Might not be AGI but I think cliched as it is that would "change everything". If not at 200 then 400 or whatever. Doesn't matter - the direction of travel seems certain.
Take any metaphor you want, it’s the same outcome: we may all be philosophical zombies.
A GAN is a neural network, does that make it an LLM?
could be a difference that makes no difference, or ...
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.
The million-year-old stuff is not what makes humans interesting.
monkeys make meseeks on purpose.
there is a difference, but will it be fun?
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)
Can say the same about a half of population, tbh
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.
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.
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.
It is remarkably good at this. But there's absolutely zero reason to believe it can solve any other problem at all.
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.
We don't know. It may turn out that we use mechanisms similar to LLMs, or it might be something entirely different.
As for the rest: nobody knows how to make ChatGPT butter a piece of toast, let alone drive a car.
ChatGPT does not reason about text, either.
There is plenty of research on LLMs successfully piloting robots.
It's but no means a solved problem but "Nobody knows how" is a stretch.
https://tidybot.cs.princeton.edu/ https://innermonologue.github.io/
>ChatGPT does not reason about text, either.
It does and there's plenty of output to demonstrate that.
("The most likely" out of what distribution? The model's distribution. So that just means "what the model thinks the answer to your question is".)
Consciousness: being “awake” and perceiving the world.
Intelligence: solving problems, finding the truth.
Consciousness is perceiving the world, whereas intelligence is understanding it.
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.
Of course there could be some wall somewhere but I don’t see why there would be
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.
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.
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
That's not the same as AGI or the singularity.
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.
This is where we differ.
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.
Ie you could technically run something like that today.
I think more VRAM on GPUs isn't necessarily a technical limitation either. I think GPU manufacturers could add a lot more VRAM to their cards if they wanted to. The question is whether it would be worth the price increase.
Yep, on higher end machines it should already be feasible. I can do 2.5-3 tok/sec on a 70B model quantized at 4 bit today with my MacBook Pro M2 MAX w/96GB. It's a little slower than a 30B, but the difference is less than I had guessed it would be. That's not super fast, but it's usable.
And that's on a machine that isn't designed for this workload. Over the next few years things should improve quite a bit. 200B does not seem like a reach.
Just checked: light travels 30cm in one nanosecond. So if the gpu is running at 4GHz it goes only 7.5 cm.
But basically GPU memory (GDDR5/6/6X/etc) is optimized for bandwidth (because GPUs need to move a lot of data, have few branches, few unknown data dependencies, high spatial locality). CPU memory is more optimized for latency (because of branchy code).