Anyone who knows anything about what a large language model is already knew this.
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Anyone who thinks intelligence means guessing the right word next probably loves AI
Current LLMs are definitely not intelligent, but predicting the future is a big part (if not the most important part) of intelligence.
Your comment is a bit like saying that humans can't be intelligent, because the biochemistry in our brains is just laws of physics in motion, and the laws of physics are not intelligent.
Intelligent is an emergent property. You can definitely be intelligent even if every component is not.
But with LLMs we found a new weird "dimension" that something can be very knowledgeable without being intelligent. Even current LLMs have more general knowledge than all humans but they lack actual intelligence.
Isn't this like saying the dictionary is knowledgeable because it knows more words than you?
The dictionary is certainly more knowledgeable about words than you.
Let's abstract if further. Rip every page out of the dictionary and put it through a shredder. All the knowledge is still there, the paper hasn't been destroyed and the knowledge can be accessed by someone patient, it's just not in a form that can be easily read.
But is that pile of shredded paper knowledgeable?
I don't get your analogy. Put your brain through a shredder. Is it still intelligent? All the atoms are still there.
Exactly? Both intelligence and knowledgeably are emergent, you can't just have all the knowledge in one place and then call it knowledgeable (or intelligent, for that matter). A book (or a chatbot) isn't knowledgeable, it merely contains knowledge.
I'm not a native speaker, but that sounds like semantics to me. How would you, when chatting, differentiate if the other end is "knowledgeable" or if it "merely contains knowledge"?
The distinction is important because an intelligent being can actually be trusted to do the things they are trained to do. LLMs can't. The "hallucination" problem comes from these things just being probability engines, they don't actually know what they're saying. B follows A, they don't know why and they don't care and they can't care. That's why LLMs are not actually able to really replace workers, at best they're productivity software that can (maybe, I'm not convinced) make human workers more productive.
One distinction is that it requires work to actually get real useful knowledge out of these things. You can't just prompt it and then always expect the answer to be correct or useful, you have to double check everything because it might just make shit up.
The knowledgeably, the intelligence, still comes from the human user.
To be fair all of what you've said applies to humans too. Look how many flat earthers there are and even more people that believe in homeopathy or think that vaccines give you autism, think that aliens built the pyramids.
But nobody calls that "hallucinations" in humans. Are LLMs perfect? Definitely not. Are they useful? Somewhat; but definitely extremely far from PhD level intelligence as some claim.
But there are things LLMs are why better than any single human already (not collectively). Giving you a hint (doesn't have to be 100% accurate) what topics to look up if you can just describe is vaguely but don't know what you would even search for in a traditional search engine.
Of course you can not trust it blindly, but you shouldn't trust humans blindly either, that's we we have the scientific method, because humans are unreliable too.
My boss trusts me to be able to accurately check the quality of parts I weld. That's literally my job. It's not blind trust, I'd lose my job if I couldn't consistently produce good results, but once I was trained to do my job I can be left to my own devices. You can't do that with an LLM because you'd still need a human to double-check to make sure it didn't hallucinate that the part was correctly welded.
That's the difference - intelligent beings, once they understand something, can be trusted. Obviously if they don't understand something, like the fact that the Earth is round, then you can't trust them. Intelligence still requires education and training, but the difference is that educating and training intelligent beings actually produces consistent results that can be relied on.
Notably, "hallucinate" is actually a term invented by the companies behind LLMs. It's not really accurate, because hallucination still implies intelligence. They're just pattern recognition engines, they don't "hallucinate" they just don't have any idea what the patterns mean or why they happen. B follows A, that's all it knows. If a sequence occurs where C follows A it makes a mistake and we call that "hallucination" even though it's really just a mindless machine churning thoughtlessly repeating the patterns it was trained on.
Your boss expects you to weld with good quality but they don't expect you to answer every question there is, without any mistakes. The problem with LLMs is that they are trained purely on text found on the internet but they have no "life experience" and thus there world model is very different from ours. There are overlaps (that's why they can produce any coherent output at all) but there are situations that make perfect sense in its world model, that's complete bogus in the real world.
It's a bit like the shadows in Platons cave allegory. LLMs are practically trained only on the shadows and so the output is completely based on that shadow world. LLMs can describe pain (because it was in the training data) but it was never smacked in the face.
That's exactly why we can't really call them intelligent or knowledgeable. They're pattern recognition engines, they mindlessly recognize and repeat patters even when they don't make any sense i.e. "hallucinate"
They're a productivity tool that can help actually intelligent and knowledgeable beings like humans do tasks, but on their own they are a parking lot covered with shredded dictionaries. If we use the Chinese room analogy, it'd be like trying to build a Chinese room with just the translation dictionary and without the human to do the translating.
Which is why LLMs make mistakes when translating too - they need a human, a real intelligence, to check.
Humans are also "pattern recognition engines". That's why optical illusions and similar completely mess with our brains. There are patterns that we perceive as moving/rotating even though the pattern is completely stationary.
But nobody would claim that you can't trust your eyes in general just because optical illusions exist.
We can tell optical illusions are fake specifically because we aren't just pattern recognition engines.
LLMs "hallucinate" because they can't do that. To them, the optical illusion is reality.
That's the difference between intelligence and knowledgeability, instead of merely containing knowledge.
Are you trying to say that the word 'knowledgeable' has some implication of intelligence? Because, depending on context, yes it can. Or are you trying to say that LLMs take a lot of time and/or energy to reassemble their shredded data? To answer your question, yes, the pile of shredded paper contains knowledge, and its accessibility is irrelevant to the conversation.
I'm saying both - a parking lot covered with shredded dictionaries isn't knowledgeable. It doesn't know anything.
Your exchange makes me think about the chinese room thought experiment.
The person inside the room has instructions and a dictonary they uses to translate chinese symbols into english words. They never leave the room and never interact with anyone. They just translate single words.
They don't understand chinese, but the output of the system (the room) gives the impression that there is thinking behind the process. If I remember correctly, it was an argument against the Turing test. The claim was that computers could be extremely efficient into constructing anwsers that seems to be backed by human consciousness/thinking.
I think you are right. IMHO the room actually does speak/understand Chinese, even of the robot/human in the room does not.
There are no neurons in your brain that "understand" English, yet you do. Intelligence is an emergent property. If you "zoom-in" enough everything is just laws of physics and those laws don't understand English or Chinese.
If we carry the thought experiment forward, the parking lot requires a human to put in energy to make the whole system knowledgeable. In order for knowledgeability or intelligence to emerge we still need a human involved in the process, whether it's a Chinese room or a parkinglot covered with shredded dictionaries or a chatbot productivity software.
We have not eliminated the human from the process, and until we do, we can not say that it is intelligent or knowledgeable.
Right, so the parking lot covered with shredded dictionaries needs a human mind or else its just a bunch of trash.
The human inside the Chinese room, or in the parking lot picking up and organizing the trash, or in a discussion with a chatbot is still critical to the overall intelligence/knowledgeability of the system. It's still needed for that spark and, without it, it's just trash.
it doesn't predict, it follows a weighted graph or the equivalent. it doesn't guess, /dev/urandom input makes the path unpredictable. any case where it looks like it predicts or guesses is purely accidental, and all in the eye of the observer.
further, it only posses knowledge to tthe degree that an encyclopedia does. the prompt is just the equivalent of a hash key pulling a bucket out of a map.
it is literally just a huge database of key-value pairs stored so as to minimize the description length of the values.
The training process evolves models to do predictions. The actual underlying mechanisms are not too relevant because the prediction function is an emergent property.
You brain is just biochemistry and biochemistry isn't intelligent and yet you are. Think of the number three and all you know about it. There is not a single neuron in your brain that has any idea what the concept of three even means. It's an emergent behavior.
there's no emergent behavior in llm's. your perception that there is, is an anthropomorphism, same as with the idea of prediction. statistically "predicting" the next word based on the frequency of input data isn't an emergent property, it exists as a staic feature of the algorithm from the start. at a certain level of complexity, llms appear to produce comprehensible text, provided you stop them in time. that's merely because of the rules of the algorithm. the illusion of intelligence comes merely from being able to select "merged buckets" from the map, which are put together mathematically.
it is a one trick pony that will never become anything else.
Excuse me but what do you think memory is other than a huge database of key-value pairs?
It makes sense that ai bros think ai is conscious and intelligent because it’s literally as conscious and intelligent as the ai bros
I think this is true for anyone who thinks LLMs are already AGI. Clearly their use case and mental capabilities are unable to see the obvious deficiencies.
You also need to consider that they really want it to be true. Either because of financial investment, or a believ that AGI will bring about paradise. I don't know why AI doomers believe LLMs will lead to AGI, thought, as they should be relieved that we're not close to AGI, though.
If we keep feeding all these LLMs with all our bullshit ... it will never be intelligent
You can't have artificial intelligence UNTIL you FIRST have artificial stupidity. No one wants to buy that.
Of course not when Organic stupidity can be locally sourced anywhere.
Experts continued that the sky is blue, water is wet, the sun shines, and according to preliminary research, fish swim.
Wow. One needs experts for that? It is a language model. It is a parrot with a dictionary, or in case of an LLM, a large dictionary.
Large Language Models aren't designed to be intelligent.
They are language models, made to generate text. Using them for anything else is just dumb user error.
I don’t really care whether LLMs are “intelligent” or not. In the real world, tools either work or they don’t. Same as a generator, radio, or water filter. They can extend your capabilities, but they never replace judgment, planning, or responsibility. Over-reliance is the real risk.
To the extent that LLM does stuff, they exist in a more middle ground of "they work... maybe". Problem being that in the ways that matter, "maybe" is really hard to take out of things.
Normally, taking the technology as it comes for what it is is reasonable, but there's a whole investment angle here around expectations. Investment is coming in as if they are expecting an emergent AGI to come out of it. The drive to build everything around executing LLM models and only LLM models is driving the cost of everything in tech up. Money that might have been spent on more general purpose friendly compute is being redirected at Grace Blackwell infrastructure connected by NVL72, which is not particularly interesting for almost all other applications. It's just sucking up RAM and storage and starving everything else.
They aren't supposed to be? An LLM is just a big ol' fabric of matrices with weights based on it's training data. It then re-weights and queries this fabric based on your input and it's other prompts and parameters, such as temperature and previous context, and returns output. LLMs are, and always will be, exactly as "intelligent" as that process, because that process is what an LLM is. The fact that data is returned from these matrices as English sentences with a bit of randomness thanks to temp settings, rather than a fixed list of results has greatly confused people.
Problem is that confusion has caused an unreasonable level of investment, with expectations that have no significant indications of being met. So we need credible experts reiterating what those in the thick of it consider 'obvious' to try to fight that irrational behavior.
Currently, they have seen a rapidly evolving tech industry with lots of things that didn't quite work right but were quickly iterated into something useful. The dot-com being an example of generally solid principles attempted too soon, and the tech companies 'fixed' a lot of the problems in short order. So they see this thing that almost seems like a conversational human and assume that 'almost' will be addressed by the tech geniuses before we know it, and whatever naysaying those same experts might be saying is just misplaced humility. The 'experts' may have nerdy reasons why they view it as limited, but the investors "common sense" experience are inconsistent with that feedback.
Of course there's survivorship bias and plenty of 'big tech' that retreated after an optimistic push if you went looking to see that tech can actually fail to close that gap, but none of those examples are anywhere near the scale of the AI push.
Yes, all the same people who thought smartphones were actually "smart" or that social media was actually "social" are the ones thinking artificial intelligence is actually "intelligent". Just cause a company calls their product "vitamin water" doesn't make it healthy, and the sooner people learn to see through the bs hype machine that major corporations have us all hypnotized by, the better for everyone.
Though in those cases while there is confusion, the outsized investment factor didn't figure in. The value proposition was more in line with realistic possibilities with the respective technologies. Smart phones may not have been that smart, but plenty capable to land in every pocket and be both a rich revenue stream in and of themselves as well as an onramp to 'app store' revenue and a goldmine of private data.
The LLMs have value, but I'm skeptical it justifies all the very large and very weird financial shenigans going on with nonsensical depreciation claims, crazy loans, and circle jerk of money going out to a customer to let the customer give it back for product...
I already said this years ago, person with basic conceptual grasp of the operative concepts at play says.
I don’t want an actual artificial intelligence for the same reason I don’t want biological research into bespoke pathogens. Both are major existential threats for humanity, and I have little hope for humanity’s survival in case of either’s unmitigated spread, given what 2020 looked like. If true AI has to happen, keep it behind unbreachable lab barriers.
The funny thing is, current AI is only a half step past passable and it’s already offered major questions about how human society will economically and sociologically manage in the near future. We’re far from ready for whatever wholistic AI will look like.
We also assume a real AI would be interested in us.
Maybe it couldn't care less. And would just disappear and hide away from us after it is created. Who knows.
They should be working on uploading and solving the nanoassembly conformational problem instead. Then we could turn all the available dumb matter into computronium and use it for processing our thoughts.
The expert is talking about Large Language Models (LLMs). You know, those chat bots that are good at pretending they know shit. We still might have AGI in the future, but LLMs are a dead end.
Benjamin Riley is a lawyer with experience in education, who started a relevant company. No degree or significant experience related to machine learning or neuroscience. He is an expert of neither human cognition nor LLMs, as implied by the headline
Fair. But to be clear, the experts without major conflicts of interest agree with this guy.
If you are downvoting this, you need to re-examine how you interact with facts.
Umm actually it would be an appeal to authority. Do u even master debate.
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