Comment by philipkglass | Hacker News Reader

Comment by philipkglass | Hacker News Reader

I appreciated the point that pure RL is insufficient for many tasks. But why also downplay the achievements of pure RL where it has matched or surpassed skilled humans?

Captioned chart:

The progression of AlphaGo Zero's skill. Note that it takes a whole day and thousands of lifetimes' worth of games to get to an ELO score of 0 (which even the weakest human can achieve easily).

I'm pretty sure that a one week old infant's ELO score will also fall short of 0. Sure, the AI did things that no human could do in order to match and then surpass human performance. Great! Half of the fun of following AI research is seeing it refute old intuitions about how human-like systems have to be to perform well on tasks previously considered to require human intelligence.

Whatever "general intelligence" or "human level intelligence" comes to mean by the 2050s, it looks like it's going to be a lot better pruned-by-counterexample than it was in the 1950s.

While I appreciate the sentiment, I thi k the fact that we can learn from fewer examples demonstrates that the learning process isn’t as efficient as ours, therefore it isn’t yet optimal. It seems like a goal should be for learning to be as efficient or more efficient for computers than for humans.

We got 86 billion neurons in our brains that all crave to get used - so you can even imagine them as single agents trying to get along.

It's like 86 billion guys that try to please that thing they simultaneously produce (our consciousness). What I want to say: The algorithm can be dumb as f*. I call it the f-star-algorithm. But the computational power in our brains is extremely high.

I don’t think throwing more computational power at the problem is the right answer to all ML problems.

Those neurons are arranged and incentivized cleverly. The structure is also very important and is necessary for the resulting intelligence.

So it's not only computational power, but also the unique structure nature found through trial and error.

I wonder how much of that cleverness will be gleaned and appropriated by those who design 3 dimensional chips.

I (author) think it is worth downplaying the achievements when perspective is lacking about their signifiance; Henry Kissinger wrote a whole op ed recently founded mostly on a misunderstanding of how big a deal AlphaGo really is https://www.theatlantic.com/magazine/archive/2018/06/henry-k...

But I did try to not just denigrate this work, rather to both praise it and discuss the sometimes ignores flaws.

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