The problem with his approach is again, the architecture is set in stone. If we keep on trying these intermediate architectures, we are never going to get to the real AI.
There are 2 areas that need focus. First is hardware accessible for everyone to experiment on. This is being worked on (Tenstorrent, Tinycorp).
The second area needs to be research into how the compute graphs organize themselves. We shouldn't be hardcoding things like temporary memory or recurrence into the models. We should be starting with some baseline model and letting it self discover constructs like memory, recurrence, e.t.c. This is the only way to get true AI.
Human species got its level of intelligence through genetic algorithm. It may turn out that this is the only way to really get a generic AI architecture. However, genetic algorithms in nature only operate on the performance scalar (i.e survival/ability to reproduce), whereas simulating these models has an additional layer of historical data (in nature, this would look like being able pick gene groups for your kid that you know resulted in better performance in a certain area). So we may be able to arrive at this faster, but this requires a shitload of memory and availability of fast compute for everyone, which is the point 1.
I like this different angle, goes to show how many unknown unknowns there are
I think most of us would agree that maximizing an architecture's flexibility, including self-improvement, will always be preferred
quite an exciting perspective I almost ignored. of course we want self-improving AI. Imagine preparing all of the hardware, computing power, and everything, and just planting that initial seed, basically telling it "here you are, we've perfected the seed of infinite ASI, now go and grow baby, this is all yours now". man, this is so exciting... wish I had no need for money so I can focus on just this for my entire life starting right now
There are 2 areas that need focus. First is hardware accessible for everyone to experiment on. This is being worked on (Tenstorrent, Tinycorp).
The second area needs to be research into how the compute graphs organize themselves. We shouldn't be hardcoding things like temporary memory or recurrence into the models. We should be starting with some baseline model and letting it self discover constructs like memory, recurrence, e.t.c. This is the only way to get true AI.
Human species got its level of intelligence through genetic algorithm. It may turn out that this is the only way to really get a generic AI architecture. However, genetic algorithms in nature only operate on the performance scalar (i.e survival/ability to reproduce), whereas simulating these models has an additional layer of historical data (in nature, this would look like being able pick gene groups for your kid that you know resulted in better performance in a certain area). So we may be able to arrive at this faster, but this requires a shitload of memory and availability of fast compute for everyone, which is the point 1.