It's such a big "just". You are just firing neurons. The stock market is just supply and demand. The internet is just a bunch of computers talking through 50 year old protocols that don't work very well.
Everything is just something else! I wonder if the first tribe to be annihilated by bronze weapons were like "that stuff is just like stone but more malleable, don't see what the big deal is".
Guess what, it apolgised immediately after and then again when I asked why it apologised even after I told it not to.
So your argument is probably more accurate for the other camp, or at least as accurate for the other camp as well.
If AI researches say, "this is just X and it can do Y!" then fine, that's just framing for "look: Y". When stochastic parrot guys say "this is just X, what's impressive about that?" it throws me for a loop coz they are are refusing to engage with Y.
I like your bronze sword analogy. From my point of view chatgtp is not a bronze sword, it’s a Stone Age sword that someone has painted bronze. It has value because people realize the advantage that a true bronze sword would have in a battle. However, when you actually put it through it’s paces you quickly realise it offers no actual value over what came before.
I understand your point, but I am struggling to see why it matters. This seems more and more an argument like “cars are not horses”. I know they are not but does it matter? Cars are superior for our use cases.
What a curious psychological study, maybe dyslexic people feel more threatened by a large language model so clearly understanding words that they’re more likely to attempt to discredit it?
Well neural networks have unpredicted emergent properties. I don't see how anyone can rule out or know future behaviour
“The bitter lesson” would like to have a word. http://www.incompleteideas.net/IncIdeas/BitterLesson.html
I appreciate your enthusiasm, but the history of ML shows that your approach is less likely to work. Maybe you’ll be the one to prove everyone else wrong. Architectural breakthroughs are few and far between, and it’s incredibly difficult to reason about. I came up with the Lion optimizer while Google was using random tree search across 300 TPUs to discover the same thing, and it’s just five lines or so.
Predicting the next word is a much deeper problem than people like you realise. To be able to be good at predicting the next word you need to have an internal model of the reality that produced that next word.
GPT-4 might be trained at predicting the next word, but in that process it learns a very deep representation of our world. That explains how it has an intuition for colours despite never having seen colours. It explains why it knows how physical objects in the real world interact.
Now, if you disagree with this hypothesis it's very easy to disprove it by presenting a problem to GPT4 that is very easy for humans to solve but not for GPT4. Like the Yann Lecun gear problem, which GPT4 is also able to solve.
Now that’s an interesting claim - that I would deeply dispute. It learns from text. Text itself is a model of reality. So chatgtp if anything proves that in order to be good at predicting the next word all you need is a good model of a model of reality. GTP knows nothing of actual reality only the statistics around symbol patterns that occur in text.
>> "good model of a model of reality"
That is just a model of reality. Also, a "model of reality" is what you'd typically call a world model. Its an intuition for how the world works, how people behave, that apples fall from trees and that orange is more similar to red than it is to grey.
Your last line shows that you still have a superficial understanding of what its learning. Yes it is statistics, but even our understanding of the world is statistical. The equations we have in our head of how the world works are not exact, they're probabilistic. Humans know that "Apples fall from the _____" should be filled with 'tree' with a high probability because that's where apples grow. Yes, we have seen them grow there, whereas the AI model has only read about the growing on trees. But that distinction is moot because both the AI model and humans express their understanding in the same way. The assertion we're making is that to be able to predict the next word well, you need an internal world model. And GPT4 has learnt that world model well, despite not having sensory inputs.
Computer-generated random numbers are not truly random, yet they are practically random in most real-world use cases. You can’t easily cheat the RNG in World of Warcraft to get critical strike every time.
The output from GPT is generally very intelligent and versatile in terms of text. It may even be capable of handling more multi-modal problems with the use of enough sensors and motors. Perhaps the same idea of "predicting the next move" or "predicting the next idea" can still apply.
Who knows, maybe humans are essentially physical creatures that "generate the next thought and generate the next move"?
One of the biggest issues with GPT is its lack of mid-term memory like human do. Instead, we need vector store and search then bolt back its short term memory instead of letting it handle everything in a more coherent way. Perhaps it could benefit from lightweight fine-tuning technologies like LoRA and hypernetworks for stable diffusion. If this issue is resolved we would see it'll get even more practical. Again, the flaw is not about "predicting the next words".
It’s a parlor trick, even if you add plugins or the ability to call other hugging face ML models - it’s just a parlor trick with fancier bells and whistles. All it is doing is using stochastic gradient descent to predict the next word in a sequence based on an enormous sophisticated training set designed to amaze people.
Thinking it has advanced because it can now get calculations correct is a fallacy. It’s still just predicting the next word, it’s just that it’s now got a post processing step that is converting those next words into code and parroting the output. It maybe be able to now answer 4567*9876 correctly (using the human hardcoded wolfram alpha engine) but it still does not fundamentally comprehend why 1+1=2 - like my 5 year old can.
Until it can generate its own internal neural networks to for example learn to logically reason about calculations we are still far from AGI. Also those calling for more data are misguided - less data, more sophisticated architectures than transformers are the only way to avoid the stochastic parrot trap.