Define intelligence.

Comprehension, something LLMs are intrinsically incapable of demonstrating, let alone possessing.

I’m pretty sure comprehension was like the original success of LLMs, and the responses were a bit wonky. When was the last time an LLM failed to comprehend?

They never comprehend. They ingest information and find close approximations for responses, but they don’t comprehend any part of the process. If they did, they wouldn’t hallucinate complete bullshit answers and spit them out like they were fact

To be honest human spew bullshit all the time when they do not understand something. Or people are confidently incorrect all the time as they are remembering incorrectly. The amount of bullshitting is more a meter for intelligence, not something that discriminate between intelligence or the lack thereof.

This gives me “are you smarter than a fifth grader” vibes

You are anthropomorphizing LLMs: This is a mistake. They are not people and they do not think, reason, or feel. They are completely incapable of these things. Even calling it "AI" is misleading at best.

I am not. I want to understand what is the difference between “feeling” as result of electrochemical reactions in our brain and “feeling” as result of math performed on a numerical matrix. I am perfectly aware those are not the same, but the result may be similar, and what we care of the result determines the conclusions we can make.

You need to clearly explain what is thinking and what in the “thinking” process of LLM is not thinking. If you define “thinking” as electromechanical reactions in human brains then LLM is not thinking, but it is not a useful definition.

I don't need to explain shit, you're the one made the claims and didn't provide proof. The burden of proof is on you. We can (and should) dismiss your baseless unproven claims about "AI" outright.

Do you even know what a claim is?

they do not think, reason, or feel. They are completely incapable of these things.

This is a claim. You made a claim. My assertion that you need to substantiate your claim definitionally does not constitute a claim.

Bullshit: The claim was made without evidence, I dismissed baseless claim.

Not even close to being the same. Do YOU know what a claim is?

Please quote the claim I made you disputed, I will try to defend it or drop it.

I'm already dropping it because extraordinary claims require extraordinary evidence. Here even mediocre evidence hasn't been provided to back up ridiculous claims anthropomorphizing "AI" so we are free to dismiss them outright.

Furthermore, you're now trying to claim there are no such claims, which is obviously ridiculous, so feel free to fuck off and troll somebody else because I am done entertaining your bullshit.

You are doing everything yourself 😂

I easily quoted your claim, you failed to do the same with mine. So I guess it is you who is dropping your claim. Seriously come to grips with reality and logic.

Oh, so, like my coworkers then.

/s

Everyone use big word. U no big word. Small words funny. Upvote

LLM’s comprehend statistical correlations. If you feed it every chess game, it can tell you which spaces the knight is likely to move to, and which it very unlikely to move to, but it does not understand how the knight moves.

You might say that’s a distinction without a difference, but programs can actually know thing, like a chess program actually does know how a knight moves, and it does so with minimal electricity.

There’s some semantics here.

Large neural networks have been shown to build parallel models of the world that they learn through their training data. A chess model trained only on chess move notation does have an interior board and state that it tracks, despite never being told about a board, the number of pieces, how they move, etc.

LLMs do something similar with language, mapping tokens to ideas in ways that can produce meaningful and grammatically correct output without needing to be formally taught any of this.

Our brains also do things like this - we observe X and then try to intuit Y, the truth of the world and reason for X.

Where this breaks down is humans can then take our guess for Y and propose Z, and try to find it out in the world. If we can’t, then Y is probably wrong, and our new understanding of the world is Y* that is consistent both with X and the absence of Z.

I cannot stress enough how far we are from machines that can do this. They can learn to approximate Y through training, but they have no means to extend that to propose an unobserved Z, no means to update their internal state given the results of that observation (without fully retraining the model on new data - which makes it a new model), and no way to question X given Z - they can talk at great length about data biases but can literally never excise bad data from their training weights. There have been experiments with training models on time-boxed datasets (e.g. stuff up to AD 1880) and asking it about modern concepts - predictably hilarious results.

All the current ‘AI’ stuff is smoke and mirrors around this very hard problem. The machines can talk, but they can’t think (Z) and can’t learn (Y*).

they can talk at great length about data biases but can literally never excise bad data from their training weights

And you can’t excise 20 years of Fox News from some boomer conservative. They can literally never back down from their biases.

Training data is a lot more permanent than context, but at the same time, it’s not absolute, either. I’ve seen plenty of thinking blocks where it knows about X from its training data, but perhaps it’s out-of-date and should check other sources. It has the web tools, finds newer information, processes it as part of its problem set, and solves the problem with newer information.

The core issue is we throw all of this data away when we’re done with the problem! A LLM only has so much context, so it starts to compact after a while. And even if it doesn’t, we voluntarily chuck that shit out the window, as soon as the session ends.

It’s like Lenny from Memento. You got a baseline person who constantly believes he’s got to find his wife’s killer. And even when he tries to supplant his lack of long-term memory with photos and tattoos, it’s a shit system with shittier outcomes. Also, he doesn’t get to choose when his memory disappears. It just happens with some kind of stress trigger. Because of how flawed Lemmy is, do we call him somebody who can’t think and can’t learn? Is he intelligent? Is he even sentient?

Though, in the case of an LLM, it doesn’t have to be set up this way. We can still stash a long-term version of memories as rules and other permanent files. In fact, this is encouraged. Everything starts with a system prompt, a set of rules, and the user prompt. All of that is adjustable.

And yes, you can train out biases. Rather easily, actually. You don’t even have to adjust weights. You post-train through rules files.

But, in terms of philosophical questions, can we call LLMs intelligent if we can edit their memories at a whim? Is a brainwashed boomer intelligent because Fox News hacked their memories and biases? Is Lenny intelligent because he can’t form new memories?

You have to remember that just about every argument for or against LLMs can also apply to humans, and you can’t really arrive at definitions without clearly separating what doesn’t apply to LLMs and does apply to humans, in all cases.

And yes, you can train out biases. Rather easily, actually. You don’t even have to adjust weights. You post-train through rules files.

The assertion is not about whether ‘you’ can do it. It is that the model cannot do this itself.

A human recognizing a bias in the model and correcting it is not evidence robot intelligence, it is further evidence for human intelligence.

Training is absolute, it determines the set of relationships the model can represent between input and output. Context, rules files, system prompts, etc. operate only on the conditional probabilities within this set and are impermanent. This is ‘priming’ in human psychology, and why a person in a herpetology class will talk about Pythonic features differently than in a coding class.

As an example of how this is not real learning and is inadequate to solve real problems, here is a paper where the authors challenged multiple models to solve unfamiliar games where they need to discover the rules through play and feedback. Even the ‘best’ model performed pitifully compared to humans.

If you feed it every chess game, it can tell you which spaces the knight is likely to move to, and which it very unlikely to move to, but it does not understand how the knight moves.

Well, it knows how the knight moves, but it might forget that’s it’s an important rule after it starts to lose context. That’s also how LLMs start to lose track of what’s actually on the board.

There are definitely differences between LLMs and Neural Networks, but at the end of the day, they are both just gigabytes of weights. And really, both types are similar to what the human brain possesses. People tend to forget that the human brain isn’t just some monolithic thing. There’s a lot of difference specialized pieces. If you ask Magnus Carlsen how he calculated the best move on the chess board in a blitz game, he literally cannot tell you! It just appears, because that part of the brain is trained and hardwired to calculate good moves, based on the current board state.

A LLM uses logic and reasoning to arrive at a solution. That’s not really compatible with intense pre-trained calculation. You can’t talk yourself into a chess move at the speed the game requires. You can quickly think about possibilities, but you have to already have pre-trained calculations to get at least 90-95% of the way there, to pick from a few different lines.

On the other hand, Neural Networks beat the shit out of the greatest grandmasters in chess. Hands down. On average, they are at least +500 ELO to the best GMs. And everybody in the chess world knows this, which is why everybody uses these tools outside of 1v1 human to human games. (Obviously, cheating is taken very seriously, and has made plenty of headlines over the years.) Every GM analyzes their own games using those engines, to pick apart mistakes.

you are just replacing one underspecified concept with another. what’s “comprehension”?

It’s what you don’t posses about the workings of LLMs (or basic English).

www.merriam-webster.com/dictionary/comprehension

anyone can quote a dictionary without comprehension.

I didn’t quote it, I sent you the entry in the hopes that you might read it yourself and learn something

Jesus fuck… did you actually read your definition?

1. the act or action of grasping with the intellect

Okay, we’ve replaced the concept of “intelligence” and just swapped it out with “grasping with the intellect”.

2. knowledge gained by comprehending

Nice, using the verb word of “comprehending” to define “comprehension”.

3. the capacity for understanding fully

Okay, maybe that’s a start, but it’s incredibly vague.

Also, clicking on the links just resolves back to the most generic circular definition possible. Remind me to never use Merriam-Webster.

At least this gets further: en.wiktionary.org/wiki/comprehension

If we settle on “A thorough understanding” and define understanding as… fuck, we’re back to this circular bullshit again.

Okay, fine, let’s just look at intelligence:

The capacity of mind, especially to understand principles, truths, facts or meanings, acquire knowledge, and apply it to practice; the ability to comprehend and learn; the ability to process sentient experience to generate true beliefs with a justified degree of confidence.

Breaking this down:

• Understand principles, truths - Sure, sounds like an LLM to me. If you ask it to define a scientific principle or fact or Newton’s laws, it can tell you what it is.
• facts or meaning - And, depending on the kind of thing we’re talking about, it can you tell you why it exists.
• acquire knowledge - While it’s not permanent (because context are just thrown away like short-term memories), LLMs have MCPs and other tools to acquire new knowledge.
• apply it in practice - It takes the knowledge it has naturally (training data), acquires new knowledge from its tools, and then applies it to the task it’s looking at.
• the ability to comprehend and learn - Here we go with “comprehension” again. But, let’s assume we all know what “learn” is. A LLM can learn about things, but the main problem is the context size and how we throw that away each session. But, that can be solved by storing facts elsewhere, Markdown rules, other techniques to condense the immediate context into long-term storage. In humans, we call that “long-term memory”. We have our own process to stash short-term memory into long-term memory. We just haven’t experimented enough with the long-term storage part in LLMs.
• the ability to process sentient experience - sentient… well, that’s a pretty weighted word. I also feel like there’s a clear difference between intelligence and sentience. We’re now entering the realm of sensation, thought, and feelings, which are entire different parts of the brain.

I also don’t think “sensations” are impossible to emulate, either.

Words have meanings, and you don’t get to change them in order to strengthen a weak argument.

I’m sorry that those meanings aren’t convenient for your untenable position.

Literal dictionary definitions:

Comprehension: the ability to understand something.

Understand: perceive the intended meaning of something.

Things LLMs are categorically incapable of embodying, and anyone who believes otherwise is demonstrating an innate challenge of grasping themselves.

what’s “perceive”?

What’s “what”?

what?

Hmmm, what?

you might be thinking you’re pulling a Socrates here, but you began sounding like a annoying 5-year-old that keeps repeating “what’s this, what’s that, why”

people of ancient Greek found Socrates so annoying they sentenced him to death by poison. so… thanks, i guess?

Alright Mr Peterson, calm down.

pretty sure it comes from latin “inter-lego” (collect or pick out information, like you would pick out the good fruit from a bush with good and bad fruit).

en.wiktionary.org/wiki/intelligent

so it’s about filtering what you want to have from what you don’t want to have. i.e. a mathematical function f : X -> R which maps an object x to a real number to figure out how desirable that object is, i.e. assign value to it.

technically you don’t even need neural networks for it, sorting algorithms employ this kind of function all the time to sort objects according to some criteria.

AI is about making machines that mimic human level intelligence. Whether they possess actual intelligence doesn’t matter. They just need to act intelligently.

You can disagree with the terminology, but it has been an active field of study since the 1950s.

Remember the Turing test? Damn goal posts move faster than the ball. I’m not sure how one could fail to be impressed by the technological advancements in the last 3 years in the field

Sort of.

I think Turing thought, “well something that could do this would surely HAVE to be intelligent”, but I seriously doubt that he would have waved the white flag for an intelligence that can convincingly mimic human communication but can’t count letters in a word.

Well the Test now includes casual tasks like counting letters, doesn’t it? You have to move your head in a certain way to spot the leopard in the shaded forest.

An important variable of the Turing Test that is never spoken about is the intelligence (or gullibility) of the user.

Then define intelligence.

You can’t just say a thing is intelligent or not intelligent without coming up with a good definition. We are ugly meatbags of mostly water that are constantly driven by chemical processes. How the hell do you call that intelligent? Prove that we are intelligent, and then prove that the “algorithmic iterator”, that uses weighted metrics with an eerie similarity to neurons, isn’t actually intelligent.

www.merriam-webster.com/dictionary/intelligence

I know you’re not a fan of the dictionary, but they are the authority on words and their meanings.

Before you get too excited about definition 3 I suggest you familiarize yourself with what it means to “compute” www.merriam-webster.com/dictionary/compute you’ll notice repeated use of the word “determine”. LLMs are by nature “non-deterministic” or in other words “incapable of determining / incapable of making a determination”.

A computer is deterministic

An LLM is a probabilistic next-word-guessing machine / software as a service

Your analysis of definition 3 also precludes the human mind as being capable of computation since biological systems contain inherent randomness that makes their output non deterministic.

I never asserted that a human mind could do computation

I enjoy speaking to the AI more than humans on most topics I find worthy of discussion.

LLMs are impressive inversely proportionally to the level of understanding and proficiency one has in the field they’re deployed. They’re also progressively corrosive to competency and credibility with one’s exposure.

All I can see is their lobotomisation of the world, and I’m sorry you’ve fallen victim to them.

I think commercial LLMs have this subtle way of writing that makes it feel addictive to talk to them. Even if you ask an innocuous question that should only require a short answer ~ they respond in a way that “keeps the conversion going” and exploits the social part of our brain. Always ending with a question or slight misunderstanding that baits you into answering or correcting. Maybe it’s to extract more information from you or something.

Speaking to even a brick wall would be better.

Sounds like something to talk to a (human) therapist about.

That’s phenomenally depressing

I bet he can set the bar lower

Ahh, buckle up for the rest of the comment thread lmao

Why does the result of algorithmic iteration seem unintelligent to you?

Sounds like ELIZA’s turn.

You are assigning a magical meaning to the word intelligence. Your objection is superficial semantics. You are effectively being a science denier proudly displaying your ignorance as evidence. This anti-vax tier anti-ai circlejerk of ignorance as evidence is just the opposite side of the same ignorant coin. You are going to get pissed on by researchers and the actual smart people when are you are anti-ai like this you are not changing minds you are ignored. Seriously move off semantics it’s just fucking embarrassing.

Your bulk discount tokens must have just hit your account, I’m honoured you chose to spend them replying to my post.

It’s always fascinating when an entity exhibits such a strong reaction to a critique of associating LLMs as intelligence, what was the prompt you engineered to conjure it?

I’ll concede my perspective of LLMs is limited, and skewed by agnostic ignorance as I’ve never actively, willingly, or consciously used one on purpose, but to accuse me of wilful ignorance smells more like Lynx Admission.

I’m not interested in the opinion or approval of anyone whose fallen for this grift, too much of my time and energy has been exhausted appeasing them. I’ll reengage with them once they wake up, or are excreted from the ‘AI’ logo sphincters, until then I need to relegate them as dark matter.

I’ll concede my perspective of LLMs is limited,

but to accuse me of wilful ignorance smells more like Lynx Admission.

I like the way you have no ability to assert your opinions and just send insults it really highlights your character and my heart is moved. You can be a better person who intelligently argues their point or you can be a MAGAt tier anti-vaxx tier science denier and fight with your feelings. Guess which one I want for you? Move off semantics it’s fucking embarrassing.