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Comment by NooneAtAll5 | Hacker News Reader

I'm frustrated with these "galactic algorithm frustration" posts

big constants are not new. We all had a laugh when matrix mul exponent went from [2.3728]639 to [2.3728]596

Even more than that - reading GMP manual and seeing all the "more efficient, but bigger constant" algorithms take over each other as the size grows causes *awe*

so this "sudden" discovery that theory is different from practice has been bloody obvious for everyone. I'm more inclined to say "if you want to went frustration at Ai - do that, stop showing your ignorance"

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the main benefit for these results isn't to show what's possible - it's to show what's *not impossible*

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also I just saw that this is the same author that wrote "Did I read this paper? Of course not. No one at OpenAI read this paper either, so why should I?"

so he expects same level of discussion he deserves himself - completely negligible

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>> also I just saw that this is the same author that wrote "Did I read this paper? Of course not. No one at OpenAI read this paper either, so why should I?"

You can excuse his, I would do that, given the absurdity of this "big" mathematical problems driven marketing campaign by the ghouls.

>> so he expects same level of discussion he deserves himself - completely negligible

The ideas are shared by a not too few people in CS/Eng/Math, probably because there is actually a vagueness of what's (exactly) the role of Complexity Theory, did the pioneers of CT have the slightest idea that the field would drift into "just find some galactic algorithm for this generic problem, ok?"? idk.

> We all had a laugh when matrix mul exponent went from [2.3728]639 to [2.3728]596

Indeed. But the frustrating thing remains. Obviously animal and human brains do something similar, and obviously it's not even N^2, because anyone can count inputs and outputs for our brains, calculate N^2 and even at picowatts per operation that is just not happening ...

1000 Hz, ~800 input nerves, 20W power usage => 800 000 input values per second, squared, would leave 0.03 nW per input value. That's not enough to turn the handle on a single protein, so that's not happening. That's not enough to propagate the result of the calculation within a cell. A 2.3 power is out of the question.

One imagines maybe you can get to N^1.4 within that budget for whatever the brain is doing. Maybe. But that would be the limit.

> One imagines maybe you can get to N^1.4 within that budget for whatever the brain is doing. Maybe. But that would be the limit.

Not for matrix multiplication of square matrices, since the output, which has to be written, is already of size O(n^2).

„Something similar” can completely change asymptotic bounds though. Compare for example Integer Linear Programming and normal Linear programming. They look “very similar”, but there is an exponential worst case gap (unless P=NP).

> Obviously animal and human brains do something similar

...what are you talking about?

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