That it bears so close a resemblance to actual thinking says more about the importance of language to cognition than the other way around.
> The specific engineering of ChatGPT has made it quite compelling. But ultimately (at least until it can use outside tools) ChatGPT is “merely” pulling out some “coherent thread of text” from the “statistics of conventional wisdom” that it’s accumulated. But it’s amazing how human-like the results are. And as I’ve discussed, this suggests something that’s at least scientifically very important: that human language (and the patterns of thinking behind it) are somehow simpler and more “law like” in their structure than we thought.
https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-...
It can solve a physics problem in Telugu close to as well as in English.
This is key. ChatGPT/GPT-4 alone are limited to reformulating what they know from their training data. Linked to search engines, databases, and computational tools such as Wolfram Alpha, they acquire much more capability. We're already seeing that with Microsoft Bing.
(Update: what happens as large language models learn Excel? Especially since Microsoft is already connecting them to Excel.)
What's striking is how fast this field is advancing. Huge advances over months, not years or decades.
We now have a much better idea of how intelligence evolved. It's mostly just more neurons. One of the great philosophical questions has, inadvertently, been answered.
Is the Singularity happening right now?
That's not true. They are extrapolating. If they weren't, they wouldn't have the problem known as "hallucination".
No, but it seems everyone loves to LARP it anyway.
I see this as analogous to the human brain; there are different structures which are particularly good at specific tasks/functions. They all work together.
The only difference between a human brain and an ANN is a difference of degree. A neuron and an artificial neuron are functionally identical. I think as we start interconnecting these models we see surprising emergent properties.
I don't understand this use of "statistical" as a diminutive to describe these models.
Why can't incredibly complicated behavior be emergent from matrix multiplication subject to optimization in the same way that our biological matter has developed complicated emergent properties also being subject to optimization?
The loss function is very different, the optimization techniques as well, but the fundamental idea of complex behavior emerging out of a substrate subject to optimization seems common. I haven't seen a single good answer to that
LLMs are trained to reproduce human text, that is different from for example AlphaGo that is trained to win Go games. Trained to reproduce data is what we mean with a statistical model, trained to win is how we got superhuman performance before, while trained to reproduce data performs worse than the original creators of the data.
You're the one supposing a thing, so the burden of proof is on you. You need to demonstrate that the "incredibly complicated behaviour" that you're referring to (I assume this is longhand for "thinking", but please correct me if I'm wrong) is indeed emerging from matrix multiplication. Especially given that what you're suggesting is unexpected, given the known way these models work and the explanations that have been put forth already that extrapolate from the known way these models work.
If science were so credulous as to accept the first proffered theory about a new development, well, we wouldn't have these interesting AI models in the first place!
What do matrix multiplication and optimisation have to do with the way the human mind, or the human brain work? That they have anything to do at all, is your assumption, that you seem absolutely convinced about- and then you go asking people why can't it be true? You say why it is true. It's your assumption.
Matrix multiplication and optimization are human mathematical techniques. They have about as good a chance of being something that exists in nature independently of humans as Magic: the Gathering and Call of Duty. They might be useful models to help us understand how things work, but to assume they are how things work is a huge leap of faith.
Ask the right questions and then an answer may even suggest itself. Ask questions that follow from your preconceived answers and you're in a world of fantasy.
Skills like fire or language, for example, had a major influence in the development of our species and are mainly culturally transmitted: trying to reason your way into creating one or the other from scratch is a surprisingly difficult task.
If that point of view is true, then it shouldn’t be surprising that a large part of what we consider human-like behaviours should be tractable simply by analysing large amounts of data. AI systems are not modelling cognition, but culture.
This does not mean that we humans might predict all the time, in fact I would argue that LLMs only predict during training. They generate otherwise. We might also learn by trying to predict. I can imagine babies doing it.