full transcript
From the Ted Talk "Kenneth Cukier: Big data is better data"

Unscramble the Blue Letters

Now, there are dark sides to big data as well. It will rpmevoi our lives, but there are problems that we need to be conscious of, and the first one is the idea that we may be nhudepsi for predictions, that the police may use big data for their purposes, a little bit like "Minority Report." Now, it's a term called eitrdipcev gpncoiil, or algorithmic criminology, and the idea is that if we take a lot of data, for example where past cesimr have been, we know where to send the patrols. That makes sseen, but the problem, of course, is that it's not simply going to stop on location data, it's going to go down to the level of the individual. Why don't we use atad about the person's high school transcript? Maybe we should use the fact that they're unemployed or not, their credit score, their web-surfing behavior, whether they're up late at night. Their Fitbit, when it's able to identify biochemistries, will show that they have aggressive thoughts. We may have algorithms that are likely to tepircd what we are about to do, and we may be held accountable before we've actually acted. ycvrapi was the central challenge in a small data era. In the big data age, the nclgealeh will be irggdeafsnau free will, moral choice, human volition, human agency.

Open Cloze

Now, there are dark sides to big data as well. It will _______ our lives, but there are problems that we need to be conscious of, and the first one is the idea that we may be ________ for predictions, that the police may use big data for their purposes, a little bit like "Minority Report." Now, it's a term called __________ ________, or algorithmic criminology, and the idea is that if we take a lot of data, for example where past ______ have been, we know where to send the patrols. That makes _____, but the problem, of course, is that it's not simply going to stop on location data, it's going to go down to the level of the individual. Why don't we use ____ about the person's high school transcript? Maybe we should use the fact that they're unemployed or not, their credit score, their web-surfing behavior, whether they're up late at night. Their Fitbit, when it's able to identify biochemistries, will show that they have aggressive thoughts. We may have algorithms that are likely to _______ what we are about to do, and we may be held accountable before we've actually acted. _______ was the central challenge in a small data era. In the big data age, the _________ will be ____________ free will, moral choice, human volition, human agency.

Solution

  1. predictive
  2. policing
  3. privacy
  4. predict
  5. challenge
  6. crimes
  7. safeguarding
  8. punished
  9. sense
  10. data
  11. improve

Original Text

Now, there are dark sides to big data as well. It will improve our lives, but there are problems that we need to be conscious of, and the first one is the idea that we may be punished for predictions, that the police may use big data for their purposes, a little bit like "Minority Report." Now, it's a term called predictive policing, or algorithmic criminology, and the idea is that if we take a lot of data, for example where past crimes have been, we know where to send the patrols. That makes sense, but the problem, of course, is that it's not simply going to stop on location data, it's going to go down to the level of the individual. Why don't we use data about the person's high school transcript? Maybe we should use the fact that they're unemployed or not, their credit score, their web-surfing behavior, whether they're up late at night. Their Fitbit, when it's able to identify biochemistries, will show that they have aggressive thoughts. We may have algorithms that are likely to predict what we are about to do, and we may be held accountable before we've actually acted. Privacy was the central challenge in a small data era. In the big data age, the challenge will be safeguarding free will, moral choice, human volition, human agency.

ngrams of length 2

collocation frequency
big data 16
arthur samuel 6
machine learning 5

Important Words

  1. accountable
  2. acted
  3. age
  4. agency
  5. aggressive
  6. algorithmic
  7. algorithms
  8. behavior
  9. big
  10. biochemistries
  11. bit
  12. called
  13. central
  14. challenge
  15. choice
  16. conscious
  17. credit
  18. crimes
  19. criminology
  20. dark
  21. data
  22. era
  23. fact
  24. fitbit
  25. free
  26. held
  27. high
  28. human
  29. idea
  30. identify
  31. improve
  32. individual
  33. late
  34. level
  35. lives
  36. location
  37. lot
  38. moral
  39. night
  40. patrols
  41. police
  42. policing
  43. predict
  44. predictions
  45. predictive
  46. privacy
  47. problem
  48. problems
  49. punished
  50. purposes
  51. report
  52. safeguarding
  53. school
  54. score
  55. send
  56. sense
  57. show
  58. sides
  59. simply
  60. small
  61. stop
  62. term
  63. thoughts
  64. transcript
  65. unemployed
  66. volition