与「evolution」相关的搜索结果

Given some of the results I'm seeing recently, it's pretty clear Codex is a good harness. But it will seem primitive in 2-3 months and we're about to go through another major evolution in how we use AI at the frontier. The next generation of models need more than your laptop.

DeepMind's Andrew Trask on why the scaling laws are pushing AI from one big model toward a protocol: "The zoomed-out picture is that in the end, AI is gonna be a protocol instead of a program. We're seeing that evolution start to really gather steam as the scaling laws constrain how much data, compute, and talent one company can bring together." "When you combine models from multiple different providers, you're implicitly combining the data, compute, and talent that they trained on. So you can get better, faster models for a lower price, which is pretty crazy when you think about it." "If you want the absolute most accurate model, ensembling the top models is always going to win, you'll get higher scores than any single model. And if you want the best accuracy relative to any unit of price, ensembling some open and closed models is likely gonna own that Pareto frontier quietly for a while." "It won't be until there's a leaderboard that widely recognizes ensembles as comparable to individual models that we start to really see it. Then in 12 to 18 months, that saturates a bunch of benchmarks across the space, and the harnesses pick it up, routing you to the best combinations of models on the fly. That's when the market really starts to change in terms of how people buy intelligence." @iamtrask @openminedorg

The biggest mistake in humanoid robotics isn't engineering. It's thinking the robot is the entire business. History says otherwise. The companies that become indispensable don't always build the machine—they build the ecosystem everyone else eventually depends on. For the past six years, PITN has been preparing for a world where humanoid robotics becomes a multilingual global economy, not just a collection of impressive machines. While headlines chased the next robot demo, we quietly assembled a strategic portfolio of 500+ robotics and AI digital assets across 30 of the world's most influential languages. Not because domains are the future. Because digital identity is. Every emerging industry reaches a tipping point where attention shifts from Can we build it? to How will the world find, trust, and connect with it? That's when gateways matter. Maybe the robotics industry hasn't reached that realization yet. Just because evolution is slow doesn’t mean you need to be! Maybe it's closer than anyone thinks. Either way, we chose to build for tomorrow instead of competing for today's headlines. The future won't speak one language. Neither will humanoid robotics. Embrace the future. #HumanoidRobotics# #ArtificialIntelligence# #Robotics# #FutureTech# #DigitalAssets# #Innovation#

Geoffrey Hinton says a big language model runs on about 1% of your brain's connections and still ends up knowing more than you: "So in your brain, you have a hundred trillion connections, roughly speaking. Okay. That's a lot. And you only live for about two billion seconds. That's not much." "If you compare how many seconds you live for, with how many connections you've got, you have a whole lot more connections than experiences." "Now with these neural nets, it's sort of the other way round. They only have of the order of a trillion connections. So like 1% of your connections, even in a big language model, many of them fewer, but they get thousands of times more experience than you." "So the big language models are solving the problem with not many connections, only a trillion. How do I make use of a huge amount of experience?" "And back propagation is really, really good at packing huge amounts of knowledge into not many connections." "But that's not the problem we're solving. We've got huge numbers of connections, not much experience. We need to sort of extract the most we can from each experience." Two to three billion seconds is the whole budget. Everything you know, you learned inside it. So evolution built you to squeeze a lot out of very little. Hinton's point is that a language model has the opposite problem and the opposite fix, and backprop turned out to be extremely good at that fix. Worth noticing what this predicts about failure. A system running on 1% of your wiring and thousands of times your experience is not going to fail the way you do. You fail from having seen too few examples. It fails from compressing too many into too little, and the compression is where the errors get made. That is a strange thing to be deploying into hospitals and courts with no way to inspect it. We test these systems by asking them questions, which tells you what came out. Nobody can yet look at a trillion connections and say what got packed in. - Geoffrey Hinton, Nobel laureate and Turing Award winner, on StarTalk (@StarTalkRadio) with Neil deGrasse Tyson.

After the interview, on Hard Lessons, Jean Eric Salata, Chair of EQT Group, joins Global Co-Head of Investment Banking Mo Assomull to discuss the evolution of private equity. As the industry matures, he explains why scale, diversification, and long-term partnerships with limited partners are becoming increasingly important.

Curious about how we can coexist and collaborate with AI? AI session speakers and exhibitors at SusHi Tech Tokyo 2026 share insights on how we can handle this dynamic AI evolution. 📅NEXT: May 20-22, 2027 🌏 #SusHiTechTokyo2026#

Did the ancient "hobbits" of human evolution really hunt big game? 🐘 A new study in Science Advances says no, they were actually scavenging raw leftovers from giant Komodo dragons! 🐉 Watch to learn how our understanding of Homo floresiensis is completely changing. #HumanEvolution# #Anthropology# #AncientDNA# #ScienceNews#

The formation of Antarctica's vast East Antarctic Ice Sheet may have been driven not only by falling atmospheric carbon dioxide but also by the slow uplift of the continent itself, according to a new study in Science. The findings highlight how interactions among tectonics, topography, climate, and ice-sheet dynamics can fundamentally shape Earth’s long-term climate evolution. Learn more: