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Can’t help but here’s a rant on people asking LLMs to “explain their reasoning” which is impossible because they can never reason (not meant to be attacking OP, just attacking the “LLMs think and reason” people and companies that spout it):
LLMs are just matrix math to complete the most likely next word. They don’t know anything and can’t reason.
Anything you read or hear about LLMs or “AI” getting “asked questions” or “explain its reasoning” or talking about how they’re “thinking” is just AI propaganda to make you think they’re doing something LLMs literally can’t do but people sure wish they could.
In this case it sounds like people who don’t understand how LLMs work eating that propaganda up and approaching LLMs like there’s something to talk to or discern from.
If you waste egregiously high amounts of gigawatts to put everything that’s ever been typed into matrices you can operate on, you get a facsimile of the human knowledge that went into typing all of that stuff.
It’d be impressive if the environmental toll making the matrices and using them wasn’t critically bad.
TLDR; LLMs can never think or reason, anyone talking about them thinking or reasoning is bullshitting, they utilize almost everything that’s ever been typed to give (occasionally) reasonably useful outputs that are the most basic bitch shit because that’s the most likely next word at the cost of environmental disaster
The environmental toll doesn’t have to be that bad. You can get decent results from single high-end gaming GPU.
You can, but the stuff that’s really useful (very competent code completion) needs gigantic context lengths that even rich peeps with $2k GPUs can’t do. And that’s ignoring the training power and hardware costs to get the models.
Techbros chasing VC funding are pushing LLMs to the physical limit of what humanity can provide power and hardware-wise. Way less hype and letting them come to market organically in 5/10 years would give the LLMs a lot more power efficiency at the current context and depth limits. But that ain’t this timeline, we just got VC money looking to buy nuclear plants and fascists trying to subdue the US for the techbro oligarchs womp womp
People don't understand what "model" means. That's the unfortunate reality.
They walk down runways and pose for magazines. Do they reason? Sometimes.
But why male models?
It's true that LLMs aren't "aware" of what internal steps they are taking, so asking an LLM how they reasoned out an answer will just output text that statistically sounds right based on its training set, but to say something like "they can never reason" is provably false.
Its obvious that you have a bias and desperately want reality to confirm it, but there's been significant research and progress in tracing internals of LLMs, that show logic, planning, and reasoning.
EDIT: lol you can downvote me but it doesn't change evidence based research
It’d be impressive if the environmental toll making the matrices and using them wasn’t critically bad.
Developing a AAA video game has a higher carbon footprint than training an LLM, and running inference uses significantly less power than playing that same video game.
Too deep on the AI propaganda there, it’s completing the next word. You can give the LLM base umpteen layers to make complicated connections, still ain’t thinking.
The LLM corpos trying to get nuclear plants to power their gigantic data centers while AAA devs aren’t trying to buy nuclear plants says that’s a straw man and you simultaneously also are wrong.
Using a pre-trained and memory-crushed LLM that can run on a small device won’t take up too much power. But that’s not what you’re thinking of. You’re thinking of the LLM only accessible via ChatGPT’s api that has a yuge context length and massive matrices that needs hilariously large amounts of RAM and compute power to execute. And it’s still a facsimile of thought.
It’s okay they suck and have very niche actual use cases - maybe it’ll get us to something better. But they ain’t gold, they ain't smart, and they ain’t worth destroying the planet.
How would you prove that someone or something is capable of reasoning or thinking?
I've read that article. They used something they called an "MRI for AIs", and checked e.g. how an AI handled math questions, and then asked the AI how it came to that answer, and the pathways actually differed. While the AI talked about using a textbook answer, it actually did a different approach. That's what I remember of that article.
But yes, it exists, and it is science, not TicTok
Oh wow thank you! That's it!
I didn't even remember now good this article was and how many experiments it collected
I don't know how I work. I couldn't tell you much about neuroscience beyond "neurons are linked together and somehow that creates thoughts". And even when it comes to complex thoughts, I sometimes can't explain why. At my job, I often lean on intuition I've developed over a decade. I can look at a system and get an immediate sense if it's going to work well, but actually explaining why or why not takes a lot more time and energy. Am I an LLM?
I agree. This is the exact problem I think people need to face with nural network AIs. They work the exact same way we do. Even if we analysed the human brain it would look like wires connected to wires with different resistances all over the place with some other chemical influences.
I think everyone forgets that nural networks were used in AI to replicate how animal brains work, and clearly if it worked for us to get smart then it should work for something synthetic. Well we've certainly answered that now.
Everyone being like "oh it's just a predictive model and it's all math and math can't be intelligent" are questioning exactly how their own brains work. We are just prediction machines, the brain releases dopamine when it correctly predicts things, it self learns from correctly assuming how things work. We modelled AI off of ourselves. And if we don't understand how we work, of course we're not gonna understand how it works.
You're definitely overselling how AI works and underselling how human brains work here, but there is a kernel of truth to what you're saying.
Neural networks are a biomimicry technology. They explicitly work by mimicking how our own neurons work, and surprise surprise, they create eerily humanlike responses.
The thing is, LLMs don't have anything close to reasoning the way human brains reason. We are actually capable of understanding and creating meaning, LLMs are not.
So how are they human-like? Our brains are made up of many subsystems, each doing extremely focussed, specific tasks.
We have so many, including sound recognition, speech recognition, language recognition. Then on the flipside we have language planning, then speech planning and motor centres dedicated to creating the speech sounds we've planned to make. The first three get sound into your brain and turn it into ideas, the last three take ideas and turn them into speech.
We have made neural network versions of each of these systems, and even tied them together. An LLM is analogous to our brain's language planning centre. That's the part that decides how to put words in sequence.
That's why LLMs sound like us, they sequence words in a very similar way.
However, each of these subsystems in our brains can loop-back on themselves to check the output. I can get my language planner to say "mary sat on the hill", then loop that through my language recognition centre to see how my conscious brain likes it. My consciousness might notice that "the hill" is wrong, and request new words until it gets "a hill" which it believes is more fitting. It might even notice that "mary" is the wrong name, and look for others, it might cycle through martha, marge, maths, maple, may, yes, that one. Okay, "may sat on a hill", then send that to the speech planning centres to eventually come out of my mouth.
Your brain does this so much you generally don't notice it happening.
In the 80s there was a craze around so called "automatic writing", which was essentially zoning out and just writing whatever popped into your head without editing. You'd get fragments of ideas and really strange things, often very emotionally charged, they seemed like they were coming from some mysterious place, maybe ghosts, demons, past lives, who knows? It was just our internal LLM being given free rein, but people got spooked into believing it was a real person, just like people think LLMs are people today.
In reality we have no idea how to even start constructing a consciousness. It's such a complex task and requires so much more linking and understanding than just a probabilistic connection between words. I wouldn't be surprised if we were more than a century away from AGI.
Maybe I am over selling current AI and underselling our brains. But the way I see it is that the exact mechanism that allowed intelligence to flourish within ourselves exists with current nural networks. They are nowhere near being AGI or UGI yet but I think these tools alone are all that are required.
The way I see it is, if we rewound the clock far enough we would see primitive life with very basic nural networks beginning to develop in existing multicellular life (something like jellyfish possibly). These nural networks made from neurons neurotransmitters and synapses or possibly something more primitive would begin forming the most basic of logic over centuries of evolution. But it wouldn't reassemble anything close to reason or intelligence, it wouldn't have eyes, ears or any need for language. At first it would probably spend its first million years just trying to control movement.
We know that this process would have started from nothing, nural networks with no training data, just a free world to explore. And yet over 500 million years later here we are.
My argument is that modern nural networks work the same way that biological brains do, at least the mechanism does. The only technical difference is with neurotransmitters and the various dampening and signal boosting that can happen along with nuromodulation. Given enough time and enough training, I firmly believe nural networks could develop reason. And given external sensors it could develop thought from these input signals.
I don't think we would need to develop a consciousness for it but that it would develop one itself given enough time to train on its own.
A large hurdle that might arguably be a good thing, is that we are largely in control of the training. When AI is used it does not learn and alter itself, only memorising things currently. But I do remember a time when various AI researchers allowed earlier models to self learn, however the internet being the internet, it developed some wildly bad habits.
If all you're saying is that neural networks could develop consciousness one day, sure, and nothing I said contradicts that. Our brains are neural networks, so it stands to reason they could do what our brains can do. But the technical hurdles are huge.
You need at least two things to get there:
- Enough computing power to support it.
- Insight into how consciousness is structured.
1 is hard because a single brain alone is about as powerful as a significant chunk of worldwide computing, the gulf between our current power and what we would need is about... 100% of what we would need. We are so woefully under resourced for that. You also need to solve how to power the computers without cooking the planet, which is not something we're even close to solving currently.
2 means that we can't just throw more power or training at the problem. Modern NN modules have an underlying theory that makes them work. They're essentially statistical curve-fitting machines. We don't currently have a good theoretical model that would allow us to structure the NN to create a consciousness. It's not even on the horizon yet.
Those are two enormous hurdles. I think saying modern NN design can create consciousness is like Jules Verne in 1867 saying we can get to the Moon with a cannon because of "what progress artillery science has made in the last few years".
Moon rockets are essentially artillery science in many ways, yes, but Jules Verne was still a century away in terms of supporting technologies, raw power, and essential insights into how to do it.
We're on the same page about consciousness then. My original comment only pointed out that current AI have problems that we have because they replicate how we work and it seems that people don't like recognising that very obvious fact that we have the exact problems that LLMs have. LLMs aren't rational because we inherently are not rational. That was the only point I was originally trying to make.
For AGI or UGI to exist, massive hurdles will need to be made, likely an entire restructuring of it. I think LLMs will continue to get smarter and likely exceed us but it will not be perfect without a massive rework.
Personally and this is pure speculation, I wouldn't be surprised if AGI or UGI is only possible with the help of a highly advanced AI. Similar to how microbiologist are only now starting to unravel protein synthesis with the help of AI. I think the shear volume of data that needs processing requires something like a highly evolved AI to understand, and that current technology is purely a stepping stone for something more.
We don't have the same problems LLMs have.
LLMs have zero fidelity. They have no - none - zero - model of the world to compare their output to.
Humans have biases and problems in our thinking, sure, but we're capable of at least making corrections and working with meaning in context. We can recognise our model of the world and how it relates to the things we are saying.
LLMs cannot do that job, at all, and they won't be able to until they have a model of the world. A model of the world would necessarily include themselves, which is self-awareness, which is AGI. That's a meaning-understander. Developing a world model is the same problem as consciousness.
What I'm saying is that you cannot develop fidelity at all without AGI, so no, LLMs don't have the same problems we do. That is an entirely different class of problem.
Some moon rockets fail, but they don't have that in common with moon cannons. One of those can in theory achieve a moon landing and the other cannot, ever, in any iteration.
They work the exact same way we do.
Two things being difficult to understand does not mean that they are the exact same.
Maybe work is the wrong word, same output. Just as a belt and chain drive does the same thing, or how fluorescent, incandescent or LED lights produce light even though they're completely different mechanisms.
What I was saying is that one is based on the other, so similar problems like irrational thought even if the right answer is conjured shouldn't be surprising. Although an animal brain and nural network are not the same, the broad concept of how they work is.
LLMs among other things lack the whole neurotransmitter "live" regulation aspect and plasticity of the brain.
We are nowhere near a close representation of actual brains. LLMs to brains are like a horse carriage compared to modern cars. Yes they have four wheels and they move, and cars also need four wheels and move, but that is far from being close to each other.
I agree. This is the exact problem I think people need to face with nural network AIs. They work the exact same way we do.
I don't think this is a fair way of summarizing it. You're making it sound like we have AGI, which we do not have AGI and we may never have AGI.
Let's get something straight, no I'm not saying we have our modern definition of AGI but we've practically got the original definition coined before LLMs were a thing. Which was that the proposed AGI agent should maximise "the ability to satisfy goals in a wide range of environments". I personally think we've just moved the goal posts a bit.
Wether we'll ever have thinking, rationalised and possibly conscious AGI is beyond the question. But I do think current AI is similar to existing brains today.
Do you not agree that animal brains are just prediction machines?
That we have our own hallucinations all the time? Think visual tricks, lapses in memory, deja vu, or just the many mental disorders people can have.
Do you think our brain doesn't follow path of least resistance in processing? Or do you think our thoughts comes from elsewhere?
I seriously don't think animal brains or human to be specific are that special that nurural networks are beneath. Sure people didn't like being likened to animals but it was the truth, and I as do many AI researches, liken us to AI.
AI is primitive now, yet it can still pass the bar, doctors exams, compute complex physics problems and write a book (soulless as it may be like some authors) in less than a few seconds.
Whilst we may not have AGI the question was about math. The paper questioned how it did 36+59 and it did things in an interesting way where it half predicted what the tens column would be and 'knew' what the units column was, then put it together. Although thats not how I or even you may do it there are probably people who do it similar.
All I argue is that AI is closer to how our brains think, and with our brains being irrational quite often it shouldn't be surprising that AI nural networks are also irrational at times.
“the ability to satisfy goals in a wide range of environments”
That was not the definition of AGI even back before LLMs were a thing.
Wether we’ll ever have thinking, rationalised and possibly conscious AGI is beyond the question. But I do think current AI is similar to existing brains today.
That's doing a disservice to AGI.
Do you not agree that animal brains are just prediction machines?
That's doing a disservice to human brains. Humans are sentient, LLMs are not sentient.
I don't really agree with you.
LLMs are damn impressive, but they are very clearly not AGI, and I think that's always worth pointing out.
The first person to be recorded talking about AGI was Mark Gubrud. He made that quote above, here's another:
The major theme of the book was to develop a mathematical foundation of artificial intelligence. This is not an easy task since intelligence has many (often ill-defined) faces. More specifically, our goal was to develop a theory for rational agents acting optimally in any environment. Thereby we touched various scientific areas, including reinforcement learning, algorithmic information theory, Kolmogorov complexity, computational complexity theory, information theory and statistics, Solomonoff induction, Levin search, sequential decision theory, adaptive control theory, and many more. Page 232 8.1.1 Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability
As UGI largely encompasses AGI we could easily argue that if modern LLMs are beginning to fit the description of UGI then it's fullfilling AGI too. Although AGI's definition in more recent times has become more nuanced to replicating a human brain instead, I'd argue that that would degrade the AI trying to replicate biology.
I don't beleive it's a disservice to AGI because AGI's goal is to create machines with human-level intelligence. But current AI is set to surpase collective human intelligence supposedly by the end of the decade.
And it's not a disservice to biological brains to summarise them to prediction machines. They work, very clearly. Sentience or not if you simulated every atom in the brain it will likely do the same job, soul or no soul. It just brings the philosophical question of "do we have free will or not?" And "is physics deterministic or not". So much text exists on the brain being prediction machines and the only time it has recently been debated is when someone tries differing us from AI.
I don't believe LLMs are AGI yet either, I think we're very far away from AGI. In a lot of ways I suspect we'll skip AGI and go for UGI instead. My firm opinion is that biological brains are just not effective enough. Our brains developed to survive the natural world and I don't think AI needs that to surpass us. I think UGI will be the equivalent of our intelligence with the fat cut off. I believe it only resembles our irrational thought patterns now because the fat hasn't been striped yet but if something truely intelligent emerges, we'll probably see these irrational patterns cease to exist.
Even if LLM "neurons" and their interconnections are modeled to the biological ones, LLMs aren't modeled on human brain, where a lot is not understood.
The first thing is that how the neurons are organized is completely different. Think about the cortex and the transformer.
Second is the learning process. Nowhere close.
The fact explained in the article about how we do math, through logical steps while LLMs use resemblance is a small but meaningful example. And it also shows that you can see how LLMs work, it's just very difficult
By design, they don't know how they work. It's interesting to see this experimentally proven, but it was already known. In the same way the predictive text function on your phone keyboard doesn't know how it works.
I'm aware of this and agree but:
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I see that asking how an LLM got to their answers as a "proof" of sound reasoning has become common
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this new trend of "reasoning" models, where an internal conversation is shown in all its steps, seems to be based on this assumption of trustable train of thoughts. And given the simple experiment I mentioned, it is extremely dangerous and misleading
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take a look at this video: https://youtube.com/watch?v=Xx4Tpsk_fnM : everything is based on observing and directing this internal reasoning, and these guys are computer scientists. How can they trust this?
So having a good written article at hand is a good idea imho
It's the anthropic article you are looking for, where they performed open brain surgery equivalent to find out that they do maths in very strange and eerily humanlike operations, like they will estimate, then if it goes over calculate the last digit like I do. It sucks as a counting technique though
Define "know".
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An LLM can have text describing how it works and be trained on that text and respond with an answer incorporating that.
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LLMs have no intrinsic ability to "sense" what's going on inside them, nor even a sense of time. It's just not an input to their state. You can build neural-net-based systems that do have such an input, but ChatGPT or whatever isn't that.
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LLMs lack a lot of the mechanisms that I would call essential to be able to solve problems in a generalized way. While I think Dijkstra had a valid point:
The question of whether a computer can think is no more interesting than the question of whether a submarine can swim.
...and we shouldn't let our prejudices about how a mind "should" function internally cloud how we treat artificial intelligence...it's also true that we can look at an LLM and say that it just fundamentally doesn't have the ability to do a lot of things that a human-like mind can. An LLM is, at best, something like a small part of our mind. While extracting it and playing with it in isolation can produce some interesting results, there's a lot that it can't do on its own: it won't, say, engage in goal-oriented behavior. Asking a chatbot questions that require introspection and insight on its part won't yield interesting result, because it can't really engage in introspection or insight to any meaningful degree. It has very little mutable state, unlike your mind.