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Comment by wamatt | Hacker News Reader

>They are incredibly slow and energy inefficient;

Human brains are energy inefficient? Well, thats a first ;)

"In 1990, the legendary Caltech engineer Carver Mead correctly predicted that our present-day computers would use ten million times more energy for a single instruction than the brain uses for a synaptic activation."

"Last March, AlphaGo, a program created by Google DeepMind, was able to beat a world-champion human player of Go, but only after it had trained on a database of thirty million moves, running on approximately a million watts. (Its opponent’s brain, by contrast, would have been about fifty thousand times more energy-thrifty, consuming twenty watts.)"

[1] http://www.newyorker.com/tech/elements/a-computer-to-rival-t...

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In terms of energy consumed for individual computations, yes. Neurons use chemical reactions to communicate and this is terribly inefficient. Transistors use very small amounts of electricity in comparison.

The main difference is computer technology is designed to be very general purpose. The brain is more like an ASIC that's hardwired to run one specific algorithm. GPUs are also computing 16 or more bits of precision, when real neurons are very low precision. There are some other differences, like how real brains are incredibly sparse and most of the synapses at any given time are dormant and not using much energy. They are also very sparsely connected to each other. While our current NNs are very dense and need to spend energy to compute every single connection each cycle.

It seems premature to make judgements about efficiency when there is so much we do not understand about brain function and consciousness. When you can replicate all brain function, lets compare efficiency. Comparing to an asic reveals the source of your error rather than defends your position.

For that to be a fair comparison, wouldn't you need to look at all the energy consumed by the human brain over the many hours it took them to become a Go champion?

Same goes for the bot, then. A back of the envelope calculation suggest Lee's brain consumed as much energy in a 80 year lifetime as AlphaGo in half a day.

Not trying to say it isn't a correct statement, or that the outcome would be different if you lined everything up properly, only that the original statement doesn't really say anything meaningful.

AlphaGo took several months of self play on a large cluster, so training probably consumed many times more energy than a human in a lifetime.

The gain is in being able to clone the bot perfectly. Once trained you can make many of them. Also, if you look at what happened in Chess, the lessons learned from the large machines was absorbed and resulted in your smartphone now being able to outclass the human world champion.

You can expect a similar thing with Go at some point.

I think that's a fair argument, but from the quote above

> "Last March, AlphaGo, a program created by Google DeepMind, was able to beat a world-champion human player of Go, but only after it had trained on a database of thirty million moves, running on approximately a million watts. (Its opponent’s brain, by contrast, would have been about fifty thousand times more energy-thrifty, consuming twenty watts.)"

Let's say alphaGo trained for a year, that would be 1 MWyr energy consumed. And lets assume that Lee Se-dol's brain consumed 20W over 34 years of his live doing nothing but working on Go, that would be 640 Wyr, still a factor 1000-ish smaller.

Totally, I'm sure it's correct, and even if you were to bring the comparison in to line then the outcome is still "computer is watt hungry". The point is that the original statement, while correct, doesn't really say anything useful.

How would you know what amount of energy the human brain spent in learning to play Go, specifically? In the same time it was doing that, the human brain was also learning to do, and doing, a whole bunch of other things that AlphaGo was never even trained on- from moving limbs and controlling bodily functions to using language and recognising faces and so on. How would you isolate the amount of energy needed for training in Go, specifically?

I mean, in principle, if you had two numbers, "human energy consumption from learning Go" and "AlphaGo energy consumption from learning Go", you could compare them. But in practice there's no way to come up with such numbers, so what's the point of comparing apples and orangutans?

That's not really the point, more that it was originally not an apples to apples comparison and therefore doesn't really tell us anything. I have no doubt that the statement is correct, it's whether or not that statement has any meaning. As another comment pointed out, even if you compared the hours, the computer still uses a few orders of magnitude more energy for a more accurate (although completely theoretical) comparison.

Not surprising that a computer expends more energy to perform a task that we [previously] thought required human-like intelligence.

I'm sure any dollar store calculator spends way less energy performing long division than the average human.

For one, normal human can do long division as fast as a calculator, and can handle numbers that will bork many calculators. (edit - look at human calculators, and the era before calculators were common place. Even now elders I know can eye ball numbers and calculate percentages / factorials and ratios)

And for another, Calculation != AI, far from actually.

One, what normal human being can perform long division as fast as a calculator?

12/43523523452. Go.

Two, AI is applied statistics. What do you think AI is?

You could, with practice

>I'm sure any dollar store calculator spends way less energy performing long division than the average human

Thats the comment.

A calculator is a one role device, with exactly specified rules.

Similarly, with training you can too. You don't need to be special, other than being practiced,which is a fair requirement for a human being.

Here is a human being who could out perform it: https://en.wikipedia.org/wiki/Shakuntala_Devi

>In 1977, at Southern Methodist University, she gave the 23rd root of a 201-digit number in 50 seconds.[1][4] Her answer—546,372,891—was confirmed by calculations done at the US Bureau of Standards by the UNIVAC 1101 computer, for which a special program had to be written to perform such a large calculation.[10]

She could easily out-perform calculators because she never needed time to key in the commands (she needs to hear the problem to solve it).

If we exclude that restriction, and the commands magically float into the calculator, and that the problem is small enough to match the calculators limits, then yes, if those arbitrary conditions are met the calculator can out-perform her brain.

Which is precisely the type of “cows are round spheres” thinking that’s being decried in the article.

People can and regularly do out-perform calculators in speed, energy and complexity of computation.

Do note that calculators weren’t allowed as exam tools in a lot of countries till a decade or so ago. Students learnt mental math techniques which were known since ancient times (think Greece).

For a human brain the answer isn’t even calculation, it becomes pattern recognition. The square root of 25 is 5, which takes about the same neural load as it takes to recognize a letter.

The calculation you provided is harder, but thats a function of lack of training/practice, not complexity.

----

AI is not in the realm of what a calculator can pull off, is what I meant to say by the compute part.

edit: I tried your computation on a store calculator, its beyond its ability to calculate,(0.0000000027)

Your example is from 1977, we've had 40 years of Moore's law since then. In the time it takes for you to recognise that you're even looking at a number (~0.08 seconds), the cheapest computer you can buy (the $5 Raspberry Pi Zero) can do around 1.92 billion normal floating maths operations. Sure, 201-digit numbers are a little slower — on my laptop, in Python, I can only do that particular calculation just under one hundred million times in the fifty seconds it took her to do it once.

But you're right to say calculators are single purpose devices and that's magically inserting the question.

So I downloaded sklearn, which contains a set of labelled hand-written digits.

It takes about 0.17-0.2 seconds for my laptop to learn to read numbers, from scratch, and thereafter it can read digits at a rate of about 8,200 per second.

For reference, "a blink of an eye" is 0.1-0.4 seconds depending on who you ask.

Dollar store calculator? I'd never have said that myself because I know calculators are rubbish. But in the context of "AI: Will it ever beat humans?" hell yes, it will and in many cases it already does.

There is the practice of anzan in China and Japan, which shifts the burden of calculation into the visual cortex by visualizing a mental abacus. Thus advanced practitioners can rapidly and correctly evaluate calculations like the one you have given. As you can see, Tsujikubo's speed in divisions appears to be limited in her ability to physically write out the answer.

https://youtu.be/-9T3P--ubQc?t=6m10s

Granted, these are people at the top of their game, so may not qualify as "normal" per your argument, but this is to illustrate that the limits of numerical calculation in "normal" people may be the algorithms we are taught rather than the computational capacity of our brains.

and he only can play Go... what a waste for a such big AI. AI can beat humain on some special part but they are designed by us, then they are wrong and bad, specially when you only need to unplug the battery for that they die, too easy, come on AI do something more than that....

And can only play well on 19x19 board, if I got it right.

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