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Study mode:
on
1
Intro
2
What do you need
3
Chess
4
Problem with Chess
5
Horizon Problem
6
Pruning
7
Pruning is dangerous
8
Better pruning
9
Minimax pruning
10
Chess bot
11
Chess branching factor
12
Go
13
Problems with Go
14
Monte Carlo Tree Search
15
How long will it take
16
Neural networks
17
Neural network playground
18
Tensorflow
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convolutional neural networks
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Reinforced network Go
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Value network Go
22
Combining all the pieces
23
The Challenger
24
The Challenger Game 1
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The Challenger Game 2
26
The Challenger Game 4
27
Conclusion
28
Nobody thought AlphaGo
29
AlphaGo went silent
30
New player MasterP
31
Healthcare
32
Questions
Description:
Explore the groundbreaking journey of AI in game-playing, from simple Tic Tac Toe to the complex world of Go, in this 50-minute Devoxx conference talk by Roy van Rijn. Delve into the algorithms and techniques that enabled Google's AlphaGo to achieve its extraordinary breakthrough, beating top human players in a game with 1.74×10^172 unique positions. Learn about chess algorithms, the horizon problem, pruning techniques, and the transition to tackling Go's complexity. Discover the power of Monte Carlo Tree Search, neural networks, and reinforced learning in AI game-playing. Gain insights into the development of AlphaGo, its matches against human champions, and the subsequent emergence of AlphaGo Master. Conclude with a discussion on the potential applications of these AI advancements in healthcare and other fields, followed by a Q&A session.

From Tic Tac Toe to AlphaGo - Playing Games with AI and Machine Learning

Devoxx
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