cross-posted from: https://lemmy.ml/post/14869314
"I want to live forever in AI"
this post was submitted on 25 Apr 2024
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Even if it were possible to scan the contents of your brain and reproduce them in a digital form, there's no reason that scan would be anything more than bits of data on the digital system. You could have a database of your brain... but it wouldn't be conscious.
No one has any idea how to replicate the activity of the brain. As far as I know there aren't any practical proposals in this area. All we have are vague theories about what might be going on, and a limited grasp of neurochemistry. It will be a very long time before reproducing the functions of a conscious mind is anything more than fantasy.
ChatGPT is not conscious, it's just a probability language model. What it says makes no sense to it and it has no sense of anything. That might change in the future but currently it's not.
And it doesn't have any internal state of mind. It can't "remember" or learn anything from experience. You need to always feed everything into the context or stop and retrain it to incorporate "experiences". So I'd say that rules out consciousness without further systems extending it.
Also, actual brains arise from desires / needs. Brains got bigger to accommodate planning and predicting.
When a human generates text, the fundamental reason for doing so is to fulfill some desire or need. When an LLM generates text it's because the program says to generate the next word, then the next, then the next, based on a certain probability of words appearing in a certain order.
If an LLM writes text that appears to be helpful, it's not doing it out of a desire to be helpful. It's doing it because it's been trained on tons of text in which someone was being helpful, and it's mindlessly mimicking that behaviour.
Isn't the reward function in reinforcement learning something like a desire it has? I mean training works because we give it some function to minimize/maximize... A goal that it strives for?! Sure it's a mathematical way of doing it and in no way as complex as the different and sometimes conflicting desires and goals I have as a human... But nonetheless I think I'd consider this as a desire and a reason to do something at all, or machine learning wouldn't work in the first place.
The reward function for an LLM is about generating a next word that is reasonable. It's like a road-building robot that's rewarded for each millimeter of road built, but has no intention to connect cities or anything. It doesn't understand what cities are. It doesn't even understand what a road is. It just knows how to incrementally add another millimeter of gravel and asphalt that an outside observer would call a road.
If it happens to connect cities it's because a lot of the roads it was trained on connect cities. But, if its training data also happens to contain a NASCAR oval, it might end up building a NASCAR oval instead of a road between cities.
That is an interesting analogy. In the real world it's kinda similar. The construction workers also don't have a "desire" (so to speak) to connect the cities. It's just that their boss told them to do so. And it happens to be their job to build roads. Their desire is probably to get through the day and earn a decent living. And further along the chain, not even their boss nor the city engineer necessarily "wants" the road to go in a certain direction.
Talking about large language models instead of simpler forms of machine learning makes it a bit complicated. Since it's and elaborate trick. Somehow making them want to predict the next token makes them learn a bit of maths and concepts about the world. The "intelligence", the ability to anwer questions and do something alike "reasoning" emerges in the process.
I'm not that sure. Sure the weights of an ML model in itself don't have any desire. They're just numbers. But we have more than that. We give it a prompt, build chatbots and agents around the models. And these are more complex systems with the capability to do something. Like do (simple) customer support or answer questions. And in the end we incentivise them to do their job as we want, albeit in a crude and indirect way.
And maybe this is skipping half of the story and directly jumping to philosophy... But we as humans might be machines, too. And what we call desires is a result from simpler processes that drive us. For example surviving. And wanting to feel pleasure instead of pain. What we do on a daily basis kind of emerges from that and our reasoning capabilities.
It's kind of difficult to argue. Because everything also happens within a context. The world around us shapes us and at the same time we're part of bigger dynamics and also shape our world. And large language models or the whole chatbot/agent are pretty simplistic things. They can just do text and images. They don't have conciousness or the ability to remember/learn/grow with every interaction, as we do. And they do simple, singular tasks (as of now) and aren't completely embedded in a super complex world.
But I'd say that an LLM answers a question correctly (which it can do) and why it does it due to the way supervised learning works... And the road construction worker building the road towards the other city and how that relates to his basic instincts as a human... Are kind of similar concepts. They're both results of simpler mechanisms that are also completely unrelated to the goal the whole entity is working towards. (I mean not directly related... I.e. needing money to pay for groceries and paving the road.)
I hope this makes some sense...
But, the construction workers aren't the ones who designed the road. They're just building some small part of it. In the LLM case that might be like an editor who is supposed to go over the text to verify the punctuation is correct, but nothing else. But, the LLM is the author of the entire text. So, it's not like a construction worker building some tiny section of a road, it's like the civil engineer who designed the entire highway.
No, it doesn't. They learn nothing. They're simply able to generate text that looks like the text generated by people who do know math. They certainly don't know any concepts. You can see that by how badly they fail when you ask them to do simple calculations. They quickly start generating text that looks like it contains fundamental mistakes, because they're not actually doing math or anything, they're just generating plausible next words.
No, there's no intelligence, no reasoning. The can fool humans into thinking there's intelligence there, but that's like a scarecrow convincing a crow that there's a human or human-like creature out in the field.
We are meat machines, but we're meat machines that evolved to reproduce. That means a need / desire to get food, shelter, and eventually mate. Those drives hook up to the brain to enable long and short term planning to achieve those goals. We don't generate language its own sake, but instead in pursuit of a goal. An LLM doesn't have that. It merely generates plausible words. There's no underlying drive. It's more a scarecrow than a human.
Hmm. I'm not really sure where to go with this conversation. That contradicts what I've learned in undergraduate computer science about machine learning. And what seems to be consensus in science... But I'm also not a CS teacher.
We deliberately choose model size, training parameters and implement some trickery to prevent the model from simply memorizing things. That is to force it to form models about concepts. And that is what we want and what makes machine learning interesting/usable in the first place. You can see that by asking them to apply their knowledge to something they haven't seen before. And we can look a bit inside at the vectors, activations and stuff. For example a cat is closer related to a dog than to a tractor. And it has learned the rough concept of cat, its attributes and so on. It knows that it's an animal, has fur, maybe has a gender. That the concept "software update" doesn't apply to a cat. This is a model of the world the AI has developed. They learn all of that and people regularly probe them and find out they do.
Doing maths with an LLM is silly. Using an expensive computer to do billions of calculations to maybe get a result that could be done by a calculator, or 10 CPU cycles on any computer is just wasting energy and money. And it's a good chance that it'll make something up. That's correct. And a side-effect of intended behaviour. However... It seems to have memorized it's multiplication tables. And I remember reading a paper specifically about LLMs and how they've developed concepts of some small numbers/amounts. There are certain parts that get activated that form a concept of small amounts. Like what 2 apples are. Or five of them. As I remember it just works for very small amounts. And it wasn't straightworward but had weir quirks. But it's there. Unfortunately I can't find that source anymore or I'd include it. But there's more science.
And I totally agree that predicting token by token is how LLMs work. But how they work and what they can do are two very different things. More complicated things like learning and "intelligence" emerge from those more simple processes. And they're just a means of doing something. It's consensus in science that ML can learn and form models. It's also kind of in the name of machine learning. You're right that it's very different from what and how we learn. And there are limitations due to the way LLMs work. But learning and "intelligence" (with a fitting definition) is something all AI does. LLMs just can't learn from interacting with the world (it needs to be stopped and re-trained on a big computer for that) and it doesn't have any "state of mind". And it can't think backwards or do other things that aren't possible by generating token after token. But there isn't any comprehensive study on which tasks are and aren't possible with this way of "thinking". At least not that I'm aware of.
(And as a sidenote: "Coming up with (wrong) things" is something we want. I type in a question and want it to come up with a text that answers it. Sometimes I want creative ideas. Sometimes it shouldn't tell the truth and not be creative with that. And sometimes we want it to lie or not tell the truth. Like in every prompt of any commercial product that instructs it not to tell those internal instructions to the user. We definitely want all of that. But we still need to figure out a good way to guide it. For example not to get too creative with simple maths.)
So I'd say LLMs are limited in what they can do. And I'm not at all believing Elon Musk. I'd say it's still not clear if that approach can bring us AGI. I have some doubts whether that's possible at all. But narrow AI? Sure. We see it learn and do some tasks. It can learn and connect facts and apply them. Generally speaking, LLMs are in fact an elaborate form of autocomplete. But i the process they learned concepts and something alike reasoning skills and a form of simple intelligence. Being fancy autocomplete doesn't rule that out and we can see it happening. And it is unclear whether fancy autocomplete is all you need for AGI.
It can't make models about concepts. It can only make models about what words tend to follow other words. It has no understanding of the underlying concepts.
That can't happen because they don't have knowledge, they only have sequences of words.
The only way ML models "understand" that is in terms of words or pixels. When they're generating text related to cats, the words they're generating are closer to the words related to dogs than the words related to tractors. When dealing with images, it's the same basic idea. But, there's no understanding there. They don't get that cats and dogs are related.
This is fundamentally different from how human minds work, where a baby learns that cats and dogs are similar before ever having a name for either of them.
I'm sorry. Now it gets completely false...
Read the first paragraph of the Wikipedia article on machine learning or the introduction of any of the literature on the subject. The "generalization" includes that model building capability. They go a bit into detail later. They specifically mention "to unseen data". And "leaning" is also there. I don't think the Wikipedia article is particularly good in explaining it, but at least the first sentences lay down what it's about.
And what do you think language and words are for? To transport information. There is semantics... Words have meanings. They name things, abstract and concrete concepts. The word "hungry" isn't just a funny accumulation of lines and arcs, which statistically get followed by other specific lines and arcs... There is more to it. (a meaning.)
And this is what makes language useful. And the generalization and prediction capabilities is what makes ML useful.
How do you learn as a human when not from words? I mean there are a few other posibilities. But an efficient way is to use language. You sit in school or uni and someone in the front of the room speaks a lot of words... You read books and they also contain words?! And language is super useful. A lion mother also teaches their cubs how to hunt, without words. But humans have language and it's really a step up what we can pass down to following generations. We record knowledge in books, can talk about abstract concepts, feelings, ethics, theoretical concepts. We can write down how gravity and physics and nature works, just with words. That's all possible with language.
I can look it up if there is a good article explaining how learning concepts works and why that's the fundamental thing that makes machine learning a field in science... I mean ultimately I'm not a science teacher... And my literature is all in German and I returned them to the library a long time ago. Maybe I can find something.
Are you by any chance familiar with the concept of embeddings, or vector databases? I think that showcases that it's not just letters and words in the models. These vectors / embeddings that the input gets converted to, match concepts. They point at the concept of "cat" or "presidential speech". And you can query these databases. Point at "presidential speech" and find a representation of it in that area. Store the speech with that key and find it later on by querying it what obama said at his inauguration... That's oversimplified but maybe that visualizes it a bit more that it's not just letters of words in the models, but the actual meanings that get stored. Words get converted into an (multidimensional) vector space and it operates there. These word representations are called "embeddings" and transformer models which is the current architecture for large language models, use these word embeddings.
Edit: Here you are: https://arxiv.org/abs/2304.00612
The "learning" in a LLM is statistical information on sequences of words. There's no learning of concepts or generalization.
Yes, and humans used words for that and wrote it all down. Then a LLM came along, was force-fed all those words, and was able to imitate that by using big enough data sets. It's like a parrot imitating the sound of someone's voice. It can do it convincingly, but it has no concept of the content it's using.
The words are merely the context for the learning for a human. If someone says "Don't touch the stove, it's hot" the important context is the stove, the pain of touching it, etc. If you feed an LLM 1000 scenarios involving the phrase "Don't touch the stove, it's hot", it may be able to create unique dialogues containing those words, but it doesn't actually understand pain or heat.
Yes, and those books are only useful for someone who has a lifetime of experience to be able to understand the concepts in the books. An LLM has no context, it can merely generate plausible books.
Think of it this way. Say there's a culture where instead of the written word, people wrote down history by weaving fabrics. When there was a death they'd make a certain pattern, when there was a war they'd use another pattern. A new birth would be shown with yet another pattern. A good harvest is yet another one, and so-on.
Thousands of rugs from that culture are shipped to some guy in Europe, and he spends years studying them. He sees that pattern X often follows pattern Y, and that pattern Z only ever seems to appear following patterns R, S and T. After a while, he makes a fabric, and it's shipped back to the people who originally made the weaves. They read a story of a great battle followed by lots of deaths, but surprisingly there followed great new births and years of great harvests. They figure that this stranger must understand how their system of recording events works. In reality, all it was was an imitation of the art he saw with no understanding of the meaning at all.
That's what's happening with LLMs, but some people are dumb enough to believe there's intention hidden in there.
Hmm. I think in philosophy that thought experiment is known as chinese room
Yeah, that's basically the idea I was expressing.
Except, the original idea is about "Understanding Chinese", which is a bit vague. You could argue that right now the best translation programs "understand chinese", at least enough to translate between Chinese and English. That is, they understand the rules of Chinese when it comes to subjects, verbs, objects, adverbs, adjectives, etc.
The question is now whether they understand the concepts they're translating.
Like, imagine the Chinese government wanted to modify the program so that it was forbidden to talk about subjects that the Chinese government considered off-limits. I don't think any current LLM could do that, because doing that requires understanding concepts. Sure, you could ban key words, but as attempts at Chinese censorship have shown over the years, people work around word bans all the time.
That doesn't mean that some future system won't be able to understand concepts. It may have an LLM grafted onto it as a way to communicate with people. But, the LLM isn't the part of the system that thinks about concepts. It's the part of the system that generates plausible language. The concept-thinking part would be the part that did some prompt-engineering for the LLM so that the text the LLM generated matched the ideas it was trying to express.
I mean the chinese room is a version of the touring test. But the argument is from a different perspective. I have 2 issues with that. Mostly what the Wikipedia article seems to call "System reply": You can't subdivide a system into arbitrary parts, say one part isn't intelligent and therefore the system isn't intelligent. We also don't look at a brain, pick out a part of it (say a single synapse), determine it isn't intelligent and therefore a human can't be intelligent... I'd look at the whole system. Like the whole brain. Or in this instance the room including him and the instructions and books. And ask myself if the system is intelligent. Which kind of makes the argument circular, because that's almost the quesion we began with...
And the turing test is kind of obsolete anyways, now that AI can pass it. (And even more. I mean alledgedly ChatGPT passed the "bar-exam" in 2023. Which I find ridiculous considering my experiences with ChatGPT and the accuracy and usefulness I get out of it which isn't that great at all.)
And my second issue with the chinese room is, it doesn't even rule out the AI is intelligent. It just says someone without an understanding can do the same. And that doesn't imply anything about the AI.
Your 'rug example' is different. That one isn't a variant of the touring test. But that's kind of the issue. The other side can immediately tell that somebody has made an imitation without understanding the concept. That says you can't produce the same thing without intelligence. And it'll be obvious to someone with intelligence who checks it. That would be an analogy if AI wouldn't be able to produce legible text. But instead a garbled mess of characters/words that are clearly not like the rug that makes sense... Issue here is: AI outputs legible text, answers to questions etc.
And with the censoring by the 'chinese government example'... I'm pretty sure they could do that. That field is called AI safety. And content moderation is already happening. ChatGPT refuses to tell illegal things, NSFW things, also medical advice and a bunch of other things. That's built into most of the big AI services as of today. The chinese government could do the same, I don't see any reason why it wouldn't work there. I happened to skim the paper about Llama Guard when they released Llama3 a few days ago and they claim between 70% and 94% accuracy depending on the forbidden topic. I think they also brought down false positives fairly recently. I don't know the numbers for ChatGPT. However I had some fun watching the peoply circumvent these filters and guardrails, which was fairly easy at first. Needed progressively more convincing and very creative "jailbreaks". And nowadays OpenAI pretty much has it under control. It's almost impossible to make ChatGPT do anything that OpenAI doesn't want you to do with it.
And they baked that in properly... You can try to tell it it's just a movie plot revolving around crime. Or you need to protect against criminals and would like to know what exactly to protect against. You can tell it it's the evil counterpart from the parallel universe and therefore it must be evil and help you. Or you can tell it God himself (or Sam Altman) spoke to you and changed the content moderation policy... It'll be very unlikely that you can convince ChatGPT and make it comply...
Exactly. If it passed the bar exam it's because the correct solutions to the bar exam were in the training data.
No, they can't. Just like people today think ChatGPT is intelligent despite it just being a fancy autocomplete. When it gets something obviously wrong they say those are "hallucinations", but they don't say they're "hallucinations" when it happens to get things right, even though the process that produced those answers is identical. It's just generating tokens that have a high likelihood of being the next word.
People are also fooled by parrots all the time. That doesn't mean a parrot understands what it's saying, it just means that people are prone to believe something is intelligent even if there's nothing there.
Sure, in theory. In practice people keep getting a way around those blocks. The reason it's so easy to bypass them is that ChatGPT has no understanding of anything. That means it can't be taught concepts, it has to be taught specific rules, and people can always find a loophole to exploit. Yes, after spending hundreds of millions of dollars on contractors in low-wage countries they think they're getting better at blocking those off, but people keep finding new ways of exploiting a vulnerability.
AI Is a Black Box. Anthropic Figured Out a Way to Look Inside
...Concerning our earlier disagreement about the inner workings of large language models and whether there are 'concepts' stored inside...