sadid

yes, but on this n-gram vs transformers; if you consider more general paradigm, self attention mechanism is basically a special form of a graph neural networks [1].

[1] Bridging Graph Neural Networks and Large Language Models: A Survey and Unified Perspective https://infoscience.epfl.ch/server/api/core/bitstreams/7e6f8...

That’s true in a narrow functional sense, but it misses the role of a world model. Intelligence isn’t just about approximating input-output mappings, it’s about building structured, causal models that let an agent generalize, simulate, and plan. Universal approximation only says you could represent those mappings, not that you can efficiently construct them. Current LLMs seem intelligent because they encode vast amounts of knowledge already expanded by biological intelligence. The real question is whether an LLM, on its own, can achieve the same kind of efficient causal and world-model building rather than just learning existing mappings. It can interpolate new intermediate representations within its learned manifold, but it still relies on the knowledge base produced by biological intelligence. It’s more of an interpolator than an extrapolator: as an analogy.

I think that’s a bit of a false take. The earlier point wasn’t pivot on a specific definition of EQ (pop-psychology take), but about the contrast between systematic intelligence (like MIT) and the storytelling ability (WWE) needed to create a coherent story that makes sense. Whatever you want to call it, we clearly need both.

The MIT vs. WWE contrast feels like a false dichotomy. MIT represents systematic, externalized intelligence (structured, formal, reductive, predictive). WWE or Pixar represent narrative and emotional intelligence. We do need both.

Also evolution is the original information-processing engine, and humans still run on it just like microbes. The difference is just the clock speed. Our intelligence, though chaotic and unstable, operates on radically faster time and complexity scales. It's an accelerator that runs in days and months instead of generations. The instability isn’t a flaw: it’s the turbulence of the way faster adaptation.

It’s hard not to see consciousness (whatever that actually is) lurking under all this you just explained. If it’s emergent, the substrate wars might just be detail; if it’s not, maybe silicon never gets a soul.

Actually, the underlying reason for at least the successful stories behind such migrations stems from non-GUI thinking. The power lies in the CLI and engagement with POSIX-compliance with an open arm. This sometimes turn out highly liberating for developers (which lead them to write such stories).

As a researcher, I'm agree with your point. One important aspect of science/scholarship is reproduciblity [1]. There is a crisis in current science regarding this. The response to this crisis is the open science movement. Some protocols on how this body of knowledge is maintained should be change but this takes time.

[1]: https://en.wikipedia.org/wiki/Replication_crisis