8 ms·

There are VERY few situations where I care even remotely how energy efficient someone's thinking is. The benefits of better thinking are nearly always far more

by mcherm 11y ago

There are VERY few situations where I care even remotely how energy efficient someone's thinking is. The benefits of better thinking are nearly always far more valuable than the energy inputs. We are frequently limited by the fact that no more intelligence can be applied to the problem no matter what we do. In many other cases we are limited by cost -- we would happily hire a team of three planners or one expensive genius, whichever was smart enough to lead our project at the cheaper price. But even in extreme cases like manned spaceflight, I've never heard of hiring someone dumber because at least they ate less.

smitherfield 11y ago
Energy efficiency does matter for small-scale stuff like mobile devices, and for medium-to-large-scale stuff like servers (unless money is no object). But yes, it's not really all that important for something like AI research. Especially considering that at the rate AlphaGo has been improving,* by some time in the next year or two it should be able to beat Sedol running on a smartphone. *And yes, there's no guarantee it will continue improving at the same rate.
ekianjo 11y ago
That's not the point at all. The point is that if you decide tomorrow to run thousands of AIs like AlphaGo to replace humans to do several stuff, does that scale well? Well, if it consumes as much power as AlphaGo, it certainly does not. The point about having something running on low watts is critical for scalability in the longer term, because no matter how you look at it, energy production is finite (you can increase it over time but there are clear limits anyway).
- visarga 11y ago
  Machine learning is compatible with low energy applications. There is a body of research in slimming down heavy neural nets to run on cell phones with very little accuracy loss. AlphaGo uses so much power because it additionally uses a brute force approach to improve its results.

by mcherm 11y ago

smitherfield 11y ago

Energy efficiency does matter for small-scale stuff like mobile devices, and for medium-to-large-scale stuff like servers (unless money is no object). But yes, it's not really all that important for something like AI research. Especially considering that at the rate AlphaGo has been improving,* by some time in the next year or two it should be able to beat Sedol running on a smartphone. *And yes, there's no guarantee it will continue improving at the same rate.

ekianjo 11y ago

That's not the point at all. The point is that if you decide tomorrow to run thousands of AIs like AlphaGo to replace humans to do several stuff, does that scale well? Well, if it consumes as much power as AlphaGo, it certainly does not. The point about having something running on low watts is critical for scalability in the longer term, because no matter how you look at it, energy production is finite (you can increase it over time but there are clear limits anyway).

visarga 11y ago
Machine learning is compatible with low energy applications. There is a body of research in slimming down heavy neural nets to run on cell phones with very little accuracy loss. AlphaGo uses so much power because it additionally uses a brute force approach to improve its results.