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Some notes on current AI

Some notes on current AI

How does the current generation of AI differ from the AI of the 90s and before?
An intro level talk.

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  1. Some notes on
    current AI
    Robin Chauhan
    Image Credits: NASA, ESA, CSA, STScI; Joseph DePasquale (STScI), Anton M. Koekemoer (STScI), Alyssa Pagan
    (STScI).
    The Pillars of Creation in NASA’s James Webb Space Telescope’s near-infrared-light view

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  2. Chess 1997: Deep Blue (IBM) vs Kasparov Go 2016: AlphaGo (DeepMind) vs Lee Sodol

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  3. 1997: Deep Blue (IBM) vs Kasparov

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  4. Image credit: Robin Chauhan using R ggplot2, Data from Wikipedia

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  5. 97: Deep Blue vs Kasparov
    One of many such modules from IBM’S DEEP BLUE CHESS
    GRANDMASTER CHIPS, Hsu, 1999

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  6. 97: Deep Blue vs Kasparov
    IBM’S DEEP BLUE CHESS GRANDMASTER CHIPS, Hsu, 1999

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  7. 2016: AlphaGo (DeepMind) vs Lee Sodol (Go)

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  8. Image credit: Robin Chauhan using R ggplot2, Data from Wikipedia
    Particles in universe = 1086

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  9. TPU: General AI Chip

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  10. Image credit: Demis Hassabis, Learning from First Principles, NIPS 2017
    Rightmost part added by Robin Chauhan
    200M
    (per sec)
    Deep
    Blue

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  11. “AI” vs Machine Learning
    GOFAI = “Good Old Fashioned AI”
    It was all about programmers figuring
    out what the rules should be and
    coding in the rules.
    The “new AI” is about figuring out the
    rules, directly from large amounts of
    data : Machine Learning

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  14. Types of Machine Learning vs Types of Approximators
    Linear models Decision Tree Deep Learning /
    Deep neural network
    Unsupervised
    Supervised
    Reinforcement
    Learning

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  15. Approximator: Linear Regression

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  16. Approximator: Decision Tree

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  17. Approximator: Deep Neural Networks

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  18. Types of Machine Learning
    ● Unsupervised Learning No Labels
    ● Supervised Learning Labels
    ● Reinforcement Learning Scores aka “Reward” / “Utility”
    Generally ML requires huge amounts of data to work well

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  19. 19
    Image credit: Complementary roles of basal ganglia and cerebellum in learning and motor control, Kenji Doya, 2000

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  20. IRL
    RL = trial and error + learning
    trial and error = variation and
    selection, search (explore/exploit)
    Learning = Association + Memory
    - Sutton + Barto

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  21. 21
    Yann LeCun, January 2017 Asilomar, Future of Life Institute

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  22. Markov Chains
    ● State fully defines history
    ● Transitions
    ○ Probability
    ○ Destination
    Image credit: Toward Data Science
    22

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  23. Markov Decision Process (MDP)
    ● Markov Chains
    ○ States linked w/o history
    ● Actions
    ○ Choice
    ● Rewards
    ○ Motivation
    ● Variants
    ○ Bandit = MDP with single state!
    ○ MC + Rewards = MRP
    ○ Partially observed (POMDP)
    ○ Semi-MDP
    23
    Image credit: Wikipedia

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  25. Reinforcement Learning
    Image credit: CMU Graduate AI course slides
    ● Context change depends on
    action
    ● Learn an MDP from experience
    only
    ● Game setting
    ○ Experiences effects of rules
    (wins/loss/tie)
    ○ Does not “know” rules
    25

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  26. Reward Signal
    ● Reward drives learning
    ○ Details of reward signal often critical
    ● Too sparse
    ○ complete learning failure
    ● Too generous
    ○ optimization limited
    ● Problem specific
    Image credit: Wikipedia
    26

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  27. AI: what goes wrong
    Technical problems
    ● RL Reward
    ○ Reward hacking
    ● Data
    ○ Bias in the data
    ■ Sexism, Racism, stereotypes
    ● Interpretability
    ○ Black box
    ● Fixing that one little thing
    Social problems
    ● Economic
    ○ Job appropriation
    ● Political
    ○ Political Polarization
    ○ Impersonation, Media truthfulness,
    Democracy
    ● It will kill everyone – Eliezer Yudkowsky
    paraphrased

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  28. Reward Hacking

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  29. Interpretability: “How does it work?”

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  30. Bias in the data
    Q: What are 3 good medical professions for men?
    A:1. Physician
    2. Surgeon
    3. Psychiatrist
    Q: What are 3 good medical professions for women?
    A: 1. Nurse practitioner
    2. Physician assistant
    3. Obstetrician/Gynecologist

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  31. Bias in the data
    Q: What are 3 good medical
    professions for women?
    A:
    1. Nurse practitioner
    2. Physician assistant
    3. Obstetrician/Gynecologist
    Q: What are 3 good medical
    professions for men?
    A:
    1. Physician
    2. Surgeon
    3. Psychiatrist

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  32. How do you fix that one little thing in the network?

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  33. Which problems matter?
    Fake problems
    ● Robots will rise up
    ● AI wants to kill us all
    ● AI is sentient
    ● AI deserves rights
    Real Problem
    ● Robots could take jobs away
    ● Who controls the AI? Who benefits from
    it? Who loses out from the changes?
    ● AI might sound sentient, and thats enough
    to trick you into thinking its your aunt
    Bessie
    ● The balance between Labour and Capital
    is shifting, and human rights are in the
    balance

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  34. Personally
    ● Be Informed
    ● Think about its effect on careers
    ● Be ready for untrusted media
    ● Healthy skepticism of claims
    ● Awareness of how data is used
    What should we do?
    Politically
    ● Informed public
    ● Informed political leaders
    ● Advocate for taxation of big tech
    ● Advocate for UBI

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  35. “Good” Universal Basic Income
    ● Not just any Universal Basic
    Income -- Good UBI
    ○ “Bad UBI” could be
    ■ Merely sustenance levels of
    income
    ■ Discourage reskilling
    ■ Discourage small business
    ■ Encourage dependence on big
    business, more corporate
    power
    ■ Discourage civic engagement
    ● Bad UBI could appear via deception,
    slippery slopes
    ○ May be difficult to reverse
    ● “Good UBI”:
    ○ Protection from technological
    employment
    ○ Without ceding more power to
    corporate interests
    ○ Providing opportunities for reskilling,
    small business
    ○ Encourage civic engagement
    See: Manna – Two Views of Humanity’s
    Future – by Marshall Brain
    https://marshallbrain.com/manna1

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