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We already have a superintelligence. It's called civilization. Humans, institutions, and now AI agents all interact among each other in one big economy. Allison Duettmann's take is that the real project in such a system is building and preparing AI enabled institutions on top of what already works. Her...

11,759 görüntüleme • 9 gün önce •via X (Twitter)

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Microsoft AI CEO Mustafa Suleyman reveals the assumption about superintelligence he thinks the other AI labs have backwards "Some of the other labs are making an assumption that a superintelligence that is smarter than all of us put together is both inevitable and even desirable. And that such a system would probably be very hard to control" "We have to reset that and make the assumption that we should only bring a system like that into the world that we are sure we can control, that operates in a subordinate way to us, that humans remain at the top of the food chain" "These tools, like any other past technology, are designed to enhance human wellbeing and serve humanity. Not exceed humanity" "Some of the things that you hear from Elon often, or even others in the field - they're fixating on a world in 2050 or 2075 when they're going off exploring other universes and conquering resources from other planets. A system like that, it is unclear to me how it would have any time for preserving us as a species" "We have to make a decision as a species to prioritize creating superintelligences that are aligned, that care about humans and want to protect humans. If we just accelerate and cut all those corners, we're taking a massive risk with the future of our species" He calls it humanist superintelligence: build it only if it is provably controllable, subordinate, and pointed at human wellbeing. The uncomfortable part: "provably controllable" is a standard nobody in the industry can verify yet.

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Jack Dorsey says the real danger isn't open source AI, it's five CEOS deciding what the world is allowed to build with AI. "These AI companies are building platforms and they're all incentivized around their own particular models. You have to ask them for permission, you have rate limits, and you have all these things. Even what the models spit back are constrained." "What are the technologies where you don't need permission from a company, from a CEO, to work on what you want, to build what you want, to build a business around that? Those are the things that are durable. That's why these open protocols are significant and important." "It limits the potential of really great ideas bringing humanity forward because it's a decision of a group of CEOs that think they know best. They may know best for their company, but they don't know the best for the creativity of the world and what people actually want to do with it." "Fortunately, there's a great movement in open source AI, and DeepSeek was an important moment in this space for exactly that reason to show a different path and to provide something that is not only competitive but better than the models that the corporations are putting out there." "We should not be reliant upon five companies telling us that they know best, and there should not be open source because it's dangerous. No, we should have these things in the open and race towards solutions that stay ahead of all the dangers." PS. If you found value in this post make sure to like and repost this tweet + follow Uncover AI to stay updated with the latest AI news. See you in the next one:

Uncover AI

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Why AI Can Now Make Discoveries - my conversation with Dan Roberts, Lead of the Foundations of Reinforcement Learning team at OpenAI 00:00 Intro: AI's wild week in mathematics 01:21 What OpenAI's Foundations of RL team does 03:08 Dan's journey: from black holes and quantum gravity to frontier AI 07:04 Are AI systems becoming useful for real science 08:21 The AI math moment: Erdős, OpenAI, DeepMind, and Anthropic 08:52 Why the OpenAI result was an act of exploration 10:25 OpenAI vs. DeepMind: informal reasoning vs. formal proof 12:13 RL 101: learning by doing, not just watching 15:10 Why reinforcement learning works 15:58 How RL breaks: sparse feedback and long-horizon tasks 17:03 RLHF: how human feedback shaped early language models 18:48 Move 37, self-play, and the search for novel strategies 22:16 Explore vs. exploit in scientific discovery 24:49 Why RL may now be "the cake," not the cherry on top 25:46 Why RL started working with large language models 27:29 Is RL "sucking supervision through a straw"? 28:47 Why language may be the grounding layer for intelligence 31:46 A contrarian take on the Bitter Lesson 32:41 What test-time compute actually is 34:50 How RL gives models the ability to think 35:40 Verifiable rewards, math, coding, and the messy real world 38:00 What physics can teach us about AI 42:08 Is there a thermodynamics of AI? 43:08 From Erdős problems to Einstein-level AI 45:16 Is AI already doing original science? 45:51 How far are we from AI automating AI research 47:41 Why Dan is excited about the future of science

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