An approximation of a very small limited subset of reality with more than a 1 in 20 error rate who produces massive amounts of tokens in quick succession is a shit representation of reality which is in every way inferior to human accounts to the point of being unusable for the industries in which they are promoted.

And that Error Rate can only spike when the training data contains errors itself, which will only grow as it samples its own content.

That was a roundabout way of admitting you have no idea what logic is or how LLMs work. Logic works with propositions regardless of their literal meaning, LLMs operate with textual tokens irrespective of their formal logical relations. The chatbot doesn't actually do the logical operations behind the scenes, it only produces the text output that looks like the operations were done (because it was trained on a lot of existing text that reflects logical operations in its content).

Yeah we’re so stupid we’ve figured out advanced maths, physics, built incredible skyscrapers and the LHC, we may as individuals be less or more intelligent but humans as a whole are incredibly intelligent

By that metric, you can argue Kasparov isn't thinking during chess, either. A lot of human chess "thinking" is recalling memorized openings, evaluating positions many moves deep, and other tasks that map to what a chess engine does. Of course Kasparov is thinking, but then you have to conclude that the AI is thinking too. Thinking isn't a magic process, nor is it tightly coupled to human-like brain processes as we like to think.

Apple always arrives late to any new tech, doesn't mean they haven't been working on it behind the scenes for just as long though...

I think that person had to choose between the drugs or hard core prison of the 1950s England where being a bit odd was enough to guarantee an incredibly difficult time as they say in England, I would've chosen the drugs as well hoping they would fix me, too bad without testosterone you're going to be suicidal and depressed, I'd rather choose to keep my hair than to be horny all the time

Fucking obviously. Until Data's positronic brains becomes reality, AI is not actual intelligence.

AI is not A I. I should make that a tshirt.

This is why I said I wasn't sure how AI works behind the scenes. But I do know that logic isn't difficult. Just to not fuck around between us. I have a CS background. Only saying this because I think you may have it as well and we can save some time.

It makes sense to me that logic is something AI can parse easily. Logic in my mind is very easy if it can tokenize some text. Wouldn't the difficulty be if the AI has the right context.

LLMs are also very good at convincing their users that they know what they are saying.

It's what they're really selected for. Looking accurate sells more than being accurate.

I wouldn't be surprised if many of the people selling LLMs as AI have drunk their own kool-aid (of course most just care about the line going up, but still).

Ok let's give a test here. Let's start with understand logic. Give me a paragraph and let's see if it can find any logical fallacies. You can provide the paragraph. Only constraint is that the context has to exist within the paragraph.

"if you put in the wrong figures, will the correct ones be output"

To be fair, an 1840 “computer” might be able to tell there was something wrong with the figures and ask about it or even correct them herself.

Babbage was being a bit obtuse there; people weren't familiar with computing machines yet. Computer was a job, and computers were expected to be fairly intelligent.

In fact I'd say that if anything this question shows that the questioner understood enough about the new machine to realise it was not the same as they understood a computer to be, and lacked many of their abilities, and was just looking for Babbage to confirm their suspicions.

an llm also works on fixed transition probabilities. All the training is done during the generation of the weights, which are the compressed state transition table. After that, it's just a regular old markov chain. I don't know why you seem so fixated on getting different output if you provide different input (as I said, each token generated is a separate independent invocation of the llm with a different input). That is true of most computer programs.

It's just an implementation detail. The markov chains we are used to has a very short context, due to combinatorial explosion when generating the state transition table. With llms, we can use a much much longer context. Put that context in, it runs through the completely immutable model, and out comes a probability distribution. Any calculations done during the calculation of this probability distribution is then discarded, the chosen token added to the context, and the program is run again with zero prior knowledge of any reasoning about the token it just generated. It's a seperate execution with absolutely nothing shared between them, so there can't be any "adapting" going on

Thata why ceo love them. When your job is 90% spewing bs a machine that does that is impressive

LLMs don't know how how they work

Dude they made chat gpt a little more boit licky and now many people are convinced they are literal messiahs. All it took for them was a chat bot and a few hours of talk.

Well, technically, yes. You're right. But they're a specific, narrow type of neural network, while I was thinking of the broader class and more traditional applications, like data analysis. I should have been more specific.

It’s an expensive carbon spewing parrot.

"Computer" meaning a mechanical/electro-mechanical/electrical machine wasn't used until around after WWII.

Babbag's difference/analytical engines weren't confusing because people called them a computer, they didn't.

"On two occasions I have been asked, 'Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?' I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question."

• Charles Babbage

If you give any computer, human or machine, random numbers, it will not give you "correct answers".

It's possible Babbage lacked the social skills to detect sarcasm. We also have several high profile cases of people just trusting LLMs to file legal briefs and official government 'studies' because the LLM "said it was real".

I think it's important to note (i'm not an llm I know that phrase triggers you to assume I am) that they haven't proven this as an inherent architectural issue, which I think would be the next step to the assertion.

do we know that they don't and are incapable of reasoning, or do we just know that for x problems they jump to memorized solutions, is it possible to create an arrangement of weights that can genuinely reason, even if the current models don't? That's the big question that needs answered. It's still possible that we just haven't properly incentivized reason over memorization during training.

if someone can objectively answer "no" to that, the bubble collapses.

What's hilarious/sad is the response to this article over on reddit's "singularity" sub, in which all the top comments are people who've obviously never got all the way through a research paper in their lives all trashing Apple and claiming their researchers don't understand AI or "reasoning". It's a weird cult.