All of hippke's Comments + Replies

I think the biggest improvement in this report can be made regarding Appendix D. The authors describe that they use "process size rather than transistor size" which is, as they correctly note, a made-up number. What should be used instead is transistor density (transistors per area), which is readily available in much detail for many past nodes, and the most recent "5nm" nodes (see e.g., wikichip).

What about the Landauer limit? We are 3 orders of magnitude from the Landauer limit (${10}^{-21}$ J/op), see my article here on Lesswrong. The authors list several physical limitations, but this one seems to be missing. It may pose the most relevant limit.

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).

The result: 

• 1911 games played
• 18 d

• With a baseline of 10 MNodes/move for SF3, I need to set SF13 to 0.375 MNodes/move for equality. That's a factor of 30. Caveat: I only ran 10 games which turned out equal, and only at 10 MNodes/move for SF3.
• Yes: Rebel6 at normal 2021 settings (40 moves in 15 min) can be approximately matched with SF13 at 20 kNodes/move. More precisely: I get parity between Rebel6 (128 MB) and SF13 (128 MB) for 16 MNodes/move vs. 20 kNodes/move (=factor of 800x). On my Intel Core-M 5Y31 (750 kNodes/s), that's 21s vs. 0.026s per move. Note that the figure shows SF8, not SF13.
• I was contacted by one person via PM, we are discussing the execution setup. Otherwise, I could do it by the end of July after my vacation.

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.

From what I understand about the computer chess community:

• Engines are optimized to win in the competitions, for reputation. There are competitions for many time controls, but most well respected are the CCC with games of 3 to 15 minutes, and TCEC which goes up to 90 minutes. So there is an incentive to tune the engines well into the many-MNodes/move regime.
• On the other hand, most testing during engine development is done at blitz or even bullet level (30s for the whole game for Stockfish). You can't just play thousands of long games after each code commit

Yes, that's correct. It is slightly off because I manually set the year 2022 to match 100,000 kNodes/s. That could be adjusted by one year. To get an engine which begins its journey right in the year 2021, we could perform a similar experiment with SF14. The curve would be virtually identical, just shifted to the right and up.

Oh, thank you for the correction about Magnus Carlsen! Indeed, my script to convert the timestamps had an error. I fixed it in the figure.

Regarding the jump in 2008 with Rybka: I think that's an artifact of that particular list. Similar lists don't have it.

Good point: SF12+ profit from NNs indirectly.

Regarding the ELO gain with compute: That's a function of diminishing returns. At very small compute, you gain +300 ELO; after ~10 doublings that reduces to +30 ELO. In between is the region with ~70 ELO; that's where engines usually operate on present hardware with minutes of think time. I currently run a set of benchmarks to plot a nice graph of this.

Yes, sorry, I got that the wrong way around. 70%=algo

The MIPS are only a "lookup table" from the year, based on a CPU list. It's for the reader's convenience to show the year (linear), plus a rough measure of compute (exponential).

The nodes/s measure has the problem that it is engine-dependent.

The real math was done by scaling down one engine (SF8) by time-per-move, and then calibrating the time to the computers of that era (e.g., a Quad i7 from 2009 has 200x the nodes/s compared to a PII-300 from 1999)

Yes, that is a correct interpretation. The SF8 numbers are:

MIPS = [139814.4, 69907, 17476, 8738, 4369, 2184, 1092, 546.2, 273.1, 136.5, 68.3, 34.1, 17.1, 8.5, 4.3]

ELO = [3407,3375,3318,3290,3260,3225,3181,3125,3051,2955,2831,2671,2470,2219,1910]

Note that the range of values is larger than the plotted range in the Figure. The Figure cuts off at a 80486DX 33 MHz, 27 MIPS, introduced May 7, 1990.

To derive an analytical result, it is reasonable to interpolate with a spline and then subtract. Let me know if you have a specific question (e.g. for the year 2000).

As you can see in my Figure in this post (https://www.lesswrong.com/posts/75dnjiD8kv2khe9eQ/measuring-hardware-overhang), Leela (Neural Network based chess engine) has a very similar log-linear ELO-FLOPs scaling as traditional algorithms. At least in this case, Neutral Networks scale slightly better for more compute, and worse for less compute. It would be interesting to determine if the bad scaling to old machines is a universal feature of NNs. Perhaps it is: NNs require a certain amount of memory, etc., which gives stronger constraints. The conclusion would be that the hardware overhang is reduced: Older hardware is less suitable for NNs.

At the Landauer kT limit, you need ${10}^{14}$ kWh to perform your ${10}^{35}$ FLOPs. That's 10,000x the yearly US electricity production. You'd need a quantum computer or a Dyson sphere to solve that problem.

Like AGI winter, a time of reduced funding

Regarding (1): Of course a step is possible; you never know. But for arithmetic, it is not a step. That may appear so from their poor Figure, but the data indicates otherwise.

True. Do these tests scale out to super-human performance or are they capped at 100%?

Except if you have an idea to monetarize one of these sub-tasks? An invest of order 10m USD in compute is not very large if you can create a Pokemon Comedy TV channel out of it, or something like that.