I am heavily involved in the Leela Chess Zero Project. LCZero We develop NN of various sizes to learn to play chess from scratch. The larger NN takes longer to train but have deeper tactical avoidance while the smaller NN is faster to train but lacks some tactical awareness. Only the rules and 1 point for a win, 0.5 for a draw and 0.0 for loss. A similar project from Google's DeepMind beat the world champ in Go last year. From there the networks play each other to explore the gamespace with some exploration (temperature) parameters and then once those are finished the next networks are trained based upon those results ( patterns that look like this led to wins and these led to draws, etc). Then the process continues for millions of games.
I test the networks vs traditional engines and other networks here
I run the traditional Alpha Beta (Alpha–beta pruning - Wikipedia )engines on a 32 core AMD CPU and the NN on 2 Nvidia GPUs. The results are astounding. In only 2 years of development, Leela Chess Zero is beating the best traditional chess engine on the planet - having itself taken 40 years of development manually by humans limited chess knowledge. In fact, Leela does things we are not sure of the reason(s) why (we can't explain the strategy per se other than to say 'placing a pawn here seems advantageous in 50 moves?) This could lead to some odd situations where we accept an assertion as true without us being able to directly explain why it's true. A NN may see a deeper harmonic in the data we cannot and determine a pattern that exists in this data.
It's amazing how just via self play the networks can learn fundamental chess theory and openings in months that took humans centuries.
I was thinking since QA is heavily reliant on data and trending if AI will play a larger part of QA work in the future. With the volume of metrics available to an AI it seems inevitable it is coming for us very soon.
What do you think? Is it feasible in your space?
The Future of Artificial Intelligence and Quality Management
How Artificial Intelligence revolutionizes Quality Assurance
How AI or machine learning can improve quality assurance: six tips
I test the networks vs traditional engines and other networks here
It's amazing how just via self play the networks can learn fundamental chess theory and openings in months that took humans centuries.
I was thinking since QA is heavily reliant on data and trending if AI will play a larger part of QA work in the future. With the volume of metrics available to an AI it seems inevitable it is coming for us very soon.
What do you think? Is it feasible in your space?
The Future of Artificial Intelligence and Quality Management
How Artificial Intelligence revolutionizes Quality Assurance
How AI or machine learning can improve quality assurance: six tips
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