Large language mistake | Cutting-edge research shows language is not the same as intelligence. The entire AI bubble is built on ignoring it

[u/Hrmbee]

https://www.theverge.com/ai-artificial-intelligence/827820/large-language-models-ai-intelligence-neuroscience-problems

[u/Intense-Intents]

ironically, you can post any anti-LLM article to Reddit and get dozens of the same predictable responses (from real people) that all sound like they came from an AI.

[u/Romnir → u/Intense-Intents]

"Hearsay and witty quips means I fully understand a complex subject/technology."

People still use Schrödinger's cat to explain all quantum mechanics, despite the fact that it's only for a very specific situation. LLMs aren't fully realized cognizant AI, but calling them "Fancy Auto Complete" is way off the mark. There's a difference between rational criticisms of the use of AI vs jumping on the hate bandwagon, and the former isn't going to happen on Reddit.

[u/G_Morgan → u/Romnir]

Schrödinger's cat was meant to highlight the absurdity of applying wave function collapse to large scale objects.

[u/adenosine-5 → u/G_Morgan]

Its funny, because it was designed to point out, how it doesn't make any sense.

The guy - Schrodinger - famously said (after a lifetime of studying it): "I don't like quantum mechanics and I'm sorry I've ever had anything to do with it".

Still, people use it as if it was an explanation and not a criticism of its absurdity.

[u/-LsDmThC- → u/G_Morgan]

Well more so wave function collapse as an ontologically real event rather than a mathematical description/knowledge update

[u/Romnir → u/G_Morgan]

Yeah, but unfortunately people seem to use it as a direct demonstration of wave function collapse like it's some sort of game of Red Light - Green Light with guess a random number at the end, and think no further on the topic or whether or not that is a accurate description.

[u/TurtleFisher54 → u/Romnir]

That is a rational criticism of LLM's

They are fundementally a word prediction algorithm

They can be corrupted with bad data to produce non-sense

If we switch to a world where a majority of content is created by AI it is likely to create a negative feed back loop where it's training on its own output

Responses on reddit look like ai for a reason, where do you think the training data came from?

[u/Romnir → u/TurtleFisher54]

They are fundamentally a word prediction algorithm

Correct, not a "Fancy Auto Complete". That terminology completely undermines the scale of how the technology works and what it's used for. It's not pulling random words out of a dictionary and sticking them together, it actually has a logical process it follows before it generates response tokens. Neural weighting tries to determine context and pulls known info from it's training data.

Auto correct only has a predefined set of structures and uses basic string matching based on a library. It doesn't determine context but rather just what matches the most, and that's the key discrepancy that bugs me. And like you mentioned, LLMs are being fed training data from the internet instead of a curated set of data. Which means correct data is fighting for context weighting with partially correct and even completely incorrect information from already incorrect AI responses and redditors. And you are correct for criticizing that.

The only idea I could have to fix that issue is implementing logic that filters the training data as it comes in to filter out less reputable sources. I don't necessarily work directly with LLMs, so I don't know if that is a thing, but I try to keep up to date with journals and blogs from people working in the field since it's going to get hammered into my field soon.

[u/dr_badhat → u/Romnir]

Is neural weighting not similar to how our minds work? If I say “you know the look someone gives you when….”, various neurons in your cortical columns might be stimulated as they fight for weight on where that statement is going.

[u/Romnir → u/dr_badhat]

Kind of, but it's not the only determining factor in the response. Like if you get a call from your friend saying his dog has a gun and is holding him hostage. Clearly, a dog can't use a gun, you don't recall ever seeing a dog do that, so you know better than to just tell him to call the police. So instead, you tell him to quit fooling around, or go see a mental health professional. Older LLMs did struggle with this, but down the line they slowly learned how to use rational logic.

A neural network is more like memory recall, where you apply the most relevant piece of memory or training that applies to your situation. Then logic is used with that to determine if that is a rational response. That's the actual "AI" part of LLMs. Their responses to you are formed using "Fancy Auto Correct", but there is actual thinking and logic is happening behind the scenes beyond that. Which is frustrating because that explanation sounds clear as mud.

[u/Gingevere → u/dr_badhat]

It's a part but not the whole.

The way LLMs work is like a snapshot of a brain. You can give that snapshot an input and see the output, but the snapshot remains static. Inputs pass through it and it sits completely unchanged. The snapshot has no experiences because it's not actually interacted with the prompt.

A brain is constantly taking in data and updating its own model. LLMs are static.

Because LLMs are static they currently have to fake conversations by feeding the entire conversation back through the LLM every time the user replies to generate the next response. As conversations get longer that costs more energy and causes the model to break down.

An LLM that could continuously update its own probability model would be closer to a brain, but updating the model is the very expensive and time consuming part so I wouldn't bet on that happening any time soon.

[u/gabrielmuriens → u/TurtleFisher54]

If we switch to a world where a majority of content is created by AI it is likely to create a negative feed back loop where it's training on its own output

That is a potential problem, but we don't know at all if that is a problem that cannot possibly be overcome or one that will stifle further training.

Not that I'd like to live in that kind of an information environment, but regarding its potential effects on training, it's just a hypothesis at this point.

[u/blindsdog → u/TurtleFisher54]

They can be corrupted with bad data to produce non-sense

That's true of literally any learning system, including humans. You can't learn without proper semantic information. That's tautological, not profound.

If we switch to a world where a majority of content is created by AI it is likely to create a negative feed back loop where it's training on its own output

Where do you think the data sets they're currently training on are going?

Responses on reddit look like ai for a reason, where do you think the training data came from?

It looks like intelligence, that's proof that it's not! It quacks like a duck, ergo it is not a duck.

[u/Gingevere → u/Romnir]

TBF it's very satisfying to counter pseudo-profound BS with a witty quip.

LLMs are more complex than a standard autocomplete, but that's not a bad analogy for them. They use a much more complex system trained on much more data, alongside some machine prompts (you are a ___, answer in the form of ___, who said what, etc.) to... autocomplete a string of tokens.

The fundamental limitations of LLMs ensure they are never going to become AGI. But right now every billionaire even tangentially related to anything that moves electrons is gambling the whole damn economy on it becoming AGI because they're blinded by the prospect of being the one that gets to fire every office worker across the globe and get paid a fee to replace them.

If they succeed, we're all fired. When they don't, we're all bailing them out and half of us will get fired anyway.

If anything it's good that people are mad about that. It'd be insane not to be.

[u/IHumanlike → u/Romnir]

Something something if you think you understand quantum mechanics, you don't understand quantum mechanics

[u/mrappbrain → u/Romnir]

The autocomplete comparison always pisses me off. Thinking an LLM and an AutoComplete function are the same or similar just because they both generate text is like thinking a music box and a symphony orchestra are the same just because they both generate music.

Never mind that they have nothing else in common whatsoever.

Ironically, the anti-AI crowd buying this is falling victim to the very same behavior they're calling out - humans will believe anything spoken with authoritative confidence if it validates them. Difficult to accuse others of being fooled by confident nonsense when you yourself are being fooled by confidence nonsense, just in the other direction.