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Comment by bra-ket | Hacker News Reader

it's really simple

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

most current AI research skips step 1

That's certainly one way to do it. However, we didn't succeed at building modern aircraft or earth moving machinery by building simulations of birds or muscles. There's enough that is unknown out there for a variety of approaches.

> 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.

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.

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

two out of three ain't bad

>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.

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.

> 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.

Medical research hasn't cracked step 1 either, at least not to a point of accurate simulation.

Besides, if you could simulate a human brain, you will end up with something that needs to sleep, something with limited and unreliable memory, something that gets bored and distracted, something emotionally needy, etc.

Then the extending of this chaotic, messy system is wildly unknown even if we could get a piece-for-piece replication to work. Such a thing would be of great benefit to medicine, but not really for AI to even start with until medicine is done reverse engineering it.

Piece-for-piece replication might not be the right level of abstraction. Blue Brain project is one unfortunate example, on the other hand the current neural nets are stuck with neural model from 1943.

In defence of AI researchers 1 is very, very hard and to the best of our knowledge there is not one way the brain works. The brain is a complex, cobbled together set of systems all using different ways of problem solving.

most AI researchers have never opened a textbook on cognitive psychology or neurobiology , or any of these 'soft' sciences.

how do you plan to build artificial intelligence with no model of intelligence, without learning about important experiments in learning and memory , it's the complete ignorance that drives me crazy.

AI is not for specialists.

Yes all of this is true. I do think studying how the brain works will provide very useful ideas of what might work in AI. At the very least it is a very interesting area to learn about.

Most of those experts aren't looking to solve general AI problems, they're looking for solutions to specific problems like basic image recognition. And you don't need a full human brain to do that, and you don't need to conform to the way humans and other biological systems do it. You're not aiming for full human intelligence, so you don't need to care too much about how humans learn.

That said, I find when trying to solve a problem with ML techniques, it's better to use someone who knows the problem domain really well than someone who only knows ML really well. Someone who really understands the problem they're trying to solve can encode that knowledge into their models when training the system. While I've seen people who really know ML but lack the specific domain knowledge labor for weeks, coming back to me with "discoveries" that are already well known.

Prof. Hinton has an interesting talk about his new 'capsule' model based on psychology of shape perception: https://www.youtube.com/watch?v=rTawFwUvnLE

We know the brain and associated sensor behaviours are too large for us to fully simulate in a reasonable way on anything resembling current hardware (We also can't fully model it but as we approached the size of hardware to do so we'd probably solve many of the problems of doing so). So which hacks and shortcuts do you want to apply to reduce the dimensionality to something runnable? Step 1) will take far too long so AI research looks for things it can do well in the category of 2) without being a full simulation. Deep Learning has been unreasonably effective here.

In industry, yes. In academia, there's computational neuroscience:

http://cocosci.mit.edu/people

https://web.stanford.edu/group/mbc/research.html

I think we should be more interested in how the mind works. Much of AI is a simulator of the mind.

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