I see your point but on the other hand a lot of conversations go: A: what will we do when AI do all the jobs, B: that's silly LLMs can't do the jobs. The thing is A didn't say LLM, they said AI as in whatever that will be a short while into the future. Which is changing rapidly because thousands of bright people are being paid to change it.
The trouble is that "AI" is also very much a leaky abstraction, which makes it tempting to see all the "AI" advances of recent years, then correctly predict that these "AI" advances will continue, but then jump to all sorts of wrong conclusions about what those advances will be.
For example, things like "AI" image and video generation are amazing, as are things like AlphaGo and AlphaFold, but none of these have anything to do with LLMs, and the only technology they share with LLMs is machine learning and neural nets. If you lump these together with LLMs, calling them all "AI", then you'll come to the wrong conclusion that all of these non-LLM advances indicate that "AI" is rapidly advancing and therefore LLMs (also being "AI") will do too ...
Even if you leave aside things like AlphaGo, and just focus on LLMs, and other future technology that may take all our jobs, then using terms like "AI" and "AGI" are still confusing and misleading. It's easy to fall into the mindset that "AGI" is just better "AI", and that since LLMs are "AI", AGI is just better LLMs, and is around the corner because "AI" is advancing rapidly ...
In reality LLMs are, like AlphaFold, something highly specific - they are auto-regressive next-word predictor language models (just as a statement of fact, and how they are trained, not a put-down), based on the Transformer architecture.
The technology that could replace humans for most jobs in the future isn't going to be a better language model - a better auto-regressive next-word predictor - but will need to be something much more brain like. The architecture itself doesn't have to be brain-like, but in order to deliver brain-like functionality it will probably need to include another half-dozen "Transformer-level" architectural/algorithmic breakthroughs including things like continual learning, which will likely turn the whole current LLM training and deployment paradigm on it's head.
Again, just focusing on LLMs, and LLM-based agents, regarding them as a black-box technology, it's easy to be misled into thinking that advances in capability are broadly advancing, and will rise all ships, when in reality progress is much more narrow. Headlines about LLMs achievement in math and competitive programming, touted as evidence of reasoning, do NOT imply that LLM reasoning is broadly advancing, but you need to get under the hood and understand RL training goals to realize why that is not necessarily the case. The correctness of most business and real-world reasoning is not as easy to check as is marking a math problem as correct or not, yet that capability is what RL training depends on.
I could go on .. LLM-based agents are also blurring the lines of what "AI" can do, and again if treated as a black box will also misinform as to what is actually progressing and what is not. Thousands of bright people are indeed working on improving LLM-adjacent low-hanging fruit like this, but it'd be illogical to conclude that this is somehow helping to create next-generation brain-like architectures that will take away our jobs.
I'll give you algorithmic breakthroughs have been quite slow to come about - I think backpropagation in 1986 and then transformers in 2017. Still the fact that LLMs can do well in things like the maths olympiad have me thinking there must be some way to tweak this to be more brain like. I recently read how LLMs work and was surprised how text focused it is, making word vectors and not physical understanding.
> Still the fact that LLMs can do well in things like the maths olympiad have me thinking there must be some way to tweak this to be more brain like
That's because you, as you admit in the next sentence, have almost no understanding of how they work.
Your reasoning is on the same level as someone in the 1950s thinking ubiquitous flying cars are just a few years away. Or fusion power, for that matter.
In your defense, that seems to be about the average level of engagement with this technology, even on this website.
Maybe but the flying cars and fusion ran into fundamental barriers of the physics being hard. With human level intelligence though we have evidence it's possible from our brains which seem to use less compute than the LLMs going by power usage so I don't see a fundamental barrier to it just needing some different code.
> Still the fact that LLMs can do well in things like the maths olympiad have me thinking there must be some way to tweak this to be more brain like.
That's like saying, well, given how fast bicycles make us, so much closer to horse speed, I wonder if we can tweak this a little to move faster than any animal can run. But cars needed more technological breakthroughs, even though some aspects of them used insights gained from tweaking bicycles.
Yes, it's a bit shocking to realize that all LLMs are doing is predicting next word (token) from samples in the training data, but the Transformer is powerful enough to do a fantastic job of prediction (which you can think of as selecting which training sample(s) to copy from), which is why the LLM - just a dumb function - appears as smart as the human training data it is copying.
The Math Olympiad results are impressive, but at the end of the day is just this same next word prediction, but in this case fine tuned by additional LLM training on solutions to math problems, teaching the LLM which next word predictions (i.e. output) will add up to solution steps that lead to correct problem solutions in the training data. Due to the logical nature of math, the reasoning/solution steps that worked for training data problems will often work for new problems it is then tested on (Math Olympiad), but most reasoning outside of logical domains like math and programming isn't so clear cut, so this approach of training on reasoning examples isn't necessarily going to help LLMs get better at reasoning on more useful real-world problems.
> a short while into the future
And what gives you that confidence? A few AI nerds already claimed that in the 80s.
We're currently exploring what LLMs can do. There is no indication that any further fundamental breakthrough is around the corner. Everybody is currently squeezing the same stone.