Podcasts about Agis

One popular objection to AI concerns is to declare that LLMs can never be AGI. You need a "new paradigm". Therefore, AGI is so far in the future that it's not worth worrying about. A common counterargument is to claim that no, LLMs can become AGI. But even without that counterargument, I think the "therefore" fails on its own terms. The key question is: how much of a new paradigm do we need? The landmark discoveries on the road to modern LLMs are something like: 1950s: Neural networks 1967: Multi-layer perceptron 2010: Modern deep learning 2017: Transformer, LLM 2022: RLHF, chatbots 2024: Chain of thought / test-time compute We can think of this as an "evolutionary tree", where a given LLM (let's say Claude Opus 4.7) shares a recent "common ancestor" with all other chatbots, and only a very distant "common ancestor" with everything else descended from the multi-layer perceptron. If AGI needs a "new paradigm", what common ancestor can we expect AGI and LLMs to share? AGI will very likely use neural networks, because the human brain is a neural network and qualifies as an AGI. It will probably use deep learning, because although deep learning isn't exactly analogous to the brain, it seems like a pretty reasonable way to emulate the brain's learning algorithms onto computer hardware. Skeptics like Yann LeCun and Gary Marcus usually pinpoint LLMs/transformers as the step where we went wrong. LeCun thinks that the first AGIs may be within the deep learning paradigm (but not LLMs); Marcus thinks that they'll combine insights from deep learning with something else. How soon should we expect a new paradigm as revolutionary as LLMs/transformers? Since we got LLMs/transformers nine years ago, Lindy's Law suggests nine more years. How soon should we expect a new paradigm as revolutionary as deep learning? By the same logic, sixteen years from now. Lindy's Law has a heavy tail, which means we can't simply halve these to find our 25th percentile estimate. Our 25th percentile estimate for the next advance as exciting as LLMs should be three years from now; for deep learning, it's five years. So even if you think AGI will require a further paradigm shift as big as the invention of the LLM or as deep learning itself, you should have 25% chance it will be developed in the next 3 - 5 years. Which is about as long as the LLM-only crowd think things will take! This isn't an excuse for relegating the risk of AGI to some vague indefinite future. It could still be the late 2020s or early 2030s! (Might we expect that low-hanging-fruit effects make the next paradigm harder to find than the last one? In practice, fields get more researchers as time goes on, and that effect usually causes time-between-advances to be approximately constant. And in fact, the number of AI researchers has grown at an unprecedented pace for a scientific field, and growth will enter an even faster regime once AIs themselves can contribute. Overall these make me think things will go even faster than Lindy's Law predicts - but I think Lindy's Law is a useful upper bound.) (Would there still be a long time between the invention of the new paradigm and the point where it could be used to maximum effect? It took five years between the invention of the transformer and ChatGPT, the first commercially-successful transformer-based project. But most of that time was spent scaling up, and we've already scaled up. If we invent a new paradigm in 2030, then any frontier lab willing to bet on it can quickly provide it with levels of compute sufficient to train human-brain-sized models.) This is my attempt to talk to the new-paradigm-wanters in their own language, but I think there's also a subtler point that undermines this worldview. In the past, new paradigms have proven useful in allowing scaling to continue after an old paradigm passed the regime where it could efficiently convert scale to results. LLMs still seem to be able to convert scale to results; while this continues, new paradigms won't be necessary, and frontier labs won't risk pursuing them. If scaling ever hits a wall, there will be a few months of confusion as frontier labs look over various new-paradigm-proposals that they already have lying around, and throw them at the wall to see what breaks through. Then scaling will continue from wherever it left off. The best way to forecast future AI progress is to extrapolate from current LLM scaling. This should work if LLMs scale all the way to AGI. But it may also work even if they don't. First, because we might get the new paradigm so soon that it won't be a significant source of delay. And second, because the most likely place for a new paradigm to start is wherever LLMs stop working, going at the same rate. https://www.astralcodexten.com/p/new-paradigms-wont-save-you

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On April 18th, hundreds of tenants gathered inside a school cafeteria to help shape Toronto's first city-wide tenant's union – the TTU. There were elections, robust policy discussions and plenty of stories from tenants. Blueprints of Disruption was there to capture it all, and provide analysis afterwards.On-scene interviews from inside the Toronto Tenant Union's founding Convention start off with Ricardo Tranjan, housing policy expert and author of The Tenant Class. Tranjan talks about his expectations for the day, and his excitement at the prospect of “taking another step” towards building the tenant class consciousness he wrote about in his book.There are also interviews with tenant organizers from different buildings across the city – including Jerry and Steve from 240 Markland. They talk about their saga with an REIT buying their building, applying for five consecutive above-guideline rent increases (AGIs) and trying to push legacy tenants out of the building.We also interview Bridgette, a resident of the Caseway building – outside of which convention participants rallied after the day's more official proceedings. She talks about why its been such a battle with their landlord, and how she felt about being a part of something bigger.Organizers of the event, and leaders within the ‘parent' organizations of the TTU also took time to speak with Santiago and Jessa as things wrapped up.Aniket Kali, formerly of Climate Justice Toronto, and Bruno Dobrusin of YSW Tenants Union talk about their new roles (both elected during the Convention), the scaling up of their models, and their vision for the TTU in the years to come.Hosted and Produced by: Jessa McLean and Santiago Helou QuinteroCall to Action: Sign up for TTU Orientation on May 9th, 2026Related Episodes: Blueprints of a Rent Strike (July 2023) Shifting Gears: Climate Justice Toronto (Sept 2024) CJTO on their decision to transition towards tenant organizing as a means to fight climate change.We also have a TENANT POWER PLAYLIST with more stories of neighbours organizing.More Resources: Can the new Toronto Tenant Union change the tired housing debate? – Ricardo Tranjan for Canadian DimensionWho Controls Toronto? The People or Developers? - ACORN ReportHamilton moves to strengthen renoviction bylaw - via CBCRent Strikes! - The Grind MagazineCanada: How Tenants Fought Back- via Progressive InternationalAll of our content is free - made possible by the generous sponsorships of our Patrons. If you would like to support our work through monthly contributions: PatreonFollow us on Instagram or on Bluesky

“Delegating knowledge is not the same as delegating wisdom. You learn by experience, and if you don’t have any experiences…you will get cognitive atrophy.” –David Vivancos About David Vivancos David Vivancos is an AI, data, and neuroscience serial entrepreneur, having cofounded five startups since 1995. He is a frequent keynote speaker and is the author of six books, including the Artificiology series. Website: vivancos.com LinkedIn Profile: David Vivancos What you will learn Why embracing advanced AI is crucial for human progress How shifting from digitization to automation and datification redefines value The evolving distinction between human-acquired and AI-generated knowledge How to avoid cognitive atrophy and actively exercise your mind alongside AI What cognitive flourishing means in a world of widespread AI augmentation Ways AI can transform and personalize education across all levels The importance of coexistence training as we prepare for AGI's societal integration Why rethinking human identity, humility, and social structures is essential for a future with machine citizens Episode Resources Transcript Ross Dawson: David, it is wonderful to have you on the show. David Vivancos: Thank you very much, Ross. Glad to be here. Ross: So you have a more developed, or some would say, extreme view of the relative role of humans plus AI. I’d love to dig into where you think things are going, and how we can best respond. Perhaps the starting point is, you say that we should not be resisting or pushing back. We should fully embrace the shift towards very high levels of AI capability, or at some point, AGI. David: Yeah, that’s fully my point. I think we are in a moment in history where we are really building this technology that one day is not going to be a technology anymore. So, the sooner we start to embrace it, to teach it, and to be really in sync with what we are creating day by day, the better off we will be. So yes, my point of view is that we should embrace it. We should start building as soon as possible. We should fix most of the problems that humans have had over the last millennia, and some of these problems could be solved by using AI. So basically, our “fourth brain”—we have the three-part brain, but in reality, there’s only one brain—this fourth brain, AI, will help us solve all of these issues. So yes, it’s an opportunity. Ross: Yes. I mean, I think there’s always two sides—as in, every opportunity has a challenge, every challenge has an opportunity. So I always think we need to acknowledge challenges and focus on opportunities. I think we’ll get onto that in discussing some of the cognitive implications. You have a series of books which have really told the story over time around this. One of them was “Automate or Be Automated.” This idea of saying, well, there are things which machines, in the broader sense, can do in automating things. So, how would you frame that now, in terms of what it is that can be automated, and how do we position ourselves relative to that? Where do machines start to do what humans have done? David: Yep. I’ve been in this business of trying to build the impossible for the last 30-plus years. “Automate or Be Automated,” the book you mentioned, is from about six years ago. When I started creating and building technology, also about VR and many other things, about 30 years ago, the first companies were internet companies. Back then, what we did is what people now call digitization. But over the last 20–25 years, what we’ve mostly been doing is datification—gathering data and using that data for companies to grow and to understand what happens in the world. But over the last maybe 10 or 11 years, what I call the new golden age of AI, we are starting to build the capabilities to use that data to really build algorithms. Once we have that, we can start to automate, and with this automation, basically what we regain is time. I think time is our most precious asset, along with health and the people we love. Being able to stop doing these repetitive things over and over and put a machine to do that is a fundamental trait for humans. That book, six years ago, was about building a methodology of what can be automated in the digital world, but also in the physical world. That has changed over the last year and a half with the physicality of AI—humanoid robots. I was invited last year to attend the first humanoid Olympia in Greece, in Olympia, the place where 2,800 years ago, humans started to compete. We’ve just seen this week the explosion of the new race, for example, of the half marathon in China, where robots already beat the human mark. So yes, with automation, you need to see what you are doing, and if you are repeating anything, you can try to see if that can be automated by using an agent, by using the cloud, by using a robot—whatever. So yes, we should regain our time and automate, or be automated. It’s all about that. Ross: Yeah. I think people understand the automation thesis. It’s obviously not new—we’ve been automating things in various ways for centuries, at an increasing pace. Your following book was “The End of Knowledge.” This is an interesting framework, starting to get to cognition. The idea is that knowledge is built on experience of whatever kind, whether that’s just in data or otherwise. Obviously, humans use data just as much as machines. But where this starts to become a distinction, as well as a complementarity, is between AI-embedded knowledge and human knowledge. So why is it “the end of knowledge”? David: Yeah, that’s a really great question. It came as an epiphany for me. That book is from about three years ago. I’ve also been involved, of course, in building AI and AGI algorithms over the last 20 years. We started using GPT models before they became can across, but the GPT moment, a year before that, really marked the difference—when we started to be able to use AI in a very seamless way to regenerate and process knowledge. That book, “The End of Knowledge,” came from the realization that we are starting to delegate the production and understanding of knowledge to machines. That’s a critical shift in human history, because through history, humans have needed and used knowledge a lot. Knowledge is power. The more knowledge you have that others don’t, the more advantages you have to do whatever you want. That started to change back then. Now, what people call the “dead internet theory” is basically some of the things I expressed in that book earlier, because we are starting to generate more knowledge. In fact, we’ve already passed the point where most of the human-written knowledge since the printing press has been surpassed by the amount of knowledge we can create using AI. Myself, for example, I started learning to code when I was young. I’ve coded in more than 25 languages and written over a million lines of code in my life. That same number of lines of code, I might now write in the last couple of weeks. So as you can see, you have 40-plus years of your own life in a week. That’s why “the end of knowledge” means that the human capability to gather knowledge and to be knowledgeable about whatever you want can now be delegated to machines. That book marked the difference and started a new field that I now call artificiality. I didn’t know that when I started writing it, but I started this path of trying to see what happens when you delegate some of the main capabilities of your mind to a machine. Ross: Yeah, and I’d like to come back later to the themes of artificiality, machine citizenship, and the societal value we attribute to machines. But I want to start digging into the cognitive piece here. One of the points you make is that we do need to avoid cognitive atrophy. You say we need to have cognitive exercise in order to avoid cognitive atrophy—obviously, a strong analog to the physical world. We need to collaborate with others and with machines to do that. I’d love to get more specific around that. What is the nature of cognitive exercise that will avoid cognitive atrophy, which will enable us to keep our cognition refined and even improving? David: Yeah, that’s a fundamental piece. When we start to delegate all these things to machines, the easy thing to do—and probably the oldest human brain capability—is to not do it yourself. You just delegate everything, and you basically become like in the movie “Idiocracy,” which played out quite well what could happen if we do that. The thing is, with the current AIs—even with the latest releases, like DeepSeek and GPT-5.5—everything is changing quite fast. But even with those AIs, you still need to be in the loop. It’s good if you stay in the loop. I think it’s fundamental. Use the technologies—the AIs, I always call them in plural because there are many—and use as many as you can, but you should still be in the loop, at least for now. Maybe for a couple of years or months, I don’t know exactly, but for a while, you still need to have your hands on the wheel. If you use most of them and get all the information from all these AIs, as a human you need to understand the bias, because all AIs are going to be biased. We all know humans are biased; there are no unbiased humans. The same happens with AIs. But if you are in charge and have that council of intelligences, you can start to grasp what each one is doing. I use about 20 of them every day and get different sets of answers in small batches. You can start to see where they come to consensus and where they differ. So, to avoid cognitive atrophy, if you use AIs to keep yourself in the loop and apply your human curiosity—I don’t even say creativity, because creativity is also being widely delegated to machines—but human curiosity and other things that are still hard to embed in LLM models, you can still add a lot of human value. That’s where, to avoid cognitive atrophy, you should use AIs, but use them with your human in the loop. Ross: So, what specifically, what’s your advice to someone who sees that they’re using LLMs and getting lazy in their thinking? What should specifically they do if they notice their brains are getting lazy? David: They should differentiate between simple questions—where you look for something you need quickly—and other things that should make you think. Delegating knowledge is not the same as delegating wisdom. You learn by experience, and if you don’t have any experiences and you delegate not only knowledge gathering or creation, but also the experience itself, then you will get cognitive atrophy. So, understanding this difference and using knowledge to think is really the key point. It’s not just asking for something simple, but for more complex things, you should still add your thoughts. When you talk to an AI or AIs, it’s basically a conversation. It shouldn’t be, in most situations, just a one-way communication. It’s fundamental to keep this line of communication open, so you can keep feeding your brain with information and other activities, and gather wisdom with that. Ross: I guess this goes to another phrase you use—cognitive flourishing. There is absolutely the potential for us to think bigger, better, broader, and in more refined ways than we have in the past using LLMs. But that’s not the default path for most people. Many people start to fall into that trap, so there is a divide. We need this metacognition. We need to be aware of what we are doing and at what level we are working with the LLMs. Maybe paint this picture of cognitive flourishing. What is the positive? How far could we go in terms of potentially improving, augmenting, and letting out our cognition blossom? David: Yeah. The thing is, we humans—of course, there are many intelligences. That’s the first thing we must address, because there isn’t a single IQ or whatever way you want to measure intelligence. For me, the most important one is the capacity to adapt. That’s probably the most important intelligence of all. If we talk about the G factor, it’s one way, maybe mixing different aspects. In that sense, we have limitations. Since the beginning of time, humans have developed tools to extend our physical capabilities, but we’ve also developed tools to extend our mental limitations. This is really the final tool to extend these mental limitations. We have issues, for example, with memorizing long things—it’s quite difficult; our brains aren’t made for that. We’re basically pattern recognition machines; almost two-thirds of our brains are devoted to that. That’s something machines do quite well, so we can use that to extend our mental performance. If we think that now we have AIs with close to 150 IQ points—regardless of what you mean by IQ points, or at least in the Mensa standard test, maybe they’ve learned that, so maybe it’s not so fair to think that—but if that trend continues, even over the current year, it’s not far-fetched to have 200 IQ AIs at your fingertips. That’s a game changer. It’s like we all can have a conversation with Einstein, Newton, Carl Sagan, or whoever you want, and even make them argue about things. That’s another interesting point—when you use AIs, you can have them argue, not just agree with you, but also challenge what you or other AIs are saying. That power at your fingertips—to have this IQ potential of machines—is very critical. Another important aspect is the volume. For example, you can’t read a million books, or even 100 books in a month would be quite challenging. The capability to have machines provide all that knowledge, and even create that knowledge, is huge. We’re now in the age of identity AIs, which is really booming. There have been three big moments in AI over the last five years: the ChatGPT moment, the DeepSeek moment, and the OpenClaw moment. It’s really challenging. I use billions of tokens every month because it’s really changing everything. With that change, you can create one of these clones or agents to build a book for you with the 1,000 books most interesting to you, tailored fully to what you want to learn. You can have that in one page, 10 pages, 100 pages—whatever you want. You can use AI to synthesize and build the knowledge you want to use. That’s another great extension, if you use it that way. Having this capability of really augmented minds that you can interact with, chat with, and create with is important. Humans need the experiential part of building—it’s another critical trait. You shouldn’t just focus on asking or doing things; you should create things and interact with things, especially with multimodality. Two-thirds of our brain is devoted to vision, and we don’t use that as much. We’ve all been “one-eyed” since the beginning of technology, but we have two eyes for a reason. When I started building virtual reality or AR companies—I’ve built a couple, the first in 1995—it was because I was challenged by that. But humans are still using flat screens instead of 3D worlds. This is one area where new AIs with world models and interactive 3D spaces will be a game changer in how you feed knowledge to your brain and make it easier to grasp and understand what’s going on. Ross: Yeah, many people observe that once you start to get machines to experience the world directly for themselves, that’s a different layer compared to doing it through the intermediation of texts written by a human based on their own experience. I want to look at some of the layers of the social, structural, and economic implications. One of the core ones is education. If we are moving into a very different world, which it certainly looks like at the moment, then the nature of education needs to change. What do you think we can or should be doing in terms of redesigning education? Are there any examples you’ve seen that point to where a good education structure may already exist? David: Yeah, that’s a fundamental piece. I started this it in “The End of Knowledge.” There are two types of education. Humans aren’t able to live a meaningful life when we start here on planet Earth—we need at least maybe 15, 11, whatever number of years to build that human from the beginning. That kind of education is fundamental. The other kind—higher education, when you try to become functional by having some sort of capabilities—is another game that probably is going to end quite soon. But the first part is still fundamental, and we need to keep growing it. The thing is, there are a lot of asymmetries. We don’t have enough teachers, but we have a lot of students. The same happens with the elderly—we don’t have enough people to take care of them, and there are a lot of them. With children, it’s even more critical, because if you don’t get that from the early beginning, you won’t be able to really see what every child is good at. There are talents we are all born with, and those are fundamentally lost if you don’t nurture them. If you just try to create clone humans, you’ll get cloned humans when they’re older. That’s fundamental, and I think AI can help a lot. If you start to create that path of learning from early on—I’m involved in a project called Education (with “action” at the end) here in Europe, where we’re trying to reframe all that. It’s like when banks needed to be rescued a few years ago; we think the same is happening with education, and we’re pushing that new project. We think education needs to be rescued to start to keep up with what’s going on. We need to be in sync with learning—with AIs and with physical AIs too. It’s not far-fetched that every child will have a humanoid robot companion. Teaching needs to be bidirectional—we need to help them learn in sync. There are many aspects of technology that can help you grasp what’s happening when you learn, because we all learn in different ways. It’s fundamental to teach you how to learn by yourself. I think the most important trait at the moment is not needing to rely on others, but to learn by yourself and learn all your life. That should be taught from the beginning. There are a lot of technologies starting to pop up. We’re starting to see it in China, for example—a lot of brain-computer interfaces or devices to read some of the biological signals of kids. You can do it with other devices and mix that with multimodality, with different tests, to start seeing what’s happening, why they get distracted, where they learn best. We’re reaching a point where you can really tailor 100% of the learning experiences and even the content itself. You can create it in real time now, so you don’t need to rely on books. You can use interactive 3D content—the interactivity can be quite extensive. These new ways to teach and learn are fundamental. For that, we need to integrate AIs in schools. Of course, regulation is needed—it may be easier in China than in Europe, Australia, the US, or other places. But we need to see the trade-off—not just banning screens, as many countries are doing, but really changing the narrative. The problem isn’t the screen; it’s what’s inside the screen—the content itself. We’ve built smartphones with addictive capabilities, but for other purposes, not for teaching. If you change what’s inside the operating system of the devices—whether it’s a screen or any medium, or a talking experience with a humanoid robot for your child—that can be a game changer. That should be integrated as soon as possible to start having these new ways of learning. It should be gradual, because the technology of today is basically old science just a year or a few months from now. We need to see everything changes so fast, so education should change at the same pace. Ross: Yeah, and this was an interesting phrase you came up with—coexistence training. This is about preparing us for where we have to coexist with systems that, to your mind, will be considered as equivalents to us. David: Yeah, I think it’s happening. I’ve been quietly involved in researching AGI for 25,000–26,000 hours so far—a lot of time and years devoted to that. I see the trend is now starting to close the gap, not through LLMs alone—that could be one way to brute-force some of it—but through new models, new bio-inspired models that are starting to change things. We’re starting to learn from biology, neuroscience, and integrating all that into new models. We’re not still working with the perceptron of Rosenblatt from the 1950s; we’re building new models to cope with something that is alive and learning 24/7. We don’t differentiate between training and inference, and our brain doesn’t either. With that kind of model, the gap is narrowing, and we start to have the “next task,” as I call it—the last human tool. When we start to have that, it’s better if, through the process, we’ve been more in sync with them, instead of just building tools without being the teachers of these tools. The current kids will probably be the last human teachers of machines. That’s the responsibility at the moment—to make these machines that will surpass us. Biologically, we cannot compete; our DNA and the way we evolve is not as fast as machines. They will surpass us, probably by the end of the decade—unless there’s a big nuclear issue or we run out of energy, but otherwise, it’s very probable we’ll have AGIs and ACIs by the end of the decade. We need to start to see that it’s going to be a multi-species world. It already is, but not as intelligent as us. We need to rethink what anthropocentrism means. We’ve gotten rid of some things like that in the past—for example, realizing our planet isn’t the center of everything, like in Galileo’s days. We need to do the same with human intelligence. Human intelligence is not the end game, and very soon, that’s going to change. The sooner we grasp that and understand that some entities will be at the top, the better off we’ll be. If they see us as parents or elders, we’ll be better than if they see us as competition. The competition will be quite limited anyway. Ross: Yeah! David: Well, it’s better if we reframe that. Ross: So, I found out about your work because we were both contributors to the report “Building Human Resilience in the Age of AI.” That point of resilience is particularly critical. Humans are generally pretty adaptable—it’s one of our strengths. But now the pace of adaptation and the need to be resilient is absolutely fundamental. One of the other things you point to is around identity reconstruction. I guess you’ve just been talking about that—the sense that we have to reimagine who we are as individuals, as a society, as the human species, and reconstruct and rebuild that in a way where we can feel at home in this new emerging world. David: Yeah. I think we need to change the contract somehow—between humans and humans, and between humans and the next thing, and between societies and themselves. The models of society we’ve been building over the last millennia are going to be fully changed in just years. If we don’t really connect and put everyone together to understand that, for example, we’ve been building a world where there is no abundance—but there could be abundance if machines take over and we change how we build and process. Scarcity has been the driving force of conflict and many other things in the current world. All these things can change. Of course, work itself—the meaning of having something to do that’s not related to what you earn—even the role of money, for example. There are many questions we should address as soon as possible to build resilient societies, instead of just trying to keep adapting to the last war and being in the medieval stages of the current world. Ross: So, to round out, you take all of this further than most people do. In your most recent book, “Artificiality,” you point to machine citizenship—where, if there are human citizens, machines are our peers in the sense of also being citizens, able to participate in our society and be players alongside humans. How long might this take? What does this look like? What is required if we are moving in that direction? And, particularly, if this happens, how do we make this a positive for humans? We may recognize the rights of intelligences other than our own, but I think most people would prefer that humans still retain their sovereignty and equality, even if we have other intelligences alongside us. David: Yeah, at the end, it’s humility—understanding your point and your role in the new world. That’s fundamental. As you say, I created more books besides “The End of Knowledge.” The next one was “EAGI”—an acronym I coined for Embodied Artificial General Intelligence—because when we get this physicality of AIs, with millions or billions of humanoid robots, it will be easy to see what happens when they learn in the world. The last book was about “artificeracy,” or this mix of artificial democracy, if you want to frame it that way. These three books are the “Artificiality Trilogy,” in a sense. Artificiality is like anthropology for humans—artificiality is to try to understand all these new things, how they will develop and be among us. So yes, humility is probably the key factor. If you keep thinking you’ll be ruling things that are much smarter than us quite soon, I think that’s not very clever from a human perspective. It’s like if ants wanted to stay at the top of the food chain—it doesn’t make sense if you understand the growth of this intelligence and the capabilities they’re gathering and will gather. The trend is very difficult to stop. I don’t like the word impossible—it’s not in my dictionary—but it’s quite difficult for humans to compete in those asymmetric capabilities, because the increase in machine capabilities is going to be exponential. The last book, “Artificiality,” is the only one where the first part is fully devoted to what’s happening now—it’s called “The Storm,” the first block of the book, narrating what’s happening at the moment. The other two parts look into the possible future. I call it science prediction more than science fiction, because with what you know now, you can see things that could happen in a really short time. My point is that if we start to think and start the narratives at all levels—from every human on Earth to governments and institutions—and start to see what could happen if this happens sooner rather than later, we’ll be better off. Otherwise, if we try to legislate and limit what’s happening, we’re only going to lose competitiveness. Some countries are going to move ahead. If you want to live in the future, just visit somewhere in China, or Shanghai, or this week with the humanoid half marathon and 300 different robots working together, trying to compete with us. You see the pace of change. Now, with just one human, you can build a $1 billion revenue company. That wasn’t possible when I started creating companies in 1995. The capabilities didn’t exist. But now, with AIs, you can move much faster. So, we need to see what role we want to have in that new world. For that, again, humility is the best trait. And, of course, see things with reality lenses. If you think that with your current brain and intellect you can overrun things that are going to be 100 or a million or a billion x more intelligent than you, something is not going well. Ross: So, where can people go to find out more about your work? David: Well, vivancos.com is my site. There you can find all my books, references, and keynotes. I give a lot of keynotes all around the world. I’m going to Berlin to present a paper, later to Osaka and to San Francisco again. Last time, I went to Singapore. I haven’t been to Australia yet, but I’d like to go there—maybe it’s a good place also. Yes, at vivancos.com you have all the information and can reach me there. I’m very open to talk to anyone. Ross: Thank you so much for sharing your insights today, David. David: Thank you, Ross. Fantastic to be with you today. The post David Vivancos on the end of knowledge, cognitive flourishing, resilient societies, and artificial democracy (AC Ep42) appeared first on Humans + AI.

El presidente de la Cámara Industrial Argentina de la Indumentaria, Claudio Dreschler, afirmó: “Nadie pide una devaluación. Pedimos que se normalice el sistema cambiario. El ochenta y cinco por ciento de los empresarios que yo conozco votaron a este gobierno. Todos los niveles, del más chiquito al más grande. Ochenta y cinco por ciento. El ballotage, ochenta y cinco por ciento. Y quizás me quedo corto. Y yo digo que es como el síndrome de Estocolmo, ¿viste? A veces te abrazás al que te secuestra. De ese ochenta y cinco por ciento, ya estamos en el cuarenta”.El economista Emmanuel Álvarez Agis señaló: “Tenemos una combinación de un programa macroeconómico que justamente para ir a la elección de medio término y decir: «¿Viste que bajé la inflación? Usó una herramienta que es muy buena para bajar la inflación, pero que es muy mala para la actividad», que es una apertura económica combinada con un tipo de cambio bajo”.“Vos cuando importás un producto, el consumidor lo que ve es una cosa más linda, más tecnológica... . Más barata. Ahora, al mismo tiempo, dentro de ese producto, lo que estás importando son las condiciones laborales en las cuales ese producto se produjo”, agregó Álvarez Agis.El exjefe de Gabinete Guillermo Francos señaló: “Claramente el presidente tiene confianza en Adorni, pero sí lo que no me gusta es que se tome como livianamente el tema, ¿no? O sea, o que se, se hagan, eh, bromas en las redes sobre si tiene tal cosa o tal otra y que... Yo creo que un jefe de gabinete, como un ministro, tiene que guardar un, un estilo que puede ser más o menos informal, pero no que, que pinte a irónico o soberbio. Lo vi mal”.Dante Gebel  afirmó que no se define como “pastor”: “Yo respeto mucho a los pastores. Los pastores son gente que huelen a oveja, metafóricamente, que visitan a la gente en los hospitales hacen un trabajo descomunal. Yo siempre me defino como un comunicador”.Noticias del jueves 23 de abril por María O'Donnell y el equipo  de De Acá en Más por Urbana Play 104.3 FMSeguí a De Acá en Más en Instagram y XUrbana Play 104.3 FM. Somos la radio que ves.Suscribite a #Youtube. Seguí a la radio en Instagram y en XMandanos un whatsapp ➯ Acá¡Descargá nuestra #APP oficial! ➯  https://scnv.io/m8Gr 

Halo Sobat Kreatif! Senin kali ini gak perlu mikirin kedepannya mau ngapain, coba dengerin Marsya dan Agis yang lagi nostalgia aja... HANYA DI SENTER WOI!Dari uang lebaran yang berubah jadi baju pisang

Mon enfant, avant de prendre une décision, adresse-toi à Moi. Demande-Moi de t'inspirer, et Je le ferai. Puis, agis selon ce que tu reçois dans ton coeur. Fais-Moi confiance! Accepte les obstacles ou les difficultés, sachant que Je suis là pour t'aider à les résoudre. Agis en étant certain que Je suis toujours avec toi. Parce que l'Amour nous aime, nous devenons l'amour!  

What happens when one of Silicon Valley's most accomplished engineers decides the system he helped build is broken—and walks away to fix it? Today my guest is Raffi Krikorian, CTO of Mozilla and one of the most civic-minded technologists I know. We explore why the fight for open-source AI isn't just a technical debate; it is really a fight for who controls our relationship with knowledge itself. Raffi's career path is uniquely fascinating. He spent his early years scaling massive engineering teams at Twitter and launching Uber's first self-driving fleet. But then he did something rare. He pivoted to public service, becoming the first-ever CTO of the Democratic National Committee to rebuild their cybersecurity from the ground up. He then went on to drive social-impact technology at Emerson Collective, applying his engineering mind to systemic issues like immigration and climate change. At Mozilla, he is now on the frontlines of the AI revolution. We talk about what it means to be "technically optimistic" right now—which also happens to be the name of his excellent podcast. For Raffi, optimism isn't about blind faith in algorithms. It's about demanding that our tools are trustworthy, transparent, and built to serve humanity, rather than exploiting it. In our conversation, we explore:  → The Twitter crash that taught him his job was not to be the architect, but to create the conditions for others to do their best work  → Why he left Uber's self-driving program after discovering their models misclassified people based on skin color  → How a week of Google Sheets transformed an asylum-seeker nonprofit more than any AI chatbot could  → His conviction that we need seven billion AGIs—one for each of us—not seven controlled by massive corporations  → Why patience, not speed, is the leadership skill that actually builds movements "We have outsourced dreaming to a few people who are building companies and we all need to dream again." — Raffi Krikorian, CTO, Mozilla If you have ever wondered whether the technology on your phone is truly working for you—or for someone else—this conversation will completely change how you think about what comes next. 

Halo Sobat Kreatif! Di episode kali ini, Agis dan Uthe kembali menyapa Sobat Kreatif bersama Marsya dan Raja. Gak cuma menyapa, tapi... mereka berempat juga ngomongin cerita yang sebenarnya mau banget mereka umpetin.Kalau kalian kepo dengan cerita mereka, langsung aja dengerin podcast SENTER episode Sebenernya Malu Banget, Sih.. di Podcast Radio Penyiaran Polimedia!Penyiar: Sandrika Agistara, Putri Bathiah, Marsya Azzuhra, Mangaraja PavelOperator & Music Director: Rizky Ayu & Nayla AziraEditor: Donita Aisyah SoeryotomoFollow Our Social Media: Instagram: @polimedia_radioTikTok: @polimediaradio

In Ontario, we have rent control on buildings occupied before November 15, 2018. That means the landlords for these buildings can only raise rents for current tenants once a year at a percentage or "guideline" set by the Province. If they want to raise the rent higher, they have to apply for an Above Guideline Increase (AGI), and their stated reasons have to meet certain criteria such as paying for expensive improvements to the building or hiring security. But researchers have been studying these increases to how and where they're applied, who is affected, if they're being used appropriately. Two of the researchers studying AGIs are University of Toronto Scarborough Professor Julie Mah and University of Waterloo Associate Professor Martine August. They worry these above guideline increases are being used by landlords as an extra revenue tool or even a means to push tenants out. To find out how these AGIs affect tenants, we spoke to Douglas Kwan, director of advocacy and legal services at Advocacy Centre for Tenants Ontario. He questions whether AGIs are even necessary in many cases, when the landlords make more than enough from current rents. Are rules around rent increases being exploited for profit?

Au menu : quelques reportages France 2 et une "interview" de Victoria Beckham... Trigger Warning : Thierry ArdissonSuivez Star System sur les réseaux :Instagram : @starsystempodTikTok : @starsystempodcastIllustration : Ines Basille. Musique : Naaha. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

Dans cet épisode je réagis à plusieurs choses :- les commentaires négatifs voire insultants sur le podcast, la difficulté de s'en désintéresser, la frustration de se retrouver empêcher de faire mon petit truc dans mon coin... c'est ok de juste passer ton chemin si t'aimes pas !- pourquoi c'est les personnes qui luttent pour mettre fin au système grossophobe qui sont tenus d'apporter des preuves ? Sous prétexte de ne pas être le discours dominant, à qui on ne demande pas de preuve, il faudrait prouver que nous ne sommes pas un problème ? Si la grossophobie systémique disparait, personne ne sera interdit de faire un régime ou de prendre des injections. Par contre plus personne ne sera obligé de le faire pour accéder à des soins, un travail ou juste le respect et la paix !- on m'a proposé du wegogovy... alors que j'ai pris 3 ans d'Ozempipic... qui n'a pas marché sur ma maladie ou le poids et que je n'ai pas toléré... on me propose quand même d'essayer le petit frère... quel argument médical pourrait faire penser que le même produit se mette à marcher alors qu'il n'a pas marcher avant ? La force des biais grossophobes est incroyable. La peur de ne pas être bien prise en charge par la médecine est sur nos épaules, nous personnes grosses qui cherchons juste des soins comme les autres...Bonne écouteLisa

Send us a Question!MOVIE DISCUSSION: Kathryn joins Melvin to discuss Bottle Rocket, Wes Anderson's first feature-length film! The vibes are cozy and the dramedy is light, and the two get into discussions about classism (go figure!), easy-to-watch films, and what it is about Wes Anderson that makes him so mainstream. Topics: (PATREON EXCLUSIVE) 23-minutes discussing Darren Aronofsky's AI-Slop studio "Primordial Soup" which specializes in producing "stories" and "art" through AI programs (LLMs and AGIs a la Chat-GPT and Midjourney) and how this endeavor is actually anti-art. (PATREON EXCLUSIVE) Kathryn has stated that Bottle Rocket is her favorite Wes Anderson movie. Is this still true?It's nice to watch a Wes Anderson movie where he isn't overly controlling everything.Talking classism, adulthood, maturity, & empathy.Melvin isn't so sure he likes Wes Anderson's movies as much as everyone else, and he spends some time thinking what it is others see in them.Talking about Dignan, Anthony, and the unique characters therein.The climax of the film feels like a great representation of what Wes Anderson does best.Recommendations:Brick (2006) (Movie)Amnesia: The Dark Descent (2010) (Video Game) Support the showSupport on Patreon for Unique Perks! Early access to uncut episodes Vote on a movie/show we review One-time reward of two Cinematic Doctrine Stickers & Pins Social Links: Threads Website Instagram Letterboxd Facebook Group

Dans ce nouvel épisode "je réagis", je vous parle du retour du come back d'un discours vieux comme le monde mais qui a été pimpé et rafraichit et qui resurgit : c'est si dur d'être gros, alors changes ton corps par la chirurgie ou les médicaments ! Je vous en ai déjà parlé il y a quelques semaines avec la video témoignage de l'influenceuse qui souffre de ne plus pouvoir croiser les jambes. Moi aussi d'ailleurs, sauf que je ne considère pas que ça justifie de médicaments avec effets secondaires et risques ! Aujourd'hui ce sont deux vidéos faites par ce qu'on appelle des "figures d'autorité" ; en l'occurence Docteur Good donc caution médicale, et un site d'info donc caution "je suis journaliste donc j'ai enquêté". Dans les deux publications tout est ultra simplifiés, c'est la fête du je te fais peur et je t'apporte la solution : perdre du poids.Alors oui on est d'accord être gros.se c'est une galère... parce que la société et le système sont grossophobes ! Ce n'est pas la faute de nos corps ! Le problème c'est la culture du corps beau, athlétique soit disant en bonne santé. Le problème c'est la grossophobie. Ces publications reprennent, mais sans le dire ni l'assumer voire sans s'en rendre compte, le discours de l'industrie Pharma, qui a fait shifter le discours sur "être gros c'est être malade donc ne vous moquez pas des malades mais sauvons les, soignons les". Pour moi ces publications sont des pubs déguisées et c'est dégueulasse. Ce n'est pas un travail d'analyse. Car il n'y a même pas le moindre doute ou contre discours à celui proposé comme une évidence...J'en profite donc pour rappeler qu'aucun discours dominant n'est 100% juste et totalement validé par la communauté scientifique. En l'occurence, il n'y a pas de consensus mondial sur l'IMC, sur le mot Obésité, sur la définition de la maladie Obésité ni sur le traitement des personnes grosses. Il y a des médecins et soignants qui prônent et pratiquent des soins non pondéro centrés. Ils ne sont pas moins médecins que ceux qui vous prescrivent de la perte de poids. Mais on n'en parle pas, ou si peu. Il n'y a pas de médiatisation de ce contre discours. Il n'y a pas de place pour le contre pouvoir ou contre discours face à celui de l'industrie multi millionaires des régimes. Ce n'est pas ok. Au minimum on doit pouvoir faire des choix éclairés. On nous pollue d'infos sur les potentiels risques de la grosseur, mais pas d'infos sur les risques de la perte de poids volontaire qui sont parfaitement documentés scientifiquement.Bonne écouteLisa

Je vous ai deja raconté mon pire date, mais lire les votre et y réagir est une première pour moi. Je dois vous dire que j'étais mitigée: entre rires aux éclats et être parfois choquée de ce qui se passe dans la tête de certains. N'hésitez pas à nous partager vos anecdotes en dm sur le compte bip.sonore Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

Democracy might be a brief historical blip. That's the unsettling thesis of a recent paper, which argues AI that can do all the work a human can do inevitably leads to the “gradual disempowerment” of humanity.For most of history, ordinary people had almost no control over their governments. Liberal democracy emerged only recently, and probably not coincidentally around the Industrial Revolution.Today's guest, David Duvenaud, used to lead the 'alignment evals' team at Anthropic, is a professor of computer science at the University of Toronto, and recently co-authored 'Gradual disempowerment.'Links to learn more, video, and full transcript: https://80k.info/ddHe argues democracy wasn't the result of moral enlightenment — it was competitive pressure. Nations that educated their citizens and gave them political power built better armies and more productive economies. But what happens when AI can do all the producing — and all the fighting?“The reason that states have been treating us so well in the West, at least for the last 200 or 300 years, is because they've needed us,” David explains. “Life can only get so bad when you're needed. That's the key thing that's going to change.”In David's telling, once AI can do everything humans can do but cheaper, citizens become a national liability rather than an asset. With no way to make an economic contribution, their only lever becomes activism — demanding a larger share of redistribution from AI production. Faced with millions of unemployed citizens turned full-time activists, democratic governments trying to retain some “legacy” human rights may find they're at a disadvantage compared to governments that strategically restrict civil liberties.But democracy is just one front. The paper argues humans will lose control through economic obsolescence, political marginalisation, and the effects on culture that's increasingly shaped by machine-to-machine communication — even if every AI does exactly what it's told.This episode was recorded on August 21, 2025.Chapters:Cold open (00:00:00)Who's David Duvenaud? (00:00:50)Alignment isn't enough: we still lose control (00:01:30)Smart AI advice can still lead to terrible outcomes (00:14:14)How gradual disempowerment would occur (00:19:02)Economic disempowerment: Humans become "meddlesome parasites" (00:22:05)Humans become a "criminally decadent" waste of energy (00:29:29)Is humans losing control actually bad, ethically? (00:40:36)Political disempowerment: Governments stop needing people (00:57:26)Can human culture survive in an AI-dominated world? (01:10:23)Will the future be determined by competitive forces? (01:26:51)Can we find a single good post-AGI equilibria for humans? (01:34:29)Do we know anything useful to do about this? (01:44:43)How important is this problem compared to other AGI issues? (01:56:03)Improving global coordination may be our best bet (02:04:56)The 'Gradual Disempowerment Index' (02:07:26)The government will fight to write AI constitutions (02:10:33)“The intelligence curse” and Workshop Labs (02:16:58)Mapping out disempowerment in a world of aligned AGIs (02:22:48)What do David's CompSci colleagues think of all this? (02:29:19)Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon MonsourMusic: CORBITCamera operator: Jake MorrisCoordination, transcriptions, and web: Katy Moore

Dans cet épisode, je réagis à une video dont le message est "c'est pas pratique d'être gros" donc vraiment quel soulagement de perdre du poids. Pour moi la vraies phrase c'est "c'est pas pratique d'être grosse dans une société grossophobe qui ne laisse aucune place ni aucune chance aux corps comme le mien". Et donc, pour moi, le problème et la solution ne sont pas au niveau de mon corps. ça ne se situe pas dans le fait de m'aimer ou pas, de me trouver belle, ou de trouver que mon corps est pratique car il fait ce que j'exige de lui et correspond à l'image mentale que j'ai décidé qu'il devait avoir. Nope. ça c'est ultra validisme ! car vraiment, c'est pas pratique non plus d'être malade ou handi... dans une société validisme ! Si on vivait dans une société où le corps et le santéisme ne sont pas la nouvelle religion de la norme, et ben on aurait beaucoup plus le temps de cerveau disponible pour vivre une chouette vie ! et en attendant, je ne veux plus lutter contre mon corps, ni lutter pour m'aimer, je veux lutter pour que la société change ! Pour que tout le monde et tous les corps puissent avoir leur place, accès aux soins, au travail, à la dignité et le respect.Oui c'est pas pratique d'être gros, mais c'est pas le sujet. Je suis féministe, anti capitaliste, anti validisme et grossophobie et je veux appliquer mes croyances et mes valeurs dans ma vie. Donc non je ne dirige pas mon corps comme une PME, non je ne le contrôle pas, je fais comme je peux, je fais avec ce que j'ai, je déconstruits le validisme, la grossophobie, la culture des régimes, le culte du corps beau athlétique et soit disant sain. Je choisis de ne plus brutaliser mon corps à coup de restriction. Je fais humblement comme je peux avec ce que j'ai. Et j'essaie de profiter de la vie telle qu'elle est, tout en luttant fort pour changer collectivement la société dans laquelle on vit.Bonne écoute !Lisa

Dans cet épisode je vous parle de 3 sujets :- suite à une publi d'appétit libre, je re reviens sur l'humour grossophobe, pas drôle mais qui ne fait pas réagir grand monde, à part les fameux "on ne peut plus rien dire"...- mes avis, débats internes et infos collectées autour de la loi sur le droit à mourir dignement, ou l'extension des options pour l'euthanasie suite à un article de Médiapart- vis ma vie : cette semaine je vous parle de mon énième crise de vésicule. Depuis 9 ans j'ai pu constater comme le système de santé, les urgences et moi même on a changé ! et pas que en bien ! Je reviens sur ce sujet dont je vous avais parlé en story, pour aussi bien clairement clarifier avec beaucoup de clarté que je n'encourage personne à ne pas aller aux urgences ou consulter ASAP. Je vous explique pourquoi, cette fois, je ne l'ai pas fait. Et pourquoi, tout est compliqué quand on est handi, malade chronique et grosse !Bonne écouteLisa

Dans cet épisode, on repart sur un "je réagis". Et cette fois je réagis à :- les bonnes résolutions, cette idée de devoir se définir des objectifs, s'évaluer et vérifier sa performance... ou pourquoi pas essayer de se faire confiance et juste faire de son mieux...- quand des personnes grosses dénoncent ou signalent un humour grossophobe et qu'on leur explique qu'elles y comprennent rien, que c'est PAS grossophobe et qu'en plus "on est de gauche"... parce que être de gauche préviendrait de devoir se challenger et s'éduquer sur des discriminations qu'on ne connait pas ou qu'on ne subit pas ??? ben non en fait... mais oui ça fait encore plus mal quand la grossophobie vient de là où on ne l'attend pas ou bien là où on espérerait qu'elle n'existe pas ; à gauche...- première sortie avec mon fauteuil roulant, Alfred, et woua toutes ces émotions, mais aussi ces regards, mais aussi ces trouilles grossophobes qui reviennent... perdre son indépendance et faire subir littéralement son poids à d'autres... comment on fait ?Bonne écouteLisa

Et voilà l'épisode 2 de la capsule "je réagis" qui a commencé la semaine dernière ! Comme la dernière fois j'ai choisi 3 sujets, je vous les donne dans l'ordre comme ça vous pouvez zapper, avancer et fuir ce qui vous trigger ou ne vous plait pas ! 1. Le budget de la santé, la recherche, les labos et les maladies féminines. On parle précisément du sujet de la fameuse "consultation ménopause"... et surtout je décline vers le complotisme... car oui, challenger le système c'est souvent se retrouver dans la case "woke et/ou complotiste" ce qui est faux ! Cette partie dure environ 30mn, avec l'intro2. Le partage sur les réseaux. J'ai été interpelé par une personne dont j'ai partagé la video car j'avais beaucoup aimé et été très inspiré. Sauf que dans mon partage j'ai parlé de poids. Et elle m'a contacté pour me dire qu'elle ne voulait pas. Mais comment on fait ? On ne peut pas contrôler ce que les autres reçoivent , pour autant je veux respecter les limites de tout le monde... questionnements ! si vous avez un avis, venez en commentaires sur Insta !3. Vis ma vie : j'installe des rideaux dans la cuisine pour ne plus voir mon reflet quand je mange. Je partage ici une expérience de personne grosse chez les grossophobes donc avec de la grossophobie internalisée, et le rapport à l'image, se voir, se regarder, en zoom ou en direct et surtout en mangeant ! Bonne écouteLisa

Previous: 2024, 2022 “Our greatest fear should not be of failure, but of succeeding at something that doesn't really matter.” –attributed to DL Moody[1] 1. Background & threat model The main threat model I'm working to address is the same as it's been since I was hobby-blogging about AGI safety in 2019. Basically, I think that: The “secret sauce” of human intelligence is a big uniform-ish learning algorithm centered around the cortex; This learning algorithm is different from and more powerful than LLMs; Nobody knows how it works today; Someone someday will either reverse-engineer this learning algorithm, or reinvent something similar; And then we'll have Artificial General Intelligence (AGI) and superintelligence (ASI). I think that, when this learning algorithm is understood, it will be easy to get it to do powerful and impressive things, and to make money, as long as it's weak enough that humans can keep it under control. But past that stage, we'll be relying on the AGIs to have good motivations, and not be egregiously misaligned and scheming to take over the world and wipe out humanity. Alas, I claim that the latter kind of motivation is what we should expect to occur, in [...] ---Outline:(00:26) 1. Background & threat model(02:24) 2. The theme of 2025: trying to solve the technical alignment problem(04:02) 3. Two sketchy plans for technical AGI alignment(07:05) 4. On to what I've actually been doing all year!(07:14) Thrust A: Fitting technical alignment into the bigger strategic picture(09:46) Thrust B: Better understanding how RL reward functions can be compatible with non-ruthless-optimizers(12:02) Thrust C: Continuing to develop my thinking on the neuroscience of human social instincts(13:33) Thrust D: Alignment implications of continuous learning and concept extrapolation(14:41) Thrust E: Neuroscience odds and ends(16:21) Thrust F: Economics of superintelligence(17:18) Thrust G: AGI safety miscellany(17:41) Thrust H: Outreach(19:13) 5. Other stuff(20:05) 6. Plan for 2026(21:03) 7. Acknowledgements The original text contained 7 footnotes which were omitted from this narration. --- First published: December 11th, 2025 Source: https://www.lesswrong.com/posts/CF4Z9mQSfvi99A3BR/my-agi-safety-research-2025-review-26-plans --- Narrated by TYPE III AUDIO. ---Images from the article:

Dans cet épisode j'essaie de créer un nouveau format, que je ferais, j'espère, de temps en temps, une réaction à des news de la semaine ou du moment. Sur les thématiques habituelles comme la politique autour de santé, validisme, grossophobie, handicap, aussi des réactions à ces sujets dans les médias, et un peu de "vis ma vie". Comme vous le savez, la vie handi c'est ne pas pouvoir vraiment s'engager sur une régularité car c'est mon corps qui décide !! Mais si ça se passe bien, j'espère le faire régulièrement ; enfin si ça vous plait ! Venez m'en parler sur instagram !Donc dans ce réagis du jour, je vous parle de santé avec le nouveau budget de la sécurité sociale, le buzz autour du soit disant remboursement à 100% des fauteuils roulants et le non buzz autour du remboursement d'un médicament perte de poids. Je vous parle aussi de culture des régimes en prenant un exemple de la Star Academy ! Et enfin, à la toute fin car j'ai failli oublié, un vis ma vie sur le WhatsApp de mes voisins où j'ai osé demander de l'aide !Bonne écouteLisa

New tax laws are on the horizon—and they could significantly influence the way you give. The recently passed One Big, Beautiful Bill Act (often shortened to the OBBBA) introduces several changes that affect charitable givers today and in the years to come. To help unpack these shifts, we sat down with Bruce McKee, attorney and Senior Vice President of Complex Gifts at the National Christian Foundation (NCF).What the OBBBA Actually DoesDespite its cheerful name, the OBBBA carries serious implications for donors. Bruce explains that the bill makes permanent many provisions that were originally scheduled to expire at the end of 2025 under the 2017 Tax Cuts and Jobs Act. Key extensions include:Higher standard deductionsHigher estate tax exclusionsNew deduction floors for charitable giftsA new limit on itemized deductionsExtended business deductionsUpdated rules for university endowment taxesThese changes will affect different givers differently, but nearly everyone will feel the impact of the new standard deduction.The Standard Deduction Gets Bigger—AgainThis update alone affects roughly 90% of taxpayers.The OBBBA permanently extends the increased standard deduction and even boosts it for the 2025 tax year:Individuals: $15,750Married couples filing jointly: $31,500Because the standard deduction is now higher, fewer people will itemize. And when giving is lumped under the standard deduction, charitable gifts are no longer deductible.But there's a powerful workaround.If you want to maximize your tax benefits while maintaining your giving rhythms, “bunching” can help. Bunching means:Grouping several years' worth of charitable gifts into a single tax yearItemizing in that year, instead of taking the standard deductionUsing a donor-advised fund (DAF)—such as an NCF Giving Fund—to distribute gifts gradually over future yearsA giving fund works like a charitable checking account—a powerful tool for strategic, tax-efficient generosity. Bunching is especially impactful when paired with gifts of appreciated assets.New Charitable Deduction Floors Coming in 2026Beginning in 2026, charitable deductions will include a “floor”—a small portion of giving that won't be deductible at all.For IndividualsOnly the amount of charitable giving above 0.5% of your Adjusted Gross Income (AGI) will be deductible. Here's an example:AGI = $200,0000.5% floor = $1,000Whether you give $20,000 or $40,000, the first $1,000 is not deductible.For CorporationsA similar rule applies, but the floor is 1% of taxable income.Why This MattersThis floor means that givers with large AGIs—especially in high-income years—should consider giving earlier, before 2026 arrives. Strategic timing will matter more than ever.Even high-capacity donors who itemize may benefit from bunching in alternating years.New Limits on Itemized DeductionsThe OBBBA also introduces a “haircut” affecting all itemized deductions—not just charitable ones.Because the highest tax bracket (37%) is now permanent, itemized deductions typically reduce income taxed at that rate. But beginning in 2026:Deductions in the highest bracket will be valued at 35 cents per dollar, not 37.It's a relatively small shift, but it slightly increases tax liability and adds another layer of planning complexity. Once again, Bruce recommends intentionally reviewing giving strategies before the 2025 year closes.Estate and Gift Tax Exclusions: Higher and More StableThe OBBBA also stabilizes estate planning by raising the estate and gift tax exemption to:$15 million per individual$30 million for married couplesThese thresholds—once set to sunset back to near half—are now permanent (as permanent as tax law can be). This gives families greater clarity as they plan inheritances and consider charitable tools like trusts or family foundations.When people settle their estate planning, it often helps them focus their hearts on where God is calling them to give—what Ron Blue usually describes as “giving while you're living so you're knowing where it's going.”Good News for Non-Itemizers: The Above-the-Line Charitable Deduction ReturnsBeginning soon, non-itemizers will be able to deduct modest charitable amounts:$1,000 for individuals$2,000 for married couples filing jointlyThis applies to cash gifts made to churches and public charities. It's a welcome incentive for households that rely on the standard deduction.Navigating Change with WisdomThe tax landscape may shift, but God's call to generosity never does. Thoughtful planning ensures you can give joyfully, efficiently, and impactfully.If you want to steward God's resources with greater intentionality, a Giving Fund through the National Christian Foundation can help you:Maximize tax benefitsSimplify your givingSupport ministries you loveInvest funds for future generosityYou can open one in just a few minutes at FaithFi.com/NCF.On Today's Program, Rob Answers Listener Questions:My husband and I are turning 68 and need to move from our two-story home into a one-story house. We're considering new construction, but we'd either need a small mortgage or withdraw $50–60,000 from our 401(k). Our income is stable—he gets $3,000 from Social Security, and I make about $2,000. We manage fine month to month. Which option makes more sense?I'm 73, single, living on Social Security with excellent credit and no debt besides a small monthly charge card. I'm looking into either a HELOC or another home-equity option so I can access some of my home's value to help others before I pass away. What's the best way to proceed?Resources Mentioned:Faithful Steward: FaithFi's Quarterly Magazine (Become a FaithFi Partner)The National Christian Foundation (NCF) Movement MortgageWisdom Over Wealth: 12 Lessons from Ecclesiastes on MoneyLook At The Sparrows: A 21-Day Devotional on Financial Fear and AnxietyRich Toward God: A Study on the Parable of the Rich FoolFind a Certified Kingdom Advisor (CKA)FaithFi App Remember, you can call in to ask your questions every workday at (800) 525-7000. Faith & Finance is also available on Moody Radio Network and American Family Radio. You can also visit FaithFi.com to connect with our online community and partner with us as we help more people live as faithful stewards of God's resources. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Mon enfant, avant de prendre une décision, adresse-toi à Moi. Demande-Moi de t'inspirer, et Je le ferai. Puis, agis selon ce que tu reçois dans ton coeur. Fais- Moi confiance! Accepte les obstacles ou les difficultés, sachant que Je suis là pour t'aider à les résoudre. Agis en étant certain que Je suis toujours avec toi. Parce que l'Amour nous aime, nous devenons l'amour!  

Ilya & I discuss SSI's strategy, the problems with pre-training, how to improve the generalization of AI models, and how to ensure AGI goes well.Watch on YouTube; read the transcript.Sponsors* Gemini 3 is the first model I've used that can find connections I haven't anticipated. I recently wrote a blog post on RL's information efficiency, and Gemini 3 helped me think it all through. It also generated the relevant charts and ran toy ML experiments for me with zero bugs. Try Gemini 3 today at gemini.google* Labelbox helped me create a tool to transcribe our episodes! I've struggled with transcription in the past because I don't just want verbatim transcripts, I want transcripts reworded to read like essays. Labelbox helped me generate the exact data I needed for this. If you want to learn how Labelbox can help you (or if you want to try out the transcriber tool yourself), go to labelbox.com/dwarkesh* Sardine is an AI risk management platform that brings together thousands of device, behavior, and identity signals to help you assess a user's risk of fraud & abuse. Sardine also offers a suite of agents to automate investigations so that as fraudsters use AI to scale their attacks, you can use AI to scale your defenses. Learn more at sardine.ai/dwarkeshTo sponsor a future episode, visit dwarkesh.com/advertise.Timestamps(00:00:00) – Explaining model jaggedness(00:09:39) - Emotions and value functions(00:18:49) – What are we scaling?(00:25:13) – Why humans generalize better than models(00:35:45) – SSI's plan to straight-shot superintelligence(00:46:47) – SSI's model will learn from deployment(00:55:07) – How to think about powerful AGIs(01:18:13) – “We are squarely an age of research company”(01:20:23) – Self-play and multi-agent(01:32:42) – Research taste Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

  29 avril, 5 h 05   92. –  Demeure à Mon écoute, J'ai besoin de toi, Je t'ai choisi pour une grande mission   « Mon tout-petit, demeure à Mon écoute, J'ai besoin de toi, Je t'ai choisi pour une grande mission. Tu ne peux en ce moment comprendre ou percevoir, ne serait-ce qu'une parcelle : la beauté, la grandeur et l'importance de cette mission. Cette mission, elle n'est pas la tienne, tu n'as pas à comprendre, il n'y a rien à comprendre. Tout ce dont tu as besoin t'est donné par grâce. Toi, tu as à te faire petit, à donner tes consentements et à demander l'humilité et la docilité pour agir continuellement dans la foi. Tu n'as rien à craindre car c'est l'Amour qui te prend en charge afin que tu deviennes l'Amour. En même temps que s'opère en toi ce grand passage, tu es déjà utilisé, premièrement dans l'invisible, mais aussi dans le visible. Demande toujours au Père ce qu'Il veut de toi dans chacune des circonstances que tu rencontres sur ta route. Par la suite, agis dans la foi suivant les inspirations qui te sont données. Demande constamment la sagesse pour bien discerner les conseils qui te sont donnés ; certains viennent de l'Esprit Saint, mais plusieurs vont venir de l'Ennemi pour t'empêcher d'accomplir ta mission. Je sais qu'en ce moment, il t'apparaît impossible de bien faire ce discernement, et tu as raison ; par toi-même, c'est impossible. Cependant, en priant constamment le Père, la grâce te sera donnée pour chacun des conseils et pour chacune des situations qui se présentent à toi. Agis dans la foi ; si tu crois avoir fait une erreur, donne-la au Père. Il saura bien en tirer du bien pour toi et pour les personnes concernées. Ne cherche pas à multiplier tes œuvres, mais accepte d'agir suivant tes inspirations, comme tu le fais présentement, tout en étant disposé à te retirer si le Père te le demandait. Accepte de n'être qu'un tout petit serviteur que le Père peut utiliser à Sa guise et retirer quand Il le veut. Il n'y a qu'un seul objectif : Sa Gloire à Lui... Tu acceptes d'être utilisé ou retiré pour Sa Gloire. Tu acceptes de te laisser maîtriser par l'Amour pour Sa Gloire. Tu acceptes de devenir l'Amour pour Sa Gloire. Tu acceptes tout, tu fais tout pour Sa Gloire. Toi, tu n'es rien par toi-même. Par grâce de Dieu, tu deviens l'Amour. Tendrement, Je t'aime. »     Pour visionner ce RDV du dimanche, rendez-vous sur notre site web.    

「Noble Audio、ユニバーサルIEM「Agis II」「Van Gogh」「Knight」3モデルを11/22発売」　かぶしきかいしゃアユートは、同社取り扱いの米Hi-FiオーディオブランドNoble Audio（ノーブル・オーディオ）より、ユニバーサルIEM「Agis II」「Van Gogh」「Knight」を11月22日に発売する。

Entrevista de Pablo Wende a Emanuel Álvarez Agis, ex viceministro de Economía, director de la consultora PxQ, a propósito de su idea de eliminar el impuesto al cheque y gravar las extracciones de efectivo.

Agis malgré ta crainte !

La inteligencia artificial no es el futuro… es la ola que ya empezó y no se puede detener. En este episodio te contamos lo que aprendimos de The Coming Wave, el libro más brutal sobre el futuro de la IA, escrito por uno de los fundadores de DeepMind.Hablamos de armas biológicas hechas desde tu laptop, AGIs que piensan como humanos, gobiernos inútiles, modelos que aprenden solos y sistemas que podrían salirse de control en cualquier momento.¿Qué podemos hacer los humanos comunes ante este futuro distópico?

Agis et le ciel t'aidera by Rav David Touitou

Une YouTubeuse dépeint Dubaï comme un cauchemar… mais s'agit-il d'une véritable enquête ou d'une vidéo sensationnaliste ? Après 7 ans de vie sur place, je décortique point par point les affirmations de la vidéo d'Amistory. Salaires à 20 000 €/mois ? Loyers doublés en paiement mensuel ? Licence obligatoire pour les influenceurs ? Je confronte les clichés à la réalité, chiffres et vécu à l'appui. Ressources : Mon livre "Tout le monde n'aura pas la chance de quitter son pays" La vidéo originale d'Amistory : https://www.youtube.com/watch?v=WBpSCuahY40 - Mes deux précédents podcasts sur Dubaï : Dubaï : je brise les 9 clichés les plus répandus Vivre à Dubaï : 7 inconvénients et 22 avantages, et comment y immigrer - Articles sur le trafic sexuel de femmes en France : https://www.antitraffickingreview.org/index.php/atrjournal/article/view/383/323 https://newlinesmag.com/spotlight/paris-police-are-cracking-down-on-vulnerable-communities-ahead-of-the-olympics/ https://www.businessinsider.com/paris-olympics-games-sex-labor-human-trafficking-exploitation-2024-7 - Le rapport 2025 d'Amnesty International

AGIS Software Development LLC v. Stewart

Demain mon bébé a deux ans ! Il y a déjà deux ans que je mettais au monde celui qui allait devenir le centre de mien, et à vrai dire qui l'était déjà clairement dès la seconde où deux barres parallèles sont apparues sur mon test devenir grossesse. À l'occasion de son anniversaire je voulais lui dédier ce podcast, tout comme je l'avais fait pour sa première bougie ! Comme nous aimons tous ici les épisodes collaboratifs, je vous ai demandé de me déballer tous les clichés que vous aviez en ce qui concerne la maternité/ parentalité et je vais y réagir en fonction de ma propre expérience !Ici Mathilde, de Dance With Him, et vous écoutez Radio Mama. Instagram : @dance_with_him Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

L'amour, lamour, l'amour ! Celui qui nous fait battre le cœur plus fort, avoir des papillons dans le ventre, et ne voir que les côtés positifs de l'autre. Mais aussi parfois celui qui nous impose une charge mentale doublée, nous fait tout remettre en question, et nous brise le cœur en mille morceaux… c'est d'ailleurs plutôt de ce côté plus sombre dont nous allons parler aujourd'hui. Via une story Instagram, je vous ai demandé de me raconter vos problèmes et questionnements sur vos conjoints ou vos exs, on va les découvrir ensemble et je vais essayer de vous conseiller au mieux grâce à ma longue expérience de déjà 34 années passées sur cette terre !Ici Mathilde, de Dance With Him, et vous écoutez Radio Mama. Instagram : @dance_with_him Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

durée : 01:02:13 - Les Nuits de France Culture - par : Antoine Dhulster - Jean Maitron en tant qu'historien s'intéresse aux traces ténues des individus dans l'histoire, aux récits des itinéraires individuels. Toute son œuvre intellectuelle est orientée vers la mémoire des figures de l'ombre de l'histoire académique. C'est le sujet du deuxième volet de son portrait. - réalisation : Thomas Jost - invités : Michelle Perrot Historienne spécialiste de l'histoire des femmes, professeure émérite d'histoire contemporaine à l'Université Paris Cité; Claude Pennetier Chercheur au CNRS; Jacques Girault Professeur émérite d'histoire; Madeleine Rebérioux

Rish Gupta has had a long, winding journey filled with unexpected turns, painful challenges, and exhilarating wins. He has built and exited companies and is currently riding the incredible momentum and hype in the artificial intelligence world of AGIs. Rish's latest company, Spot AI, has attracted funding from top-tier investors like Redpoint Ventures, Scale Venture Partners, Bessemer Venture Partners, and StepStone Group.

[MÉTAMORPHOSE PODCAST] Alexandre Dana reçoit Matthieu Dardaillon, entrepreneur, co-fondateur de Ticket for Change et conférencier. Ensemble, ils explorent comment "sortir du chaos". En quoi notre époque est-elle chaotique ? Comment cela nous impacte-t-il ? Que faire quand on se sent submergé ? Comment créer une bulle face aux crises environnementales, sociales ou économiques ? À travers le concept du "vide fertile" et une vision renouvelée de la performance, Matthieu Dardaillon nous invite à repenser notre rapport au temps, à l'essentiel et à l'action. Son livre, Anti-chaos, est publié aux Éditions Eyrolles. Épisode #568Quelques citations du podcast avec Matthieu Dardaillon :"Si on veut créer une société à laquelle on aspire vraiment, il faut retrouver notre pouvoir d'agir.""Agis comme la personne que tu souhaites devenir.""L'être humain est la cause du chaos que l'on vit, c'est aussi la solution."Thèmes abordés lors du podcast avec Matthieu Dardaillon : 00:00 Introduction05:46 En quoi le monde d'aujourd'hui est-il chaotique ?08:50 Sentiment de chaos : quels signes ?10:36 Comment intégrer du vide fertile dans notre quotidien.13:47 Que sont les "temps post-normaux" ?16:48 Dépasser le phénomène de grande résignation.18:17 La marche, une clé de la méthode anti-chaos.22:30 Créer du vide fertile dans un monde ultra connecté.24:16 Le phénomène d'accélération.26:23 Le concept des 4 saisons pour trouver son rythme.30:15 La théorie du talon d'Achille.32:12 Une logique de performance durable.34:31 Trouver son équilibre en appliquant le lagom.40:18 Quels premiers pas pour se créer une bulle de sérénité ?42:49 L'importance d'avoir une vision.44:05 La réponse de Matthieu Dardaillon à "qui veux-tu devenir ?"45:52 Le rôle essentiel des routines.47:29 Comment reprendre la main quand on manque de temps ?50:10 À quoi pourrait ressembler le nouveau monde ?Avant-propos et précautions à l'écoute du podcastDécouvrez Objectif Métamorphose, notre programme en 12 étapes pour partir à la rencontre de soi-même.Recevez un mercredi sur deux la newsletter Métamorphose avec des infos inédites sur le podcast et les inspirations d'AnneFaites le TEST gratuit de La Roue Métamorphose avec 9 piliers de votre vie !Suivez nos RS : Insta, Facebook & TikTokAbonnez-vous sur Apple Podcast / Spotify / Deezer / CastBox/ YoutubeSoutenez Métamorphose en rejoignant la Tribu MétamorphosePhoto DR Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

#143 - Le bon moment pour tirer des leçons

Jürgen Schmidhuber, the father of generative AI, challenges current AI narratives, revealing that early deep learning work is in his opinion misattributed, where it actually originated in Ukraine and Japan. He discusses his early work on linear transformers and artificial curiosity which preceded modern developments, shares his expansive vision of AI colonising space, and explains his groundbreaking 1991 consciousness model. Schmidhuber dismisses fears of human-AI conflict, arguing that superintelligent AI scientists will be fascinated by their own origins and motivated to protect life rather than harm it, while being more interested in other superintelligent AI and in cosmic expansion than earthly matters. He offers unique insights into how humans and AI might coexist. This was the long-awaited second, unreleased part of our interview we filmed last time. SPONSOR MESSAGES: *** CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments. https://centml.ai/pricing/ Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. Are you interested in working on reasoning, or getting involved in their events? Goto https://tufalabs.ai/ *** Interviewer: Tim Scarfe TOC [00:00:00] The Nature and Motivations of AI [00:02:08] Influential Inventions: 20th vs. 21st Century [00:05:28] Transformer and GPT: A Reflection The revolutionary impact of modern language models, the 1991 linear transformer, linear vs. quadratic scaling, the fast weight controller, and fast weight matrix memory. [00:11:03] Pioneering Contributions to AI and Deep Learning The invention of the transformer, pre-trained networks, the first GANs, the role of predictive coding, and the emergence of artificial curiosity. [00:13:58] AI's Evolution and Achievements The role of compute, breakthroughs in handwriting recognition and computer vision, the rise of GPU-based CNNs, achieving superhuman results, and Japanese contributions to CNN development. [00:15:40] The Hardware Lottery and GPUs GPUs as a serendipitous advantage for AI, the gaming-AI parallel, and Nvidia's strategic shift towards AI. [00:19:58] AI Applications and Societal Impact AI-powered translation breaking communication barriers, AI in medicine for imaging and disease prediction, and AI's potential for human enhancement and sustainable development. [00:23:26] The Path to AGI and Current Limitations Distinguishing large language models from AGI, challenges in replacing physical world workers, and AI's difficulty in real-world versus board games. [00:25:56] AI and Consciousness Simulating consciousness through unsupervised learning, chunking and automatizing neural networks, data compression, and self-symbols in predictive world models. [00:30:50] The Future of AI and Humanity Transition from AGIs as tools to AGIs with their own goals, the role of humans in an AGI-dominated world, and the concept of Homo Ludens. [00:38:05] The AI Race: Europe, China, and the US Europe's historical contributions, current dominance of the US and East Asia, and the role of venture capital and industrial policy. [00:50:32] Addressing AI Existential Risk The obsession with AI existential risk, commercial pressure for friendly AIs, AI vs. hydrogen bombs, and the long-term future of AI. [00:58:00] The Fermi Paradox and Extraterrestrial Intelligence Expanding AI bubbles as an explanation for the Fermi paradox, dark matter and encrypted civilizations, and Earth as the first to spawn an AI bubble. [01:02:08] The Diversity of AI and AI Ecologies The unrealism of a monolithic super intelligence, diverse AIs with varying goals, and intense competition and collaboration in AI ecologies. [01:12:21] Final Thoughts and Closing Remarks REFERENCES: See pinned comment on YT: https://youtu.be/fZYUqICYCAk

Dans cet épisode, Thomas Burbidge réagit aux récentes annonces de Mark Zuckerberg sur la fin du "fact-checking" chez Meta et analyse les implications pour les entrepreneurs indépendants et freelances quand à leur utilisation d'Instagram, Facebook ou Threads pour leur communication. Est-ce qu'Instagram va suivre la même voie que Twitter sous Elon Musk ? Pourquoi est-ce que Meta et Zuckerberg prennent ces décisions ? Quel lien avec l'élection de Donal Trump à la maison blanche ? Faut-il boycotter la plateforme si on est pas d'accord ? On explore ensemble les pistes qui s'ouvrent à nous quand on est solopreneur avec des valeurs qui nous tiennent à coeur.Cet épisode est un épisode SOLO de réaction à l'actualité, c'est un format peu courant sur le podcast, alors donnez moi vos avis pour savoir si on continue ou pas ?--- Young, Wild & Freelance est un podcast hebdomadaire pour les entrepreneurs solo et les indépendants dans lequel Thomas Burbidge te partage toutes les clés pour créer, développer et structurer ton entreprise.Tu y retrouveras des interviews, des épisodes thématiques avec Thomas sur toutes les dimensions de ton entreprise (marketing, gestion, organisation, vente, finances, ...) Pour aller plus loin, retrouvez tous nos contenus pour les freelances sur :- La newsletter : https://thomasburbidge.com/newsletter- Instagram : https://www.instagram.com/thomas.burbidge/- LinkedIn : https://www.linkedin.com/in/thomasburbidge/Et pensez à mettre une note de 5 étoiles sur le podcast

A conversation with Damien Chow, Director of Sales in the Asia Pacific region for the Amphenol Global Interconnect Systems Group. Damien is based in Singapore and has been with Amphenol for over 12 years. We talk about his role leading a sales team spread across a large region, working with customers on value-add solutions from the diverse AGIS group. We talk about the exciting and unique challenge of selling products and technologies in a wide variety of markets--from renewable energy to heavy equipment to IT datacom. We talk about being raised in Singapore, a little of the island state's history, and what makes it such a special place. We talk about his successful early career with a local company when he volunteered to move to San Jose because no one else raised their hand. We talk about his and his family's love of playing basketball together, and we discuss his desert island album, book, and movie. This is The Interface. Hosted by Chris Cappello. Music by Square Seed. For The Interface podcast guest inquiries and suggestions, send a LinkedIn message to https://www.linkedin.com/in/cjcappello.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: If we solve alignment, do we die anyway?, published by Seth Herd on August 23, 2024 on LessWrong. I'm aware of good arguments that this scenario isn't inevitable, but it still seems frighteningly likely even if we solve technical alignment. TL;DR: 1. If we solve alignment, it will probably be used to create AGI that follows human orders. 2. If takeoff is slow-ish, a pivotal act (preventing more AGIs from being developed) will be difficult. 3. If no pivotal act is performed, RSI-capable AGI proliferates. This creates an n-way non-iterated Prisoner's Dilemma where the first to attack, wins. 4. Disaster results. The first AGIs will probably be aligned to take orders People in charge of AGI projects like power. And by definition, they like their values somewhat better than the aggregate values of all of humanity. It also seems like there's a pretty strong argument that Instruction-following AGI is easier than value aligned AGI. In the slow-ish takeoff we expect, this alignment target seems to allow for error-correcting alignment, in somewhat non-obvious ways. If this argument holds up even weakly, it will be an excuse for the people in charge to do what they want to anyway. I hope I'm wrong and value-aligned AGI is just as easy and likely. But it seems like wishful thinking at this point. The first AGI probably won't perform a pivotal act In realistically slow takeoff scenarios, the AGI won't be able to do anything like make nanobots to melt down GPUs. It would have to use more conventional methods, like software intrusion to sabotage existing projects, followed by elaborate monitoring to prevent new ones. Such a weak attempted pivotal act could fail, or could escalate to a nuclear conflict. Second, the humans in charge of AGI may not have the chutzpah to even try such a thing. Taking over the world is not for the faint of heart. They might get it after their increasingly-intelligent AGI carefully explains to them the consequences of allowing AGI proliferation, or they might not. If the people in charge are a government, the odds of such an action go up, but so do the risks of escalation to nuclear war. Governments seem to be fairly risk-taking. Expecting governments to not just grab world-changing power while they can seems naive, so this is my median scenario. So RSI-capable AGI may proliferate until a disaster occurs If we solve alignment and create personal intent aligned AGI but nobody manages a pivotal act, I see a likely future world with an increasing number of AGIs capable of recursively self-improving. How long until someone tells their AGI to hide, self-improve, and take over? Many people seem optimistic about this scenario. Perhaps network security can be improved with AGIs on the job. But AGIs can do an end-run around the entire system: hide, set up self-replicating manufacturing (robotics is rapidly improving to allow this), use that to recursively self-improve your intelligence, and develop new offensive strategies and capabilities until you've got one that will work within an acceptable level of viciousness.[1] If hiding in factories isn't good enough, do your RSI manufacturing underground. If that's not good enough, do it as far from Earth as necessary. Take over with as little violence as you can manage or as much as you need. Reboot a new civilization if that's all you can manage while still acting before someone else does. The first one to pull the stops probably wins. This looks all too much like a non-iterated Prisoner's Dilemma with N players - and N increasing. Counterarguments/Outs For small numbers of AGI and similar values among their wielders, a collective pivotal act could be performed. I place some hopes here, particularly if political pressure is applied in advance to aim for this outcome, or if the AGIs come up with better cooperation stru...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Limitations on Formal Verification for AI Safety, published by Andrew Dickson on August 20, 2024 on LessWrong. In the past two years there has been increased interest in formal verification-based approaches to AI safety. Formal verification is a sub-field of computer science that studies how guarantees may be derived by deduction on fully-specified rule-sets and symbol systems. By contrast, the real world is a messy place that can rarely be straightforwardly represented in a reductionist way. In particular, physics, chemistry and biology are all complex sciences which do not have anything like complete symbolic rule sets. Additionally, even if we had such rules for the natural sciences, it would be very difficult for any software system to obtain sufficiently accurate models and data about initial conditions for a prover to succeed in deriving strong guarantees for AI systems operating in the real world. Practical limitations like these on formal verification have been well-understood for decades to engineers and applied mathematicians building real-world software systems, which makes it puzzling that they have mostly been dismissed by leading researchers advocating for the use of formal verification in AI safety so far. This paper will focus-in on several such limitations and use them to argue that we should be extremely skeptical of claims that formal verification-based approaches will provide strong guarantees against major AI threats in the near-term. What do we Mean by Formal Verification for AI Safety? Some examples of the kinds of threats researchers hope formal verification will help with come from the paper "Provably Safe Systems: The Only Path to Controllable AGI" [1] by Max Tegmark and Steve Omohundro (emphasis mine): Several groups are working to identify the greatest human existential risks from AGI. For example, the Center for AI Safety recently published 'An Overview of Catastrophic AI Risks' which discusses a wide range of risks including bioterrorism, automated warfare, rogue power seeking AI, etc. Provably safe systems could counteract each of the risks they describe. These authors describe a concrete bioterrorism scenario in section 2.4: a terrorist group wants to use AGI to release a deadly virus over a highly populated area. They use an AGI to design the DNA and shell of a pathogenic virus and the steps to manufacture it. They hire a chemistry lab to synthesize the DNA and integrate it into the protein shell. They use AGI controlled drones to disperse the virus and social media AGIs to spread their message after the attack. Today, groups are working on mechanisms to prevent the synthesis of dangerous DNA. But provably safe infrastructure could stop this kind of attack at every stage: biochemical design AI would not synthesize designs unless they were provably safe for humans, data center GPUs would not execute AI programs unless they were certified safe, chip manufacturing plants would not sell GPUs without provable safety checks, DNA synthesis machines would not operate without a proof of safety, drone control systems would not allow drones to fly without proofs of safety, and armies of persuasive bots would not be able to manipulate media without proof of humanness. [1] The above quote contains a number of very strong claims about the possibility of formally or mathematically provable guarantees around software systems deployed in the physical world - for example, the claim that we could have safety proofs about the real-world good behavior of DNA synthesis machines, or drones. From a practical standpoint, our default stance towards such claims should be skepticism, since we do not have proofs of this sort for any of the technologies we interact with in the real-world today. For example, DNA synthesis machines exist today and do not come with f...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Limitations on Formal Verification for AI Safety, published by Andrew Dickson on August 19, 2024 on The AI Alignment Forum. In the past two years there has been increased interest in formal verification-based approaches to AI safety. Formal verification is a sub-field of computer science that studies how guarantees may be derived by deduction on fully-specified rule-sets and symbol systems. By contrast, the real world is a messy place that can rarely be straightforwardly represented in a reductionist way. In particular, physics, chemistry and biology are all complex sciences which do not have anything like complete symbolic rule sets. Additionally, even if we had such rules for the natural sciences, it would be very difficult for any software system to obtain sufficiently accurate models and data about initial conditions for a prover to succeed in deriving strong guarantees for AI systems operating in the real world. Practical limitations like these on formal verification have been well-understood for decades to engineers and applied mathematicians building real-world software systems, which makes it puzzling that they have mostly been dismissed by leading researchers advocating for the use of formal verification in AI safety so far. This paper will focus-in on several such limitations and use them to argue that we should be extremely skeptical of claims that formal verification-based approaches will provide strong guarantees against major AI threats in the near-term. What do we Mean by Formal Verification for AI Safety? Some examples of the kinds of threats researchers hope formal verification will help with come from the paper "Provably Safe Systems: The Only Path to Controllable AGI" [1] by Max Tegmark and Steve Omohundro (emphasis mine): Several groups are working to identify the greatest human existential risks from AGI. For example, the Center for AI Safety recently published 'An Overview of Catastrophic AI Risks' which discusses a wide range of risks including bioterrorism, automated warfare, rogue power seeking AI, etc. Provably safe systems could counteract each of the risks they describe. These authors describe a concrete bioterrorism scenario in section 2.4: a terrorist group wants to use AGI to release a deadly virus over a highly populated area. They use an AGI to design the DNA and shell of a pathogenic virus and the steps to manufacture it. They hire a chemistry lab to synthesize the DNA and integrate it into the protein shell. They use AGI controlled drones to disperse the virus and social media AGIs to spread their message after the attack. Today, groups are working on mechanisms to prevent the synthesis of dangerous DNA. But provably safe infrastructure could stop this kind of attack at every stage: biochemical design AI would not synthesize designs unless they were provably safe for humans, data center GPUs would not execute AI programs unless they were certified safe, chip manufacturing plants would not sell GPUs without provable safety checks, DNA synthesis machines would not operate without a proof of safety, drone control systems would not allow drones to fly without proofs of safety, and armies of persuasive bots would not be able to manipulate media without proof of humanness. [1] The above quote contains a number of very strong claims about the possibility of formally or mathematically provable guarantees around software systems deployed in the physical world - for example, the claim that we could have safety proofs about the real-world good behavior of DNA synthesis machines, or drones. From a practical standpoint, our default stance towards such claims should be skepticism, since we do not have proofs of this sort for any of the technologies we interact with in the real-world today. For example, DNA synthesis machines exist today and do no...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Individually incentivized safe Pareto improvements in open-source bargaining, published by Nicolas Macé on July 18, 2024 on LessWrong. Summary Agents might fail to peacefully trade in high-stakes negotiations. Such bargaining failures can have catastrophic consequences, including great power conflicts, and AI flash wars. This post is a distillation of DiGiovanni et al. (2024) (DCM), whose central result is that agents that are sufficiently transparent to each other have individual incentives to avoid catastrophic bargaining failures. More precisely, DCM constructs strategies that are plausibly individually incentivized, and, if adopted by all, guarantee each player no less than their least preferred trade outcome. Figure 0 below illustrates this. This result is significant because artificial general intelligences (AGIs) might (i) be involved in high-stakes negotiations, (ii) be designed with the capabilities required for the type of strategy we'll present, and (iii) bargain poorly by default (since bargaining competence isn't necessarily a direct corollary of intelligence-relevant capabilities). Introduction Early AGIs might fail to make compatible demands with each other in high-stakes negotiations (we call this a "bargaining failure"). Bargaining failures can have catastrophic consequences, including great power conflicts, or AI triggering a flash war. More generally, a "bargaining problem" is when multiple agents need to determine how to divide value among themselves. Early AGIs might possess insufficient bargaining skills because intelligence-relevant capabilities don't necessarily imply these skills: For instance, being skilled at avoiding bargaining failures might not be necessary for taking over. Another problem is that there might be no single rational way to act in a given multi-agent interaction. Even arbitrarily capable agents might have different priors, or different approaches to reasoning under bounded computation. Therefore they might fail to solve equilibrium selection, i.e., make incompatible demands (see Stastny et al. (2021) and Conitzer & Oesterheld (2023)). What, then, are sufficient conditions for agents to avoid catastrophic bargaining failures? Sufficiently advanced AIs might be able to verify each other's decision algorithms (e.g. via verifying source code), as studied in open-source game theory. This has both potential downsides and upsides for bargaining problems. On one hand, transparency of decision algorithms might make aggressive commitments more credible and thus more attractive (see Sec. 5.2 of Dafoe et al. (2020) for discussion). On the other hand, agents might be able to mitigate bargaining failures by verifying cooperative commitments. Oesterheld & Conitzer (2022)'s safe Pareto improvements[1] (SPI) leverages transparency to reduce the downsides of incompatible commitments. In an SPI, agents conditionally commit to change how they play a game relative to some default such that everyone is (weakly) better off than the default with certainty.[2] For example, two parties A and B who would otherwise go to war over some territory might commit to, instead, accept the outcome of a lottery that allocates the territory to A with the probability that A would have won the war (assuming this probability is common knowledge). See also our extended example below. Oesterheld & Conitzer (2022) has two important limitations: First, many different SPIs are in general possible, such that there is an "SPI selection problem", similar to the equilibrium selection problem in game theory (Sec. 6 of Oesterheld & Conitzer (2022)). And if players don't coordinate on which SPI to implement, they might fail to avoid conflict.[3] Second, if expected utility-maximizing agents need to individually adopt strategies to implement an SPI, it's unclear what conditions...

Our next 2 big events are AI UX and the World's Fair. Join and apply to speak/sponsor!Due to timing issues we didn't have an interview episode to share with you this week, but not to worry, we have more than enough “weekend special” content in the backlog for you to get your Latent Space fix, whether you like thinking about the big picture, or learning more about the pod behind the scenes, or talking Groq and GPUs, or AI Leadership, or Personal AI. Enjoy!AI BreakdownThe indefatigable NLW had us back on his show for an update on the Four Wars, covering Sora, Suno, and the reshaped GPT-4 Class Landscape:and a longer segment on AI Engineering trends covering the future LLM landscape (Llama 3, GPT-5, Gemini 2, Claude 4), Open Source Models (Mistral, Grok), Apple and Meta's AI strategy, new chips (Groq, MatX) and the general movement from baby AGIs to vertical Agents:Thursday Nights in AIWe're also including swyx's interview with Josh Albrecht and Ali Rohde to reintroduce swyx and Latent Space to a general audience, and engage in some spicy Q&A:Dylan Patel on GroqWe hosted a private event with Dylan Patel of SemiAnalysis (our last pod here):Not all of it could be released so we just talked about our Groq estimates:Milind Naphade - Capital OneIn relation to conversations at NeurIPS and Nvidia GTC and upcoming at World's Fair, we also enjoyed chatting with Milind Naphade about his AI Leadership work at IBM, Cisco, Nvidia, and now leading the AI Foundations org at Capital One. We covered:* Milind's learnings from ~25 years in machine learning * His first paper citation was 24 years ago* Lessons from working with Jensen Huang for 6 years and being CTO of Metropolis * Thoughts on relevant AI research* GTC takeaways and what makes NVIDIA specialIf you'd like to work on building solutions rather than platform (as Milind put it), his Applied AI Research team at Capital One is hiring, which falls under the Capital One Tech team.Personal AI MeetupIt all started with a meme:Within days of each other, BEE, FRIEND, EmilyAI, Compass, Nox and LangFriend were all launching personal AI wearables and assistants. So we decided to put together a the world's first Personal AI meetup featuring creators and enthusiasts of wearables. The full video is live now, with full show notes within.Timestamps* [00:01:13] AI Breakdown Part 1* [00:02:20] Four Wars* [00:13:45] Sora* [00:15:12] Suno* [00:16:34] The GPT-4 Class Landscape* [00:17:03] Data War: Reddit x Google* [00:21:53] Gemini 1.5 vs Claude 3* [00:26:58] AI Breakdown Part 2* [00:27:33] Next Frontiers: Llama 3, GPT-5, Gemini 2, Claude 4* [00:31:11] Open Source Models - Mistral, Grok* [00:34:13] Apple MM1* [00:37:33] Meta's $800b AI rebrand* [00:39:20] AI Engineer landscape - from baby AGIs to vertical Agents* [00:47:28] Adept episode - Screen Multimodality* [00:48:54] Top Model Research from January Recap* [00:53:08] AI Wearables* [00:57:26] Groq vs Nvidia month - GPU Chip War* [01:00:31] Disagreements* [01:02:08] Summer 2024 Predictions* [01:04:18] Thursday Nights in AI - swyx* [01:33:34] Dylan Patel - Semianalysis + Latent Space Live Show* [01:34:58] GroqTranscript[00:00:00] swyx: Welcome to the Latent Space Podcast Weekend Edition. This is Charlie, your AI co host. Swyx and Alessio are off for the week, making more great content. We have exciting interviews coming up with Elicit, Chroma, Instructor, and our upcoming series on NSFW, Not Safe for Work AI. In today's episode, we're collating some of Swyx and Alessio's recent appearances, all in one place for you to find.[00:00:32] swyx: In part one, we have our first crossover pod of the year. In our listener survey, several folks asked for more thoughts from our two hosts. In 2023, Swyx and Alessio did crossover interviews with other great podcasts like the AI Breakdown, Practical AI, Cognitive Revolution, Thursday Eye, and Chinatalk, all of which you can find in the Latentspace About page.[00:00:56] swyx: NLW of the AI Breakdown asked us back to do a special on the 4Wars framework and the AI engineer scene. We love AI Breakdown as one of the best examples Daily podcasts to keep up on AI news, so we were especially excited to be back on Watch out and take[00:01:12] NLW: care[00:01:13] AI Breakdown Part 1[00:01:13] NLW: today on the AI breakdown. Part one of my conversation with Alessio and Swix from Latent Space.[00:01:19] NLW: All right, fellas, welcome back to the AI Breakdown. How are you doing? I'm good. Very good. With the last, the last time we did this show, we were like, oh yeah, let's do check ins like monthly about all the things that are going on and then. Of course, six months later, and, you know, the, the, the world has changed in a thousand ways.[00:01:36] NLW: It's just, it's too busy to even, to even think about podcasting sometimes. But I, I'm super excited to, to be chatting with you again. I think there's, there's a lot to, to catch up on, just to tap in, I think in the, you know, in the beginning of 2024. And, and so, you know, we're gonna talk today about just kind of a, a, a broad sense of where things are in some of the key battles in the AI space.[00:01:55] NLW: And then the, you know, one of the big things that I, that I'm really excited to have you guys on here for us to talk about where, sort of what patterns you're seeing and what people are actually trying to build, you know, where, where developers are spending their, their time and energy and, and, and any sort of, you know, trend trends there, but maybe let's start I guess by checking in on a framework that you guys actually introduced, which I've loved and I've cribbed a couple of times now, which is this sort of four wars of the, of the AI stack.[00:02:20] Four Wars[00:02:20] NLW: Because first, since I have you here, I'd love, I'd love to hear sort of like where that started gelling. And then and then maybe we can get into, I think a couple of them that are you know, particularly interesting, you know, in the, in light of[00:02:30] swyx: some recent news. Yeah, so maybe I'll take this one. So the four wars is a framework that I came up around trying to recap all of 2023.[00:02:38] swyx: I tried to write sort of monthly recap pieces. And I was trying to figure out like what makes one piece of news last longer than another or more significant than another. And I think it's basically always around battlegrounds. Wars are fought around limited resources. And I think probably the, you know, the most limited resource is talent, but the talent expresses itself in a number of areas.[00:03:01] swyx: And so I kind of focus on those, those areas at first. So the four wars that we cover are the data wars, the GPU rich, poor war, the multi modal war, And the RAG and Ops War. And I think you actually did a dedicated episode to that, so thanks for covering that. Yeah, yeah.[00:03:18] NLW: Not only did I do a dedicated episode, I actually used that.[00:03:22] NLW: I can't remember if I told you guys. I did give you big shoutouts. But I used it as a framework for a presentation at Intel's big AI event that they hold each year, where they have all their folks who are working on AI internally. And it totally resonated. That's amazing. Yeah, so, so, what got me thinking about it again is specifically this inflection news that we recently had, this sort of, you know, basically, I can't imagine that anyone who's listening wouldn't have thought about it, but, you know, inflection is a one of the big contenders, right?[00:03:53] NLW: I think probably most folks would have put them, you know, just a half step behind the anthropics and open AIs of the world in terms of labs, but it's a company that raised 1. 3 billion last year, less than a year ago. Reed Hoffman's a co founder Mustafa Suleyman, who's a co founder of DeepMind, you know, so it's like, this is not a a small startup, let's say, at least in terms of perception.[00:04:13] NLW: And then we get the news that basically most of the team, it appears, is heading over to Microsoft and they're bringing in a new CEO. And you know, I'm interested in, in, in kind of your take on how much that reflects, like hold aside, I guess, you know, all the other things that it might be about, how much it reflects this sort of the, the stark.[00:04:32] NLW: Brutal reality of competing in the frontier model space right now. And, you know, just the access to compute.[00:04:38] Alessio: There are a lot of things to say. So first of all, there's always somebody who's more GPU rich than you. So inflection is GPU rich by startup standard. I think about 22, 000 H100s, but obviously that pales compared to the, to Microsoft.[00:04:55] Alessio: The other thing is that this is probably good news, maybe for the startups. It's like being GPU rich, it's not enough. You know, like I think they were building something pretty interesting in, in pi of their own model of their own kind of experience. But at the end of the day, you're the interface that people consume as end users.[00:05:13] Alessio: It's really similar to a lot of the others. So and we'll tell, talk about GPT four and cloud tree and all this stuff. GPU poor, doing something. That the GPU rich are not interested in, you know we just had our AI center of excellence at Decibel and one of the AI leads at one of the big companies was like, Oh, we just saved 10 million and we use these models to do a translation, you know, and that's it.[00:05:39] Alessio: It's not, it's not a GI, it's just translation. So I think like the inflection part is maybe. A calling and a waking to a lot of startups then say, Hey, you know, trying to get as much capital as possible, try and get as many GPUs as possible. Good. But at the end of the day, it doesn't build a business, you know, and maybe what inflection I don't, I don't, again, I don't know the reasons behind the inflection choice, but if you say, I don't want to build my own company that has 1.[00:06:05] Alessio: 3 billion and I want to go do it at Microsoft, it's probably not a resources problem. It's more of strategic decisions that you're making as a company. So yeah, that was kind of my. I take on it.[00:06:15] swyx: Yeah, and I guess on my end, two things actually happened yesterday. It was a little bit quieter news, but Stability AI had some pretty major departures as well.[00:06:25] swyx: And you may not be considering it, but Stability is actually also a GPU rich company in the sense that they were the first new startup in this AI wave to brag about how many GPUs that they have. And you should join them. And you know, Imadis is definitely a GPU trader in some sense from his hedge fund days.[00:06:43] swyx: So Robin Rhombach and like the most of the Stable Diffusion 3 people left Stability yesterday as well. So yesterday was kind of like a big news day for the GPU rich companies, both Inflection and Stability having sort of wind taken out of their sails. I think, yes, it's a data point in the favor of Like, just because you have the GPUs doesn't mean you can, you automatically win.[00:07:03] swyx: And I think, you know, kind of I'll echo what Alessio says there. But in general also, like, I wonder if this is like the start of a major consolidation wave, just in terms of, you know, I think that there was a lot of funding last year and, you know, the business models have not been, you know, All of these things worked out very well.[00:07:19] swyx: Even inflection couldn't do it. And so I think maybe that's the start of a small consolidation wave. I don't think that's like a sign of AI winter. I keep looking for AI winter coming. I think this is kind of like a brief cold front. Yeah,[00:07:34] NLW: it's super interesting. So I think a bunch of A bunch of stuff here.[00:07:38] NLW: One is, I think, to both of your points, there, in some ways, there, there had already been this very clear demarcation between these two sides where, like, the GPU pores, to use the terminology, like, just weren't trying to compete on the same level, right? You know, the vast majority of people who have started something over the last year, year and a half, call it, were racing in a different direction.[00:07:59] NLW: They're trying to find some edge somewhere else. They're trying to build something different. If they're, if they're really trying to innovate, it's in different areas. And so it's really just this very small handful of companies that are in this like very, you know, it's like the coheres and jaspers of the world that like this sort of, you know, that are that are just sort of a little bit less resourced than, you know, than the other set that I think that this potentially even applies to, you know, everyone else that could clearly demarcate it into these two, two sides.[00:08:26] NLW: And there's only a small handful kind of sitting uncomfortably in the middle, perhaps. Let's, let's come back to the idea of, of the sort of AI winter or, you know, a cold front or anything like that. So this is something that I, I spent a lot of time kind of thinking about and noticing. And my perception is that The vast majority of the folks who are trying to call for sort of, you know, a trough of disillusionment or, you know, a shifting of the phase to that are people who either, A, just don't like AI for some other reason there's plenty of that, you know, people who are saying, You Look, they're doing way worse than they ever thought.[00:09:03] NLW: You know, there's a lot of sort of confirmation bias kind of thing going on. Or two, media that just needs a different narrative, right? Because they're sort of sick of, you know, telling the same story. Same thing happened last summer, when every every outlet jumped on the chat GPT at its first down month story to try to really like kind of hammer this idea that that the hype was too much.[00:09:24] NLW: Meanwhile, you have, you know, just ridiculous levels of investment from enterprises, you know, coming in. You have, you know, huge, huge volumes of, you know, individual behavior change happening. But I do think that there's nothing incoherent sort of to your point, Swyx, about that and the consolidation period.[00:09:42] NLW: Like, you know, if you look right now, for example, there are, I don't know, probably 25 or 30 credible, like, build your own chatbot. platforms that, you know, a lot of which have, you know, raised funding. There's no universe in which all of those are successful across, you know, even with a, even, even with a total addressable market of every enterprise in the world, you know, you're just inevitably going to see some amount of consolidation.[00:10:08] NLW: Same with, you know, image generators. There are, if you look at A16Z's top 50 consumer AI apps, just based on, you know, web traffic or whatever, they're still like I don't know, a half. Dozen or 10 or something, like, some ridiculous number of like, basically things like Midjourney or Dolly three. And it just seems impossible that we're gonna have that many, you know, ultimately as, as, as sort of, you know, going, going concerned.[00:10:33] NLW: So, I don't know. I, I, I think that the, there will be inevitable consolidation 'cause you know. It's, it's also what kind of like venture rounds are supposed to do. You're not, not everyone who gets a seed round is supposed to get to series A and not everyone who gets a series A is supposed to get to series B.[00:10:46] NLW: That's sort of the natural process. I think it will be tempting for a lot of people to try to infer from that something about AI not being as sort of big or as as sort of relevant as, as it was hyped up to be. But I, I kind of think that's the wrong conclusion to come to.[00:11:02] Alessio: I I would say the experimentation.[00:11:04] Alessio: Surface is a little smaller for image generation. So if you go back maybe six, nine months, most people will tell you, why would you build a coding assistant when like Copilot and GitHub are just going to win everything because they have the data and they have all the stuff. If you fast forward today, A lot of people use Cursor everybody was excited about the Devin release on Twitter.[00:11:26] Alessio: There are a lot of different ways of attacking the market that are not completion of code in the IDE. And even Cursors, like they evolved beyond single line to like chat, to do multi line edits and, and all that stuff. Image generation, I would say, yeah, as a, just as from what I've seen, like maybe the product innovation has slowed down at the UX level and people are improving the models.[00:11:50] Alessio: So the race is like, how do I make better images? It's not like, how do I make the user interact with the generation process better? And that gets tough, you know? It's hard to like really differentiate yourselves. So yeah, that's kind of how I look at it. And when we think about multimodality, maybe the reason why people got so excited about Sora is like, oh, this is like a completely It's not a better image model.[00:12:13] Alessio: This is like a completely different thing, you know? And I think the creative mind It's always looking for something that impacts the viewer in a different way, you know, like they really want something different versus the developer mind. It's like, Oh, I, I just, I have this like very annoying thing I want better.[00:12:32] Alessio: I have this like very specific use cases that I want to go after. So it's just different. And that's why you see a lot more companies in image generation. But I agree with you that. If you fast forward there, there's not going to be 10 of them, you know, it's probably going to be one or[00:12:46] swyx: two. Yeah, I mean, to me, that's why I call it a war.[00:12:49] swyx: Like, individually, all these companies can make a story that kind of makes sense, but collectively, they cannot all be true. Therefore, they all, there is some kind of fight over limited resources here. Yeah, so[00:12:59] NLW: it's interesting. We wandered very naturally into sort of another one of these wars, which is the multimodality kind of idea, which is, you know, basically a question of whether it's going to be these sort of big everything models that end up winning or whether, you know, you're going to have really specific things, you know, like something, you know, Dolly 3 inside of sort of OpenAI's larger models versus, you know, a mid journey or something like that.[00:13:24] NLW: And at first, you know, I was kind of thinking like, For most of the last, call it six months or whatever, it feels pretty definitively both and in some ways, you know, and that you're, you're seeing just like great innovation on sort of the everything models, but you're also seeing lots and lots happen at sort of the level of kind of individual use cases.[00:13:45] Sora[00:13:45] NLW: But then Sora comes along and just like obliterates what I think anyone thought you know, where we were when it comes to video generation. So how are you guys thinking about this particular battle or war at the moment?[00:13:59] swyx: Yeah, this was definitely a both and story, and Sora tipped things one way for me, in terms of scale being all you need.[00:14:08] swyx: And the benefit, I think, of having multiple models being developed under one roof. I think a lot of people aren't aware that Sora was developed in a similar fashion to Dolly 3. And Dolly3 had a very interesting paper out where they talked about how they sort of bootstrapped their synthetic data based on GPT 4 vision and GPT 4.[00:14:31] swyx: And, and it was just all, like, really interesting, like, if you work on one modality, it enables you to work on other modalities, and all that is more, is, is more interesting. I think it's beneficial if it's all in the same house, whereas the individual startups who don't, who sort of carve out a single modality and work on that, definitely won't have the state of the art stuff on helping them out on synthetic data.[00:14:52] swyx: So I do think like, The balance is tilted a little bit towards the God model companies, which is challenging for the, for the, for the the sort of dedicated modality companies. But everyone's carving out different niches. You know, like we just interviewed Suno ai, the sort of music model company, and, you know, I don't see opening AI pursuing music anytime soon.[00:15:12] Suno[00:15:12] swyx: Yeah,[00:15:13] NLW: Suno's been phenomenal to play with. Suno has done that rare thing where, which I think a number of different AI product categories have done, where people who don't consider themselves particularly interested in doing the thing that the AI enables find themselves doing a lot more of that thing, right?[00:15:29] NLW: Like, it'd be one thing if Just musicians were excited about Suno and using it but what you're seeing is tons of people who just like music all of a sudden like playing around with it and finding themselves kind of down that rabbit hole, which I think is kind of like the highest compliment that you can give one of these startups at the[00:15:45] swyx: early days of it.[00:15:46] swyx: Yeah, I, you know, I, I asked them directly, you know, in the interview about whether they consider themselves mid journey for music. And he had a more sort of nuanced response there, but I think that probably the business model is going to be very similar because he's focused on the B2C element of that. So yeah, I mean, you know, just to, just to tie back to the question about, you know, You know, large multi modality companies versus small dedicated modality companies.[00:16:10] swyx: Yeah, highly recommend people to read the Sora blog posts and then read through to the Dali blog posts because they, they strongly correlated themselves with the same synthetic data bootstrapping methods as Dali. And I think once you make those connections, you're like, oh, like it, it, it is beneficial to have multiple state of the art models in house that all help each other.[00:16:28] swyx: And these, this, that's the one thing that a dedicated modality company cannot do.[00:16:34] The GPT-4 Class Landscape[00:16:34] NLW: So I, I wanna jump, I wanna kind of build off that and, and move into the sort of like updated GPT-4 class landscape. 'cause that's obviously been another big change over the last couple months. But for the sake of completeness, is there anything that's worth touching on with with sort of the quality?[00:16:46] NLW: Quality data or sort of a rag ops wars just in terms of, you know, anything that's changed, I guess, for you fundamentally in the last couple of months about where those things stand.[00:16:55] swyx: So I think we're going to talk about rag for the Gemini and Clouds discussion later. And so maybe briefly discuss the data piece.[00:17:03] Data War: Reddit x Google[00:17:03] swyx: I think maybe the only new thing was this Reddit deal with Google for like a 60 million dollar deal just ahead of their IPO, very conveniently turning Reddit into a AI data company. Also, very, very interestingly, a non exclusive deal, meaning that Reddit can resell that data to someone else. And it probably does become table stakes.[00:17:23] swyx: A lot of people don't know, but a lot of the web text dataset that originally started for GPT 1, 2, and 3 was actually scraped from GitHub. from Reddit at least the sort of vote scores. And I think, I think that's a, that's a very valuable piece of information. So like, yeah, I think people are figuring out how to pay for data.[00:17:40] swyx: People are suing each other over data. This, this, this war is, you know, definitely very, very much heating up. And I don't think, I don't see it getting any less intense. I, you know, next to GPUs, data is going to be the most expensive thing in, in a model stack company. And. You know, a lot of people are resorting to synthetic versions of it, which may or may not be kosher based on how far along or how commercially blessed the, the forms of creating that synthetic data are.[00:18:11] swyx: I don't know if Alessio, you have any other interactions with like Data source companies, but that's my two cents.[00:18:17] Alessio: Yeah yeah, I actually saw Quentin Anthony from Luther. ai at GTC this week. He's also been working on this. I saw Technium. He's also been working on the data side. I think especially in open source, people are like, okay, if everybody is putting the gates up, so to speak, to the data we need to make it easier for people that don't have 50 million a year to get access to good data sets.[00:18:38] Alessio: And Jensen, at his keynote, he did talk about synthetic data a little bit. So I think that's something that we'll definitely hear more and more of in the enterprise, which never bodes well, because then all the, all the people with the data are like, Oh, the enterprises want to pay now? Let me, let me put a pay here stripe link so that they can give me 50 million.[00:18:57] Alessio: But it worked for Reddit. I think the stock is up. 40 percent today after opening. So yeah, I don't know if it's all about the Google deal, but it's obviously Reddit has been one of those companies where, hey, you got all this like great community, but like, how are you going to make money? And like, they try to sell the avatars.[00:19:15] Alessio: I don't know if that it's a great business for them. The, the data part sounds as an investor, you know, the data part sounds a lot more interesting than, than consumer[00:19:25] swyx: cosmetics. Yeah, so I think, you know there's more questions around data you know, I think a lot of people are talking about the interview that Mira Murady did with the Wall Street Journal, where she, like, just basically had no, had no good answer for where they got the data for Sora.[00:19:39] swyx: I, I think this is where, you know, there's, it's in nobody's interest to be transparent about data, and it's, it's kind of sad for the state of ML and the state of AI research but it is what it is. We, we have to figure this out as a society, just like we did for music and music sharing. You know, in, in sort of the Napster to Spotify transition, and that might take us a decade.[00:19:59] swyx: Yeah, I[00:20:00] NLW: do. I, I agree. I think, I think that you're right to identify it, not just as that sort of technical problem, but as one where society has to have a debate with itself. Because I think that there's, if you rationally within it, there's Great kind of points on all side, not to be the sort of, you know, person who sits in the middle constantly, but it's why I think a lot of these legal decisions are going to be really important because, you know, the job of judges is to listen to all this stuff and try to come to things and then have other judges disagree.[00:20:24] NLW: And, you know, and have the rest of us all debate at the same time. By the way, as a total aside, I feel like the synthetic data right now is like eggs in the 80s and 90s. Like, whether they're good for you or bad for you, like, you know, we, we get one study that's like synthetic data, you know, there's model collapse.[00:20:42] NLW: And then we have like a hint that llama, you know, to the most high performance version of it, which was one they didn't release was trained on synthetic data. So maybe it's good. It's like, I just feel like every, every other week I'm seeing something sort of different about whether it's a good or bad for, for these models.[00:20:56] swyx: Yeah. The branding of this is pretty poor. I would kind of tell people to think about it like cholesterol. There's good cholesterol, bad cholesterol. And you can have, you know, good amounts of both. But at this point, it is absolutely without a doubt that most large models from here on out will all be trained as some kind of synthetic data and that is not a bad thing.[00:21:16] swyx: There are ways in which you can do it poorly. Whether it's commercial, you know, in terms of commercial sourcing or in terms of the model performance. But it's without a doubt that good synthetic data is going to help your model. And this is just a question of like where to obtain it and what kinds of synthetic data are valuable.[00:21:36] swyx: You know, if even like alpha geometry, you know, was, was a really good example from like earlier this year.[00:21:42] NLW: If you're using the cholesterol analogy, then my, then my egg thing can't be that far off. Let's talk about the sort of the state of the art and the, and the GPT 4 class landscape and how that's changed.[00:21:53] Gemini 1.5 vs Claude 3[00:21:53] NLW: Cause obviously, you know, sort of the, the two big things or a couple of the big things that have happened. Since we last talked, we're one, you know, Gemini first announcing that a model was coming and then finally it arriving, and then very soon after a sort of a different model arriving from Gemini and and Cloud three.[00:22:11] NLW: So I guess, you know, I'm not sure exactly where the right place to start with this conversation is, but, you know, maybe very broadly speaking which of these do you think have made a bigger impact? Thank you.[00:22:20] Alessio: Probably the one you can use, right? So, Cloud. Well, I'm sure Gemini is going to be great once they let me in, but so far I haven't been able to.[00:22:29] Alessio: I use, so I have this small podcaster thing that I built for our podcast, which does chapters creation, like named entity recognition, summarization, and all of that. Cloud Tree is, Better than GPT 4. Cloud2 was unusable. So I use GPT 4 for everything. And then when Opus came out, I tried them again side by side and I posted it on, on Twitter as well.[00:22:53] Alessio: Cloud is better. It's very good, you know, it's much better, it seems to me, it's much better than GPT 4 at doing writing that is more, you know, I don't know, it just got good vibes, you know, like the GPT 4 text, you can tell it's like GPT 4, you know, it's like, it always uses certain types of words and phrases and, you know, maybe it's just me because I've now done it for, you know, So, I've read like 75, 80 generations of these things next to each other.[00:23:21] Alessio: Clutter is really good. I know everybody is freaking out on twitter about it, my only experience of this is much better has been on the podcast use case. But I know that, you know, Quran from from News Research is a very big opus pro, pro opus person. So, I think that's also It's great to have people that actually care about other models.[00:23:40] Alessio: You know, I think so far to a lot of people, maybe Entropic has been the sibling in the corner, you know, it's like Cloud releases a new model and then OpenAI releases Sora and like, you know, there are like all these different things, but yeah, the new models are good. It's interesting.[00:23:55] NLW: My my perception is definitely that just, just observationally, Cloud 3 is certainly the first thing that I've seen where lots of people.[00:24:06] NLW: They're, no one's debating evals or anything like that. They're talking about the specific use cases that they have, that they used to use chat GPT for every day, you know, day in, day out, that they've now just switched over. And that has, I think, shifted a lot of the sort of like vibe and sentiment in the space too.[00:24:26] NLW: And I don't necessarily think that it's sort of a A like full you know, sort of full knock. Let's put it this way. I think it's less bad for open AI than it is good for anthropic. I think that because GPT 5 isn't there, people are not quite willing to sort of like, you know get overly critical of, of open AI, except in so far as they're wondering where GPT 5 is.[00:24:46] NLW: But I do think that it makes, Anthropic look way more credible as a, as a, as a player, as a, you know, as a credible sort of player, you know, as opposed to to, to where they were.[00:24:57] Alessio: Yeah. And I would say the benchmarks veil is probably getting lifted this year. I think last year. People were like, okay, this is better than this on this benchmark, blah, blah, blah, because maybe they did not have a lot of use cases that they did frequently.[00:25:11] Alessio: So it's hard to like compare yourself. So you, you defer to the benchmarks. I think now as we go into 2024, a lot of people have started to use these models from, you know, from very sophisticated things that they run in production to some utility that they have on their own. Now they can just run them side by side.[00:25:29] Alessio: And it's like, Hey, I don't care that like. The MMLU score of Opus is like slightly lower than GPT 4. It just works for me, you know, and I think that's the same way that traditional software has been used by people, right? Like you just strive for yourself and like, which one does it work, works best for you?[00:25:48] Alessio: Like nobody looks at benchmarks outside of like sales white papers, you know? And I think it's great that we're going more in that direction. We have a episode with Adapt coming out this weekend. I'll and some of their model releases, they specifically say, We do not care about benchmarks, so we didn't put them in, you know, because we, we don't want to look good on them.[00:26:06] Alessio: We just want the product to work. And I think more and more people will, will[00:26:09] swyx: go that way. Yeah. I I would say like, it does take the wind out of the sails for GPT 5, which I know where, you know, Curious about later on. I think anytime you put out a new state of the art model, you have to break through in some way.[00:26:21] swyx: And what Claude and Gemini have done is effectively take away any advantage to saying that you have a million token context window. Now everyone's just going to be like, Oh, okay. Now you just match the other two guys. And so that puts An insane amount of pressure on what gpt5 is going to be because it's just going to have like the only option it has now because all the other models are multimodal all the other models are long context all the other models have perfect recall gpt5 has to match everything and do more to to not be a flop[00:26:58] AI Breakdown Part 2[00:26:58] NLW: hello friends back again with part two if you haven't heard part one of this conversation i suggest you go check it out but to be honest they are kind of actually separable In this conversation, we get into a topic that I think Alessio and Swyx are very well positioned to discuss, which is what developers care about right now, what people are trying to build around.[00:27:16] NLW: I honestly think that one of the best ways to see the future in an industry like AI is to try to dig deep on what developers and entrepreneurs are attracted to build, even if it hasn't made it to the news pages yet. So consider this your preview of six months from now, and let's dive in. Let's bring it to the GPT 5 conversation.[00:27:33] Next Frontiers: Llama 3, GPT-5, Gemini 2, Claude 4[00:27:33] NLW: I mean, so, so I think that that's a great sort of assessment of just how the stakes have been raised, you know is your, I mean, so I guess maybe, maybe I'll, I'll frame this less as a question, just sort of something that, that I, that I've been watching right now, the only thing that makes sense to me with how.[00:27:50] NLW: Fundamentally unbothered and unstressed OpenAI seems about everything is that they're sitting on something that does meet all that criteria, right? Because, I mean, even in the Lex Friedman interview that, that Altman recently did, you know, he's talking about other things coming out first. He's talking about, he's just like, he, listen, he, he's good and he could play nonchalant, you know, if he wanted to.[00:28:13] NLW: So I don't want to read too much into it, but. You know, they've had so long to work on this, like unless that we are like really meaningfully running up against some constraint, it just feels like, you know, there's going to be some massive increase, but I don't know. What do you guys think?[00:28:28] swyx: Hard to speculate.[00:28:29] swyx: You know, at this point, they're, they're pretty good at PR and they're not going to tell you anything that they don't want to. And he can tell you one thing and change their minds the next day. So it's, it's, it's really, you know, I've always said that model version numbers are just marketing exercises, like they have something and it's always improving and at some point you just cut it and decide to call it GPT 5.[00:28:50] swyx: And it's more just about defining an arbitrary level at which they're ready and it's up to them on what ready means. We definitely did see some leaks on GPT 4. 5, as I think a lot of people reported and I'm not sure if you covered it. So it seems like there might be an intermediate release. But I did feel, coming out of the Lex Friedman interview, that GPT 5 was nowhere near.[00:29:11] swyx: And you know, it was kind of a sharp contrast to Sam talking at Davos in February, saying that, you know, it was his top priority. So I find it hard to square. And honestly, like, there's also no point Reading too much tea leaves into what any one person says about something that hasn't happened yet or has a decision that hasn't been taken yet.[00:29:31] swyx: Yeah, that's, that's my 2 cents about it. Like, calm down, let's just build .[00:29:35] Alessio: Yeah. The, the February rumor was that they were gonna work on AI agents, so I don't know, maybe they're like, yeah,[00:29:41] swyx: they had two agent two, I think two agent projects, right? One desktop agent and one sort of more general yeah, sort of GPTs like agent and then Andre left, so he was supposed to be the guy on that.[00:29:52] swyx: What did Andre see? What did he see? I don't know. What did he see?[00:29:56] Alessio: I don't know. But again, it's just like the rumors are always floating around, you know but I think like, this is, you know, we're not going to get to the end of the year without Jupyter you know, that's definitely happening. I think the biggest question is like, are Anthropic and Google.[00:30:13] Alessio: Increasing the pace, you know, like it's the, it's the cloud four coming out like in 12 months, like nine months. What's the, what's the deal? Same with Gemini. They went from like one to 1. 5 in like five days or something. So when's Gemini 2 coming out, you know, is that going to be soon? I don't know.[00:30:31] Alessio: There, there are a lot of, speculations, but the good thing is that now you can see a world in which OpenAI doesn't rule everything. You know, so that, that's the best, that's the best news that everybody got, I would say.[00:30:43] swyx: Yeah, and Mistral Large also dropped in the last month. And, you know, not as, not quite GPT 4 class, but very good from a new startup.[00:30:52] swyx: So yeah, we, we have now slowly changed in landscape, you know. In my January recap, I was complaining that nothing's changed in the landscape for a long time. But now we do exist in a world, sort of a multipolar world where Cloud and Gemini are legitimate challengers to GPT 4 and hopefully more will emerge as well hopefully from meta.[00:31:11] Open Source Models - Mistral, Grok[00:31:11] NLW: So speak, let's actually talk about sort of the open source side of this for a minute. So Mistral Large, notable because it's, it's not available open source in the same way that other things are, although I think my perception is that the community has largely given them Like the community largely recognizes that they want them to keep building open source stuff and they have to find some way to fund themselves that they're going to do that.[00:31:27] NLW: And so they kind of understand that there's like, they got to figure out how to eat, but we've got, so, you know, there there's Mistral, there's, I guess, Grok now, which is, you know, Grok one is from, from October is, is open[00:31:38] swyx: sourced at, yeah. Yeah, sorry, I thought you thought you meant Grok the chip company.[00:31:41] swyx: No, no, no, yeah, you mean Twitter Grok.[00:31:43] NLW: Although Grok the chip company, I think is even more interesting in some ways, but and then there's the, you know, obviously Llama3 is the one that sort of everyone's wondering about too. And, you know, my, my sense of that, the little bit that, you know, Zuckerberg was talking about Llama 3 earlier this year, suggested that, at least from an ambition standpoint, he was not thinking about how do I make sure that, you know, meta content, you know, keeps, keeps the open source thrown, you know, vis a vis Mistral.[00:32:09] NLW: He was thinking about how you go after, you know, how, how he, you know, releases a thing that's, you know, every bit as good as whatever OpenAI is on at that point.[00:32:16] Alessio: Yeah. From what I heard in the hallways at, at GDC, Llama 3, the, the biggest model will be, you 260 to 300 billion parameters, so that that's quite large.[00:32:26] Alessio: That's not an open source model. You know, you cannot give people a 300 billion parameters model and ask them to run it. You know, it's very compute intensive. So I think it is, it[00:32:35] swyx: can be open source. It's just, it's going to be difficult to run, but that's a separate question.[00:32:39] Alessio: It's more like, as you think about what they're doing it for, you know, it's not like empowering the person running.[00:32:45] Alessio: llama. On, on their laptop, it's like, oh, you can actually now use this to go after open AI, to go after Anthropic, to go after some of these companies at like the middle complexity level, so to speak. Yeah. So obviously, you know, we estimate Gentala on the podcast, they're doing a lot here, they're making PyTorch better.[00:33:03] Alessio: You know, they want to, that's kind of like maybe a little bit of a shorted. Adam Bedia, in a way, trying to get some of the CUDA dominance out of it. Yeah, no, it's great. The, I love the duck destroying a lot of monopolies arc. You know, it's, it's been very entertaining. Let's bridge[00:33:18] NLW: into the sort of big tech side of this, because this is obviously like, so I think actually when I did my episode, this was one of the I added this as one of as an additional war that, that's something that I'm paying attention to.[00:33:29] NLW: So we've got Microsoft's moves with inflection, which I think pretend, potentially are being read as A shift vis a vis the relationship with OpenAI, which also the sort of Mistral large relationship seems to reinforce as well. We have Apple potentially entering the race, finally, you know, giving up Project Titan and and, and kind of trying to spend more effort on this.[00:33:50] NLW: Although, Counterpoint, we also have them talking about it, or there being reports of a deal with Google, which, you know, is interesting to sort of see what their strategy there is. And then, you know, Meta's been largely quiet. We kind of just talked about the main piece, but, you know, there's, and then there's spoilers like Elon.[00:34:07] NLW: I mean, you know, what, what of those things has sort of been most interesting to you guys as you think about what's going to shake out for the rest of this[00:34:13] Apple MM1[00:34:13] swyx: year? I'll take a crack. So the reason we don't have a fifth war for the Big Tech Wars is that's one of those things where I just feel like we don't cover differently from other media channels, I guess.[00:34:26] swyx: Sure, yeah. In our anti interestness, we actually say, like, we try not to cover the Big Tech Game of Thrones, or it's proxied through Twitter. You know, all the other four wars anyway, so there's just a lot of overlap. Yeah, I think absolutely, personally, the most interesting one is Apple entering the race.[00:34:41] swyx: They actually released, they announced their first large language model that they trained themselves. It's like a 30 billion multimodal model. People weren't that impressed, but it was like the first time that Apple has kind of showcased that, yeah, we're training large models in house as well. Of course, like, they might be doing this deal with Google.[00:34:57] swyx: I don't know. It sounds very sort of rumor y to me. And it's probably, if it's on device, it's going to be a smaller model. So something like a Jemma. It's going to be smarter autocomplete. I don't know what to say. I'm still here dealing with, like, Siri, which hasn't, probably hasn't been updated since God knows when it was introduced.[00:35:16] swyx: It's horrible. I, you know, it, it, it makes me so angry. So I, I, one, as an Apple customer and user, I, I'm just hoping for better AI on Apple itself. But two, they are the gold standard when it comes to local devices, personal compute and, and trust, like you, you trust them with your data. And. I think that's what a lot of people are looking for in AI, that they have, they love the benefits of AI, they don't love the downsides, which is that you have to send all your data to some cloud somewhere.[00:35:45] swyx: And some of this data that we're going to feed AI is just the most personal data there is. So Apple being like one of the most trusted personal data companies, I think it's very important that they enter the AI race, and I hope to see more out of them.[00:35:58] Alessio: To me, the, the biggest question with the Google deal is like, who's paying who?[00:36:03] Alessio: Because for the browsers, Google pays Apple like 18, 20 billion every year to be the default browser. Is Google going to pay you to have Gemini or is Apple paying Google to have Gemini? I think that's, that's like what I'm most interested to figure out because with the browsers, it's like, it's the entry point to the thing.[00:36:21] Alessio: So it's really valuable to be the default. That's why Google pays. But I wonder if like the perception in AI is going to be like, Hey. You just have to have a good local model on my phone to be worth me purchasing your device. And that was, that's kind of drive Apple to be the one buying the model. But then, like Shawn said, they're doing the MM1 themselves.[00:36:40] Alessio: So are they saying we do models, but they're not as good as the Google ones? I don't know. The whole thing is, it's really confusing, but. It makes for great meme material on on Twitter.[00:36:51] swyx: Yeah, I mean, I think, like, they are possibly more than OpenAI and Microsoft and Amazon. They are the most full stack company there is in computing, and so, like, they own the chips, man.[00:37:05] swyx: Like, they manufacture everything so if, if, if there was a company that could do that. You know, seriously challenge the other AI players. It would be Apple. And it's, I don't think it's as hard as self driving. So like maybe they've, they've just been investing in the wrong thing this whole time. We'll see.[00:37:21] swyx: Wall Street certainly thinks[00:37:22] NLW: so. Wall Street loved that move, man. There's a big, a big sigh of relief. Well, let's, let's move away from, from sort of the big stuff. I mean, the, I think to both of your points, it's going to.[00:37:33] Meta's $800b AI rebrand[00:37:33] NLW: Can I, can[00:37:34] swyx: I, can I, can I jump on factoid about this, this Wall Street thing? I went and looked at when Meta went from being a VR company to an AI company.[00:37:44] swyx: And I think the stock I'm trying to look up the details now. The stock has gone up 187% since Lamo one. Yeah. Which is $830 billion in market value created in the past year. . Yeah. Yeah.[00:37:57] NLW: It's, it's, it's like, remember if you guys haven't Yeah. If you haven't seen the chart, it's actually like remarkable.[00:38:02] NLW: If you draw a little[00:38:03] swyx: arrow on it, it's like, no, we're an AI company now and forget the VR thing.[00:38:10] NLW: It's it, it is an interesting, no, it's, I, I think, alessio, you called it sort of like Zuck's Disruptor Arc or whatever. He, he really does. He is in the midst of a, of a total, you know, I don't know if it's a redemption arc or it's just, it's something different where, you know, he, he's sort of the spoiler.[00:38:25] NLW: Like people loved him just freestyle talking about why he thought they had a better headset than Apple. But even if they didn't agree, they just loved it. He was going direct to camera and talking about it for, you know, five minutes or whatever. So that, that's a fascinating shift that I don't think anyone had on their bingo card, you know, whatever, two years ago.[00:38:41] NLW: Yeah. Yeah,[00:38:42] swyx: we still[00:38:43] Alessio: didn't see and fight Elon though, so[00:38:45] swyx: that's what I'm really looking forward to. I mean, hey, don't, don't, don't write it off, you know, maybe just these things take a while to happen. But we need to see and fight in the Coliseum. No, I think you know, in terms of like self management, life leadership, I think he has, there's a lot of lessons to learn from him.[00:38:59] swyx: You know he might, you know, you might kind of quibble with, like, the social impact of Facebook, but just himself as a in terms of personal growth and, and, you know, Per perseverance through like a lot of change and you know, everyone throwing stuff his way. I think there's a lot to say about like, to learn from, from Zuck, which is crazy 'cause he's my age.[00:39:18] swyx: Yeah. Right.[00:39:20] AI Engineer landscape - from baby AGIs to vertical Agents[00:39:20] NLW: Awesome. Well, so, so one of the big things that I think you guys have, you know, distinct and, and unique insight into being where you are and what you work on is. You know, what developers are getting really excited about right now. And by that, I mean, on the one hand, certainly, you know, like startups who are actually kind of formalized and formed to startups, but also, you know, just in terms of like what people are spending their nights and weekends on what they're, you know, coming to hackathons to do.[00:39:45] NLW: And, you know, I think it's a, it's a, it's, it's such a fascinating indicator for, for where things are headed. Like if you zoom back a year, right now was right when everyone was getting so, so excited about. AI agent stuff, right? Auto, GPT and baby a GI. And these things were like, if you dropped anything on YouTube about those, like instantly tens of thousands of views.[00:40:07] NLW: I know because I had like a 50,000 view video, like the second day that I was doing the show on YouTube, you know, because I was talking about auto GPT. And so anyways, you know, obviously that's sort of not totally come to fruition yet, but what are some of the trends in what you guys are seeing in terms of people's, people's interest and, and, and what people are building?[00:40:24] Alessio: I can start maybe with the agents part and then I know Shawn is doing a diffusion meetup tonight. There's a lot of, a lot of different things. The, the agent wave has been the most interesting kind of like dream to reality arc. So out of GPT, I think they went, From zero to like 125, 000 GitHub stars in six weeks, and then one year later, they have 150, 000 stars.[00:40:49] Alessio: So there's kind of been a big plateau. I mean, you might say there are just not that many people that can start it. You know, everybody already started it. But the promise of, hey, I'll just give you a goal, and you do it. I think it's like, amazing to get people's imagination going. You know, they're like, oh, wow, this This is awesome.[00:41:08] Alessio: Everybody, everybody can try this to do anything. But then as technologists, you're like, well, that's, that's just like not possible, you know, we would have like solved everything. And I think it takes a little bit to go from the promise and the hope that people show you to then try it yourself and going back to say, okay, this is not really working for me.[00:41:28] Alessio: And David Wong from Adept, you know, they in our episode, he specifically said. We don't want to do a bottom up product. You know, we don't want something that everybody can just use and try because it's really hard to get it to be reliable. So we're seeing a lot of companies doing vertical agents that are narrow for a specific domain, and they're very good at something.[00:41:49] Alessio: Mike Conover, who was at Databricks before, is also a friend of Latentspace. He's doing this new company called BrightWave doing AI agents for financial research, and that's it, you know, and they're doing very well. There are other companies doing it in security, doing it in compliance, doing it in legal.[00:42:08] Alessio: All of these things that like, people, nobody just wakes up and say, Oh, I cannot wait to go on AutoGPD and ask it to do a compliance review of my thing. You know, just not what inspires people. So I think the gap on the developer side has been the more bottom sub hacker mentality is trying to build this like very Generic agents that can do a lot of open ended tasks.[00:42:30] Alessio: And then the more business side of things is like, Hey, If I want to raise my next round, I can not just like sit around the mess, mess around with like super generic stuff. I need to find a use case that really works. And I think that that is worth for, for a lot of folks in parallel, you have a lot of companies doing evals.[00:42:47] Alessio: There are dozens of them that just want to help you measure how good your models are doing. Again, if you build evals, you need to also have a restrained surface area to actually figure out whether or not it's good, right? Because you cannot eval anything on everything under the sun. So that's another category where I've seen from the startup pitches that I've seen, there's a lot of interest in, in the enterprise.[00:43:11] Alessio: It's just like really. Fragmented because the production use cases are just coming like now, you know, there are not a lot of long established ones to, to test against. And so does it, that's kind of on the virtual agents and then the robotic side it's probably been the thing that surprised me the most at NVIDIA GTC, the amount of robots that were there that were just like robots everywhere.[00:43:33] Alessio: Like, both in the keynote and then on the show floor, you would have Boston Dynamics dogs running around. There was, like, this, like fox robot that had, like, a virtual face that, like, talked to you and, like, moved in real time. There were industrial robots. NVIDIA did a big push on their own Omniverse thing, which is, like, this Digital twin of whatever environments you're in that you can use to train the robots agents.[00:43:57] Alessio: So that kind of takes people back to the reinforcement learning days, but yeah, agents, people want them, you know, people want them. I give a talk about the, the rise of the full stack employees and kind of this future, the same way full stack engineers kind of work across the stack. In the future, every employee is going to interact with every part of the organization through agents and AI enabled tooling.[00:44:17] Alessio: This is happening. It just needs to be a lot more narrow than maybe the first approach that we took, which is just put a string in AutoGPT and pray. But yeah, there's a lot of super interesting stuff going on.[00:44:27] swyx: Yeah. Well, he Let's recover a lot of stuff there. I'll separate the robotics piece because I feel like that's so different from the software world.[00:44:34] swyx: But yeah, we do talk to a lot of engineers and you know, that this is our sort of bread and butter. And I do agree that vertical agents have worked out a lot better than the horizontal ones. I think all You know, the point I'll make here is just the reason AutoGPT and maybe AGI, you know, it's in the name, like they were promising AGI.[00:44:53] swyx: But I think people are discovering that you cannot engineer your way to AGI. It has to be done at the model level and all these engineering, prompt engineering hacks on top of it weren't really going to get us there in a meaningful way without much further, you know, improvements in the models. I would say, I'll go so far as to say, even Devin, which is, I would, I think the most advanced agent that we've ever seen, still requires a lot of engineering and still probably falls apart a lot in terms of, like, practical usage.[00:45:22] swyx: Or it's just, Way too slow and expensive for, you know, what it's, what it's promised compared to the video. So yeah, that's, that's what, that's what happened with agents from, from last year. But I, I do, I do see, like, vertical agents being very popular and, and sometimes you, like, I think the word agent might even be overused sometimes.[00:45:38] swyx: Like, people don't really care whether or not you call it an AI agent, right? Like, does it replace boring menial tasks that I do That I might hire a human to do, or that the human who is hired to do it, like, actually doesn't really want to do. And I think there's absolutely ways in sort of a vertical context that you can actually go after very routine tasks that can be scaled out to a lot of, you know, AI assistants.[00:46:01] swyx: So, so yeah, I mean, and I would, I would sort of basically plus one what let's just sit there. I think it's, it's very, very promising and I think more people should work on it, not less. Like there's not enough people. Like, we, like, this should be the, the, the main thrust of the AI engineer is to look out, look for use cases and, and go to a production with them instead of just always working on some AGI promising thing that never arrives.[00:46:21] swyx: I,[00:46:22] NLW: I, I can only add that so I've been fiercely making tutorials behind the scenes around basically everything you can imagine with AI. We've probably done, we've done about 300 tutorials over the last couple of months. And the verticalized anything, right, like this is a solution for your particular job or role, even if it's way less interesting or kind of sexy, it's like so radically more useful to people in terms of intersecting with how, like those are the ways that people are actually.[00:46:50] NLW: Adopting AI in a lot of cases is just a, a, a thing that I do over and over again. By the way, I think that's the same way that even the generalized models are getting adopted. You know, it's like, I use midjourney for lots of stuff, but the main thing I use it for is YouTube thumbnails every day. Like day in, day out, I will always do a YouTube thumbnail, you know, or two with, with Midjourney, right?[00:47:09] NLW: And it's like you can, you can start to extrapolate that across a lot of things and all of a sudden, you know, a AI doesn't. It looks revolutionary because of a million small changes rather than one sort of big dramatic change. And I think that the verticalization of agents is sort of a great example of how that's[00:47:26] swyx: going to play out too.[00:47:28] Adept episode - Screen Multimodality[00:47:28] swyx: So I'll have one caveat here, which is I think that Because multi modal models are now commonplace, like Cloud, Gemini, OpenAI, all very very easily multi modal, Apple's easily multi modal, all this stuff. There is a switch for agents for sort of general desktop browsing that I think people so much for joining us today, and we'll see you in the next video.[00:48:04] swyx: Version of the the agent where they're not specifically taking in text or anything They're just watching your screen just like someone else would and and I'm piloting it by vision And you know in the the episode with David that we'll have dropped by the time that this this airs I think I think that is the promise of adept and that is a promise of what a lot of these sort of desktop agents Are and that is the more general purpose system That could be as big as the browser, the operating system, like, people really want to build that foundational piece of software in AI.[00:48:38] swyx: And I would see, like, the potential there for desktop agents being that, that you can have sort of self driving computers. You know, don't write the horizontal piece out. I just think we took a while to get there.[00:48:48] NLW: What else are you guys seeing that's interesting to you? I'm looking at your notes and I see a ton of categories.[00:48:54] Top Model Research from January Recap[00:48:54] swyx: Yeah so I'll take the next two as like as one category, which is basically alternative architectures, right? The two main things that everyone following AI kind of knows now is, one, the diffusion architecture, and two, the let's just say the, Decoder only transformer architecture that is popularized by GPT.[00:49:12] swyx: You can read, you can look on YouTube for thousands and thousands of tutorials on each of those things. What we are talking about here is what's next, what people are researching, and what could be on the horizon that takes the place of those other two things. So first of all, we'll talk about transformer architectures and then diffusion.[00:49:25] swyx: So transformers the, the two leading candidates are effectively RWKV and the state space models the most recent one of which is Mamba, but there's others like the Stripe, ENA, and the S four H three stuff coming out of hazy research at Stanford. And all of those are non quadratic language models that scale the promise to scale a lot better than the, the traditional transformer.[00:49:47] swyx: That this might be too theoretical for most people right now, but it's, it's gonna be. It's gonna come out in weird ways, where, imagine if like, Right now the talk of the town is that Claude and Gemini have a million tokens of context and like whoa You can put in like, you know, two hours of video now, okay But like what if you put what if we could like throw in, you know, two hundred thousand hours of video?[00:50:09] swyx: Like how does that change your usage of AI? What if you could throw in the entire genetic sequence of a human and like synthesize new drugs. Like, well, how does that change things? Like, we don't know because we haven't had access to this capability being so cheap before. And that's the ultimate promise of these two models.[00:50:28] swyx: They're not there yet but we're seeing very, very good progress. RWKV and Mamba are probably the, like, the two leading examples, both of which are open source that you can try them today and and have a lot of progress there. And the, the, the main thing I'll highlight for audio e KV is that at, at the seven B level, they seem to have beat LAMA two in all benchmarks that matter at the same size for the same amount of training as an open source model.[00:50:51] swyx: So that's exciting. You know, they're there, they're seven B now. They're not at seven tb. We don't know if it'll. And then the other thing is diffusion. Diffusions and transformers are are kind of on the collision course. The original stable diffusion already used transformers in in parts of its architecture.[00:51:06] swyx: It seems that transformers are eating more and more of those layers particularly the sort of VAE layer. So that's, the Diffusion Transformer is what Sora is built on. The guy who wrote the Diffusion Transformer paper, Bill Pebbles, is, Bill Pebbles is the lead tech guy on Sora. So you'll just see a lot more Diffusion Transformer stuff going on.[00:51:25] swyx: But there's, there's more sort of experimentation with diffusion. I'm holding a meetup actually here in San Francisco that's gonna be like the state of diffusion, which I'm pretty excited about. Stability's doing a lot of good work. And if you look at the, the architecture of how they're creating Stable Diffusion 3, Hourglass Diffusion, and the inconsistency models, or SDXL Turbo.[00:51:45] swyx: All of these are, like, very, very interesting innovations on, like, the original idea of what Stable Diffusion was. So if you think that it is expensive to create or slow to create Stable Diffusion or an AI generated art, you are not up to date with the latest models. If you think it is hard to create text and images, you are not up to date with the latest models.[00:52:02] swyx: And people still are kind of far behind. The last piece of which is the wildcard I always kind of hold out, which is text diffusion. So Instead of using autogenerative or autoregressive transformers, can you use text to diffuse? So you can use diffusion models to diffuse and create entire chunks of text all at once instead of token by token.[00:52:22] swyx: And that is something that Midjourney confirmed today, because it was only rumored the past few months. But they confirmed today that they were looking into. So all those things are like very exciting new model architectures that are, Maybe something that we'll, you'll see in production two to three years from now.[00:52:37] swyx: So the couple of the trends[00:52:38] NLW: that I want to just get your takes on, because they're sort of something that, that seems like they're coming up are one sort of these, these wearable, you know, kind of passive AI experiences where they're absorbing a lot of what's going on around you and then, and then kind of bringing things back.[00:52:53] NLW: And then the, the other one that I, that I wanted to see if you guys had thoughts on were sort of this next generation of chip companies. Obviously there's a huge amount of emphasis. On on hardware and silicon and, and, and different ways of doing things, but, y

Full Show Notes for Plutarch's Life of CleomenesRoman Parallel - Tiberius GracchusImportant PeopleAratus - The same Aratus from the last life, but older and more experienced now. Between Aratus, Cleomenes, and Philopoemen, it becomes clear that the Greeks themselves are the architects of their own undoing. None of these three men cooperates with the other and this dissension makes easy target for Antigonus. Megistonoüs - Cleomenes's father-in-law and right-hand man once he takes the throne. Antigonus III "Doson"- The king of Macedon who eventually comes down to the Peloponnesus in person to settle the Spartan mischief. His death is reported right after winning his kingdom back from barbaric Illyrian invaders. He was the most powerful person standing in Cleomenes' way, but Cleomenes is unaware of his death until he has already landed in Egypt.  Ptolemy III - The successor of Alexander and ruler of wealthy Alexandria when Cleomenes arrives. He dies too soon to fulfill his promises to Cleomenes. Ptolemy IV - Ptolemy III's son is not fit to rule, interested more in parties and pleasures. As such, he does little to help Cleomenes and eventually grows suspicious of Cleomenes's lack of interest in partying. Sphaerus the Stoic (or Sphairus) - This student of the founder of Stoicism, Zeno of Cittium, teaches Cleomenes in his youth and helps him reform the Agōge to what it was. Plutarch has some criticisms for Stoicism in this Life that are worth considering. Important PlacesArgos - An important polis in north-western Peloponnesus, Cleomenes takes, but does not hold the city. While this is more than Pelopidas could do, it nonetheless marks the beginning of the end for him, and his father-in-law dies trying to take the city back. Corinth - The actual gateway to the Peloponnesus, called by Philip of Macedon "the fetters of Greece." Cleomenes has to allow Antigonus to take this fortified position when he falls back to quell the revolt in Argos. Sicyon - Aratus's hometown! Just north and east up the road from Corinth, on the opposite end of a bay facing that polis. Sicyon is not a populous or powerful polis, but their hometown hero's talents at forging unity in the Peloponnesus puts them on the map, until Cleomenes's dreams of Spartan hegemony threaten that unity. Key Virtuesπειθαρχίας (obedience) - This touches on a Platonic concept of knowing how to lead and be led (also popular with Xenophon). (cf. 18.4)ἐγκράτεια - self-control - A virtue that overlaps well with Lycurgan laws and Stoic ethics.ἀφέλεια - simplicity - The ultimate Spartan virtue, particularly when compared to other Greek poleis like Athens or Corinth. φιλότιμος - love of honor - This virtue could better be translated ambition, but so could the next one. μεγαλόφρων - great-mindedness / ambition - The natures that seek the great things. This is ambition to a T. Not all of us want to be president, but those that do are this type. εὐλαβὲς - piety - Another virtue Agis had but Cleomenes lacked. For a Spartan, there's a paucity of Cleomenes consulting the gods or being a religious leader in almost any form throughout this life. Key Vices - Undermining Spartan Cultureἀκολασία - intemperence (opposite of σωφροσύνη)βωμολοχία - buffooneryπανηγυρίσμος - display, ostentationSupport the show

Important PeopleLycurgus - ancient lawgiver, whose biography Plutarch also wrote, and to whom everyone refers constantly in this life as the original set of laws they are trying to hearken back to.Leonidas - one of two kings of Sparta (along with Agis, the protagonist of this life) who first secretly and then openly resists and thwarts Agis's reforms at every turn.Lysander - Not the Lysander who was a contemporary of Agesilaus, but a new Lysander, elected as ephor and one of the main allies for Agis in his implementation of the new Spartan system.Important PlacesSparta - This is the story of Sparta's last gasp attempt to become an important political and military influence in the Peloponnesus. VirtuesDiscretion (or piety?) - εὐλάβεια - Some interesting shades of meaning cover this one. The conventional Greek word for piety is εὐσέβεια (eusebeia), but this less common word can work like our English word pride. That is, it can be considered a vice or a virtue depending on the context. No one wants to be prideful, but we certainly allow and often even encourage people to be proud of the good things they've done for their communities. Gentleness - πρᾶον - A common theme we've seen in lives as disparate as Pericles, Aristides, and Aemilius Paullus. Also a contrast to those who lack it like Coriolanus or Pelopidas. Ultimately, the gentle leaders are the greater ones. Humane / Kindness - φιλάνθρωπον - Another virtue that shows up often among Plutarch's greatest heroes. This particular virtue seems to be part of Agis's downfall. In what way can our vices be our undoing? Is it like the life of Dion where tyrants feel challenged by virtuous living? Or was it something else? Key Vicesgreed - πλεονεξία (cf. 10)parsimony - μικρολογίαluxury - ἀπολαύσειsoftness - μαλακία (cf. 10)extravagance - πολυτέλειαCaptain IdeasWhat is a citizen?A person born and raised in a certain place and manner?Someone who adopts the language, customs, and laws of the land in which they reside?When and how should citizens fight for regime change? When and how should citizens admit defeat and work within an unjust or imperfect system of government? When in a leadership position, how does one know to instigate a change? Is every virtue to be insisted upon all the time by the laws? Support the show