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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
  2. 97: Deep Blue vs Kasparov One of many such modules

  3. Image credit: Demis Hassabis, Learning from First Principles, NIPS 2017

    Rightmost part added by Robin Chauhan 200M (per sec) Deep Blue
  4. “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
  5. Types of Machine Learning vs Types of Approximators Linear models

    Decision Tree Deep Learning / Deep neural network Unsupervised Supervised Reinforcement Learning
  6. 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
  7. 19 Image credit: Complementary roles of basal ganglia and cerebellum

    in learning and motor control, Kenji Doya, 2000
  8. IRL RL = trial and error + learning trial and

    error = variation and selection, search (explore/exploit) Learning = Association + Memory - Sutton + Barto
  9. Markov Chains • State fully defines history • Transitions ◦

    Probability ◦ Destination Image credit: Toward Data Science 22
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. “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