Here's what I can tell you - the fact that Ezra Klein can't figure out how to use AI in his daily work is solely the problem of Ezra Klein. Even with my lack of intelligence, I have somehow landed a job in cancer research, specifically within the field of tumor immunology. In this field, as in many others, AI is able to provide invaluable insight - via pulling vast amounts of research - towards integrating multiple biomarkers across studies into coherent mechanistic models of cancer biology which are already accelerating the discovery of new therapies. In the very near future, synergy between AI and essential academic personnel will be mandatory for any lab hoping to have a competitive standing for grant funding. When I say "this is no longer a matter of science" what I mean is that even at the current state of affairs, we have achieved an architecture displaying intelligence capabilities very similar in effect to that of specialists across many domains. Further iterations of massive LLMs, whose inner workings are quite similar to the learning principles in the brain, are likely to achieve symbolic and abstract reasoning that will be able to productively delve into advanced math subjects, like topology, number theory, etc. (search matrix multiplication and DeepMind for some impressive results already here).
"LLMs, whose inner workings are quite similar to the learning principles in the brain." Really, you don't know what you're talking about lol. Not even people at the vanguard of mechanistic interpretability research know how neural networks work. How can you know they work like the brain (which, by the way, we also don't fully know how it works)? Let me tell you what I think: you just read Leopold's Situational Awareness essay and were mindblown haha.
Actually, I hadn't but it very much seems on point haha
As far as knowing "how neural networks work" is concerned - I think we are talking about different things. We very much know how neural networks work, and neuromorphic architectures specifically (check Intel's Loihi) closely mimic neuronal message passing although they're still lacking in defined learning algorithms similar to the way backprop functions for traditional NNs. What is surprising (but it should not have been in my opinion) is the emergent capabilities of NNs on certain tasks as their complexity passes fuzzy thresholds. In addition (and as it turns out in agreement with Mr. Aschenbrenner's essay), people working at OpenAI and DeepMind have a lot to show for their work. What do the AGI sceptics have to show on the other side? Rule based bs from the 80s? The reason why I'm stuck on your article is that I think it's important for people to realize the magnitude of what's about to hit us instead of opening up debates on whether AI is getting boring or not.
Okay, I buy your last point. But how do you make people pay attention to that if they really *are bored*. I have a very good bird's eye view on this and believe me, half the world is already at something else. If Leopold is right (I don't think so but worth thinking about) how do you make those people pay attention back except by acknowledging their experience and contrasting it with a different point of view, like yours?
Saying "this is no longer a matter of science" reveals that you, indeed, have no intelligence. No wonder you can't wait for it to be a commodity!
Here's what I can tell you - the fact that Ezra Klein can't figure out how to use AI in his daily work is solely the problem of Ezra Klein. Even with my lack of intelligence, I have somehow landed a job in cancer research, specifically within the field of tumor immunology. In this field, as in many others, AI is able to provide invaluable insight - via pulling vast amounts of research - towards integrating multiple biomarkers across studies into coherent mechanistic models of cancer biology which are already accelerating the discovery of new therapies. In the very near future, synergy between AI and essential academic personnel will be mandatory for any lab hoping to have a competitive standing for grant funding. When I say "this is no longer a matter of science" what I mean is that even at the current state of affairs, we have achieved an architecture displaying intelligence capabilities very similar in effect to that of specialists across many domains. Further iterations of massive LLMs, whose inner workings are quite similar to the learning principles in the brain, are likely to achieve symbolic and abstract reasoning that will be able to productively delve into advanced math subjects, like topology, number theory, etc. (search matrix multiplication and DeepMind for some impressive results already here).
"LLMs, whose inner workings are quite similar to the learning principles in the brain." Really, you don't know what you're talking about lol. Not even people at the vanguard of mechanistic interpretability research know how neural networks work. How can you know they work like the brain (which, by the way, we also don't fully know how it works)? Let me tell you what I think: you just read Leopold's Situational Awareness essay and were mindblown haha.
Actually, I hadn't but it very much seems on point haha
As far as knowing "how neural networks work" is concerned - I think we are talking about different things. We very much know how neural networks work, and neuromorphic architectures specifically (check Intel's Loihi) closely mimic neuronal message passing although they're still lacking in defined learning algorithms similar to the way backprop functions for traditional NNs. What is surprising (but it should not have been in my opinion) is the emergent capabilities of NNs on certain tasks as their complexity passes fuzzy thresholds. In addition (and as it turns out in agreement with Mr. Aschenbrenner's essay), people working at OpenAI and DeepMind have a lot to show for their work. What do the AGI sceptics have to show on the other side? Rule based bs from the 80s? The reason why I'm stuck on your article is that I think it's important for people to realize the magnitude of what's about to hit us instead of opening up debates on whether AI is getting boring or not.
Okay, I buy your last point. But how do you make people pay attention to that if they really *are bored*. I have a very good bird's eye view on this and believe me, half the world is already at something else. If Leopold is right (I don't think so but worth thinking about) how do you make those people pay attention back except by acknowledging their experience and contrasting it with a different point of view, like yours?