Podcasts about riemann

Epoch AI researchers reveal why Anthropic might beat everyone to the first gigawatt datacenter, why AI could solve the Riemann hypothesis in 5 years, and what 30% GDP growth actually looks like. They explain why "energy bottlenecks" are just companies complaining about paying 2x for power instead of getting it cheap, why 10% of current jobs will vanish this decade, and the most data-driven take on whether we're racing toward superintelligence or headed for history's biggest bubble. Resources:Follow Yafah Edelman on X: https://x.com/YafahEdelmanFollow David Owen on X: https://x.com/everysumFollow Marco Mascorro on X: https://x.com/MascobotFollow Erik Torenberg on X: https://x.com/eriktorenberg Stay Updated:If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X: https://x.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see http://a16z.com/disclosures.   Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Podcast on SpotifyListen to the a16z Podcast on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

My fellow pro-growth/progress/abundance Up Wingers in America and around the world:What really gets AI optimists excited isn't the prospect of automating customer service departments or human resources. Imagine, rather, what might happen to the pace of scientific progress if AI becomes a super research assistant. Tom Davidson's new paper, How Quick and Big Would a Software Intelligence Explosion Be?, explores that very scenario.Today on Faster, Please! — The Podcast, I talk with Davidson about what it would mean for automated AI researchers to rapidly improve their own algorithms, thus creating a self-reinforcing loop of innovation. We talk about the economic effects of self-improving AI research and how close we are to that reality.Davidson is a senior research fellow at Forethought, where he explores AI and explosive growth. He was previously a senior research fellow at Open Philanthropy and a research scientist at the UK government's AI Security Institute.In This Episode* Making human minds (1:43)* Theory to reality (6:45)* The world with automated research (10:59)* Considering constraints (16:30)* Worries and what-ifs (19:07)Below is a lightly edited transcript of our conversation. Making human minds (1:43). . . you don't have to build any more computer chips, you don't have to build any more fabs . . . In fact, you don't have to do anything at all in the physical world.Pethokoukis: A few years ago, you wrote a paper called “Could Advanced AI Drive Explosive Economic Growth?,” which argued that growth could accelerate dramatically if AI would start generating ideas the way human researchers once did. In your view, population growth historically powered kind of an ideas feedback loop. More people meant more researchers meant more ideas, rising incomes, but that loop broke after the demographic transition in the late-19th century but you suggest that AI could restart it: more ideas, more output, more AI, more ideas. Does this new paper in a way build upon that paper? “How quick and big would a software intelligence explosion be?”The first paper you referred to is about the biggest-picture dynamic of economic growth. As you said, throughout the long run history, when we produced more food, the population increased. That additional output transferred itself into more people, more workers. These days that doesn't happen. When GDP goes up, that doesn't mean people have more kids. In fact, the demographic transition, the richer people get, the fewer kids they have. So now we've got more output, we're getting even fewer people as a result, so that's been blocked.This first paper is basically saying, look, if we can manufacture human minds or human-equivalent minds in any way, be it by building more computer chips, or making better computer chips, or any way at all, then that feedback loop gets going again. Because if we can manufacture more human minds, then we can spend output again to create more workers. That's the first paper.The second paper double clicks on one specific way that we can use output to create more human minds. It's actually, in a way, the scariest way because it's the way of creating human minds which can happen the quickest. So this is the way where you don't have to build any more computer chips, you don't have to build any more fabs, as they're called, these big factories that make computer chips. In fact, you don't have to do anything at all in the physical world.It seems like most of the conversation has been about how much investment is going to go into building how many new data centers, and that seems like that is almost the entire conversation, in a way, at the moment. But you're not looking at compute, you're looking at software.Exactly, software. So the idea is you don't have to build anything. You've already got loads of computer chips and you just make the algorithms that run the AIs on those computer chips more efficient. This is already happening, but it isn't yet a big deal because AI isn't that capable. But already, one year out, Epoch, this AI forecasting organization, estimates that just in one year, it becomes 10 times to 1000 times cheaper to run the same AI system. Just wait 12 months, and suddenly, for the same budget, you are able to run 10 times as many AI systems, or maybe even 1000 times as many for their most aggressive estimate. As I said, not a big deal today, but if we then develop an AI system which is better than any human at doing research, then now, in 10 months, you haven't built anything, but you've got 10 times as many researchers that you can set to work or even more than that. So then we get this feedback loop where you make some research progress, you improve your algorithms, now you've got loads more researchers, you set them all to work again, finding even more algorithmic improvements. So today we've got maybe a few hundred people that are advancing state-of-the-art AI algorithms.I think they're all getting paid a billion dollars a person, too.Exactly. But maybe we can 10x that initially by having them replaced by AI researchers that do the same thing. But then those AI researchers improve their own algorithms. Now you have 10x as many again, you have them building more computer chips, you're just running them more efficiently, and then the cycle continues. You're throwing more and more of these AI researchers at AI progress itself, and the algorithms are improving in what might be a very powerful feedback loop.In this case, it seems me that you're not necessarily talking about artificial general intelligence. This is certainly a powerful intelligence, but it's narrow. It doesn't have to do everything, it doesn't have to play chess, it just has to be able to do research.It's certainly not fully general. You don't need it to be able to control a robot body. You don't need it to be able to solve the Riemann hypothesis. You don't need it to be able to even be very persuasive or charismatic to a human. It's not narrow, I wouldn't say, it has to be able to do literally anything that AI researchers do, and that's a wide range of tasks: They're coding, they're communicating with each other, they're managing people, they are planning out what to work on, they are thinking about reviewing the literature. There's a fairly wide range of stuff. It's extremely challenging. It's some of the hardest work in the world to do, so I wouldn't say it's now, but it's not everything. It's some kind of intermediate level of generality in between a mere chess algorithm that just does chess and the kind of AGI that can literally do anything.Theory to reality (6:45)I think it's a much smaller gap for AI research than it is for many other parts of the economy.I think people who are cautiously optimistic about AI will say something like, “Yeah, I could see the kind of intelligence you're referring to coming about within a decade, but it's going to take a couple of big breakthroughs to get there.” Is that true, or are we actually getting pretty close?Famously, predicting the future of technology is very, very difficult. Just a few years before people invented the nuclear bomb, famous, very well-respected physicists were saying, “It's impossible, this will never happen.” So my best guess is that we do need a couple of fairly non-trivial breakthroughs. So we had the start of RL training a couple of years ago, became a big deal within the language model paradigm. I think we'll probably need another couple of breakthroughs of that kind of size.We're not talking a completely new approach, throw everything out, but we're talking like, okay, we need to extend the current approach in a meaningfully different way. It's going to take some inventiveness, it's going to take some creativity, we're going to have to try out a few things. I think, probably, we'll need that to get to the researcher that can fully automate OpenAI, is a nice way of putting it — OpenAI doesn't employ any humans anymore, they've just got AIs there.There's a difference between what a model can do on some benchmark versus becoming actually productive in the real world. That's why, while all the benchmark stuff is interesting, the thing I pay attention to is: How are businesses beginning to use this technology? Because that's the leap. What is that gap like, in your scenario, versus an AI model that can do a theoretical version of the lab to actually be incorporated in a real laboratory?It's definitely a gap. I think it's a pretty big gap. I think it's a much smaller gap for AI research than it is for many other parts of the economy. Let's say we are talking about car manufacturing and you're trying to get an AI to do everything that happens there. Man, it's such a messy process. There's a million different parts of the supply chain. There's all this tacit knowledge and all the human workers' minds. It's going to be really tough. There's going to be a very big gap going from those benchmarks to actually fully automating the supply chain for cars.For automating what OpenAI does, there's still a gap, but it's much smaller, because firstly, all of the work is virtual. Everyone at OpenAI could, in principle, work remotely. Their top research scientists, they're just on a computer all day. They're not picking up bricks and doing stuff like that. So also that already means it's a lot less messy. You get a lot less of that kind of messy world reality stuff slowing down adoption. And also, a lot of it is coding, and coding is almost uniquely clean in that, for many coding tasks, you can define clearly defined metrics for success, and so that makes AI much better. You can just have a go. Did AI succeed in the test? If not, try something else or do a gradient set update.That said, there's still a lot of messiness here, as any coder will know, when you're writing good code, it's not just about whether it does the function that you've asked it to do, it needs to be well-designed, it needs to be modular, it needs to be maintainable. These things are much harder to evaluate, and so AIs often pass our benchmarks because they can do the function that you asked it to do, the code runs, but they kind of write really spaghetti code — code that no one wants to look at, that no one can understand, and so no company would want to use that.So there's still going to be a pretty big benchmark-to-reality gap, even for OpenAI, and I think that's one of the big uncertainties in terms of, will this happen in three years versus will this happen in 10 years, or even 15 years?Since you brought up the timeline, what's your guess? I didn't know whether to open with that question or conclude with that question — we'll stick it right in the middle of our chat.Great. Honestly, my best guess about this does change more often than I would like it to, which I think tells us, look, there's still a state of flux. This is just really something that's very hard to know about. Predicting the future is hard. My current best guess is it's about even odds that we're able to fully automate OpenAI within the next 10 years. So maybe that's a 50-50.The world with AI research automation (10:59). . . I'm talking about 30 percent growth every year. I think it gets faster than that. If you want to know how fast it eventually gets, you can think about the question of how fast can a kind of self-replicating system double itself?So then what really would be the impact of that kind of AI research automation? How would you go about quantifying that kind of acceleration? What does the world look like?Yeah, so many possibilities, but I think what strikes me is that there is a plausible world where it is just way, way faster than almost everyone is expecting it to be. So that's the world where you fully automate OpenAI, and then we get that feedback loop that I was talking about earlier where AIs make their algorithms way more efficient, now you've got way more of them, then they make their algorithms way more efficient again, now they're way smarter. Now they're thinking a hundred times faster. The feedback loop continues and maybe within six months you now have a billion superintelligent AIs running on this OpenAI data center. The combined cognitive abilities of all these AIs outstrips the whole of the United States, outstrips anything we've seen from any kind of company or entity before, and they can all potentially be put towards any goal that OpenAI wants to. And then there's, of course, the risk that OpenAI's lost control of these systems, often discussed, in which case these systems could all be working together to pursue a particular goal. And so what we're talking about here is really a huge amount of power. It's a threat to national security for any government in which this happens, potentially. It is a threat to everyone if we lose control of these systems, or if the company that develops them uses them for some kind of malicious end. And, in terms of economic impacts, I personally think that that again could happen much more quickly than people think, and we can get into that.In the first paper we mentioned, it was kind of a thought experiment, but you were really talking about moving the decimal point in GDP growth, instead of talking about two and three percent, 20 and 30 percent. Is that the kind of world we're talking about?I speak to economists a lot, and —They hate those kinds of predictions, by the way.Obviously, they think I'm crazy. Not all of them. There are economists that take it very seriously. I think it's taken more seriously than everyone else realizes. It's like it's a bit embarrassing, at the moment, to admit that you take it seriously, but there are a few really senior economists who absolutely know their stuff. They're like, “Yep, this checks out. I think that's what's going to happen.” And I've had conversation with them where they're like, “Yeah, I think this is going to happen.” But the really loud, dominant view where I think people are a little bit scared to speak out against is they're like, “Obviously this is sci-fi.”One analogy I like to give to people who are very, very confident that this is all sci-fi and it's rubbish is to imagine that we were sitting there in the year 1400, imagine we had an economics professor who'd been studying the rate of economic growth, and they've been like, “Yeah, we've always had 0.1 percent growth every single year throughout history. We've never seen anything higher.” And then there was some kind of futurist economist rogue that said, “Actually, I think that if I extrapolate the curves in this way and we get this kind of technology, maybe we could have one percent growth.” And then all the other economists laugh at them, tell them they're insane – that's what happened. In 1400, we'd never had growth that was at all fast, and then a few hundred years later, we developed industrial technology, we started that feedback loop, we were investing more and more resources in scientific progress and in physical capital, and we did see much faster growth.So I think it can be useful to try and challenge economists and say, “Okay, I know it sounds crazy, but history was crazy. This crazy thing happened where growth just got way, way faster. No one would've predicted it. You would not have predicted it.” And I think being in that mindset can encourage people to be like, “Yeah, okay. You know what? Maybe if we do get AI that's really that powerful, it can really do everything, and maybe it is possible.”But to answer your question, yeah, I'm talking about 30 percent growth every year. I think it gets faster than that. If you want to know how fast it eventually gets, you can think about the question of how fast can a kind of self-replicating system double itself? So ultimately, what the economy is going to be like is it's going to have robots and factories that are able to fully create new versions of themselves. Everything you need: the roads, the electricity, the robots, the buildings, all of that will be replicated. And so you can look at actually biology and say, do we have any examples of systems which fully replicate themselves? How long does it take? And if you look at rats, for example, they're able to double the number of rats by grabbing resources from the environment, and giving birth, and whatnot. The doubling time is about six weeks for some types of rats. So that's an example of here's a physical system — ultimately, everything's made of physics — a physical system that has some intelligence that's able to go out into the world, gather resources, replicate itself. The doubling time is six weeks.Now, who knows how long it'll take us to get to AI that's that good? But when we do, you could see the whole physical economy, maybe a part that humans aren't involved with, a whole automated city without any humans just doubling itself every few weeks. If that happens, and the amount of stuff we're able to reduce as a civilization is doubling again on the order of weeks. And, in fact, there are some animals that double faster still, in days, but that's the kind of level of craziness. Now we're talking about 1000 percent growth, at that point. We don't know how crazy it could get, but I think we should take even the really crazy possibilities, we shouldn't fully rule them out.Considering constraints (16:30)I really hope people work less. If we get this good future, and the benefits are shared between all . . . no one should work. But that doesn't stop growth . . .There's this great AI forecast chart put out by the Federal Reserve Bank of Dallas, and I think its main forecast — the one most economists would probably agree with — has a line showing AI improving GDP by maybe two tenths of a percent. And then there are two other lines: one is more or less straight up, and the other one is straight down, because in the first, AI created a utopia, and in the second, AI gets out of control and starts killing us, and whatever. So those are your three possibilities.If we stick with the optimistic case for a moment, what constraints do you see as most plausible — reduced labor supply from rising incomes, social pushback against disruption, energy limits, or something else?Briefly, the ones you've mentioned, people not working, 100 percent. I really hope people work less. If we get this good future, and the benefits are shared between all — which isn't guaranteed — if we get that, then yeah, no one should work. But that doesn't stop growth, because when AI and robots can do everything that humans do, you don't need humans in the loop anymore. That whole thing is just going and kind of self-replicating itself and making as many goods as services as we want. Sure, if you want your clothes to be knitted by a human, you're in trouble, then your consumption is stuck. Bad luck. If you're happy to consume goods and services produced by AI systems or robots, fine if no one wants to work.Pushback: I think, for me, this is the biggest one. Obviously, the economy doubling every year is very scary as a thought. Tech progress will be going much faster. Imagine if you woke up and, over the course of the year, you go from not having any telephones at all in the world, to everyone's on their smartphones and social media and all the apps. That's a transition that took decades. If that happened in a year, that would be very disconcerting.Another example is the development of nuclear weapons. Nuclear weapons were developed over a number of years. If that happened in a month, or two months, that could be very dangerous. There'd be much less time for different countries, different actors to figure out how they're going to handle it. So I think pushback is the strongest one that we might as a society choose, “Actually, this is insane. We're going to go slower than we could.” That requires, potentially, coordination, but I think there would be broad support for some degree of coordination there.Worries and what-ifs (19:07)If suddenly no one has any jobs, what will we want to do with ourselves? That's a very, very consequential transition for the nature of human society.I imagine you certainly talk with people who are extremely gung-ho about this prospect. What is the common response you get from people who are less enthusiastic? Do they worry about a future with no jobs? Maybe they do worry about the existential kinds of issues. What's your response to those people? And how much do you worry about those things?I think there are loads of very worrying things that we're going to be facing. One class of pushback, which I think is very common, is worries about employment. It's a source of income for all of us, employment, but also, it's a source of pride, it's a source of meaning. If suddenly no one has any jobs, what will we want to do with ourselves? That's a very, very consequential transition for the nature of human society. I think people aren't just going to be down to just do it. I think people are scared about three AI companies literally now taking all the revenues that all of humanity used to be earning. It is naturally a very scary prospect. So that's one kind of pushback, and I'm sympathetic with it.I think that there are solutions, if we find a way to tax AI systems, which isn't necessarily easy, because it's very easy to move physical assets between countries. It's a lot easier to tax labor than capital already when rich people can move their assets around. We're going to have the same problem with AI, but if we can find a way to tax it, and we maintain a good democratic country, and we can just redistribute the wealth broadly, it can be solved. So I think it's a big problem, but it is doable.Then there's the problem of some people want to stop this now because they're worried about AI killing everyone. Their literally worry is that everyone will be dead because superintelligent AI will want that to happen. I think there's a real risk there. It's definitely above one percent, in my opinion. I wouldn't go above 10 percent, myself, but I think it's very scary, and that's a great reason to slow things down. I personally don't want to stop quite yet. I think you want to stop when the AI is a bit more powerful and a bit more useful than it is today so it can kind of help us figure out what to do about all of this crazy stuff that's coming.On what side of that line is AI as an AI researcher?That's a really great question. Should we stop? I think it's very hard to stop just after you've got the AI researcher AI, because that's when it's suddenly really easy to go very, very fast. So my out-of-the-box proposal here, which is probably very flawed, would be: When we're within a few spits distance — not spitting distance, but if you did that three times, and we can see we're almost at that AI automating OpenAI — then you pause, because you're not going to accidentally then go all the way. It is actually still a little bit a fair distance away, but it's actually still, at that point, probably a very powerful AI that can really help.Then you pause and do what?Great question. So then you pause, and you use your AI systems to help you firstly solve the problem of AI alignment, make extra, double sure that every time we increase the notch of AI capabilities, the AI is still loyal to humanity, not to its own kind of secret goals.Secondly, you solve the problem of, how are we going to make sure that no one person in government or no one CEO of an AI company ensures that this whole AI army is loyal to them, personally? How are we going to ensure that everyone, the whole world gets influenced over what this AI is ultimately programmed to do? That's the second problem.And then there's just a whole host of other things: unemployment that we've talked about, competition between different countries, US and China, there's a whole host of other things that I think you want to research on, figure out, get consensus on, and then slowly ratchet up the capabilities in what is now a very safe and controlled way.What else should we be working on? What are you working on next?One problem I'm excited about is people have historically worried about AI having its own goals. We need to make it loyal to humanity. But as we've got closer, it's become increasingly obvious, “loyalty to humanity” is very vague. What specifically do you want the AI to be programmed to do? I mean, it's not programmed, it's grown, but if it were programmed, if you're writing a rule book for AI, some organizations have employee handbooks: Here's the philosophy of the organization, here's how you should behave. Imagine you're doing that for the AI, but you're going super detailed, exactly how you want your AI assistant to behave in all kinds of situations. What should that be? Essentially, what should we align the AI to? Not any individual person, probably following the law, probably loads of other things. I think basically designing what is the character of this AI system is a really exciting question, and if we get that right, maybe the AI can then help us solve all these other problems.Maybe you have no interest in science fiction, but is there any film, TV, book that you think is useful for someone in your position to be aware of, or that you find useful in any way? Just wondering.I think there's this great post called “AI 2027,” which lays out a concrete scenario for how AI could go wrong or how maybe it could go right. I would recommend that. I think that's the only thing that's coming top of mind. I often read a lot of the stuff I read is I read a lot of LessWrong, to be honest. There's a lot of stuff from there that I don't love, but a lot of new ideas, interesting content there.Any fiction?I mean, I read fiction, but honestly, I don't really love the AI fiction that I've read because often it's quite unrealistic, and so I kind of get a bit overly nitpicky about it. But I mean, yeah, there's this book called Harry Potter and the Methods of Rationality, which I read maybe 10 years ago, which I thought was pretty fun.On sale everywhere The Conservative Futurist: How To Create the Sci-Fi World We Were Promised Faster, Please! is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit fasterplease.substack.com/subscribe

Riemann, Jasper www.deutschlandfunk.de, Tag für Tag

In this episode, Momo sits down with his friend and long-time flatmate, Sohail, a mathematician, for a relaxed conversation about RSA cryptography—how it works and why it was revolutionary. Sohail breaks down the math behind RSA with clear, accessible examples and shares bonus fun facts about mathematics, broken predictions, and the future of math in an open-source world.---Hardy writes: "The 'real' mathematics of the 'real' mathematicians, the mathematics of Fermat and Euler and Gauss and Abel and Riemann, is almost wholly 'useless'."A similar quote is attributed to Gauss, Sohail's beloved mathematician. He jokingly said "I must have committed blasphemy by attributing it to someone else." Here's the exact quote:"Mathematics is the queen of the sciences, and number theory is the queen of mathematics. She often condescends to render service to astronomy and other natural sciences, but under all circumstances she is entitled to first rank."---00:58 – Sohail's background in mathematics03:23 – Math and real-world applications08:11 – Asymmetric cryptography (like public-key cryptography) vs. symmetric cryptography (like the Caesar cipher)11:18 – Remembering Gauss and Gauss Junior15:55 – Is asymmetric cryptography mind-blowing?17:53 – Why RSA cryptography was ground-breaking21:01 – Explaining RSA through the “suitcase” analogy25:09 – The math behind RSA32:18 – What kinds of functions can be used in RSA?34:58 – Clock-like modular functions in RSA40:59 – Fermat's Little Theorem as the basis of RSA48:11 – A more complex function than Fermat's Little Theorem used in RSA50:43 – How your password reaches your bank securely using RSA59:41 – Do my function and my bank's function need to match in RSA?01:01:19 – The importance of prime numbers in cryptography01:04:06 – Accessible resources for math enthusiasts01:05:40 – Nuance: which exponentiation operations are invalid in RSA01:10:25 – Can a hacker intercept and decode an RSA-encrypted message?01:12:28 – Why the move to elliptic curves?01:14:00 – Other real-world applications of number theory01:19:03 – The future of mathematics---Fermat's little theorem explanation:https://en.wikipedia.org/wiki/Fermat's_little_theoremThe channel for explaining math concepts in simple words, 3Blue1Brown. This source doesn't explain Fermat's Little Theorem, but it is an accessible source for math enthusiasts without specialized training.https://www.youtube.com/@3blue1brownEnigma Cipher Center, the cryptography museum in Poznan, Polandhttps://csenigma.pl/en/My Nostr post about the internet and cryptography:https://primal.net/e/nevent1qqs9x3rxx3s9fhg6jwzvafgh6vvvxe658junc0vt4lphmcdl4w9ccrs9rk8dd---

The Riemann Key: Unlocking Modern Nobel Discoveries of the Past with a 19th-Century Thesis--Please note the views of this podcast represent those of my guest and I, and AI may have been used in the production of the episode. Please see a board-certified medical professional, that is state licensed for medical or professional advice. We disclaim any loss in any way.

In dieser Folge nehmen Jan und Florian das Riemann-Thomann-Modell unter die Lupe und zeigen, wie es Teams hilft, Persönlichkeiten und Dynamiken besser zu verstehen. Außerdem teilen sie ihren selbstgebauten KI-Prompt, der das Modell interaktiv erlebbar macht und neue Einblicke in die Zusammenarbeit ermöglicht.

Warum fürchten wir Spinnen, Höhen oder enge Räume – obwohl keine reale Gefahr droht? In dieser Episode gehen wir den Phobien nach: was sie über unsere Psyche verraten, wie sie entstehen und warum sie sich so hartnäckig halten. Ein psychoanalytischer Blick auf die „kleinen großen“ Ängste, die unser Leben prägen können. Fallgeschichte Saskia: Die Angst vor dem Dunkel: https://www.patreon.com/posts/138717136 Das Skript zur Folge: https://www.patreon.com/posts/121184696 Link zum Gespräch mit Dr. Gerhard Schneider, dessen Denken unseren Podcast sehr beeinflusst hat: "Die Psychoanalyse ist ein Humanismus": https://www.patreon.com/posts/dr-gerhard-die-136345449 **Literaturempfehlungen** - Freud, S. (1909): Analyse der Phobie eines fünfjährigen Knaben. Gesammelte Werke, Bd. VII. - Freud, S. (1915): Die Verdrängung. Gesammelte Werke, Bd. X. - Fenichel, O. (1945): The Psychoanalytic Theory of Neurosis. New York: Norton. - Mentzos, S. (1975): Angstneurose. Psychodynamische und psychotherapeutische Aspekte. Stuttgart: Kohlhammer. - Mentzos, S. (2017): Lehrbuch der Psychodynamik. Die Funktion der Dysfunktionalität psychischer Störungen. Göttingen: Vandenhoeck & Ruprecht. - Riemann, F. (1961): Grundformen der Angst. München: Ernst Reinhardt Verlag. - Ebrecht-Laermann, A. (2014): Angst. Gießen: Psychosozial-Verlag. - Bestellung unseres Buches über genialokal: https://www.genialokal.de/Produkt/Cecile-Loetz-Jakob-Mueller/Mein-groesstes-Raetsel-bin-ich-selbst_lid_50275662.html und überall, wo es Bücher gibt. Auch als Hörbuch (z.B. bei Audible oder Bookbeats)! - Link zu unserer Website mit weiteren Informationen: www.psy-cast.de - **Wir freuen uns auch über eine Förderung unseres Projekts via Paypal**: https://www.paypal.com/donate/?hosted_button_id=VLYYKR3UXK4VE&source=url - Anmeldung zum Newsletter: https://dashboard.mailerlite.com/forms/394929/87999492964484369/share

Je časť začať ďalšiu sériu, táto bude o veľkých výzvach v matematike, sedem úlohu pre toto tisícročie. Kto bol Riemann? Od čoho ho zachránil Gauss? A v čom je významná jeho funckia? O tom všetkom diskutujú Jozef a Samuel. Podcast vzniká v spolupráci so SME.    Hlasovať za nás môžete v súťaži Podcast roka https://podcastroka.sk/ Podcastové hrnčeky a ponožky nájdete na stránke https://vedator.space/vedastore/ Vedátora môžete podporiť cez stránku Patreon https://www.patreon.com/Vedator_sk   Všetko ostatné nájdete tu https://linktr.ee/vedatorsk Vedátorský newsletter http://eepurl.com/gIm1y5

Los extrañólogos de Podcaliptus nos juntamos para hablar del caso OVNI y contactismo por excelencia en España, el de los Ummitas, que se convirtió en icono popular por décadas y ha tenido diversas variables. Ahora con nuevo eco gracias a documentales como "Ummo: La España alienígena" o el excelente libro de Eduardo Bravo "Ummo, lo increible es la verdad". Adelantamos un pequeño extracto de una carta de los ummitas, porque lo dejaban todo muy claro: "Nuestra imagen del WAAM, pese a ser considerada por nosotros como un UXGIIGIIAM pluridimensional que sufre en su estructura múltiples curvaturas (que llamamos masas) en nada se parece ni al concepto de Espacio Tridimensional Euclideo elaborado por los clásicos terrestres, ni es un fiel reflejo de las modernas concepciones terrestres de RIEMANN, BOLYAI o LOBATCHEWSKY que suponen un N-Espacio o espacio pluridimensional, indicando que el Cosmos pueda adoptar la forma de una Hiperesfera de curvatura positiva o de curvatura negativa". La música tiene licencia Creative Commons ("Into the Storm") o está cedida (cierre por el Almirante Stargazer del fantabuloso podcast "Torpedo Rojo") o es de dominio público. Recordad que ahora también nos podéis visitar en nuestra página podcaliptus.com :-) ENLACES DE INTERÉS: —Fantabuloso pódcast de ciencia "Mundo Gilipoy: https://www.ivoox.com/podcast-mundo-gilipoy_sq_f11501113_1.html —Fantabulosos "Torpedos Ocultos": https://www.ivoox.com/torpedo-oculto-10x03-expediente-x-la-trama-audios-mp3_rf_143648934_1.html https://www.ivoox.com/torpedo-oculto-10x02-especial-criptidos-baba-audios-mp3_rf_143241845_1.html https://www.ivoox.com/torpedo-oculto-9x27-especial-vampiros-con-audios-mp3_rf_138555357_1.html https://www.ivoox.com/torpedo-oculto-9x19-especial-revista-mas-alla-audios-mp3_rf_136030656_1.html https://www.ivoox.com/torpedo-oculto-9x12-especial-psicofonias-con-audios-mp3_rf_134054554_1.html https://www.ivoox.com/torpedo-oculto-9x05-especial-sectas-con-audios-mp3_rf_128737039_1.html https://www.ivoox.com/torpedo-oculto-9x02-especial-conspiranoia-con-audios-mp3_rf_126910227_1.html —La revista de Ciencia ficción "Anticipación", con participación de Antonio Ribera y referencias a Fernando Sesma: https://blogcaliptusbonbon.blogspot.com/2025/04/la-primera-revista-espanola-de-ciencia.html —Pódcast sobre la secta Edelweiss: https://www.ivoox.com/xstra-edelweiss-cronologia-del-horror-rtve-2021-audios-mp3_rf_78309081_1.html Pódcast "El sotano sellado": https://www.ivoox.com/podcast-sotano-sellado_sq_f12392918_1.html —Pódcast "Terra Incognita": https://www.ivoox.com/podcast-terra-incognita_sq_f11544_1.html —Pódcast "Dimensión Límite": https://www.ivoox.com/podcast-dimension-limite_sq_f14606_1.html —Reediciones Anómalas: https://www.reedicionesanomalas.com/ —Algunos de los artículos sobre Ummo en "El ojo crítico": http://elojocritico.info/ummo-quien-como-cuando-y-por-que/ http://elojocritico.info/ummo-la-teoria-de-la-conspiracion/ https://elojocritico.info/mas-alla-de-ummo/

Riemann, Katja www.deutschlandfunkkultur.de, Studio 9

Wir sprechen heute über die verrückte 2. Liga, eine sieglose Top 5 und reihenweise punktende Abstiegskandidaten. Wir sprechen über die FC-Spieler im Detail, viele vergebene Torchancen und ein gerechtes Unentschieden. Und wir sprechen über ersatzgeschwächte, aber effiziente Fortunen, einen zu frühen Pfiff und einen über die Außenbahn stürmenden Torwart.

Ricarda Riemann aus Augsburg tritt als freie Sängerin bei Trauungen, Taufen und Beerdigungen auf. Zudem gibt sie Gesangsunterricht für Kinder und Jugendliche bei Young Stage Augsburg e.V. und am Musikinstitut Martina Brix & Co. Ricarda ist es wichtig, ihren Schüler:innen auf Augenhöhe zu begegnen und sie dabei zu unterstützen, ihren eigenen Weg zu gehen. Darüber sprechen wir im Backstage Podcast, außerdem erzählt Ricarda von ihrer eigenen Suche nach ihrem Platz in der Musikwelt und von einer großen Musicalproduktion, bei der sie mitwirken durfte. Webseite: https://www.ricardariemann.com/ Instagram: https://www.instagram.com/ricardariemann/ Facebook: https://www.facebook.com/hochzeitssaengerinricardariemann/ Youtube: https://www.youtube.com/@RickyRena Erwähnt: https://young-stage.info/, https://www.martina-brix.de/, https://www.instagram.com/notanotherweddingband/ BACKSTAGE unterstützen? ♥ Hier entlang: https://backstage.podcaster.de/unterstuetzen/ RSS-Feed: https://backstage.podcaster.de/BackstagePodcast.rss Blog: https://backstage.podcaster.de Instagram: https://www.instagram.com/backstage_podcast Kontakt: backstagepodcast@gmx.de Über Leni Bohrmann: https://www.lenibohrmann.de

Pada episode ini kita lanjutkan bahasan mengenai Riemann Hypothesis, bagaimana perkembangan Riemann Hypothesis, apa yang sudah tercapai dan apa signifikansinya bagi kehidupan kita.Bahasan utama mulai dari (47:02)

En este primer programa del 2025 aprovechamos la cercanía del Día de Reyes para pedir en voz alta lo que nos gustaría que ocurriese en la ciencia durante este año. Es posible que Sus Majestades estuvieran muy ocupadas y no nos escucharan ese día, pero por probar no se pierde nada. Santi García Cremades solicita que en el año 2025 se demuestren tres resultados matemáticos de calado casi mitológico. En primer lugar, pide que nos llegue al fin la demostración de la Hipótesis de Riemann, una afirmación que se relaciona íntimamente con la densidad de números primos. Básicamente, sabemos que la cantidad de números primos menores que N viene dada aproximadamente por el logaritmo integral de N; si la hipótesis de Riemann se demuestra cierta, podemos afinar "cuánto se equivoca" el logaritmo integral, cómo de buena aproximación es. Además, Santi también solicita que se demuestre la Conjetura de Goldbach, que dice que cualquier número par debe poderse escribir como una suma de dos primos. Y ya, abusando de Sus Majestades, pide también la demostración de la Conjetura de Collatz, que emerge de un pequeño juego numérico que todos podemos hacer en casa. Por su parte, Alberto Aparici pide dos cosas: en primer lugar, que se encuentre la primera ribozima autorreplicativa. Una ribozima es una máquina molecular hecha de ARN; una de las hipótesis sobre el origen de la vida es que el ARN fue la primera "materia no viva" que mostró características similares a las de la vida. En concreto, sabemos que existen ribozimas capaces de hacer copias de pequeños pedazos de ADN, pero no conocemos ninguna que sea capaz de copiarse a sí misma, que es lo que hacemos los seres vivos. La sospecha es que en los días en que se estaba cocinando la vida sí que existían este tipo de máquinas de ARN, y que después fueron sustituidas por proteínas, ADN y mecanismos más eficientes. Si logramos crear en el laboratorio una ribozima autorreplicativa no sería la prueba de que la vida empezó con el ARN, pero sí sería una indicación de que ése es el camino correcto. Además, Alberto también pide que en 2025 podamos ver, de una vez, una supernova en nuestra galaxia. La última que se pudo observar fue en el año 1604, pero sospechamos que en una galaxia tan grande como la Vía Láctea debe de explotar una supernova cada 50 años. La mayoría no son visibles porque estallan muy lejos y quedan ocultadas por el polvo y la porquería que hay en el disco de nuestra galaxia, pero a día de hoy, en el año 2025, ya tenemos las herramientas para ver *cualquier* supernova que ocurra en la Vía Láctea. Quizá no la veamos con nuestros ojos, pero la veremos de otras maneras... Este programa se emitió originalmente el 2 de enero de 2025. Podéis escuchar el resto de audios de Más de Uno en la app de Onda Cero y en su web, ondacero.es

Kali ini kita bahas tentang salah satu masalah yang berhadiah satu juta dolar yakni Hipotesis Riemann. Kenapa sih masalah ini sangat penting bagi para matematikawan? Bahasan Utama mulai dari (47:57)

Rambo-Riemann lager kaosballett av årets klareste utvisning. Kan Niko Kovac få fart på et Borussia Dortmund som ligger som våt slaps i veikanten? En legende i Karlsruhe og broren til en verdensmester er blant Asbjørns favoritter i Zweite Bundesliga Superliga. En norsk 18-åring skal snu kaostrenden til HSV. Alexander Røsling Lelesiit drar fra LSK til Hamburg!See omnystudio.com/listener for privacy information.

In this episode, we explore how Danish startup Agrain is revolutionizing food sustainability by upcycling spent grains from breweries into nutritious food ingredients. Aviaja Riemann-Andersen shares how these grains, far from being "waste," actually develop enhanced nutritional profiles and unique flavors through the brewing process. The conversation spans from the technical aspects of grain processing to broader themes of circular economy and sustainable food systems.About Aviaja Riemann-AndersenAviaja is one of the pioneers of the Scandinavian food tech scene. In 2018 she co-founded Agrain by Circular Food Technology. Agrain's mission is to change the food system to become circular, by developing delicious and nutritious ingredients made from upcycled spent grains. Agrain is working with unique and patented processes.Circular economy and respect for our planet are the guiding principles for Aviaja, and through her work with Agrain and in several committees, she is promoting a more sustainable future. Aviaja is a Board member in the Danish Plantbased Food Association and a board member in The Danish Food and Drink Federation. From 2021-2022 she was a member of the Government's Green Advisory Board. Agrain has since June 2022 been a proud member of the EIT Rising Food Star Programme.Privately, she lives in Copenhagen with her two daughters, she is a hot yoga heavy user. She has a MSc in economic and Japanese from Copenhagen Business School and worked 15 years in cosmetics before she joined the food industry.Connect with Agrain:Website: www.agrainproducts.comInstagram: @agrainproductsLinkedIn: Agrain by Circular Food TechnologyFit, Healthy & Happy Podcast Welcome to the Fit, Healthy and Happy Podcast hosted by Josh and Kyle from Colossus...Listen on: Apple Podcasts SpotifyDiscounts Get 10% off delicious local farm-fresh food delivered to your door with my link for FarmMatch: https://farmmatch.com/jane Get 15% off high-quality Italian olive oil with code FARMTOFUTURE: https://shop.vignolifood.com/FARMTOFUTURE Get 40% the CircleDNA's Premium DNA test with code JANEZHANG: https://circledna.com/premium Connect with Jane Z. Instagram: @farm.to.future Email: jane@farmtofuture.co

Joseph Bennish discusses math as a "concept factory." The concept of prime numbers came from a desire to break numbers down to their simplest atoms. This simple concept led to simple questions like the twin prime conjecture that no one has been able to answer. Those questions in turn led to deep research. The concepts of new geometries grew out of failed attempts to prove that Euclid's geometry was the only geometry. Gauss' "most wonderful theorem" of surfaces led to Riemann's higher dimensional manifolds. This, combined with Minkowski's space-time geometry, led to Einstein's relativity, "the most beautiful theory of physics."

Julian Nagelsmann hat angekündigt im letzten Nations-League-Gruppenspiel gegen Ungarn kräftig zu rotieren. Manuel Riemann ist ins Bochum-Training zurückgekehrt und Paris Brunner hat in Belgien Probleme auf Spielzeit zu kommen.

Die Messer waren gewetzt - doch der BVB überzeugt gegen Leipzig wie lange nicht - wie kann das sein? Unser Cheftrainer versucht sich an einer Erklärung, dazu Frankfurts Ballermänner, St.Pauli macht´s wieder in BW, Hecking in Bochum vielleicht mit Riemann? - und Pokal ohne VAR ist natürlich auch Thema...

Bierverbot, Raute, Riemann. Allein diese drei Themen beschäftigen den VfL Bochum. Wie sehr wackelt bereits jetzt der Stuhl von Trainer Zeidler? Philipp Rentsch beleuchtet die Lage am Tabellenende.

rWotD Episode 2705: Curve Welcome to Random Wiki of the Day, your journey through Wikipedia’s vast and varied content, one random article at a time.The random article for Sunday, 29 September 2024 is Curve.In mathematics, a curve (also called a curved line in older texts) is an object similar to a line, but that does not have to be straight.Intuitively, a curve may be thought of as the trace left by a moving point. This is the definition that appeared more than 2000 years ago in Euclid's Elements: "The [curved] line is […] the first species of quantity, which has only one dimension, namely length, without any width nor depth, and is nothing else than the flow or run of the point which […] will leave from its imaginary moving some vestige in length, exempt of any width."This definition of a curve has been formalized in modern mathematics as: A curve is the image of an interval to a topological space by a continuous function. In some contexts, the function that defines the curve is called a parametrization, and the curve is a parametric curve. In this article, these curves are sometimes called topological curves to distinguish them from more constrained curves such as differentiable curves. This definition encompasses most curves that are studied in mathematics; notable exceptions are level curves (which are unions of curves and isolated points), and algebraic curves (see below). Level curves and algebraic curves are sometimes called implicit curves, since they are generally defined by implicit equations.Nevertheless, the class of topological curves is very broad, and contains some curves that do not look as one may expect for a curve, or even cannot be drawn. This is the case of space-filling curves and fractal curves. For ensuring more regularity, the function that defines a curve is often supposed to be differentiable, and the curve is then said to be a differentiable curve.A plane algebraic curve is the zero set of a polynomial in two indeterminates. More generally, an algebraic curve is the zero set of a finite set of polynomials, which satisfies the further condition of being an algebraic variety of dimension one. If the coefficients of the polynomials belong to a field k, the curve is said to be defined over k. In the common case of a real algebraic curve, where k is the field of real numbers, an algebraic curve is a finite union of topological curves. When complex zeros are considered, one has a complex algebraic curve, which, from the topological point of view, is not a curve, but a surface, and is often called a Riemann surface. Although not being curves in the common sense, algebraic curves defined over other fields have been widely studied. In particular, algebraic curves over a finite field are widely used in modern cryptography.This recording reflects the Wikipedia text as of 00:13 UTC on Sunday, 29 September 2024.For the full current version of the article, see Curve on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Mastodon at @wikioftheday@masto.ai.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm neural Arthur.

Adding code to LLM training data is a known method of improving a model's reasoning skills. But wouldn't math, the basis of all reasoning, be even better? Up until recently, there just wasn't enough usable data that describes mathematics to make this feasible. A few years ago, Vlad Tenev (also founder of Robinhood) and Tudor Achim noticed the rise of the community around an esoteric programming language called Lean that was gaining traction among mathematicians. The combination of that and the past decade's rise of autoregressive models capable of fast, flexible learning made them think the time was now and they founded Harmonic. Their mission is both lofty—mathematical superintelligence—and imminently practical, verifying all safety-critical software. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital  Mentioned in this episode: IMO and the Millennium Prize: Two significant global competitions Harmonic hopes to win (soon) Riemann hypothesis: One of the most difficult unsolved math conjectures (and a Millenium Prize problem) most recently in the sights of MIT mathematician Larry Guth Terry Tao: perhaps the greatest living mathematician and Vlad's professor at UCLA Lean: an open source functional language for code verification launched by Leonardo de Moura when at Microsoft Research in 2013 that powers the Lean Theorem Prover mathlib: the largest math textbook in the world, all written in Lean Metaculus: online prediction platform that tracks and scores thousands of forecasters Minecraft Beaten in 20 Seconds: The video Vlad references as an analogy to AI math Navier-Stokes equations: another important Millenium Prize math problem. Vlad considers this more tractable that Riemann John von Neumann: Hungarian mathematician and polymath that made foundational contributions to computing, the Manhattan Project and game theory Gottfried Wilhelm Leibniz: co-inventor of calculus and (remarkably) creator of the “universal characteristic,” a system for reasoning through a language of symbols and calculations—anticipating Lean and Harmonic by 350 years! 00:00 - Introduction 01:42 - Math is reasoning 06:16 - Studying with the world's greatest living mathematician 10:18 - What does the math community think of AI math? 15:11 - Recursive self-improvement 18:31 - What is Lean? 21:05 - Why now? 22:46 - Synthetic data is the fuel for the model 27:29 - How fast will your model get better? 29:45 - Exploring the frontiers of human knowledge 34:11 - Lightning round

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: Open Problems in AIXI Agent Foundations, published by Cole Wyeth on September 13, 2024 on LessWrong. I believe that the theoretical foundations of the AIXI agent and variations are a surprisingly neglected and high leverage approach to agent foundations research. Though discussion of AIXI is pretty ubiquitous in A.I. safety spaces, underscoring AIXI's usefulness as a model of superintelligence, this is usually limited to poorly justified verbal claims about its behavior which are sometimes questionable or wrong. This includes, in my opinion, a serious exaggeration of AIXI's flaws. For instance, in a recent post I proposed a simple extension of AIXI off-policy that seems to solve the anvil problem in practice - in fact, in my opinion it has never been convincingly argued that the anvil problem would occur for an AIXI approximation. The perception that AIXI fails as an embedded agent seems to be one of the reasons it is often dismissed with a cursory link to some informal discussion. However, I think AIXI research provides a more concrete and justified model of superintelligence than most subfields of agent foundations [1]. In particular, a Bayesian superintelligence must optimize some utility function using a rich prior, requiring at least structural similarity to AIXI. I think a precise understanding of how to represent this utility function may be a necessary part of any alignment scheme on pain of wireheading. And this will likely come down to understanding some variant of AIXI, at least if my central load bearing claim is true: The most direct route to understanding real superintelligent systems is by analyzing agents similar to AIXI. Though AIXI itself is not a perfect model of embedded superintelligence, it is perhaps the simplest member of a family of models rich enough to elucidate the necessary problems and exhibit the important structure. Just as the Riemann integral is an important precursor of Lebesgue integration, despite qualitative differences, it would make no sense to throw AIXI out and start anew without rigorously understanding the limits of the model. And there are already variants of AIXI that surpass some of those limits, such as the reflective version that can represent other agents as powerful as itself. This matters because the theoretical underpinnings of AIXI are still very spotty and contain many tractable open problems. In this document, I will collect several of them that I find most important - and in many cases am actively pursuing as part of my PhD research advised by Ming Li and Marcus Hutter. The AIXI (~= "universal artificial intelligence") research community is small enough that I am willing to post many of the directions I think are important publicly; in exchange I would appreciate a heads-up from anyone who reads a problem on this list and decides to work on it, so that we don't duplicate efforts (I am also open to collaborate). The list is particularly tilted towards those problems with clear, tractable relevance to alignment OR philosophical relevance to human rationality. Naturally, most problems are mathematical. Particularly where they intersect recursion theory, these problems may have solutions in the mathematical literature I am not aware of (keep in mind that I am a lowly second year PhD student). Expect a scattering of experimental problems to be interspersed as well. To save time, I will assume that the reader has a copy of Jan Leike's PhD thesis on hand. In my opinion, he has made much of the existing foundational progress since Marcus Hutter invented the model. Also, I will sometimes refer to the two foundational books on AIXI as UAI = Universal Artificial Intelligence and Intro to UAI = An Introduction to Universal Artificial Intelligence, and the canonical textbook on algorithmic information theory Intro to K = An...

Riemann, Jasper www.deutschlandfunk.de, Tag für Tag

Riemann, Jasper www.deutschlandfunkkultur.de, Studio 9

In this episode of Discover Daily by Perplexity, we explore groundbreaking developments in prime number theory that could reshape our understanding of mathematics and impact internet security. Mathematicians James Maynard and Larry Guth have made significant progress towards understanding the hidden structure of prime numbers, providing new insights into the famous Riemann Hypothesis. Their work improves bounds on where the nontrivial zeros of the Riemann zeta function cannot lie, crucial for understanding prime number distribution.Meanwhile, researchers from City University of Hong Kong and North Carolina State University claim to have developed a "Periodic Table of Primes" (PTP), challenging the long-held belief that prime numbers are unpredictable. This innovative approach claims to accurately predict the occurrence of prime numbers, with potential applications in finding future primes, factoring integers, and identifying twin primes. While still awaiting peer review, this breakthrough could have far-reaching implications for cryptography and data security.These advancements in prime number theory highlight the unexpected ways abstract mathematics can impact our daily lives. From enhancing internet security to advancing quantum physics, prime numbers continue to play a crucial role in shaping our digital world and pushing the boundaries of scientific knowledge. As mathematicians inch closer to resolving long-standing conjectures like the Riemann Hypothesis, we may be on the brink of a new era in number theory and its applications.Perplexity is the fastest and most powerful way to search the web. Perplexity crawls the web and curates the most relevant and up-to-date sources (from academic papers to Reddit threads) to create the perfect response to any question or topic you're interested in. Take the world's knowledge with you anywhere. Available on iOS and Android Join our growing Discord community for the latest updates and exclusive content. Follow us on: Instagram Threads X (Twitter) YouTube Linkedin

En el episodio de hoy de "10 Minutos con Sami", exploramos tres noticias fascinantes del mundo científico. Comenzamos con la emocionante predicción del próximo máximo solar, que promete espectaculares auroras boreales entre 2024 y 2026. Luego, nos adentramos en un ambicioso proyecto para crear un biorrepositoriooo lunar, diseñado para preservar muestras biológicas de especies en peligro de extinción de la Tierra. Finalmente, discutimos los recientes avances en la teoría de números primos, incluyendo progresos hacia la comprensión de la Hipótesis de Riemann y un método revolucionario que podría predecir la aparición de números primos. Estos descubrimientos no solo desafían nuestro entendimiento actual, sino que también abren nuevas posibilidades en campos como la criptografía y la conservación de la biodiversidad. Fuentes: https://spaceplace.nasa.gov/aurora/en/ , https://www.scientificamerican.com/article/solar-maximum-could-hit-us-harder-and-sooner-than-we-thought-how-dangerous-will-the-suns-chaotic-peak-be/ , https://education.nationalgeographic.org/resource/aurora/ , https://twin-cities.umn.edu/news-events/scientists-propose-plan-store-bio-samples-moon , https://www.scientificamerican.com/article/the-riemann-hypothesis-the-biggest-problem-in-mathematics-is-a-step-closer/ Redes: Puedes buscarme por redes sociales como Threads, Twitter e Instagram con @olivernabani, y puedes encontrarme habitualmente en Twitch: http://twitch.tv/olivernabani Puedes encontrar tanto este Podcast como otro contenido original en YouTube: https://youtube.com/olivernabani Además si quieres participar en la comunidad mashain, tenemos un server de Discord donde compartimos nuestras inquietudes: https://discord.gg/5JbqEhYv Un canal de Telegram donde os aviso de novedades y contenidos: https://t.me/sedicemashain Y un canal de Whatsapp: https://whatsapp.com/channel/0029VaCSKOzFCCoavMoLwX43 Y por supuesto lo más importante, recuerda: No se dice Machine, se dice Mashain

Alon Amit, prolific Quora math answerer, argues that an honest representation of mathematical ideas is enough to spark interest in math. It's not necessary to exaggerate the role of math; the golden ratio does not drive the stock market, the solution of the Riemann hypothesis will not kill cryptography, and Grothendieck did not advance robotics. History and seeing the thought process and the struggle behind the tight finished proof are ways to make math compelling.

Der FC Bayern steht unmittelbar vor der Verpflichtung von Palhinha. Toni Kroos gibt sich auf der DFB-Pressekonferenz ganz cool und das Kapitel von Manuel Riemann beim VfL Bochum scheint endgültig beendet.

Stephen Wolfram answers questions from his viewers about the future of science and technology as part of an unscripted livestream series, also available on YouTube here: https://wolfr.am/youtube-sw-qa Questions include: Can AI be swallowed by more advanced AI by feeding it via "virtual" input? The motivation could be the increased efficiency of "larger" AI. - Do you think anyone will solve the Riemann hypothesis in your lifetime? ​​​- Aren't there thousands upon thousand of written papers that assume the Riemann hypothesis is true? - Will AIs be the ones to explore space? - ​It's ​4.37 light years to Alpha Centauri. - We should harness light/light waves so that we can take a picture of the planets there, then bring them back and produce pictures in 8+ years. - Do you think Ray Kurzweil's longevity predictions are likely to happen in our own lifetime? - Would it make sense for alien AI civilizations to broadcast radio or other signals with information on how to build such AI as a way to propagate in the universe faster than material space travel? - People automatically assume that AI will create this type of step function upward in everything in the world or in the economy. This may not be the case due to diminishing returns.

In vielen Bundesländern sind Wohnungslose von Wahlen ausgeschlossen. Baden-Württemberg hat das geändert. Seit diesem Jahr dürfen dort Wohnungslose in der Kommunalpolitik ihre Stimme abgeben. Gespräche mit Menschen, die das betrifft. Riemann, Jasper www.deutschlandfunk.de, Dlf-Magazin

0:3 gegen Atalanta im EuroLeague-Finale? Wie konnte das passieren? Wir sprechen über Leverkusens erste Saison-Niederlage zur Unzeit mit Tobias Nordmann von ntv.de. Auch über unsere anstehende Kooperation während der EM, Bochum ohne den aussortierten Leader Riemann vor der Relegation gegen Düsseldorf und Vince Kompany, den vermutlich-wahrscheinlich neuen Bayern-Trainer, wenn er nicht noch absagt...

Siste runde ble dramatisk. Marco Reus åpnet lommeboka for tørste fans, Christian Streich lot tårene renne allerede før kampstart, mens Hansa Rostock røykla halve Øst-Tyskland da de sa takk for seg. Nå gjenstår kvalik og et tjuetalls cupfinaler.See omnystudio.com/listener for privacy information.

Seit letztem Wochenende ist klar: Der VfL Bochum muss nachsitzen und in der Relegation gegen Fortuna Düsseldorf ran. Am Donnerstag geht's mit dem Hinspiel los und Stammkeeper Manuel Riemann wird nicht dabei sein. Schwächt das die Bochumer Mannschaft oder ist das ein Mentalitäts-Pluspunkt? Außerdem in den News: Toni Kroos beendet nach der EM seine Karriere und Karl-Heinz Schnellinger ist im Alter von 85 Jahren verstorben.

Hammer in Bochum! Der VfL verzichtet in der Relegation auf Torwart Manuel Riemann. Unser Reporter Max Backhaus erklärt die Gründe. BVB-Sportdirektor Sebastian Kehl ist Kandidat als Sport-Geschäftsführer in Wolfsburg und der FC Bayern sucht in England nach einem neuen Trainer.

Diese Woche schaut mit Manuel Riemann ein absoluter KMD-Liebling im Podcast vorbei! Der Bochumer Keeper spricht über das Derby in Dortmund, ärgert sich über die ein oder andere Regelauslegung der Schiedsrichter und träumt von der NFL. Außerdem pflügen Alex und Benni durch den abgelaufenen Bundesliga-Spieltag und lassen sich von kicker-Reporter Thomas Böker das Ende der Klopp-Ära in Liverpool einordnen...

Sinkende Bestände und ein miserabler Zustand der Ostsee haben die Zahl der Fischereibetriebe in den letzten 20 Jahren halbiert. Elf Menschen lassen sich derzeit für den uralten Beruf weiterbilden - als Meeresförster mit neuen Aufgaben. Riemann, Jasper / Fehrle, Moritzwww.deutschlandfunk.de, Deutschland heute

In questa puntata di fine ottobre, Leonardo ci parla di un articolo su Nature, di Agosto, che studia l'effetto degli algoritmi di Facebook sulla creazione di camere dell'eco e sulla polarizzazione degli utenti. Con un po' di polemica che ci sta sempre bene.Per l'intervento esterno, torna Scientifibook con Giuliana e Andrea Vico, che questo mese ci suggerisce:“Contro lo smartphone - Per una tecnologia più democratica” di Juan Carlos de Martin – ADD editore.“Il futuro è decrescita - Guida per un mondo post-capitalista” di Matthias Schmelzer, Andrea Vetter, Aaron Vansintijan – Ledizioni.“Orizzonti. Una storia globale della scienza” di James Poskett – Einaudi“Animali non umani” di Carl Safina – Adelphi.Per bambini/ragazzi: “Fiori in famiglia” di Elena Accati – Editoriale Scienza.Romina ci parla, invece, di un altro articolo riguardante la percezione dei colori. Fino ad ora, l'insieme dei colori era stato descritto matematicamente usando le varietà Riemanniane nello spazio tridimensionale. Un nuovo studio, invece, ha mostrato che questa descrizione non è molto accurata quando si parla di come percepiamo colori molto diversi tra loro.

Hat die SGE endlich ihr Sturmproblem gelöst? Derweil trifft Guirassy weiter, Union kann nicht mehr gewinnen, Leroy Sané zaubert und Riemann wird zum Helden. Während Augsburg ein großes Problem hat: Zu viele Augsburgs.

Drei Tore den Leverkusenern hoch im Licht, Sieben Punkte den Darmstädtern in ihren Hallen aus Stein, Den Augsburgern, ewig dem Abstiegskampf verfallen, neun, Einer dem Dunklen Herrn auf dunklem Thron Im Lande München, wo die Hoeneß wohn. Ein King(sley), sie zu knechten, sie alle zu finden, Ins Dunkel zu treiben und ewig zu binden Im Lande München, wo die Hoeneß wohn. Du möchtest mehr über unsere Werbepartner erfahren? Hier findest du alle Infos & Rabatte: https://linktr.ee/50plus2

Buschis NFL Reise... kein Spaß für die ganze Familie! Aber für ihn. Schmiso extatisch beim Handball. Kiel raus! Melsungen verliert. Häfner/Heine-Wahsinn. Buschi bremst und feiert bei Volleyball und Turnen. Dauser und Grozer! Nagelsmann zündet Schmiso wieder für die deutschen Fußballer an - Buschi macht sich nur drüber lustig. Beide feiern Riemann und Guirassy. Und verzweifeln mal wieder an der Fifa. Du möchtest mehr über unsere Werbepartner erfahren? Hier findest du alle Infos & Rabatte: https://linktr.ee/Lauschangriff_Podcast

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: Logical Share Splitting, published by DaemonicSigil on September 11, 2023 on LessWrong. Are mathematicians just not trying hard enough? The Riemann hypothesis is one of the most important open problems in math. There's a $1 million prize from the Clay mathematics institute for a proof or disproof of the Riemann hypothesis. At the time of writing, it remains unsolved. From this, we may conclude that one cannot simply buy a solution to difficult mathematical problems. Or could we? How do we know that buying a difficult maths proof is impossible? Perhaps the Clay mathematics institute is somehow not asking the question in the right way. And it's true that the value of a million dollars has been eroded over time by inflation. One might guess that a Riemann proof would be worth at least 100 million. Would that be enough to conjure it from the collective intelligence of humanity? Simply directly declaring a $100 million reward for a solution would probably not work. For one thing, there's the issue of corollary-sniping where the prize wouldn't give anyone an incentive to publish solutions to hard intermediate steps of the problem, since the prize as a whole only goes to the one who solves the entire problem as a whole. For another, even the million dollar prize on its own would be plenty of reason for a money-motivated person to solve the problem if a solution were within their grasp. The issue is not merely one of funding, we humans are somehow failing to combine our efforts properly. Prediction markets are pretty cool One of the standard ways to buy knowledge is prediction markets. Can we try that here? John Wentworth describes here a scheme for using markets to get mathematical proofs. Here's a scheme that's very similar in the overall idea, though the exact rules are slightly different: Shares on mathematical propositions are traded on the market. Propositions should be the kind of things that might be theorems, i.e. they are syntactically meaningful, and contain no free variables, though it's fine if they are false or unprovable. Shares on provable propositions are worth $1, at least if anyone knows or can find the proof. How this works out in practice is that a share in ⊤ can be redeemed in exchange for $1, together with the next rule. Shares in logically equivalent propositions can be exchanged for each other. So if it's the case that A⟺B then a share in A can be exchanged for a share in B. This is a trading rule that will obviously have to be called the Law of Equivalent Exchange. We only allow exchanges that can be seen to be equivalent in one step, but for anything that can be proved equivalent in any number of steps, we can simply make multiple exchanges to get the shares we want. How do people get these shares they're trading to begin with? Simple! You can buy shares in ⊤ for $1. You can also buy shares in ⊥ for $0, i.e. they're free. We allow traders to perform a logical share splitting operation. This is a special operation that is described in the next section. As Wentworth explains, the virtue of this system is that in order to redeem shares for a proposition you can prove, you need to reveal your proof to the people running the market. Not to other traders necessarily, but whatever the market mechanism is that is enabling the above rules, it can see the math proofs implied in the trades people make. Logical share splitting A common theme in mathematics is to split proofs up like this: First we show A⟹B, then we show A. This allows us to prove B. Part of the point of using a market is to combine the intelligence of the various traders in the market into something greater than any individual trader. So ideally, we'd like to be able to prove B, even if one trader can only prove A⟹B and the other trader can only prove A. So let's say there's some profit in pro...

Back on BMS ep. 229, Bob invited Steve Patterson back to the show to discuss Riemann's Rearrangement Theorem, which was such a counterintuitive result that Bob changed his mind and agreed with Steve that mathematicians had smuggled nonsense into their field. Math PhD Ian Deters now joins Bob to defend the use of infinity in higher mathematics.Mentioned in the Episode and Other Links of Interest:The YouTube version of this interview (with mathematical images overlaid).The BMS ep 229 featuring Steve Patterson's critique of infinity.Help support the Bob Murphy Show.The audio production for this episode was provided by Podsworth Media.

Justin and Jason discuss Justin's plan to integrate daily physical exercise, how excess fructose could be the primary driver for elevated levels of uric acid and metabolic diseases generally, the million-dollar prize for a solution to the Riemann hypothesis, Justin's idea for a website called AmISheeple.com, the non-replication of the room-temperature superconductor, whether AI is a solution in search of a problem or the exact opposite, Elon Musk's response to the anti-Tesla ETF closing its doors, his decisions to change the name of Twitter to X and to get rid of the bock functionality, and how he's paid more taxes than anyone in history, why a wealth tax is unworkable, how Justin has absolutely, positively not given up on NitroNote, Math Academy's free-response functionality, whether Jason's approach to automated testing has changed, and the upcoming marketing push, how Justin once brought down his company's entire network (by accident), Colby's work on his game's tech tree and how his play testing is working out, the movie The Godfather and the Hulu series The Bear. Artwork by https://sonsofcrypto.com. Join our Discord, chat with us and fellow listeners! https://discord.gg/2EbBwdHHx8

Justin and Jason discuss Justin's plan to integrate daily physical exercise, how excess fructose could be the primary driver for elevated levels of uric acid and metabolic diseases generally, the million-dollar prize for a solution to the Riemann hypothesis, Justin's idea for a website called AmISheeple.com, the non-replication of the room-temperature superconductor, whether AI is a solution in search of a problem or the exact opposite, Elon Musk's response to the anti-Tesla ETF closing its doors, his decisions to change the name of Twitter to X and to get rid of the bock functionality, and how he's paid more taxes than anyone in history, why a wealth tax is unworkable, how Justin has absolutely, positively not given up on NitroNote, Math Academy's free-response functionality, whether Jason's approach to automated testing has changed, and the upcoming marketing push, how Justin once brought down his company's entire network (by accident), Colby's work on his game's tech tree and how his play testing is working out, the movie The Godfather and the Hulu series The Bear. Artwork by https://sonsofcrypto.com. Join our Discord, chat with us and fellow listeners! https://discord.gg/2EbBwdHHx8

An introduction to the conceptual and mathematical framework of Einstein's General Theory of Relativity. We begin by considering the key insight of gravity as a geometric phenomenon, and how the curvature of spacetime by matter explains the equality of inertial and gravitational mass. We then discuss the mathematics of general relativity, including geodesics, differential manifolds, covariant derivatives, the metric tensor, Christoffel symbols, the Riemann curvature tensor, the Ricci tensor, and the energy-momentum tensor. The episode concludes with a derivation and explanation of the significance of Einstein's Field Equations. Recommended pre-listening is Episodes 114 and 115: Special Relativity 1 and 2.  If you enjoyed the podcast please consider supporting the show by making a PayPal donation or becoming a Patreon supporter. https://www.patreon.com/jamesfodor https://www.paypal.me/ScienceofEverything  

You've likely heard about the countless benefits of magnesium for overall health, but did you know it also plays a crucial role in sleep? As one of The 3 Pillars of VIGOR, getting sufficient quality sleep must be a nonnegotiable for anyone who wants to maintain good health. Unfortunately, many people struggle with sleep, with almost 10% of Americans taking sleep medication. One way to enhance sleep quality is by getting enough magnesium. This guide explores the connection between magnesium and sleep, covers various magnesium supplements, and helps you find the best magnesium for sleep to optimize your rest. Why Magnesium Matters for Sleep Magnesium is a vital mineral involved in over 300 biochemical reactions in the body, including nerve and muscle function, maintaining a healthy immune system, and regulating blood pressure.de Baaij, J. H., Hoenderop, J. G., & Bindels, R. J. (2015). Magnesium in man: implications for health and disease. Physiological reviews, 95(1), 1-46. One of the most significant roles magnesium plays is in sleep quality. Magnesium contributes to the production of melatonin, a hormone that regulates sleep-wake cycles, and supports the function of GABA, a neurotransmitter that promotes relaxation and sleep.Abbasi, B., Kimiagar, M., Sadeghniiat, K., Shirazi, M. M., Hedayati, M., & Rashidkhani, B. (2012). The effect of magnesium supplementation on primary insomnia in elderly: A double-blind placebo-controlled clinical trial. Journal of research in medical sciences, 17(12), 1161. Unfortunately, magnesium deficiency is quite common, with studies suggesting that up to 68% of adults in the United States do not meet the recommended daily intake.Moshfegh, A., Goldman, J., Ahuja, J., Rhodes, D., & LaComb, R. (2009). What We Eat in America, NHANES 2005-2006: Usual Nutrient Intakes from Food and Water Compared to 1997 Dietary Reference Intakes for Vitamin D, Calcium, Phosphorus, and Magnesium. US Department of Agriculture, Agricultural Research Service. A lack of magnesium can result in poor sleep quality, insomnia, and even restless leg syndrome.Hornyak, M., Haas, P., Veit, J., Gann, H., & Riemann, D. (2004). Magnesium therapy for periodic leg movements-related insomnia and restless legs syndrome: an open pilot study. Sleep, 27(5), 1040-1048. Types of Magnesium Supplements There are several types of magnesium supplements available, each with unique pros and cons: Magnesium oxide: A common, low-cost option with a high magnesium content but low absorption rate.Lindberg, J. S., Zobitz, M. M., Poindexter, J. R., & Pak, C. Y. (1990). Magnesium bioavailability from magnesium citrate and magnesium oxide. Journal of the American College of Nutrition, 9(1), 48-55. Due to its poor bioavailability, magnesium oxide may not be the best choice for sleep improvement. Magnesium citrate: More readily absorbed than magnesium oxide but may cause gastrointestinal side effects, such as diarrhea, in some individuals.Walker, A. F., Marakis, G., Christie, S., & Byng, M. (2003). Mg citrate found more bioavailable than other Mg preparations in a randomised, double-blind study. Magnesium research, 16(3), 183-191. Although it's more bioavailable than magnesium oxide, its potential side effects make it less suitable for sleep improvement. Magnesium glycinate: A well-absorbed form that is gentle on the stomach and may improve sleep quality.Cao, Y., Zhen, S., Taylor, A. W., Appleton, S., Atlantis, E., & Shi, Z. (2018). Magnesium Intake and Sleep Disorder Symptoms: Findings from the Jiangsu Nutrition Study of Chinese Adults at Five-Year Follow-Up. Nutrients, 10(10), 1354. This chelated form of magnesium binds magnesium to the amino acid glycine, which has calming effects on the brain and nervous system, making it an excellent choice for sleep improvement. Magnesium malate: Known for its energy-boosting properties, it may not be the best option for sleep.Uysal, N., Kizildag, S., Yuce, Z., Guvendi, G., Kandis, S.,

Cluster B This show aims to educate the audience from a scientifically informed perspective about the major cluster B personality disorders: narcissism, histrionic, borderline, and antisocial. Resources:  Bleidorn, W., Hopwood, C. J., Ackerman, R. A., Witt, E. A., Kandler, C., Riemann, R., … Donnellan, M. B. (2019). The healthy personality from a basic trait perspective. Journal of Personality and Social Psychology. Want more mental health content? Check out our other Podcasts: Mental Health // Demystified with Dr. Tracey Marks  True Crime Psychology and Personality Healthy // Toxic Here, Now, Together with Rou Reynolds Links for Dr. Grande Dr. Grande on YouTube Produced by Ars Longa Media Learn more at arslonga.media. Produced by: Erin McCue Executive Producer: Patrick C. Beeman, MD Legal Stuff The information presented in this podcast is intended for educational and entertainment purposes only and is not professional advice. Learn more about your ad choices. Visit megaphone.fm/adchoices