I am impressed that with multimodal modals in the hands of everyone people still say this. I mean it’s true on the surface but the ability to take a picture of a data sheet and wiring diagram and get it to give me step by step instructions to wire two components together and an example of the UART protocol in cpp that while not directly functional captures all the essential information I need. That’s amazing and it’s not just word prediction, the source is images of documents with diagrams!
Anyone who does this and comes away jaded has lost the ability to dream.
It's not just true on the surface—it's true at its core. All those features you mention are based on statistical relationships. This doesn't mean that these systems can't be very useful, but it also doesn't mean that they are _intelligent_, as much as we like to call them that. They have no understanding of their input or output, beyond being able to pattern match and mechanically decide what the next token should be based on their training data.
Then you need to explain why statistics isn't enough to be intelligent. We are at the point where that isn't an obvious argument anymore and I'll point to the best models as to why you might be wrong.
Because being able to find patterns in large amounts of data will never make the system intuitively understand math[1], or history[2], or any other field. It will always depend on the data and biases we feed it, at least with the current approaches.
You might say that this is how humans learn and demonstrate intelligence, but it's not the same thing. We have the ability to advance our understanding of the universe without being explicitly trained on every topic. Until we can build systems that can do this, I wouldn't label them as intelligent.
But, again, this doesn't mean that they can't be useful.
I never said it wasn’t statistical at any level so I’m not sure what your point is. My point was almost everything we do is an approximation (I mean, all of science and technology and engineering) and very little of it isn’t. Fake it until you make it is pervasive in human endeavors. Over time the errors and issues will be smoothed away until it’s still there but it’s not noticeable enough to be a major problem.
It being statistical doesn’t make it useless. It not being some metaphysical concept of awareness doesn’t make it useless.
But the multimodal model support puts the lie to it being just a stochastic parrot of language. The domains it works in -is- more abstract than syntax and grammar even if it’s grounded in it. That’s part of the entire power of the model. In the end it leads to a predicted token but the path it takes is more subtle and complex than a simple Markov chain of tokens.
> I never said it wasn’t statistical at any level so I’m not sure what your point is.
You said it was true on the surface, and I'm pointing out that it's true at its core. Your amazement at how well it works is based on our inability as humans to find the same patterns in the data as these models do. Ascribing some higher level sense of intelligence or understanding to these systems because of this party trick is anthropomorphizing what they actually are. We would all be better served by keeping in mind how they work, instead of being surprised when they do output the wrong pattern.
> Over time the errors and issues will be smoothed away until it’s still there but it’s not noticeable enough to be a major problem.
Why do you think this is guaranteed? We can keep throwing data and compute at these systems, and while we might continue to get better results, the current systems will never intuitively understand that e.g. 9.11 is lower than 9.2[1], unless those specific examples are in their training data. As we reach the limits of the data we can feed them, and we have to generate synthetic data, there's no reason to believe that the current approaches will ever fix these problems at their core.
> It being statistical doesn’t make it useless. It not being some metaphysical concept of awareness doesn’t make it useless.
I never said it was. I agree that this can be useful, but again, as long as we're aware of their limits. It's good keeping this in mind as we're reaching the peak of inflated expectations of the hype cycle.
I meant, the answer to the question of why does AI hallucinate is the same answer to the question "why does any statistical system ever produce an incorrect result" which I thought was well established.
How can it be well established when the LLMs are still actively changing and improving.
Don't think we can say, statistical systems can have incorrect results, hence we no longer need to study how statistical system produce incorrect results, because we already know they can have incorrect results.
</Sarcasm> Are we in 2024 and people are still just saying "duh its statistics, nothing to see here".