Whatever your ideas for regulating AI, I say start with China. Do not put China as an afterthought at the end of your proposal, mentioned in a vague wish that something good ought to happen and that maybe the future of humanity can be secured.
In verifiable domains, model capability scaling should remain unbounded. Models will simply keep improving by "absorbing more and more of the computational universe", which is infinite by construction.
Yeah, I mean it's a little different, but I think uh you're going to see more and more uh uh high performance and um low energy uh inference hardware systems because I think everyone is now realizing that inference is the key to making you know these agent-based systems be available to more and more people and that latency is really important and that specialization of the hardware is a really key way you can make uh things that are more energy efficient and lower latency than more general purpose uh computational devices like say GPUs or TPUs
I think the case of TLA+ and most, not all, but most formal methods, they shine the most in highly computational domains, where most of the problems are highly technical and not like business embedded.
What is happening is that GenAI's climate harm comes from the system design, such as Google's AI overview triggering constantly whether users want it or not, in addition to significantly worse digital bloat tools dominating overconsumption.
A very good and readable book on this interaction, with excellent discussions of Donna Summer, Stevie Wonder, La Monte Young, and Penderecki, among many others.
Almost all discussions of it center on “data”, but it’s actually a fight about payments, and whether banks have a right to monopolize and charge for all economic activity their users engage in, irrespective of whether the bank operates the payment method.
Another challenge for Class II biotechs is that even if you have a nifty computational drug discovery platform, you are still often bottlenecked by the rest of the drug development process. It takes 1,000 (or more!) small steps to advance a drug through discovery, development, and past the regulators.
The bar I typically suggest is that the people manager doesn't need to have the most technical depth on the team, but they need enough depth that they can follow most discussions without slowing them down, understand who's correct in most debates without needing to rely on trust, and generally stay oriented easily.
Discussions of housing policy often focus on issues of zoning, regulation, and other supply restrictions which manifest as increased land prices, but for most American housing, the largest cost comes from building the physical structure itself.
Similarly, most recent discussions of how to promote fertility discuss how we might promote housing, inequality, schooling, day-care, etc., but few consider directly paying parents big amounts to have kids. As indirect approaches require us to guess what are the actual main obstacles to fertility, they are more likely to fail.
Instead of meetings, I used Workplace posts (Meta’s internal version of Facebook Group posts) to share thoughts, start discussions, and make announcements.
Though this book frequently digresses into discussions of the cultural impact of the Star Wars franchise, it also contains one of the most thoroughly-researched biographies of Lucas currently available.
Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known.
My extensive discussions with Andrew led me to conclude that the focus on value versus growth doesn’t serve investors well in the fast-changing world in which we live.
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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