Secret sauce for intelligence

Secret sauce for intelligence (also known as one algorithm, simple core algorithm, lumpy AI progress, intelligibility of intelligence, and many other phrases) is a disagreement or a cluster of disagreements within AI safety. Resolving this is important for thinking about the shape of AI takeoff.

The common forms/framings of the disagreement are:

• whether progress in AI is "lumpy", i.e. coming in a small number of chunks that contribute greatly to AI capabilities; there are a small number of discrete insights required to create an AGI rather than many small/messy insights
• whether intelligence is "simple" in some sense, maybe Kolmogorov complexity
• whether the core function of intelligence is understandable/intelligible in some sense
• whether it is possible to specialize in intelligence itself (i.e. whether intelligence can be considered a single narrow technology, like a car, rather than something that is an umbrella term for a whole collection of separate narrow technologies and cannot be specialized in, like a city)

Proponents of the secret sauce view believe that progress is lumpy, and that intelligence is simple and understandable.

History

This disagreement was a (the?) core part of the Hanson-Yudkowsky AI Foom debate.

Evidence

What kinds of evidence would shift our beliefs to one side of the disagreement or the other? This section lists all the things that have been given as reasons for believing or not believing in a secret sauce.

• Looking at things like The MIT Encyclopedia of the Cognitive Sciences and Judea Pearl's work on causality and trying to estimate how many insights are required to build an AGI.
• Looking at citation lumpiness in CS/AI/ML.
• The information content of the human genome is about 750 megabytes, and gives one upper bound of how much information must be baked into an AI (rather than learned by interacting with the environment). Taking into account the fact that most of the DNA is junk and that the genes for the brain are only a part of the entire genome further reduces the bound.
• Comparing scientific ability in chimps vs humans suggests that some sort of "missing gear" was added which made human intelligence much more general.
• Variation in scientific ability among humans (what does this imply?)
• Variation in cognitive ability among chimps (what does this imply?)
• the science argument: that science is this general "architectural insight" which allowed humans to have much more control over the world, and that we should expect something like that to happen with AI as well (that there is some sort of core insight that allows an AI to have much more control over the world, rather than it being a bunch of incremental progress). [1] (search "you look at human civilization and there's this core trick called science") See (5) in [2] for Robin Hanson's response.

See also

• Simple core of consequentialist reasoning
• Prosaic AI

Notes

Even if there is a secret sauce, AI takeoff could still be continuous if hardware is a bottleneck (i.e. we might discover all the important facts about intelligence early on but not have enough computing power to run it cheaply).

To what extent is this the same debate as something like realism about rationality? It seems like if intelligence/rationality is simple, then there will be a secret sauce. But if it's not simple it could go either way (maybe there's a final essential "gear" that needs to be added to make things really work, in which case there is a secret sauce, or maybe everything just gradually improves and there's nothing like a "last gear"). So my initial thought is that intelligibility of intelligence implies secret sauce, but not conversely. Now, about rationality realism specifically, I'm not sure.

https://sideways-view.com/2018/02/24/takeoff-speeds/

https://jacoblagerros.wordpress.com/2018/03/09/brains-and-backprop-a-key-timeline-crux/

https://web.archive.org/web/20200218080005/https://lw2.issarice.com/posts/4Q5s8qGyCtzfYtCZX/is-there-a-compute-efficient-algorithm-for-agency (i guess this one argues against)

http://benjaminrosshoffman.com/openai-makes-humanity-less-safe/#comment-128508

https://srconstantin.wordpress.com/2017/02/21/strong-ai-isnt-here-yet/

from https://arbital.com/p/general_intelligence/

An Artificial General Intelligence would have the same property; it could learn a tremendous variety of domains, including domains it had no inkling of when it was switched on.

More specific hypotheses about how general intelligence operates have been advanced at various points, but any corresponding attempts to define general intelligence that way, would be theory-laden. The pretheoretical phenomenon to be explained is the extraordinary variety of human achievements across many non-instinctual domains, compared to other animals.

[…]

To the extent one credits the existence of 'significantly more general than chimpanzee intelligence', it implies that there are common cognitive subproblems of the huge variety of problems that humans can (learn to) solve, despite the surface-level differences of those domains. Or at least, the way humans solve problems in those domains, the cognitive work we do must have deep commonalities across those domains. These commonalities may not be visible on an immediate surface inspection.

'But in general, the hypothesis of general intelligence seems like it should cash out as some version of: "There's some set of new cognitive algorithms, plus improvements to existing algorithms, plus bigger brains, plus other resources--we don't know how many things like this there are, but there's some set of things like that--which, when added to previously existing primate and hominid capabilities, created the ability to do better on a broad set of deep cognitive subproblems held in common across a very wide variety of humanly-approachable surface-level problems for learning and manipulating domains. And that's why humans do better on a huge variety of domains simultaneously, despite evolution having not preprogrammed us with new instinctual knowledge or algorithms for all those domains separately."' -- this doesn't really tell us which of those things it was that helped the most.

Rob Bensinger makes the same argument here: https://www.greaterwrong.com/posts/D3NspiH2nhKA6B2PE/what-evidence-is-alphago-zero-re-agi-complexity/comment/awsEzHzgD5Rv2YGPo

see also discussion at https://www.facebook.com/yudkowsky/posts/10154018209759228?comment_id=10154018937319228

"Yes, IF there are just one or two insights that can create a very general AGI which is far more capable than previous systems, and if that fact is unanticipated, then it might happen that a small team creates this AGI, and it stays better than other systems for a sufficient time to have a big differential effect. So as I've said our key dispute is about the lumpiness and number of key insights needed to create a general capable AGI." [3]

Robin Hanson:

You might claim that once we have enough good simple tools, complexity will no longer be required. With enough simple tools (and some data to crunch), a few simple and relatively obvious combinations of those tools will be sufficient to perform most all tasks in the world economy at a human level. And thus the first team to find the last simple general tool needed might “foom” via having an enormous advantage over the entire rest of the world put together. At least if that one last tool were powerful enough. I disagree with this claim, but I agree that neither view can be easily and clearly proven wrong.