Human vs. Machine: Intelligence per Watt

Contemplating the possibility that machines won't win everywhere all at once

There’s no law of physics that forbids in silico intelligence from performing all of the functions that biological intelligence currently performs—and for that reason alone, there’s no reason to believe that they eventually do so.

But one must distinguish what is technically possible from what is economically viable.

This requires some understanding of the intersections between the microeconomics of watt-per-intelligent-operation vs. the fundamental physical properties that limit computation. The Landauer Limit gives us insight into the latter, and it turns out that human brains operate very close to this limit: in other words, it would be very hard to optimize more energy efficiency out of the system. Evolution does an excellent job at these energy-optimization problems over long time spans.

Our very efficient 12 watt brain does amazing things, but it is very slow compared to silicon because speed needs a lot of energy. We are very effective problem solvers, navigating massive amounts of complexity, performing complicated mental simulations—but we don’t do it extremely fast, and we don’t really multitask. To have those features would require our brains to consume far more energy, which would mean we need a lot more food and generate a lot more heat.

Artificial neural networks may be able to do many of the computations that our brains do, but it may be possible that getting artificial neural networks (ANNs) to outperform humans on cost will take longer. Qualcomm noted this a few years back:

From 2018 Qualcomm presentation on AI energy-efficiency

I’m thinking a lot about what this means in terms of identifying the most interesting and disruptive opportunities over the near and long term. A few of the variables in this multidimensional system include: