41–50 of 529 posts
Earlier quoted context omitted.
Europeans have free healthcare and retirement. They consider putting their money with long term benefits not just become CEO on Tuesday and declare bankruptcy on Wednesday.
It is not free, we just pay taxes.
Adds up : We are seeing a clear exodus of both capital and talent from the US - with the current US administration’s shift toward cronyism - and the EU stands as the most compelling alternative with a uniform market of 500 million people and the last major federation truly committed to the rule of law.
Earlier quoted context omitted.
I don't understand this view. How I see it the fundamental bottleneck to AGI is continual learning and backpropagation. Models today are static, and human brains don't learn or adapt themselves with anything close to backpropagation. World models don't solve any of these problems; they are fundamentally the same kind of deep learning architectures we are used to work with. Heck, if you think learning from the world i…
I don't understand why online learning is that necessary. If you took Einstein at 40 and surgically removed his hippocampus so he can't learn anything he didn't already know (meaning no online learning), that's still a very useful AGI. A hippocampus is a nice upgrade to that, but not super obviously on the critical path.
Justifiable. There are a lot more degrees of freedom in world models. LLMs are fundamentally capped because they only learn from static text -- human communications about the world -- rather than from the world itself, which is why they can remix existing ideas but find it all but impossible to produce genuinely novel discoveries or inventions. A well-funded and well-run startup building physical world models (ground…
There will be no "unlocking of AGI" until we develop a new science capable of artificial comprehension. Comprehension is the cornucopia that produces everything we are, given raw stimulus an entire communicating Universe is generated with a plethora of highly advanceds predator/prey characters in an infinitely complex dynamic, and human science and technology have no lead how to artificially make sense of that in a s…
Earlier quoted context omitted.
Whether it is text or an image, it is just bits for a computer. A token can represent anything.
Sure, but don't conflate the representation format with the structure of what's being represented. Everything is bits to a computer, but text training data captures the flattened, after-the-fact residue of baseline human thought: Someone's written description of how something works. (At best!) A world model would need to capture the underlying causal, spatial, and temporal structure of reality itself -- the thing its…
So when we think about capturing any underlying structure of reality itself, we are constrained by the tools at hand.
The capability of the tool forms the description which grants the level of understanding.
Justifiable. There are a lot more degrees of freedom in world models. LLMs are fundamentally capped because they only learn from static text -- human communications about the world -- rather than from the world itself, which is why they can remix existing ideas but find it all but impossible to produce genuinely novel discoveries or inventions. A well-funded and well-run startup building physical world models (ground…
why LLMs (transformers trained on multimodal token sequences, potentially containing spatiotemporal information) can't be a world model?
> One major critique LeCun raises is that LLMs operate only in the realm of language, which is a simple, discrete space compared to the continuous, complex physical world we live in. LLMs can solve math problems or answer trivia because such tasks reduce to pattern completion on text, but they lack any meaningful grounding in physical reality. LeCun points out a striking paradox: we now have language models that can pass the bar exam, solve equations, and compute integrals, yet “where is our domestic robot? Where is a robot that’s as good as a cat in the physical world?” Even a house cat effortlessly navigates the 3D world and manipulates objects — abilities that current AI notably lacks. As LeCun observes, “We don’t think the tasks that a cat can accomplish are smart, but in fact, they are.”
Earlier quoted context omitted.
I don't understand this view. How I see it the fundamental bottleneck to AGI is continual learning and backpropagation. Models today are static, and human brains don't learn or adapt themselves with anything close to backpropagation. World models don't solve any of these problems; they are fundamentally the same kind of deep learning architectures we are used to work with. Heck, if you think learning from the world i…
I don't understand why online learning is that necessary. If you took Einstein at 40 and surgically removed his hippocampus so he can't learn anything he didn't already know (meaning no online learning), that's still a very useful AGI. A hippocampus is a nice upgrade to that, but not super obviously on the critical path.
But one might say that the brain is not lossless ... True, good point. But in what way is it lossy? Can that be simulated well enough to learn an Einstein? What gives events significance is very subjective.
Earlier quoted context omitted.
I don't understand this view. How I see it the fundamental bottleneck to AGI is continual learning and backpropagation. Models today are static, and human brains don't learn or adapt themselves with anything close to backpropagation. World models don't solve any of these problems; they are fundamentally the same kind of deep learning architectures we are used to work with. Heck, if you think learning from the world i…
I don't understand why online learning is that necessary. If you took Einstein at 40 and surgically removed his hippocampus so he can't learn anything he didn't already know (meaning no online learning), that's still a very useful AGI. A hippocampus is a nice upgrade to that, but not super obviously on the critical path.
Justifiable. There are a lot more degrees of freedom in world models. LLMs are fundamentally capped because they only learn from static text -- human communications about the world -- rather than from the world itself, which is why they can remix existing ideas but find it all but impossible to produce genuinely novel discoveries or inventions. A well-funded and well-run startup building physical world models (ground…
I don't understand this view. How I see it the fundamental bottleneck to AGI is continual learning and backpropagation. Models today are static, and human brains don't learn or adapt themselves with anything close to backpropagation. World models don't solve any of these problems; they are fundamentally the same kind of deep learning architectures we are used to work with. Heck, if you think learning from the world i…
While I suspect latter is a real problem (because all mammal brains* are much more example-efficient than all ML), the former is more about productisation than a fundamental thing: the models can be continuously updated already, but that makes it hard to deal with regressions. You kinda want an artefact with a version stamp that doesn't change itself before you release the update, especially as this isn't like normal software where specific features can be toggled on or off in isolation of everything else.
* I think. Also, I'm saying "mammal" because of an absence of evidence (to my *totally amateur* skill level) not evidence of absence.