That's an excellent question, pondered by the brightest minds. The great Freeman Dyson proposed a solution dubbed eternal intelligence (Dyson 1979, Reviews of Modern Physics, Volume 51, Issue 3, July 1979, pp.447-460). Basically, some finite amount of matter=energy is stored. As the universe cools over time, energy costs per computation decrease (logarithmically, but forever). After each cooling time period, one can use some fraction of the remaining energy, which will thus never go to zero, leading to eternal consciousness.
It was later understood that the expansion of the universe is accelerating. If that holds, the concept breaks down, as Dyson admitted. In the far future, any two observers will be separated, making the remaining subjects very lonely.
I think this calculation is invalid. A human is created from a seed worth 700 MB of information, encoded in the form of DNA. This was created in millions of years of evolution, compressing/worth a large (but finite) amount of information (energy). A relevant fraction of hardware and software is encoded in this information. Additional learning is done during 20 years worth 3 MWh. The fractional value of this learning part is unknown.
How can we know that "it is possible to train a 200 IQ equivalent intelligence for at most 3 MW-hr"?
How did von Neumann come close to taking over the world? Perhaps Hitler, but von Neumann?
Sure! I argue that we just don't know whether such a thing as "much more intelligent than humans" can exist. Millions of years of monkey evolution have increased human IQ to the 50-200 range. Perhaps that can go 1000x, perhaps it would level of at 210. The AGI concept makes the assumption that it can go to a big number, which might be wrong.
From what I understand about "ELO inflation", it refers to the effect that the Top 100 FIDE players had 2600 ELO in 1970, but 2700 ELO today. It has been argued that simply the level increased, as more very good players entered the field. The ELO number as such should be fair in both eras (after playing infinitely many games...). I don't think that it is an issue for computer chess comparisons. Let me know if you have other data/information!
I ran the experiment "Rebel 6 vs. Stockfish 13" on Amazon's AWS EC2. I rented a Xeon Platinum 8124M which benched at 18x 1.5 MNodes/s. I launched 18 concurrent single-threaded game sets with 128 MB of RAM for each engine. Again, ponder was of, no books, no tables. Time settings were 40 moves in 60s + 0.6 per move, corresponding to 17.5 MNodes/move. For reference, SF13 benches at ELO 3630 at this setting (entry "64 bit"); Rebel 6.0 got 2415 on a Pentium 90 (SSDF Computer Rating List (01-DEC-1996).txt, 90 kN/move).
Answer: ELO gain with compute is not a linear function, but one with diminishing returns. Thus, the percentage "due to algo" increases, the longer the time frame. Thus, a fixed percentage is not a good answer.But we can give the percentage as a function of time gap:
With data from other sources (SF8, Houdini 3) I made this figure to show the effect more clearly. The dashed black line is a double-log fit function: A base-10 log for the exponential increase of compute with time, and a natural log for the exponential search tree of chess. The parameter values are engine-dependent, but should be similar for engines of the same era (here: Houdini 3 and SF8). With more and more compute, the ELO gain approaches zero. In the future, we can expect engines whose curve is shifted to the right side of this plot.
OK, I have added the Houdini data from this experiment to the plot:
The baseline ELO is not stated, but likely close to 3200:
Mhm, good point. I must admit that the "70 ELO per doubling" etc. is forum wisdom that is perhaps not the last word. A similar scaling experiment was done with Houdini 3 (2013) which dropped below 70 ELO per doubling when exceeding 4 MNodes/move. In my experiment, the drop is already around 1 MNode/move. So there is certainly an engine dependence.