Why would you need brain emulation to get superhuman intelligence?
- LLMs have better long term memory (they know more than any human) and more working memory (LLMs have fast, uniform access to their whole context window).
- LLMs are faster than we are.
- Humans have online learning (we can do simultaneous learning and inference), giving us advantages in many novel tasks.
- We can learn concepts from far less data. And we can manage our mental context more smoothly.
- We seem to have better world models than current models. AI video just doesn't look right, somehow.
I expect that these remaining weaknesses can be overcome without resorting to human brain emulation. I see no reason to think that current LLMs are at the limit of what technology is capable of.
For example, it seems that even at Fable scale, simple concepts like the passage of time or (gasp) timezones elude them. I live in UTC+10 and with any RFC8339 data LLMs are constantly confused - is it Sunday the 10th or Sunday the 9th, etc. I have tried many solutions for this and every time it finds a way to get it wrong.
Getting confused about timezones does not place LLMs behind that many humans. (But doing so repeatedly does highlight the lack of online learning).
How do you square that then? They can do amazing things, but they're also not smart? Do you think its possible to solve Erdos problems without any "smarts"? Can you do it without even understanding mathematics?
I find it very hard to hold the idea that LLMs don't understand anything. They can explain concepts, translate them, simplify them and implement them in code. From the outside, LLMs seem to understands most concepts better than most humans do. Do you understand anything? Couldn't I make the same argument? How would you prove that you understand what a for loop is, or that you know what calculus is? I assume you'd demonstrate your knowledge by using a for loop in a program, or explain calculus back to me. But LLMs can do that too.
> For example, it seems that even at Fable scale, simple concepts like the passage of time or (gasp) timezones elude them.
Funny example, because lots of human struggle with this too. The number of meetings I've had with people in the US! "Lets meet on thursday morning australia time!". Only, they actually meant thursday night US time, which is friday morning australia time. "Oooh that's so weird! Its the next day for you!". ...... Yes, I know.
I think LLMs are just a different kind of intelligence than humans. They're better at some things than us, and worse than others. They can find latent security vulnerabilities in the linux kernel, but struggle to count the Rs in strawberry. They're not as smart as humans in many ways. But we're not as smart as LLMs in plenty of ways too. I didn't find those linux bugs.
Are you making a serious argument that it's not?
Because you'll need to explain leading-edge mathematics advances that have come from LLMs, among other things.
Also, you may have noticed in passing that humans aren't getting any smarter, while AI models are.
That's not all of what we are doing for at least a year, possibly few. LLMs are trained increasingly on generated inputs. Soon human sourced material is going to be rounding error in the process of training.
If you add two random numbers and calculate the result and those happened to be numbers noone else ever had idea to add you created a new piece of information. Template is not new, but the piece of information is. And sure, this template might be very simple, too simple, but you can come up with more complex one. And metadata is data. You can create templates in similar manner to how you create new pieces of information using them.
And labs training AI are doing it for years at this point. And it is ever increasing fraction of all training.