Past Performance is Not Indicative of Future Results (2020)

It's probably true that you could imagine a "perfectly designed" brain which could perform better on some tasks with less complexity, but I think it's also true that there's been a lot of selection pressure towards increased intelligence, so this is probably fairly well optimized.

> why don’t we have a normal abstraction for sending signals? Instead, we have like 10s of slightly different ones with different failings each, but each having many repetitive machinery leading to inefficient “spaghetti code”.

What do you mean exactly by this? Like different neurotransmitter systems? Because I think it's actually quite elegant how the properties of different neurohormones lead to different processing modalities. It's like we have purpose-built hardware on the scale of individual proteins specialized for different purposes. I'm not so sure a more homogenized process for neural signaling would be an improvement.

It might be obvious for most people here on HN that we are very far away from true artificial intelligence, but most normal people aren’t and the marketing bullshit around calling statistical models "artificial intelligence" paints the wrong picture. This article shows why.

I have to be honest, I would take any such comparison from the 1950's with a huge pinch of salt. I think perceptions about how "dumb" an individual neuron is as a processing unit have shifted quite a bit since then.

> Also, I don’t think that comparing the training of a neural network to the brain is fair from an energy usage point of view - compare the usage of the final NN with it.

I'm not considering this in terms of the training efficiency, I'm looking at it in terms of the ratio between operational utility and energy used. There's no trained ANN with anything remotely close to the overall utility of the human brain at any scale, let alone one that weighs 3lbs, fits into a human skull and runs on 20 Watts of power.

I actually think that AGI is deceptively simple. I don't have a proof, but I have a (rather embryonic, frankly) theory of how is it gonna work.

I believe AGI is an analogue of third Futamura projection, but for (reinforcement) learners and not compilers.

So the first level is you have problem and a learner, and you teach learner to solve the problem. The representation of the problem is implicit in the learner.

The second level is that you have a language, which can describe the problem and its solution, and a (2nd level) learner, and you teach the 2nd level learner to create (1st level) solvers of the problem based on the problem description language. The ability to interpret the problem description language is implicit in the 2nd level learner.

The third level is, you have a general description language that is capable of describing any problem description language, and you teach the 3rd level learner to take a description of the problem description language, and produce 2nd level learners that can use this language to solve problems created in it.

Now, just like in Futamura projections, this is where it stops. You have a "generally intelligent" creature on the 3rd level. You can talk to them on level of how to effectively describe or solve problems (create a specialized language for it) and they will come all the way down with the way to attack (solve) them.

In humans, the 3rd level, general intelligence (AKA "sentience"), evolved eventually from the 2nd level, and it was a creation of the general internal language (which probably co-evolved to be shared). The 2nd level is an internal representation of the world that can be manipulated, but only ever refer to the external world, not itself, so it allows creatures to make conscious plans, but lack the ability to reflect on the planning (and also learning) process itself. The "bicameral mind" is a theory how we acquired 3rd level from the 2nd, and the 3rd level is why "we are strange loops".

Anway, the problem is, the higher you go up the chain, the harder it becomes to create the learner, it's a lot more general problem. But I think the ladder must be, and should be, climbed. I believe that Deepmind (and RL research) has solved the 1st level, is now working on the 2nd level, but they already somewhat dimly see the 3rd level.