4 ms·
Your point is generally valid, but it's worth noting that power =/= energy. It is conceivable that at 100 watts of power, a human could use on the order megawat
by demosthenes111 10y ago
Your point is generally valid, but it's worth noting that power =/= energy. It is conceivable that at 100 watts of power, a human could use on the order megawatt hours of energy while practicing to achieve top-level performance. 10000 hours of practice is not unreasonable to achieve mastery which would be 1MWh.
It's also likely that AlphaGo is reasonably efficient, given that it used custom ASICs.
- argonaut 10y agoAccording to Google they used 280 GPUs and 1,920 CPUs. I'm sure they used custom chips in addition.
- Cybiote 10y agoThere is also a difference between learning and playing. During play, the human operated at ~20 watts on computation while the machine ran at a rate of anywhere from 26,000 watts to 260,000 watts, depending on how efficient the TPUs are (and assuming 10x as the ideal case). The human is also learning new things about Go as it plays, planning complex muscle firing programs, filtering audio and managing attention, working on subconscious goals, running complex vision tasks, all while running its autonomic subsystem. Low power is also still important due to issues of heat and energy availability. Low power also implies high efficiency which is important for several reasons. The human brain is estimated at 20 watts (when people talk about computing systems they tend to not include the power needed for all the auxiliary infrastructure needed to keep it networked and cooled); it's also estimated that beyond 4 hours a day, learning effectiveness drops precipitously. If we take the case of Go, you can take a 4 year old human and have a professional player by 13. This is about 950 megajoules spent by the brain while learning Go. For the machine, if you look at the learning part (self play, value and policy on 50 GPUs for several weeks) the estimate on energy spend is about 30,000 megajoules. The policy network is itself ~20,000 MJ, while the full AlphaGo system playing on a single GPU and 48 CPUs is just a strong amateur. But this is not even an apples to apples comparison since the brain is not spending all of its energy on learning Go. In fact, learning how to play Go is very far from the most difficult thing the brain is learning how to do.
- louprado 10y agoTPU defined: https://en.wikipedia.org/wiki/Tensor_processing_unit https://en.wikipedia.org/wiki/Tensor_processing_unit
- deleted 10y ago[deleted]
- lern_too_spel 10y agoYou're confusing professional level with world champion level. How many megajoules will it take to create a world champion Go player using the human brain? It would take multiple brains, each teaching each other. We can now train a professional-level Go player pretty cheaply — Zen Go plays at a professional level and runs on commodity hardware.
- argonaut 10y agoNo, that is a ridiculous comparison. Then you should start counting the energy required to construct the Google server farm, the energy of all the computers used by all the engineers who built the farm while they were in university, on and on and on.
- lern_too_spel 10y agoNow you're suggesting calculating the energy used by the human champion's ancestors, which I am not suggesting. The computer can train itself with just records of past games and self-play. The world champion level human cannot. You must account for the difference in training.
- argonaut 10y agoNo, the computer can't. The computer was itself trained/programmed by humans.
- 10y ago
- Animats 10y agoPatience. It's early for Go. Remember when it took Deep Blue to play grandmaster-level chess? Now your laptop can do it. All of the better commercial chess programs now play above human level. Well above human level; the top programs are rated around 3300 on a good laptop, and the highest human rating is currently 2882.