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Any problem can be treated as a pattern recognition problem if your training data covers a sufficiently dense sampling of the problem space. What's interesting is what happens when your training data is a sparse sampling of the space -- to extrapolate, you will need intelligence.
Sometimes, it's not about the outcome, it's about what you can learn from the process.
If OpenAI Five beats professional players, it would be interesting to see if that is so because it learned some new "tricks" humans didn't discover before. Thus, it might help human players get even better.
Learning something about DotA that helps human players to improve is a likely outcome of OpenAI Five, but that is using AI to research on DotA, not viceversa.
How is this different from AlphaGO?
Do you know of any good benchmarks for generalization in machine learning algorithms?
Compensate the sparse training data with a physics or rule based model?
I do not agree, completely. This is not supervised learning, this is Reinforcement Learning, where the agents extrapolate a complex behavior from raw rewards coming from an environment. If this is pattern recognition, then humans are so.
We can model human intelligence as pattern recognition, but we are *extremely good* at it We need zero samples, a description (words) may be enough to create a pattern (BTW it is holistic/hierarchical), recognize it, and be disappointed (high entropy) by it, because WTF is this:pic.twitter.com/hUQu35h8ev
I may be biased, but IMHO StarCraft is a different beast due to fog of war, e.g. may choose to lose a unit to see something For the first time, actively seeking to update a theory of mind/strategy of the rival and adapting to it _might be necessary_ to defeat best human players
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