Really? I can guess at what spicy autocomplete might actually mean but I doubt a LLM ... OK ChatGPT did a pretty good job of it (I've just asked it), whilst sidestepping the definition of spicy in this context. It is after all a very good next word guesser, given a context, and its not ... me! I am capable of hallucinating but it was 30 odd years ago since I hunted out certain fungi on Dartmoor, or smoked hemp.
To be fair, we humans do often interrupt each other to second guess a sentence completion. Done correctly it is a brief satisfying collaboration. Done wrong ... I've been married for 18 years and know when to bite my tongue, but I still get it wrong from time to time - sometimes deliberately. Despite that, me and the wiff can autocomplete each other's sentences with uncanny accuracy and end up with perfect harmony or a cough slight disagreement as a result.
We are getting some phenomenal slide rules these days but the darleks are not going to be flying up the stairwell just yet, nor will SkyNet be taking over tomorrow.
That said, you just know that some noddy is trying to sell a nuclear "deterrent" LLM AI thingie somewhere. Thankfully, production military equipment takes quite a while to get to deployment. There is a good chance that we will get to grips with all this stuff before SkyNet is let loose for real 8)
No, a human isn't born with a set of knowledge like a freshly trained LLM, keeping the model fixed and responding to input. The analog to the model changes based on the human's experience. Just making bigger and bigger LLMs won't give you this.
I have been thinking about this lately. Things that are recently developed like language skills are easiest to replicate by machines. But things that too took a time to develop like walking and grabbing are still something that machines struggle with.
Ah, but I believe you forget the implicit biases of genetic programming. Instincts in my experience are the skeleton, and in a sense the default basis functions for the structure of how we live, see, do, and learn.
No, I don't forget that. There's obviously a starting point, behaviors and abilities that newborns already have. The point is that the model is not static.
Whoa, imagine you get a good base LLM model and save all conversations with it. Run a batch process every night to fine tune a LORA on convo dataset. If I ever came across such a chat bot I'd probably freak out as to why it remembers things outside of the context window, without summarisation
Bit of an unfair comparison when humans also have a bunch of senses that LLMs don't have. They might be trained on orders of magnitude more words, but more data? Doubtful.
That's the key. I'm reminded of the Helen Keller story. She was completely blind and deaf. Her teacher spent a very long time signing into her hand. It took a very long time before she realized that the sign for "water" designated the thing she could feel flowing onto her hand; before that breakthrough the signs were meaningless to her. An LLM only knows the structure of language. It doesn't know that there is an external physical world that the language refers to. It only can predict what words follow which other words, and which output is preferred. Without any senses (and the huge bandwidth of information provided by them) an LLM is very crippled.
Crippled, yes, but I would disagree that it is fundamentally limited, or that an input stream of human language is inadequate to bootstrap "meaning", or in some way philosophically inferior to native biological senses.
It's very interesting you bring up Helen Keller because she's generally regarded as possessing the same level of sentience - and indeed intelligence - as anyone else, despite the extreme narrowness of her sensory input. It took her much longer to get going, but it's not as if she only understood concepts that directly related to touch. The experience with "water" taught her the concept of a symbol, and from there she could bootstrap everything else. LLMs already work with symbols - that is their sense.
In fact we're all a bit like Helen Keller, in the sense that if sensory input is the basis for our entire world model, then it is a very small foundation supporting an incredibly vast and intricate edifice. There is a considerable abstraction gap between concepts like "capitalism" and any direct sensory input. We all of us, all the time, manipulate concepts without thinking through what they "mean" all the way to something we can see and touch.
No they don't. You're "just" doing what everyone else in the past has done with the brain/human intelligence and using the latest technology as a metaphor without realizing it.
We want to think we’re exceptional but all we can do is say “human consciousness is special” without having any way of measuring it or disproving the assertion that we’re just really fancy pattern matchers.
Take any metaphor you want, it’s the same outcome: we may all be philosophical zombies.
I’m conscious, maybe you’re not? Not that I really believe that. I think you probably see colors and hear sounds, even in your dreams! But engineering types tend to be persuaded by a particular view of the world, failing to understand that it is a view and not nature itself.
The irony in your statement is immense. Yes, Kurzeweil has been saying this for decades. No it doesn't mean AGI is close. These llms do nothing to advance AGI. There is no theoretical basis to the belief in emergent intelligence from statistical language models and the answers are amazingly good, highly unreliable and parrot meaning at best. There is no inductance, and no inteospection and no understanding of the deep semantic meaning of the language presented. There's no intelligence.
The lack of concept of "knowledge" is a big one for me - if that's an emergent thing it hasn't even shown hints of this yet. This to me seems a pretty hard line right now, as it limits their capability of things even inexperienced humans can do - namely decide if they actually know something, and identify when they don't know something and attempt to fix that - e.g. asking for clarification on vague inputs, or deciding if something is actually truth or fiction.
That then ties into another limitation right now - how after a training the model is pretty static, so cannot learn and has no state outside it's context buffer. This could just be another point where a few orders of magnitude more computing power can "fix" it, doing whole training steps between each input to actually incorporate new information and corrections into itself instead of relying on a fixed size small context.
But I'm not deep enough into things to say if they're fundamental issues, or current techniques will start displaying those effects as emergent characteristics as the complexity and training increases. There's been a few other examples when "known" techniques start to show unexpected characteristics down the line as they are scaled up, so can't really say for sure they'll /never/ be shown, just that the current examples don't seem to show even the beginnings of that sort of thing.
Why do you say they do nothing to advance AGI? Do you know what it takes to advance AGI? It's hard to state that without knowing how AGI would work yourself.
LLMs would be considered magic just a couple years ago. Sure, not AGI but behaves just like one for certain workloads. I find hard to believe we're not a bit closer now - or maybe even a lot closer.
AGI should have morals, opinions, self-reflection, learn continuously from sensor data, reason, realize when they’re proven wrong and update their model of the world, and be creative. So far LLMs exhibit none of those. But LLMs exhibit a digestible distillation of a very large body of data which may be a component of an AGI.
But you can have an AGI that doesn’t have encyclopedic knowledge but it’s still highly intelligent, so I don’t think LLMs have to be an intrinsic component.
That is not what AGI means. AGI = Artificial General Intelligence.
1. Artificial = we made it
2. General = it can solve problems in any field
3. Intelligence = the ability to solve problems
A chess engine is a very strong Artificial Intelligence. But it’s not very General, it can only evaluate chess positions.
GPT-4 is very General, you can ask it about any question and get a somewhat reasonable answer. But it’s not very intelligent, often the answer is wrong.
You’re talking about an Artificial Human. That’s a different problem. Intelligence is not species dependent. Dolphins are intelligent (a bit), aliens can be intelligent and have zero emotions or conception of self. There’s certainly plenty of amoral intelligent serial killers.
That's news to me. AGI (or strong AI) is typically defined as "human-level intelligence", or "perform any task that a human or animal can." Humans and animals often perform tasks that are critically reliant on being conscious, emoting, reading body language, reasoning, etc.
Not only that but prominent thinkers who have carved out the notion of AGI (or Strong AI) tend to have consciousness, mental states, and emotions at the core of it.
I think what you're talking about is a multi-task AI, not an AGI.
We don't have a good computer model of dolphin intelligence and the llms are not even remotely close to consciousness or dolphins, dogs, parrots on the intelligence Front.
How do humans write if not by intuiting what word comes after another?
Intelligence is the ability of that next word decision procedure to determine a next word that is aligned with our human intuition and model of truth.
I believe what you’re getting at is modality, that GPT-4 only provides responses in text. You can’t ask it to drive a car, or paint like Dall-e. And that’s a fair criticism, but it’s mostly just because it would make the models too large and slow, not because we don’t know how to do it. The thing we don’t know how to do is make a model reason as well as a human, and it makes sense to try to solve that in the text domain first rather than making highly multimodal models that reason poorly in all domains.
> the answers are amazingly good, highly unreliable and parrot meaning at best. There is no inductance, and no inteospection and no understanding of the deep semantic meaning of the language presented. There's no intelligence.
You have a metric for human brains worth of computing power which hasn't already been exceeded? I can't do infinite precision arithmetic or the RSA algorithm in my head, or index a billion strings into lexical sort order.
But I am human, I am conscious and no visible VLSI work or algorithmic model will lead to AGI or a human equivalent computing power by 2029. Let alone for $1000.
well, you could just be hallucinating your own consciousness. By 2029 it seems not unreasonable to expect that the most sophisticated models will carry out visual and auditory interactions which could fool even the most sophisticated viewer. At that point, what really does consciousness mean? If the robot insisted to me it was conscious, how can I really say no?
By most measures you could think of for intelligence languages models are improving, so I don’t see why you think this wouldn’t lead to something at least almost human-level if you scaled it up enough
Of course there could be some wall somewhere but I don’t see why there would be
That's "we need a larger cowbell" thinking. It's not a theory of mind, it's wishful thinking that it will.. emerge. Absent theory I don't think moar will make it emerge, no.
If you want theory there’s this: https://arxiv.org/abs/2001.08361 (I haven’t actually read this but I know roughly what it’s about)
It’s saying that so far the abilities of an LLMs have scaled up with its parameter count and training data size. Of course there’s no way to be sure without actually training larger models but I don’t see why the point where it stops would be just after our current best LLMs. Many properties have already emerged from making it bigger so I don’t see why this would be the exception
While it may be true that new data is coming in at a trickle these days, due to things like Discord, Slack, et al. all locking conversation and context up, as well as the daily volume of chapter is small relative to what is out there now.
The fact is that training data can be used in many different ways and I bet you we see the products of that fairly quickly as those who see this same as I do reach a point where they want to show n tell and test.
>The fact is that training data can be used in many different ways and I bet you we see the products of that fairly quickly as those who see this same as I do reach a point where they want to show n tell and test.
Sounds like wishful thinking to overcome the limitations of LLMs.
At the same time we get more and more texts generated by LLMs so it gets harder to get actual man made texts.
