full transcript

From the Ted Talk by Sinan Aral: How we can protect truth in the age of misinformation

Unscramble the Blue Letters


Now, everything that I have told you so far, unfortunately for all of us, is the good news.

The reason is because it's about to get a whole lot wosre. And two specific technologies are going to make it worse. We are going to see the rise of a tremendous wave of synthetic media. Fake video, fake audio that is very ccnivninog to the human eye. And this will powered by two technologies.

The first of these is known as "generative adversarial networks." This is a machine-learning model with two networks: a domrsiaticinr, whose job it is to determine whether something is true or false, and a generator, whose job it is to generate synthetic media. So the synthetic generator generates sihtntyec veido or audio, and the discriminator tries to tell, "Is this real or is this fake?" And in fact, it is the job of the generator to maximize the lhoeiokild that it will fool the discriminator into thinking the synthetic video and audio that it is creating is actually true. Imagine a machine in a hyperloop, trying to get better and better at fooling us.

Open Cloze


Now, everything that I have told you so far, unfortunately for all of us, is the good news.

The reason is because it's about to get a whole lot _____. And two specific technologies are going to make it worse. We are going to see the rise of a tremendous wave of synthetic media. Fake video, fake audio that is very __________ to the human eye. And this will powered by two technologies.

The first of these is known as "generative adversarial networks." This is a machine-learning model with two networks: a _____________, whose job it is to determine whether something is true or false, and a generator, whose job it is to generate synthetic media. So the synthetic generator generates _________ _____ or audio, and the discriminator tries to tell, "Is this real or is this fake?" And in fact, it is the job of the generator to maximize the __________ that it will fool the discriminator into thinking the synthetic video and audio that it is creating is actually true. Imagine a machine in a hyperloop, trying to get better and better at fooling us.

Solution


  1. discriminator
  2. synthetic
  3. convincing
  4. worse
  5. video
  6. likelihood

Original Text


Now, everything that I have told you so far, unfortunately for all of us, is the good news.

The reason is because it's about to get a whole lot worse. And two specific technologies are going to make it worse. We are going to see the rise of a tremendous wave of synthetic media. Fake video, fake audio that is very convincing to the human eye. And this will powered by two technologies.

The first of these is known as "generative adversarial networks." This is a machine-learning model with two networks: a discriminator, whose job it is to determine whether something is true or false, and a generator, whose job it is to generate synthetic media. So the synthetic generator generates synthetic video or audio, and the discriminator tries to tell, "Is this real or is this fake?" And in fact, it is the job of the generator to maximize the likelihood that it will fool the discriminator into thinking the synthetic video and audio that it is creating is actually true. Imagine a machine in a hyperloop, trying to get better and better at fooling us.

Frequently Occurring Word Combinations


ngrams of length 2

collocation frequency
false news 11
presidential election 4
white house 3
social media 3
true news 3
synthetic media 3
united states 2
fake news 2
news stories 2
false tweets 2
news exhibited 2
exhibited significantly 2
generate synthetic 2
synthetic video 2
artificial intelligence 2
press pass 2
economic motive 2
define truth 2

ngrams of length 3

collocation frequency
news exhibited significantly 2
generate synthetic media 2

Important Words


  1. adversarial
  2. audio
  3. convincing
  4. creating
  5. determine
  6. discriminator
  7. eye
  8. fact
  9. fake
  10. false
  11. fool
  12. fooling
  13. generate
  14. generates
  15. generator
  16. good
  17. human
  18. hyperloop
  19. imagine
  20. job
  21. likelihood
  22. lot
  23. machine
  24. maximize
  25. media
  26. model
  27. networks
  28. news
  29. powered
  30. real
  31. reason
  32. rise
  33. specific
  34. synthetic
  35. technologies
  36. thinking
  37. told
  38. tremendous
  39. true
  40. video
  41. wave
  42. worse