This is a somewhat nihilistic take with an optimistic ending. I believe humans will never fix hallucinations. Amount of totally or partially untrue statements people make is significant. Especially in tech, it's rare for people to admit that they do not know something. And yet, despite all of that the progress keeps marching forward and maybe even accelerating.
I think most humans are perfectly capable of admitting to themselves when they do not know something. Computers ought to do better.
You must interact with a very different set of humans than most.
I think the hard question is whether those wins can be realized with less effort than what we’re already doing, though. What I mean is this: A brain today is obviously far more efficient at intelligence than our current approaches to AI. But a brain is a highly specialized chemical computer that evolved over hundreds of millions of years. That leaves a lot of room for inefficient and implausible strategies to play ou…
Also, a brain evolved to be a stable compute platform in body that finds itself in many different temperature and energy regimes. And the brain can withstand and recover from some pretty severe damage. So I'd suspect an intelligence that is designed to run in a tighter temp/power envelope with no need for recovery or redundancy could be significantly more efficient than our brain.
Most brain damage would not be considered in the realm of what most people would consider "recoverable".
I don't think you need to be an ML researcher to understand his point of view. He wants to do fundamental research. Optimizing LLMs is not fundamental research. There are numerous other potential approaches, and it's obvious that LLMs have weaknesses that other approaches could tackle.
If he was Hinton's age then maybe he would also want to retire and be happy with transformers and LLMs. He is still an ambitious researcher that wants to do foundational research to get to the next paradigm.
Having said all of that, it is a misjudgement for him to be disparaging the incredible capabilities of LLMs to the degree he has.
The model still has some representation of whether the word after an/a is more likely to start with a vowel or not when it outputs a/an. You can trivially understand this is true by asking LLMs to answer questions with only one correct answer. "The animal most similar to a crocodile is:" https://chatgpt.com/share/67d493c2-f28c-8010-82f7-0b60117ab2... It will always say "an alligator". It chooses "an" because somewher…
Again, that's not a good example I think because everything about the answer is in the prompt, so obviously from the start the "alligator" is high, but then it's just waiting for an "an" to occur to have an occasion to put that. That doesn't mean it knows "in advance" what it want to say, it's just that at every step the alligator is lurking in the logits because it directly derives from the prompt.
You write: "it's just that at every step the alligator is lurking in the logits because it directly derives from the prompt" - but isn't that the whole point: at the moment the model writes "an", it isn't just spitting out a random article (or a 50/50 distribution of articles or other words for that matter); rather, "an" gets a high probability because the model internally knows that "alligator" is the correct thing after that. While it can only emit one token in this step, it will emit "an" to make it consistent with its alligator knowledge "lurking". And btw while not even directly relevant, the word alligator isn't in the prompt. Sure, it derives from the prompt but so does every an LLM generates, and same for any other AI mechanism for generating answers.
Sligtly related: Energy Based Models (EBMs) are better in theory and yet too resource intensive. I tried to sell using EBMs to my org, but the price for even a small use case was prohibitive. I learned it from: https://youtube.com/playlist?list=PLLHTzKZzVU9eaEyErdV26ikyo... Yann LeCun, and Michael Bronstein and his colleagues have some similarities in trying to properly Sciencify Deep Learning. Yann LeCun's approach,…
Jürgen Schmidhuber has a paper with Lucas Beyer? I'm not aware of it. Which do you mean?
A transformer will attend to previous tokens, and so it is free to ignore prior errors. I don't get LeCun's comment on error propagation, and look forward to a more thorough exposition of the problem.
The next-gen LLMs are going to use something like mipmaps in graphics: a stack of progressively smaller versions of the image, with a 1x1 image at the top. The same concept applies to text. When you're writing something, your have a high-level idea in mind that serves as a guide. That idea is such a mipmap. Perhaps the next-gen LLMs will be generating a few parallel sequencies, the top-level will be a slow-pace anchor and the bottom-level being the actual text that depends on slower upper levels.
I don't think you need to be an ML researcher to understand his point of view. He wants to do fundamental research. Optimizing LLMs is not fundamental research. There are numerous other potential approaches, and it's obvious that LLMs have weaknesses that other approaches could tackle. If he was Hinton's age then maybe he would also want to retire and be happy with transformers and LLMs. He is still an ambitious rese…
> it is a misjudgement for him to be disparaging the incredible capabilities of LLMs to the degree he has.
I agree. Spiking neural networks are usually mentioned in this context, but there is no hardware ecosystem behind them that can compete with Nvidia and CUDA.
Investments in AI are now counting by billions of dollars. Would that be enough to create an initial ecosystem for a new architecture?
A new HW architecture for an unproven SW architecture is never going to happen. The SW needs to start working initially and demonstrate better performance. Of course, as with the original deep neural net stuff, it took computers getting sufficiently advanced to demonstrate this is possible. A different SW architecture would have to be so much more efficient to work. Moreover, HW and SW evolve in tandem - HW takes existing SW and tries to optimize it (e.g. by adding an abstraction layer) or SW tries to leverage existing HW to run a new architecture faster. Coming up with a new HW/SW combo seems unlikely given the cost of bringing HW to market. If AI speedup of HW ever delivers like Jeff Dean expects, then the cost of prototyping might come down enough to try to make these kinds of bets.
I have a paper coming up that I modestly hope will clarify some of this. The short answer should be that it's obvious LLM training and inference are both ridiculously inefficient and biologically implausible, and therefore there has to be some big optimization wins still on the table.
Honest question: Given that the only wide consensus of anything approaching general intelligence are humans and that humans are biological systems that have evolved in physical reality, is there any arguments that better efficiency is even possible without relying on leveraging the nature of reality?
For example, analog computers can differentiate near instantly by leveraging the nature of electromagnetism and you can do very basic analogs of complex equations by just connecting containers of water together in certain (very specific) configurations. Are we sure that these optimizations to get us to AGI are possible without abusing the physical nature of the world? This is without even touching the hot mess that is quantum mechanics and its role in chemistry which in turn affects biology. I wouldn't put it past evolution to have stumbled upon some quantum mechanic that allowed for the emergence of general intelligence.
I'm super interested in anything discussing this but have very limited exposure to the literature in this space.