It seems more likely that there will be multiple avenues to AGI, all with their strengths and weaknesses. But perhaps the "God AI" will be a multifaceted model composed of many different models acting in unison.

There's really no God algorithm needed, just something good enough to assist with research of the next tier of hardware, energy, and code for AI.

Algorithm is too simple, but yes - what does the system look like

I think the transformers architecture, or something very similar with eventually-on-policy time series forecasting in a markov decision process, is the right answer actually and was what I have been trying to make progress on for a long time[1].

[1]https://kemendo.com/research/streaminference.html

We're searching for an efficient algorithm that leads to AGI. Given sufficient time and compute, I'm sure that we could get there with existing stuff, by accident, and we wouldn't realize it before moving on to the next thing... and there'd be a poor orphan AGI, lost in a Git repo, waiting for runtime.

No, it’s a much simpler architecture. Anything that evolved can’t require a really complicated niche structure.

The difficulty is the scale. Every synapse of a neuron is effectively a neuron itself, and every synapse acts on the synapses around it. So before you’ve even got to the neuron as a whole you’ve already got the equivalent of thousands of neurons and logic gates. Then the final result gets passed on to thousands more neurons.

I don’t know how you would recreate such complexity in programming. It’s not just the scale, it’s the flexibility of the structure.

There's this nice new paper ("Simplifying Transformer Blocks" https://arxiv.org/abs/2311.01906) about getting rid of all the unnecessary parts of a transformer. On their dataset, they manage to get rid of the layernorm, the skip connection, the value matrix, and the projection matrix.

As it stands, each token processed by transformer requires a constant amount of computation and energy. For an AGI system, this would imply the ability to solve problems of any complexity with a fixed amount of energy. But if this were true, it would essentially mean that P equals NP, a major theoretical breakthrough in computational complexity theory. IMO we are still missing something.

If they were we'd stop searching already, no?

Theory of everything is supposed to be beautiful.

Theory of AI is just going to be some weird network with a shit-ton of compute power, where the latter is more important to the outcome than the former.

No, not at all. Just looking for the next step of many, stacking S-curves atop each other.