a recognition that AI is powerful (and will reshape power relations among humans) but that the consequences will flow messily through multiple complex processes that we really need to start mapping and understanding
researchers started by seeking a 1:1 mapping between neurons and concepts, like a neuron that always fired when the AI was thinking about cats, but quickly learned that nothing like that existed.
Incredible watering down -- the Singularity is now redefined to mean "the rate of new firm creation has increased somewhat"
Vernor Vinge described the Singularity as an event horizon past which everything (e.g. what happens tomorrow) becomes entirely unimaginable and unpredictable to human understanding
there's not a technical reason that they couldn't have plaid lunchboxes and shoelaces or be singing a song, but it would probably be more distracting and just take away from that instant recognition that you're aiming for.
I genuinely believe that if you took an open weights model from 2025 and built a pentest harness for it, it could do this kind of sandbox escape and scan/hack in most networks. This is only surprising because you assume OpenAI has sounder sandboxes.
As mentioned before, Uber and Lyft are better regulators than the State’s paper-based taxi medallions, email is superior to the USPS, and SpaceX is out-executing NASA.
I loved this, but partly this was the pleasant shock of recognition: much of the action revolves around the National Science Foundation and, physically, its old headquarters in Arlington, which Robinson captures extremely well (even the atmosphere of review panels).
we feel this sense of ownership of products that we rely on every day. And we're angry when they change. Even if they change for the better, we're like angry because we go through life faster because of pattern recognition.
I will tell you though what the most surprising thing has been. The most surprising thing has been the lack of public recognition of how close we are to the end of the exponential.
At least for me, when I remember myself being 5 years old, my I was I was very excited about cars back then, and I'm pretty sure my car recognition was more than adequate for self-driving already.
And actually, you don't actually need or want the knowledge. I actually think that's probably actually holding back the neural networks overall because it's actually like getting them to rely on the knowledge a little too much sometimes.
You know, interestingly, LM themselves are quite bad at playing chess. Like, they hallucinate moves. They look at patterns, right? They're they're very good at pattern recognition, but not so good at going super super super deep on a specific chess thing.
This is a short (~150 pp), brisk first book on the topic for physicists. Most of the book --- the first ten chapters --- are good introduction to common models of spin glasses.
I'm now fairly pessimistic about ambitious interpretability (i.e. complete reverse-engineering), and I'm excited about model biology (studying qualitative high-level properties of models) and applied interpretability (rigorously doing useful things with interp).
This means that working on neural networks is NOT getting us closer to AGI, except indirectly.
Their words now
It’s become very difficult for me to maintain the belief in the stupidity of ChatGPT when every time I laugh at it, it ends up ridiculing me 6 months later.
It follows that a true AGI with full, human-level autonomy is inconceivable in the Kantian sense without also gaining our recognition as a moral subject.
AI competition needs to be tempered with a recognition that China isn’t going away as a player in AI, and that coexistence must also be a part of America’s strategy.
Websites outside of these handful of social networks are harder and harder to even find, and partially because of that, they have a harder and harder time sustaining themselves.
Mind candy portal fantasy, in which Our Protagonist's struggles to escape back to Mundania are rather complicated by her growing recognition that her life there sucked, actually, and maybe fighting strange beasts and stranger people isn't so bad in comparison...
The biggest issue is probably that we don’t control neutral networks enough to be able to ensure AI doesn’t harm humans. We can’t even control AI to not reveal internal prompts.
What we could we could train people by saying, "Here's the recognition prime decision model. Now, you know, follow this." And that would be useless. Because of uh telling people the strategy isn't going to buy them anything. The intuition part is a reflection of the patterns they've built up through experience. And so there's no shortcut for that.
in 1985, um my colleagues and I came up with a description of how people actually make life-and-death decisions under extreme time pressure and certainty. And we called it the recognition prime decision model. And it's a blend of intuition plus analysis. And we found we studied firefighters. We found that most fire that accounted for almost 90% of the tough decisions firefighters made.
This is a sad, depressing love story of a father to his son. It’s pure sacrifice. The narrator’s melancholy mindset and recognition of impossible odds never derails his duty to his boy. It’s the pinnacle of grace.
Neural network pruning techniques can reduce the parameter counts of trained networks by over 90%, decreasing storage requirements and improving computational performance of inference without compromising accuracy.
Neural network pruning techniques can reduce the parameter counts of trained networks by over 90%, decreasing storage requirements and improving computational performance of inference without compromising accuracy.
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