#sedol

So this article just reached the top of HN: Another Way Of Looking At Lee Sedol vs AlphaGo

I've seen a lot of "AI denial" and moving goalposts in the discussions about AlphGo. Go was, for the longest time, a huge milestone for AI research. And now that it's finally succeeded, there's a lot of discussion about how it's not that significant.

But this article takes the cake for moving goalposts. It declares that the Go match wasn't fair because AlphaGo got to use far more energy than Sedol.

On the surface this claim is kind of absurd. You could give Sedol an electrical outlet with as much power as he wants. It wouldn't help at all. Humans can't make their brains bigger, or use more power, even if they want to. Which is one advantage the AI has. And that's totally fair.

But beyond that, until now no has really cared about the energy usage of AIs. The fact an AI could even do a task on the level of a human was incredible, regardless how much energy was required. The Energy usage isn't important in this domain. It's board game playing. It's shifting goalposts to goals that don't even matter!

But there is a valid reason to care about energy usage. It does cost money to run AlphaGo, and in other domains that could matter a lot. But even from the economic perspective, it's not accurate. I bet the food budget of a human is way more than the electricity budget of AlphaGo.

For this economic estimate, you need to factor in more than just energy. A human takes 20 years to mature. AlphaGo took less than a week to get good, and a few months to be the best. And over the human's life, they will use lots of energy and food.

Humans are extremely expensive, as can be estimated just from wages. If it requires only a rack of GPUs to replace an expert human, it may very well be worth it economically.

Additionally, AlphaGo doesn't require a huge distributed network of GPUs. That improves it's performance only slightly. Google claims that a version of AlphaGo that runs on a single machine, was able to beat the distributed version 25% of the time. Which would still give it a higher Elo than Sedol, who only beat AlphaGo 20% of the time.

Now besides all that, there is an important point here. Once you get past the accusations of "unfairness", it's still very interesting that human brains are so energy efficient.

But the reason for that is that artificial neural networks are run on general purpose hardware. Human brains are highly specialized hardware. General purpose computers always consume vastly more energy than specialized circuits, optimized for energy usage. GPUs compute every possible synapse to 32 bit floating point precision. Most that computation is unnecessary. The majority of synapses are 0, and only a few bits of accuracy are required on active synapses.

I know this because there has been a lot of recent research on efficiently running NNs. Especially on low budget hardware, like mobile phones or embedded devices. This is something that is already possible, and is only going to get better over time.

http://www.theverge.com/2016/3/12/11210650/alphago-deepmind-go-match-3-result

AlphaGo beats Lee Se-dol again to take Google DeepMind Challenge series

http://www.wired.com/2016/03/russia-thinks-can-use-nukes-fly-mars-90-days-can-find-rubles/

Russia has ambitions to build nuke powered space ships to fly to Mars and back... really fast.

https://www.youtube.com/watch?v=CKCvf8E7V1g

Rich people don't create jobs.

https://youtu.be/VBceREwF7SA?t=23m1s

Stuart Russel succinctly illustrates what makes DeepMind's artificial intelligence algorithms so amazing

http://www.kurzweilai.net/ibm-simulates-530-billon-neurons-100-trillion-synapses-on-worlds-fastest-supercomputer