Is AlphaZero a Scientific Breakthrough in AI?

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Discussion Overview

The discussion centers on whether AlphaZero represents a scientific breakthrough in artificial intelligence, particularly in the context of its application to chess and potential implications for mathematics and theorem proving. Participants explore the capabilities of AlphaZero compared to traditional algorithms and the broader implications of self-learning algorithms in solving complex problems.

Discussion Character

  • Exploratory
  • Debate/contested
  • Technical explanation

Main Points Raised

  • Some participants express excitement about AlphaZero's performance in chess, noting its ability to search for patterns rather than calculating all potential moves, contrasting it with Stockfish.
  • One participant raises the question of whether an algorithm could teach itself mathematics and solve currently unattainable problems, citing Gödel's Incompleteness Theorem as a potential limitation.
  • Another participant mentions the advancements in theorem-proving software, suggesting that while some problems may remain unsolvable, significant progress has been made in the field.
  • There is a reference to Roger Penrose's views on the limitations of computation in understanding all of mathematics, indicating a skepticism towards the Church-Turing thesis.
  • Participants share personal experiences with machine learning and neural networks, highlighting their growing interest in these technologies.
  • Links to external articles are shared, indicating a mix of support and skepticism regarding AlphaZero's status as a breakthrough.

Areas of Agreement / Disagreement

Participants exhibit a mix of enthusiasm and skepticism regarding AlphaZero's impact on AI and mathematics. There is no consensus on whether it constitutes a scientific breakthrough, with some expressing doubts and others highlighting its innovative aspects.

Contextual Notes

Some claims about the capabilities of AlphaZero and its comparison to traditional algorithms are based on personal experiences and interpretations, which may vary among participants. The discussion includes references to theoretical limitations in mathematics that are not universally accepted.

Ackbach
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You chess fans out there might find https://chess24.com/en/read/news/deepmind-s-alphazero-crushes-chess interesting. No domain-specific knowledge required!
 
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That's amazing! (Yes)
 
...which leads me to the question "Can we construct an algorithm that, given the rules of math, teaches itself math and consequently arrives at solutions for problems currently unattainable for humans". Maybe not in this era but I certainly don't know that.
 
greg1313 said:
...which leads me to the question "Can we construct an algorithm that, given the rules of math, teaches itself math and consequently arrives at solutions for problems currently unattainable for humans". Maybe not in this era but I certainly don't know that.

I think Gödel's Incompleteness Theorem puts the lid on finding out all of mathematics (there are infinitely many theorems). But we already have theorem-provers doing some pretty amazing things. You can pay to have a theorem named after you, for crying out loud. I believe Mathematica can do things people can't do, even given enough time.

All this to say, I am no strong Church-Turing thesis guy. I'm more with Roger Penrose in his book The Emperor's New Mind, one that I would highly recommend.
 
I work with machine learning on a daily basis in my day job, so I've really become interested in these types of projects. Neural networks have become absolutely amazing with regards to the types of problems they can solve. Stockfish has been the gold standard of chess for years now and AlphaZero actually calculated less potential moves than Stockfish on average. Instead it will search for patterns that lead to optimal outcomes.

This is a great video series on neural networks if you are interested. I find them truly fascinating.

[YOUTUBE]aircAruvnKk[/YOUTUBE]
 
Jameson said:
I work with machine learning on a daily basis in my day job, so I've really become interested in these types of projects. Neural networks have become absolutely amazing with regards to the types of problems they can solve. Stockfish has been the gold standard of chess for years now and AlphaZero actually calculated less potential moves than Stockfish on average. Instead it will search for patterns that lead to optimal outcomes.

I am working on deep learning as well but as part of the Masters program. For those interested to see some battles between stockfish and AlphaZero watch this with some commentary

[YOUTUBE]lFXJWPhDsSY[/YOUTUBE]
 

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