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2016: An Intelligence Odyssey
2016 An Intelligence
Odyssey
March, 2016
Ju Han Kim, M.D., Ph.D.
2016: An Intelligence Odyssey
Ju Han Kim, M.D., Ph.D.
March, 2016 入 試 說 明 會
2016: An Intelligence Odyssey
A Space Odyssey
March, 2016
Ju Han Kim, M.D., Ph.D.
2016: An Intelligence Odyssey
The Dawn of Man
• 25s
• 1m
• 3m 10s
• 5m 30s
2016: An Intelligence Odyssey
Discovery One
2016: An Intelligence Odyssey
EVA Pod
Ex Machina
α
2016: An Intelligence Odyssey
An Intelligence Odyssey
March, 2016
Ju Han Kim, M.D., Ph.D.
2016: An Intelligence Odyssey
Deep Thought에서 DeepMind까지
2016: An Intelligence Odyssey
Deep Thought, Deep Blue, and DeepMind
• Average branching factors for
Chess (35) and Go (250)
• Combinatorial explosion!
• Brut force search, infeasible!
• Pruning algorithm can cut down
the branching factor (i.e., alpha-
beta pruning and minmax algorithm, a
branch and bound algorithm)
2016: An Intelligence Odyssey
Time complexity: Θ(bd) b= branching factor, d=depth
형세판단: Value networks, Policy networks
Daylen Yang, University of
California at Berkeley, TNG
Technology Consulting,
https://www.youtube.com/wat
ch?v=pUyURF1Tqvg
A ridiculously big number!!
Search Space Reduction
2016: An Intelligence Odyssey
“알파고 승리는 희대의 사기극”
Time complexity: Θ(bd) b= branching factor, d=depth A ridiculously big number!!
2016: An Intelligence Odyssey
The Mystery of Go (WIRED, 2014.12.05)
• “The Mystery of Go”, 1965, New Scientist
• Impossibly high branching factor (250, cf. 35 for Chess) and
state space makes MinMax extremely costly.
• Alfred Zorist’s first Go program in 1968
• In Search of the Mental Leap: Many people peak out at a certain level of amateur and never get any stronger. Bruce Wilcox won James Kerwin in 1979 with divide and conquer mainly with expert modeling. Mark Boon (Goliath), David Fotland (Many Faces of Go), Chen Zhixing (Handtalk and Goemate) were all excellent players.
• The Monte Carlo Bet: (Coulom’s Crazy stone ) MC reduces search space with statistical sampling. Crazy stone MCTS won its first tournament in 2006, commercialized in 2011, winning Yoshio in 2013. “After the match, I ask Coulom when a machine will win without a handicap, “I think maye 10 years, but I don’t like to make predictions.” Feng-Hsing Hsu (Deep Thought) also favored alpha-beta search over MC.
• When AI Is Not AI “Even more surpprising was that no programmers
think of their creations as ‘intelligent’”
• IBM Watson becomes the gatekeeper of a new era.
2016: An Intelligence Odyssey
Deep Thought, Deep Blue, and DeepMind
ChipTest (1985), Deep Thought (by Feng-hsiung Hsu, a Taiwanese American Scientist at Carnegie Mellon University), and Deep Blue (IBM)
Deep Fritz (Germani), Deep Junior (Israeli)
Deep Blue (IBM) won its first game against a world champion, Garry Kasparov, on February 10, 1996, but defeated by 4-2. Deep Blue was then heavily upgraded, and defeated Kasparov again in May 1997, becoming the first computer system to defeat a reigning world champion in a match under standard chess tournament.
Blue Gene is an IBM supercomputers that can reach operating speeds in the PFLOPS (petaFLOPS) range.
Watson is a QA computer system capable of answering questions posed in natural language,[2] developed in IBM's DeepQA project, specifically developed to answer questions on the quiz show Jeopardy! Watson won Jeopardy in 2011 and awared $1M.
DeepMind & AlphaGo is claimed to be not pre-programmed. Technically it uses deep learning on a convolutional neural network, with a novel form of Q-learning, a form of model-free reinforcement learning.
2016: An Intelligence Odyssey
Deep Thought, Deep Blue, and DeepMind
Deep Thought does not know the Ultimate Question to Life, the Universe and Everything, but offers to design an even more powerful computer, (planet) Earth, to calculate it. After 10M years of calculation, the Earth is destroyed by Vogons* 5 min. before the computation is complete.
1971
1979.10.12
Deep Thought is a super computer created by some hyper-intelligent, pan-galactic beings (whose three-dimensional protrusions into
our universe are white mice). Deep Thought is the size of a small city. Deep Thought takes 7.5M years to compute the Answer to “The Ultimate Question of Life, the Universe, and
Everything“, which turns out to be 42,
quite definitely after very thorough checking.
*Vogons are a fictional alien race from the planet Vogsphere who are responsible for the
destruction of the Earth, in order to facilitate an intergalactic highway construction project.
2016: An Intelligence Odyssey
A Towel and A Screwdriver
Bring your towel.
May 25th.
2016: An Intelligence Odyssey
Riddles, Tricks and Intelligence
March, 2016
Ju Han Kim, M.D., Ph.D.
The first riddle of the sphinx
What goes on four legs in the morning, on two legs
at noon, and on three legs in the evening?
Oedipus solved the riddle, and the Sphinx destroyed herself. 17
The second riddle of the sphinx
"There are two sisters: one gives birth to
the other and she, in turn, gives birth to
the first. Who are the two sisters?" 18
2016: An Intelligence Odyssey
A Riddle of My Son, 양말을 꿀꺽 삼켜버린 수학
2016: An Intelligence Odyssey
Bachet’s game or Nim
• The 21 game The first player says "1" and each player in turn increases the number by 1, 2, or 3, but may not exceed 21; the player forced to say "21" loses.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
노오오오오~오~력!
• The 100 game Two players start from 0 and alternatively add a number from 1 to 10 to the sum. The player who reaches 100 wins. The winning strategy is to reach a number in which the digits are subsequent (e.g. 01, 12, 23, 34,...) and control the game by jumping through all the numbers of this sequence. Once reached 89, the opponent has lost (he can only tell numbers from 90 to 99, and the next answer can in any case be 100).
2016: An Intelligence Odyssey
Nimatron, an Electric Brain?
• At the 1940 New York World's Fair, Westinghouse displayed a machine, the Nimatron, that played Nim. It was also one of the first ever electronic computerized games.
The Nimrod, built in Britain by Ferranti for the 1951 Festival of Britain, was an early computer custom-built to play a computer game, one of the first games developed in the early history of video games.
2016: An Intelligence Odyssey
Can Machine Think?
March, 2016
Ju Han Kim, M.D., Ph.D.
2016: An Intelligence Odyssey
Monte, Nim, Chess, Go, and Human Insight
1060
2016: An Intelligence Odyssey
The Imitation Game
Alan Turing’s Machine Intelligence (since 1941)
Computing Machinery and Intelligence (1950) "I propose to
consider the question, 'Can machines think?'"
Because "thinking" is difficult to define, Turing chooses to replace
the question, "Are there imaginable digital computers which
would do well (what we (as thinking entities) can do) in the
Imitation Game?", one that can actually be answered.
But it never happens that it arranges its speech in various ways, in order to reply
appropriately to everything that may be said in its presence, as even the lowest type
of man can do. (René Descartes in 1637, in his Discourse on the Method)
2016: An Intelligence Odyssey
Monte, Nim, Chess, Go, Elisa, and Human
• Eliza, the psychotherapist, is the grandma of Siri.
• Turing test for Elisa in Blade Runner
• Eugene Goostman
• Siri by Apple
29s
2016: An Intelligence Odyssey
The Chinese Room (Strong AI hypothesis)
• John Searle, a philosopher, proposed a thought experiment to challenge
the claim that it is possible for a computer running a program to have a
"mind" and "consciousness" in the same sense that people do,
simply by virtue of running the right program.
"The appropriately programmed computer with the right inputs and outputs would thereby have a
mind in exactly the same sense human beings have minds."
2016: An Intelligence Odyssey
Strong vs. Weak AI
• Strong AI: Performing any intellectual task
that a human being can.
• The primary goal of AI
• SF writers and futurists
• Artificial General Intelligence
• Consciousness, sentience, sapience, self-
awareness
• Weak AI: applied or narrow AI
• Do not necessarily have a mind, mental states,
or consciousness.
2016: An Intelligence Odyssey
A Strong AI, w/ Emergent Properties
2016: An Intelligence Odyssey
A Strong AI, having emotion/motivation?
•알파고 승리 배경
2016: An Intelligence Odyssey
알파고 승리 배경 A Strong AI, having emotion/motivation?
, .
Feel
2016: An Intelligence Odyssey
A Strong AI, showing delinquent behavior?
2016: An Intelligence Odyssey
Reverse Turing Test
2016: An Intelligence Odyssey
Stupidity or ‘Natural’ Intelligence
2016: An Intelligence Odyssey
Artificial Stupidity vs. Human Stupidity
2016: An Intelligence Odyssey
An Odyssey for Intelligence or Search?
March, 2016
Ju Han Kim, M.D., Ph.D.
2016: An Intelligence Odyssey
Time complexity: Θ(bd) b= branching factor, d=depth
형세판단: Value networks, Policy networks
Daylen Yang, University of
California at Berkeley, TNG
Technology Consulting,
https://www.youtube.com/wat
ch?v=pUyURF1Tqvg
A ridiculously big number!!
2016: An Intelligence Odyssey
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Graph and Tree, Path and Search
2016: An Intelligence Odyssey
Tree Search
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2016: An Intelligence Odyssey
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S : Start G : Goal N(x) : list of 1st degree neighbors Path : [S, D, E, F, G] Paths : [[S, A, B, C], [S, D, E, F, G],….]
[[S]] [[S,A], [S,D]] [[S,D], [S,A,B], [S,A,D]] [[S,A,B], [S,A,D], [S,D,A], [S,D,E]] [[S,A,D], [S,D,A], [S,D,E], [S,A,B,C], [S,A,B,E]] [[S,D,A], [S,D,E], [S,A,B,C], [S,A,B,E], [S,A,D,E]] [[S,D,E], [S,A,B,C], [S,A,B,E], [S,A,D,E], [S,D,A,B]] [[S,A,B,C], [S,A,B,E], [S,A,D,E], [S,D,A,B], [S,D,E,B], [S,D,E,F]] [[S,A,B,E], [S,A,D,E], [S,D,A,B], [S,D,E,B], [S,D,E,F], [S,A,B,C]] [[S,A,D,E], [S,D,A,B], [S,D,E,B], [S,D,E,F], [S,A,B,C], [S,A,B,E,D], [S,A,B,E,F]] [[S,D,A,B], [S,D,E,B], [S,D,E,F], [S,A,B,C], [S,A,B,E,D], [S,A,B,E,F], [S,A,D,E,B], [S,A,D,E,F]] [[S,D,E,B], [S,D,E,F], [S,A,B,C], [S,A,B,E,D], [S,A,B,E,F], [S,A,D,E,B], [S,A,D,E,F], [S,D,A,B,C], [S,D,A,B,E]] [[S,D,E,F], [S,A,B,C], [S,A,B,E,D], [S,A,B,E,F], [S,A,D,E,B], [S,A,D,E,F], [S,D,A,B,C], [S,D,A,B,E], [S,D,E,B,A], [S,D,E,B,C]] [[S,D,E,F], [S,A,B,C], [S,A,B,E,D], [S,A,B,E,F], [S,A,D,E,B], [S,A,D,E,F], [S,D,A,B,C], [S,D,A,B,E], [S,D,E,B,A], [S,D,E,B,C]] [[S,A,B,C], [S,A,B,E,D], [S,A,B,E,F], [S,A,D,E,B], [S,A,D,E,F], [S,D,A,B,C], [S,D,A,B,E], [S,D,E,B,A], [S,D,E,B,C], [S,D,E,F,G]] [[S,A,B,E,D], [S,A,B,E,F], [S,A,D,E,B], [S,A,D,E,F], [S,D,A,B,C], [S,D,A,B,E], [S,D,E,B,A], [S,D,E,B,C], [S,D,E,F,G], [S,A,B,C]] [[S,A,B,E,F], [S,A,D,E,B], [S,A,D,E,F], [S,D,A,B,C], [S,D,A,B,E], [S,D,E,B,A], [S,D,E,B,C], [S,D,E,F,G], [S,A,B,C], [S,A,B,E,D]] [[S,A,D,E,B], [S,A,D,E,F], [S,D,A,B,C], [S,D,A,B,E], [S,D,E,B,A], [S,D,E,B,C], [S,D,E,F,G], [S,A,B,C], [S,A,B,E,D], [S,A,B,E,F,G]] [[S,A,D,E,F], [S,D,A,B,C], [S,D,A,B,E], [S,D,E,B,A], [S,D,E,B,C], [S,D,E,F,G], [S,A,B,C], [S,A,B,E,D], [S,A,B,E,F,G], [S,A,D,E,B,C]] [[S,D,A,B,C], [S,D,A,B,E], [S,D,E,B,A], [S,D,E,B,C], [S,D,E,F,G], [S,A,B,C], [S,A,B,E,D], [S,A,B,E,F,G], [S,A,D,E,B,C], [S,A,D,E,F,G]] [[S,D,A,B,E], [S,D,E,B,A], [S,D,E,B,C], [S,D,E,F,G], [S,A,B,C], [S,A,B,E,D], [S,A,B,E,F,G], [S,A,D,E,B,C], [S,A,D,E,F,G], [S,D,A,B,C]]
S
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Graph and Tree, Path and Search
2016: An Intelligence Odyssey
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S : Start G : Goal N(x) : list of 1st degree neighbors Path : [S, D, E, F, G] Paths : [[S, A, B, C], [S, D, E, F, G],….]
[[S]] [[S,A], [S,D]] [[S,A,D], [S,A,B], [S,D]] [[S,A,D,E], [S,A,B], [S,D]] [[S,A,D,E,B], [S,A,D,E,F], [S,A,B], [S,D]] [[S,A,D,E,B,C], [S,A,D,E,F], [S,A,B], [S,D]] [[S,A,D,E,F], [S,A,B], [S,D], [S,A,D,E,BC]] [[S,A,D,E,F,G],[S,A,B], [S,D], [S,A,D,E,BC]] [[S,A,B], [S,D], [S,A,D,E,BC], [S,A,D,E,F,G]] [[S,A,B,C], [S,A,B,E], [S,D], [S,A,D,E,BC], [S,A,D,E,F,G]] [[S,A,B,E], [S,D], [S,A,D,E,BC], [S,A,D,E,F,G], [S,A,B,C]] [[S,A,B,E,D], [S,A,B,E,F], [S,D], [S,A,D,E,BC], [S,A,D,E,F,G], [S,A,B,C]] [[S,A,B,E,F], [S,D], [S,A,D,E,BC], [S,A,D,E,F,G], [S,A,B,C], [S,A,B,E,D]] [[S,A,B,E,F,G], [S,D], [S,A,D,E,BC], [S,A,D,E,F,G], [S,A,B,C], [S,A,B,E,D]] [[S,D], [S,A,D,E,BC], [S,A,D,E,F,G], [S,A,B,C], [S,A,B,E,D], [S,A,B,E,F,G],]
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Graph and Tree, Path and Search
2016: An Intelligence Odyssey
g = ((0,1),(0,4),(1,4),(1,2),(2,3),(4,5),(2,5),(5,6),(6,7)) def nextNodes(graph, paths, goal): path = paths[0] for each g[i] if g[i] has path[-1] and g[i] not in path: paths = paths + [path + g[i]] if goal == g[i]: BINGO!! return paths def dfs(graph, paths, goal): if ?nextNodes for all: exit() else: return dfs(graph, nextNodes(graph, paths, goal), goal)
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Depth First Search, Breath First Search
2016: An Intelligence Odyssey
Create a Web-bot!
ALTRUISM-
.| .||
PL---ASMA ALTRUI
.|..--
PLAS
2016: An Intelligence Odyssey
Graph and tree search algorithms
•α — β •A* •B* •Beam •Bellman–Ford •Best-first •Bidirectional •Borůvka •Branch & bound •BFS •British Museum •D* •DFS •Depth-limited •Dijkstra •Edmonds •Floyd-Warshall •Hill climbing •Iterative deepening •Kruskal •Johnson •Lexicographic BFS •Prim •Uniform-cost Listings •Graph algorithms •Search algorithms •List of graph algorithms Related topics •Dynamic programming •Graph traversal •Tree traversal •Search games
Most AI problems are search problems!
• (Deep) Neural Nets • SVMs • Genetic Algorithms • Graph Algorithms
• Bayse Net • Semi-supervised MLs • Unsupervised MLs • Reinforcement Learning
…
2016: An Intelligence Odyssey
Deep Learning
• The modern reincarnation of Artificial Neural Networks from the 80s and 90s.
• A multi-layer feed forward NNs, w/ an input layer and multiple layers for non-linear transformations.
• Hinton and Salakhutdinov (2000’s): many-layered feedforward NN can effectively pre-train one layer
at a time, treating each layer as an unsupervised restricted Boltzmann machine, then fine-tune using
supervised backpropagation.
• Good in Speech (signal) and Image Recognition: Deep Learning outperforms the traditional GMM-
HMM large-scale speech recognition around 2010.
• A set of algorithms attempting to model higher level abstractions in data by using multi-layered
architectures.
• General algorithms that learn from observations in big-compute, big-data era.
• Critic: Black box, mostly empirical, rather than theoretical.
2016: An Intelligence Odyssey
• Learn action policy: s a to
maximize rewards
• Value function: expected future rewards
• Temporal difference error
• Exploration vs. Exploitation
Reinforcement Learning and Co-evolution
r
a
State s
2016: An Intelligence Odyssey
Reinforcement Learning and Co-evolution
• Reinforcement learning is an area of ML inspired by behaviorism (ako
Skinnerian), concerned with how software agents ought to take actions in an
environment so as to maximize some notion of cumulative reward.
• Claimed to differ from standard supervised learning in that correct input
/output pairs are never presented, nor sub-optimal actions explicitly corrected.
• Approximate Dynamic Programming
• Markov Decision Process: a discrete time stochastic control process.
• Exploration (of uncharted territory) vs. Exploitation (of current knowledge)
trade-off
• Q-learning is a model-free RL technique for an optimal action-selection policy
for any given (finite) MDP.
• Co-evolution strategy by self-playing
2016: An Intelligence Odyssey
The Go Space
March, 2016
Ju Han Kim, M.D., Ph.D.
2016: An Intelligence Odyssey
Go Problem Space
• Max. branching factor: 19 * 19
• Max. Depth: 150
• Ko rule (覇), 삼패빅, 공배
• Finite vs. infinite
• Computability, Provability, Definability
♥ α
2016: An Intelligence Odyssey
Go space
• Problem space
Local optima
global optima
perturbation
Hill climbing Optimization Brut force?
Curse of dimensionality, the long narrow valley problem
2016: An Intelligence Odyssey
AlphaGo vs. Sedol Lee
2016: An Intelligence Odyssey
`
DeathGo, 제국의 구조
• 바둑의 문제공간 크기는 생각했던 것보다 작다.
• 특히, 바둑이 진행될수록 문제공간은 급격히 좁혀진다.
• ‘패’는 생각보다 쉽게 피하거나 무시할 수 있다.
• 그렇다면 바둑은 그저 좀 큰 체스에 불과하다.
• Balance A* w/ Deep Learning ‘near’ brut-force search.
Win. prob.
“딱 이길 만큼만 둔다!”
Space size
2016: An Intelligence Odyssey
“At the Moment of Truth” Strategy!
1. Go space as latent ko rule 승부수
실수 없이 마무리
2. 흉내바둑 승부수 low-tech. brut force attack
Winning strategy, AlphaGo
“딱 이길 만큼만 둔다!”
Space size
Win. prob.
2016: An Intelligence Odyssey
AlphaGo vs. Lee Sedol, Match 4
• . 78 . . .
• : , . . 78 .
. .
2016: An Intelligence Odyssey
DeathGo, 제국의 구조
• Troopers, Darth Vader, Dark Lord, Death Star
• When Luke Skywalker solved the riddle, the Death Star destroyed herself.
Space size Win. prob.
2016: An Intelligence Odyssey
Death Go, the Dark Lord
1. 흉내바둑 승부수 low-tech. brut force attack
Space size Win. prob.
2016: An Intelligence Odyssey
The Last Hero
2016: An Intelligence Odyssey
The Hero’s Odyssey
2016: An Intelligence Odyssey
The Last Hero
Space size Win. prob.
2016: An Intelligence Odyssey
Taking Count of Crosses
March, 2016
Ju Han Kim, M.D., Ph.D.
計 家
2016: An Intelligence Odyssey
Take Count the Crosses
•패자는?
•승자는?
•대책은?
2016: An Intelligence Odyssey
Don’t Panic Bring Your Towel
March, 2016
Ju Han Kim, M.D., Ph.D.
2016: An Intelligence Odyssey
I think the problem,
to be quite honest with you
is that you’ve never known
what the question was.
Too Deep Thought…
2016: An Intelligence Odyssey
• Formal System
• Computability, Definability, Provability
• Hilbert's problem No. 2 vs. Gödel's Incompleteness Theorems
The Question Was..
“ .”
“( ) (Formal System) ”
( ).
“( )
.
,
.”
“ ”, “
.”
“
.”
David Hilbert Kurt Gödel
Gerhard Karl Erich Gentzen
2016: An Intelligence Odyssey
AI BI CI
• AI: Artificial Intelligence
• BI: Biomedical Informatics
• CI: Clinical Intelligence
• DI: Dasein Intelligence
2016: An Intelligence Odyssey
Always Know Where Your Towel Is
Return Of the Jedi opened
on May 25, 1983.
Bring Your Towel, May 25
2016: An Intelligence Odyssey
AlphaGo Strikes Back
March, 2016
Ju Han Kim, M.D., Ph.D.
2016: An Intelligence Odyssey
When AlphaGo Strikes Back…
2016: An Intelligence Odyssey
The Problem Begins
• Problem space
2016: An Intelligence Odyssey
From the Dawn of Computer…
2016: An Intelligence Odyssey
고용의 미래 소비
생산
Luddite, Chartist, Suffragettes
Hitchhikers, Hackers, Heroes
2016: An Intelligence Odyssey
의료의 미래
Dr.
2016: An Intelligence Odyssey
When the Empire Strikes Back…
Space size
2016: An Intelligence Odyssey
Hitchhikers, Hackers, and Heroes
2016: An Intelligence Odyssey
The Troopers
우리가 모르고 있는 것 때문에 문제가 일어나는
것이 아니다. 문제는 우리가 진실이라고 믿고
있는 것이 틀렸을 때 발생한다. -- 마크 트웨인
2016: An Intelligence Odyssey
An Eternal Odyssey
March, 2016
Ju Han Kim, M.D., Ph.D.
2016: An Intelligence Odyssey
The Final Odyssey…
2016: An Intelligence Odyssey
After the Odyssey…
♥ α
2016: An Intelligence Odyssey
Ju Han Kim, M.D., Ph.D.
March, 2016 入 試 說 明 會
2016: An Intelligence Odyssey
What if the space is not the one that we’ve known?
2016: An Intelligence Odyssey
What if the space is not the one that we’ve known?
2016: An Intelligence Odyssey
Q&A
CU on May 25th