I have a PhD in Computational Neuroscience from UCSD (Bachelor’s was in Biomedical Engineering with Math and Computer Science minors). Ever since junior high, I’ve been trying to figure out how to engineer artificial minds, and I’ve been coding up artificial neural networks ever since I first learned to program. Obviously, all my early designs were almost completely wrong/unworkable/poorly defined, but I think my experiences did prime my brain with inductive biases that are well suited for working on AGI.
Although I now work as a data scientist in R&D at a large medical device company, I continue to spend my free time studying the latest developments in AI/ML/DL/RL and neuroscience and trying to come up with models for how to bring it all together into systems that could actually be implemented. Unfortnately, I don’t seem to have much time to develop my ideas into publishable models, but I would love to have the opportunity to share ideas with those who do.
Of course, I’m also very interested in AI Alignment (hence the account here). My ideas on that front mostly fall into the “learn (invertible) generative models of human needs/goals and hook those up to the AI’s own reward signal” camp. I think methods of achieving alignment that depend on restricting the AI’s intelligence or behavior are about as destined to failure in the long term as Prohibition or the War on Drugs in the USA. We need a better theory of what reward signals are for in general (probably something to do with maximizing (minimizing) the attainable (dis)utility with respect to the survival needs of a system) before we can hope to model human values usefully. This could even extend to modeling the “values” of the ecological/socioeconomic/political supersystems in which humans are embedded or of the biological subsystems that are embedded within humans, both of which would be crucial for creating a better future.
I largely agree with the main thrust of the argument. What would this line of thought imply for the possibility of mind-uploading? Do we need to simulate every synapse to recreate a person, or might there be a way to take advantage of certain regularities in the computational structure of the brain to convert someone’s memories/behavioral policies/personality/etc. into some standard format that could be imprinted on a more generic architecture?
A couple of quibbles, though:
Depending on what exactly you mean by “neuromorphic”, I take issue with this. If you want to use traditional CPU/GPU technology, I imagine that you could simulate an AGI on a small server farm and use that to control a robot body (physically or virtually embedded). However, if you want to have anywhere near human-level power/space efficiency, I think that something like neuromorphic hardware will be essential.
You can run a large neural network in software using continuous values for neuron activations, but the hardware it’s running on is only optimized for generic computations. “Neurons that spike” offer many advantages like power efficiency and event-based Monte Carlo sampling. Dedicated hardware that runs on spiking neuron analogs could implement brain-like AGI models far better than existing CPUs/GPUs in terms of efficiency, at the cost of generality of computation (no free lunch).
Does AGI itself require neuromorphic hardware *per se*? No. Will the first implementation of scalable AGI algorithms and data structures be done in software running on non-AGI-dedicated hardware? Probably. Will those algorithms involve simulating Na/K/Ca currents, gene regulation, etc. directly? Probably not. But will it be necessary to convert those algorithms and data structures into something that could be run on spiking/event-based neuromorphic hardware to make it competitive, affordable, and scalable? I think so. Eventually. At least if you want to have robots with human-level intelligence running on human-brain-sized computers.
This is wrong unless “key operating principles” means something different each time you say it (i.e. it refers to the algorithms and data structures running on the human brain, but then it refers to the molecular-level causal graph describing the worm’s nervous system). Which is what I assume you meant.