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Comment by Chipshuffle | Hacker News Reader

The more I think about LLMs the stranger it feels trying to grasp what they are. To me, when I'm working with them, they don't feel intelligence but rather an attempt at mimicking it. You can never trust, that the AI actually did something smart or dump. The judge always has to be you.

It's ability to pattern match it's way through a code base is impressive until it's not and you always have to pull it back to reality when it goes astray.

It's ability to plan ahead is so limited and it's way of "remembering" is so basic. Every day it's a bit like 50 first dates.

Nonetheless seeing what can be achieved with this pseudo intelligence tool makes me feel a little in awe. It's the contrast between not being intelligence and achieving clearly useful outcomes if stirred correctly and the feeling that we just started to understand how to interact with this alien.

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> they don't feel intelligence but rather an attempt at mimicking it

Because that's exactly what they are. An LLM is just a big optimization function with the objective "return the most probabilistically plausible sequence of words in a given context".

There is no higher thinking. They were literally built as a mimicry of intelligence.

> Because that's exactly what they are. An LLM is just a big optimization function with the objective "return the most probabilistically plausible sequence of words in a given context". > There is no higher thinking. They were literally built as a mimicry of intelligence.

Maybe real intelligence also is a big optimization function? Brain isn't magical, there are rules that govern our intelligence and I wouldn't be terribly surprised if our intelligence in fact turned out to be kind of returning the most plausible thoughs. Might as well be something else of course - my point is that "it's not intelligence, it's just predicting next token" doesn't make sense to me - it could be both!

I don't understand why this point is NOT getting across to so many on HN.

LLM's do not think, understand, reason, reflect, comprehend and they never shall. I have commented elsewhere but this bears repeating

If you had enough paper and ink and the patience to go through it, you could take all the training data and manually step through and train the same model. Then once you have trained the model you could use even more pen and paper to step through the correct prompts to arrive at the answer. All of this would be a completely mechanical process. This really does bear thinking about. It's amazing the results that LLM's are able to acheive. But let's not kid ourselves and start throwing about terms like AGI or emergence just yet. It makes a mechanical process seem magical (as do computers in general).

I should add it also makes sense as to why it would, just look at the volume of human knowledge (the training data). It's the training data with the mass quite literally of mankind's knowledge, genius, logic, inferences, language and intellect that does the heavy lifting.

> If you had enough paper and ink and the patience to go through it, you could take all the training data and manually step through and train the same model.

But you could make the exact same argument for a human mind? (could just simulate all those neural interactions with pen and paper)

The only way to get out of it is to basically admit magic (or some other metaphysical construct with a different name).

> But you could make the exact same argument for a human mind?

It would be an argument and you are free to make it. What the human mind is, is an open scientific and philosophical problem many are working on.

The point is that LLM's are NOT the same because we DO know that LLM's are. Please see the myriad of tutorials 'write an LLM from scratch'

We do know that they are different, and that there are some systematic shortcomings in LLMs for now (e.g. no mechanism for online learning).

But we have no idea how many "essential" differences there are (if any!).

Dismissing LLMs as avenues toward intelligence just because they are simpler and easier to understand than our minds is a bit like looking at a modern phone from a 19th century point of view and dismissing the notion that it could be "just a Turing machine": Sure, the phone is infinitely more complex, but at its core those things are the same regardless.

I'm not so sure "a human mind" is the kind of newtonian clockwork thingiemabob you "could just simulate" within the same degree of complexity as the thing you're simulating, at least not without some sacrifices.

Can you give examples of how that "LLM's do not think, understand, reason, reflect, comprehend and they never shall" or that "completely mechanical process" helps you understand better when LLM works and when they don't?

Many people are throwing around that they don't "think", that they aren't "conscious", that they don't "reason", but I don't see those people sharing interesting heuristics to use LLMs well. The "they don't reason" people tend to, in my opinion/experience, underestimate them by a lot, often claiming that they will never be able to do <thing that LLMs have been able to do for a year>.

To be fair, the "they reason/are conscious" people tend to, in my opinion/experience, overestimate how much a LLM being able to "act" a certain way in a certain situation says about the LLM/LLMs as a whole ("act" is not a perfect word here, another way of looking at it is that they visit only the coast of a country and conclude that the whole country must be sailors and have a sailing culture).

We know what an LLM is in fact you can build one from scratch if you like. e.g. https://www.manning.com/books/build-a-large-language-model-f...

It's an algorithm and a completely mechanical process which you can quite literally copy time and time again. Unless of course you think 'physical' computers have magical powers that a pen and paper Turing machine doesn't?

> Many people are throwing around that they don't "think", that they aren't "conscious", that they don't "reason", but I don't see those people sharing interesting heuristics to use LLMs well.

My digital thermometer doesn't think. Imbibing LLM's with thought will start leading to some absurd conclusions.

A cursory read of basic philosophy would help elucidate why casually saying LLM's think, reason etc is not good enough.

What is thinking? What is intelligence? What is consciousness? These questions are difficult to answer. There is NO clear definition. Some things are so hard to define (and people have tried for centuries) e.g. what is consciousness? That they are a problem set within themselves please see Hard problem of consciousness.

https://en.wikipedia.org/wiki/Hard_problem_of_consciousness

>My digital thermometer doesn't think. Imbibing LLM's with thought will start leading to some absurd conclusions.

What kind of absurd conclusions? And what kind of non absurd conclusions can you make when you follow your let's call it "mechanistic" view?

>It's an algorithm and a completely mechanical process which you can quite literally copy time and time again. Unless of course you think 'physical' computers have magical powers that a pen and paper Turing machine doesn't?

I don't, just like I don't think a human or animal brain has any magical power that imbues it with "intelligence" and "reasoning".

>A cursory read of basic philosophy would help elucidate why casually saying LLM's think, reason etc is not good enough.

I'm not saying they do or they don't, I'm saying that from what I've seen having a strong opinion about whether they think or they don't seem to lead people to weird places.

>What is thinking? What is intelligence? What is consciousness? These questions are difficult to answer. There is NO clear definition.

You see pretty certain that whatever those three things are a LLM isn't doing it, a paper and pencil aren't doing it even when manipulated by a human, the system of a human manipulating a paper and pencil isn't doing it.

A cursory read of basic philosophy would surely include the arguments against Searle's Chinese room, no? It's hardly settled.

Life is more fun as a scruffie.

[0] http://www.catb.org/~esr/jargon/html/N/neats-vs--scruffies.h...

> The judge always has to be you.

But you can automate much of that work by having good tests. Why vibe-test AI code when you can code-test it? Spend your extra time thinking how to make testing even better.

It's a compressed database with diffuse indices. It's using probability matching rather than pattern matching. Write operations are called 'training' and 'fine-tuning'.

If you find yourself 50-first-dating your LLMs, it may be worth it to invest some energy into building up some better context indexing of both the codebase itself and of your roadmap.

Yeah, I admit I'm probably not doing that quite optimally. I'm still just letting the LLM generate ephemeral .md files that I delete after a certain task is done.

The other day I found [beads](https://github.com/steveyegge/beads) and thought maybe that could be a good improvement over my current state.

But I'm quite hesitant because I also have seen these AGENTS.md files become stale and then there is also the question of how much information is too much especially with the limited context windows.

Probably all things that could again just be solved by leveraging AI more and I'm just an LLM noob. :D

Beads is basically what github issues is, but local and built in a way that LLMs can easily use it. I had a self-made solution that was close, but moved to beads because it worked out of the box without disrupting my workflow that much.

I've used it quite a bit, but now that Gas Town is a thing Beads getting a bit bloated and they're adding new features left and right, dunno why.

Might have to steal the best bits of Beads (the averaged out cli experience and JSONL for storing issues in the repo + local sqlite cache) and build my one with none of the extra bells and whistles.

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