I like the idea of predicting the user's response. How's this as an architecture for a helpful agent:

Given a user question, before you generate an answer you predict the user's ideal response to the model's answer (e.g. "thanks, that was helpful", or more likely a distribution over such responses), then generate an answer and iteratively optimize it to make the ideal user response more likely.

This way you're explicitly modeling the user's intent, and you can adapt the amount of computation appropriately for the complexity of the question by controlling the number of iterations on the answer.

To me the browser plugin is the only one you need. Wolfram Alpha is a website, Instacart is a website, everything is a website. Just have it use the website, done. Plugins seem like a way to get people excited about giving the AI permission to use their stuff, but it's not technically necessary.

> performs as well as text-davinci-003

No it doesn't! The researchers don't claim that either, they claim "often behaves similarly to text-davinci-003" which is much more believable. I've seen a lot of people claiming things like this with little evidence. We need some people evaluating these claims objectively. Can someone start a third party model review site?

Alignment isn't my main concern. I fear AIs that are "aligned" with people who want to e.g. fight wars, or worse.

Very cool. Literature is tough without being that familiar with the authors. Even so, I think longer snippets would be pretty easy. A sentence of only ten or so words out of context is not really much to go on.

Ooh, shadows is a good one.

71/100 exactly here too. I found Midjourney most convincing. Easiest tells I found are hands (obviously), signatures or any other lettering, malformed objects in general, and anything with symmetry or duplication. Funny that AI would be bad at duplication!

Without the requirement for exact repeatability you can use analog circuits instead of digital, and your manufacturing tolerances are greatly relaxed. You can use error-prone methods like self assembly instead of EUV photolithography in ten billion dollar cleanrooms.

Again, I don't really buy it but there's an argument to be made.

He calls it "mortal computation". Like instead of loading identical pretrained weights into every robot brain you actually train each brain individually, and then when they die their experience is lost. Just like humans! (Except you can probably train them in simulation, "The Matrix"-style.) But the advantage is that by relaxing the repeatability requirement you get hardware that is orders of magnitude cheaper and more efficient, so for any given budget it is much, much more capable. Maybe. I tend to think that won't be the case, but who knows.

Well no one knows exactly what the brain is up to in there, but we don't see enough backwards connections or activation storage to make backprop plausible, so this is a way of learning without backwards connections, and that alone makes it more biologically plausible.

This seems more interesting than the capsule stuff he was working on before. Biologically plausible learning rules are cool. Does it work on imagenet though?