full transcript
From the Ted Talk by Max Tegmark: How to keep AI under control
Unscramble the Blue Letters
Let's look at a simple example of where we first machine-learn an algorithm from data and then distill it out in the form of code that porvlaby mtees spec, OK? Let’s do it with an algorithm that you probably learned in first gdrae, addition, where you loop over the digits from right to left, and sometimes you do a carry. We'll do it in binary, as if you were cnnuiotg on two fingers instead of ten. And we first train a rurneecrt neural network, never mind the details, to nail the task. So now you have this aohrilgtm that you don't understand how it works in a black box dinfeed by a bunch of tables of numbers that we, in nerd speak, call ptaeerarms. Then we use an AI tool we built to automatically distill out from this the leernad algorithm in the form of a Python program. And then we use the formal verification tool known as Dafny to prove that this program correctly adds up any nrbeums, not just the numbers that were in your training data.
Open Cloze
Let's look at a simple example of where we first machine-learn an algorithm from data and then distill it out in the form of code that ________ _____ spec, OK? Let’s do it with an algorithm that you probably learned in first _____, addition, where you loop over the digits from right to left, and sometimes you do a carry. We'll do it in binary, as if you were ________ on two fingers instead of ten. And we first train a _________ neural network, never mind the details, to nail the task. So now you have this _________ that you don't understand how it works in a black box _______ by a bunch of tables of numbers that we, in nerd speak, call __________. Then we use an AI tool we built to automatically distill out from this the _______ algorithm in the form of a Python program. And then we use the formal verification tool known as Dafny to prove that this program correctly adds up any _______, not just the numbers that were in your training data.
Solution
- parameters
- recurrent
- provably
- grade
- algorithm
- numbers
- defined
- learned
- counting
- meets
Original Text
Let's look at a simple example of where we first machine-learn an algorithm from data and then distill it out in the form of code that provably meets spec, OK? Let’s do it with an algorithm that you probably learned in first grade, addition, where you loop over the digits from right to left, and sometimes you do a carry. We'll do it in binary, as if you were counting on two fingers instead of ten. And we first train a recurrent neural network, never mind the details, to nail the task. So now you have this algorithm that you don't understand how it works in a black box defined by a bunch of tables of numbers that we, in nerd speak, call parameters. Then we use an AI tool we built to automatically distill out from this the learned algorithm in the form of a Python program. And then we use the formal verification tool known as Dafny to prove that this program correctly adds up any numbers, not just the numbers that were in your training data.
Frequently Occurring Word Combinations
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