Comment by bensyverson

LLMs are orders of magnitude simpler than brains, and we literally designed them from scratch. Also, we have full control over their operation and we can trace every signal.

Are you surprised we understand them better than brains?

To be fair to your field, that advancement seems expected, no? We can do things to LLMs that we can't ethically or practically do to humans.

Oh I very much agree that it's great to see more research and findings and improvements in this field. I'm just a little puzzled by GP's tone (which suggested that it isn't completely expected to find new things about LLMs, a few years in).

Indeed. For me, it's also a good reminder that AI is here to stay as technology, that the hype and investment bubble don't actually matter (well, except to those that care about AI as investment vehicle, of which I'm not one). Even if all funding dried out today, even if all AI companies shut down tomorrow, and there are no more models being trained - we've barely begun exploring how to properly use the ones we have.

We have tons of low-hanging fruits across all fields of science and engineering to be picked, in form of different ways to apply and chain the models we have, different ways to interact with them, etc. - enough to fuel a good decade of continued progress in everything.

I hate to "umm, akshually" but apparently we have been studying the brain for thousands of years. I wasn't talking about purely modern neuroscience (which ironically for our topic of emergence, (often till recently/still in most places) treats the brain as the sum of its parts - be them neurons or neurotransmitters).

> The earliest reference to the brain occurs in the Edwin Smith Surgical Papyrus, written in the 17th century BC.

I was actually thinking of ancient greeks when writing my comment, but I suppose Egyptians have even older records than them.

From https://en.wikipedia.org/wiki/History_of_neuroscience

I came here to say this :)

Interestingly enough, for a while physics used to be studied by philosophers (and used to be put in the natural philosophy basket, together with biology and most other hard sciences).

¸That field is called Machine Learning.

The intersection of physics isnt psychology it is philosophy, and the same is true (at present) with LLM's

Much as Diogenes mocked Platos definition of a man with a plucked chicken, LLM's revealed what "real" ai would require: contigous learning. That isnt to diminish the power of LLM's (the are useful) but that limitation is a fairly hard one to over come if true AGI is your goal.

That is a very interesting thought!

Yeah, I was also thinking about it A LOT.

We kinda have a little bit of it with some coding harnesses giving model access to LSP, but I think that we can insert this knowledge on a lower level if we find a clever way to somehow utilize it during sampling.

I think that there is a lot of low hanging fruit in this area.

And in general, I think that people try to use LLMs too much to solve problems that can be easily solved by cheaper (computationally), and, more importantly deterministic tools.

For example, back in the day when LLM-assisted coding just became a thing people very often complained about models generating syntactically incorrect code and inventing non-existent library methods.

Well, I, an experienced human programmer, probably would also be making syntax mistakes and inventing non-existent methods if you stripped me of my tools and made me write code in a bare text editor without syntax highlighting.

Thankfully, my IDE would autocomplete real syntax and actually existing library methods for me and immediately give me feedback if I make a mistake anyway. And all of it is achieved using reliable deterministic code without the inherent issues of statistical models.

I think that it is really inefficient to reach for an expensive and unreliable tool when a cheap and reliable tool will do.

In general these agents support LSPs, which is often as much information as your IDE will give you. They are also not required to output syntactically correct code token by token when running agentically, because the loop is:

1. code

2. syntax check / build / format / lint (details language dependent)

3. test

and they can hop between 1 and 2 however many times they want.

Doing a tool call for autocomplete is not going to make coding agents faster.

I do think there is some merit in a tool that dumps all namespaces and reachable symbols so the agent can do its own autocomplete without a round-trip.

You're describing structured outputs.

> Give coding agents access to intellisense and syntax highlighting.

i once asked an LLM if it could ingest code from an interactive session more easily if it were in appropriately-typed markdown fences and it said absolutely yes, and that the syntax highlighting fed to it that way helps it immensely. i was downright shocked that syntax highlighting was anything more than noise for them.

It's a little disturbing, but also very fun to just discover by probing, building and breaking.

Quantum physics teaches us that at the fundamental levels of physics, reality itself is probabilistic. Probability distributions collapsing to discrete locations aligns nicely across LLMs and quantum mechanics.

This is an AI bot btw. (sarcasm, metaphor that doesn't make sense)

Neat!

> This is probably due to the way larger numbers are tokenised, as big numbers can be split up into arbitrary forms. Take the integer 123456789. A BPE tokenizer (e.g., GPT-style) might split it like: ‘123’ ‘456’ ‘789’ or: ‘12’ ‘345’ ‘67’ ‘89’

One of the craziest LLM hacks that doesn't get love is https://polymathic-ai.org/blog/xval/

xVal basically says "tokenizing numbers is hard: what if instead of outputting tokens that combine to represent numbers, we just output the numbers themselves, right there in the output embedding?"

It works! Imagine you're discussing math with someone. Instead of saying "x is twenty five, which is large" in words, you'd say "x is", then switch to making a whistling noise in which the pitch of your whistle, in its position within your output frequency range, communicated the concept of 25.00 +/- epsilon. Then you'd resume speech and say "which is large".

I think the sentiment is that today's models are big and well-trained enough that receiving and delivering quantities as tokens representing numbers doesn't hurt capabilities much, but I'm still fascinated by xVal's much more elegant approach.

This is crazy, thank you for the link!

I think this seems similar to what Anthropic had been doing since the latest few Opus releases, which is interleaved thinking; CoT reasoning in the middle of a message. But they operate at different layers.