Congratulations to Anthropic for getting an LLM to act as a Turing machine - though that particular achievement shouldn't be surprising. Of greater practical interest is, how efficiently can it act as a Turing machine, and how efficiently should we want it to act. After all, it's far more efficient to implement your Turing machine as a few lines of specialized code. 

On the other hand, the ability to be a (universal) Turing machine could, in principle, be the foundation of the ability to reliably perform complex rigorous calculation and cognition - the kind of tasks where there is an exact right answer, or exact constraints on what is a valid next step, and so the ability to pattern-match plausibly is not enough. And that is what people always say is missing from LLMs. 

I also note the claim that "given only existing tapes, it learns the rules and computes new sequences correctly". Arguably this ability is even more important than the ability to follow rules exactly, since this ability is about discovering unknown exact rules, i.e., the LLM inventing new exact models and theories. But there are bounds on the ability to extrapolate sequences correctly (e.g. complexity bounds), so it would be interesting to know how closely Claude approaches those bounds. 

Standard model coupled to gravitons is already kind of a unified theory. There are phenomena at the edges (neutrino mass, dark matter, dark energy) which don't have a consensus explanation, as well as unresolved theoretical issues (Higgs finetuning, quantum gravity at high energies), but a well-defined "theory of almost everything" does already exist for accessible energies. 

OK, maybe I understand. If I put it in my own words: You think "consciousness" is just a word denoting a somewhat arbitrary conjunction of cognitive abilities, rather than a distinctive actual thing which people are right or wrong about in varying degrees, and that the hard problem of consciousness results from reifying this conjunction. And you suspect that LeCun in his own thinking e.g. denies that LLMs can reason, because he has added unnecessary extra conditions to his personal definition of "reasoning". 

Regarding LeCun: It strikes me that his best-known argument about the capabilities of LLMs rests on a mathematical claim, that in pure autoregression, the probability of error necessarily grows. He directly acknowledges that if you add chain of thought, it can ameliorate the problem... In his JEPA paper, he discusses what reasoning is, just a little bit. In Kahneman's language, he calls it a system-2 process, and characterizes it as "simulation plus optimization". 

Regarding your path to eliminativism: I am reminded of my discussion with Carl Feynman last year. I assume you both have subjective experience that is made of qualia from top to bottom, but also have habits of thought that keep you from seeing this as ontologically problematic. In his case, the sense of a problem just doesn't arise and he has to speculate as to why other people feel it; in your case, you felt the problem, until you decided that an AI civilization might spontaneously develop a spurious concept of phenomenal consciousness. 

As for me, I see the problem and I don't feel a need to un-see it. Physical theory doesn't contain (e.g.) phenomenal color; reality does; therefore we need a broader theory. The truth is likely to sound strange, e.g. there's a lattice of natural qubits in the cortex, the Cartesian theater is how the corresponding Hilbert space feels from the inside, and decohered (classical) computation is unconscious and functional only. 

So long as generative AI is just a cognitive prosthesis for humans, I think the situation is similar to social media, or television, or print, or writing; something is lost, something is found. The new medium has its affordances, its limitations, its technicalities, it does create a new layer of idiocracy; but people who want to learn, can learn, and people who master the novelty, and becomes power users of the new medium, can do things that no one in history was previously able to do. In my opinion, humanity's biggest AI problem is still the risk of being completely replaced, not of being dumbed down. 

I would like to defer any debate over your conclusion for a moment, because that debate is not new. But this is: 

I think one of the main differences in worldview between LeCun and me is that he is deeply confused about notions like what is true "understanding," what is "situational awareness," and what is "reasoning," and this might be a catastrophic error.

This is the first time I've heard anyone say that LeCun's rosy views of AI safety stem from his philosophy of mind! Can you say more?

Completely wrong conclusion - but can you also explain how this is supposed to relate to Yann LeCun's views on AI safety? 

AI futurists ... We are looking for a fourth speaker

You should have an actual AI explain why it doesn't want to merge with humans. 

Would you say that you yourself have achieved some knowledge of what is true and what is good, despite irreducibility, incompleteness, and cognitive bias? And that was achieved with your own merely human intelligence. The point of AI alignment is not to create something perfect, it is to tilt the superhuman intelligence that is coming, in the direction of good things rather than bad things. If humans can make some progress in the direction of truth and virtue, then super-humans can make further progress. 

Many people outside of academic philosophy have written up some kind of philosophical system or theory of everything (e.g. see vixra and philpapers). And many of those works would, I think, sustain at least this amount of analysis. 

So the meta-question is, what makes such a work worth reading? Many such works boil down to a list of the author's opinions on a smorgasbord of topics, with none of the individual opinions actually being original. 

Does Langan have any ideas that have not appeared before?