Josh Patterson's talk on Artificial Intelligence from the October 2017 conference in Raleigh NC "All Things Distributed"

1. What is Artificial Intelligence?
Oct 2017
All Things Open Conference
Raleigh, NC

2. Josh Patterson
•Co-author of Deep Learning
book
•Co-creator of DL4J
•VP Field Eng @ Skymind

3. Central Thesis for “What is Artificial Intelligence?”
“Artificial
Intelligence” is a
term for algorithms
that increase user
productivity
•Quite artificial, no where close
to being “alive”
•Historically we get over-excited
about AI
•Because its an existential threat
•Accelerated in our minds by our
fixation on social media fueled
over-marketed narratives
•Can be hard to define
•Goal posts tend to move
•Much like Data Science

4. History and Definitions
Of Artificial Intelligence
One of the best books written on the subject of AI (if not the best) is
Stuart Russell and Peter Norvig’s Artificial Intelligence: A Modern Approach.
I can’t recommend this book enough for you to get a more complete idea of the depth and history of AI.

5. The Study of Intelligence
•Study of intelligence formally initiated in 1956 at Dartmouth
•Yet is at least 2,000 years old.
•The field is based on understanding intelligent entities and studying
topics such as these:
•Seeing
•Learning
•Remembering
•Reasoning
•These topics are components of what we’d consider intelligent
function
•to the capacity we have to understand intelligence

6. Building Blocks of Intelligent Study
•Philosophy (400 BC)
•Philosophers began to suggest the
mind as a mechanical machine
that encodes knowledge in some
form inside the brain.
•Mathematics
•Mathematicians developed the
core ideas of working with
statements of logic along with the
groundwork for reasoning about
algorithms.
•Psychology
•This field of study is built on the
ideas that animals and humans
have a brain that can process
information.
•Computer science
•Practitioners came up with
hardware, data structures, and
algorithms to support reverse
engineering basic components of
the brain.

7. Defn. “AI”
•Methods considered AI
•Linear modeling
•Neural Networks
•Random Forrests
•Expert Systems
•Rule Bases
Algorithms that automate parts, or all of, tasks
Russell and Norvig’s book on AI:
The intellectual establishment, by and large, preferred to believe that a
“machine can never do X.”
Problem is, AI researchers have
systematically responded by
demonstrating one X after
another.

8. Why is AI Popular Now? Deep Learning
•Three major contributors are
driving interest in AI today:
•The big jump in computer-vision
technology in the late 2000s
(Hinton’s team, others)
•The big data wave of the early
2010s
•Advancements in applications of
deep learning by top technology
firms

9. Quick History of Neural Networks
•Rough approximation of biological neuron
•1950s saw perceptron developed in
hardware
•Changes in activation function allowed for
non-linear functions
•Multi-layer perceptron becomes more
modern version of “neural networks”

10. Applications
Of Artificial Intelligence

11. Convolutional Network Architecture

13. Practical Use Cases of AI Today
•Computer Vision Applications
•Is there damage on this asset?
(Insurance)
•Sensor Applications
•Which machine on this assembly
line needs maintenance?
•(Automotive, manufacturing)
•Control and Planning
•Robots that navigate a warehouse
(logistics, retail)
•Machines today are getting
better at
•Making sense of images from
cameras
•Working w sensor data
•Making a plan to interact with the
world based on vision and sensors

14. •Developed Deep Reinforcement
Learning techniques to play
different types of games
•Atari
•Go
•Developed system that beat the
world’s best Go player
•AlphaGo beat Lee Sedol the world
champion in a five game
tournament
“Alpha Go Zero.”
Later on deepmind developed a
new variant that was able to
defeat Alpha Go at its own game
only 40 days later
Deepmind

15. IBM Watson Cancer
Reality check:
• uses expert system (programmed by
Memorial Sloan Kettering) to
recommend treatments
• uses NLP to discover and summarize
relevant studies, research, etc., to
back up recommendation
• does NOT automatically learn from
patient record data
• apparently uninterpretable despite
being an expert system!
Pros
• high concordance with common practice --
at hospitals similar to MSK
• can make medical decision making more
efficient
• can improve patient outcomes in regions
with limited resources, no experts
Cons
• recommendations are biased toward MSK
doctors, patient population
• cannot take, e.g., local regulations into
account (maybe recommended treatment not
covered by insurance)
• human-in-the-loop training is slow, labor
intensive; makes adaptation to local
population difficult

16. Why Does the term AI
attract so much Attention?
•Existential
•Will it take my job?
•Will robots take over the world?
•More subtly: “can it answer all of my
questions?”
•So often people expect technology to “give
them the answers”
•Being human is about the intuition to know
which questions to ask
•And leveraging the right technology to answer
these questions

17. Elon Musk says “Regulate AI”

18. Irrational Fear in a Non-Linear World
•When we start out with existential threats on our way of life
•Coupled with irrational exuberance in marketing narratives
•We arrive at some crazy end games, which tend to not be realistic as its hard
to project outcomes in the non-linear world (that we live in)
•Ever notice that in horror movies the narrative always requires the
characters to make irrational choices to move forward?
•“how about we *not* land on that planet?”
•Society tends to self adjust in emergent fashion
•Lots of agents make small local decisions to adapt to changing environments
•Tends to make global changes in the system in ways that are hard to predict

19. Also: Sort of hard to regulate games of linear
algebra and methods of optimization
Sorry, Elon.

20. Guns, Germs, and …
Artificial Intelligence

21. Modern AI Marketing Narratives
•Narratives such as
•Requires a phd to do anything related to
“AI” (“expert gating”)
•Only top 5 tech shops can hire anyone
good at AI, NFL-level salaries
•Techniques are impossible to fathom,
basically
•A lot of hype

22. The Hype Cycle
•Previous hype cycles:
•cloud, smart grid, big data
•Why is AI different than most hype cycles?
•Existential threat
•Companies tack on the big terms of the cycle to get
marketing and funding
•Most have no real claim to the term they tack on
•“AI spreadsheets”, “AI calendars”, “AI for HR”, etc
•What do they really mean?
•Most of the time: “we use linear modeling in our product in some
tangential way”

23. All of This Has Happened Before
Periods of interest have been the result of the sector being unrealisti‐
cally overhyped followed by a cycle of predictably underwhelming
results.
AI Winter I: (1974–1980). The lead-up to the first AI winter saw
machine translation fail to live up to the hype. Connectionism (neural
networks) interest waned in the 1970s, and speech understanding
research overpromised and underdelivered.
AI Winter II: Late 1980s. In the late 1980s and early 1990s, there was
overpromotion of technologies such as expert systems and LISP
machines, both of which failed to live up to expectations. The
Strategic Computing Initiative canceled new spending at the end of
this cycle. The fifth-generation computer also failed to meet its goals.

24. AI, Experts, and Commodity
•Hadoop, 2009: only a few experts at the top shops can do this
(Google, Yahoo, FB)
•2017: Hadoop becomes commodotized
•Although the narrative is that AI-experts are compensated like NFL
stars today, and that only the big-tech co’s can get them…
•Reality is that it will get commoditized through the combined forces of
tooling, open source, integrated AI-apps
•Along a long enough timeline, all technology converges on “Big 6 Consultant”

25. Post Trough AI-Technologies
We’ve seen this with the following:
•Informatics
•Machine learning
•Knowledge-based systems
•Business rules management
•Cognitive systems
•Intelligent systems
•Computational intelligence
The name change might be partly because they consider their field to be
fundamentally different from AI.

26. The Red Queen Always Wins
•We Survived the spreadsheet
from the 90’s
•Radiologists will survive AI
today
Automation will shift and evolve
the workforce as it has for the
past 1000 years
“My dear, here we must run as fast as we can, just to stay in
place. And if you wish to go anywhere you must run twice as fast
as that.”

27. Thank you for your time!
Open Source Stories series:
The Road to A.I. film -
www.redhat.com/opensourcesto
ries

29. A Style Guide for Writing About AI
•Don’t talk about “AI” as if it is a noun
•Its not
•Don’t take anything Elon Musk says about AI too seriously
•He’s great at cars and rockets, but for other things --- Its marketing,