Comment by dragontamer | Hacker News Reader

Comment by dragontamer | Hacker News Reader

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> 1) learn how the brain works 2) build a simulator

I disagree that step #1 is important.

Consider the "Air-foil", which led to flight. In one sense, its an approximation of the wings of birds and other animals.

But ultimately, the discovery that the "Air-foil" shape turns sideways blowing wind into an upward force now called "lift" is completely different from how most people understand bird wings.

Bird Wings flap, but Airplane Air Foils do not.

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Another example: Neural Networks are one of the best mathematical simulations of the human brain (as we understand it, as well as a few simplifications to make Artificial Neural Networks possible to run on modern GPUs / CPUs).

However, the big advances in "Game AI" the past few years are:

1. Monte Carlo Tree Search -- AlphaGo (although some of it is Neural Network training, the MCTS is the core of the algorithm)

2. Counterfactual Regret Minimization -- The Poker AI that out-bluffed humans

There are other methodologies which have proven very successful, despite little to no biological roots. IIRC, Bayesian Inference is a widely deployed machine learning technique (for some definition of Machine Learning at least), but has almost nothing to do with how a human brain works.

An interesting field of AI is "Genetic Algorithms", which have biological roots but not anything based on the biology of brains, to achieve machine learning. Overall, a "Genetic Algorithm" is really just a randomized search in a multidimensional problem, but the idea of it was inspired by Darwinian Evolution.

> Monte Carlo Tree Search -- AlphaGo (although some of it is Neural Network training, the MCTS is the core of the algorithm)

AFAIK, this is not correct. Many of the Go playing algorithms before AlphaGo used MCTS or some variant. The true breakthrough of AlphaGo was deep reinforcement learning.

> AlphaGo's performance without search The AlphaGo team then tested the performance of the policy networks. At each move, they chose the actions that were predicted by the policy networks to give the highest likelihood of a win. Using this strategy, each move took only 3 ms to compute. They tested their best-performing policy network against Pachi, the strongest open-source Go program, and which relies on 100,000 simulations of MCTS at each turn. AlphaGo's policy network won 85% of the games against Pachi! I find this result truly remarkable. A fast feed-forward architecture (a convolutional network) was able to outperform a system that relies extensively on search. https://www.tastehit.com/blog/google-deepmind-alphago-how-it...

I don't know whether AlphaGo Master (the next version of AlphaGo that was trained purely with self-played games and has not been beaten in 60+ games) even uses MTCS.

Disclaimer: I have no expertise in AI.

That said, I agree that learning how the brain works seems unimportant and unnecessary. Evolution doesn't know how a brain works, but it's given us Einstein, Michelangelo, and conversations on HN.

It seems really important to learn how to build evolution into attempts at AI, given that evolution is the only known mechanism that leads to what we recognize as intelligence.

>evoluyion doesn't

you use antropomorphy to reflect on your own standpoint. we don't know how the brain works? we can feel it and psychologist have a huge body of work concerned with the topic and that is already having influence on competition and fitness.

> it's given us Einstein, Michelangelo, and conversations on HN

two out of three ain't bad

You could have made exactly that this argument about flight before the Wright brothers.

Consider the "Air-foil", which led to flight. In one sense, its an approximation of the wings of birds and other animal

Not true; "lift" was well known for thousands of years, horizontal "lift" is how ships sail upwind. The breakthrough for the Wright bros was making something light enough to make use of this phenomenon vertically.

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