Moore's Law and AI

By now you've probably heard that Moore's Law is really dead. So dead that the semiconductor industry roadmap for keeping it on track has more or less been abandoned: see, e.g., here, here or here. (Reported on this blog 2 years ago!)

What I have not yet seen discussed is how a significantly reduced rate of improvement in hardware capability will affect AI and the arrival of the dreaded (in some quarters) Singularity. The fundamental physical problems associated with ~ nm scale feature size could take decades or more to overcome. How much faster are today's cars and airplanes than those of 50 years ago?

Hint to technocratic planners: invest more in physicists, chemists, and materials scientists. The recent explosion in value from technology has been driven by physical science -- software gets way too much credit. From the former we got a factor of a million or more in compute power, data storage, and bandwidth. From the latter, we gained (perhaps) an order of magnitude or two in effectiveness: how much better are current OSes and programming languages than Unix and C, both of which are ~50 years old now?

HLMI = ‘high–level machine intelligence’ = one that can carry out most human professions at least as well as a typical human. (From Minds and Machines.)

Of relevance to this discussion: a big chunk of AlphaGo's performance improvement over other Go programs is due to raw compute power (link via Jess Riedel). The vertical axis is ELO score. You can see that without multi-GPU compute, AlphaGo has relatively pedestrian strength.

ELO range 2000-3000 spans amateur to lower professional Go ranks. The compute power certainly affects depth of Monte Carlo Tree Search. The initial training of the value and policy neural networks using KGS Go server positions might have still been possible with slower machines, but would have taken a long time.