Technology

These models are nothing more than glorified autocomplete algorithms parroting the responses to questions that already existed in their input.

They're completely incapable of critical thought or even basic reasoning. They only seem smart because people tend to ask the same stupid questions over and over.

If they receive an input that doesn't have a strong correlation to their training, they just output whatever bullshit comes close, whether it's true or not. Which makes them truly dangerous.

And I highly doubt that'll ever be fixed because the brainrotten corporate middle-manager types that insist on implementing this shit won't ever want their "state of the art AI chatbot" to answer a customer's question with "sorry, I don't know."

I can't wait for this stupid AI craze to eat its own tail.

Of course they don't, logical reasoning isn't just guessing a word or phrase that comes next.

As much as some of these tech bros want human thinking and creativity to be reducible to mere pattern recognition, it isn't, and it never will be.

But the corpos and Capitalists don't care, because their whole worldview is based in the idea that humans are only as valuable as the profitability they generate for a company.

They don't see any value in poetry, or philosophy, or literature, or historical analysis, or visual arts unless it can be patented, trademarked, copyrighted, and sold to consumers at a good markup.

As if the only difference between Van Goh's art and an LLM is the size of sample data and efficiency of an algorithm.

I work for a consulting company and they're truly going off the deep end pushing consultants to sell this miracle solution. They are now doing weekly product demos and all of them are absolutely useless hype grifts. It's maddening.

Apple's study proves that LLM-based AI models are flawed because they cannot reason

This really isn't a good title, I think. It was understood that LLM-based models don't reason, not on their own.

A better one would be that researchers at Apple proposed a metric that better accounts for reasoning capability, a better sort of "score" for an AI's capability.

I still think it's better to refer to LLMs as "stochastic lexical indexes" than AI

Research paper : https://arxiv.org/pdf/2410.05229

What, reasoning was an expected feature?

I still fail to see how people expect LLMs to reason. It's like expecting a slice of pizza to reason. That's just not what it does.

Although Porsche managed to make a car with the engine in the most idiotic place win literally everything on Earth, so I guess I'm leaving a little possibility that the slice of pizza will outreason GPT 4.

I tried it myself (changing the name and changing the values) but lost interest after 3 attempts and always getting the right answer:

https://chatgpt.com/share/670af65d-da08-800f-8ad4-c67782ee5477

https://chatgpt.com/share/670af672-45dc-800f-ac91-cc2811fa89c7

https://chatgpt.com/share/6709e80b-e5a8-800f-90d0-1af3418675ef

Water is wet. More at 11

Do we know how human brains reason? Not really... Do we have an abundance of long chains of reasoning we can use as training data?

...no.

So we don't have the training data to get language models to talk through their reasoning then, especially not in novel or personable ways.

But also - even if we did, that wouldn't produce 'thought' any more than a book about thought can produce thought.

Thinking is relational. It requires an internal self awareness. We can't discuss that in text so much that a book is suddenly conscious.

This is the idea that"Sentience can't come from semantics"... More is needed than that.

@Timely_Jellyfish_2077 interesting read, thanks for sharing

Here's a simple test showing lack of logic skills of LLM-based chatbots.

1. Pick some public figure (politician, celebrity, etc.), whose parents are known by name, but not themselves public figures.
2. Ask the bot of your choice "who is the [father|mother] of [public person]?", to check if the bot contains such piece of info.
3. If the bot contains such piece of info, start a new chat.
4. In the new chat, ask the opposite question - "who is the [son|daughter] of [parent mentioned in the previous answer]?". And watch the bot losing its shit.

I'll exemplify it with ChatGPT-4o (as provided by DDG) and Katy Perry (parents: Mary Christine and Maurice Hudson).

Note that step #3 is not optional. You must start a new chat; plenty bots are able to retrieve tokens from their previous output within the same chat, and that would stain the test.

Failure to consistently output correct information shows that those bots are unable to perform simple logic operations like "if A is the parent of B, then B is the child of A".

I'll also pre-emptively address some ad hoc idiocy that I've seen sealions lacking basic reading comprehension (i.e. the sort of people who claims that those systems are able to reason) using against this test:

• "Ackshyually the bot is forgerring it and then reminring it. Just like hoominz" - cut off the crap.
• "Ackshyually you wouldn't remember things from different conversations." - cut off the crap.
• [Repeats the test while disingenuously = idiotically omitting step 3] - congrats for proving that there's a context window and nothing else, you muppet.
• "You can't prove that it is not smart" - inversion of the burden of the proof. You can't prove that your mum didn't get syphilis by sharing a cactus-shaped dildo with Hitler.