I don’t know I assume not but everyone has a product that could easily be profitable, it would just be dumb to do it because you will lose out to everyone else running at a loss to capture market share. I just mean the guy seems to have an aversion to business sensibility generally. I think he’s really in it for the love of the research. He’s of course rightly lauded for everything he’s done, he’s extremely brilliant, and in person (at a distance) very kind and reasonable (something that is very different than his LinkedIn personality which is basically a daily pissing contest). But I would not give him one cent of investment personally.

This goes for any experienced senior SWE individual with a sharp attention to detail can easily tell if an AI wrote a project or not.

Right now the definition of AGI has been hijacked so much that it can mean absolutely anything.

When I look at these cars, I don't see them going faster, I see them hovering higher and higher above the ground. They're already flying.

Good thing nobody listened to the LeCuns saying cars were a deadend back in the 1910s.

I can see why it's so intoxicating though, it seemed magical that scaling got us as far as it did.

Can you point me to some of the actual research you're talking about? I'd love to read.

You're going to be eating so much crow shortly.

Sure and that is fair. Seldom are extreme viewpoints likely scenarios anyways, but my disagreement with him stems from his unwarranted confidence in his own abilities to predict the future when he's already wrong about LLMs.

He has zero epistemic humility.

We don't know the nature of intelligence. His difficulties in scaling up his research is a testament to this fact. This means we really have no theoretical basis upon which to rest the claim that superintelligence cannot in principle emerge from LLM adjacent architectures--how can we make such a statement, when we don't even know what such thing looks like?

We could be staring at an imperative definition of superintelligence and not know it, nevermind that approximations to such a function could in principle be learned by LLMs (universal approximation theorem). It sounds exceedingly unlikely, but would you rather be comforted by false confidence or be told the honest truth of what our current understanding of the sciences can tell us?

I should clarify that LLMs trained on the internet are necessarily a dead end, theoretically, because the internet both (1) lacks specialist knowledge and knowledge that cannot be encoded in text / language, and (2) is polluted with not just false, but irrelevant knowledge for general tasks. LLMs (or rather, transformers and deep models tuned by gradient descent) trained on synthetic data or more curated / highly-specific data where there are actual costs / losses we can properly model (e.g. AlphaFold) could still have tremendous potential. But "LLM" in the usual, everyday sense in which people use this label, are very limited.

A good example would be trying to make an LLM trained on the entire internet do math proofs. Almost everything in its dataset tells it that the word "orthogonal" means "unrelated to", because this is how it is used colloquially. Only in a tiny amount of math forums / resources it digested does this actually mean something about the dot product, so clearly an LLM that does math well only does so by ignoring the majority of the space it is trained on. Similar considerations apply for attempting to use e.g. vision-language models trained on "pop" images to facilitate the analysis of, say, MRI scans, or LIDAR data. That we can make some progress in these domains tells us there is some substantial overlap in the semantics, but it is obvious there are limits to this.

There is no reason to believe these (often: irrelevant, incorrect) semantics learned from the entire web are going to be helpful for the LLM to produce deeply useful math / MRI analysis / LIDAR interpretation. Broadly, not all semantics useful in one domain are useful in another, and, even more clearly, linguistic semantics clearly have limited relevance to much of what we consider intelligence (which includes visual, auditory, proprioceptive/kinaesthetic, and, arguably, mathematical abstractions). But, it could well be that curve-fitting huge amounts of data from the relevant semantic space (e.g. feeding transformers enough Lean / MRI / LIDAR data) is in fact all we need, so that e.g. transformers are "good enough" for achieving most basic AI aims. It just is clearly the case that the internet can't provide all that data for all / most domains.

EDIT: Also Anthropic's writeups are basically fraud if you actually understand the math, there is no "thinking ahead" or "planning in advance" in any sense, literally just if you head down certain paths due to pre-training, yes, of course, you can "already see" weight activations of future tokens: this is just what curve-fitting in N-D looks like, there is no where else for the model to go. Actual thinking ahead means things like backtracking / backspace tokens, i.e. actually retracing your path, which current LLMs simply cannot do.

Yes, LLMs will continue to progress until they hit the limits of LLMs.

The idea that LLMs will reach AGI is entirely speculative, not least because AGI is undefined and speculative.