full transcript

From the Ted Talk by Nabiha Saklayen: Could you recover from illness ... using your own stem cells?

Unscramble the Blue Letters


When I started my PhD, I joined a lesar physics lab, because lasers are the coolest. But I also decided to dabble in biology. I started using lasers to engineer hmaun cells, and when I talked to biologists about it, they were amazed. Here's why: scientists are always looking for ways to make biology more precise. Sometimes cell cultrue can feel a lot like cooking: take some chemicals, put it in a pot, stir it, heat it, see what happens, try it all over again. In contrast, lasers are so precise, you can target one cell in millions at precise invlrteas — every second, every minute, every hour — you name it. I realized that instead of doing this tedious pecosrs of stem cell culture by hand, we could use lersas to remove the unwanted cells. And to automate the etirne process, we decided to use machine learning to identify those unwanted cells and zap them. Algorithms tadoy are great at finindg useful information and imaegs, mknaig this a perfect use case for machine learning.

Open Cloze


When I started my PhD, I joined a _____ physics lab, because lasers are the coolest. But I also decided to dabble in biology. I started using lasers to engineer _____ cells, and when I talked to biologists about it, they were amazed. Here's why: scientists are always looking for ways to make biology more precise. Sometimes cell _______ can feel a lot like cooking: take some chemicals, put it in a pot, stir it, heat it, see what happens, try it all over again. In contrast, lasers are so precise, you can target one cell in millions at precise _________ — every second, every minute, every hour — you name it. I realized that instead of doing this tedious _______ of stem cell culture by hand, we could use ______ to remove the unwanted cells. And to automate the ______ process, we decided to use machine learning to identify those unwanted cells and zap them. Algorithms _____ are great at _______ useful information and ______, ______ this a perfect use case for machine learning.

Solution


  1. images
  2. human
  3. laser
  4. entire
  5. process
  6. today
  7. intervals
  8. making
  9. finding
  10. lasers
  11. culture

Original Text


When I started my PhD, I joined a laser physics lab, because lasers are the coolest. But I also decided to dabble in biology. I started using lasers to engineer human cells, and when I talked to biologists about it, they were amazed. Here's why: scientists are always looking for ways to make biology more precise. Sometimes cell culture can feel a lot like cooking: take some chemicals, put it in a pot, stir it, heat it, see what happens, try it all over again. In contrast, lasers are so precise, you can target one cell in millions at precise intervals — every second, every minute, every hour — you name it. I realized that instead of doing this tedious process of stem cell culture by hand, we could use lasers to remove the unwanted cells. And to automate the entire process, we decided to use machine learning to identify those unwanted cells and zap them. Algorithms today are great at finding useful information and images, making this a perfect use case for machine learning.

Frequently Occurring Word Combinations


ngrams of length 2

collocation frequency
stem cells 11
stem cell 8
unwanted cells 5
blood cells 4
machine learning 3
immune system 2
laser physics 2
personalized stem 2
cell bank 2
million dollars 2
cell culture 2
miniature human 2
human replica 2

ngrams of length 3

collocation frequency
miniature human replica 2

Important Words


  1. algorithms
  2. amazed
  3. automate
  4. biologists
  5. biology
  6. case
  7. cell
  8. cells
  9. chemicals
  10. contrast
  11. coolest
  12. culture
  13. dabble
  14. decided
  15. engineer
  16. entire
  17. feel
  18. finding
  19. great
  20. hand
  21. heat
  22. hour
  23. human
  24. identify
  25. images
  26. information
  27. intervals
  28. joined
  29. lab
  30. laser
  31. lasers
  32. learning
  33. lot
  34. machine
  35. making
  36. millions
  37. minute
  38. perfect
  39. phd
  40. physics
  41. pot
  42. precise
  43. process
  44. put
  45. realized
  46. remove
  47. scientists
  48. started
  49. stem
  50. stir
  51. talked
  52. target
  53. tedious
  54. today
  55. unwanted
  56. ways
  57. zap