what did alphago do to beat the strongest human go player?
TRANSCRIPT
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March 2016
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Mainstream Media
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1997
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Ing cup 1985 – 2000(up to 1,400,000$)
(1985-2000)
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5d win 1998
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October 2015
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This is the first time that a computer program has defeated a human professional player in the
full-sized game of Go, a featpreviously thought to be at
least a decade away.
Silver, D. et al., 2016. Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), p.484-489.
January 2016
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Go
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Computational Challenge
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Monte Carlo Method
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Neural Networks
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Revolution with Neural Networks
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What did we learn?
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Go
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Computational Challenge
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Go vs. Chess
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Complex vs. Complicated
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„While the Baroque rules of chess could only have been created by humans, the rules of
go are so elegant, organic, and rigorously logical that if intelligent life forms exist
elsewhere in the universe, they almost certainly play go.“
Edward Lasker (chess grandmaster)
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Larger board19x19 vs. 8x8
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Almost every move is legal
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Average branching factor:250 vs 35
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State Space Complexity:10 171 vs 1047
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1080
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Global impact of moves
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68957966354765
685766345
857635
563
6
MAX
MIN
MAX
MIN
MAX
Traditional Seach
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68957966354765
685766345
857635
563
6
MAX
MIN
MAX
MIN
MAX
Evaluation Function
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Monte Carlo Method
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What is Pi?
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How do you determine Pi?
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2006
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Browne, Cb, and Edward Powley. 2012. A survey of monte carlo tree search methods. Intelligence and AI 4, no. 1: 1-49
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2/4
1/1 0/1 1/1 0/1
A1D5
F13C7
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2/4
1/1 0/1 1/1 0/1
A1D5
F13C7
Selection
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2/4
1/1 0/1 1/1 0/1
A1D5
F13C7
0/0
B5
Expansion
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2/4
1/1 0/1 1/1 0/1
A1D5
F13C7
0/0
B5
Simulation
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Random
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Not Human like?
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3/5
2/2 0/1 1/1 0/1
A1D5
F13C7
1/1
B5
Backpropagation
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3/5
2/2 0/1 1/1 0/1
A1D5
F13C7
1/1
B5
Perspective
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2/5
1/2 0/1 1/1 0/1
A1D5
F13C7
1/1
B5
Perspective
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Multi Armed Bandit
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Multi Armed Bandit
Exploitation vs Exploration
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winsvisits
+explorationFactor √ ln (totalVisits)visits
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15042
86/193
0/1 1/2 0/2
36/1116
2/2
58/151
1/2 0/23/3
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15042
86/193
0/1 1/2 0/2
36/1116
2/2
58/151
1/2 0/23/3
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15042
86/193
0/1 1/2 0/2
36/1116
2/2
58/151
1/2 0/23/3
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Generate a valid random move
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Who has won?
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General Game Playing
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Anytime
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Lazy
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Expert Knowledge
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Neural Networks
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2014
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What does this even mean?
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Neural Networks
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Input“Hidden” Layer
Output
Neural Networks
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Weights
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Bias/Threshold
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4
2
-3
3.2
Activation
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5.2 >= 4
2
-3
3.2
Activation
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2.2 <= 4
2
-3
3.2
Activation
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Activation
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Training
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Adjust parameters
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Supervised Learning
InputExpectedOutput
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Backpropagation
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Data set
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Training data + test data
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Training
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Verify
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Overfitting
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Deep Neural Networks
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Convolutional Neural Networks
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Local Receptive Field
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Feature Map
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Stride
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Shared weights and biases
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19 x 19 3 x 17 x 17
Multiple Feature maps/filters
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Architecture
...
Input Features
12 layers with 64 – 192 filters
Output
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Architecture
...
Input Features
12 layers with 64 – 192 filters
Output
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Architecture
...
Input Features
12 layers with 64 – 192 filters
Output
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2.3 million parameters630 million connections
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● Stone Colour x 3● Liberties x 4● Liberties after move played x 6● Legal Move x 1● Turns since x 5● Capture Size x 7 ● Ladder Move x 1● KGS Rank x 9
Input Features
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Training on game data predicting the next move
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55% Accuracy
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Mostly beats GnuGo
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Combined with MCTS in the Selection
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Asynchronous GPU Power
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Revolution
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Silver, D. et al., 2016. Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), p.484-489.
Networks in Training
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Silver, D. et al., 2016. Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), p.484-489.
Networks in Training
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Silver, D. et al., 2016. Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), p.484-489.
AlphaGo Search
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Selection
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Action ValuePrior Probability
Visit Count
Selection
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Action ValuePrior Probability
Visit Count
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Action ValuePrior Probability
Visit Count
Selection
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Action ValuePrior Probability
Visit Count
Selection
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0.8
1.2 0.5 1.1 0.9
Action Value + Bonues
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0.8
1.2 0.5 1.1 0.9
Expansion
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0.8
1.2 0.5 1.1 0.9
Expansion
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0.8
1.2 0.5 1.1 0.9
Prior Probability
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0.8
1.2 0.5 1.1 0.9
Evalution
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0.8
1.2 0.5 1.1 0.9
Evalution
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0.8
1.2 0.5 1.1 0.9
Rollout
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0.8
1.2 0.5 1.1 0.9
Value Network
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0.81
1.3 0.5 1.1 0.9
Backup
1 .6
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1202 CPUs 176 GPUS0.8
1.2 0.5 1.1 0.9
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Tensor
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Human Instinct Policy NetworkReading Capability SearchPositional Judgement Value Network
3 Strengths of AlphaGo
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Human Instinct Policy NetworkReading Capability SearchPositional Judgement Value Network
Most Important Strength
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More Natural
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Lee Sedol match
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Style
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So when AlphaGo plays a slack looking move,we may regard it as a mistake,
but perhaps it should more accurately be viewedas a declaration of victory?
An Younggil 8p
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Game 2
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Game 4
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Game 4
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Game 4
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What can we learn?
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Making X fastervs
Doing less of X
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Benchmark everything
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Solving problems the human wayvs
Solving problems the computer way
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Don't blindly dismiss approaches as infeasible
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One Approach vs
Combination of Approaches
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Joy of Creation
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PragTob/Rubykon
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Sources
● Maddison, C.J. et al., 2014. Move Evaluation in Go Using Deep Convolutional Neural Networks.
● Silver, D. et al., 2016. Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), p.484-489.
● Michael A. Nielsen, "Neural Networks and Deep Learning", Determination Press, 2015 http://neuralnetworksanddeeplearning.com
● Gelly, S. & Silver, D., 2011. Monte-Carlo tree search and rapid action value estimation in computer Go. Artificial Intelligence, 175(11), p.1856-1876.
● I. Althöfer, “On the Laziness of Monte-Carlo Game Tree Search In Non-tight Situations,” Friedrich-Schiller Univ., Jena, Tech. Rep., 2008.
● Browne, C. & Powley, E., 2012. A survey of monte carlo tree search methods. IEEE Transactions on Intelligence and AI in Games, 4(1), p.1-49.
● Gelly, S. & Silver, D., 2007. Combining online and offline knowledge in UCT. Machine Learning, p.273-280.
● https://www.youtube.com/watch?v=LX8Knl0g0LE&index=9&list=WL
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Photo Credit● http://www.computer-go.info/events/ing/2000/images/bigcup.jpg
● https://en.wikipedia.org/wiki/File:Kasparov-29.jpg
● http://www.geforce.com/hardware/desktop-gpus/geforce-gtx-titan-black/product-images
● http://giphy.com/gifs/dark-thread-after-lCP95tGSbMmWI
● https://cloudplatform.googleblog.com/2016/05/Google-supercharges-machine-learning-tasks-with-custom-chip.html
● https://gogameguru.com/i/2016/01/Fan-Hui-vs-AlphaGo-550x364.jpg
● http://makeitstranger.com/
● CC BY 2.0
– https://en.wikipedia.org/wiki/File:Deep_Blue.jpg
– https://www.flickr.com/photos/luisbg/2094497611/
● CC BY-SA 3.0
– https://en.wikipedia.org/wiki/Alpha%E2%80%93beta_pruning#/media/File:AB_pruning.svg
● CC BY-SA 2.0
– https://flic.kr/p/cPUtny
– https://flic.kr/p/dLSKTQ
– https://www.flickr.com/photos/83633410@N07/7658272558/
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Photo Credit● CC BY-NC-ND 2.0
– https://flic.kr/p/q15pzb
– https://flic.kr/p/bHSj7D
– https://flic.kr/p/ixSsfM
– https://www.flickr.com/photos/waxorian/4228645447/
– https://www.flickr.com/photos/pennstatelive/8972110324/
– https://www.flickr.com/photos/dylanstraub/6428496139/
● https://en.wikipedia.org/wiki/Alphabet_Inc.#/media/File:Alphabet_Inc_Logo_2015.svg
● CC BY 3.0
– https://en.wikipedia.org/wiki/File:Pi_30K.gif