Machine Learning is becoming a more and more important part of everything Google does, but can seem quite inaccessible to learn about. This presentation doesn't try to teach you how to do ML, but focuses instead on showing you the types of problems that ML can address, how Google have used it previously, and how they might use it in the future.

1. @TomAnthonySEO
April 2015 - BrightonSEO
HOW TO SPOT A BEAR
A Machine Learning Introduction for SEOs

3. Can you define a list of
rules for spotting
bears?

4. 1) Four legs.
Let’s start with:

5. Bear!

6. List of rules (first half):
(when I asked in the office)
1. Four legs.
2.Breathes.
3.Furry.
4.Long snout.

7. Bear!

8. List of rules:
1. Four legs.
2.Breathes.
3.Furry.
4.Long snout.
5.Brown.
6.Not always brown.
7. Mammal.
8.No tail.
(how do you spot
a mammal?!)

9. Let’s check our
rules…

10. Rules say:
Bear

11. Rules say:
Harmless Furry Thing
(less than 4 legs)

12. Rules say:
Odd Grey Creature
(no long snout)

13. Remove ‘long snout’, and rules say:
Bear
(Extra-terrestrial bear?!)

14. Our rules suck.

15. A different bear:
Google’s Panda

16. Can you define a list of
rules for spotting
spammy pages?
Same problem as
bears!

17. NBED GOOD PAGE
Good page

18. NBED GOOD PAGE
Commercial page, still good.

19. Hrm…

20. Seems legit…

21. WTF!

22. Google can’t write
rules.

23. What we can do is
identify spammy or
non-spammy
attributes.

24. Are there adverts on the page?
Are there lots of spelling mistakes?
Is there little text content?
Are there Calls To Action in ALL CAPS?
Some Possible Spam Signals

25. Smooth segue to:
Machine Learning

26. List of pages we’ve
manually classified.
List of attributes that we
believe are important to
classifying pages.

27. adverts
on page?
more than 5
spelling
mistakes?
less than 200
words of
content?
CTA in ALL
CAPS?
site A Y Y Y Y Spam Site
site B N N Y Y Good Site
site C Y N N N Spam Site
site D N Y N Y Spam Site
site E N Y N N Good Site
Example Data

28. Neural Networks: A Perceptron
Inputs
Output
Neuron

29. Neural Networks: A Perceptron
Inputs
Output
1
if:
inputs >= 1
output TRUE
0
1
0
0.5
0.5
0.5
0.5

30. 1 x 0.5 = 0.5
0 x 0.5 = 0
1 x 0.5 = 0.5
0 x 0.5 = 0
1
______
Total:
Output: TRUE
1
if:
inputs >= 1
output TRUE
0
1
0
0.5
0.5
0.5
0.5
TRUE

31. 1 x 0.5 = 0.5
0 x 0.5 = 0
0 x 0.5 = 0
0 x 0.5 = 0
0.5
______
Total:
Output: FALSE
1
if:
inputs >= 1
output TRUE
0
0
0
0.5
0.5
0.5
0.5
FALSE

32. 1 x 0.5 = 0.5
0 x 0.5 = 0
1 x 0.4 = 0.4
0 x 0.5 = 0
0.9
______
Total:
Output: FALSE
1
if:
inputs >= 1
output TRUE
0
1
0
0.5
0.5
0.4
0.5
FALSE

33. adverts
on page?
more than 5
spelling
mistakes?
less than 200
words of
content?
CTA in ALL
CAPS?
site A Y Y Y Y Spam Site
site B N N Y Y Good Site
site C Y N N N Spam Site
site D N Y N Y Spam Site
site E N Y N N Good Site
Example Data

34. Untrained Neuron
Is site
spam?
adverts
>5 spelling
mistakes
< 200 words
content
CTA in ALL
CAPS
if:
inputs >= 1
output TRUE
0.5
0.5
0.5
0.5

35. Training
adverts
>5 spelling
mistakes
< 200 words
content
CTA in ALL
CAPS
if:
inputs >= 1
output TRUE
0.5
0.5
0.5
0.5
0
0
1
1
SPAM!

36. Training
adverts
>5 spelling
mistakes
< 200 words
content
CTA in ALL
CAPS
if:
inputs >= 1
output TRUE
0.5
0.5
0.6
0.6

37. After training: 4/5 sites correct
Is site
spam?
adverts
>5 spelling
mistakes
< 200 words
content
CTA in ALL
CAPS
if:
inputs >= 1
output TRUE
0.2
0.7
0.4
0.5

38. ANNs typically have many neurons
source: http://www.teco.edu/~albrecht/neuro/html/node18.html

39. Deep Learning

40. Humans are good at pattern matching

41. We’re better than machines…
source: Pawan Sinha (http://web.mit.edu/bcs/sinha/papers/sinha_recog_review_NN.pdf)

42. ML can learn to recognise cats from examples

43. Deep Learning learns more like us

44. Ok, so what does this have to
do with Google?

45. PandaML based algorithm updates

46. Old index Caffeine
Caffeine - Infrastructure Update
(we believe this made Panda+Penguin possible)

47. Hummingbird is to ???
as
Caffeine is to Panda+Penguin

48. Hummingbird
Is it similar to Caffeine? Is it the basis for new natural language algorithms?

49. Where is Google
going next with ML?

50. Idea
Image Search 2.0

51. Image Labelling

52. Image Labelling

53. Video Labelling

54. ML Generated Image Descriptions
“Two pizzas sitting on top of a stove top oven”

55. Natural Language
Faceted Search
Idea

56. ‘show me olympic athletes' ‘show me the women'

57. “Find well rated vegetarian cooking
books written after 1990”
How about:

58. Idea
Factual Accuracy
as a
Ranking Factor

59. Fact CheckingKnowledge Vault

60. Idea: Bad Facts
NBED- shot of Google
talking about this shit
Estimating ‘Trustworthiness’

61. Idea
Entirely ML Generated
Algorithm?

62. http://dis.tl/ml-algo

63. Thanks! :)
@TomAnthonySEO