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This is: Birds, Brains, Planes, and AI: Against Appeals to the Complexity/Mysteriousness/Efficiency of the Brain, published by Daniel Kokotajlo on the LessWrong.
Crossposted from the AI Alignment Forum. May contain more technical jargon than usual.
[Epistemic status: Strong opinions lightly held, this time with a cool graph.]
I argue that an entire class of common arguments against short timelines is bogus, and provide weak evidence that anchoring to the human-brain-human-lifetime milestone is reasonable.
In a sentence, my argument is that the complexity and mysteriousness and efficiency of the human brain (compared to artificial neural nets) is almost zero evidence that building TAI will be difficult, because evolution typically makes things complex and mysterious and efficient, even when there are simple, easily understood, inefficient designs that work almost as well (or even better!) for human purposes.
In slogan form: If all we had to do to get TAI was make a simple neural net 10x the size of my brain, my brain would still look the way it does.
The case of birds & planes illustrates this point nicely. Moreover, it is also a precedent for several other short-timelines talking points, such as the human-brain-human-lifetime (HBHL) anchor.
Plan:
Illustrative Analogy
Exciting Graph
Analysis
Extra brute force can make the problem a lot easier
Evolution produces complex mysterious efficient designs by default, even when simple inefficient designs work just fine for human purposes.
What’s bogus and what’s not
Example: Data-efficiency
Conclusion
Appendix
1909 French military plane, the Antionette VII.
By Deep silence (Mikaël Restoux) - Own work (Bourget museum, in France), CC BY 2.5,
Illustrative Analogy
AI timelines, from our current perspective Flying machine timelines, from the perspective of the late 1800’s:
Shorty: Human brains are giant neural nets. This is reason to think we can make human-level AGI (or at least AI with strategically relevant skills, like politics and science) by making giant neural nets. Shorty: Birds are winged creatures that paddle through the air. This is reason to think we can make winged machines that paddle through the air.
Longs: Whoa whoa, there are loads of important differences between brains and artificial neural nets: [what follows is a direct quote from the objection a friend raised when reading an early draft of this post!]
- During training, deep neural nets use some variant of backpropagation. My understanding is that the brain does something else, closer to Hebbian learning. (Though I vaguely remember at least one paper claiming that maybe the brain does something that's similar to backprop after all.)
- It's at least possible that the wiring diagram of neurons plus weights is too coarse-grained to accurately model the brain's computation, but it's all there is in deep neural nets. If we need to pay attention to glial cells, intracellular processes, different neurotransmitters etc., it's not clear how to integrate this into the deep learning paradigm.
- My impression is that several biological observations on the brain don't have a plausible analog in deep neural nets: growing new neurons (though unclear how important it is for an adult brain), "repurposing" in response to brain damage, .
Longs: Whoa whoa, there are loads of important differences between birds and flying machines:
- Birds paddle the air by flapping, whereas current machine designs use propellers and fixed wings.
- It’s at least possible that the anatomical diagram of bones, muscles, and wing surfaces is too coarse-grained to accurately model how a bird flies, but that’s all there is to current machine designs (replacing bones with struts and muscles with motors, that is). If we need to pay attention to the percolation of air through and between feathers, micro-eddies in t...
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