One of the opening keynote talks for Big Data Spain 2016 in Madrid. https://www.bigdataspain.org/ Artificial Intelligence has arrived. After decades of wishful thinking and false-starts, we're seeing consistent evidence of industry applications where generalized approaches of automation can beat human experts. Major technology firms now battle for top experts, capabilities, market share, etc., and a robust ecosystem begins to emerge. Arguably, the lion's share of the discussion goes toward Deep Learning, although other areas of AI applications are emerging as well. Recently, O'Reilly Media hosted its first Artificial Intelligence conference, including key insights from Peter Norvig, Tim O'Reilly, Yann LeCun, Lili Cheng, Genevieve Bell, Oren Etzioni, et al. Met with unexpectedly high demand from the audience and compelling engagement by vendors, the forum surfaced non-intuitive problems encountered with AI applications at scale and their cognitive embrace. Of course, it also surfaced the plaguing questions about ethics, anthropology, and the future of work. This talk will give a brief survey through the state of AI in 2016, showing examples, caveats, trends -- along with insights gathered from practitioners. Notably, what is the impact for those of us already immersed in Data Science, Machine Learning, Distributed Systems, Cloud technologies, DevOps Practice, etc.?

1. Has AI Arrived?
Big Data Spain
Madrid, 2016-11-17
Paco Nathan, @pacoid
Director, Learning Group @ O’Reilly Media
1

2. A rhetorical question:
from:
Beyond the AI Winter
goo.gl/tKug8u
Can you name ten successful tech start-ups which lack
any application of Machine Learning on their roadmaps?
2

3. An interesting perspective:
To paraphrase Peter Norvig, Google @ AI Conference 2016:
Marc Andreessen noted famously how software
was disrupting so many incumbents … and now
Machine Learning is disrupting many incumbents
from:
Software engineering of systems that learn in uncertain domains
safaribooksonline.com/library/view/oreilly-ai-conference/
9781491973912/video260721.html
3

4. A related perspective:
Pedro Domingos believes we’re getting closer to realizing
a “universal learner”
The future belongs to those who understand at
a very deep level how to combine their unique
expertise with what algorithms do best.
from:
The Master Algorithm
goodreads.com/book/show/24612233-the-master-algorithm
4

5. A related perspective:
Domingos describes “five tribes” of machine learning
(see especially on page 54):
• symbolists: inverse deduction, e.g., rule systems
• connectionists: what the brain does, e.g., deep learning
• evolutionaries: natural selection, e.g., genetic programming
• bayesians: uncertainty, e.g., probabilistic inference
• analogizers: similarities, e.g., support vectors
from:
The Master Algorithm
goodreads.com/book/show/24612233-the-master-algorithm
5

6. In retrospect:
During the past few years applications of deep learning have
exploded. Among those tribes, “connectionists” now prevail.
Even so, deep learning is only a portion of machine learning.
Moreover machine learning does not represent the entirety
of machine intelligence.
What else will be needed?
6

7. Where are the examples?
7

8. Major tech firms (just a sample):
8

9. “An Ecosystem of Machine Intelligence”
oreilly.com/ideas/the-current-state-of-machine-intelligence-3-0
Shivon Zilis, James Cham, Heidi Skinner
9

10. Reaching Human Parity:
Historic Achievement: Microsoft researchers reach human parity
in conversational speech recognition
blogs.microsoft.com/next/2016/10/18/historic-achievement-microsoft-
researchers-reach-human-parity-conversational-speech-recognition/
10

11. Reaching Human Parity:
Historic Achievement: Microsoft researchers reach human parity
in conversational speech recognition
blogs.microsoft.com/next/2016/10/18/historic-achievement-microsoft-
researchers-reach-human-parity-conversational-speech-recognition/
Shades of HAL:
openreview.net/pdf?
id=BkjLkSqxg
11

12. Realistically…
consider the control system at the heart of, say, Uber –
manipulating supply chains of resources for particular outcomes
12

13. Some favorite examples in arts & lit:
Benjamin.ai / Sunspring
youtu.be/LY7x2Ihqjmc
13

14. Some favorite examples in arts & lit:
Flash Forward: “The Witch Who Came From Mars”
flashforwardpod.com/2016/09/05/episode-20-something-martian-witch-way-comes/
14

15. Artificial Intelligence conference series:
New York City (last Sep)
conferences.oreilly.com/artificial-intelligence/ai-ny-2016
San Francisco (last Oct)
conferences.oreilly.com/artificial-intelligence/bot-ca
New York City, Jun 26-29 2017
conferences.oreilly.com/artificial-intelligence/ai-ny
(CFP open through Jan 18)
15

16. Artificial Intelligence conference series:
New York City (last Sep)
conferences.oreilly.com/artificial-intelligence/ai-ny-2016
San Francisco (last Oct)
conferences.oreilly.com/artificial-intelligence/bot-ca
New York City, Jun 26-29
conferences.oreilly.com/artificial-intelligence/ai-ny
(CFP open through Jan 18)
As one might imagine, the
presenters discussed much
deep learning – although
there were other important
points… let’s consider those
16

17. AI requires sophisticated engineering?
Software engineering of systems that learn in uncertain domains
safaribooksonline.com/library/view/oreilly-ai-conference/
9781491973912/video260721.html
17

18. Observations by Peter Norvig:
• difficult to debug, revise incrementally, verify
• less transparency into algorithms
• components are hard to isolate, for debugging
• automated integration introduces unusual risks
• tech debt accumulates more readily
Machine Learning: The High Interest Credit Card of Technical Debt
research.google.com/pubs/pub43146.html
Software engineering of systems that learn in uncertain domains
safaribooksonline.com/library/view/oreilly-ai-conference/
9781491973912/video260721.html
AI requires sophisticated engineering?
18

19. Why should I trust you? Explaining the predictions of any classifier
safaribooksonline.com/library/view/strata-hadoop/
9781491944660/video282744.html
kdd.org/kdd2016/subtopic/view/why-should-i-trust-you-
explaining-the-predictions-of-any-classifier
Carlos Guestrin: LIME
19

20. Impact on Big Data, Cloud, etc.:
Overall, AI drives product features
That process in turn drives cloud consumption
(look at the major players)
What’s the impact for those already immersed
in Big Data, Data Science, Machine Learning,
Distributed Systems, Cloud technologies,
DevOps practice, etc.? In word: Good
The results will be in health
care, manufacturing, agriculture,
energy, transportation, etc.
20

21. Artificial intelligence: making a human connection
safaribooksonline.com/library/view/oreilly-ai-conference/
9781491973912/video260723.html
AI work is mostly human?
21

22. Observations by Genevieve Bell @ Intel:
An anthropologist would ask: “Who raised you?
Who were your mummies and your daddies?” ...
AI has had a lot of daddies.
If we understand the founders, we can ask what
do we need to bring back into the conversation?
Artificial intelligence: making a human connection
safaribooksonline.com/library/view/oreilly-ai-conference/
9781491973912/video260723.html
AI work is mostly human?
22

23. AI work is mostly human?
The Future of AI, Oren Etzioni @ AI2
safaribooksonline.com/library/view/oreilly-ai-conference/
9781491973912/video282377.html
23

24. Etzioni stressed the key role of humans-in-the-loop:
99% of machine learning is human work
AI work is mostly human?
24

25. Over-anthropomorphization may become problematic:
• does this analysis introduce unneeded bias?
• machine intelligence differs from human cognition,
e.g., abductive reasoning (e.g., C.S. Peirce)
• consider examples of evolved antenna
AI work is mostly human?
25

26. Jobs won’t be displaced by AI?
Why we’ll never run out of jobs
safaribooksonline.com/library/view/oreilly-ai-conference/
9781491973912/video260722.html
26

27. Observations by Tim O’Reilly:
We won’t run out of work until we run out of problems
Our main advances have come when we invested in
other people's children – massive investment in EU
following WWII, built from something that resembles
Syria today
21st c great question: “Who’s black box do you trust?”
Jobs won’t be displaced by AI?
Why we’ll never run out of jobs
safaribooksonline.com/library/view/oreilly-ai-conference/
9781491973912/video260722.html
27

28. US voting by state
g.co/kgs/PSq9JS
Jobs won’t be displaced by AI?
28

29. US jobs by state
npr.org/sections/money/2015/02/05/382664837/map-the-most-common-job-in-every-state
Jobs won’t be displaced by AI?
29

30. Realistically, fully self-driving trucks are a bit further away
fool.com/investing/2016/10/30/despite-ubers-self-driving-truck-
delivery-truck-dr.aspx
Some contend that no existing economic model addresses
the accelerating pull of technological deflation
Meanwhile, social reforms regarding health care and
Universal Basic Income become
urgent priorities
Jobs won’t be displaced by AI?
30

31. Does AI = Deep Learning?
Obstacles to progress in AI
safaribooksonline.com/library/view/oreilly-ai-conference/
9781491973912/video260902.html
31

32. Yann LeCun described some necessary components of AI:
• perception
• predictive model
• memory
• reasoning and planning
Obstacles to progress in AI
safaribooksonline.com/library/view/oreilly-ai-conference/
9781491973912/video260902.html
Does AI = Deep Learning?
32

33. AI is much more than Deep Learning
Perception, prediction, memory – these are necessary;
however, they do not address understanding
Winograd Schemas show the need for common sense and
contextual understanding – replacement for Turing Test
see:
The Winograd Schema Challenge
Hector Levesque
commonsensereasoning.org/2011/papers/Levesque.pdf
33

34. AI is much more than Deep Learning
Common sense and context: for example, without ample
knowledge of the world, a sentence cannot be understood
embodied cognition (prevailed for a while)
ontology (more difficult, likely much more useful)
34

35. A lesson from history
see:
Why AM and Eurisko Appear to Work
Doug Lenat, John Seely Brown
aaaipress.org/Papers/AAAI/1983/AAAI83-059.pdf
Eurisko, The Computer With A Mind Of Its Own
George Johnson
aliciapatterson.org/stories/eurisko-computer-mind-its-own
Eurisko, and a mobius strip memory cell
Learning, rules, patterns – these only go so far
Ontology and the quest for common sense
35

36. Some Missing Pieces
With ML, we assume there’s structure embedded in the
data, then build ML models to validate those assumptions
However, which tools serve to identify structure?
see:
Persistent Homology: An Introduction and a New Text Representation
for Natural Language Processing
Xiaojin Zhu
pages.cs.wisc.edu/~jerryzhu/pub/homology.pdf
Topological Data Analysis
Chad Topaz
dsweb.siam.org/TheMagazine/Article/TabId/823/ArtMID/1971/ArticleID/777/
Topological-Data-Analysis.aspx
36

37. AI transformations
Recently launched our own AI project within O’Reilly Media…
We’re not a high-tech company; even so, the value of our data
gets unlocked through AI
This project makes use of cloud, Spark, Mesos, Kubernetes,
Docker, etc., leveraging the tools we know, but in more
complex use cases now.
37

38. 13K lexemes: our “universe” for customer interaction
Too much cognitive load for any editor or engineer to master;
however, not so difficult for a small cluster.
Curation is hard; you don’t want it full automated – related to
what Norvig calls the “Inattention Valley”
AI transformations
38

39. Challenge: generating an implicit graph versus curating
an explicit graph, then maintaining integrity between:
A
C
B
E
D
ML, Big Data, etc.:
computed similarity,
inferred links, etc.
(empiricists)
Curated ontology:
graph queries,
search rewrites, etc.
(rationalists)
a
c
b
e
d
AI transformations
39

40. A
C
B
E
D
a
c
b
e
d
Needs better tooling
(SPARQL and triple store crowd haven’t gotten the memo
yet about containers, orchestration, microservices, etc.)
AI transformations
BTW, this repo is fantastic: github.com/danielricks/penseur
40

41. David Beyer: Reshaping global industries
Machine intelligence in the wild: How AI will reshape global industries
safaribooksonline.com/library/view/strata-hadoop/9781491944660/
video282803.html
41

42. To paraphrase:
Consider the shift from steam to electric power:
it took a generation before factory managers
understood they could reconfigure the physical
arrangement
AI may be quicker adoption, but faces similar
extremes of cognitive embrace
Machine intelligence in the wild: How AI will reshape global industries
safaribooksonline.com/library/view/strata-hadoop/9781491944660/
video282803.html
David Beyer: Reshaping global industries
42

43. Looking ahead…
We have a need now to distinguish between what
humans and computers can do well, respectively
cognitive load, speed, scale, repeatability:
computers > humans
curation (captchas, as an example):
computers < humans
Organizations which focus on this
expertise for AI applications will
have a distinct advantage
43

44. presenter:
Just Enough Math
O’Reilly (2014)
justenoughmath.com
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@pacoid