A concern about the “evolutionary anchor” of Ajeya Cotra’s report

tl;dr: The report underestimates the amount of compute used by evolution because it only looks at what it would take to simulate neurons, rather than neurons in agents inside a complex environment. It’s not clear to me what the magnitude of the error is, but it could range many, many orders of magnitude. This makes it a less forceful outside view.

Background

Within Effective Altruism, Ajeya Cotra’s report on artificial general intelligence (AGI) timelines has been influential in justifying or convincing members and organizations to work on AGI safety. The report has a section on the “evolutionary anchor”, i.e., an upper bound on how much compute it would take to reach artificial general intelligence. The section can be found in pages 24-28 of this Google doc. As a summary, in the report’s own words:

This hypothesis states that we should assume on priors that training computation requirements will resemble the amount of computation performed in all animal brains over the course of evolution from the earliest animals with neurons to modern humans, because we should expect our architectures and optimization algorithms to be about as efficient as natural selection.

This anchor isn’t all that important in the report’s own terms: it only gets a 10% probability assigned to it in the final weighted average. But this bound is personally important to me because I do buy that if you literally reran evolution, or if you use as much computation as evolution, you would have a high chance of producing something as intelligent as humans, and so I think that it is particularly forceful as an “outside view”.

Explanation of my concern

I don’t buy the details of how the author arrives at the estimate of the compute used by evolution:

The amount of computation done over evolutionary history can roughly be approximated by the following formula: (Length of time since earliest neurons emerged) * (Total amount of computation occurring at a given point in time). My rough best guess for each of these factors is as follows:

• Length of evolutionary time: Virtually all animals have neurons of some form, which means that the earliest nervous systems in human evolutionary history likely emerged around the time that the Kingdom Animalia diverged from the rest of the Eukaryotes. According to timetree.org, an online resource for estimating when different taxa diverged from one another, this occurred around \~6e8 years ago. In seconds, this is \~1e16 seconds.

• Total amount of computation occurring at a given point in time: This blog post attempts to estimate how many individual creatures in various taxa are alive at any given point in time in the modern period. It implies that the total amount of brain computation occurring inside animals with very few neurons is roughly comparable to the amount of brain computation occurring inside the animals with the largest brains. For example, the population of nematodes (a phylum of small worms including C. Elegans) estimated to be \~1e20 to \~1e22 individuals. Assuming that each nematode performs \~10,000 FLOP/s,the number of FLOP contributed by the nematodes every second is \~1e21 * 1e4 = \~1e25; this doesn’t count non-nematode animals with similar or fewer numbers of neurons. On the other hand, the number of FLOP/s contributed by humans is (\~7e9 humans) * (\~1e15 FLOP/s / person) = \~7e24. The human population is vastly larger now than it was during most of our evolutionary history, whereas it is likely that the population of animals with tiny nervous systems has stayed similar. This suggests to me that the average ancestor across our entire evolutionary history was likely tiny and performed very few FLOP/s. I will assume that the “average ancestor” performed about as many FLOP/s as a nematode and the “average population size” was \~1e21 individuals alive at a given point in time. This implies that our ancestors were collectively performing \~1e25 FLOP every second on average over the \~1 billion years of evolutionary history.

This implies that the total amount of computation done over the course of evolution from the first animals with neurons to humans was (\~1e16 seconds) * (\~1e25 FLOP/s) = \~1e41 FLOP

Unlike what the reader might suspect, I don’t particularly want to take issue with the assumption that “…the total amount of brain computation occurring inside animals with very few neurons is roughly comparable to the amount of brain computation occurring inside the animals with the largest brains”. I haven’t looked at the literature on this, and the assumption seems a prima facie plausible.

Instead, I don’t buy the assumption that to simulate evolution, it would be enough to simulate the behaviour of all the neurons throughout history. Instead, I think that one would also have to simulate the stimuli to which these neurons are exposed in order to compute how neurons behave, and for this one also has to simulate the environment. For a simple example, in the case of AlphaGo, one not only has to simulate the inner workings of the model, but also the state of the Go board. Likewise, to simulate evolution, one would not only have to simulate the neurons of the brains in it, but also the state of the world in which they are.

It’s not clear to me how big a world would one need to spawn intelligent life. For instance, I think that Minecraft requires on the order of ~500 GFLOPs to run¹. Conversely, if we want a planet as large as Earth, with a complex atmosphere & so on, this might vastly exceed the computing power of current supercomputers². Working through these details might be a neat research project, and perhaps a good fit for an organization such as AI Impacts or Epoch, though I’m not sure how many estimates other than my own it would change. In any case, this would involve:

1. Coming up with estimates of what the least fine-grained world that we would expect might be able to produce intelligent life if we simulated natural selection in it.
2. Calculating how much compute it would take to in fact simulate it.

Propagation of beliefs

After reading this, I think that the reader should:

1. Make AI timelines (slightly) wider to the right
2. Slightly reduce credence in the argument chain in The Most Important Century series, which leans heavily on the argument that AGI will be developed this century.
3. Slightly increase credence that something like The Patient Philanthropy Project, which pretty much conditions on no AGI soon, is a good idea.
4. The meta update: about whether arguments in favor of shorter timelines are found more often, and about how intensely Open Philanthropy reports have been scrutinized.

I think that the first three updates should be relatively small, on the order of 0.1% to 10%:

• Something like 0.1% if you are keeping careful track of your AI timelines in a spreadsheet and don’t particularly care about the evolutionary anchor, but want to update a bit to not fall prey to confirmation bias.
• Something like 10% if you are replacing the whole of Ajeya Cotra’s weight on the previous evolutionary anchor timelines with some unspecified but much longer timelines, and if you greatly favored Cotra’s estimate over Carlsmith’s or Davidson’s
• You could also have a much larger update if you more predominantly cared about the evolutionary anchor.

I haven’t given much thought about how the meta update should look like.

Discussion

Alex Guzey mentions:

indeed by only considering neurons fired instead information observed by the organisms you probably save yourself orders of magnitude of computational requirements. But you don’t know a priori how exactly to make these neurons fire without looking at all of the environment and doing computation over it.

In conversation with Misha Yagudin, he mentions:

1. that it is not clear whether the report is talking about “simulating all of evolution” when saying something like “we should expect our architectures and optimization algorithms to be about as efficient as natural selection.”
2. That he personally expects AI to require less compute than evolution.
3. Than even if you have to simulate an environment, this could have comparable computational requirements to computing the neurons firing in the ML model. But it could also be much greater. “It’s important if the computational requirements are compatible or greater. If it’s 10% more than baseline, this is within model’s precision. If it’s ×1 — that’s concerning, if it’s ×10 it’s a mistake, ×10¹⁰ — that’s a disaster.”

I broadly agree with these points. On 3., I don’t like to use the term “Knightian uncertainty”, but I’ve given an overly broad range between “Minecraft” and “simulate all of the earth at similar levels of precision as we currently simulate the atmosphere”.

Acknowledgements

Thanks to Alexey Guzey and Misha Yagudin for reviewing this post. Otherwise, my work is generally supported by the Quantified Uncertainty Research Institute.