TedTest

From the Ted Talk by The TED Interview: The race to build AI that benefits humanity with Sam Altman

Unscramble the Blue Letters

Hello there, this is cirhs ardonsen, and I am hlgeuy, hugely, tmenruoseldy excited to welcome you to a new series of the TED interview. Now, then, this season, we're trying something new. We're organising the whole season around a single theme, albeit a theme that some of you may consider inappropriate. But hear me out. The theme is the case for optimism. And yes, I know the world has been hit with etdnraxirliaory ugly things in the last few years. Political division, a racial reckoning, technology run amuck, not to mention a golbal pandemic and impending cimtale catastrophe. What on earth are we thinking in this context? Optimism just seems so nvaie and unwanted, almost annoynig. So here's my position. Don't think of optimism as a feeling. It's not just this sort of shallow feeling of hope. Optimism is a search. It's a determination to look for a pathway forward somewhere out there. I believe I truly believe there are amazing people whose minds contain the ideas, the visions, the solutions that can actually create that pathway forward. If given the support and rsceoeurs they need, they may very well lhigt the path out of this dark place we're in. So these are the people who can present not optimism, but a case for oipsmitm. They're the people I'm talking to this season. So let's see if they can persuade us now. Then the place I want to satrt is with A.I. artificial intelligence. This, of course, is the next innovative technology that is going to chgnae everything as we know it, for better or for worse. Today was painted not with the usual dsiayoptn brush, but by someone who truly believes in its potential. Sam Altman is the former pseneirdt of Y Combinator, the legendary startup accelerator. And in 2015, he and a team launched a company called Open Eye, dedicated to one noble purpose to develop A.I. so that it benefits humanity as a whole. You may have heard, by the way, recently a lot of buzz around in A.I. technology called T3 that was developed by open eye improve the quality of the amazing team of researchers and developers they have work in. There will be hearing a lot about three in the crasoitenovn ahead. But sticking to this lotfy mission of doenpliveg A.I. for humanity and finding the resources to realize it haven't been simple. Open A.I. is certainly not without its critics, but their goal couldn't be more important. And honestly, I found it really quite exciting to hear Sam's vision for where all this could lead. OK, let's do this. So, Sam Altman, welcome. Thank you for having me. So, Sam, here we are in 2021. A lot of people are fearful of the future at this moment in world history. How would you describe your attitude to the future? I think that the combination of scientific and technological progress and better societal dociisen making, better societal governance is going to solve in the next couple of decades all of our current most pressing permlbos, there will be new ones. But I think we are going to get very safe, very inexpensive, carbon free nuclear energy to work. And I think we're going to talk about that time that the climate disaster looks so bad and how lucky we are. We got svead by science and technology, I think. And we've already now seen this with the rptiiady that we were able to get vaccines deployed. We are going to find that we are able to cure or at least taret a significant percentage of haumn disease, including I think we'll just actually make progress in helping people have much longer decades, longer health spans. And I think in the next cpluoe of decades, that will look pretty clear. I think we will build systems with AI and otherwise that make access to an iendlicrby high quality education more possible than ever before. I think the lives we look forward like one hundred yreas, ftfiy years, even the quality of life available to anyone then will be much better than the qiutlay of life available in the very best case to anyone taody, to any single preson today. So, yeah, I'm super optimistic. I think, like, it's always easy to do scroll and think about how bad are the bad things are, but the good things are really good and getting much better. Is it your sincere belief that artificial intelligence can actually make that future better? Certainly. How look, with any technology. I don't think it will all be better. I think there are always positive and negative use cseas of anything new, and it's our job to maximize the positive ones, minimize the negative ones. But I truly, genuinely believe that the positive impacts will be orders of magnitude bigger than the nevagtie ones. I think we're seeing a glimpse of that now. Now that we have the first general purpose built out in the world and available via things like RPI, I think we are seeing evidence of just the breadth of services that we will be able to offer as the sort of technological revolution really takes hold. And we will have people irtnaect with scerives that are smart, really smart, and it will feel like as strange as the world before mobile phones feels now to us. Hmm, yeah, you minoneted your API, I gsues that stands for what, application programming inrtacfee? It's the technology that allows complex technology to be aesiscbcle to others. So give me a sense of a couple of things that have got you most excited that are already out there and then how that gives you visibility to a pathway forward that is even more exciting. So I think that the things that we're seeing now are very much glimpse of the future. We released three, which is a general-purpose natural language text model in the summer of tnwtey twenty. You know, there's hdrednus of applications that are now using it in production that's ramping up all of the time. But there are things where people use three to really understand the intent behind the search query and deliver results and sort of understand not only intent, but all of the data and deliver the thing of what you want. So you can sort of describe a fuzzy thing and it'll understand documents. It can understand, you know, short documents, not full books yet, but bring you back to the context of what you want. There's been a lot of excitement about using the generative capabilities to create sort of games or sort of interactive stories or letting people develop characters or chat with a sort of vutaril friend. There are applications that, for example, help a job seeekr polish a teilraod aoppiaitlcn for each individual company. There's the bnniiengg of tutors that can sort of tceah people about different concepts and take on different personas. And we can go on for a long time. But I think anything that you can imagine that you do today via computer that you would like to really understand and get to know you. And not only that, but understand all of the data and knowledge in the world and help you have the best experience that is is possible that that will happen. So what gets opened up? What new adjacent possible state is that as a result of these powers from this question, from the point of view of someone who's starting out on a career, for example, they're trying to figure out what would be a really interesting thing to do in the future that has only recently become possible. What are some new things that this opens up in a world where you can talk to a computer? And get. The output that would normally require you hiring the world erpxets back imlmeeiadty for almost no money, I would say think about what's possible there. So that could be like, as you said, what can normally only the best programmer in the world or a really great programmer do for me. And can I now instead just ask in English and have that prgarom written? So all these people that, you know, want to develop an app and they have an idea, but they don't know how to program. Now they can have it. You know, what is the service look like when anyone on Earth who wants really great mdiecal advice? Can get better medical advice than any single doctor could ever get, because this has the total medical knowledge and reasoning ability that the some humanity has ever produced. When you want to laren something, you have sort of a tutor that understands your exact style, how you best learn everything you know, and custom tehecas you whatever concept you want to learn someday. You can imignae that like. You have an eye that reads your email and your task list and your calendar and the documents you've been sent and in any meeting maximally perfectly peprraes you and has all of the information that you need in all the context of your entire career right there for you to go on for a long time. But I think this will just be powerful smtseys. So it's really fun piynlag around with Chapatti three, one compelling example of someone who's more tax base is try Googling The Guardian essay that was written entirely by different GP2 three queries and stitched together. It's an essay on why artificial intelligence isn't a threat to humanity. And that's impressive. It's very compelling. I actually tried inputting one of the three online uses. I asked the question what is interesting about some ollman? Oh no. Here's what it came back with. It was it was rather plchshpiiaool, actually. Came back with. I don't understand what you mean by interesting. It seems to me that this word has no unambiguous meaning in the context of human society beyond its literal definition as somewhat pleasing or entertaining. I do not believe there to be any such thing as INTERESTINGNESS except in the mind of a human or other sentient being that to my knowledge, this is an entirely sijuvbcete sttae that vireas from person to person. However, I will ganrt that this is an interesting question to ask. This does not mean it has been answered. There is no answer to be found. Well, so you can agree that somewhere between profound and gibberish is that almost well, with the state of play is I mean, that's where we are today. I think somewhere between profound and jbsirbeih is the right way to think about the current capabilities of CGP three. I think they would definitely had a bbulbe of hype about three last summer. But the thing about bubbles is the reason that smrat people fall for them is there's a kernel of something really real and really interesting that people get overexcited about. And I think people definitely got and still are overexcited about 3:00 today, but still probably underestimated the potential of where these models will go in the future. And so maybe there's this like short term overhyped and long term under hype for the etirne field, for tax models, for whatever you'd like. It's going on. And as you said, there's clearly some gibberish in there. But on the other hand, those were like well-formed sentences. And there were a couple of ideas and there that I was like, oh, like they actually maybe that's right. And I think if artificial intelligence, even in its crenrut very larval state, can make us confront new things and sort of inspire new iades, that's already pretty impressive. Give us a sense of what's actually happening in the background there. I think it's hard to understand because you read these words seem like someone is trying to mean something. Obviously, I think you believe that there's whatever you've built there, that there's a sort of thinking, sentient thing that's going, oh, I must answer this question. So so what how would you describe what's going on? You've got something that has read the entire Internet, essentially all of Wikipedia, etc. We've read something that's read like a small fitrocan of a random sampling of the Internet. We will eventually tiarn something that has read as much of the Internet or more of the Internet than we've done right now. But we have a very long way to go. I mean, we're still, I think, relative to what we will have orpaeetd at quite small scale with quite small eyes. But what is happening is there is a mdeol that is ingesting lots of text and it is trying to predict the next word. So we use Transformer's they take in a ctnexot, which is a particular architecture of an A.I. model, they take in a context of a lot of words, let's say like a thousand or something like that. And they try to predict the word that comes next in the sequence. And there's like a lot of other things that happen, but fundamentally that's it, and I think this is interesting because in the pcosers of playing that little game of trying to predict the next word, these models have to develop a representation and understanding of what is likely to come next and. I think it is maybe not perfectly accurate, but certainly worth considering to say that intelligence is very near the ability to make accurate predictions. What's confusing about this is that there are so many wdors on the Internet which are foolish as well as the words that are wise. And and how do you build a model that can distinguish between those two? And this is prompted actually by another example that I typed in. Like I asked, you know, what is a powerful idea, very interested in ideas. That was my question as a powerful idea. And it came back with several things, some of which seemed moeladtrey pronouncements, which seemed moderately gibberish. But then he was he was one that it came back with the idea that the human race has, qutoe, evolved, unquote, is false evolution or adaptation within a species was abandoned by biology and genetics long ago. Wait a sec. That's news to me. What have you been reading? And I presume this has been pulled out of some recesses of the Internet, but how is it possible, even in trehoy, to imagine how a model can gaivratte towards truth, wisdom, as opposed to just like majority views? Or how how how do you avoid something taking us further into the sort of the maze of errors and bad tiinnhkg and so forth that has already been a worrying feature for the last few years ? It's a fantastic question, and I think it is the most interesting area of research that we need to pursue. Now, I think at this pniot, the questions of whether we can build really powerful general-purpose AI system, I won't say there in the rearview mirror. We still have a lot of hard engineering work to do, but I'm ptrety confident we're going to be able to. And now the questions are like, what should we build? And how and why and what data should we train on and how do we build systems not just that can do these like phenomenally iispvmsere things, but that we can ensure do the things that we want and that understand the concepts of truth and falsehood and, you know, alignment with human values and mmnielsanigt with human values. One of the pieces of raeecsrh that we put out last year that I was most proud of and most excited about is what we call reinforcement learning from human feedback. And we sewohd that we can take these giant models that are trained on a bnuch of stuff, some of it good, some of the bad, and then with a really quite small amuont of feedback from human judgment about, hey, this is good, this is bad, this is wrong, this is the behavior I want I don't want this behavior. We can feed that information from the human judges back into the model and we can teach the model, behave more like this and less like that. And it works better than I ever imagined it would. And that gives me a lot of hope that we can build an aligned system. We'll do other things, too, like I think curating data sets where there's just less sort of bad data to train on. It will go a very long way. And as these models get smarter, I think they ienelrhnty deolevp the ability to sort out bad data from good data. And as they get really smart, they'll even start to do something we call active lenianrg, which is where they ask us for exactly the data they need when they're missing something, when they're uurnse, when they don't understand. But I think as a result of siplmy scaling these models up, building better, I hate to use the word cognition because it sounds so anthropomorphic, but let's say bndiilug a better ailbity to resoan into the moelds, to think, to cllngeahe, to try to understand and combining that with this idea of online into human vuleas via this technique we developed, that's going to go a very long way. Now, there's another question, which you sort of just kicked the ball down the field, too, which is how do we as a society decide to which set of human values do we align these powerful systems? Yeah, indeed. So if I if I understand rightly what you're saying, that you're saying that it's possible to look at the otuupt at any one time of three. And if we don't like what it's coming up with, some ways human can say, no, that was off, don't do that. Whatever algorithm or process led you to that, undo it. Yeah. And that the system is that incredibly powerful at avoiding that same kind of mistake in future because it sort of replicates the instructions , correct? Yeah. And eventually and not much longer, I believe that we'll be able to not only say that was good, that was bad, but say that was bad for this reason. And also tell me how you got to that answer so I can make sure I understand. But at the end of the day, someone needs to decide who is the wise human or short humans who are looking at the results. So it's a big difference. Someone who who grew up with intelligent design wrold view could look at that and go, that's a brlalinit outmoce. Well, Goldstar done. And someone else would say something is done awfully wrong here. So how do you avoid and this is a version of the pebrlom that a lot of the, I guess, sliicon Valley companies are facing right now in terms of the pushback they're getting on the output of social media and so forth. How do you asblmese that pool of experts who santd for human values that we actually want? I mean, we talk about this all the time, I don't think this is like solely or even not even clsoe to majorly up to opening night to decide, I think we need to begin a societal conversation now about how we're going to make those decisions, how we're going to make sure we have representational input in that, and how we sort of make these very difficult global governance systems. My personal beleif is that we should have pretty baord rules about what these systems will never do and will always do. But then the individual user should get a system that kind of beevahs like they want. And there will be people do have very different value systems. Some of them are just fundamentally ictpialmobne. No one gets to use eye to, like, exploit other people, for example, and hopefully we can all agree on. But do you want the AI to like. You know, support you and your belief of intelligent design, like, do I think openly, I should say it can't, even though I disagree with that is like a scientific conclusion. No, I wouldn't take that stance. I think the thing to remmeebr about all of this is that history is still quite extraordinarily weak. It's still has such big problems and it's still so unreliable that for most use cases it's still unsuitable. But when we think about a system that is like a thousand times more powerful and let's say a million tiems more reliable, it just doesn't it doesn't say gibberish very often. It doesn't totally lose the plot and get distracted or system like that is going to be one that a lot of the economic activity in the world comes to rely on. And I think it's very important that we don't have a small group of people sort of saying you can never use it for this thing that, like most of the world wants to use it for because it doesn't match our personal beliefs. Talk a bit more about some of the other uses of it, because one of the things that's most snuririspg is it's not just about sort of text responses. It's it can take gnazieereld human instructions and build things up. For example, you can say to it, write a Python program that is designed to put a flashing cursor in one corner of the screen, in the gogloe logo in the other corner. And and it can go your way and do something like that. Shockingly, quite well, effectively. Yeah, I it can. That's amazing. I mean, this is aamnizg to me. That opens the door to. An entirely way to think about programers for the future, that you could you could have people who can program just in human natural language potentially and gain rapid efficiency. I do the engineering. We're not that far away from that world. We're not that far away from the world where you will write a spec in English. And for a slpmie enough program, I will just write the code for you. As you said, you can see glimpses of that even in this very week three which was not trained to code like. I think this is important to remember. We trained it on the language on the Internet very rarely, you know, Internet let language on the Internet also iulecnds some code snippets. And that was enough, so if we really try to go train a model on code itself and that's where we decide to put the horsepower of the model into, just imagine what will be possible will be quite impressive. But I think what you're pointing to there is that because models like three to some degree or other, and it's like very hard to know exactly how much understand the underlying concepts of what's going on. And they're not just regurgitating things they found in a wibtese, but they can really apply them and say, oh, yeah, I kind of like know about this word and this idea and code. And this is probably what you're trying to do. And I won't get it right always. But sometimes I will just graneete this like a brand new program for nothing that anyone has ever asked before. And it will work. That's pretty cool. And data is data. So it can do that from English to code. It can do that from elsignh to French. Again, we never told it to learn about translation. We never told it about the concepts of English and frcenh, but it learned them, even though we never said this is what English is and this is what French is and this is what it means to tsatarnle, it can still do it. Wow, I mean, for carvitee people, is there a world coming where the sort of the palette of possibility that they can be exposed to is just explodes? I mean, if you're a miaiuscn, is there a near future where you can say to your eye, OK, I'm going to bed now, but in the morning I'd love you to present me with a thousand tuba jingles with words attached that you have of a sort of mean factor to the and you come down in the mnionrg and the cuemtpor shows you the stuff. And one of them, you go, wow, that is it. That is a top 10 hit and you build a song from it. Or is that going to be released? Actually be the value add. We released something last year cllead Jukebox, which is very near what you described, where you can say I want music generated for me in this style or this kind of stuff, and it can come up with the words as well. And it's like pretty cool. And I really enjoy listening to misuc that it cetaers. And I can sort of do four songs, two bars of a jingle, whatever you'd like. And one of my very firtaove asttris reached out, called to open it after we reaelse this and said that he wanted to talk. And I was like, well, I like total fanboy here. I'd love to join that call. And I was so nervous that he was going to say, this is terrible. This is like a really sad thing for human creativity. Like, you know, why are you doing this? This is like whatever. And he was so excited. And he's like, this has been so inrinpsig. I want to do a new aulbm with this. You know, it's like, give me all these new ideas. It's making me much better at my job. I'm going to make better music because of this tool. And that was awesome. And I hope that's how it all continues to go. And I think it is going to lead to this. We see a similar thing now with Dolly, where graphic dirseegns sometimes tell us that they just they see this new set of possibilities because there's new creative inspiration and they're cycle time, like the amount of time it teaks to just come up with an idea and be able to look at it and then decide whether to go down that path or head in a different direction goes down so much. And so I think it's going to just be this like incredible creative explosion for humans. And how far away are we some before? And I it comes up with a genuinely powerful new idea, an idea that solves the problem that humans have been wrestling with. It doesn't have to be as quite on the sacle as of, OK, we've got a virus cinomg. Please dsierbce to us what a what a national rational response should look like, but some kind of genuinely innovative idea or solution like one one ianetrnl question we've asked ourselves is, when will the first genuinely interesting, purely AI written TED talk show up? I think that's a great milestone. I will say it's always hard to guess timeline's I'm sure I'll be wrong on this, but I would guess the first genuinely iinserettng. Ted talk, thought of written delivered by an AIDS within the kind of the seven ish year time frame. Maybe a little bit less. And it flees like I mean, just reading that Guardian essay that was kind of it was a composite of several different GPG three rpeonesss to questions about, you know, the threats of robotics or whatever. If you throw in a human editor into the mix, you could probably imagine something much sooner. Indeed. Like tooomrrw. Yeah. So the hybrid the hybrid version where it's basically a tool assisted TED talk, but that it is better than any TED talk a human could generate in one hundred hours or whatever, if you can sort of combine human discretion with A.I. heeroowpsr. I suspect that's like our next year or two years from now kind of thing where it's just really quite good. That's that's really interesting. How do you view the impact of A.I. on jobs? There's obviously been the familiar story is that every White-Collar job is now up for drctioetsun. What's what's your view there? You know, it's I think it's always hard to make these proiendicts. That is definitely the familiar story now. Five years ago, it was every blue collar job is up for destruction, maybe like last year it was. Every creative job is up for destruction because of things like Jukebox I. I think there will be an enormous impact on. The job market, and I really hate it, I think it's kind of gross when people like working on I petrend like there's not going to be or sort of say, oh, don't worry about it. It'll just all obviously better. It doesn't always obviously get better. I think what is true is. Every technological revolution produces a change in jobs, we always find new ones, at least so far. It's difficult to pdrecit from where we're sitting now what the new ones will be and this technological revolution is likely to be. Again, it's always tempting to say this time it's different. Maybe I'll be totally wrong. But from what I see now, this technological revolution is likely to be more. dtamaric. More of a staaccto note than most, and I think we as a society need to figure out how we're going to cushion everybody through that. I've got my own ideas about how to do that. I, I wouldn't say that I have any reason to believe they're the right ones, but doing nothing and not really engaging with the magnitude of what's about to happen, I think it's like not an aetpaccble ansewr. So there's going to be huge impact. It's dfulifcit to predict where it shows up the most. I think previous predictions have mostly been wrong, but I I'd like to see us all as a society, certainly as a field, engage in what what the shifts we want to make to the siacol ctcrnaot are to kind of get through that in a way that is milmaaxly bfnieecail to everybody. I mean, in every past rouvitolen, there's always been a spcae for humans to move to. That is, if you like, mnoivg up the food chain, it's sort of we've retreated to the things that humans could uiunlqey do, think better, be more creative and so forth. I guess the wrroy about A.I. is that in principle, I believe this, that there is no human cognitive feat that won't ultimately be doable, probably better by artificial general touch, simply because of the extra frpoieewr that ultimately they can have, the vast knowledge they bring to the table and so forth. Is that basclaily right, that there is ultimately no safe sort of space where we can say, oh, but that would never be able to do that on a very long time horizon? I agree with you, but that's such a long time hioorzn. I think that, you know, like maybe we've merged by that point, like maybe we're all plugged in and then, like, we're this sort of symbiotic thing. Like, I think there's an interesting example, as we were talking about a few mtnieus ago, where right now we have these systems that have sort of enormous horsepower but no serietng wheel. It's like, you know , incredible capabilities, but no judgment. And there's like these obvious ways in which today even a human plus three is far better than either on their own. Many people speak about a world where it's sort of A.I. as this external threat you speak about. At some point, we actually merge with eyes in some way. What do you mean by that? There's a lot of different vsiornes of what I think is possible there, you know, in some sense, I'd argue the merge has already like begun the human technology mrgee like we have this thing in our hands that sort of dictates a lot of what we think, but it gives us real superpowers and that can go much, much further. Maybe it goes all the way to like the Elon Musk vision of neuro link and having our brains plugged into computers and sort of like literally we have a computer on the back of our head or goes the other direction and we get uploaded into one. Or maybe it's just that we all have a chat bot that kind of cntoatlsny steers us and hlpes us make better decisions than we could. But in any case, I think the fundamental thing is it's not like the humans versus the eyes cnopetimg to be the. Smartest sentient thing on earth or beyond. But it's that this idea of being on the same team. Hmm. I certainly get very excited by the sort of the medium term pinottael for creative people of all sorts if they're willing to expand their palette of plitbssiiieos. But with the use of A.I. to be willing to. I mean, the one thing that the history of technology has shown again and again is that something this powerful and with this much benefit is unstoppable and you will get rewarded for ebmcnraig it the most and the earliest. So talk about what can go wrnog with that, so let's move away from just the sort of emniococ displacement fatcor. You were a co-founder of Open Eye because you saw eiasntixtel risks to humanity from high today. What would you put as the sort of the most wyrirnog of those risks? And how is open eye working to minimize? I still think all of the really hrriiyfnog risks exist. I am more cifnnodet, much more confident than I was five years ago when we statred that there are technical things we can do about. How we build these systems and the research and the alignnemt that make us much more likely to end up in the kind of really wonderful camp, but, you know, like maybe open I fall behind and maybe somebody else feels ajai that thinks about it in a very different way or doesn't care as much as we'd like about safety and the risks or how to strike a different trade off of how fast we should go with this and where we should sort of just say, like, you know, like let's push on for the economic benefits. But I think all of this sort of like, you know, tatiiolldanry what's been in the realm of sci fi risks are real and we should not igrone them. And I still lose sleep over them. And just to update people is artificial general intelligence. Right now, we have incredible examples of powerful AI operating on specific areas. Ajai is the ability of a computer mind to connect the dots and to make decisions at the same level of breadth that that huanms have had. What's your sort of elevator pitch on Ajai about how to ifdnteiy and how to think of it? Yeah, I mean, the way that I would say it is that for a while we were in this world of like very narrow A.I. , you know, that could like clsifsay images of cats or whatever, more advanced stuff in that. But that kind of thing. We are now in the era of general purpose, AI, where you have these systems that are still very much iferpmcet tools, but that can generalize. And one thing like GPP three can write essays and translate between languages and write computer code and do very cmeactolpid search. It's like a single model that understands enough of what's really going on to do a broad array of tasks and learn new things quickly, sort of like people can. And then eunlveatly we'll get to this other realm. Some people call it ajai, some people call ostler things. But I think it implies that the systems are like to some degree self directed, have some intentionality of their own is a simple summary to say that, like the fundamental risk is that there's the potential with general artificial intelligence of a sort of rnaawuy effect of self-improvement that can happen far faster than any kind of humans can even keep up with, so that the day after you get to ajai, suddenly computers are tdnohsuas of times more advanced than us and we have no way of controlling what they do with that power. Yeah, and that is certainly in the risk space, which is that we build this thing and at some point somewhat suddenly, it's much more powerful than we are, we haven't really done the full merge yet. There's an event horizon there and it's sort of hard to see to the other side of it. Again, lots of reasons to think it will go OK. Lots of reasons to think we won't even get to that scenario. But that is something that. I don't think people should brush under the rug as much as they do, it's in the possibility space for sure, and in the possibility subspace of that is one where, like, we didn't actually do as good of a job on the alignment work as we thought. And this sort of child of humanity kind of acts in a very different way than we think. A framework that I find useful is to sort of think about like a two by two matrix, which is sroht timelines to ajai and long timelines to ajai and a slow take off and a fast take off on the other axis. And in the short timelines, fast take off quadrant, which is not where I think we're going to be. But if we get there, I think there's a lot of sncieaors in the direction that you are describing that are worrisome. And we would want to spend a lot of effort planning for. I mean, the fact that a computer could start editing its own code and improving itself while we're asleep and you wake up in the morning and it's got smarter, that is the start of something super powerful and potentially scary. I have tremendous misgivings about letting my system, not one we have today, but one that we might not have and too many more years start editing its own code while we're not paying attention. I think that's the kind of thing that is worth a gerat deal of societal diusssocin about, you know, just because we can do that. Should we? Yes, because one of the things that's that's been most snkcohig to you about the last few years has been just the power of unintended consequences. It's like you don't have to have a belief that there's some sort of wkiang up of of an alien intelligence that suddenly decided it wants to wraek havoc on humans. That may never happen. What you can have is just incredible pwoer that goes amok. So a lot of people would argue that what's happened in technology in the last few years is actually an example of that. You know, social media companies created these inetglnelcies that were programmed to maximally hrasvet attention, for example, for sure. And they understand this from that turned out to be in some ways horrifying and extraordinarily damaging. Is that a meaningful sort of canary in the coal mine saying, look out, humanity, this could be really dangerous? And how how on earth do you protect against those kinds of unintended consequences? I think you raise a great point in general, which is these systems don't have to wish ill to humanity to cause ill just when you have, like, very powerful systems. I mean, unenidnted cqensucnoees for sure. But another version of that is and I think this applies at the technical level, at the company level, at the societal lveel, incentives are superpower's. Charlie Munger had this thing on, which is incentives are so powerful that if you can spend any time wevhotsaer working on the incentive system, that's what you should do before you work on anything else. And I really believe that. And I think that apeilps to the ivdndaiuil models we bilud and what their reward ftuocinns look like. I think it applies to society in a big way, and I think it applies to our cprtarooe structure at open. I you know, we sort of observe that if you have very well-meaning people, but they have this incentive to sort of maximize attention harvesting and profit forever through no one's ill intentions, that leads to a quite undesirable outcome. And so we set up opening is this thing called a capped profit model specifically so that we don't have the system incentive to just generate maximum value forever with an AGI that seems like obviously quite broken. But even though we knew that was bad and even though we all like to think of ourselves as good people, it took us a long time to figure out the right sttucrrue, to figure out a charter that's going to govern us and a set of incentives that we believe will let us do our work. And kind of these we have these like three elements that we talk about a lot research sort of engineering, development and deployment policy and safety. Put those all together under a system where you don't have to rely on. Anything but the natural ieecvntnis to push in a direction that we hope will minimize the sort of negative unintended consequences. So help me understand this, because this is I think this is confusing to some people. So you started oeninpg. I initially I think Elon Musk, the co-founder, and there was a group of you and the argument was this technology is too powerful to be left, developed in sceret and to be left developed purely by corporations who have whatever incentive they may have. We need a nifoonprt that will develop and shrae knowledge openly. First of all, just even at that early stage, some people were confused about this. It was saying if this thing is so dangerous, why on earth would you want to make it secrets even more available? Well, maybe givnig the tools to that sort of AI trseorrit in his bedroom somewhere, I think I think we got misunderstood in the way we were talking about that. We certainly don't think that the right thing to do is to, like, build this a super woepan and hand it to a terrorist. That's obviously afuwl. One of the reasons that we like our API model is it lets us make the most powerful AI thoeolcngy anyone in the world has, as far as we know, available to ever would like to use it, but to put some controls on its usage. And also, if we make a mistake, to be able to pull it back or change it or tweak it or improve it or whatever. But we do want to put and this is continued will continue to be true with appropriate restrictions and gadrlruais, very powerful technology in the hands of people. I think that is fair. I think that will lead to the best results for the society as a whole. And I think it will sort of maximize bneifet. But that's very different than sort of sihpipng the whole model and saying, here, do whatever you want with it. We're able to enforce rules on it. We also think and this is part of the msisoin that like something the field was doing a lot of that we didn't feel good about was sort of saying like, oh, we're going to keep the pace of progress and capabilities secret. That doesn't feel right, because I think we do need a satoceil conversation about what's what's going on here, what the itcpmas are going to be. And so we although we don't always say, like, you know, here's the suepr weapon, hopefully we do try to say, like, this is really serious. This is a big deal. This is going to affect all of us. We need to have a big conversation about what to do with it. Help me understand the structure a bit better, because you definitely surprised much people when you announced that msfrcoiot were putting a billion dalorls into the organization and in return, I guess they get certain exclusive licensing rights. And so, for example, they are the exclusive licensee of CP3. So talk about that structure of how you win. Microsoft presumably have invested not purely for aritlutisc purposes. They think that they will make money on that billion dollars. I sure hope they do. I love capitalism, but I think that I really leovd even more about Microsoft as a partner. And I'll talk about the structure and the exclusive license in a minute is that we like went around to people that might find us. And we said one of the things here is that we're going to try to make you some money. But like Adjei going well is more important. And we need you to sign this document that says if things don't go the way we think and we can't make you money like you just cheerfully walk away from it and we do the right thing for humanity. And they were like, yes, we are enthusiastic about that. We get that the mission comes first here. So again, I hope a phenomenal investment for them. But they were like they really pstenalaly surprised us on the upside of how aligned they were with us, about how strange the world may get here and the need for us to have flexibility and put our mission first, even if that means they lose all their money, which I hope they don't and don't think they will. So the way it's set up is that if at some point in the coming year or two, two years, Microsoft decide that there's some incredible commercial opportunity that they could realize out of the eye that you've built and you feel actually, no, that's that's damaging. You can block it. You can veto it. Correct. So the four most powerful version of three and its successors are available via the API, and we intend for that to ctuoinne. What Microsoft has is the ability to sort of put that model directly into their own technology. If they want to do that. We don't plan to do that with other people because we can't have all these controls that we talked about earlier. But they're like a close trusted partner and they really care about safety, too. But our goal is that anybody who wants to use the API can have the most poferuwl versions of what we've trained. And the structure of the API lets us continue to increase the safety and fix problems when we find them. But but the structure. So we start out as a non-profit, as you said, we realized pretty quickly that although we went into this thinking that the way to get to ajai would be about smarter and srtmear amrtlhogis, that we just needed bigger and bggeir curteopms as well. And that was going to require a scale of capital that no one will, at least certainly not me, could figure out how to raise is a nonprofit. We also needed to sort of be able to compensate very highly compensated, talented individuals that do this, but are full for profit company had runaway incentives problem, among other things. Also just one about sort of fairness in society and wealth concentration that didn't feel right to us either. And so we came up with this kind of hybrid where we have a nonprofit that governs what we do, and it has a subsidiary, LLC, that we structure in a way to make a fixed amount of profit so that all of our investors and employees, hopefully if things go how we like, if not no one gets any money, but hopefully they get to make this one time great ruetrn on their investment or the time that they snpet it open their equity here. And then beyond that, all the value flows back to the nonprofit and we figure out how to share it as fairly as we can with the world. And I think that this structure and this nonprofit with this very srtong ctearhr in place and everybody who jnios snignig up for the mission come in first and the fact the world may get strange, I think that. That was at least the best idea we could come up with, and I think it feels so far like the incentive system is wniokrg, just as I sort of wctah the way that we and our partners make decisions. But if I read it right, the cap on the gain that investors can make is 100 Axum. It's a massive call that was for our very first round investors. It's way, way lower. Like as we now take a bit of capital, it's way, way lower. So your deal with Microsoft isn't you can only make the first hundred billion dollars. I don't know. It's way lower than after that. We're giving it to the world. It's way lower than that. Have you disclosed what I don't know if we have, so I won't accidentally do it now. All right. OK, so explain a bit more about the charter and how it is that you. Hope to aivod or I guess help contribute to an eye that is safe for humanity. What do you see as the keys to us avoiding the wrsot mistakes and really hdnilog on to something that's that's beneficial for humanity? My answer there is actually more about, like technical and societal issues than the charter. So if it's OK for me to answer it from that ptceirpevse, sure. OK, I'm happy to talk about the charter to. I think this question of alignment that we telakd about a little elairer is paramount, and then I think to undnerastd that it's useful to dtinfaeefrtie between accidental misuse of a system and intentional misuse of a system. So like intentional would be a bad actor saying, I've got this powerful system, I'm going to use it to like hack into all the computers in the world and wreak havoc on the power gdris. And accidental would be kind of the Nick Bostrom make a lot of paper clips and view humans as collateral damage in both cases. But to vnraiyg deerges, if we can really, truly, technically solve the alignment problem and the societal problem of deciding to which set of human values do we align, then the systems understand right and wrong, and they understand probably better than we ever can, unintended consequences from cmolpex ancoits and very complex systems. And, you know, if we can train a system which is like. Don't harm humanity and the system can really understand what we mean when we say that, again, who is we and what does that have some asterisks on them? Sorry, go ahead. Well, that's if they could understand what it means to not harm humanity, that there's a lot wrapped up in that sentence. Because what's been so striking to me about efforts so far is that they seem to have been based on a very naive view of human nature. Go back to the sort of Facebook and Twitter examples of, well, the engineers building some of the systems would say we've just designed them around what humans want to do. You said, well, if someone wants to cclik on something, we will give them more of that thing. And what could psobisly be wrong with that? We're just snpoutiprg human choice, ignoring the fact that humans are complicated, farshid animals for sure, who are constantly making ccieohs, that a more effective vrioesn of themselves would agree is not in their long term interests. So that's one part of it. And then you've got layered on top of that or the complications of systemic complexity where, you know, mupillte choices by thousands of people end up creating a reality that possibly have designed for how how to cut through that. Like an AI has to make a decision based on a moment, on a spcfeiic data set. As those decisions get more powerful, how can we be confident that they don't lead to this sort of system crashing basically in some way? I think that I've heard a lot of behavioral psychologists and other people that have studied this say in different ways, are that I hate to keep picking on Facebook, but we can do it one more time since we're on the topic. Maybe you can't in any given moment in ngiht where you're tired and you have a stressful day, stop yourself from the dopamine hit of soinlrlcg and Instagram, even though you know that's bad for you and it's not leading to your best life. But if you were akesd in a reflective moment where you were sort of fully alert and tugufhtohl, do you want to spend as much time as you do scrolling through Instagram? Does it make you happier or not? You would actually be able to give like the right long term answer? It's sort of the spirit is willing, but the flesh is weak kind of monemt. And one thing that I am hfuepol is that humans do know what we want and what. On the whole, and penteserd with research or sort of an objective view about what makes us happy and doesn't we're pretty, what's so great about it, they're pretty good. But in any particular moment, we are subjected to our animal instincts and it is easy for the lower bairn to take over the eye. Well , I think be an even higher brain. And as we can teach it, you know, here is what we really do value. Here's what we really do want. It will help us make better dsienoics than we are capable of, even in our best moments. So is that being proposed and talked about as an actual rule? Because it skreits me that there is something peallointty super profound here to introduce some kind of rule for development of AIDS that they have to tap into not. What humans one, which is an ill diefend question, but as to what humans in reflective mode want. Yeah, we talk about this a lot. I mean, do you see a real ccnhae where something like that could be incorporated as a sort of an absolute gdloen rule and and if you like, searpd around the community so that it seeps into corporations and elsewhere? Because that I've seen no evidence that, well, a little corporation that was potentially a game changer. Corporations have this weird incentive problem. Right. What I was trying to speak about was something that I think should be technologically possible , and that's something that we as a society should demand. And I think it is technically possible for this to be sort of like a layer above the neocortex that makes even better decisions for us and our wfaelre and our long term happiness and fulfillment than we could make on our own. And I think it is possible for us as a steiocy to demand that. And if we can do like a penicr move between what the technology is capable of and what we what we as society demand, maybe we can make everybody in the middle that way. I mean, there are instances of even though companies have their incentives to make money and so forth, they also in the knowledge age. Can't make moeny if they have pissed off too many of their employees and customers and investors by aoaglny of the climate space right now, you can see more and more companies, even those that are emitting huge amounts of carbon dioxide, saying, wait a sec, we're struggling to recruit talented people because they don't want to work for someone who's evil. And their customers are saying, we don't want to buy something that is evil. And so, you know, ultimately you can picture processes where they do better. And I I believe that most engineers, for example, work in Silicon Valley. Companies are actually good people who want to degsin great products for humanity. I think that the people who run these companies want to be a net contribution to htmnuiay. It's we've we've rushed really quickly and design stuff without thinking it through properly. And it's led to a mess up. So it's like, OK, don't move fast, break things, slow down and build beautiful things that are bliut on a real version of human nature and on a real version of system complexity and the risks associated with sstemyic complexity. Is that the adnega that fundamentally you think that you can push somehow? Yes, but I think the way we can push it is by getting the iivcnnete system right. I think most people are flmdntualenay erxtleemy good. Very few people wake up in the morning thinking about how can I make the world a worse place? But the incentive systems that we're in are so powerful. And even those eeirnegns who join with the absolute best of intentions get sucked into this world where they're like trying to go up from it all for and five or whatever Facebook calls those things and you like, it's pretty exciting. You get caught up playing the game, you're rewarded for kind of doing things that move the company's key metrics. It's like fun to get promoted. It feels good to make more money and the incentive systems of the company. And that's what it rewards. An individual performance are maybe like not what we all want. And here I don't want to pick on Facebook at all because I think there's versions of this at play it like every big tech company, including in some ways I'm sure it open I but to the degree that we can better agiln the incentives of companies with the welfare of society and then the incentives of an individual at those companies within the now realign incentives for those companies, the more likely we are to be able to have things like ajai that. Follow an incentive ssetym of. What we want in our most reflective best mentmos and are even better than what we could think of ourselves is is it still the vision for open eye that you will get to? Artificial greenal intelligence ahead of. The cotonripoars, so that you can somehow put a stake in the ground and build it the right way. Is that really a realistic thing to to dream for? And if not, how do you live up to the mission and help ensure that this thing doesn't go off the rails? I think it is. Look, I certainly don't think we will be the only gourp to build an AGI, but I think we could be the first. And I think if you are the first, you have a lot of norms that empower. And I think you've already seen that. You know, we have released some of the most powerful systems to date. And I think the way that we have done that kind of in controlled release where we've released a bigger model than a bigger one than a bigger one, and we sort of try and talk about the potential misuse cases and we try to like talk about the importance of releasing this behind an API so that you can make changes. Other groups have followed suit in some of those directions, and I think that's good. So, yes, I don't think we can be the only I do think we can be ahead. And if we are ahead, I think we can use that leverage to hopefully push people in a better dorictien or maybe we're wrong and somebody else has a better direction. We're doing something about do you have a sucruttral advantage in that your mission is to do this for everyone as opposed to for some corporate obitcevje. And that that that allows you that. Why is it that we came out of open eye and not someone else? It's like it's surprising in some ways when you're up against so much money and so much talent in these other cpnaeioms that you came up with this platform ahead of. You know, in some sesne it's surprising and in some sense, like the startup wins most of the time, like I'm a huge believer in startups as the best force for innovation we have in the world today. I talked a little bit about how we combine these three. Different clans of research, engineering and sort of safety and policy that don't normally combine well and I think we have an unusual strength, there were clearly like well funded. We have super talented ppolee. But what we really have is like intense focus and self belief that what we're doing is possible and good. And I appreciate the implied compliment. But, you know, we, like, work really hard. And if we stop doing that, I'm sure someone would run by us fast. Tell us a bit more about some of your prior life sentences. Yeah, for several years, you were running Y Combinator, which had incredible imcpat on some 70 companies. There are so many startup stories that began at Y Combinator. What were key drivers in your own life that took you on the path you're on? And how did that path end up at Y Combinator? No exaggeration. I think I have back to back had the two jobs that are at least the most interesting to me in all of Silicon vllaey. I, I was I went to clgeloe to study computer science. I was a mjoar computer nerd growing up. I knew like a little bit about startups, but not very much. I started working on this project the same year I started working on that. This thing called Y Combinator started and funded me and my co-founders. And we doreppd out of school and did this cnmpaoy, which I ran for like seven years. And then after that I got acquired. I had stayed close to my comment the whole time. I thought it was just this incredible group of people and spirit and set of incentives and just bdaly misunderstood by most of the world, but obvuios to everyone within it that it was going to create huge amounts of value and do a lot of new things. My company had acquired PJI, who is the founder of ICI, and like truly one of the most incredible humans and business people. And Paul Burrell, Paul Graham asked me if I wnetad to run it. And kind of like the central learning of my cearer, why I individual srupttas has been that if you really scale them up, remarkable things can happen. And I. I did it and I was like, one of the things that would make this exciting for me personally mtiiovntag would be if I could sort of push it in the direction of doing these hard tech companies, one of which became open. I describe actually what Y Combinator is, you know, how many people come through it to give us a couple of stories of its impact. Yeah. So you basically apply as a handful of people and an idea, maybe a prototype and say, I would like to start a company and will you please fund me? And we review those apipnoltiacs and we I shouldn't say we anymore. I guess they fund four hundred companies a year. You get about one hundred and fifty tusanohd dollars while she takes about seven percent oehrsnwip and then gives you lots of advice and then networking and sort of this like fast track program for sttraing a startup. I haven't looked at this in a while, but at one point a significant fraction of the biillon dollar plus companies in the US that got started. It all came through the Wiki program, some recently in the news ones have been like anbrib, jahdocre, Coinbase, insta card stripe. And I think it's just it has become an incredible way to help. People who understand technology get a three month course in business, but instead of like herding you with an MBA, we actually teach you the things that matter and kind of go on to do incredible, incredible work. What is it about eernrepnrteus? Why do they matter? Some people just find them kind of annoying. But I think you would aruge I think I would argue that they have done as much as anyone to spahe the future. Why ? What is it about them? I think it is the ability to take. And idea and by force of will to make it hpeapn in the world and in an incentive system that rewards you for making the most impact on the most people like in our system. That's how we get most of the things that that we use. That's how we got the computer that I'm using, the software I'm using to talk to you on it. Like all of this, you know, everyone in life, everything has a balance sheet. There's pntely of very annoying things about them. And there's plenty of very annoying things about the system that sort of ildeiozs them. But we do get something really ipnamrtot in return. And I think that as a force for mikang things that make all of our lives better happen, it's very cool. Otherwise, you know, like if you have, like, a great idea, but you don't actually do anything useful with it for people, that's still cool. It's still intellectually interesting. But like, there's got to be something about the reward fcontiun in society that is like, did you actually do something useful? Did you create value? And I think entrepreneurship and startups are a wonderful way to do that. You know, we get all these great software companies. But I also think it's like how we're going to get ajai, how we're going to get nuclear fusion, how we're going to get life estxieonn. And like on any of those topics are a long list of other things I could point to. There's like a number of startups that I think are doing incredible work, some of which will actually deliver. It is a truly amazing thing when you put the camera back and to believe that a human being could be lynig awake at night and something pops inside their mind as a patterning of the noreuns in their brain that is effectively them saying, aha, I can see a way where the future could be better and and they can actually picture it. And then they wake up and then they talk to other people and they persuade them and they persuade investors and so forth. And the fact that this this system can happen and that they can then actually change the history changes in some sense. It is mind bnlgoigg that that happens that way and it happens again and again. So you've seen so many of these stories happen. What would you say? Is the is there a key thing that differentiates good entrepreneurs from others? If you could dolbue down on one trait, what would it be? If I could pick only one, I would pick determination. I think that is the biggest predictor of success, the biggest differentiator predictor. And if you would allow a second, I would pick like communication skills or egaevlsnim or something in that direction as well. There are all of the obvious ones that matter, like intinlelcgee, but there's like a lot of smart people in the world. And when I look back at kind of the thousands of entrepreneurs I've worked with, all of many of whom were like quite capable, I would say that's like one and two of the surprisingly differentiated characteristics. What it's it's what I look at, the different things that you've built and you're working on. I mean, it could not be more foundational for the future. I mean, entrepreneurship. I know this is I agree that this is really what has driven the future. Do you see some people get really now they look at Silicon Valley and they look at this srtoy and they worry about the culture. Right. That it's this is a bro clrtuue. Do you see prospects of that changing atmyine soon? And would you welcome it? Can we get better companies by really working to expand a group of people who can be entrepreneurs and who can contribute to aid, for example? For sure. And in fact, I think I'm hopeful, since these are the two things I've thought the most about. I'm excited for the day when someone combines them and uses A.I. to better select who did more fairly, maybe even scleet who to fund and how to aisvde them and really kind of make entrepreneurship super widely available that will lead to like better outcomes and sort of more societal wealth for all of us. So are. So, yeah, I think. Broadening the set of people able to start companies and that sort of get the resources that you need, that is like an uoalunqcveliy good thing and it's something that I think Silicon Valley is making some progress in. But I hope we see a lot more. And I do really, truly think that the technology industry entrepreneurship is one of the greatest forces for self betterment. If we can just figure out how to be a little bit more inclusive in how we do things. My last question today is about ideas were spreading. If you could inject one idea into the mind of everyone listening, what what would the idea be? We've touched on it a bunch, but the one idea would be the ajai really is going to happen. You have to engage with it seriously, and you shouldn't just listen to this and then brush aside and go about life as if it's not going to happen because it is going to affect everything. And we will all we all, I think, have an obligation, but also an opportunity to figure out what not means and how we want the world and this sort of one time shift to go on. I'm kind of awed by the breadth of things are engaged with. Thank you so much for spending so much time sharing your vision. Thanks so much for having me. OK, that's it for today. You can read more about open eyes, vision and pegosrrs at open eye dotcom. If you want to try playing with yourself, it's a little tricky. You have to find a website that has licensed the API. The one I went to was popoliheshr ehi dot com, where you just you pay a few dollars to get access to a very strange mind. That's actually quite a lot of fun. The ireveitnw is part of the TED Audio Collective, a collection of podcasts dedicated to sparking curiosity and sharing ideas that matter. This show is produced by Kim Net2Phone Pittas and edited by Grace Rubenstein and Sheila bfnfaoo, Sambor Islamic Sir. Fact Check is by Paul Durbin and special thanks to Michele Quent, Colin hmlees and Anna flein. If you like the show, please write and review it. It helps other people find us. We read every reievw, so thanks so much for lnneistig. See you next time.

Open Cloze

Hello there, this is _____ ________, and I am ______, hugely, ____________ excited to welcome you to a new series of the TED interview. Now, then, this season, we're trying something new. We're organising the whole season around a single theme, albeit a theme that some of you may consider inappropriate. But hear me out. The theme is the case for optimism. And yes, I know the world has been hit with _______________ ugly things in the last few years. Political division, a racial reckoning, technology run amuck, not to mention a ______ pandemic and impending _______ catastrophe. What on earth are we thinking in this context? Optimism just seems so _____ and unwanted, almost ________. So here's my position. Don't think of optimism as a feeling. It's not just this sort of shallow feeling of hope. Optimism is a search. It's a determination to look for a pathway forward somewhere out there. I believe I truly believe there are amazing people whose minds contain the ideas, the visions, the solutions that can actually create that pathway forward. If given the support and _________ they need, they may very well _____ the path out of this dark place we're in. So these are the people who can present not optimism, but a case for ________. They're the people I'm talking to this season. So let's see if they can persuade us now. Then the place I want to _____ is with A.I. artificial intelligence. This, of course, is the next innovative technology that is going to ______ everything as we know it, for better or for worse. Today was painted not with the usual _________ brush, but by someone who truly believes in its potential. Sam Altman is the former _________ of Y Combinator, the legendary startup accelerator. And in 2015, he and a team launched a company called Open Eye, dedicated to one noble purpose to develop A.I. so that it benefits humanity as a whole. You may have heard, by the way, recently a lot of buzz around in A.I. technology called T3 that was developed by open eye improve the quality of the amazing team of researchers and developers they have work in. There will be hearing a lot about three in the ____________ ahead. But sticking to this _____ mission of __________ A.I. for humanity and finding the resources to realize it haven't been simple. Open A.I. is certainly not without its critics, but their goal couldn't be more important. And honestly, I found it really quite exciting to hear Sam's vision for where all this could lead. OK, let's do this. So, Sam Altman, welcome. Thank you for having me. So, Sam, here we are in 2021. A lot of people are fearful of the future at this moment in world history. How would you describe your attitude to the future? I think that the combination of scientific and technological progress and better societal ________ making, better societal governance is going to solve in the next couple of decades all of our current most pressing ________, there will be new ones. But I think we are going to get very safe, very inexpensive, carbon free nuclear energy to work. And I think we're going to talk about that time that the climate disaster looks so bad and how lucky we are. We got _____ by science and technology, I think. And we've already now seen this with the ________ that we were able to get vaccines deployed. We are going to find that we are able to cure or at least _____ a significant percentage of _____ disease, including I think we'll just actually make progress in helping people have much longer decades, longer health spans. And I think in the next ______ of decades, that will look pretty clear. I think we will build systems with AI and otherwise that make access to an __________ high quality education more possible than ever before. I think the lives we look forward like one hundred _____, _____ years, even the quality of life available to anyone then will be much better than the _______ of life available in the very best case to anyone _____, to any single ______ today. So, yeah, I'm super optimistic. I think, like, it's always easy to do scroll and think about how bad are the bad things are, but the good things are really good and getting much better. Is it your sincere belief that artificial intelligence can actually make that future better? Certainly. How look, with any technology. I don't think it will all be better. I think there are always positive and negative use _____ of anything new, and it's our job to maximize the positive ones, minimize the negative ones. But I truly, genuinely believe that the positive impacts will be orders of magnitude bigger than the ________ ones. I think we're seeing a glimpse of that now. Now that we have the first general purpose built out in the world and available via things like RPI, I think we are seeing evidence of just the breadth of services that we will be able to offer as the sort of technological revolution really takes hold. And we will have people ________ with ________ that are smart, really smart, and it will feel like as strange as the world before mobile phones feels now to us. Hmm, yeah, you _________ your API, I _____ that stands for what, application programming _________? It's the technology that allows complex technology to be __________ to others. So give me a sense of a couple of things that have got you most excited that are already out there and then how that gives you visibility to a pathway forward that is even more exciting. So I think that the things that we're seeing now are very much glimpse of the future. We released three, which is a general-purpose natural language text model in the summer of ______ twenty. You know, there's ________ of applications that are now using it in production that's ramping up all of the time. But there are things where people use three to really understand the intent behind the search query and deliver results and sort of understand not only intent, but all of the data and deliver the thing of what you want. So you can sort of describe a fuzzy thing and it'll understand documents. It can understand, you know, short documents, not full books yet, but bring you back to the context of what you want. There's been a lot of excitement about using the generative capabilities to create sort of games or sort of interactive stories or letting people develop characters or chat with a sort of _______ friend. There are applications that, for example, help a job ______ polish a ________ ___________ for each individual company. There's the _________ of tutors that can sort of _____ people about different concepts and take on different personas. And we can go on for a long time. But I think anything that you can imagine that you do today via computer that you would like to really understand and get to know you. And not only that, but understand all of the data and knowledge in the world and help you have the best experience that is is possible that that will happen. So what gets opened up? What new adjacent possible state is that as a result of these powers from this question, from the point of view of someone who's starting out on a career, for example, they're trying to figure out what would be a really interesting thing to do in the future that has only recently become possible. What are some new things that this opens up in a world where you can talk to a computer? And get. The output that would normally require you hiring the world _______ back ___________ for almost no money, I would say think about what's possible there. So that could be like, as you said, what can normally only the best programmer in the world or a really great programmer do for me. And can I now instead just ask in English and have that _______ written? So all these people that, you know, want to develop an app and they have an idea, but they don't know how to program. Now they can have it. You know, what is the service look like when anyone on Earth who wants really great _______ advice? Can get better medical advice than any single doctor could ever get, because this has the total medical knowledge and reasoning ability that the some humanity has ever produced. When you want to _____ something, you have sort of a tutor that understands your exact style, how you best learn everything you know, and custom _______ you whatever concept you want to learn someday. You can _______ that like. You have an eye that reads your email and your task list and your calendar and the documents you've been sent and in any meeting maximally perfectly ________ you and has all of the information that you need in all the context of your entire career right there for you to go on for a long time. But I think this will just be powerful _______. So it's really fun _______ around with Chapatti three, one compelling example of someone who's more tax base is try Googling The Guardian essay that was written entirely by different GP2 three queries and stitched together. It's an essay on why artificial intelligence isn't a threat to humanity. And that's impressive. It's very compelling. I actually tried inputting one of the three online uses. I asked the question what is interesting about some ollman? Oh no. Here's what it came back with. It was it was rather _____________, actually. Came back with. I don't understand what you mean by interesting. It seems to me that this word has no unambiguous meaning in the context of human society beyond its literal definition as somewhat pleasing or entertaining. I do not believe there to be any such thing as INTERESTINGNESS except in the mind of a human or other sentient being that to my knowledge, this is an entirely __________ _____ that ______ from person to person. However, I will _____ that this is an interesting question to ask. This does not mean it has been answered. There is no answer to be found. Well, so you can agree that somewhere between profound and gibberish is that almost well, with the state of play is I mean, that's where we are today. I think somewhere between profound and _________ is the right way to think about the current capabilities of CGP three. I think they would definitely had a ______ of hype about three last summer. But the thing about bubbles is the reason that _____ people fall for them is there's a kernel of something really real and really interesting that people get overexcited about. And I think people definitely got and still are overexcited about 3:00 today, but still probably underestimated the potential of where these models will go in the future. And so maybe there's this like short term overhyped and long term under hype for the ______ field, for tax models, for whatever you'd like. It's going on. And as you said, there's clearly some gibberish in there. But on the other hand, those were like well-formed sentences. And there were a couple of ideas and there that I was like, oh, like they actually maybe that's right. And I think if artificial intelligence, even in its _______ very larval state, can make us confront new things and sort of inspire new _____, that's already pretty impressive. Give us a sense of what's actually happening in the background there. I think it's hard to understand because you read these words seem like someone is trying to mean something. Obviously, I think you believe that there's whatever you've built there, that there's a sort of thinking, sentient thing that's going, oh, I must answer this question. So so what how would you describe what's going on? You've got something that has read the entire Internet, essentially all of Wikipedia, etc. We've read something that's read like a small ________ of a random sampling of the Internet. We will eventually _____ something that has read as much of the Internet or more of the Internet than we've done right now. But we have a very long way to go. I mean, we're still, I think, relative to what we will have ________ at quite small scale with quite small eyes. But what is happening is there is a _____ that is ingesting lots of text and it is trying to predict the next word. So we use Transformer's they take in a _______, which is a particular architecture of an A.I. model, they take in a context of a lot of words, let's say like a thousand or something like that. And they try to predict the word that comes next in the sequence. And there's like a lot of other things that happen, but fundamentally that's it, and I think this is interesting because in the _______ of playing that little game of trying to predict the next word, these models have to develop a representation and understanding of what is likely to come next and. I think it is maybe not perfectly accurate, but certainly worth considering to say that intelligence is very near the ability to make accurate predictions. What's confusing about this is that there are so many _____ on the Internet which are foolish as well as the words that are wise. And and how do you build a model that can distinguish between those two? And this is prompted actually by another example that I typed in. Like I asked, you know, what is a powerful idea, very interested in ideas. That was my question as a powerful idea. And it came back with several things, some of which seemed __________ pronouncements, which seemed moderately gibberish. But then he was he was one that it came back with the idea that the human race has, _____, evolved, unquote, is false evolution or adaptation within a species was abandoned by biology and genetics long ago. Wait a sec. That's news to me. What have you been reading? And I presume this has been pulled out of some recesses of the Internet, but how is it possible, even in ______, to imagine how a model can _________ towards truth, wisdom, as opposed to just like majority views? Or how how how do you avoid something taking us further into the sort of the maze of errors and bad ________ and so forth that has already been a worrying feature for the last few years ? It's a fantastic question, and I think it is the most interesting area of research that we need to pursue. Now, I think at this _____, the questions of whether we can build really powerful general-purpose AI system, I won't say there in the rearview mirror. We still have a lot of hard engineering work to do, but I'm ______ confident we're going to be able to. And now the questions are like, what should we build? And how and why and what data should we train on and how do we build systems not just that can do these like phenomenally __________ things, but that we can ensure do the things that we want and that understand the concepts of truth and falsehood and, you know, alignment with human values and ____________ with human values. One of the pieces of ________ that we put out last year that I was most proud of and most excited about is what we call reinforcement learning from human feedback. And we ______ that we can take these giant models that are trained on a _____ of stuff, some of it good, some of the bad, and then with a really quite small ______ of feedback from human judgment about, hey, this is good, this is bad, this is wrong, this is the behavior I want I don't want this behavior. We can feed that information from the human judges back into the model and we can teach the model, behave more like this and less like that. And it works better than I ever imagined it would. And that gives me a lot of hope that we can build an aligned system. We'll do other things, too, like I think curating data sets where there's just less sort of bad data to train on. It will go a very long way. And as these models get smarter, I think they __________ _______ the ability to sort out bad data from good data. And as they get really smart, they'll even start to do something we call active ________, which is where they ask us for exactly the data they need when they're missing something, when they're ______, when they don't understand. But I think as a result of ______ scaling these models up, building better, I hate to use the word cognition because it sounds so anthropomorphic, but let's say ________ a better _______ to ______ into the ______, to think, to _________, to try to understand and combining that with this idea of online into human ______ via this technique we developed, that's going to go a very long way. Now, there's another question, which you sort of just kicked the ball down the field, too, which is how do we as a society decide to which set of human values do we align these powerful systems? Yeah, indeed. So if I if I understand rightly what you're saying, that you're saying that it's possible to look at the ______ at any one time of three. And if we don't like what it's coming up with, some ways human can say, no, that was off, don't do that. Whatever algorithm or process led you to that, undo it. Yeah. And that the system is that incredibly powerful at avoiding that same kind of mistake in future because it sort of replicates the instructions , correct? Yeah. And eventually and not much longer, I believe that we'll be able to not only say that was good, that was bad, but say that was bad for this reason. And also tell me how you got to that answer so I can make sure I understand. But at the end of the day, someone needs to decide who is the wise human or short humans who are looking at the results. So it's a big difference. Someone who who grew up with intelligent design _____ view could look at that and go, that's a _________ _______. Well, Goldstar done. And someone else would say something is done awfully wrong here. So how do you avoid and this is a version of the _______ that a lot of the, I guess, _______ Valley companies are facing right now in terms of the pushback they're getting on the output of social media and so forth. How do you ________ that pool of experts who _____ for human values that we actually want? I mean, we talk about this all the time, I don't think this is like solely or even not even _____ to majorly up to opening night to decide, I think we need to begin a societal conversation now about how we're going to make those decisions, how we're going to make sure we have representational input in that, and how we sort of make these very difficult global governance systems. My personal ______ is that we should have pretty _____ rules about what these systems will never do and will always do. But then the individual user should get a system that kind of _______ like they want. And there will be people do have very different value systems. Some of them are just fundamentally ____________. No one gets to use eye to, like, exploit other people, for example, and hopefully we can all agree on. But do you want the AI to like. You know, support you and your belief of intelligent design, like, do I think openly, I should say it can't, even though I disagree with that is like a scientific conclusion. No, I wouldn't take that stance. I think the thing to ________ about all of this is that history is still quite extraordinarily weak. It's still has such big problems and it's still so unreliable that for most use cases it's still unsuitable. But when we think about a system that is like a thousand times more powerful and let's say a million _____ more reliable, it just doesn't it doesn't say gibberish very often. It doesn't totally lose the plot and get distracted or system like that is going to be one that a lot of the economic activity in the world comes to rely on. And I think it's very important that we don't have a small group of people sort of saying you can never use it for this thing that, like most of the world wants to use it for because it doesn't match our personal beliefs. Talk a bit more about some of the other uses of it, because one of the things that's most __________ is it's not just about sort of text responses. It's it can take ___________ human instructions and build things up. For example, you can say to it, write a Python program that is designed to put a flashing cursor in one corner of the screen, in the ______ logo in the other corner. And and it can go your way and do something like that. Shockingly, quite well, effectively. Yeah, I it can. That's amazing. I mean, this is _______ to me. That opens the door to. An entirely way to think about programers for the future, that you could you could have people who can program just in human natural language potentially and gain rapid efficiency. I do the engineering. We're not that far away from that world. We're not that far away from the world where you will write a spec in English. And for a ______ enough program, I will just write the code for you. As you said, you can see glimpses of that even in this very week three which was not trained to code like. I think this is important to remember. We trained it on the language on the Internet very rarely, you know, Internet let language on the Internet also ________ some code snippets. And that was enough, so if we really try to go train a model on code itself and that's where we decide to put the horsepower of the model into, just imagine what will be possible will be quite impressive. But I think what you're pointing to there is that because models like three to some degree or other, and it's like very hard to know exactly how much understand the underlying concepts of what's going on. And they're not just regurgitating things they found in a _______, but they can really apply them and say, oh, yeah, I kind of like know about this word and this idea and code. And this is probably what you're trying to do. And I won't get it right always. But sometimes I will just ________ this like a brand new program for nothing that anyone has ever asked before. And it will work. That's pretty cool. And data is data. So it can do that from English to code. It can do that from _______ to French. Again, we never told it to learn about translation. We never told it about the concepts of English and ______, but it learned them, even though we never said this is what English is and this is what French is and this is what it means to _________, it can still do it. Wow, I mean, for ________ people, is there a world coming where the sort of the palette of possibility that they can be exposed to is just explodes? I mean, if you're a ________, is there a near future where you can say to your eye, OK, I'm going to bed now, but in the morning I'd love you to present me with a thousand tuba jingles with words attached that you have of a sort of mean factor to the and you come down in the _______ and the ________ shows you the stuff. And one of them, you go, wow, that is it. That is a top 10 hit and you build a song from it. Or is that going to be released? Actually be the value add. We released something last year ______ Jukebox, which is very near what you described, where you can say I want music generated for me in this style or this kind of stuff, and it can come up with the words as well. And it's like pretty cool. And I really enjoy listening to _____ that it _______. And I can sort of do four songs, two bars of a jingle, whatever you'd like. And one of my very ________ _______ reached out, called to open it after we _______ this and said that he wanted to talk. And I was like, well, I like total fanboy here. I'd love to join that call. And I was so nervous that he was going to say, this is terrible. This is like a really sad thing for human creativity. Like, you know, why are you doing this? This is like whatever. And he was so excited. And he's like, this has been so _________. I want to do a new _____ with this. You know, it's like, give me all these new ideas. It's making me much better at my job. I'm going to make better music because of this tool. And that was awesome. And I hope that's how it all continues to go. And I think it is going to lead to this. We see a similar thing now with Dolly, where graphic _________ sometimes tell us that they just they see this new set of possibilities because there's new creative inspiration and they're cycle time, like the amount of time it _____ to just come up with an idea and be able to look at it and then decide whether to go down that path or head in a different direction goes down so much. And so I think it's going to just be this like incredible creative explosion for humans. And how far away are we some before? And I it comes up with a genuinely powerful new idea, an idea that solves the problem that humans have been wrestling with. It doesn't have to be as quite on the _____ as of, OK, we've got a virus ______. Please ________ to us what a what a national rational response should look like, but some kind of genuinely innovative idea or solution like one one ________ question we've asked ourselves is, when will the first genuinely interesting, purely AI written TED talk show up? I think that's a great milestone. I will say it's always hard to guess timeline's I'm sure I'll be wrong on this, but I would guess the first genuinely ___________. Ted talk, thought of written delivered by an AIDS within the kind of the seven ish year time frame. Maybe a little bit less. And it _____ like I mean, just reading that Guardian essay that was kind of it was a composite of several different GPG three _________ to questions about, you know, the threats of robotics or whatever. If you throw in a human editor into the mix, you could probably imagine something much sooner. Indeed. Like ________. Yeah. So the hybrid the hybrid version where it's basically a tool assisted TED talk, but that it is better than any TED talk a human could generate in one hundred hours or whatever, if you can sort of combine human discretion with A.I. __________. I suspect that's like our next year or two years from now kind of thing where it's just really quite good. That's that's really interesting. How do you view the impact of A.I. on jobs? There's obviously been the familiar story is that every White-Collar job is now up for ___________. What's what's your view there? You know, it's I think it's always hard to make these ___________. That is definitely the familiar story now. Five years ago, it was every blue collar job is up for destruction, maybe like last year it was. Every creative job is up for destruction because of things like Jukebox I. I think there will be an enormous impact on. The job market, and I really hate it, I think it's kind of gross when people like working on I _______ like there's not going to be or sort of say, oh, don't worry about it. It'll just all obviously better. It doesn't always obviously get better. I think what is true is. Every technological revolution produces a change in jobs, we always find new ones, at least so far. It's difficult to _______ from where we're sitting now what the new ones will be and this technological revolution is likely to be. Again, it's always tempting to say this time it's different. Maybe I'll be totally wrong. But from what I see now, this technological revolution is likely to be more. ________. More of a ________ note than most, and I think we as a society need to figure out how we're going to cushion everybody through that. I've got my own ideas about how to do that. I, I wouldn't say that I have any reason to believe they're the right ones, but doing nothing and not really engaging with the magnitude of what's about to happen, I think it's like not an __________ ______. So there's going to be huge impact. It's _________ to predict where it shows up the most. I think previous predictions have mostly been wrong, but I I'd like to see us all as a society, certainly as a field, engage in what what the shifts we want to make to the ______ ________ are to kind of get through that in a way that is _________ __________ to everybody. I mean, in every past __________, there's always been a _____ for humans to move to. That is, if you like, ______ up the food chain, it's sort of we've retreated to the things that humans could ________ do, think better, be more creative and so forth. I guess the _____ about A.I. is that in principle, I believe this, that there is no human cognitive feat that won't ultimately be doable, probably better by artificial general touch, simply because of the extra _________ that ultimately they can have, the vast knowledge they bring to the table and so forth. Is that _________ right, that there is ultimately no safe sort of space where we can say, oh, but that would never be able to do that on a very long time horizon? I agree with you, but that's such a long time _______. I think that, you know, like maybe we've merged by that point, like maybe we're all plugged in and then, like, we're this sort of symbiotic thing. Like, I think there's an interesting example, as we were talking about a few _______ ago, where right now we have these systems that have sort of enormous horsepower but no ________ wheel. It's like, you know , incredible capabilities, but no judgment. And there's like these obvious ways in which today even a human plus three is far better than either on their own. Many people speak about a world where it's sort of A.I. as this external threat you speak about. At some point, we actually merge with eyes in some way. What do you mean by that? There's a lot of different ________ of what I think is possible there, you know, in some sense, I'd argue the merge has already like begun the human technology _____ like we have this thing in our hands that sort of dictates a lot of what we think, but it gives us real superpowers and that can go much, much further. Maybe it goes all the way to like the Elon Musk vision of neuro link and having our brains plugged into computers and sort of like literally we have a computer on the back of our head or goes the other direction and we get uploaded into one. Or maybe it's just that we all have a chat bot that kind of __________ steers us and _____ us make better decisions than we could. But in any case, I think the fundamental thing is it's not like the humans versus the eyes _________ to be the. Smartest sentient thing on earth or beyond. But it's that this idea of being on the same team. Hmm. I certainly get very excited by the sort of the medium term _________ for creative people of all sorts if they're willing to expand their palette of _____________. But with the use of A.I. to be willing to. I mean, the one thing that the history of technology has shown again and again is that something this powerful and with this much benefit is unstoppable and you will get rewarded for _________ it the most and the earliest. So talk about what can go _____ with that, so let's move away from just the sort of ________ displacement ______. You were a co-founder of Open Eye because you saw ___________ risks to humanity from high today. What would you put as the sort of the most ________ of those risks? And how is open eye working to minimize? I still think all of the really __________ risks exist. I am more _________, much more confident than I was five years ago when we _______ that there are technical things we can do about. How we build these systems and the research and the _________ that make us much more likely to end up in the kind of really wonderful camp, but, you know, like maybe open I fall behind and maybe somebody else feels ajai that thinks about it in a very different way or doesn't care as much as we'd like about safety and the risks or how to strike a different trade off of how fast we should go with this and where we should sort of just say, like, you know, like let's push on for the economic benefits. But I think all of this sort of like, you know, _____________ what's been in the realm of sci fi risks are real and we should not ______ them. And I still lose sleep over them. And just to update people is artificial general intelligence. Right now, we have incredible examples of powerful AI operating on specific areas. Ajai is the ability of a computer mind to connect the dots and to make decisions at the same level of breadth that that ______ have had. What's your sort of elevator pitch on Ajai about how to ________ and how to think of it? Yeah, I mean, the way that I would say it is that for a while we were in this world of like very narrow A.I. , you know, that could like ________ images of cats or whatever, more advanced stuff in that. But that kind of thing. We are now in the era of general purpose, AI, where you have these systems that are still very much _________ tools, but that can generalize. And one thing like GPP three can write essays and translate between languages and write computer code and do very ___________ search. It's like a single model that understands enough of what's really going on to do a broad array of tasks and learn new things quickly, sort of like people can. And then __________ we'll get to this other realm. Some people call it ajai, some people call ostler things. But I think it implies that the systems are like to some degree self directed, have some intentionality of their own is a simple summary to say that, like the fundamental risk is that there's the potential with general artificial intelligence of a sort of _______ effect of self-improvement that can happen far faster than any kind of humans can even keep up with, so that the day after you get to ajai, suddenly computers are _________ of times more advanced than us and we have no way of controlling what they do with that power. Yeah, and that is certainly in the risk space, which is that we build this thing and at some point somewhat suddenly, it's much more powerful than we are, we haven't really done the full merge yet. There's an event horizon there and it's sort of hard to see to the other side of it. Again, lots of reasons to think it will go OK. Lots of reasons to think we won't even get to that scenario. But that is something that. I don't think people should brush under the rug as much as they do, it's in the possibility space for sure, and in the possibility subspace of that is one where, like, we didn't actually do as good of a job on the alignment work as we thought. And this sort of child of humanity kind of acts in a very different way than we think. A framework that I find useful is to sort of think about like a two by two matrix, which is _____ timelines to ajai and long timelines to ajai and a slow take off and a fast take off on the other axis. And in the short timelines, fast take off quadrant, which is not where I think we're going to be. But if we get there, I think there's a lot of _________ in the direction that you are describing that are worrisome. And we would want to spend a lot of effort planning for. I mean, the fact that a computer could start editing its own code and improving itself while we're asleep and you wake up in the morning and it's got smarter, that is the start of something super powerful and potentially scary. I have tremendous misgivings about letting my system, not one we have today, but one that we might not have and too many more years start editing its own code while we're not paying attention. I think that's the kind of thing that is worth a _____ deal of societal __________ about, you know, just because we can do that. Should we? Yes, because one of the things that's that's been most ________ to you about the last few years has been just the power of unintended consequences. It's like you don't have to have a belief that there's some sort of ______ up of of an alien intelligence that suddenly decided it wants to _____ havoc on humans. That may never happen. What you can have is just incredible _____ that goes amok. So a lot of people would argue that what's happened in technology in the last few years is actually an example of that. You know, social media companies created these _____________ that were programmed to maximally _______ attention, for example, for sure. And they understand this from that turned out to be in some ways horrifying and extraordinarily damaging. Is that a meaningful sort of canary in the coal mine saying, look out, humanity, this could be really dangerous? And how how on earth do you protect against those kinds of unintended consequences? I think you raise a great point in general, which is these systems don't have to wish ill to humanity to cause ill just when you have, like, very powerful systems. I mean, __________ ____________ for sure. But another version of that is and I think this applies at the technical level, at the company level, at the societal _____, incentives are superpower's. Charlie Munger had this thing on, which is incentives are so powerful that if you can spend any time __________ working on the incentive system, that's what you should do before you work on anything else. And I really believe that. And I think that _______ to the __________ models we _____ and what their reward _________ look like. I think it applies to society in a big way, and I think it applies to our _________ structure at open. I you know, we sort of observe that if you have very well-meaning people, but they have this incentive to sort of maximize attention harvesting and profit forever through no one's ill intentions, that leads to a quite undesirable outcome. And so we set up opening is this thing called a capped profit model specifically so that we don't have the system incentive to just generate maximum value forever with an AGI that seems like obviously quite broken. But even though we knew that was bad and even though we all like to think of ourselves as good people, it took us a long time to figure out the right _________, to figure out a charter that's going to govern us and a set of incentives that we believe will let us do our work. And kind of these we have these like three elements that we talk about a lot research sort of engineering, development and deployment policy and safety. Put those all together under a system where you don't have to rely on. Anything but the natural __________ to push in a direction that we hope will minimize the sort of negative unintended consequences. So help me understand this, because this is I think this is confusing to some people. So you started _______. I initially I think Elon Musk, the co-founder, and there was a group of you and the argument was this technology is too powerful to be left, developed in ______ and to be left developed purely by corporations who have whatever incentive they may have. We need a _________ that will develop and _____ knowledge openly. First of all, just even at that early stage, some people were confused about this. It was saying if this thing is so dangerous, why on earth would you want to make it secrets even more available? Well, maybe ______ the tools to that sort of AI _________ in his bedroom somewhere, I think I think we got misunderstood in the way we were talking about that. We certainly don't think that the right thing to do is to, like, build this a super ______ and hand it to a terrorist. That's obviously _____. One of the reasons that we like our API model is it lets us make the most powerful AI __________ anyone in the world has, as far as we know, available to ever would like to use it, but to put some controls on its usage. And also, if we make a mistake, to be able to pull it back or change it or tweak it or improve it or whatever. But we do want to put and this is continued will continue to be true with appropriate restrictions and __________, very powerful technology in the hands of people. I think that is fair. I think that will lead to the best results for the society as a whole. And I think it will sort of maximize _______. But that's very different than sort of ________ the whole model and saying, here, do whatever you want with it. We're able to enforce rules on it. We also think and this is part of the _______ that like something the field was doing a lot of that we didn't feel good about was sort of saying like, oh, we're going to keep the pace of progress and capabilities secret. That doesn't feel right, because I think we do need a ________ conversation about what's what's going on here, what the _______ are going to be. And so we although we don't always say, like, you know, here's the _____ weapon, hopefully we do try to say, like, this is really serious. This is a big deal. This is going to affect all of us. We need to have a big conversation about what to do with it. Help me understand the structure a bit better, because you definitely surprised much people when you announced that _________ were putting a billion _______ into the organization and in return, I guess they get certain exclusive licensing rights. And so, for example, they are the exclusive licensee of CP3. So talk about that structure of how you win. Microsoft presumably have invested not purely for __________ purposes. They think that they will make money on that billion dollars. I sure hope they do. I love capitalism, but I think that I really _____ even more about Microsoft as a partner. And I'll talk about the structure and the exclusive license in a minute is that we like went around to people that might find us. And we said one of the things here is that we're going to try to make you some money. But like Adjei going well is more important. And we need you to sign this document that says if things don't go the way we think and we can't make you money like you just cheerfully walk away from it and we do the right thing for humanity. And they were like, yes, we are enthusiastic about that. We get that the mission comes first here. So again, I hope a phenomenal investment for them. But they were like they really __________ surprised us on the upside of how aligned they were with us, about how strange the world may get here and the need for us to have flexibility and put our mission first, even if that means they lose all their money, which I hope they don't and don't think they will. So the way it's set up is that if at some point in the coming year or two, two years, Microsoft decide that there's some incredible commercial opportunity that they could realize out of the eye that you've built and you feel actually, no, that's that's damaging. You can block it. You can veto it. Correct. So the four most powerful version of three and its successors are available via the API, and we intend for that to ________. What Microsoft has is the ability to sort of put that model directly into their own technology. If they want to do that. We don't plan to do that with other people because we can't have all these controls that we talked about earlier. But they're like a close trusted partner and they really care about safety, too. But our goal is that anybody who wants to use the API can have the most ________ versions of what we've trained. And the structure of the API lets us continue to increase the safety and fix problems when we find them. But but the structure. So we start out as a non-profit, as you said, we realized pretty quickly that although we went into this thinking that the way to get to ajai would be about smarter and _______ __________, that we just needed bigger and ______ _________ as well. And that was going to require a scale of capital that no one will, at least certainly not me, could figure out how to raise is a nonprofit. We also needed to sort of be able to compensate very highly compensated, talented individuals that do this, but are full for profit company had runaway incentives problem, among other things. Also just one about sort of fairness in society and wealth concentration that didn't feel right to us either. And so we came up with this kind of hybrid where we have a nonprofit that governs what we do, and it has a subsidiary, LLC, that we structure in a way to make a fixed amount of profit so that all of our investors and employees, hopefully if things go how we like, if not no one gets any money, but hopefully they get to make this one time great ______ on their investment or the time that they _____ it open their equity here. And then beyond that, all the value flows back to the nonprofit and we figure out how to share it as fairly as we can with the world. And I think that this structure and this nonprofit with this very ______ _______ in place and everybody who _____ _______ up for the mission come in first and the fact the world may get strange, I think that. That was at least the best idea we could come up with, and I think it feels so far like the incentive system is _______, just as I sort of _____ the way that we and our partners make decisions. But if I read it right, the cap on the gain that investors can make is 100 Axum. It's a massive call that was for our very first round investors. It's way, way lower. Like as we now take a bit of capital, it's way, way lower. So your deal with Microsoft isn't you can only make the first hundred billion dollars. I don't know. It's way lower than after that. We're giving it to the world. It's way lower than that. Have you disclosed what I don't know if we have, so I won't accidentally do it now. All right. OK, so explain a bit more about the charter and how it is that you. Hope to _____ or I guess help contribute to an eye that is safe for humanity. What do you see as the keys to us avoiding the _____ mistakes and really _______ on to something that's that's beneficial for humanity? My answer there is actually more about, like technical and societal issues than the charter. So if it's OK for me to answer it from that ___________, sure. OK, I'm happy to talk about the charter to. I think this question of alignment that we ______ about a little _______ is paramount, and then I think to __________ that it's useful to _____________ between accidental misuse of a system and intentional misuse of a system. So like intentional would be a bad actor saying, I've got this powerful system, I'm going to use it to like hack into all the computers in the world and wreak havoc on the power _____. And accidental would be kind of the Nick Bostrom make a lot of paper clips and view humans as collateral damage in both cases. But to _______ _______, if we can really, truly, technically solve the alignment problem and the societal problem of deciding to which set of human values do we align, then the systems understand right and wrong, and they understand probably better than we ever can, unintended consequences from _______ _______ and very complex systems. And, you know, if we can train a system which is like. Don't harm humanity and the system can really understand what we mean when we say that, again, who is we and what does that have some asterisks on them? Sorry, go ahead. Well, that's if they could understand what it means to not harm humanity, that there's a lot wrapped up in that sentence. Because what's been so striking to me about efforts so far is that they seem to have been based on a very naive view of human nature. Go back to the sort of Facebook and Twitter examples of, well, the engineers building some of the systems would say we've just designed them around what humans want to do. You said, well, if someone wants to _____ on something, we will give them more of that thing. And what could ________ be wrong with that? We're just __________ human choice, ignoring the fact that humans are complicated, farshid animals for sure, who are constantly making _______, that a more effective _______ of themselves would agree is not in their long term interests. So that's one part of it. And then you've got layered on top of that or the complications of systemic complexity where, you know, ________ choices by thousands of people end up creating a reality that possibly have designed for how how to cut through that. Like an AI has to make a decision based on a moment, on a ________ data set. As those decisions get more powerful, how can we be confident that they don't lead to this sort of system crashing basically in some way? I think that I've heard a lot of behavioral psychologists and other people that have studied this say in different ways, are that I hate to keep picking on Facebook, but we can do it one more time since we're on the topic. Maybe you can't in any given moment in _____ where you're tired and you have a stressful day, stop yourself from the dopamine hit of _________ and Instagram, even though you know that's bad for you and it's not leading to your best life. But if you were _____ in a reflective moment where you were sort of fully alert and __________, do you want to spend as much time as you do scrolling through Instagram? Does it make you happier or not? You would actually be able to give like the right long term answer? It's sort of the spirit is willing, but the flesh is weak kind of ______. And one thing that I am _______ is that humans do know what we want and what. On the whole, and _________ with research or sort of an objective view about what makes us happy and doesn't we're pretty, what's so great about it, they're pretty good. But in any particular moment, we are subjected to our animal instincts and it is easy for the lower _____ to take over the eye. Well , I think be an even higher brain. And as we can teach it, you know, here is what we really do value. Here's what we really do want. It will help us make better _________ than we are capable of, even in our best moments. So is that being proposed and talked about as an actual rule? Because it _______ me that there is something ___________ super profound here to introduce some kind of rule for development of AIDS that they have to tap into not. What humans one, which is an ill _______ question, but as to what humans in reflective mode want. Yeah, we talk about this a lot. I mean, do you see a real ______ where something like that could be incorporated as a sort of an absolute ______ rule and and if you like, ______ around the community so that it seeps into corporations and elsewhere? Because that I've seen no evidence that, well, a little corporation that was potentially a game changer. Corporations have this weird incentive problem. Right. What I was trying to speak about was something that I think should be technologically possible , and that's something that we as a society should demand. And I think it is technically possible for this to be sort of like a layer above the neocortex that makes even better decisions for us and our _______ and our long term happiness and fulfillment than we could make on our own. And I think it is possible for us as a _______ to demand that. And if we can do like a ______ move between what the technology is capable of and what we what we as society demand, maybe we can make everybody in the middle that way. I mean, there are instances of even though companies have their incentives to make money and so forth, they also in the knowledge age. Can't make _____ if they have pissed off too many of their employees and customers and investors by _______ of the climate space right now, you can see more and more companies, even those that are emitting huge amounts of carbon dioxide, saying, wait a sec, we're struggling to recruit talented people because they don't want to work for someone who's evil. And their customers are saying, we don't want to buy something that is evil. And so, you know, ultimately you can picture processes where they do better. And I I believe that most engineers, for example, work in Silicon Valley. Companies are actually good people who want to ______ great products for humanity. I think that the people who run these companies want to be a net contribution to ________. It's we've we've rushed really quickly and design stuff without thinking it through properly. And it's led to a mess up. So it's like, OK, don't move fast, break things, slow down and build beautiful things that are _____ on a real version of human nature and on a real version of system complexity and the risks associated with ________ complexity. Is that the ______ that fundamentally you think that you can push somehow? Yes, but I think the way we can push it is by getting the _________ system right. I think most people are _____________ _________ good. Very few people wake up in the morning thinking about how can I make the world a worse place? But the incentive systems that we're in are so powerful. And even those _________ who join with the absolute best of intentions get sucked into this world where they're like trying to go up from it all for and five or whatever Facebook calls those things and you like, it's pretty exciting. You get caught up playing the game, you're rewarded for kind of doing things that move the company's key metrics. It's like fun to get promoted. It feels good to make more money and the incentive systems of the company. And that's what it rewards. An individual performance are maybe like not what we all want. And here I don't want to pick on Facebook at all because I think there's versions of this at play it like every big tech company, including in some ways I'm sure it open I but to the degree that we can better _____ the incentives of companies with the welfare of society and then the incentives of an individual at those companies within the now realign incentives for those companies, the more likely we are to be able to have things like ajai that. Follow an incentive ______ of. What we want in our most reflective best _______ and are even better than what we could think of ourselves is is it still the vision for open eye that you will get to? Artificial _______ intelligence ahead of. The ____________, so that you can somehow put a stake in the ground and build it the right way. Is that really a realistic thing to to dream for? And if not, how do you live up to the mission and help ensure that this thing doesn't go off the rails? I think it is. Look, I certainly don't think we will be the only _____ to build an AGI, but I think we could be the first. And I think if you are the first, you have a lot of norms that empower. And I think you've already seen that. You know, we have released some of the most powerful systems to date. And I think the way that we have done that kind of in controlled release where we've released a bigger model than a bigger one than a bigger one, and we sort of try and talk about the potential misuse cases and we try to like talk about the importance of releasing this behind an API so that you can make changes. Other groups have followed suit in some of those directions, and I think that's good. So, yes, I don't think we can be the only I do think we can be ahead. And if we are ahead, I think we can use that leverage to hopefully push people in a better _________ or maybe we're wrong and somebody else has a better direction. We're doing something about do you have a __________ advantage in that your mission is to do this for everyone as opposed to for some corporate _________. And that that that allows you that. Why is it that we came out of open eye and not someone else? It's like it's surprising in some ways when you're up against so much money and so much talent in these other _________ that you came up with this platform ahead of. You know, in some _____ it's surprising and in some sense, like the startup wins most of the time, like I'm a huge believer in startups as the best force for innovation we have in the world today. I talked a little bit about how we combine these three. Different clans of research, engineering and sort of safety and policy that don't normally combine well and I think we have an unusual strength, there were clearly like well funded. We have super talented ______. But what we really have is like intense focus and self belief that what we're doing is possible and good. And I appreciate the implied compliment. But, you know, we, like, work really hard. And if we stop doing that, I'm sure someone would run by us fast. Tell us a bit more about some of your prior life sentences. Yeah, for several years, you were running Y Combinator, which had incredible ______ on some 70 companies. There are so many startup stories that began at Y Combinator. What were key drivers in your own life that took you on the path you're on? And how did that path end up at Y Combinator? No exaggeration. I think I have back to back had the two jobs that are at least the most interesting to me in all of Silicon ______. I, I was I went to _______ to study computer science. I was a _____ computer nerd growing up. I knew like a little bit about startups, but not very much. I started working on this project the same year I started working on that. This thing called Y Combinator started and funded me and my co-founders. And we _______ out of school and did this _______, which I ran for like seven years. And then after that I got acquired. I had stayed close to my comment the whole time. I thought it was just this incredible group of people and spirit and set of incentives and just _____ misunderstood by most of the world, but _______ to everyone within it that it was going to create huge amounts of value and do a lot of new things. My company had acquired PJI, who is the founder of ICI, and like truly one of the most incredible humans and business people. And Paul Burrell, Paul Graham asked me if I ______ to run it. And kind of like the central learning of my ______, why I individual ________ has been that if you really scale them up, remarkable things can happen. And I. I did it and I was like, one of the things that would make this exciting for me personally __________ would be if I could sort of push it in the direction of doing these hard tech companies, one of which became open. I describe actually what Y Combinator is, you know, how many people come through it to give us a couple of stories of its impact. Yeah. So you basically apply as a handful of people and an idea, maybe a prototype and say, I would like to start a company and will you please fund me? And we review those ____________ and we I shouldn't say we anymore. I guess they fund four hundred companies a year. You get about one hundred and fifty ________ dollars while she takes about seven percent _________ and then gives you lots of advice and then networking and sort of this like fast track program for ________ a startup. I haven't looked at this in a while, but at one point a significant fraction of the _______ dollar plus companies in the US that got started. It all came through the Wiki program, some recently in the news ones have been like ______, ________, Coinbase, insta card stripe. And I think it's just it has become an incredible way to help. People who understand technology get a three month course in business, but instead of like herding you with an MBA, we actually teach you the things that matter and kind of go on to do incredible, incredible work. What is it about _____________? Why do they matter? Some people just find them kind of annoying. But I think you would _____ I think I would argue that they have done as much as anyone to _____ the future. Why ? What is it about them? I think it is the ability to take. And idea and by force of will to make it ______ in the world and in an incentive system that rewards you for making the most impact on the most people like in our system. That's how we get most of the things that that we use. That's how we got the computer that I'm using, the software I'm using to talk to you on it. Like all of this, you know, everyone in life, everything has a balance sheet. There's ______ of very annoying things about them. And there's plenty of very annoying things about the system that sort of ________ them. But we do get something really _________ in return. And I think that as a force for ______ things that make all of our lives better happen, it's very cool. Otherwise, you know, like if you have, like, a great idea, but you don't actually do anything useful with it for people, that's still cool. It's still intellectually interesting. But like, there's got to be something about the reward ________ in society that is like, did you actually do something useful? Did you create value? And I think entrepreneurship and startups are a wonderful way to do that. You know, we get all these great software companies. But I also think it's like how we're going to get ajai, how we're going to get nuclear fusion, how we're going to get life _________. And like on any of those topics are a long list of other things I could point to. There's like a number of startups that I think are doing incredible work, some of which will actually deliver. It is a truly amazing thing when you put the camera back and to believe that a human being could be _____ awake at night and something pops inside their mind as a patterning of the _______ in their brain that is effectively them saying, aha, I can see a way where the future could be better and and they can actually picture it. And then they wake up and then they talk to other people and they persuade them and they persuade investors and so forth. And the fact that this this system can happen and that they can then actually change the history changes in some sense. It is mind ________ that that happens that way and it happens again and again. So you've seen so many of these stories happen. What would you say? Is the is there a key thing that differentiates good entrepreneurs from others? If you could ______ down on one trait, what would it be? If I could pick only one, I would pick determination. I think that is the biggest predictor of success, the biggest differentiator predictor. And if you would allow a second, I would pick like communication skills or __________ or something in that direction as well. There are all of the obvious ones that matter, like ____________, but there's like a lot of smart people in the world. And when I look back at kind of the thousands of entrepreneurs I've worked with, all of many of whom were like quite capable, I would say that's like one and two of the surprisingly differentiated characteristics. What it's it's what I look at, the different things that you've built and you're working on. I mean, it could not be more foundational for the future. I mean, entrepreneurship. I know this is I agree that this is really what has driven the future. Do you see some people get really now they look at Silicon Valley and they look at this _____ and they worry about the culture. Right. That it's this is a bro _______. Do you see prospects of that changing _______ soon? And would you welcome it? Can we get better companies by really working to expand a group of people who can be entrepreneurs and who can contribute to aid, for example? For sure. And in fact, I think I'm hopeful, since these are the two things I've thought the most about. I'm excited for the day when someone combines them and uses A.I. to better select who did more fairly, maybe even ______ who to fund and how to ______ them and really kind of make entrepreneurship super widely available that will lead to like better outcomes and sort of more societal wealth for all of us. So are. So, yeah, I think. Broadening the set of people able to start companies and that sort of get the resources that you need, that is like an _____________ good thing and it's something that I think Silicon Valley is making some progress in. But I hope we see a lot more. And I do really, truly think that the technology industry entrepreneurship is one of the greatest forces for self betterment. If we can just figure out how to be a little bit more inclusive in how we do things. My last question today is about ideas were spreading. If you could inject one idea into the mind of everyone listening, what what would the idea be? We've touched on it a bunch, but the one idea would be the ajai really is going to happen. You have to engage with it seriously, and you shouldn't just listen to this and then brush aside and go about life as if it's not going to happen because it is going to affect everything. And we will all we all, I think, have an obligation, but also an opportunity to figure out what not means and how we want the world and this sort of one time shift to go on. I'm kind of awed by the breadth of things are engaged with. Thank you so much for spending so much time sharing your vision. Thanks so much for having me. OK, that's it for today. You can read more about open eyes, vision and ________ at open eye dotcom. If you want to try playing with yourself, it's a little tricky. You have to find a website that has licensed the API. The one I went to was ___________ ehi dot com, where you just you pay a few dollars to get access to a very strange mind. That's actually quite a lot of fun. The _________ is part of the TED Audio Collective, a collection of podcasts dedicated to sparking curiosity and sharing ideas that matter. This show is produced by Kim Net2Phone Pittas and edited by Grace Rubenstein and Sheila _______, Sambor Islamic Sir. Fact Check is by Paul Durbin and special thanks to Michele Quent, Colin ______ and Anna _____. If you like the show, please write and review it. It helps other people find us. We read every ______, so thanks so much for _________. See you next time.

Solution

applies
helmes
dollars
argue
plenty
interview
felin
teaches
worst
strikes
ignore
companies
pleasantly
belief
silicon
horsepower
thousands
classify
dystopian
close
happen
problems
constantly
creates
challenge
change
factor
moving
analogy
showed
continue
inspiring
designers
called
career
loved
structural
wrong
confident
joins
microsoft
basically
musician
start
spent
complicated
major
broad
dropped
imagine
objective
hugely
competing
multiple
staccato
interact
moderately
artists
technology
identify
bunch
social
power
societal
chance
motivating
asked
functions
virtual
watch
corporate
train
night
impact
great
interface
includes
times
choices
possibly
anytime
extraordinarily
tremendously
years
current
takes
humans
jordache
difficult
entire
embracing
nonprofit
build
company
light
direction
tomorrow
design
learning
firepower
grids
mentioned
humanity
presented
subjective
destruction
algorithms
computers
rapidity
values
unequivocally
making
perspective
state
developing
bigger
double
weapon
potential
startups
amount
surprising
alignment
strong
cases
contract
mission
amazing
philosophical
thoughtful
creative
extremely
predict
acceptable
incentive
whatsoever
misalignment
english
saved
sense
output
shipping
twenty
pretend
wanted
worrying
function
incentives
anderson
extension
ownership
review
negative
supporting
signing
existential
computer
fifty
optimism
resources
beginning
moments
consequences
neurons
pretty
process
answer
unintended
brain
uniquely
short
college
ideas
starting
model
discussion
align
remember
prepares
treat
potentially
guess
applications
hundreds
experts
intelligences
secret
corporations
ability
immediately
runaway
medical
conversation
understand
guardrails
evangelism
varying
decisions
learn
words
interesting
stand
impressive
program
super
boffano
money
merge
badly
horrifying
outcome
generate
album
agenda
horizon
climate
unsure
research
culture
version
impacts
reason
quote
moment
avoid
lofty
charter
click
helps
welfare
morning
systems
people
differentiate
french
context
group
listening
structure
couple
degrees
naive
jibberish
scrolling
global
annoying
models
idolizes
fundamentally
release
imperfect
entrepreneurs
thousand
working
beneficial
awful
problem
playing
opening
services
bubble
coming
level
website
system
seeker
incompatible
golden
airbnb
chris
varies
worry
harvest
incredibly
develop
complex
today
grant
brilliant
revolution
teach
shocking
wreak
person
individual
altruistic
general
thinking
tailored
valley
feels
defined
powerful
philosopher
simply
terrorist
earlier
actions
boggling
human
assemble
building
steering
engineers
application
responses
accessible
scale
society
decision
started
obvious
built
giving
google
possibilities
advise
economic
waking
share
fraction
operated
talked
president
world
smarter
theory
intelligence
scenarios
internal
story
specific
behaves
minutes
quality
point
music
translate
lying
generalized
spread
predictions
space
systemic
pincer
billion
benefit
favorite
inherently
gravitate
select
describe
important
dramatic
versions
traditionally
hopeful
simple
smart
maximally
holding
progress
eventually
return
shape

Original Text

Hello there, this is Chris Anderson, and I am hugely, hugely, tremendously excited to welcome you to a new series of the TED interview. Now, then, this season, we're trying something new. We're organising the whole season around a single theme, albeit a theme that some of you may consider inappropriate. But hear me out. The theme is the case for optimism. And yes, I know the world has been hit with extraordinarily ugly things in the last few years. Political division, a racial reckoning, technology run amuck, not to mention a global pandemic and impending climate catastrophe. What on earth are we thinking in this context? Optimism just seems so naive and unwanted, almost annoying. So here's my position. Don't think of optimism as a feeling. It's not just this sort of shallow feeling of hope. Optimism is a search. It's a determination to look for a pathway forward somewhere out there. I believe I truly believe there are amazing people whose minds contain the ideas, the visions, the solutions that can actually create that pathway forward. If given the support and resources they need, they may very well light the path out of this dark place we're in. So these are the people who can present not optimism, but a case for optimism. They're the people I'm talking to this season. So let's see if they can persuade us now. Then the place I want to start is with A.I. artificial intelligence. This, of course, is the next innovative technology that is going to change everything as we know it, for better or for worse. Today was painted not with the usual dystopian brush, but by someone who truly believes in its potential. Sam Altman is the former president of Y Combinator, the legendary startup accelerator. And in 2015, he and a team launched a company called Open Eye, dedicated to one noble purpose to develop A.I. so that it benefits humanity as a whole. You may have heard, by the way, recently a lot of buzz around in A.I. technology called T3 that was developed by open eye improve the quality of the amazing team of researchers and developers they have work in. There will be hearing a lot about three in the conversation ahead. But sticking to this lofty mission of developing A.I. for humanity and finding the resources to realize it haven't been simple. Open A.I. is certainly not without its critics, but their goal couldn't be more important. And honestly, I found it really quite exciting to hear Sam's vision for where all this could lead. OK, let's do this. So, Sam Altman, welcome. Thank you for having me. So, Sam, here we are in 2021. A lot of people are fearful of the future at this moment in world history. How would you describe your attitude to the future? I think that the combination of scientific and technological progress and better societal decision making, better societal governance is going to solve in the next couple of decades all of our current most pressing problems, there will be new ones. But I think we are going to get very safe, very inexpensive, carbon free nuclear energy to work. And I think we're going to talk about that time that the climate disaster looks so bad and how lucky we are. We got saved by science and technology, I think. And we've already now seen this with the rapidity that we were able to get vaccines deployed. We are going to find that we are able to cure or at least treat a significant percentage of human disease, including I think we'll just actually make progress in helping people have much longer decades, longer health spans. And I think in the next couple of decades, that will look pretty clear. I think we will build systems with AI and otherwise that make access to an incredibly high quality education more possible than ever before. I think the lives we look forward like one hundred years, fifty years, even the quality of life available to anyone then will be much better than the quality of life available in the very best case to anyone today, to any single person today. So, yeah, I'm super optimistic. I think, like, it's always easy to do scroll and think about how bad are the bad things are, but the good things are really good and getting much better. Is it your sincere belief that artificial intelligence can actually make that future better? Certainly. How look, with any technology. I don't think it will all be better. I think there are always positive and negative use cases of anything new, and it's our job to maximize the positive ones, minimize the negative ones. But I truly, genuinely believe that the positive impacts will be orders of magnitude bigger than the negative ones. I think we're seeing a glimpse of that now. Now that we have the first general purpose built out in the world and available via things like RPI, I think we are seeing evidence of just the breadth of services that we will be able to offer as the sort of technological revolution really takes hold. And we will have people interact with services that are smart, really smart, and it will feel like as strange as the world before mobile phones feels now to us. Hmm, yeah, you mentioned your API, I guess that stands for what, application programming interface? It's the technology that allows complex technology to be accessible to others. So give me a sense of a couple of things that have got you most excited that are already out there and then how that gives you visibility to a pathway forward that is even more exciting. So I think that the things that we're seeing now are very much glimpse of the future. We released three, which is a general-purpose natural language text model in the summer of twenty twenty. You know, there's hundreds of applications that are now using it in production that's ramping up all of the time. But there are things where people use three to really understand the intent behind the search query and deliver results and sort of understand not only intent, but all of the data and deliver the thing of what you want. So you can sort of describe a fuzzy thing and it'll understand documents. It can understand, you know, short documents, not full books yet, but bring you back to the context of what you want. There's been a lot of excitement about using the generative capabilities to create sort of games or sort of interactive stories or letting people develop characters or chat with a sort of virtual friend. There are applications that, for example, help a job seeker polish a tailored application for each individual company. There's the beginning of tutors that can sort of teach people about different concepts and take on different personas. And we can go on for a long time. But I think anything that you can imagine that you do today via computer that you would like to really understand and get to know you. And not only that, but understand all of the data and knowledge in the world and help you have the best experience that is is possible that that will happen. So what gets opened up? What new adjacent possible state is that as a result of these powers from this question, from the point of view of someone who's starting out on a career, for example, they're trying to figure out what would be a really interesting thing to do in the future that has only recently become possible. What are some new things that this opens up in a world where you can talk to a computer? And get. The output that would normally require you hiring the world experts back immediately for almost no money, I would say think about what's possible there. So that could be like, as you said, what can normally only the best programmer in the world or a really great programmer do for me. And can I now instead just ask in English and have that program written? So all these people that, you know, want to develop an app and they have an idea, but they don't know how to program. Now they can have it. You know, what is the service look like when anyone on Earth who wants really great medical advice? Can get better medical advice than any single doctor could ever get, because this has the total medical knowledge and reasoning ability that the some humanity has ever produced. When you want to learn something, you have sort of a tutor that understands your exact style, how you best learn everything you know, and custom teaches you whatever concept you want to learn someday. You can imagine that like. You have an eye that reads your email and your task list and your calendar and the documents you've been sent and in any meeting maximally perfectly prepares you and has all of the information that you need in all the context of your entire career right there for you to go on for a long time. But I think this will just be powerful systems. So it's really fun playing around with Chapatti three, one compelling example of someone who's more tax base is try Googling The Guardian essay that was written entirely by different GP2 three queries and stitched together. It's an essay on why artificial intelligence isn't a threat to humanity. And that's impressive. It's very compelling. I actually tried inputting one of the three online uses. I asked the question what is interesting about some ollman? Oh no. Here's what it came back with. It was it was rather philosophical, actually. Came back with. I don't understand what you mean by interesting. It seems to me that this word has no unambiguous meaning in the context of human society beyond its literal definition as somewhat pleasing or entertaining. I do not believe there to be any such thing as INTERESTINGNESS except in the mind of a human or other sentient being that to my knowledge, this is an entirely subjective state that varies from person to person. However, I will grant that this is an interesting question to ask. This does not mean it has been answered. There is no answer to be found. Well, so you can agree that somewhere between profound and gibberish is that almost well, with the state of play is I mean, that's where we are today. I think somewhere between profound and jibberish is the right way to think about the current capabilities of CGP three. I think they would definitely had a bubble of hype about three last summer. But the thing about bubbles is the reason that smart people fall for them is there's a kernel of something really real and really interesting that people get overexcited about. And I think people definitely got and still are overexcited about 3:00 today, but still probably underestimated the potential of where these models will go in the future. And so maybe there's this like short term overhyped and long term under hype for the entire field, for tax models, for whatever you'd like. It's going on. And as you said, there's clearly some gibberish in there. But on the other hand, those were like well-formed sentences. And there were a couple of ideas and there that I was like, oh, like they actually maybe that's right. And I think if artificial intelligence, even in its current very larval state, can make us confront new things and sort of inspire new ideas, that's already pretty impressive. Give us a sense of what's actually happening in the background there. I think it's hard to understand because you read these words seem like someone is trying to mean something. Obviously, I think you believe that there's whatever you've built there, that there's a sort of thinking, sentient thing that's going, oh, I must answer this question. So so what how would you describe what's going on? You've got something that has read the entire Internet, essentially all of Wikipedia, etc. We've read something that's read like a small fraction of a random sampling of the Internet. We will eventually train something that has read as much of the Internet or more of the Internet than we've done right now. But we have a very long way to go. I mean, we're still, I think, relative to what we will have operated at quite small scale with quite small eyes. But what is happening is there is a model that is ingesting lots of text and it is trying to predict the next word. So we use Transformer's they take in a context, which is a particular architecture of an A.I. model, they take in a context of a lot of words, let's say like a thousand or something like that. And they try to predict the word that comes next in the sequence. And there's like a lot of other things that happen, but fundamentally that's it, and I think this is interesting because in the process of playing that little game of trying to predict the next word, these models have to develop a representation and understanding of what is likely to come next and. I think it is maybe not perfectly accurate, but certainly worth considering to say that intelligence is very near the ability to make accurate predictions. What's confusing about this is that there are so many words on the Internet which are foolish as well as the words that are wise. And and how do you build a model that can distinguish between those two? And this is prompted actually by another example that I typed in. Like I asked, you know, what is a powerful idea, very interested in ideas. That was my question as a powerful idea. And it came back with several things, some of which seemed moderately pronouncements, which seemed moderately gibberish. But then he was he was one that it came back with the idea that the human race has, quote, evolved, unquote, is false evolution or adaptation within a species was abandoned by biology and genetics long ago. Wait a sec. That's news to me. What have you been reading? And I presume this has been pulled out of some recesses of the Internet, but how is it possible, even in theory, to imagine how a model can gravitate towards truth, wisdom, as opposed to just like majority views? Or how how how do you avoid something taking us further into the sort of the maze of errors and bad thinking and so forth that has already been a worrying feature for the last few years ? It's a fantastic question, and I think it is the most interesting area of research that we need to pursue. Now, I think at this point, the questions of whether we can build really powerful general-purpose AI system, I won't say there in the rearview mirror. We still have a lot of hard engineering work to do, but I'm pretty confident we're going to be able to. And now the questions are like, what should we build? And how and why and what data should we train on and how do we build systems not just that can do these like phenomenally impressive things, but that we can ensure do the things that we want and that understand the concepts of truth and falsehood and, you know, alignment with human values and misalignment with human values. One of the pieces of research that we put out last year that I was most proud of and most excited about is what we call reinforcement learning from human feedback. And we showed that we can take these giant models that are trained on a bunch of stuff, some of it good, some of the bad, and then with a really quite small amount of feedback from human judgment about, hey, this is good, this is bad, this is wrong, this is the behavior I want I don't want this behavior. We can feed that information from the human judges back into the model and we can teach the model, behave more like this and less like that. And it works better than I ever imagined it would. And that gives me a lot of hope that we can build an aligned system. We'll do other things, too, like I think curating data sets where there's just less sort of bad data to train on. It will go a very long way. And as these models get smarter, I think they inherently develop the ability to sort out bad data from good data. And as they get really smart, they'll even start to do something we call active learning, which is where they ask us for exactly the data they need when they're missing something, when they're unsure, when they don't understand. But I think as a result of simply scaling these models up, building better, I hate to use the word cognition because it sounds so anthropomorphic, but let's say building a better ability to reason into the models, to think, to challenge, to try to understand and combining that with this idea of online into human values via this technique we developed, that's going to go a very long way. Now, there's another question, which you sort of just kicked the ball down the field, too, which is how do we as a society decide to which set of human values do we align these powerful systems? Yeah, indeed. So if I if I understand rightly what you're saying, that you're saying that it's possible to look at the output at any one time of three. And if we don't like what it's coming up with, some ways human can say, no, that was off, don't do that. Whatever algorithm or process led you to that, undo it. Yeah. And that the system is that incredibly powerful at avoiding that same kind of mistake in future because it sort of replicates the instructions , correct? Yeah. And eventually and not much longer, I believe that we'll be able to not only say that was good, that was bad, but say that was bad for this reason. And also tell me how you got to that answer so I can make sure I understand. But at the end of the day, someone needs to decide who is the wise human or short humans who are looking at the results. So it's a big difference. Someone who who grew up with intelligent design world view could look at that and go, that's a brilliant outcome. Well, Goldstar done. And someone else would say something is done awfully wrong here. So how do you avoid and this is a version of the problem that a lot of the, I guess, Silicon Valley companies are facing right now in terms of the pushback they're getting on the output of social media and so forth. How do you assemble that pool of experts who stand for human values that we actually want? I mean, we talk about this all the time, I don't think this is like solely or even not even close to majorly up to opening night to decide, I think we need to begin a societal conversation now about how we're going to make those decisions, how we're going to make sure we have representational input in that, and how we sort of make these very difficult global governance systems. My personal belief is that we should have pretty broad rules about what these systems will never do and will always do. But then the individual user should get a system that kind of behaves like they want. And there will be people do have very different value systems. Some of them are just fundamentally incompatible. No one gets to use eye to, like, exploit other people, for example, and hopefully we can all agree on. But do you want the AI to like. You know, support you and your belief of intelligent design, like, do I think openly, I should say it can't, even though I disagree with that is like a scientific conclusion. No, I wouldn't take that stance. I think the thing to remember about all of this is that history is still quite extraordinarily weak. It's still has such big problems and it's still so unreliable that for most use cases it's still unsuitable. But when we think about a system that is like a thousand times more powerful and let's say a million times more reliable, it just doesn't it doesn't say gibberish very often. It doesn't totally lose the plot and get distracted or system like that is going to be one that a lot of the economic activity in the world comes to rely on. And I think it's very important that we don't have a small group of people sort of saying you can never use it for this thing that, like most of the world wants to use it for because it doesn't match our personal beliefs. Talk a bit more about some of the other uses of it, because one of the things that's most surprising is it's not just about sort of text responses. It's it can take generalized human instructions and build things up. For example, you can say to it, write a Python program that is designed to put a flashing cursor in one corner of the screen, in the Google logo in the other corner. And and it can go your way and do something like that. Shockingly, quite well, effectively. Yeah, I it can. That's amazing. I mean, this is amazing to me. That opens the door to. An entirely way to think about programers for the future, that you could you could have people who can program just in human natural language potentially and gain rapid efficiency. I do the engineering. We're not that far away from that world. We're not that far away from the world where you will write a spec in English. And for a simple enough program, I will just write the code for you. As you said, you can see glimpses of that even in this very week three which was not trained to code like. I think this is important to remember. We trained it on the language on the Internet very rarely, you know, Internet let language on the Internet also includes some code snippets. And that was enough, so if we really try to go train a model on code itself and that's where we decide to put the horsepower of the model into, just imagine what will be possible will be quite impressive. But I think what you're pointing to there is that because models like three to some degree or other, and it's like very hard to know exactly how much understand the underlying concepts of what's going on. And they're not just regurgitating things they found in a website, but they can really apply them and say, oh, yeah, I kind of like know about this word and this idea and code. And this is probably what you're trying to do. And I won't get it right always. But sometimes I will just generate this like a brand new program for nothing that anyone has ever asked before. And it will work. That's pretty cool. And data is data. So it can do that from English to code. It can do that from English to French. Again, we never told it to learn about translation. We never told it about the concepts of English and French, but it learned them, even though we never said this is what English is and this is what French is and this is what it means to translate, it can still do it. Wow, I mean, for creative people, is there a world coming where the sort of the palette of possibility that they can be exposed to is just explodes? I mean, if you're a musician, is there a near future where you can say to your eye, OK, I'm going to bed now, but in the morning I'd love you to present me with a thousand tuba jingles with words attached that you have of a sort of mean factor to the and you come down in the morning and the computer shows you the stuff. And one of them, you go, wow, that is it. That is a top 10 hit and you build a song from it. Or is that going to be released? Actually be the value add. We released something last year called Jukebox, which is very near what you described, where you can say I want music generated for me in this style or this kind of stuff, and it can come up with the words as well. And it's like pretty cool. And I really enjoy listening to music that it creates. And I can sort of do four songs, two bars of a jingle, whatever you'd like. And one of my very favorite artists reached out, called to open it after we release this and said that he wanted to talk. And I was like, well, I like total fanboy here. I'd love to join that call. And I was so nervous that he was going to say, this is terrible. This is like a really sad thing for human creativity. Like, you know, why are you doing this? This is like whatever. And he was so excited. And he's like, this has been so inspiring. I want to do a new album with this. You know, it's like, give me all these new ideas. It's making me much better at my job. I'm going to make better music because of this tool. And that was awesome. And I hope that's how it all continues to go. And I think it is going to lead to this. We see a similar thing now with Dolly, where graphic designers sometimes tell us that they just they see this new set of possibilities because there's new creative inspiration and they're cycle time, like the amount of time it takes to just come up with an idea and be able to look at it and then decide whether to go down that path or head in a different direction goes down so much. And so I think it's going to just be this like incredible creative explosion for humans. And how far away are we some before? And I it comes up with a genuinely powerful new idea, an idea that solves the problem that humans have been wrestling with. It doesn't have to be as quite on the scale as of, OK, we've got a virus coming. Please describe to us what a what a national rational response should look like, but some kind of genuinely innovative idea or solution like one one internal question we've asked ourselves is, when will the first genuinely interesting, purely AI written TED talk show up? I think that's a great milestone. I will say it's always hard to guess timeline's I'm sure I'll be wrong on this, but I would guess the first genuinely interesting. Ted talk, thought of written delivered by an AIDS within the kind of the seven ish year time frame. Maybe a little bit less. And it feels like I mean, just reading that Guardian essay that was kind of it was a composite of several different GPG three responses to questions about, you know, the threats of robotics or whatever. If you throw in a human editor into the mix, you could probably imagine something much sooner. Indeed. Like tomorrow. Yeah. So the hybrid the hybrid version where it's basically a tool assisted TED talk, but that it is better than any TED talk a human could generate in one hundred hours or whatever, if you can sort of combine human discretion with A.I. horsepower. I suspect that's like our next year or two years from now kind of thing where it's just really quite good. That's that's really interesting. How do you view the impact of A.I. on jobs? There's obviously been the familiar story is that every White-Collar job is now up for destruction. What's what's your view there? You know, it's I think it's always hard to make these predictions. That is definitely the familiar story now. Five years ago, it was every blue collar job is up for destruction, maybe like last year it was. Every creative job is up for destruction because of things like Jukebox I. I think there will be an enormous impact on. The job market, and I really hate it, I think it's kind of gross when people like working on I pretend like there's not going to be or sort of say, oh, don't worry about it. It'll just all obviously better. It doesn't always obviously get better. I think what is true is. Every technological revolution produces a change in jobs, we always find new ones, at least so far. It's difficult to predict from where we're sitting now what the new ones will be and this technological revolution is likely to be. Again, it's always tempting to say this time it's different. Maybe I'll be totally wrong. But from what I see now, this technological revolution is likely to be more. Dramatic. More of a staccato note than most, and I think we as a society need to figure out how we're going to cushion everybody through that. I've got my own ideas about how to do that. I, I wouldn't say that I have any reason to believe they're the right ones, but doing nothing and not really engaging with the magnitude of what's about to happen, I think it's like not an acceptable answer. So there's going to be huge impact. It's difficult to predict where it shows up the most. I think previous predictions have mostly been wrong, but I I'd like to see us all as a society, certainly as a field, engage in what what the shifts we want to make to the social contract are to kind of get through that in a way that is maximally beneficial to everybody. I mean, in every past revolution, there's always been a space for humans to move to. That is, if you like, moving up the food chain, it's sort of we've retreated to the things that humans could uniquely do, think better, be more creative and so forth. I guess the worry about A.I. is that in principle, I believe this, that there is no human cognitive feat that won't ultimately be doable, probably better by artificial general touch, simply because of the extra firepower that ultimately they can have, the vast knowledge they bring to the table and so forth. Is that basically right, that there is ultimately no safe sort of space where we can say, oh, but that would never be able to do that on a very long time horizon? I agree with you, but that's such a long time horizon. I think that, you know, like maybe we've merged by that point, like maybe we're all plugged in and then, like, we're this sort of symbiotic thing. Like, I think there's an interesting example, as we were talking about a few minutes ago, where right now we have these systems that have sort of enormous horsepower but no steering wheel. It's like, you know , incredible capabilities, but no judgment. And there's like these obvious ways in which today even a human plus three is far better than either on their own. Many people speak about a world where it's sort of A.I. as this external threat you speak about. At some point, we actually merge with eyes in some way. What do you mean by that? There's a lot of different versions of what I think is possible there, you know, in some sense, I'd argue the merge has already like begun the human technology merge like we have this thing in our hands that sort of dictates a lot of what we think, but it gives us real superpowers and that can go much, much further. Maybe it goes all the way to like the Elon Musk vision of neuro link and having our brains plugged into computers and sort of like literally we have a computer on the back of our head or goes the other direction and we get uploaded into one. Or maybe it's just that we all have a chat bot that kind of constantly steers us and helps us make better decisions than we could. But in any case, I think the fundamental thing is it's not like the humans versus the eyes competing to be the. Smartest sentient thing on earth or beyond. But it's that this idea of being on the same team. Hmm. I certainly get very excited by the sort of the medium term potential for creative people of all sorts if they're willing to expand their palette of possibilities. But with the use of A.I. to be willing to. I mean, the one thing that the history of technology has shown again and again is that something this powerful and with this much benefit is unstoppable and you will get rewarded for embracing it the most and the earliest. So talk about what can go wrong with that, so let's move away from just the sort of economic displacement factor. You were a co-founder of Open Eye because you saw existential risks to humanity from high today. What would you put as the sort of the most worrying of those risks? And how is open eye working to minimize? I still think all of the really horrifying risks exist. I am more confident, much more confident than I was five years ago when we started that there are technical things we can do about. How we build these systems and the research and the alignment that make us much more likely to end up in the kind of really wonderful camp, but, you know, like maybe open I fall behind and maybe somebody else feels ajai that thinks about it in a very different way or doesn't care as much as we'd like about safety and the risks or how to strike a different trade off of how fast we should go with this and where we should sort of just say, like, you know, like let's push on for the economic benefits. But I think all of this sort of like, you know, traditionally what's been in the realm of sci fi risks are real and we should not ignore them. And I still lose sleep over them. And just to update people is artificial general intelligence. Right now, we have incredible examples of powerful AI operating on specific areas. Ajai is the ability of a computer mind to connect the dots and to make decisions at the same level of breadth that that humans have had. What's your sort of elevator pitch on Ajai about how to identify and how to think of it? Yeah, I mean, the way that I would say it is that for a while we were in this world of like very narrow A.I. , you know, that could like classify images of cats or whatever, more advanced stuff in that. But that kind of thing. We are now in the era of general purpose, AI, where you have these systems that are still very much imperfect tools, but that can generalize. And one thing like GPP three can write essays and translate between languages and write computer code and do very complicated search. It's like a single model that understands enough of what's really going on to do a broad array of tasks and learn new things quickly, sort of like people can. And then eventually we'll get to this other realm. Some people call it ajai, some people call ostler things. But I think it implies that the systems are like to some degree self directed, have some intentionality of their own is a simple summary to say that, like the fundamental risk is that there's the potential with general artificial intelligence of a sort of runaway effect of self-improvement that can happen far faster than any kind of humans can even keep up with, so that the day after you get to ajai, suddenly computers are thousands of times more advanced than us and we have no way of controlling what they do with that power. Yeah, and that is certainly in the risk space, which is that we build this thing and at some point somewhat suddenly, it's much more powerful than we are, we haven't really done the full merge yet. There's an event horizon there and it's sort of hard to see to the other side of it. Again, lots of reasons to think it will go OK. Lots of reasons to think we won't even get to that scenario. But that is something that. I don't think people should brush under the rug as much as they do, it's in the possibility space for sure, and in the possibility subspace of that is one where, like, we didn't actually do as good of a job on the alignment work as we thought. And this sort of child of humanity kind of acts in a very different way than we think. A framework that I find useful is to sort of think about like a two by two matrix, which is short timelines to ajai and long timelines to ajai and a slow take off and a fast take off on the other axis. And in the short timelines, fast take off quadrant, which is not where I think we're going to be. But if we get there, I think there's a lot of scenarios in the direction that you are describing that are worrisome. And we would want to spend a lot of effort planning for. I mean, the fact that a computer could start editing its own code and improving itself while we're asleep and you wake up in the morning and it's got smarter, that is the start of something super powerful and potentially scary. I have tremendous misgivings about letting my system, not one we have today, but one that we might not have and too many more years start editing its own code while we're not paying attention. I think that's the kind of thing that is worth a great deal of societal discussion about, you know, just because we can do that. Should we? Yes, because one of the things that's that's been most shocking to you about the last few years has been just the power of unintended consequences. It's like you don't have to have a belief that there's some sort of waking up of of an alien intelligence that suddenly decided it wants to wreak havoc on humans. That may never happen. What you can have is just incredible power that goes amok. So a lot of people would argue that what's happened in technology in the last few years is actually an example of that. You know, social media companies created these intelligences that were programmed to maximally harvest attention, for example, for sure. And they understand this from that turned out to be in some ways horrifying and extraordinarily damaging. Is that a meaningful sort of canary in the coal mine saying, look out, humanity, this could be really dangerous? And how how on earth do you protect against those kinds of unintended consequences? I think you raise a great point in general, which is these systems don't have to wish ill to humanity to cause ill just when you have, like, very powerful systems. I mean, unintended consequences for sure. But another version of that is and I think this applies at the technical level, at the company level, at the societal level, incentives are superpower's. Charlie Munger had this thing on, which is incentives are so powerful that if you can spend any time whatsoever working on the incentive system, that's what you should do before you work on anything else. And I really believe that. And I think that applies to the individual models we build and what their reward functions look like. I think it applies to society in a big way, and I think it applies to our corporate structure at open. I you know, we sort of observe that if you have very well-meaning people, but they have this incentive to sort of maximize attention harvesting and profit forever through no one's ill intentions, that leads to a quite undesirable outcome. And so we set up opening is this thing called a capped profit model specifically so that we don't have the system incentive to just generate maximum value forever with an AGI that seems like obviously quite broken. But even though we knew that was bad and even though we all like to think of ourselves as good people, it took us a long time to figure out the right structure, to figure out a charter that's going to govern us and a set of incentives that we believe will let us do our work. And kind of these we have these like three elements that we talk about a lot research sort of engineering, development and deployment policy and safety. Put those all together under a system where you don't have to rely on. Anything but the natural incentives to push in a direction that we hope will minimize the sort of negative unintended consequences. So help me understand this, because this is I think this is confusing to some people. So you started opening. I initially I think Elon Musk, the co-founder, and there was a group of you and the argument was this technology is too powerful to be left, developed in secret and to be left developed purely by corporations who have whatever incentive they may have. We need a nonprofit that will develop and share knowledge openly. First of all, just even at that early stage, some people were confused about this. It was saying if this thing is so dangerous, why on earth would you want to make it secrets even more available? Well, maybe giving the tools to that sort of AI terrorist in his bedroom somewhere, I think I think we got misunderstood in the way we were talking about that. We certainly don't think that the right thing to do is to, like, build this a super weapon and hand it to a terrorist. That's obviously awful. One of the reasons that we like our API model is it lets us make the most powerful AI technology anyone in the world has, as far as we know, available to ever would like to use it, but to put some controls on its usage. And also, if we make a mistake, to be able to pull it back or change it or tweak it or improve it or whatever. But we do want to put and this is continued will continue to be true with appropriate restrictions and guardrails, very powerful technology in the hands of people. I think that is fair. I think that will lead to the best results for the society as a whole. And I think it will sort of maximize benefit. But that's very different than sort of shipping the whole model and saying, here, do whatever you want with it. We're able to enforce rules on it. We also think and this is part of the mission that like something the field was doing a lot of that we didn't feel good about was sort of saying like, oh, we're going to keep the pace of progress and capabilities secret. That doesn't feel right, because I think we do need a societal conversation about what's what's going on here, what the impacts are going to be. And so we although we don't always say, like, you know, here's the super weapon, hopefully we do try to say, like, this is really serious. This is a big deal. This is going to affect all of us. We need to have a big conversation about what to do with it. Help me understand the structure a bit better, because you definitely surprised much people when you announced that Microsoft were putting a billion dollars into the organization and in return, I guess they get certain exclusive licensing rights. And so, for example, they are the exclusive licensee of CP3. So talk about that structure of how you win. Microsoft presumably have invested not purely for altruistic purposes. They think that they will make money on that billion dollars. I sure hope they do. I love capitalism, but I think that I really loved even more about Microsoft as a partner. And I'll talk about the structure and the exclusive license in a minute is that we like went around to people that might find us. And we said one of the things here is that we're going to try to make you some money. But like Adjei going well is more important. And we need you to sign this document that says if things don't go the way we think and we can't make you money like you just cheerfully walk away from it and we do the right thing for humanity. And they were like, yes, we are enthusiastic about that. We get that the mission comes first here. So again, I hope a phenomenal investment for them. But they were like they really pleasantly surprised us on the upside of how aligned they were with us, about how strange the world may get here and the need for us to have flexibility and put our mission first, even if that means they lose all their money, which I hope they don't and don't think they will. So the way it's set up is that if at some point in the coming year or two, two years, Microsoft decide that there's some incredible commercial opportunity that they could realize out of the eye that you've built and you feel actually, no, that's that's damaging. You can block it. You can veto it. Correct. So the four most powerful version of three and its successors are available via the API, and we intend for that to continue. What Microsoft has is the ability to sort of put that model directly into their own technology. If they want to do that. We don't plan to do that with other people because we can't have all these controls that we talked about earlier. But they're like a close trusted partner and they really care about safety, too. But our goal is that anybody who wants to use the API can have the most powerful versions of what we've trained. And the structure of the API lets us continue to increase the safety and fix problems when we find them. But but the structure. So we start out as a non-profit, as you said, we realized pretty quickly that although we went into this thinking that the way to get to ajai would be about smarter and smarter algorithms, that we just needed bigger and bigger computers as well. And that was going to require a scale of capital that no one will, at least certainly not me, could figure out how to raise is a nonprofit. We also needed to sort of be able to compensate very highly compensated, talented individuals that do this, but are full for profit company had runaway incentives problem, among other things. Also just one about sort of fairness in society and wealth concentration that didn't feel right to us either. And so we came up with this kind of hybrid where we have a nonprofit that governs what we do, and it has a subsidiary, LLC, that we structure in a way to make a fixed amount of profit so that all of our investors and employees, hopefully if things go how we like, if not no one gets any money, but hopefully they get to make this one time great return on their investment or the time that they spent it open their equity here. And then beyond that, all the value flows back to the nonprofit and we figure out how to share it as fairly as we can with the world. And I think that this structure and this nonprofit with this very strong charter in place and everybody who joins signing up for the mission come in first and the fact the world may get strange, I think that. That was at least the best idea we could come up with, and I think it feels so far like the incentive system is working, just as I sort of watch the way that we and our partners make decisions. But if I read it right, the cap on the gain that investors can make is 100 Axum. It's a massive call that was for our very first round investors. It's way, way lower. Like as we now take a bit of capital, it's way, way lower. So your deal with Microsoft isn't you can only make the first hundred billion dollars. I don't know. It's way lower than after that. We're giving it to the world. It's way lower than that. Have you disclosed what I don't know if we have, so I won't accidentally do it now. All right. OK, so explain a bit more about the charter and how it is that you. Hope to avoid or I guess help contribute to an eye that is safe for humanity. What do you see as the keys to us avoiding the worst mistakes and really holding on to something that's that's beneficial for humanity? My answer there is actually more about, like technical and societal issues than the charter. So if it's OK for me to answer it from that perspective, sure. OK, I'm happy to talk about the charter to. I think this question of alignment that we talked about a little earlier is paramount, and then I think to understand that it's useful to differentiate between accidental misuse of a system and intentional misuse of a system. So like intentional would be a bad actor saying, I've got this powerful system, I'm going to use it to like hack into all the computers in the world and wreak havoc on the power grids. And accidental would be kind of the Nick Bostrom make a lot of paper clips and view humans as collateral damage in both cases. But to varying degrees, if we can really, truly, technically solve the alignment problem and the societal problem of deciding to which set of human values do we align, then the systems understand right and wrong, and they understand probably better than we ever can, unintended consequences from complex actions and very complex systems. And, you know, if we can train a system which is like. Don't harm humanity and the system can really understand what we mean when we say that, again, who is we and what does that have some asterisks on them? Sorry, go ahead. Well, that's if they could understand what it means to not harm humanity, that there's a lot wrapped up in that sentence. Because what's been so striking to me about efforts so far is that they seem to have been based on a very naive view of human nature. Go back to the sort of Facebook and Twitter examples of, well, the engineers building some of the systems would say we've just designed them around what humans want to do. You said, well, if someone wants to click on something, we will give them more of that thing. And what could possibly be wrong with that? We're just supporting human choice, ignoring the fact that humans are complicated, farshid animals for sure, who are constantly making choices, that a more effective version of themselves would agree is not in their long term interests. So that's one part of it. And then you've got layered on top of that or the complications of systemic complexity where, you know, multiple choices by thousands of people end up creating a reality that possibly have designed for how how to cut through that. Like an AI has to make a decision based on a moment, on a specific data set. As those decisions get more powerful, how can we be confident that they don't lead to this sort of system crashing basically in some way? I think that I've heard a lot of behavioral psychologists and other people that have studied this say in different ways, are that I hate to keep picking on Facebook, but we can do it one more time since we're on the topic. Maybe you can't in any given moment in night where you're tired and you have a stressful day, stop yourself from the dopamine hit of scrolling and Instagram, even though you know that's bad for you and it's not leading to your best life. But if you were asked in a reflective moment where you were sort of fully alert and thoughtful, do you want to spend as much time as you do scrolling through Instagram? Does it make you happier or not? You would actually be able to give like the right long term answer? It's sort of the spirit is willing, but the flesh is weak kind of moment. And one thing that I am hopeful is that humans do know what we want and what. On the whole, and presented with research or sort of an objective view about what makes us happy and doesn't we're pretty, what's so great about it, they're pretty good. But in any particular moment, we are subjected to our animal instincts and it is easy for the lower brain to take over the eye. Well , I think be an even higher brain. And as we can teach it, you know, here is what we really do value. Here's what we really do want. It will help us make better decisions than we are capable of, even in our best moments. So is that being proposed and talked about as an actual rule? Because it strikes me that there is something potentially super profound here to introduce some kind of rule for development of AIDS that they have to tap into not. What humans one, which is an ill defined question, but as to what humans in reflective mode want. Yeah, we talk about this a lot. I mean, do you see a real chance where something like that could be incorporated as a sort of an absolute golden rule and and if you like, spread around the community so that it seeps into corporations and elsewhere? Because that I've seen no evidence that, well, a little corporation that was potentially a game changer. Corporations have this weird incentive problem. Right. What I was trying to speak about was something that I think should be technologically possible , and that's something that we as a society should demand. And I think it is technically possible for this to be sort of like a layer above the neocortex that makes even better decisions for us and our welfare and our long term happiness and fulfillment than we could make on our own. And I think it is possible for us as a society to demand that. And if we can do like a pincer move between what the technology is capable of and what we what we as society demand, maybe we can make everybody in the middle that way. I mean, there are instances of even though companies have their incentives to make money and so forth, they also in the knowledge age. Can't make money if they have pissed off too many of their employees and customers and investors by analogy of the climate space right now, you can see more and more companies, even those that are emitting huge amounts of carbon dioxide, saying, wait a sec, we're struggling to recruit talented people because they don't want to work for someone who's evil. And their customers are saying, we don't want to buy something that is evil. And so, you know, ultimately you can picture processes where they do better. And I I believe that most engineers, for example, work in Silicon Valley. Companies are actually good people who want to design great products for humanity. I think that the people who run these companies want to be a net contribution to humanity. It's we've we've rushed really quickly and design stuff without thinking it through properly. And it's led to a mess up. So it's like, OK, don't move fast, break things, slow down and build beautiful things that are built on a real version of human nature and on a real version of system complexity and the risks associated with systemic complexity. Is that the agenda that fundamentally you think that you can push somehow? Yes, but I think the way we can push it is by getting the incentive system right. I think most people are fundamentally extremely good. Very few people wake up in the morning thinking about how can I make the world a worse place? But the incentive systems that we're in are so powerful. And even those engineers who join with the absolute best of intentions get sucked into this world where they're like trying to go up from it all for and five or whatever Facebook calls those things and you like, it's pretty exciting. You get caught up playing the game, you're rewarded for kind of doing things that move the company's key metrics. It's like fun to get promoted. It feels good to make more money and the incentive systems of the company. And that's what it rewards. An individual performance are maybe like not what we all want. And here I don't want to pick on Facebook at all because I think there's versions of this at play it like every big tech company, including in some ways I'm sure it open I but to the degree that we can better align the incentives of companies with the welfare of society and then the incentives of an individual at those companies within the now realign incentives for those companies, the more likely we are to be able to have things like ajai that. Follow an incentive system of. What we want in our most reflective best moments and are even better than what we could think of ourselves is is it still the vision for open eye that you will get to? Artificial general intelligence ahead of. The corporations, so that you can somehow put a stake in the ground and build it the right way. Is that really a realistic thing to to dream for? And if not, how do you live up to the mission and help ensure that this thing doesn't go off the rails? I think it is. Look, I certainly don't think we will be the only group to build an AGI, but I think we could be the first. And I think if you are the first, you have a lot of norms that empower. And I think you've already seen that. You know, we have released some of the most powerful systems to date. And I think the way that we have done that kind of in controlled release where we've released a bigger model than a bigger one than a bigger one, and we sort of try and talk about the potential misuse cases and we try to like talk about the importance of releasing this behind an API so that you can make changes. Other groups have followed suit in some of those directions, and I think that's good. So, yes, I don't think we can be the only I do think we can be ahead. And if we are ahead, I think we can use that leverage to hopefully push people in a better direction or maybe we're wrong and somebody else has a better direction. We're doing something about do you have a structural advantage in that your mission is to do this for everyone as opposed to for some corporate objective. And that that that allows you that. Why is it that we came out of open eye and not someone else? It's like it's surprising in some ways when you're up against so much money and so much talent in these other companies that you came up with this platform ahead of. You know, in some sense it's surprising and in some sense, like the startup wins most of the time, like I'm a huge believer in startups as the best force for innovation we have in the world today. I talked a little bit about how we combine these three. Different clans of research, engineering and sort of safety and policy that don't normally combine well and I think we have an unusual strength, there were clearly like well funded. We have super talented people. But what we really have is like intense focus and self belief that what we're doing is possible and good. And I appreciate the implied compliment. But, you know, we, like, work really hard. And if we stop doing that, I'm sure someone would run by us fast. Tell us a bit more about some of your prior life sentences. Yeah, for several years, you were running Y Combinator, which had incredible impact on some 70 companies. There are so many startup stories that began at Y Combinator. What were key drivers in your own life that took you on the path you're on? And how did that path end up at Y Combinator? No exaggeration. I think I have back to back had the two jobs that are at least the most interesting to me in all of Silicon Valley. I, I was I went to college to study computer science. I was a major computer nerd growing up. I knew like a little bit about startups, but not very much. I started working on this project the same year I started working on that. This thing called Y Combinator started and funded me and my co-founders. And we dropped out of school and did this company, which I ran for like seven years. And then after that I got acquired. I had stayed close to my comment the whole time. I thought it was just this incredible group of people and spirit and set of incentives and just badly misunderstood by most of the world, but obvious to everyone within it that it was going to create huge amounts of value and do a lot of new things. My company had acquired PJI, who is the founder of ICI, and like truly one of the most incredible humans and business people. And Paul Burrell, Paul Graham asked me if I wanted to run it. And kind of like the central learning of my career, why I individual startups has been that if you really scale them up, remarkable things can happen. And I. I did it and I was like, one of the things that would make this exciting for me personally motivating would be if I could sort of push it in the direction of doing these hard tech companies, one of which became open. I describe actually what Y Combinator is, you know, how many people come through it to give us a couple of stories of its impact. Yeah. So you basically apply as a handful of people and an idea, maybe a prototype and say, I would like to start a company and will you please fund me? And we review those applications and we I shouldn't say we anymore. I guess they fund four hundred companies a year. You get about one hundred and fifty thousand dollars while she takes about seven percent ownership and then gives you lots of advice and then networking and sort of this like fast track program for starting a startup. I haven't looked at this in a while, but at one point a significant fraction of the billion dollar plus companies in the US that got started. It all came through the Wiki program, some recently in the news ones have been like Airbnb, Jordache, Coinbase, insta card stripe. And I think it's just it has become an incredible way to help. People who understand technology get a three month course in business, but instead of like herding you with an MBA, we actually teach you the things that matter and kind of go on to do incredible, incredible work. What is it about entrepreneurs? Why do they matter? Some people just find them kind of annoying. But I think you would argue I think I would argue that they have done as much as anyone to shape the future. Why ? What is it about them? I think it is the ability to take. And idea and by force of will to make it happen in the world and in an incentive system that rewards you for making the most impact on the most people like in our system. That's how we get most of the things that that we use. That's how we got the computer that I'm using, the software I'm using to talk to you on it. Like all of this, you know, everyone in life, everything has a balance sheet. There's plenty of very annoying things about them. And there's plenty of very annoying things about the system that sort of idolizes them. But we do get something really important in return. And I think that as a force for making things that make all of our lives better happen, it's very cool. Otherwise, you know, like if you have, like, a great idea, but you don't actually do anything useful with it for people, that's still cool. It's still intellectually interesting. But like, there's got to be something about the reward function in society that is like, did you actually do something useful? Did you create value? And I think entrepreneurship and startups are a wonderful way to do that. You know, we get all these great software companies. But I also think it's like how we're going to get ajai, how we're going to get nuclear fusion, how we're going to get life extension. And like on any of those topics are a long list of other things I could point to. There's like a number of startups that I think are doing incredible work, some of which will actually deliver. It is a truly amazing thing when you put the camera back and to believe that a human being could be lying awake at night and something pops inside their mind as a patterning of the neurons in their brain that is effectively them saying, aha, I can see a way where the future could be better and and they can actually picture it. And then they wake up and then they talk to other people and they persuade them and they persuade investors and so forth. And the fact that this this system can happen and that they can then actually change the history changes in some sense. It is mind boggling that that happens that way and it happens again and again. So you've seen so many of these stories happen. What would you say? Is the is there a key thing that differentiates good entrepreneurs from others? If you could double down on one trait, what would it be? If I could pick only one, I would pick determination. I think that is the biggest predictor of success, the biggest differentiator predictor. And if you would allow a second, I would pick like communication skills or evangelism or something in that direction as well. There are all of the obvious ones that matter, like intelligence, but there's like a lot of smart people in the world. And when I look back at kind of the thousands of entrepreneurs I've worked with, all of many of whom were like quite capable, I would say that's like one and two of the surprisingly differentiated characteristics. What it's it's what I look at, the different things that you've built and you're working on. I mean, it could not be more foundational for the future. I mean, entrepreneurship. I know this is I agree that this is really what has driven the future. Do you see some people get really now they look at Silicon Valley and they look at this story and they worry about the culture. Right. That it's this is a bro culture. Do you see prospects of that changing anytime soon? And would you welcome it? Can we get better companies by really working to expand a group of people who can be entrepreneurs and who can contribute to aid, for example? For sure. And in fact, I think I'm hopeful, since these are the two things I've thought the most about. I'm excited for the day when someone combines them and uses A.I. to better select who did more fairly, maybe even select who to fund and how to advise them and really kind of make entrepreneurship super widely available that will lead to like better outcomes and sort of more societal wealth for all of us. So are. So, yeah, I think. Broadening the set of people able to start companies and that sort of get the resources that you need, that is like an unequivocally good thing and it's something that I think Silicon Valley is making some progress in. But I hope we see a lot more. And I do really, truly think that the technology industry entrepreneurship is one of the greatest forces for self betterment. If we can just figure out how to be a little bit more inclusive in how we do things. My last question today is about ideas were spreading. If you could inject one idea into the mind of everyone listening, what what would the idea be? We've touched on it a bunch, but the one idea would be the ajai really is going to happen. You have to engage with it seriously, and you shouldn't just listen to this and then brush aside and go about life as if it's not going to happen because it is going to affect everything. And we will all we all, I think, have an obligation, but also an opportunity to figure out what not means and how we want the world and this sort of one time shift to go on. I'm kind of awed by the breadth of things are engaged with. Thank you so much for spending so much time sharing your vision. Thanks so much for having me. OK, that's it for today. You can read more about open eyes, vision and progress at open eye dotcom. If you want to try playing with yourself, it's a little tricky. You have to find a website that has licensed the API. The one I went to was philosopher ehi dot com, where you just you pay a few dollars to get access to a very strange mind. That's actually quite a lot of fun. The interview is part of the TED Audio Collective, a collection of podcasts dedicated to sparking curiosity and sharing ideas that matter. This show is produced by Kim Net2Phone Pittas and edited by Grace Rubenstein and Sheila Boffano, Sambor Islamic Sir. Fact Check is by Paul Durbin and special thanks to Michele Quent, Colin Helmes and Anna Felin. If you like the show, please write and review it. It helps other people find us. We read every review, so thanks so much for listening. See you next time.

Frequently Occurring Word Combinations

ngrams of length 2

collocation	frequency
open eye	6
human values	6
long time	5
silicon valley	5
artificial intelligence	4
technological revolution	4
long term	4
unintended consequences	4
incentive system	4
powerful systems	3
artificial general	3
billion dollars	3
build systems	2
natural language	2
guardian essay	2
smart people	2
bad data	2
valley companies	2
social media	2
societal conversation	2
pretty cool	2
ted talk	2
familiar story	2
general intelligence	2
powerful ai	2
people call	2
start editing	2
wreak havoc	2
human nature	2
systemic complexity	2
huge amounts	2
talented people	2
real version	2
incentive systems	2
started working	2

ngrams of length 3

collocation	frequency
silicon valley companies	2
artificial general intelligence	2

Important Words

abandoned
ability
absolute
accelerator
acceptable
access
accessible
accidental
accidentally
accurate
acquired
actions
active
activity
actor
acts
actual
adaptation
add
adjacent
adjei
advanced
advantage
advice
advise
affect
age
agenda
agi
agree
aha
ai
aid
aids
airbnb
ajai
albeit
album
alert
algorithm
algorithms
alien
align
aligned
alignment
altman
altruistic
amazing
amok
amount
amounts
amuck
analogy
anderson
animal
animals
anna
announced
annoying
answer
answered
anthropomorphic
anymore
anytime
api
app
application
applications
applies
apply
architecture
area
areas
argue
argument
array
artificial
artists
asked
asleep
assemble
assisted
asterisks
attached
attention
attitude
audio
avoid
avoiding
awake
awed
awesome
awful
axis
axum
background
bad
badly
balance
ball
bars
base
based
basically
beautiful
bed
bedroom
began
beginning
begun
behave
behaves
behavior
behavioral
belief
beliefs
believer
believes
beneficial
benefit
benefits
betterment
big
bigger
biggest
billion
biology
bit
block
blue
boffano
boggling
books
bostrom
bot
brain
brains
brand
breadth
break
brilliant
bring
bro
broad
broadening
broken
brush
bubble
bubbles
build
building
built
bunch
burrell
business
buy
buzz
calendar
call
called
calls
camera
camp
canary
cap
capabilities
capable
capital
capitalism
capped
carbon
card
care
career
case
cases
catastrophe
cats
caught
central
cgp
chain
challenge
chance
change
changer
changing
chapatti
characteristics
characters
charlie
charter
chat
check
cheerfully
child
choice
choices
chris
clans
classify
clear
click
climate
clips
close
coal
code
cognition
cognitive
coinbase
colin
collar
collateral
collection
collective
college
combination
combinator
combine
combines
combining
coming
comment
commercial
communication
community
companies
company
compelling
compensate
compensated
competing
complex
complexity
complicated
complications
compliment
composite
computer
computers
concentration
concept
concepts
conclusion
confident
confront
confused
confusing
connect
consequences
constantly
context
continue
continued
continues
contract
contribute
contribution
controlled
controlling
controls
conversation
cool
corner
corporate
corporation
corporations
correct
couple
crashing
create
created
creates
creating
creative
creativity
critics
culture
curating
cure
curiosity
current
cursor
cushion
custom
customers
cut
cycle
damage
damaging
dangerous
dark
data
date
day
deal
decades
decide
decided
deciding
decision
decisions
dedicated
defined
definition
degree
degrees
deliver
delivered
demand
deployed
deployment
describe
describing
design
designed
designers
destruction
determination
develop
developed
developers
developing
development
dictates
difference
differentiate
differentiated
differentiates
differentiator
difficult
dioxide
directed
direction
directions
disagree
disaster
disclosed
discretion
discussion
disease
displacement
distinguish
distracted
division
doable
doctor
document
documents
dollar
dollars
dolly
door
dopamine
dot
dotcom
dots
double
dramatic
dream
driven
drivers
dropped
durbin
dystopian
earlier
earliest
early
earth
easy
economic
edited
editing
editor
education
effect
effective
effectively
efficiency
effort
efforts
ehi
elements
elevator
elon
email
embracing
emitting
employees
empower
energy
enforce
engage
engaged
engaging
engineering
engineers
english
enjoy
enormous
ensure
entertaining
enthusiastic
entire
entrepreneurs
entrepreneurship
equity
era
errors
essay
essays
essentially
evangelism
event
eventually
evidence
evil
evolution
evolved
exact
exaggeration
examples
excited
excitement
exciting
exclusive
exist
existential
expand
experience
experts
explain
explodes
exploit
explosion
exposed
extension
external
extra
extraordinarily
extremely
eye
eyes
facebook
facing
fact
factor
fair
fairness
fall
false
falsehood
familiar
fanboy
fantastic
farshid
fast
faster
favorite
fearful
feat
feature
feed
feedback
feel
feeling
feels
felin
fi
field
fifty
figure
find
finding
firepower
fix
fixed
flashing
flesh
flexibility
flows
focus
follow
food
foolish
force
forces
foundational
founder
fraction
frame
framework
free
french
friend
fulfillment
full
fully
fun
function
functions
fund
fundamental
fundamentally
funded
fusion
future
fuzzy
gain
game
games
general
generalize
generalized
generate
generated
generative
genetics
genuinely
giant
gibberish
give
giving
glimpse
glimpses
global
goal
golden
goldstar
good
google
googling
govern
governance
governs
gpg
gpp
grace
graham
grant
graphic
gravitate
great
greatest
grew
grids
gross
ground
group
groups
growing
guardian
guardrails
guess
hack
hand
handful
hands
happen
happened
happening
happier
happiness
happy
hard
harm
harvest
harvesting
hate
havoc
head
health
hear
heard
hearing
helmes
helping
helps
herding
hey
high
higher
highly
hiring
history
hit
hmm
hold
holding
honestly
hope
hopeful
horizon
horrifying
horsepower
hours
huge
hugely
human
humanity
humans
hundreds
hybrid
hype
ici
idea
ideas
identify
idolizes
ignore
ignoring
ill
images
imagine
imagined
immediately
impact
impacts
impending
imperfect
implied
implies
importance
important
impressive
improve
improving
inappropriate
incentive
incentives
includes
including
inclusive
incompatible
incorporated
increase
incredible
incredibly
individual
individuals
industry
inexpensive
information
ingesting
inherently
initially
inject
innovation
innovative
input
inputting
inspiration
inspire
inspiring
insta
instagram
instances
instincts
instructions
intellectually
intelligence
intelligences
intelligent
intend
intense
intent
intentional
intentionality
intentions
interact
interactive
interested
interesting
interestingness
interests
interface
internal
internet
interview
introduce
invested
investment
investors
ish
islamic
issues
jibberish
jingle
jingles
job
jobs
join
joins
jordache
judges
judgment
jukebox
kernel
key
keys
kicked
kim
kind
kinds
knew
knowledge
language
languages
larval
launched
layer
layered
lead
leading
leads
learn
learned
learning
led
left
legendary
lets
letting
level
leverage
license
licensed
licensee
licensing
life
light
link
list
listen
listening
literal
literally
live
lives
llc
lofty
logo
long
longer
looked
lose
lot
lots
love
loved
lucky
lying
magnitude
major
majority
majorly
making
market
massive
match
matrix
matter
maximally
maximize
maximum
maze
mba
meaning
meaningful
means
media
medical
medium
meeting
mention
mentioned
merge
merged
mess
metrics
michele
microsoft
middle
milestone
million
mind
minds
minimize
minute
minutes
mirror
misalignment
misgivings
missing
mission
mistake
mistakes
misunderstood
misuse
mix
mobile
mode
model
models
moderately
moment
moments
money
month
morning
motivating
move
moving
multiple
munger
music
musician
musk
naive
narrow
national
natural
nature
needed
negative
neocortex
nerd
nervous
net
networking
neuro
neurons
news
nick
night
noble
nonprofit
norms
note
nuclear
number
objective
obligation
observe
obvious
offer
ollman
online
open
opened
opening
openly
opens
operated
operating
opportunity
opposed
optimism
optimistic
orders
organising
organization
ostler
outcome
outcomes
output
overexcited
overhyped
ownership
pace
painted
palette
pandemic
paper
paramount
part
partner
partners
path
pathway
patterning
paul
pay
paying
people
percent
percentage
perfectly
performance
person
personal
personally
personas
perspective
persuade
phenomenal
phenomenally
philosopher
philosophical
phones
pick
picking
picture
pieces
pincer
pissed
pitch
pittas
pji
place
plan
planning
platform
play
playing
pleasantly
pleasing
plenty
plot
plugged
podcasts
point
pointing
policy
polish
political
pool
pops
position
positive
possibilities
possibility
possibly
potential
potentially
power
powerful
powers
predict
predictions
predictor
prepares
present
presented
president
pressing
presume
pretend
pretty
previous
principle
prior
problem
problems
process
processes
produced
produces
production
products
profit
profound
program
programers
programmed
programmer
programming
progress
project
promoted
prompted
pronouncements
properly
proposed
prospects
protect
prototype
proud
psychologists
pull
pulled
purely
purpose
purposes
pursue
push
pushback
put
putting
python
quadrant
quality
quent
queries
query
question
questions
quickly
quote
race
racial
rails
raise
ramping
ran
random
rapid
rapidity
rarely
rational
reached
read
reading
reads
real
realign
realistic
reality
realize
realized
realm
rearview
reason
reasoning
reasons
recesses
reckoning
recruit
reflective
regurgitating
reinforcement
relative
release
released
releasing
reliable
rely
remarkable
remember
replicates
representation
representational
require
research
researchers
resources
response
responses
restrictions
result
results
retreated
return
review
revolution
reward
rewarded
rewards
rightly
rights
risk
risks
robotics
rpi
rubenstein
rug
rule
rules
run
runaway
running
rushed
sad
safe
safety
sam
sambor
sampling
saved
scale
scaling
scary
scenario
scenarios
school
sci
science
scientific
screen
scroll
scrolling
search
season
sec
secret
secrets
seeker
seeps
select
sense
sentence
sentences
sentient
sequence
series
service
services
set
sets
shallow
shape
share
sharing
sheet
sheila
shift
shifts
shipping
shocking
shockingly
short
show
showed
shown
shows
side
sign
significant
signing
silicon
similar
simple
simply
sincere
single
sir
sitting
skills
sleep
slow
small
smart
smarter
smartest
snippets
social
societal
society
software
solely
solution
solutions
solve
solves
song
songs
sooner
sort
sorts
sounds
space
spans
sparking
speak
spec
special
species
specific
specifically
spend
spending
spent
spirit
spread
spreading
staccato
stage
stake
stance
stand
stands
start
started
starting
startup
startups
state
stayed
steering
steers
sticking
stitched
stop
stories
story
strange
strength
stressful
strike
strikes
striking
stripe
strong
structural
structure
struggling
studied
study
stuff
style
subjected
subjective
subsidiary
subspace
success
successors
sucked
suddenly
suit
summary
summer
super
superpowers
support
supporting
surprised
surprising
surprisingly
suspect
symbiotic
system
systemic
systems
table
tailored
takes
talent
talented
talk
talked
talking
tap
task
tasks
tax
teach
teaches
team
tech
technical
technically
technique
technological
technologically
technology
ted
tempting
term
terms
terrible
terrorist
text
theme
theory
thinking
thinks
thought
thoughtful
thousand
thousands
threat
threats
throw
time
timelines
times
tired
today
told
tomorrow
tool
tools
top
topic
topics
total
totally
touch
touched
track
trade
traditionally
train
trained
trait
translate
translation
treat
tremendous
tremendously
tricky
true
trusted
truth
tuba
turned
tutor
tutors
tweak
twenty
twitter
typed
ugly
ultimately
unambiguous
underestimated
underlying
understand
understanding
understands
undesirable
undo
unequivocally
unintended
uniquely
unquote
unreliable
unstoppable
unsuitable
unsure
unusual
unwanted
update
uploaded
upside
usage
user
usual
vaccines
valley
values
varies
varying
vast
version
versions
veto
view
views
virtual
virus
visibility
vision
visions
wait
wake
waking
walk
wanted
watch
ways
weak
wealth
weapon
website
week
weird
welfare
whatsoever
wheel
widely
wiki
wikipedia
win
wins
wisdom
wise
wonderful
word
words
work
worked
working
works
world
worrisome
worry
worrying
worse
worst
worth
wow
wrapped
wreak
wrestling
write
written
wrong
yeah
year
years