21–30 of 179 posts
Earlier quoted context omitted.
Interesting ! i didn't understand why they stopped the alphastar project. They pretended they reached their goal, but clearly haven't.
The goal was never to beat all pros. https://deepmind.com/blog/article/AlphaStar-Grandmaster-leve...
Still what they have accomplished is a miracle.
I wonder if the code and network weights will ever see the light of day. I wonder what the eventual value proposition of working on this sort of stuff is. I suppose they are just going to try to apply the algorithms to better things. I've been interested in the application of AlphaZero to chess. It's sad that this many resources were devoted to something which we can't even use to play chess as of now. Leela (the ope…
It seems to me that, if you only take it as a marketing operation, it has been already very valuable.
We don't run on those fancy V100 cards though, just regular old gaming cards suffice, and I suppose if we bought the "industrial" nvidia versions it would a take a bit longer to recoup, but still definitely within the year.
Anyway what I'm saying is that it's probably possible to to this a lot cheaper than 36M, though maybe not in such a short time. Our startup is extremely cash intensive, and I bet machine learning companies are as well (I suppose machine learning experts aren't cheap ;)), so if we can put in some work and safe a big portion off our hardware costs that really goes the distance.
Another way of thinking about how efficient the brain is: By the article’s numbers, about 5.5 million TPU hours were required to train the machine to play as well as a Go champion. A Go champion might have trained for 8 hours a day, for 15 years (age 5 to 20). That is about 40 000 hours. In other words, machines required 137 times longer to learn the game, and at twice the power consumption! There is still a lot of r…
Another way of thinking about how efficient the brain is: By the article’s numbers, about 5.5 million TPU hours were required to train the machine to play as well as a Go champion. A Go champion might have trained for 8 hours a day, for 15 years (age 5 to 20). That is about 40 000 hours. In other words, machines required 137 times longer to learn the game, and at twice the power consumption! There is still a lot of r…
Go champions don't learn from zero. They learn from teachers, books, and playing against each other. This knowledge is built over hundreds, or thousands of years.
"AlphaGo was initially trained to mimic human play by attempting to match the moves of expert players from recorded historical games, using a database of around 30 million moves".
Another way of thinking about how efficient the brain is: By the article’s numbers, about 5.5 million TPU hours were required to train the machine to play as well as a Go champion. A Go champion might have trained for 8 hours a day, for 15 years (age 5 to 20). That is about 40 000 hours. In other words, machines required 137 times longer to learn the game, and at twice the power consumption! There is still a lot of r…
This comparison is not entirely fair because the human brain also benefits from priors baked in over the entire course of evolution.
Earlier quoted context omitted.
Go champions don't learn from zero. They learn from teachers, books, and playing against each other. This knowledge is built over hundreds, or thousands of years.
Alphago didn't learn from zero either. It has a pre-processor that identifies sets of patterns with known features, and also: "AlphaGo was initially trained to mimic human play by attempting to match the moves of expert players from recorded historical games, using a database of around 30 million moves".
>The power consumption of the experiment is equivalent to 12,760 human brains running continuously.
But the problem is this "brains" unit on AlphaZero doesn't seems to take into account of GPU, CPU and Memory involved. It only took the TPU numbers.
Then there is another problem.
> a TPU consumes about 40 watts,[1]
The TPU referred to was a first Gen TPU built on 28nm running at 40W, more like a proof of concept. Currently Google is with Cloud TPU v3 [2], The latest-generation Cloud TPU v3 Pods are liquid-cooled for maximum performance. And each TPU v3 is actually a four chip module. [3]. If a single chip is 100W that is 400W per TPU.
Edit: Turns out Wiki list TPU v3 as 250W. [4]. Not sure if that is 250W per chip or 250W for 4 Chips.
That is on the assumption they are very high powered and hence would require liquid cooling. Although that might not always be the case.
So adding CPU, GPU, Memory, and TPU figures. That original estimate of 12,760 human brains may be off by a factor of 10 if not more.
Still pretty impressive. Considering we now only get about 1.8x improvement with each generation node. We would get about 19x by 2030. ( Assuming the same algorithm ). Which means AI is good, but human brain on its own is still very much magical in its efficiency :)
Correct me If I am wrong on the numbers.
My other questions is, that was how much energy it used to learn Go. But what about energy it used during the Game?
How would AlphaGo Zero perform if it was limited to 20W?
[1] https://cloud.google.com/blog/products/gcp/an-in-depth-look-...
[2] https://cloud.google.com/blog/products/ai-machine-learning/g...
[3] https://techcrunch.com/2019/05/07/googles-newest-cloud-tpu-p...
Another way of thinking about how efficient the brain is: By the article’s numbers, about 5.5 million TPU hours were required to train the machine to play as well as a Go champion. A Go champion might have trained for 8 hours a day, for 15 years (age 5 to 20). That is about 40 000 hours. In other words, machines required 137 times longer to learn the game, and at twice the power consumption! There is still a lot of r…
Now, the bot has many advantages. It never sleeps, never gets distracted, never dies and can be copied to another system to obtain a copy of the bot with the same playing performance.
The bot is also more accessible. Any player now can train with a bot, all day if you want, for almost free. You cannot do that with a professional.
Another way of thinking about how efficient the brain is: By the article’s numbers, about 5.5 million TPU hours were required to train the machine to play as well as a Go champion. A Go champion might have trained for 8 hours a day, for 15 years (age 5 to 20). That is about 40 000 hours. In other words, machines required 137 times longer to learn the game, and at twice the power consumption! There is still a lot of r…
Go champions don't learn from zero. They learn from teachers, books, and playing against each other. This knowledge is built over hundreds, or thousands of years.