Podcasts about ERD

My guest today is Robert Hart, The Verge's London-based AI reporter. Robert recently wrote a fantastic story for us about the debate raging inside the world of mathematics — and the existential crisis over what it means that new frontier AI models have become very good at math in a shockingly short period of time.  I wanted to dive into all of this with Robert, who actually spoke to some of the most accomplished mathematicians working today to figure out what's hype and what's real — and to get a sense of just how exciting, and how scary, all of this is.  Read the full interview transcript on The Verge. Links:  The AI takeover of mathematics has begun | The Verge Ten advances in mathematics and theoretical computer science | OpenAI An unreleased Anthropic model made progress on the Riemann hypothesis| TechCrunch OpenAI's amazing — but vastly oversold — new model Astra | Gary Marcus OpenAI's math breakthrough played to AI's strengths | Understanding AI Why the legendary Erdős problems are falling to AI | Quanta Subscribe to The Verge to access the ad-free version of Decoder! Credits: Decoder is a production of The Verge and part of the Vox Media Podcast Network. Decoder is produced by Kate Cox and Nick Statt. This episode was edited by Ursa Wright. Our supervising producer is Greg Ott, and our editorial director is Kevin McShane. The Decoder music is by Breakmaster Cylinder. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Nagyon izgalmas téma a 21. században a 10 parancsolat! Félelmetes mumus, vagy életgyógyító lehetőség? Műsorunkban Dr. Lőrincz Zoltán református lelkész, professzor emeritus  művészettörténésszel beszélgetünk legújabb könyvéről. Várjuk teológiai, lelki, morális, társadalmi vagy személyes dilemmákkal kapcsolatos kérdések, nehézségek, bizonyságtételek előzetes megosztását is az erdos.eszter.marta@gmail.com e-mail címre. Szeretettel várom Önöket: Erdős Eszter református lelkész, szerkesztő-műsorvezető

Fantasy Baseball Live – August 9, 2026 @ 3pmSegments 1 and 2 – Review of the weekend gamesAdditional Questions/Topics1.Hunter Greene is having elbow surgery again.2.Kaelen Culpepper is getting the call3.Kade Anderson, Joshua Baez, and other top prospect math – After August 13, players can be promoted and still stay under the 45-day threshold for losing rookie eligibility. The other threshold is 130 AB and 50 IP.4.Jackson Jobe's long awaiting return to the mound occurred on Saturday where he shoved it against the Giants – 5.0 IP, 1 hit, 4K/1BB with the win. He maxed out at 100.1 mph.5.Traded starter's first outing – some tough startsa.Tarik Skubal (LHP, LAD) – 6.0 IP, 4 hits, 2 ER, 6K/2BB – Lossb.Casey Mize (RHP, SD) – 3.1 IP, 9 hits, 8 ER – Lossc.Freddy Peralta (RHP, TB) – 3.2 IP, 9 hits, 7 ERd.Robbie Ray (LHP, SD) – 5.0 IP, 7 hits, 4 HR, 2K/5BB6.The Athletics fired their General Manager, David Forst. After all, it was his fault for moving the team out of Oakland and into Sacramento – smile.Segment 3 – Waiver ReportSegment 4 – Closer Report

Az Orbán-kormány vízügyi államtitkára is ellentmondott Orbán korábbi kijelentésének Orbán Viktor a Mol vezérigazgató-helyettesével és Hatvanpuszta főépítészével bukkant fel egy szerb fesztiválon Baka András: Kétharmaddal sem lehet mindent megcsinálni Izrael elutasítja Trump 15 pontos gázai tervét Menczer Tamás Rogán Antalról: Nagyon okos, vannak dolgok, amiket nem értek, de nem kell nekem mindent érteni Súlyos fájdalmai vannak Joe Bidennek, rákbetegsége már a csontszöveteket is elérte Ismét fellángolt a vita arról, hogy kell-e duzzasztómű a Dunára Megtámadták a mentőket Erdélyben Európa gáztartalékai alacsony szinten: nehéz tél előtt állunk? Vége az urambátyám-rendszernek az állami földek hasznosításában is A magyar válogatott szerepelt a legeredményesebben az isztambuli öttusa Európa-bajnokságon Szoboszlaiék kikaptak a Monacotól Rekordmélyen a Duna: mit jelentenek valójában a centiméterek? A további adásainkat keresd a podcast.hirstart.hu oldalunkon. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Az Orbán-kormány vízügyi államtitkára is ellentmondott Orbán korábbi kijelentésének Orbán Viktor a Mol vezérigazgató-helyettesével és Hatvanpuszta főépítészével bukkant fel egy szerb fesztiválon Baka András: Kétharmaddal sem lehet mindent megcsinálni Izrael elutasítja Trump 15 pontos gázai tervét Menczer Tamás Rogán Antalról: Nagyon okos, vannak dolgok, amiket nem értek, de nem kell nekem mindent érteni Súlyos fájdalmai vannak Joe Bidennek, rákbetegsége már a csontszöveteket is elérte Ismét fellángolt a vita arról, hogy kell-e duzzasztómű a Dunára Megtámadták a mentőket Erdélyben Európa gáztartalékai alacsony szinten: nehéz tél előtt állunk? Vége az urambátyám-rendszernek az állami földek hasznosításában is A magyar válogatott szerepelt a legeredményesebben az isztambuli öttusa Európa-bajnokságon Szoboszlaiék kikaptak a Monacotól Rekordmélyen a Duna: mit jelentenek valójában a centiméterek? A további adásainkat keresd a podcast.hirstart.hu oldalunkon. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Egy világbajnoki címet nem adnak ingyen. Nem Lászlóval beszélgettünk, aki az ultrafutás 24 és 48 órász korosztályos világbajnoka, valamint világcsúcstartója. Mi kellett ahhoz, hogy ezeket a sikereket elérje 56 évesen? Erről beszélgetettünk vele, és edzőjével, Erdélyi Nándival. #ensport #pulzuspodcast

2026. június 30-án Kanadában egy salto mortale performansszal vetett véget életének minden idők legkövetkezetesebb és egyben legrejtélyesebb magyar spionja, az 1978-ban külföldre távozott Molnár Gergely, alias Gregor Davidow, Helmut Spiel, Spiel! stb. stb., sok minden egyéb mellett a magyar neoavantgárd és a punk legnagyobb hatású művésze. Bár a sajtóban hirtelen mindenhol megemlékeztek munkásságáról, mégis meglehetősen sokatmondó, hogy a többség átvette a személytelen hírt és csak kevesen merészkedtek saját gondolatokat tartalmazó nekrológot írni Spiel! munkássága kapcsán. Amennyire zavarba ejtő volt a maga idejében a Spions működése, koncertjei, felvételei, annyira zavarba ejtő 2026-ban is, ráadásul a kontextus és a gesztusok sora e generáció kiöregedésével kezd egyre értelmezhetetlenebbé válni, pedig a Spions nélkül nagy valószínűséggel nincs URH, Kontroll Csoport, Európa Kiadó, nincs TÁP Színház és sorolhatnánk. Ezért gyűlt össze egy rakás könyv, szamizdat, folyóirat, fotó társaságában a Wanted három régi szerkesztője, Marton László Távolodó, Uj Péter és Bihari Balázs, hogy átbeszéljék a korszakot, meghatározó személyiségeit Balaskó Jenőtől, Galántai Györgyön át Najmányi Lászlóig, Csányi Attiláig, Erdély Miklósig, Ladik Katalinig, Szombathy Bálintig, Monty Cantsinig vagy hogy Molnár Gergely tényleg annyira rejtélyes figura volt vagy ennyire ráégett a spion imázs és mennyire dokumentált a munkássága.A Wanted podcast adása az NKA Hangfoglaló program támogatásával készült.

Magyar Péter: Hajnali fél kettőkor az utolsó előtti egységet is leállítják az atomerőműben Meghalt Nirmal Purja neves nepáli hegymászó tíz másik emberrel együtt egy lavinában Erdőtűz a nyaralás közelében: mikor véd és mikor nem az utasbiztosítás? Megjelent a Magyar Közlönyben a lekapcsolhatóságról szóló kormányrendelet Az űrből is látszik a divat pusztítása: 60 ezer tonna ruha borít egy chilei sivatagot Két új elektromos SUV modellel tört be az autópiacra a Xiaomi Rengetegen pályáztak a NAV-elnöki posztra Négyéves gyerek lett a család hőse – mentőt hívott, miután édesanyja összeesett a kertben Hőség- és áramkrízis – Megszólalt az átmeneti államfő "Betolták Erikát a műtőbe" - Zoltán Erika és a férje a kamerák előtt sírták el magukat Megtartották az 56 centis tóban a Balaton-átúszást, Révfülöpnél 28 fokos vízben indult a mezőny Gulácsi Péter spanyol csapattal egyezett meg? Záporok enyhíthetik néhol a 40 fokot vasárnap A további adásainkat keresd a podcast.hirstart.hu oldalunkon. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Magyar Péter: Hajnali fél kettőkor az utolsó előtti egységet is leállítják az atomerőműben Meghalt Nirmal Purja neves nepáli hegymászó tíz másik emberrel együtt egy lavinában Erdőtűz a nyaralás közelében: mikor véd és mikor nem az utasbiztosítás? Megjelent a Magyar Közlönyben a lekapcsolhatóságról szóló kormányrendelet Az űrből is látszik a divat pusztítása: 60 ezer tonna ruha borít egy chilei sivatagot Két új elektromos SUV modellel tört be az autópiacra a Xiaomi Rengetegen pályáztak a NAV-elnöki posztra Négyéves gyerek lett a család hőse – mentőt hívott, miután édesanyja összeesett a kertben Hőség- és áramkrízis – Megszólalt az átmeneti államfő "Betolták Erikát a műtőbe" - Zoltán Erika és a férje a kamerák előtt sírták el magukat Megtartották az 56 centis tóban a Balaton-átúszást, Révfülöpnél 28 fokos vízben indult a mezőny Gulácsi Péter spanyol csapattal egyezett meg? Záporok enyhíthetik néhol a 40 fokot vasárnap A további adásainkat keresd a podcast.hirstart.hu oldalunkon. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Damit sie nicht zu Hause rumhängen, können Kinder im Kindergartenalter auch 12 Stunden am Tag in einer Raffinerie arbeiten. Das nennt man ganztägige Erdölförderung. Von Ulrich Winters.

Erdöl wurde diese Woche mehr gehandlet als Bitcoin und das auf Hyperliquid. Erleben wir gerade RWAs in Form von Erdöl? 00:00 Intro 00:26 Hinweis 00:31 Vorschau 00:41 Bitcoin 03:11 Ethereum 05:02 Uniswap 07:18 Verabschiedung #krypto #cryptonews #cryptotrading #swissquote _____

Der reichste Mann der Welt ist heute so reich wie die ärmsten vier Milliarden Menschen zusammen. Nach wie vor werden Kriege um Erdöl und Ressourcen geführt. Unser Wirtschaftssystem stellt Wachstum vor das Wohl unseres Planeten. Steuern wir gleich auf mehrere Katastrophen zu? Und wie können wir das verhindern? Darüber haben wir beim vergangenen Alpen Klimagipfel mit Impact-Investor Charly Kleissner gesprochen.

Kommentiert werden der Stellenabbau beim Automobilkonzern BMW sowie der heutige sogenannte Erdüberlastungstag. Zunächst geht es aber um die Personalrochade in der Bundesregierung und in der CDU, die Bundeskanzler und Parteichef Merz viel Kritik eingebracht hat. www.deutschlandfunk.de, Presseschau

Wenn die Angst vor Krankheiten das Leben prägt; Erdüberlastungstag 2026; Duft Mimikry: Käferlarven duften wie Blüten, um Bienen zu manipulieren; Powerbanks im Flugzeug; Plastik - teuflisch oder göttlich?; Organspende - Warum so wenig gespendet wird Moderation: Marija Bakker. Von WDR 5.

„[...] Pont ennek a workshopnap a végén meg kellett fogalmaznunk egy statementet. Akkor az volt, hogy visszaadni az embereknek a rácsodálkozás élményét. Egyébként nem az, hogy megismerjék Erdély történelmi, kulturális, bármilyen értékeit. Az egy másodlagos célunk.” P. Kristály Bea podcastgyártó, a Hetedhét Erdély társalapítója Itt érhető elFacebook | Instagram DBA-közösségimédia-felületekFacebook | Instagram | LinkedIn | YouTube

Ab dem Erdüberlastungstag lebt die Menschheit rechnerisch über der ökologischen Grenze der Erde für das Jahr, berichtet Stefanie Peyk. "Immer mehr Menschen erleben die Folgen des Klimawandels wie Unwetter, Hitze und Dürren", sagt BUND-Vorsitzender Olaf Bandt. Von WDR 5.

Heute ist der Earth Overshoot Day, also der Tag im Jahr, ab dem die Menschheit sozusagen „ökologisch auf Pump“ lebt. Das genaue Datum wird jedes Jahr neu berechnet, doch die Methode dahinter wird immer wieder kritisiert. Was kann das Bild vom Erdüberlastungstag leisten, was nicht? Stefanie Peyk hat recherchiert

Kohlenwasserstoffe werden weiterhin das Rückgrat der modernen Zivilisation bilden, allerdings in einer anderen Funktion. Nämlich bei der Herstellung von Polymeren, aus denen unser gesamtes Lebensumfeld entsteht. Das Verbrennen von Erdöl und Erdgas in einem Motor wird bald als Zeichen von Verschwendung gelten. Von Gleb Prostakow

Du suchst ein Einfamilienhaus mit viel Platz, flexiblen Räumen und hochwertiger Ausstattung – ohne dabei dein Budget zu sprengen? In dieser Podcast-Folge stellt Bauexperte Tobias Stahl das neue Haus Lebensraum vor: Ein durchdachtes Familienhaus mit 151 m² Wohnfläche, bis zu sechs nutzbaren Zimmern und einer intelligenten Raumaufteilung für moderne Familien.

In recent months, the open vs closed, and US vs China discussions on model ownership and sovereign/local AI have heated up to a fever pitch. So it is very very good news that Poolside AI are finally emerging with new models, like Laguna S 2.1, that are beating Thinking Machines' recent release nearly 10 times their size.Poolside's recent tech report got a lot of praise due to their level of detail, and Vibhu first covered Laguna's recent technical report on our paper club:From spending $12 million building language models for code before the world cared to creating a Model Factory that can take a model from pre-training to release in eight weeks, Eiso Kant has spent more than a decade betting that code is the path to AGI. In this episode, the Poolside co-founder joins swyx and Vibhu to explain why ChatGPT felt like vindication, why Poolside embraced open weights and open research, and why he would rather live in a world with 100 foundation model companies than five even if Poolside were one of the five.We go deep on Poolside's Model Factory: the engineering systems behind 10,000–20,000 experiments per month, streaming data directly into training, reproducible experimentation, low-precision compute, and agents that increasingly write code, launch jobs, evaluate results, and modify the pipelines used to train future models. Eiso also unpacks their recent launch Laguna S, why persistence, verification, and backtracking may matter more than raw intelligence, how much capability remains inside smaller models, why reinforcement learning will move earlier into pre-training, and why next-token prediction is still extracting too little from the web.We also discuss model-harness co-design, Poolside's path from coding agents to AGI, why Eiso thinks MCP and traditional tool calls are “stupid,” the real economics behind frontier-model training, Poolside's $500 million raise, open-source AI, regulation, NVIDIA and TSMC's influence, engineering productivity in the agent era, high-agency teams, and hiring at Poolside.We discuss:* How Andrej Karpathy's RNN work inspired Eiso to start building language models for code in 2015* Why Eiso spent four years and $12 million pursuing an idea before the market cared* Why ChatGPT felt like vindication and brought Poolside back to open source* Why Eiso would prefer 100 foundation model companies over an oligopoly of five* The difference between releasing open weights and publishing genuinely open research* Why Poolside deliberately built a global research organization outside the Bay Area talent war* Why model building is ultimately 90% engineering* The Model Factory: Poolside's end-to-end system for rapidly training and improving models* How fewer than 70 researchers run roughly 10,000–20,000 experiments each month* How Poolside moved from six-month model cycles to five- and eight-week launches* Why streaming data directly into training unlocked faster experimentation* How immutable data, versioned code, and reproducibility enable rigorous model research* Why Eiso wants capable researchers to leave their labs and become Poolside's competitors* Why 95% of model building can be reduced to better data or compute efficiency* Laguna S and why persistence, verification, and backtracking can outperform raw intelligence* Why smaller models may handle far more knowledge work than previously expected* Why reinforcement learning will move earlier into pre-training* Why next-token prediction is still failing to extract enough knowledge from the web* Why distillation and environments have become the AI industry's favorite “drugs”* Why mid-training is really an early form of curriculum design* Low-precision training, networking bottlenecks, and the next gains in compute efficiency* Laguna S: 118 billion total parameters, 8 billion active, and eight weeks from training to launch* Why model builders can often evaluate a new checkpoint within its first 30 minutes* Model versus harness: where agent capabilities actually come from* Why Poolside sees coding and long-horizon software tasks as a path to AGI* Why Eiso thinks MCP and traditional tool calls are “stupid”* Why future agents will write scripts instead of choosing from dozens of predefined tools* The case for minimal harnesses, containers, and model freedom* Why Poolside is prioritizing vision but does not expect to work on audio soon* Why language may be the most compute-efficient modality for encoding knowledge and reasoning* The real cost of model development and why the final training run is anticlimactic* The story behind the Poolside name and why it represents refusing to lower ambitions* How Poolside raised $500 million while investors still questioned whether AGI was real* Why intelligence could become the world's most demanded and commoditized resource* When open models may become too capable to release without restrictions* Why unilateral AI safety does not work in a globally competitive environment* How regulation could accidentally lock in an oligopoly of two or three AI companies* NVIDIA, TSMC, and the hardware systems underpinning foundation-model progress* Why reinforcement-learning wall-clock time is one of Poolside's biggest bottlenecks* Why Poolside trains models from scratch instead of simply distilling larger models* How AI changes the way companies should measure engineering productivity* Why agency may become the most important quality for employees in the AI era* How leaders align high-agency people through shared goals and clear constraints* Hiring across research, post-training, pre-training, architecture, evals, and engineering at PoolsideEiso KantLinkedIn: https://www.linkedin.com/in/eisokantX: https://x.com/eisokantPoolside: https://poolside.aiTimestamps00:00:00 Introduction00:00:54 Karpathy, RNNs, and Building Code Models Before Transformers00:02:26 The $12M Failure and ChatGPT Vindication00:03:39 Open Source and the Case for 100 Foundation Model Companies00:09:22 Open Weights, Open Research, and Poolside's Global Team00:16:04 The Model Factory: Why Model Building Is 90% Engineering00:20:19 Agents, Automated Experiments, and Early Signs of RSI00:24:04 Streaming Data, Reproducibility, and Scientific Rigor00:30:35 Creating More Foundation Model Companies00:36:07 Laguna S: Persistence vs. Raw Intelligence00:43:01 Reinventing Pre-Training, RL, and Curriculum Design00:52:33 Low-Precision Training and Squeezing More From Smaller Models00:58:37 Model Harnesses, Coding Agents, and the Path to AGI01:09:26 Why MCP and Traditional Tool Calls Are “Stupid”01:13:04 Vision, Multimodality, and Why Language Still Matters01:18:15 Scaling Models and the Real Economics of Training01:20:40 Why Poolside Is Called Poolside and Raising $500M01:27:37 Open Models, AI Safety, and the Risk of an Oligopoly01:33:53 NVIDIA, TSMC, and the Reinforcement-Learning Bottleneck01:41:52 Smaller Models, Distillation, Engineering Productivity, and HiringTranscriptIntroduction: Eiso Kant, Poolside, and Open ModelsSwyx [00:00:00]: All right, we're here in the studio with Eiso Kant from Poolside, together with Vibhu. Welcome.Eiso Kant [00:00:08]: Thanks. Thanks for having me, guys. Good to be here.Swyx [00:00:10]: Yeah, fresh on the plane. You texted me, you were like, “Hey, I'm on my way to SF.” I was like, “You're on a plane right now, right?” Like, hey.Eiso Kant [00:00:16]: I know. After I texted you, I realized that probably coming in with major jet lag was gonna offer some fun experiences today, but let's do it.Swyx [00:00:23]: I mean, I think the thing I would tell guests is that they don't have to prepare that much because if you're truly working on this every single day, then even, like, what you hazily remember is going to be new for a lot of the audience that don't live in your world every day, right? so 10 years ago, you did a talk at Google Slush, talking about the democratization of AI. and, now here you are, like, open sourcing an incredible new model that we're gonna talk about. But I guess, like, what got you into democratization of AI? Like, it's not obvious from your LinkedIn or something.From Karpathy's RNN Post to SourcedEiso Kant [00:00:57]: No, it's not at all. I don't think it's obvious how I got in this space. I owe getting into this space to Andrej Karpathy.Eiso Kant [00:01:05]: In 2015, he wrote an article called “The Unreasonable Effectiveness of Recurrent Neural Nets.”Swyx [00:01:10]: Neural Nets, yep.Eiso Kant [00:01:11]: And that article, I read it, and I pivoted my startup at the time overnight to working on RNNs, and later LSTMs and Transformer models to be able to write code. If you go to this article and you scroll down, you can start seeing, like, this was the precursor to what ended up becoming language models. So, at least when he was character-level language models that were starting to predict letters, he has an example out here. There's a little Paul Graham generator, and you can read it, and the text makes sense, but it doesn't. and there's a little-- There's an example of code a little bit further down. Yeah, so Shakespeare.Swyx [00:01:47]: Shakespeare.Swyx [00:01:49]: CoolEiso Kant [00:01:49]: And for some reason, I read this, and I went down the rabbit hole of learning everything I could about RNNs and LSTMs, right? This is Transformer paper. And I had built a completely unreasonable belief, that neural nets should be able to generalize to anything and everything, and that language should be able to generalize, to a lot of things that are intelligent and the ability to write code. And so I started building Sourced, which was a fully open source company trying to build, what we used to call machine learning on code, language models on code. And we spent about four or five years on this, till the end of 2019. And that sounds really cool today, but back then, no one cared.Eiso Kant [00:02:29]: Right? Like, no one cared. We were in the dark. Like, we did things along the way. We tried applying convolutional neural nets to, like, the structure of code. We were. when attention came out, we were applying it to LSTMs, and then the Transformer paper came out. And it - it wasn't obvious, and what we missed throughout that entire journey, that we were on the right track, but we should have just kept scaling up. And today, to all of us, the scaling laws and scaling up seems like the most obvious thing. But having spent four or five years of my life on working on language models on code, it wasn't obvious. So I have a lot of respect to folks at Google and OpenAI and others who took that confidence and kept going. we failed ultimately at the time, and it was, like, biggest failure of my career, right? You blew $12 million of investors' money, which was a lot back then.Swyx [00:03:18]: Yep.Eiso Kant [00:03:19]: You spent, still a lot, but, And you spent years with, like, a group of 40 people just obsessing over this problem. And life took a different turn, And it was, and family became a focus, and I kept my heads down and really, didn't really look at language models for the following two years. big mistake considering Following years are gonna be really interesting. And then ChatGPT came out And it was like a vindication. It's like people started texting me. I found, like, my old, work decks and these old talks. And throughout that whole journey, we,ChatGPT, Vindication, and Returning to Open SourceEiso Kant [00:03:56]: We really had a strong point of view at the time that, like, as you're building more capable intelligence, it should be open and open source.Eiso Kant [00:04:04]: When we started Poolside, that wasn't the case at all, and I wanna be very open about it. When we started Poolside, we were like, there was a premise of two things. One is this technology is not gonna stop compounding in capabilities. I think to most people obvious today, but three-plus years ago when we started, most people were still arguing if these were stochastic parrots or not.Eiso Kant [00:04:23]: And the second was that reinforcement learning was gonna be the biggest driver for LLM capabilities. Today, very obvious. Three years ago, was not an opinion held or direction held at either OpenAI or Google or Anthropic or others. And so people looked down on us a little bit. They were like, “ is this really gonna work?” And so we just started working the problem, and we never really thought about open source again. We just kept our heads down and we built our, like, knowledge, understanding from scratch, right? We didn't roll out of an existing lab. So we picked up the papers and started writing code and figuring things out.Eiso Kant [00:04:59]: And it wasn't until the beginning of this year that me and my founder, Jason, picked up the open source conversation again.Eiso Kant [00:05:07]: And if you go back to some of the early things on our website, it was very straightforward. It was we wanna get to AGI, we wanna support a world of abundance, and we wanna be the first company that gets there.Eiso Kant [00:05:20]: But we started talking at the beginning of this year because it became obvious that the world was going in a direction that was starting to like, pick at us a little bit. Like, it didn't, this didn't happen overnight. It was, like, a little bit we were seeing this and we're like, “Okay, The world's going down a path.” And Throughout this journey, there was something that I used as a, as an analogy or thing. So I said well, if I go back to back in those days, 2015 or 2016, we're working on this, and I picked up a fi book off the shelf, and I was reading the book about 2035. AGI is achieved, and the story would be over the following, decades. And it would have that first chapter where everyone's trying to figure things out. You'd get the chapter of ChatGPT coming out And then you would get to the chapter where the world was at a fork in the road, and the one that it picked was one where three or four or a handful of companies were going to create all of intelligence moving forward.Eiso Kant [00:06:21]: And when I thought about that story, it felt like a dystopian fi book, not a utopian fi book. And the reality is, I'm a utopian fi guy. Like, and so We took a step back and said, “Hey, can we play a role here?” Now it was easy for us to do so because we were not at the frontier.Eiso Kant [00:06:41]: If we were at the frontier, I don't think we could have changed our mind. and I don't mean this like it's when the moment there's too much capital involved, too much expectations, you've built up things, right? We're a small team, just improving and improving. And so we knew that we could make that decision now, but it would be a lot harder to make as we got closer and closer to the frontier and caught up to others. And did a lot of soul-searching and a lot of conversations, and said, “No, this makes sense,” Even if there's big unanswered questions, like how the hell do you build a business model with foundation models about open source? Big open-ended question that we do not fully have the answer to yet, right? At what point do you no longer wanna release open source models because misuse of models has, real potential risks associated with it? how is the government gonna respond to open source? but I think it all just came down to one thing, and I'll stop the monologue, is the fact that I rather live in a world that has 100 foundation model companies than a world that has five, even if I was one of the five. And the smallest and most meaningful contribution we can make for 100 to exist is to open up our research and open up, like, our weights right now and figure out along the way how we can, like, do more.Neo-Labs, Model Choice, and the Token EconomySwyx [00:08:01]: Yeah. I think if anything, over the past three years, that has become a bit more true. you are one of a cohort of Neo labsEiso Kant [00:08:10]: YeahSwyx [00:08:10]: That people are now calling that. And, we're, we're doing this on the day that Thinky launched their, new model and you are outperforming them on their, on some benchmarks that they released, right? Like, they just don't have it yet. so it goes to show that I think, like, this is one of those things where, like, there is room for multiple players, and you are seeing a little bit more of the future. Maybe more like 20, not 100, but, like, you are one of the 20.Eiso Kant [00:08:36]: I really hope so, right? I think we I'm, I'm excited about their release, and I'm excited about everyone releasing because, like, ultimately, like, choice competition is both gonna drive progress in the right direction. But the fact that like, we create models and while we all, drink out of the same well of data effectively, we do introduce very different behaviors and biases in our models. Some are intended biases, some are completely unintended biases.Swyx [00:09:03]: Yeah.Eiso Kant [00:09:03]: And if we shape up in an ecosystem in the world where open models are gonna be a part of the token economy, like, I don't think there's any question about it anymore Then we want to be able to live in a world where companies, countries, people can choose and say, “Hey, I am most aligned and I trust most this provider for these things.”Swyx [00:09:25]: Yeah.Vibhu [00:09:26]: I think more than just one of the 20 Neo labs, up until recently, most of open source innovation was coming from the Chinese labs, right? So there's the DeepSeek of the West. Is it today? Okay, maybe it's thinking machines reflection, but there aren't many, right? So, one of the things you guys started in France, Europe, but very much now you're taking that American standpoint and more than just that, the point is the Chinese models that we see, they're not super open research. the work you put out is, I think, some of the best. So every few months you get not only frontier models, but also here's a breakdown blog, paper, technical report of here's everything for state of the art to build, frontier intelligence and you're filling that gap too, right? So not just only open weight, not just Western, but also pretty open research.Open Weights vs. Open ResearchEiso Kant [00:10:20]: No, I appreciate it. Look, I think it's, I think it's the most meaningful contribution, right? Weights are a binary. Let's call them what they are. Yes, we can modify them, we can change them, but, like, giving someone the weights does not allow them ultimately to recreate what you're doing, right? And so now there's challenges around releasing data sets, challenges around like releasing certain things, but being able to share your research, like, right, how do we do it? What are the lessons we learned that we spent, tens of thousands of experiments of compute on? I think very much so. One correction though, Vibhu, and I say this because it's been haunting us for quite a few years. We from day zero were an American company.Swyx [00:10:55]: Yeah. They movedPoolside's Global Team and American Company StorySwyx [00:10:56]: To France.Eiso Kant [00:10:56]: So the story once and for all is very. We start as an American company. We have always been an American company, and early on we made a very conscious decision. We said, “We're not gonna hire any researchers in the Bay Area. We're gonna look for talent everywhere else in the world.” and that is everything from Middle Americas, Seattle to, Serbia, and to Taiwan and Singapore and other places. And it was because we took a view that this was gonna become a talent war for this, and I think it has over the years now. Three years ago, that wasn't fully obvious yet. I think today it very much is. And we also realized that, like, some of the world's most capable people with, like, the most interesting, innovative ideas were not just gonna be here. And so it led us to create like a fully remote company. and we ended up opening an office in Paris and London and different places and we have a lot of the team in the US and a lot of team outside. But we always took this view of like, we're an American company, but if we want the best of the best to work with us, we need to take a global view. Now we do also have people here in Silicon Valley, like the company's grown and others, but I think one of the things that, it slowed us down at the beginning, but it has sped us up now, and it's why you're seeing like the progress, I think, on our models and the cadence at which we release, is because we didn't roll out of an existing lab. Right? we didn't, we didn't have a lot of the information that's freely flowing around here at the time. We just took this point of view as like, “Okay, well, let's just work the problem. Let's just go and, like, read the few papers that are out there, and let's just figure this stuff out.” And we made some hilarious mistakes in model training because of that over the yearsEiso Kant [00:12:35]: Like especially in the first 12 months. there's a few that I think still haunt me and scare me. We can talk about them later. but it created a, like, a resiliency and persistency in the team, right? with extremely few people have left us over the years, that, like, told us, “Okay, we can do this.” When we first wrote our first training code base completely from scratch, it wasn't a fork of any open source. It was just like, “Okay, let's build it from scratch.” I remember we had this one moment where we spent three weeks working out an optimizer bug. Like, it was like training just couldn't get stable. We, like, obsessed over it, and we thought, like, maybe we were wrong. Maybe we should have just forked this repo, or we should have. But then when we solved it, I still remember at the time we were like five people in the company. when we solved it, we were like, “Oh, we can do things,” like if we're just willing to work hard. and I think that culture with a very strong engineering bias has helped us, like, get to where we were. And so there's this notion of open source and talent and these things. I think we, We just took different decisions from a different starting point. and I think we are lucky. I do want to definitely call it lucky. And there was a lot of hard work at the team that now, like, that's starting to show up in results.Swyx [00:13:52]: Just ‘cause we probably won't revisit this again, but, and this is a fun recruiting challenge if someone knows the answer. What was the bug? And then we won't tell the solution, but we'An Optimizer Bug and the Value of Building From ScratchEiso Kant [00:14:01]: So the - This - You're gonna test my memory here,Swyx [00:14:04]: Oh, okayEiso Kant [00:14:04]: So but I thinkSwyx [00:14:05]: DirectlyEiso Kant [00:14:05]: I think I can recall. So if you, so if you look at, So if you take like Adam as an optimizer, you have epsilonSwyx [00:14:12]: YeahEiso Kant [00:14:13]: Which is, right, like in the denominatorSwyx [00:14:14]: Momentum and weights. YeahEiso Kant [00:14:15]: Is exactly, in the denominator. And at the time, if I recall, you looked at like the early Llama papers and things like that. People were juicing epsilon, like, quite a bit. Like, they were, like, adding, I don't know if it was E minus four or whatever, like a high value for epsilon.Eiso Kant [00:14:31]: And if you think about this during training, it's like a bit weird and counterintuitive that we're adding noise to our optimizer by just adding effectively, like, a random number in the denominator, right? Like behind the decimal point. And I don't recall the exact bug, but it had - What I remember is once we solved it, we no longer had to juice epsilon as much as, like, was happening in the Llama paper and other places. and it was like one of those fundamental moments where we had trusted this paper that was out there, and we're like, “Oh, no, it has to be this way. It has to have this high value of epsilon.” But it made no sense to us intuitively. Like, why do you have to have this so high? Like, if you're just trying to avoid division by zero, why can't the value be extremely small? and that was like one of those moments where you realize like, okay, finding things out from scratch yourself builds a better intuition. Because the one thing you learn very quickly with model building is that your intuitions that you start with are gonna get beaten up so hard.Eiso Kant [00:15:33]: Right? Like - It's such an experimental science, that the things that seem obvious, you very quickly get to learn, like, you were wrong, and hopefully you figure out why, and sometimes you don't even.Swyx [00:15:45]: Yeah. yeah, so, one of the reasons that you, when you released your new models, Vibhu got really excited. I mean, everyone got really excited. But Vibhu led our paper club on it, and you guys sawEiso Kant [00:15:58]: YeahSwyx [00:15:58]: Obviously. maybe talk through some lessons learned in that, whatever you can disclose. we can focus on the model factory stuff, whatever you think is a good starting point.Model Building as EngineeringEiso Kant [00:16:08]: So I would say that our view from very early on in the company was that model building is ultimately 90% engineering.Eiso Kant [00:16:18]: And I think we all know it in the industry because if you look at where's every researcher spending their time, they're spending their time writing code, right? Looking at data and writing code. And so we said, okay, The state at the moment, like three years ago, was bash scripts and Slurm and spaghetti code bases for training and, like, data pipelines that were patched together. And we looked at this and said, “Well, ultimately, model building is a process.” You're going from raw data, right? Like training raw material, the web, et cetera. you're doing a whole bunch of filtering, cleaning up, transformations, analyzing. These days, that's, far more complex than it was three years ago. then you're training a model, which is effectively a large distributed systems problem, right? Across hardware that has still-- It's become a lot more reliable. It was extremely flaky back then. and now with every new generation, we get our new sets of challenges. And then you go into the next stages, right? There was no training back then, but, like, you got, your post-training and then your reinforcement learning. And so we looked at this and we said, “Well, this looks like an industrialized process. This looks like an end process, that every single part of it has its machinery,” right? If it's your big data pipelines, if it's your crawling ingestion of the web, if it's your, large-scale distributed training, and then you've got your reliability. And we said, “Well, why don't we take some of the world's smartest distributed systems engineers that we knew and make them part of the process of research from day zero?” Not retrofitting it later on, but, like, really from the beginning. And that became our model factory. And so our model factory started with a handful of components. Today, it's thousands of components, and I try to equate it to, if you think about, like, someone who was at the very early days of Foxconn, if they had been there for the following, decade, they would be able to rebuild Foxconn because they saw every decision that led to building that system and all the complexity. If you and I walk into Foxconn today, no chance.The Model Factory and Experiment VelocityEiso Kant [00:18:18]: Right? Because we don't have the lineage and history of decisions that led to that. And so we built early on from the beginning- with a team that really understood that, well, the metric that we are optimizing for is the speed of an idea from a researcher to an experimental result that we can trust to then being part of the next model training.Eiso Kant [00:18:42]: And in the. And because it's such an experimental science, ultimately, in the beginning when it wasn't that complex, you could patch your way around it, right? But now, at any foundation model company, you are running. I mean, we're a small team, right? We're less than 70 researchers, another 35 engineers. and we are running, I haven't checked the latest count, but far more than 10,000, maybe 10 to 20,000 experiments a month that we cut. And so if you look at that scale of every model run that is, like it's ultimately it's, it's you need to be able to trust it as an infra problem. And so what we have now done over the years is gotten really good at that, and just by working it and improving it and obsessing over those end decisions. So now what that means is that you looked up Laguna XS 2 that we launched. It was five weeks from the beginning of training to launch. The model that we're gonna talk about today was eight weeks from start of training, to launch. We started the next model literally yesterday because we now finished the post-training required for the model we're launching, next week or by the time this comes out today. and we move that compute to the much larger Laguna M model that we're now training. And so the model should be an artifact of someone's process. It shouldn't be really a thing in itself. Like, and we treat this like the way you would look at like a SpaceX factory where, yes, the first rocket, really hard to build, but the much harder challenge was building the factory. And now they're rolling off, and no one is really thinking about the next launch anymore. So it's just another launch, it's another launch, another rocket comes off. And that's what we're trying to do with model building.Eiso Kant [00:20:22]: And what has been, which was not planned from day zero, it was in the back of our mind like this will happen one day, is that when you build a really good end model factory with really good APIs and really good engineering systems, Well, what is it perfect for? It's perfect for agents.Agents Inside the Model FactoryEiso Kant [00:20:40]: Because agents are now starting to take over more and more work in our model factory.Vibhu [00:20:43]: Yeah.Eiso Kant [00:20:44]: So I look at the screens when I walk, like when we're, we come together, in our monthly, we do monthly onsites, and I walk behind people's screens and I stop by and I talk to our researchers. And the default is all of these different agents running on their screen that are writing the code. They're launching the jobs. They're evaluating the results that are coming back from the model runs. They are, making the changes. And we're still in the driver's seat. We're still coming up with the ideas. We're still helping with the debugging. But more and more, and this is right now very profound on the data side of our pipelines in both pre and post and the synthetic data pipelines, it's starting to become more on the architecture side as well. You're starting to see these twinklings of what RSI is gonna look like.Eiso Kant [00:21:27]: And that's. So when we talk about, like to your question about our models, every talk about the model factory, And my coolest example of these things is always that when we kick off a new run, doesn't matter if it's a training like big run or if it's now a post, like one of 10 post-training versions we do for like release or many experiments, is that at any given moment, the changes that somebody made that they had experimental results from the day before make it into that run.Eiso Kant [00:21:57]: So there's not like a cutoff 90 days before. Like no, it's like literally from that moment because we can now trust the machine enough. And then you also have to invest in the reliability. So one of my favorite metrics about like Laguna S is that there was no call events, Right? Like completely zero. And we haven't had a meaningful call event, like something to wake up for, as far as I recall this entire year. now there is one asterisk to that. In usually the first six hours of launching a new model run, something breaks because you set a config wrong, you made a small mistake, et cetera. So that's usually there's a little bit of intervention, but that's always within like call periods, right? Not on call. And I think that's starting to now compound. So the model we're releasing now, I love it. It's amazing, but we're already onto the next one. and I think that's the way it should be.Laguna, Five-Week Builds, and Zero On-Call EventsVibhu [00:22:50]: Hey, I also just wanna point out, so for context, this was like a month ago. we found it in the tech report, so we just came in with, “Okay, new model's dropped. Haven't heard about it.” We wereEiso Kant [00:23:02]: Yeah, we're very used to doing this every few months.Vibhu [00:23:03]: We're, we're very much like, “ okay, look, it's like, on par with Kimi, DeepSeek, whatnot, the small ones, Gemma level. Oh, it's a very cool paper on what goes into building.” And then we hit this page, right? Like literally page two of tech report is, “This process allowed us to build the small model from scratch to delivery within five weeks applying the lessons”. And then I'm like, oh, this paper is not about here's a tech report of benchmarks and here's how many tokens it was trained on. Like for people that wanna dive more from what we're not gonna discuss on the podcast, it's all laid out here, right? FromEiso Kant [00:23:38]: YeahVibhu [00:23:39]: Custom software that agents can use to interface with training code, training data.Eiso Kant [00:23:45]: Yeah. Well, link the paper correctly, so yeah.Vibhu [00:23:47]: Yeah. All that stuff. read the paper here, but,Technical Report Principles and Streaming Training DataEiso Kant [00:23:50]: But I would like to. I love principles, and I think that is a good starting off point for maybe telling some stories. Maybe we can go one by one past the principles. I'll just call out that Dagster just got bought by a Prefect.Vibhu [00:24:01]: Yeah.Eiso Kant [00:24:01]: Isn't it fun? But yes, I'm very familiar with Dagster. just anything where like they trigger some story.Vibhu [00:24:07]: So, well, I would say, well, experiments code's obvious, but I think one of my favorite things is, I don't know where it is in here, but early on, and I still think this is the case a lot of foundation model companies, people prepare their training data sets, they get packaged up, then they get copied over to a training cluster distributed across all of the nodes, and then training starts.Vibhu [00:24:30]: And we looked at this like three years ago and we were like That makes no senseEiso Kant [00:24:36]: You lose so much time because the moment you have to rematerialize the data set, you have to make a change, you have to fix something, et cetera, you've got all this time of like repackaging it, right? Toca- tokenizing it, repacking it, moving it over to a cluster, then distributing it across the nodes. The bigger your clusters are, you start using fancy like torrent-like algorithms to like distribute your data. So why aren't we streaming data into training? Right? Something that's very common and like just basicVibhu [00:25:00]: Like just in timeEiso Kant [00:25:01]: Just in time, like good computer science like principle. And that was one of the first things that I think unlocked - the model factory. Because the moment you start thinking about, well, a training job, it doesn't matter if it's a big hero run or a small like, post-training experiment, consumes a certain number of tokens per second, right? And it's not a lot, right? From a like a data, moving data perspective. So we said, well, we have our training cluster, and then we've got like our AWS kinda setup where we can build these amazing big data pipelines. We can set things up. We use Spark underneath the hood, like all these things.Vibhu [00:25:36]: But when you say AWS, it's not actual AWS, it's your internal AWS.Eiso Kant [00:25:39]: It's our internal-- No, it's our internal like just running like our infrastructureVibhu [00:25:42]: Site web servicesEiso Kant [00:25:43]: Exactly. Our stuff running on like an AWS account or on like any hardware, right?Vibhu [00:25:47]: Yeah.Eiso Kant [00:25:48]: And so once we made that shift into I can stream data into training, all of a sudden you realize a lot of things unlock. Because now you don't have to wait for the whole data set to materialize.Immutable Data, Experiments as Code, and Scientific RigorEiso Kant [00:26:00]: You now all of a sudden when you're running data experiments about mixing data, it's a config. Because you've got these data sources that are coming in, and you just - we have this service called Blender that's in the report, where we then say, “Okay, for this run, I want 20% of this source, 10% of this source. I want this much, so many epochs of repetition. I want this to be, shuffled in a certain way,” and your training job can start while the rest of the data is even still materializing. also what it does is because all of this underneath-- So for us, we treated the data layer underneath as like an immutable data layer, and that was really important. Like experiments as code, immutable data layer means that you can always go back and understand literally down to the single token at which cursor it went in on which version of the code.Vibhu [00:26:47]: Yeah.Eiso Kant [00:26:48]: And it took us a I have to admit, like the first year of Poolside, we understood that engineering had to get great, But we didn't understand yet, that this is ultimately in support of like a good rigorous scientific progress. We were quite a - We were a very small number of people, so a lot of it was YOLO ideas and YOLO runs.Vibhu [00:27:08]: Yeah.Eiso Kant [00:27:09]: And we built great infra for the YOLO runs. But once we realized that we treated data as immutable and code as always versioned, and you could always track and trace every experiment end to end perfectly, you could repeat everything perfectly, right? You have perfect reproducibility. I can still reproduce runs from two years ago if I wanted to, right? It enables the scientific progress, like the scientific process, and I think that took us probably about a year and a half into the company to figure out. We also had some great hires, like our head of applied research, Nikolai, who joined us from Yandex, who'd been working on language models since like the early 2020s, I think brought that into the company of like, “Hey, we wanna have even more rigor.” And then once we kinda had the combination of like increasingly more capable platform that allowed people to do more, but had this immutability, we were able to start “Okay, every experiment is truly an ablation. We truly need to understand it.” And I think we became much more scientifically rigorous in the last couple of years, and the infra underneath enabled it. and then there's just fun stuff like, andVibhu [00:28:16]: Yeah, a lot of it's fun, like even just the, one, you share all the ablations, two, picking the data sets, right? There's like a random small paragraph in here where it's just like, “Oh yeah, training data, we have some, we have an auto mixer.” it trains eight small models, scales them up, picks the training data set. We don't even need to look at it. I'm like, “Wow, a lot of engineering rigor there.” And there's just, there's just a lot in here.Publishing Research and Giving BackEiso Kant [00:28:40]: Yeah, and it'- and look, and we wanna put out more. Like we, We treat writing papers as something that we haven't earned the right for yet for a long time. So you earn the right to spend time, publishing research once you're at the frontier, because until then, you're catching up, and every minute and hour in this industry matters. Like I obsess over it, not just the wall clock time from idea to result, but just general like time every day that we, waste is one that doesn't allow us to catch up. But in this case, we said, “Okay, we're gonna give ourselves.” I think we gave the team like three or four days while still doing their work, like give everything in there. And to your point earlier, if your stuff, it's easy to like put it out. And so there's so many more things that we wanna talk about over time, and we will definitely start doing. And as we earn more of the right, but also now have like added to our mission that we want more foundation model companies to exist, you'll see us like be way more proactive, and just trying to keep dropping some of those like things that we've learned along the way that can help others like speed up.Vibhu [00:29:40]: Which is the other cool side of this, right? It's, it's not like, back to your point, it's not just here's the benchmarks of our training. If you want to replicate, here's experiments of optimizers, data sets, post-training. you lay out a lot of it here alongside here's your system for how to do it? So it's, it's really like promotingEiso Kant [00:29:59]: No, thank youVibhu [00:29:59]: Other people can do the same.Eiso Kant [00:30:00]: And by the way, I also wanna make clear, right, we have been incredible-- Like we've taken a lot of advantage of the fact of all the open research that others have published, Right? And you mentioned, the Chinese labs, and we I think it's important that there's, from every country and every culture and background, including like Western companies like us, there's different models that come out that people can choose to trust. But I think we do have to give credit where credit's due, right? The incredible Chinese lab have done an amazing job at sharing their research, and we have definitely like been on the receiving end of taking advantage of that. So when you're on the receiving end of something coming to you, I think it's, you also have an obligation to give back.Swyx [00:30:39]: Do you have a favorite or underrated Chinese lab that you wanna shout out? Everyone shout outs DeepSeek.Chinese Labs, Zhipu, and PersistenceEiso Kant [00:30:44]: That's a good question.Swyx [00:30:45]: Moaan obviously for Therapsi. Yeah.Eiso Kant [00:30:48]: Yeah, look, I think, I think obviously everyone's been talking about Zhipu lately, with 5.2. I think what most people don't realize is when they started.Swyx [00:30:59]: Yeah.Eiso Kant [00:30:59]: Right? They started years before ChatGPT.Swyx [00:31:02]: They just rebranded. YeahEiso Kant [00:31:03]: And so, I've like, I remember how hard it was to work on these things Before the rest of the world got excited about it. And so I have an immense amount of respect for people, who were working on improving models when it wasn't the sexy thing to do, when believing in LLMs, was gonna get you ridiculed. I remember like back in 2016 when we were doing what we'd call, machine learning on code with some of these models. we would-- people would just laugh at us, like they'd be like, “This makes no sense. Like why are you wasting all these, like, millions of dollars on trying to figure this out?” And so I would say they're probably the one that, I think deserves a shout-out, not just because their latest model is very good, but because they fought to get here. And I think, I think every foundation model company it takes time to get here, right? It took us three years to get to the model that we're, that we're now gonna be releasing. and now the time in between the models is coming, is counted in weeks. It's no longer counted in months or years. But this stuff's hard. and if we can make it a little bit easier for the next person, like we should all do so. Because if we don't do so, we're, we've got a small window before models are really impacting recursive self-improvement to a level where catching up otherwise might become unfeasible. And we should try to, in that window, encourage as many labs or however we wanna call them, like to start. And so one of my currentEiso Kant [00:32:36]: Mission, but qualm is like I wanna encourage whoever is a researcher right now who thinks they can tackle this to go and leave and become my competitor.Eiso Kant [00:32:45]: Like start another foundation model company because I think we need it. I think otherwise we're not gonna be in the world where, I don't want to just be the fifth or the sixth company that wins. I wanna look at a world where there's lots of choice.Starting a Foundation Model CompanyVibhu [00:32:57]: What else do people not see in starting a foundation model? it's, there's a lot of compute, there's a lot of capital required, a lot of compute. You lay out model factory and how to do the training, but there's a lot there, right? That's,Eiso Kant [00:33:10]: Well, look, it's, I in turn-- this is an oversimplification, and I always asterisk it with that because it can land a little bit the wrong way in people's minds. But I think you can sum down, And I saw it, 95% of model building to just doing, you're just doing two things. You're improving data or you're improving compute efficiency. And I know that feels like an oversimplification for the incredible, like, Gifted and skilled work people do. But if you really look at it, like what are we doing? We are looking at data, we're generating new data, we're improving data. and the only way to do that is to look at the data, right? That's a big part of foundation model building. And on the other hand, we come up with these incredible breakthroughs in inference, in architecture, and new attention mechanisms. But what are they really doing? They're bringing compute efficiency. Now, we have definitely had some breakthroughs over the years that allow for more model capabilities. But at the limit, if you could train a large enough model, right, like, and you had infinite compute, we probably-- if you had infinite compute, you'd be at AGI probably already tomorrow.Eiso Kant [00:34:12]: Right? Like it's not. And so, and let me say that infinite compute with infinite ability of much faster networking because networking ends up being more of the bottleneck than compute. But, so I do think that's, those are the main things. And to just realize that this is engineering. I think it's become more obvious, but I think for quite a few years, people have held foundation model companies and researchers and others on this pedestal of like you're doing incredible magic or rocket science, or only like, Nobel laureate physicists can do this. And don't get me wrong, there are some really hard problems that need to be solved, but a lot of the work that all of us are doing on a day Is not sitting down trying to solve a math theorem. A lot of the work that we're doing is just really doing the basics right, writing good code, looking at data, improving it, running experiments, looking at plots, trying to see like, hey, trying to shape our intuitions. And a lot more people could be highly capable researchers. and I think that's, it feels far for people to do so. But I've seen in our own company, we've seen engineers become researchers because the model factory allowed them to be, have a much lower hurdle of running experiments and trying things. And one of the guys on our team who started as an engineer building our agents is a legit reinforcement learning researcher now, making real progress. and that happened in the span of like six months. that would've not been what I think most people assumed was possible, a couple of years ago.Swyx [00:35:46]: Yeah. I think one of the interesting moments is when you can self-host, like, if in a programming language, like if you can compile the language in the language, the equivalent is can you use your own tools, right? You have the pool CLI, you have your own models. presumably you're not only using your own models. There's no way. But like, what's that percentage over time?Laguna S, Persistence, and Behavioral GainsEiso Kant [00:36:10]: This is the first model that we're releasing that is starting to meaningfully contribute to our own work. It's not a it's not state-art model yet. Fable and other, they're, they're very capable models, but Laguna S Is really interesting. I'm gonna pull up the quote. Peng Ming, one of our heads of applied research, said something, last week as the model came out about 10 days ago, much better than we had hoped for or expected. And he said, I have the feeling that a lot of the gains in Laguna S come not from more intelligence, but more from different behavior, more verification, less taking things for granted, not declaring victory early, and being way more persistent. And to be honest, those are more predictive than raw intelligence for success in human also to some degree. And this was, he wrote me this on 5th of July on a Sunday, and it's been burned in my brain ever since because the Laguna S model, as you'll see it and why it does so well on benchmarks and why it does so well in using it on a day basis, is that it's just incredibly persistent. It reasons a lot. I do call that out. We have work to do on making it more efficient. We have to work to do on offering different reasoning modes. But this is the model that has been able to do things that I never thought it could do. A hundred eighteen billion 8B active model, which is not that large. It fits on a DGX Spark and still runs at, thirty, forty tokens a second on a Spark, is able to solve Erdős 397 independently. It's able to do complex programming tasks. It's able to. I asked it this morning to make me a Fi scanner without using any external libraries on my Mac, and it's, like, figuring out, like, the core WLAN API by really persistently trying to understand it without access to the internet. And more, I love vibe checking. I've probably spent eight to ten hours a day with this model for the last ten days.Eiso Kant [00:38:05]: I'm not exaggerating. I was on my eleven-hour flight yesterday. I spent ten hours reading trajectories and traces and, like, of the model.Eiso Kant [00:38:12]: And what I take away from it is exactly what Peng Ming said. We are gonna be able to squeeze so much more out of smaller models than I think we had imagined in the industry because, yes, there's intelligence and larger models are more intelligent. Like, no doubt about it. We should continue to scale up. but the behaviors of being really persistent, of being able to backtrack when you're wrong, of, like, understanding how to interact with your environment show us that we can get a lot more out of it. And this, for me, has created a bit of a Question in my mind the last couple of days. If you think about where we're using models today, right? We are using models, say, for knowledge work. Represents twenty-five percent of the global economy, twenty-five trillion dollars of work.Eiso Kant [00:39:00]: As we scale up models and they become more intelligent, we are excited about using them more and more for pushing the frontier of science.Small Models, Knowledge Work, and CommoditizationEiso Kant [00:39:08]: And if you look at the frontier of science, like true breakthroughs in science, they have been linked, they are linked to more intelligence in many places. Einstein figuring out general relativity is able to bring ideas together that other people would have not brought together. And I think one of the many dimensions of intelligence is the ability to do that, and it's something we clearly see that as models get larger and more capable, they're able to pull more ideas and threads together that a smaller model wouldn't be able to.Eiso Kant [00:39:36]: And we're starting to see examples of that in medicine and, like, in bio and other things. But if you think about the majority of knowledge work that we do, and it includes building software. I'm a software developer at heart first and foremost probably, although I probably can't say it that much anymore as I don't write production code in years, is that what makes us good is our persistence. It's our ability to encounter a problem and backtrack and say, “I need to go figure out this bug. I need to go research this. I need to go look at the documentation. I need to, like, try different, five different ways to see, like, if I can solve it.” But it is not necessarily bringing three ideas together from radically different fields. And so if we are now seeing, and I think Laguna S is an example, that we are able to make a relatively small model much more capable than I had definitely predicted or any previous, like, benchmarks had shown for any model remotely this size or even larger, At least on coding tasks, that it's because of the behaviors. And so now the question I have, and I don't have an answer, it is I know at the limit, so infinite model size, right, extremely large model, and the cost of that model is gonna be very expensive to run. We know this, right? So larger model ROI.Eiso Kant [00:40:52]: So I know that at the very limit, I'm not gonna use the world's largest model one day, quadrillion parameter, whatever crazy, like, scale we scale up, to do a basic coding task. Already today, I'm starting to size down for certain tasks.Eiso Kant [00:41:07]: So it means that there is an optimal. It means there's some curve that goes as we go up to model size for knowledge work, at some point we're at the peak, and after that, the return on investment of using a bigger model, just doesn't make sense.Eiso Kant [00:41:22]: Now, I think the question is, before I would have thought that peak was extremely very far away.Eiso Kant [00:41:30]: This model for me is the first sign that Maybe that peak is At a trillion, five trillion, ten trillion. Maybe we can just squeeze way more out of these models. I'm no longer thinking that we need two or three orders of magnitude on the largest models to be able to, solve knowledge work, the accounting, the legal, the code that we write. And so if that holds true, It is an argument for the commoditization of models. It's an argument that open source can win and, like, succeed in this world. And now it's of course a self-serving argument and it's a hopeful argument, but theoretically at the limit it works. We just have to go discover in the next couple of years of how much more we can squeeze out. Now, I do want to put a big asterisk. This does not mean I'm against scaling models. I think we ultimately only succeed if we scale our models as large as our competition. I do not like. I think we should not put our head in the sand and say we're gonna be king of open source small models. I think that's, It's a out. It's trying to be king of your own kingdom, but not realizing what the rest of the world's doing. All of us rather use a smarter, faster, more model. It's a sign of hope. And so I don't wanna overly state this is a good model. We have a long way to go to get to the state-art. But what hopefully people take away when they use this model is that the behaviors inside of it are what push it to be far more capable, less than necessarily the number of parameters.Pre-Training, Mid-Training, and RL Moving EarlierVibhu [00:43:03]: Is that mostly post-training? LikeEiso Kant [00:43:05]: YesVibhu [00:43:05]: Right.Eiso Kant [00:43:06]: It's entirely post-training.Vibhu [00:43:08]: Are we done improving anything on training? Is, like, training done?Eiso Kant [00:43:12]: No.Vibhu [00:43:12]: Okay.Eiso Kant [00:43:13]: SoVibhu [00:43:13]: I just wanted to cover training, and then we go post-trainingEiso Kant [00:43:15]: Training is not done. I mean, look, there's a part of training of just dealing with skill, right? Every new order of magnitude of model skill, you are going to get new things you gotta solve for. That'- but those are ultimately, engineering challenges.Eiso Kant [00:43:31]: I have a, I would say, a not commonly held opinion that reinforcement learning Will move earlier and earlier into training.Vibhu [00:43:42]: Yeah, training.Eiso Kant [00:43:44]: Not even training. Like training today, right, is, like if you look at - So we've been working on this for years already. and I think the best-- I think the first time we saw it out in public was the DeepSeek Zero paper. this is a year and a half ago, I think, if I recall correctly. where, you can Very early on in a model as it starts capable of being able to use language, et cetera, induce reasoning. and so the question that I have is like, we have this- we have the dataset that's the web. and the web, I think we could arguably say probably has The totality of humanity's knowledge somewhere encoded in different places. It's a huge variance degree of quality, from garbage data, and like once you look at training data, you really get humbled of like what the web is, to like, the most greatest scientific papers and best blog posts and like, best transcripts and whatnot.Eiso Kant [00:44:39]: And so now What we are trying to figure out, and have been doing a lot of work on, and it's a place where maybe not as open as we're on other things, but we will become more over time. we've been spending a couple of years really doing research on how can we turn the web into not just next token prediction, but into a way to teach the model to think earlier in its training. and I think there's a huge amount of gold to be found there. I think we are right now in, we've got some drugs in the industry. One of the drugs is distillation. Another drug is, more environments. Like, and they're great, and they make us feel good, and they make the models better, and like we're all addicted to them, and we'll use them, right? in various different ways. and but ultimately, I think we are still barely squeezing out of the web what we should be getting out of the web.Eiso Kant [00:45:33]: I think just next token prediction during training is not enough.Eiso Kant [00:45:36]: AndVibhu [00:45:38]: YeahEiso Kant [00:45:38]: I think we'll see some very interesting things still happen. and that RL in post-training to induce behaviors, to improve things, like I think - the whole world knows how to do this now. I think we're, we're scaling it up. Everyone is. But I wonder if we need to go as far as we're going today with environments. I'm not sure yetVibhu [00:46:01]: You mean we're going too far?Eiso Kant [00:46:02]: I'm, I'm not sure if the path to AGI is justVibhu [00:46:06]: Is more environmentEiso Kant [00:46:07]: More environments.Vibhu [00:46:08]: It seems like a never-ending, “Okay, I want instruction manual for this table, right? Am I gonna environment out building furniture? Or are we just gonna tail end like we need some general solution?”Eiso Kant [00:46:19]: I think there is, I think there's an ability to generalize more from the web. but I also am very encouraged, like when I look at Laguna S and, which is post-training is, well, is the big impact there. and I see like, oh, wait a second, just by making some of these behaviors much better, we're able to get so much more out of it. It just changes a little bit the way you think about intelligence.Vibhu [00:46:40]: Yeah. The analogy people draw often is the RL phase is where you don't learn as much new knowledge. You shiftEiso Kant [00:46:46]: Yeah.Vibhu [00:46:46]: Yeah. So, you shift distribution, and you can have it reason towards what you want. on your point about training, a lot of training is still just continue training in a domain, say medicine, then you do RL. So still justEiso Kant [00:47:00]: It's just better data, right? Like, I mean, training, ooh, I like how we invented this word. Like it's effectively just like,Vibhu [00:47:06]: Second phaseEiso Kant [00:47:07]: It's the second phase of training With like a really dumb way to do a curriculum. But like ultimately, what you'd want is a curriculum from token zero to token 30 whatever or 40 trillion tokens that really truly is the optimal curriculum for the model to learn. But training is essentially a stage curriculum on the web because we do not have to compute, And, effectively to try to ablate the perfect curriculum, right? And so I'm pretty sure that you'll start to see people talking soon about some other term, and there's two or - ‘cause now we do this, right? We talk stage two and stage three and stage four training and like. But ultimately, all we're doing is we're trying to assign a curriculum to the web data that we have to allow the model to learn better. I think at some point, as things get compute, as models get cheaper to run, as the next generations of compute, this will become more of a continuous spectrum. I also think the reason, by the way, you have training and like stage two and stage three is organizational, Right? It'- this is, I think, a thing where-- that we really try to avoid with the model factory is like Training exists because there's a training team now, right? There's people, or like people in training decide to focus on like a training effort. but what you really want is engineering and scale of experiments that allows for a much more continuous spectrum that you don't, you have infinite stages. Now, we're not there. Compute's not there. Organization design is not there for it yet. but I think we'll get there. we'll look back on a couple of years and be like, “Oh my God, it was so cute that we did our training data like this in such a like naïve way. Like we barely ordered it. We didn't really do a good job at likeCurriculum, Auto Research, and New ObjectivesVibhu [00:48:48]: The building that curriculum will get you that in the industry.Eiso Kant [00:48:51]: And I'll confirm that, when I talk to some researchers that this is a lot of the focus now is like how does training change and what is the next objective other than, next token prediction. I assume you don't have the answers, but you have some ideas.Vibhu [00:49:02]: We have some ideas. We're not ready to talk about it yet.Eiso Kant [00:49:05]: Yeah.Vibhu [00:49:05]: We've been working on them for years, and I think that's the one thing that's also like you asked earlier about, like what's not obvious about building a foundation model company is that you are constantly balancing the table stakes work, the recipe worksEiso Kant [00:49:19]: Yeah.Vibhu [00:49:19]: Versus like your, my crazyEiso Kant [00:49:22]: Pure researchVibhu [00:49:22]: Breakthrough.Eiso Kant [00:49:22]: Yeah.Vibhu [00:49:22]: Pure research and finding that balance and adjusting the percentage to it based on where you are in the race is really important.Eiso Kant [00:49:31]: I mean, so like, this is a nice way. I was gonna bring up auto research at some pointVibhu [00:49:35]: YesEiso Kant [00:49:35]: As another Andrej invention, or coinage, which is like, I honestly, like how many objective functions can there be, right? Like just try 1,000 of them, set it running, whatever.Vibhu [00:49:47]: Man, it's alsoEiso Kant [00:49:48]: Like what you're looking for. You're looking for loss curves like that, likeVibhu [00:49:51]: It's also a thing people take bets on, right? When you say more Neo labs, you're doing a version of we'll do foundation models, scale them up, next token predictors. A lot of other Neo labs that we see want to take a completely different approach, right? At some level, you're right. It's all, compute efficiency, and that's the net objective. But some are okay, different architecture, like vastly different amounts of compute spend. So some are different. They're not justEiso Kant [00:50:19]: YeahVibhu [00:50:19]: They're like, 99% not balancing, here's the vanilla and scale up. They're 99% on, here's novel research that'll change everything.Eiso Kant [00:50:27]: And I think, Luke, I think you. It depends when you started as well, right?Pure Research vs. Table StakesVibhu [00:50:30]: Yeah.Eiso Kant [00:50:30]: When we started, like the novel thing we did was reinforcement learning on code. No long- that's no longer novel by far, but we were like, - that's where we obsessed over when no one believed in RL. So you have to when you start the company, you have to have your own idea. You have to have something that's different that allows you to speed up, right? For us, it was RL to LLMs that later became common, like, Knowledge. But in the beginning, it wasn'tVibhu [00:50:53]: It's cool. this was like your original 2023 blogEiso Kant [00:50:57]: YeahVibhu [00:50:57]: Of purpose.Eiso Kant [00:50:58]: Yeah.Vibhu [00:50:59]: And like you do lay it all out here.Eiso Kant [00:51:01]: We laidVibhu [00:51:01]: The blog is pretty underrated, right? The whole RL on code was very early on.Eiso Kant [00:51:06]: Very early. And even we had to argue with people, like we say here things like to push beyond current capability, to train your own foundation model. We had to argue with people that it mattered that you had your own like, base model. you can fine-tune your way to success, right? major capabilities emerge from training a base model made accurate and useful during fine-tuning.Vibhu [00:51:23]: Which like, for perspective at the time, we knew closed models, OpenAI, Anthropic were huge. The open models we had were like Mistral 7B, a 30B, a 70B.Eiso Kant [00:51:35]: When weVibhu [00:51:35]: YeahEiso Kant [00:51:36]: The date on this thing is wrong. When we published this, it was April 2023. I think this was justVibhu [00:51:42]: YeahEiso Kant [00:51:42]: Happened on a migration, probably found it on archive.org.Vibhu [00:51:45]: Mistral.Eiso Kant [00:51:46]: Mistral had started, we started on the same month, right?Vibhu [00:51:49]: Yeah.Eiso Kant [00:51:49]: So this wasn't even, there was only, I think, Llama out at the timeVibhu [00:51:52]: SnellEiso Kant [00:51:52]: And that's it, right? And so, but I agree. I think we wan

Angola will seine Wirtschaft breiter aufstellen. Neben dem Ölsektor sollen jetzt auch andere Branchen gefördert werden, etwa der Tourismus. Denn bislang dominiert der Erdölexport und von dem profitiert nur eine kleine reiche Elite. Von Julian Hilgers, Anna Hoffmann-Kwanga und Yana Adu www.deutschlandfunkkultur.de, Weltzeit

Deutschland gibt jedes Jahr 80 Milliarden Euro für Erdöl und Erdgas aus. Für Jan Lozek nichts anderes als eine unsichtbare Steuer, denn das Geld fließt in die USA, den Nahen Osten, aber auch Norwegen. "Diese Summe ist weg. Für immer. Die kann man nicht reinvestieren", sagt der Chef des Risikokapitalgebers Future Energy Ventures. "Das ist ein Wettbewerbsnachteil für den Standort Deutschland und Europa." Die Lösung ist Lozek zufolge die Energiewende. Der Staat sollte kein Geld für fossile Brennstoffe ausgeben, sondern Unternehmen und jedem Haushalt bei der Anschaffung von Batteriespeicher, Wärmepumpe und E-Auto unterstützen. "Erneuerbarer Strom und erneuerbare Wärme sind wesentlich preiswerter als fossile Energie oder fossile Wärme", sagt der Investor. "Aber viele Menschen haben das Geld dafür nicht." Diese Anschubfinanzierung muss ihm zufolge keine Subvention und auch kein zinsloser Kredit sein. "Wichtig ist, dass man die Rückzahlung mit den Einsparungen verbindet", sagt er. Gelingt das, sei der Pfad klar abgesteckt: Bereits in zwei Jahren wird Strom günstiger als im alten System sein - und Deutschland wettbewerbsfähiger. Außerdem im Podcast: # Das fossile Importmanagement der schwarz-roten Regierung # Würde Jan Lozek als Risikokapitalgeber derzeit auf Deutschland setzen? # Wie bekommt man die Interessen von 27 EU-Staaten unter einen Hut? Gast: Jan Lozek, seit 2016 Gründer und Geschäftsführer von Future Energy Ventures. Zuvor hatte er leitende Positionen bei RWE, der RWE-Tochter Innogy und Eon inne. Moderation: Clara Pfeffer und Christian Herrmann Wir freuen uns über Feedback und Zuschriften: klimalabor@ntv.de Ihr möchtet uns unterstützen? Dann bewertet das "Klima-Labor" bei Apple Podcasts oder Spotify Das Interview als Text? Einfach hier klicken. Dieser Podcast wird vermarktet von Julep Media: sales@julep.de Wir verarbeiten im Zusammenhang mit dem Angebot unserer Podcasts Daten. Wenn Sie der automatischen Übermittlung der Daten widersprechen wollen, melden Sie sich hier: datenschutz@julep.de

Weil der Iran die Strasse von Hormus blockiert, suchen viele Golfstaaten nach alternativen Routen für ihre Erdölexporte. Vor allem Saudi-Arabien setzt zunehmend auf andere Handelswege. Doch es gebe nicht wirklich eine Alternative, die sich rechne, sagt Nahost-Korrespondent Thomas Gutersohn. Alle Themen: (00:00) Intro und Schlagzeilen (01:37) Was für Alternativen zur Strasse von Hormus gibt es? (08:39) Nachrichtenübersicht (13:33) ICC-Chefankläger könnte diese Woche abgesetzt werden (18:56) Knickt die Sportwelt bei der Russland-Frage ein? (23:03) Chinas wachsender Einfluss in Kirgistan (29:23) Mit KI und Drohnen gegen die Quagga-Muschel

Műsorunkban Tóth Andrással beszélgetünk, aki 71 évesen keresztelkedett meg. Vajon mi indította erre?  Várjuk teológiai, lelki, morális, társadalmi vagy személyes dilemmákkal kapcsolatos kérdések, nehézségek, bizonyságtételek előzetes megosztását is az erdos.eszter.marta@gmail.com e-mail címre. Szeretettel várom Önöket: Erdős Eszter református lelkész, szerkesztő-műsorvezető

A short rant based on my new article this week, "The Database Is Not the Data Model""In discussions with data practitioners, I keep seeing the same confusion. Someone pulls up a DDL file, a folder of dbt or SQL, or an ERD reverse-engineered from a Postgres instance and says: “Here's our data model.”Not to be pedantic, but that's a schema. Schemas are great. But data modeling is more than just schema design."Let's dive into the difference in this podcast and the article.Article: https://practicaldatamodeling.substack.com/p/the-database-is-not-the-data-model

Eine grosse Vielfalt Früchte ist im Juli erntereif: Kirsche, Aprikose, Erd- und Himbeere, Heidel- und Johannisbeere und auch erste Brombeeren sind schwarz und süss. Die Fruchtpflanzen müssen im Sommer fachgerecht gepflegt und geschnitten werden, um auch im nächsten Jahr eine schöne Ernte abzugeben. Früchte an Kirsch- und Aprikosenbäumen schützen · Schattiernetze anbringen: Bei Spalierbäumen an Wänden senken Netze die Temperatur und schützen vor Sonnenbrand. · Beschädigte Früchte entfernen, damit sich keine Schädlinge einnisten und vermehren und keine Fäulnisherde bilden. · Kirschessigfliegen-Fallen im Innern der Bäume aufhängen und Lockmittel regelmässig erneuern (s. Merkblatt Kirschessigfliege). Bewässerung und Bodenschutz bei Fruchtbäumen · Aprikosenbäume reagieren während der Reifezeit empfindlich auf Trockenheit. Deshalb mindestens einmal kräftig giessen. · Baumscheiben mulchen: Eine 2–3 cm hohe Schicht aus Rasenschnitt, Schilfmulch oder Hanfeinstreu hält den Boden länger feucht und schattiert. Baumschnitt · Sommerschnitt verschieben: Wegen der aktuellen Hitze sollte man mit dem Sommerschnitt (z. B. dem Entfernen von Wasserschoss) bis in den August warten. Die Triebe spenden derzeit wichtigen Schatten. · Säulenbäume zurückschneiden: Bei Säulen-Kirschbäumen müssen die Seitenzweige jetzt auf etwa 15 cm eingekürzt werden, falls das noch nicht erledigt wurde. Hitzeschutz für Topfpflanzen · Töpfe beschatten: Auf Balkonen und Terrassen überhitzt die Erde in den Kübeln schnell. Deshalb kleinere Blumentöpfe davorstellen oder die Haupttöpfe weiss anstreichen, damit das Sonnenlicht reflektiert wird. Wer zu viele reife Früchte hat oder in den Ferien ist, kann Apps wie «Mundraub» oder «MeinObstgarten» nutzen, um Erntehelfer zu finden oder Pflegepatenschaften zu schliessen.

In this Diving Deep episode, Dr. Robert Pearl and Jeremy Corr explore what a breakthrough in mathematics can teach healthcare about generative AI, outdated assumptions and the future of medical practice. The conversation begins with Paul Erdős, the brilliant Hungarian mathematician whose unit-distance problem stumped experts for 80 years. Generations of mathematicians tried to prove Erdős' conjecture using the tools and assumptions of geometry. Then OpenAI announced that one of its models had found a different path, one that challenged the assumptions of humans. For Pearl, the lesson for medicine is not about math. It is about the danger of staying trapped inside old models of thought. American healthcare faces persistent crises in quality, access and affordability, yet the proposed fixes focus on small adjustments to a system that has failed for decades. Pearl argues that generative AI will not reach its full potential if clinicians continue to use it merely for administrative tasks, documentation support or occasional diagnostic assistance. Instead, he says, medicine must be willing to abandon three longstanding fallacies. The second half of the episode shifts the discussion from assumptions to urgency. Pearl compares generative AI in medicine to the sudden invention of cars in a world where people could only travel by foot, bicycle or horseback. If faster transportation could save far more lives than it cost, society would not wait until cars were risk-free before building roads and teaching people to drive. He argues medicine should think similarly about GenAI: not as a finished product, but as a rapidly improving tool that could save lives if implemented wisely and quickly. In the episode, Pearl highlights three areas where GenAI could have immediate impact. In each case, he returns to the same point: the greatest risk is not only that AI might make mistakes, but that medicine will ignore the harms already caused by today's failures. For more, tune into this month's episode and check out the links below. Helpful links: What GenAI's Math Breakthrough Means For Medicine (Forbes) GenAI Is Ready To Change Medicine. America Isn't Prepared (Forbes) Monthly Musings on American Healthcare (RobertPearlMD.com) * * * Dr. Robert Pearl is the author of ChatGPT, MD: How AI-Empowered Patients & Doctors Can Take Back Control of American Medicine. Fixing Healthcare is a co-production of Dr. Robert Pearl and Jeremy Corr. Subscribe to the show via Apple, Spotify or wherever you find podcasts. Join the conversation or suggest a guest by following the show on X and LinkedIn. The post FHC #220: How AI is challenging old assumptions in medicine appeared first on Fixing Healthcare.

Angesichts der Krisen auf der Welt wird verstärkt über den Ersatz von Erdölprodukten gesprochen. Biodiesel und Bioethanol gehören dazu. Eine Entwicklung, über die sich der Chef der Firma Verbio, Claus Sauter, freut. Von Andreas Oppermann

Achtung: Dies ist Folge 1 von 2, Teil 2 erscheint in einer Woche. Worum geht's? Worum geht's? Texas Instruments ist heute vor allem als Zulieferer von Halbleitern bekannt, doch Ende der 70er Jahre baute das Unternehmen aus Dallas einen eigenen Heimcomputer: den TI-99/4. Ein schwarz-silbernes Gerät mit 16-Bit-Prozessor, das schon 1979 auf den Markt kam und damit technisch seiner Zeit voraus war. Oder zumindest hätte sein können. Henner und Chris zeichnen nach, wie es dazu kamm und warum der Weg dorthin von Anfang an mit problematischen Entscheidungen gepflastert war. Sie beginnen bei den Ursprüngen von Texas Instruments, das als „Geophysical Service Incorporated“ mit seismischen Messungen zur Erdölerkundung anfing und sich über die Halbleiterfertigung zum Technologiekonzern entwickelte. 1958 erfand Jack Kilby dort den integrierten Schaltkreis – eine der folgenreichsten Erfindungen der Technikgeschichte. TI baute darauf Taschenrechner, Sprachsynthesechips und 1978 den Speak & Spell, einen der ersten Consumer-Artikel mit digitaler Sprachausgabe. Zu Wort kommt dabei auch der ehemalige TI-Ingenieur Karl Guttag, der damals an mehreren Mikrochips bei Texas Instruments gearbeitet hat und die verworrene Entstehungsgeschichte des Heimcomputers aus erster Hand kennt, inklusive der internen Rivalitäten zwischen den Abteilungen, dem gescheiterten Versuch einer eigenen Heimcomputer-CPU und der legendär schlechten Entscheidung, die gesamte Entwicklungsabteilung in die texanische Kleinstadt Lubbock zu verlegen. Dies ist der erste Teil einer zweiteiligen Folge. Spiele, Marktgeschichte und das Ende von TIs Heimcomputer-Ambitionen folgen in Teil 2. Produktions-Credits: Sprecher, Redaktion: Henner Thomsen, Christian Schmidt Audioproduktion: Matthias Kuhlmann, Christian Schmidt Titelgrafik: Johannes DuBois Vielen Dank an Karl Guttag und Paul Urbanus.

In this episode:

Die Aufhebung der Sanktionen auf iranisches Erdöl ist einer der Kernpunkte des Rahmenabkommens zwischen der USA und dem Iran. Wie die USA heute veröffentlichten, habe sie ihre Sanktionen auf den Handel mit iranischem Erdöl bis 21. August vorläufig aufgehoben.

Über hundert Tage lang war die Strasse von Hormuz blockiert. Doch während Analysten einen dramatischen Preissprung befürchteten, blieb die globale Wirtschaft verschont. In dieser Podcast-Folge erklärt NZZ-Pro-Redaktorin Catherine Bosley die Hintergründe. Anfang März griffen die USA und Israel Iran an. Als Reaktion blockierte Iran die Meerenge bei Hormuz - der Ölpreis stieg an, Analysten rechneten mit dem Schlimmsten. Drei Monate später lässt sich feststellen: Länder wie Indien und die Philippinen haben mit Knappheiten bei Brennstoff zu kämpfen. In den Industrieländern in Europa und Nordeuropa ist ein unkontrollierbarer Ölschock aber  ausgeblieben. Einerseits habe es China geschafft, die Öl-Importe drastisch herunterzufahren, sagt Bosley. Andererseits hätten die USA in den letzten Wochen mehr Erdöl exportiert, als vor dem Krieg. Auch die strategischen Notreserven der internationalen Energieagentur halfen, die Notlage zu überbrücken. Heutiger Gast: Catherine Bosley, Redaktorin NZZ Pro Host: Alice Grosjean Redaktion: David Vogel Die Analyse von Catherine könnt ihr auch in der NZZ nachlesen: https://www.nzz.ch/pro/dank-china-und-den-usa-ist-die-ganz-grosse-energiekrise-ausgeblieben-doch-fuer-entwarnung-ist-es-noch-zu-frueh-ld.10011338 Hier findet ihr weitere Artikel von Catherine zum Thema Geoökonomie: https://www.nzz.ch/impressum/catherine-bosley-ld.1918458

Wir stecken mitten im Wandel von der Erd- in die Luftepoche – markiert durch die seltene “Barbobas”-Konstellation. Der Sommeranfang lenkt den Blick auf die eigene Wurzel, während Mars ab dem 28. Juni wie ein schnüffelnder Hund durch die Zwillinge zieht und die Energie zerstreut. Der Vollmond entlarvt den Verlust echter Sprache, der rückläufige Merkur wird zur Einladung einer Überarbeitung und Jupiter im Löwen stellt die große Frage: starke Persönlichkeit oder aufgeblähtes Ego... Lebensfreude oder Gier? MUSIK

A hatalom és a hatalomnak alárendeltek viszonyáról, felszabadulási lehetőségekről és az emberi test csodálatos mivoltáról is szól Tompa Andrea pár hete megjelent, hatodik regénye, a Kiváló testek. A Ceaușescu-korszak Kolozsvárján, az erdélyi magyar értelmiségi közegben játszódó regényben egyéni és közösségi útkeresések, besúgók és besúgások, vágyak és megalkuvások között bolyonghatunk, így adta magát, hogy mindezekről a kérdésekről beszélgessünk Tompa Andreával a Nem rossz könyvek évadzáró epizódjában. A tartalomból: 00:00 Vendégünk Tompa Andrea, akinek pár hete jelent meg legújabb regénye. Milyen érzés útjára engedni egy könyvet? És hogyan születik meg egyáltalán egy regény? Az elején mindig egy fojtogató fekete lyuk van. És a vágy az olyan irodalom iránt, amiben nincsenek emberek. A drámák, 07:30 Kézzel írt nagyregények, és amikor zaklatottá válik a kézzel írt szöveg. Test és szellem kapcsolata. 15:00 Erdélyi magyar értelmiségiek a rendszerváltás előtt, és a besúgók köztük. A gyanakvások és az állandó meglepetés, ami kíséri az ügynökkérdést. 22:30 Hogyan lehet megalkotni úgy egy történetet, amiben mi, sokan vagyunk, és mi csinálunk valamit egymással? Ahhoz, hogy láthatóvá váljanak az elnyomás vagy a szolidaritás alakzatai, sok ember kell. 30:15 Testek ellenőrzése és a testek felszabadulása. Amikor nem az a kérdés, hogy ki vagy, hanem hogy mit csinálsz. 39:11 Ügynökakták megnyitása, bármit is jelentsen a megnyitás. És lehetnek-e az akták művészet tárgyai? Hogyan lehet kifordulni a megfigyelők nyelvéből és perspektívájából? 43:30 Jelen és múlt viszonya az írásban. És a közelmúltból a fotó a gyűrött ágyról és a tánc a választás után. 48:35 Hogyan kapnak nyelvet a testek? A tánc, ami táncol bennünk. És ki kéne hirdetni, hogy kevesebb pszichológiát, és több filozófiát és szociológiát az irodalomba. 58:10 Három könyv Tompa Andrea ajánlásában: David Graeber - Bullshit munkák, Hannah Arendt - The Human Condition, Rüdiger Safranski - Idő. See omnystudio.com/listener for privacy information.

Claude Fable 5 d'Anthropic désactivé sous pression, OpenAI invalide une conjecture d'Erdős, Microsoft présente ses modèles MAI et Majorana 2. SpaceX explose les records en Bourse, l'IA alimente la dette des géants de la tech, les data centers spatiaux deviennent une piste industrielle, et les annonces Xbox/Nintendo saturent déjà les plannings de joueurs.  Me soutenir sur Patreon Me retrouver sur YouTube On discute ensemble sur Discord Fables de la quinzaine Maître Dario, sur son arbre perché, avait un modèle de langage, Maître Trump, par un rapport contrarié, lui fit alors ce chantage. Vibe mathémating : GPT aime bien les jeux Erdős. Joli moi de MAI : c'était aussi la Build de Microsoft. Culture pub : parce que Claude Code est un Netflix comme les autres. La bulle voulait être aussi grosse que le bœuf. Projet Dernière Chance IPO griffes : SpaceX fait le casse du siècle. Et tout ça grâce à xAI, AI1 et Starship. Que des valeurs sûres. En matière d'espace, la Chine ne manque pas de sail. Le Donut aurait finalement un fourrage crémeux. Jeux vidéo Xbox Showcase : 25 ans, 25 jeux et un avenir heureux ? Nintendo Direct : du neuf avec du vieux. Summer Game Fest : un show Spyrotechnique. Participants Une émission préparée par Guillaume Poggiaspalla Présenté par Guillaume Vendé

Műsorunkban Bakó Zita református gyülekezeti diakóniai gondnokkal beszélgetünk, aki elismert HR-es munkája mellett vállalta a szolgálatot. Vajon mi indította erre? Hogyan szolgálhatjuk Istent különleges elhívásokban és a hétköznapokban? Várjuk teológiai, lelki, morális, társadalmi vagy személyes dilemmákkal kapcsolatos kérdések, nehézségek, bizonyságtételek előzetes megosztását is az erdos.eszter.marta@gmail.com e-mail címre. Szeretettel várom Önöket: Erdős Eszter református lelkész, szerkesztő-műsorvezető

Terence Tao was a child prodigy, and became a mathematics professor in the United States at age 24. In his early 30s he won the Fields Medal, known as 'the Nobel Prize of Mathematics'. Tao is considered one of the greatest living mathematicians, in part because of the breadth of his contributions to the field – from finding new patterns in prime numbers to solving several of the "unsolvable" Erdős problems. On Monday, the King's Birthday, Tao was awarded Australia's highest civilian honour, the Companion of the Order of Australia. In this bonus episode of The Briefing, Terence Tao, AC, speaks with Natarsha Belling about the pleasures of solving problems, and how maths makes the world a less scary place. If you want more Terence Tao, one of the YouTube channels he mentions is 3Blue1Brown, and Tao is featured on an episode. Follow The Briefing: TikTok: @thebriefingpodInstagram: @thebriefingpodcast YouTube: @TheBriefingPodcastSee omnystudio.com/listener for privacy information.

Mai vendégem Bozsoky Szabolcs Zoltán, erdélyi származású magyar futballedző, testnevelő tanár és sportmenedzsment szakember, aki immár több mint 12 éve Kanadában él és dolgozik a labdarúgás világában.   A beszélgetés a Torontói Magyar Fesztiválon, a Hungarofesten készült, ahol Szabolccsal nemcsak a fociról, hanem magyar identitásról, kanadai újrakezdésről, sportkultúráról, közösségről és életútról is beszélgettünk.   Szabolcs Erdélyből indult, Romániában született, magyar állampolgárként érkezett Kanadába, ma pedig kanadai állampolgárként dolgozik vezetőedzőként. A beszélgetésben elmeséli, hogyan jutott el a testnevelő tanári pályától a kanadai futballrendszerig, milyen volt 100 kilónyi csomaggal új életet kezdeni, és mit jelent számára az, hogy büszke magyar emberként dolgozhat Észak-Amerikában.   Szóba kerül a kanadai és európai futball közötti különbség, az utánpótlás-nevelés, a szülők szerepe, a gyerekek sportolási lehetőségei, a Toronto FC, az MLS, a kanadai profi bajnokság, valamint az is, hogy milyen fejlődésen ment keresztül a kanadai labdarúgás az elmúlt években.   Szabolcs jelenleg a University of Toronto Mississauga férfi csapatának vezetőedzője, emellett komoly szerepet vállal a kanadai magyar származású fiatal tehetségek támogatásában is. A célja, hogy minél több magyar gyökerű sportolónak segítsen lehetőséget találni, akár Magyarországon, akár a nemzetközi futball világában.   Beszélünk arról is, hogy mit jelent a CONCACAF A edzői diploma, miben hasonlít vagy különbözik az UEFA rendszerétől, és miért különleges, hogy Szabolcs magyar származású edzőként ezen az úton is komoly szakmai elismerést szerzett.   A beszélgetés végén egy teljesen másik oldala is előkerül: a zene. Szabolcs gyerekkora óta dobol, magyar, román és angol zenekarokban is játszott, több hivatalosan kiadott lemezen szerepelt. Így a futball mellett arról is beszélgetünk, hogyan kapcsolódik össze a ritmus, a csapatmunka, a fegyelem és az edzői gondolkodás.   Ez az epizód nemcsak futballrajongóknak szól. Ez egy történet újrakezdésről, magyarságról, alkalmazkodásról, munkáról, közösségről és arról, hogyan lehet egy idegen országban is felépíteni valamit, amire az ember büszke lehet.   Ha érdekel, hogyan látja egy erdélyi magyar futballedző Kanada sportvilágát, a magyar futball jövőjét és a 2026-os világbajnokság lehetőségeit Észak-Amerikában, akkor ez a beszélgetés neked szól.   Zene:   https://youtu.be/6cPTzGlAwOY?si=5UAJqqNnfxRXfRmM https://youtu.be/9VKlJI0pU3E?si=pHynxf6Vj9Da3aoI https://youtu.be/dzum0pfvdTU?si=3fbU5bC-ovbBm1su https://youtu.be/G5ZxZI2llqs?si=KcirExIvUlFzhMMb https://youtu.be/YlALXoZK100?si=Pc_ptbAeyO-K9XF7   Website: www.mayersharvest.com (http://www.mayersharvest.com/) Kuponkód: MOKA 20% kedvezményért Amazon Store: https://rb.gy/j1eiuy   Iratkozz fel a csatornára további magyar New York-i interjúkért és podcast epizódokért.   https://bit.ly/MOKAPodcatsSign  Kövess minket Facebookon: @mokapodcast Instagramon: @mokapodcastusa Web: mokapodcast.com Spotify  (https://bit.ly/mokapodcast) Apple Podcast  (https://bit.ly/moka2021) [Google Podcast](https://bit.ly/MokaGoogle) [Deezer](https://bit.ly/MokaDeezer) [LibSyn](https://bit.ly/MokaLibsyn) [Facebook](https://bit.ly/MokaFB)   00:00 Bevezető, ambíció és újrakezdés 00:48 Bozsoky Szabolcs a torontói Hungarofesten 02:58 Erdélyből Kanadába 08:11 Sportcsalád, fegyelem és példaképek 12:49 Magyar identitás Erdélyben és Kanadában 14:14 Edző, pedagógus és tehetségkutató 17:37 Magyar foci és kanadai magyar tehetségek 23:26 Kanadai futball, MLS és sportkultúra 30:10 Rush Academy, UTM és CONCACAF A diploma 43:36 Zene, család és magyar sikertörténetek

Are we witnessing the first real signs of AI becoming a scientist? In this episode of The MAD Podcast, Matt Turck sits down with Dan Roberts, lead of the Foundations of Reinforcement Learning team at OpenAI, to explore one of the biggest shifts happening in AI: the rise of reasoning models, test-time compute, and reinforcement learning as engines of scientific discovery. Dan brings a rare perspective - from theoretical physics, black holes, quantum information, and deep learning theory - to explain how models are learning to “think,” why language may be such a powerful foundation for intelligence, what recent AI math breakthroughs really mean, and whether we are beginning to see AI systems that can contribute to science itself.(00:00) Intro: AI's wild week in mathematics(01:21) What OpenAI's Foundations of RL team does(03:08) Dan's journey: from black holes and quantum gravity to frontier AI(07:04) Are AI systems becoming useful for real science?(08:21) The AI math moment: Erdős, OpenAI, DeepMind, and Anthropic(08:52) Why the OpenAI result was an act of exploration(10:25) OpenAI vs. DeepMind: informal reasoning vs. formal proof(12:13) RL 101: learning by doing, not just watching(15:10) Why reinforcement learning works(15:58) How RL breaks: sparse feedback and long-horizon tasks(17:03) RLHF: how human feedback shaped early language models(18:48) Move 37, self-play, and the search for novel strategies(22:16) Explore vs. exploit in scientific discovery(24:49) Why RL may now be "the cake," not the cherry on top(25:46) Why RL started working with large language models(27:29) Is RL "sucking supervision through a straw"?(28:47) Why language may be the grounding layer for intelligence(31:46) A contrarian take on the Bitter Lesson(32:41) What test-time compute actually is(34:50) How RL gives models the ability to think(35:40) Verifiable rewards, math, coding, and the messy real world(38:00) What physics can teach us about AI(42:08) Is there a thermodynamics of AI?(43:08) From Erdős problems to Einstein-level AI(45:16) Is AI already doing original science?(45:51) How far are we from AI automating AI research?(47:41) Why Dan is excited about the future of science

──────────────────────────────────────── [00:03:00] AI Solved an 80-Year-Old Math Problem by Defying Conventional Wisdom — Which Is What Science Is Supposed to Do OpenAI's model disproved the Erdős unit distance conjecture in 32 hours; Princeton mathematicians said they would have accepted the paper without hesitation. ──────────────────────────────────────── [00:12:00] Sam Altman: 'Homo Sapiens Will Be the First Species to Design Our Own Descendants' — Knight: This Is Luciferian Religion The Guardian identifies Silicon Valley transhumanism as a full religion — Larry Page wants digital beings to spread across the galaxy; Musk calls humanity 'a biological bootloader.' ──────────────────────────────────────── [00:23:00] Man Arrested for Organizing a Facebook Protest Against a Data Center — Police Said a Comment by Someone Else Was a Threat Fusion center police charged a Virginia man with stalking for planning a public protest and arrested him while he asked them to quote the supposed threat. ──────────────────────────────────────── [00:38:00] Fusion Centers Are Classifying Data Center Protesters as Anti-Tech Extremists — Trump's Memo Targets 'Anti-American Beliefs' Wired obtained fusion center documents showing a national shift to surveilling AI opposition; Trump's security memo instructs DOJ to target anti-American, anti-Christian, and anti-capitalist beliefs. ──────────────────────────────────────── [00:50:00] Transhumanists Don't Know What Consciousness Is — But They Want to Transfer It Into a Machine to Live Forever Zoltan Istvan couldn't say whether it would be him or a copy; Altman subscribed to a startup to upload his brain to the cloud; Musk concedes it would only be a copy. ──────────────────────────────────────── [01:05:00] 99% of Corporate Executives Plan AI Job Cuts in Two Years — Two-Thirds Want to Eliminate Human Roles Entirely The 2026 Mercer Global Talent Trends report: 825 executives surveyed, 99% expect headcount reductions, only 32% believe humans and machines can work in optimal combination. ──────────────────────────────────────── [01:14:00] Trump's Name Was Removed From the Kennedy Center by Court Order — He Claimed He Canceled His Involvement The statute forbids naming the center for anyone other than Kennedy; Trump raged from the golf course and said 'I canceled my involvement' — the judge canceled it. ──────────────────────────────────────── [01:24:00] Nine Acts Booked for Trump's 250th Celebration — All But Two Canceled, So He Said He'd Perform and He's More Popular Than Elvis Trump said he needs only a microphone to draw a bigger crowd than Elvis in his prime and posted AI slop of himself dunking on the New York governor. ──────────────────────────────────────── [01:33:00] A Couple Paid $640 for a Trump Watch That Arrived Saying 'RUMP' — the T Was Missing From the Face A radio ad using Trump's voice sold the watches as limited edition, one of 250 — fine print discloses no connection to the Trump Organization. Knight: sold out — he has sold us out. ──────────────────────────────────────── [01:42:00] Pam Bondi Testified Under a Throat Bandage — She Recalled Nothing, Praised Blanche, and Refused to Mention Trump Bondi has cancer with a good prognosis — unlike the Trump administration she served. She answered every question by saying she did not recall or telling the committee to ask Blanche. ──────────────────────────────────────── Money should have intrinsic value AND transactional privacy: Go to https://davidknight.gold/ for great deals on physical gold/silver For 10% off Gerald Celente's prescient Trends Journal, go to https://trendsjournal.com/ and enter the code “KNIGHT” For high quality made in America products go to HomeSteadProducts.shop and use promo code “Knight” for 10% off your purchases Find out more about the show and where you can watch it at TheDavidKnightShow.com If you would like to support the show and our family please consider subscribing monthly here: SubscribeStar https://www.subscribestar.com/the-david-knight-show Or you can send a donation throughMail: David Knight POB 994 Kodak, TN 37764Zelle: @DavidKnightShow@protonmail.comCash App at: $davidknightshowBTC to: bc1qkuec29hkuye4xse9unh7nptvu3y9qmv24vanh7Become a supporter of this podcast: https://www.spreaker.com/podcast/the-david-knight-show--2653468/support.

──────────────────────────────────────── [00:03:00] AI Solved an 80-Year-Old Math Problem by Defying Conventional Wisdom — Which Is What Science Is Supposed to Do OpenAI's model disproved the Erdős unit distance conjecture in 32 hours; Princeton mathematicians said they would have accepted the paper without hesitation. ──────────────────────────────────────── [00:12:00] Sam Altman: 'Homo Sapiens Will Be the First Species to Design Our Own Descendants' — Knight: This Is Luciferian Religion The Guardian identifies Silicon Valley transhumanism as a full religion — Larry Page wants digital beings to spread across the galaxy; Musk calls humanity 'a biological bootloader.' ──────────────────────────────────────── [00:23:00] Man Arrested for Organizing a Facebook Protest Against a Data Center — Police Said a Comment by Someone Else Was a Threat Fusion center police charged a Virginia man with stalking for planning a public protest and arrested him while he asked them to quote the supposed threat. ──────────────────────────────────────── [00:38:00] Fusion Centers Are Classifying Data Center Protesters as Anti-Tech Extremists — Trump's Memo Targets 'Anti-American Beliefs' Wired obtained fusion center documents showing a national shift to surveilling AI opposition; Trump's security memo instructs DOJ to target anti-American, anti-Christian, and anti-capitalist beliefs. ──────────────────────────────────────── [00:50:00] Transhumanists Don't Know What Consciousness Is — But They Want to Transfer It Into a Machine to Live Forever Zoltan Istvan couldn't say whether it would be him or a copy; Altman subscribed to a startup to upload his brain to the cloud; Musk concedes it would only be a copy. ──────────────────────────────────────── [01:05:00] 99% of Corporate Executives Plan AI Job Cuts in Two Years — Two-Thirds Want to Eliminate Human Roles Entirely The 2026 Mercer Global Talent Trends report: 825 executives surveyed, 99% expect headcount reductions, only 32% believe humans and machines can work in optimal combination. ──────────────────────────────────────── [01:14:00] Trump's Name Was Removed From the Kennedy Center by Court Order — He Claimed He Canceled His Involvement The statute forbids naming the center for anyone other than Kennedy; Trump raged from the golf course and said 'I canceled my involvement' — the judge canceled it. ──────────────────────────────────────── [01:24:00] Nine Acts Booked for Trump's 250th Celebration — All But Two Canceled, So He Said He'd Perform and He's More Popular Than Elvis Trump said he needs only a microphone to draw a bigger crowd than Elvis in his prime and posted AI slop of himself dunking on the New York governor. ──────────────────────────────────────── [01:33:00] A Couple Paid $640 for a Trump Watch That Arrived Saying 'RUMP' — the T Was Missing From the Face A radio ad using Trump's voice sold the watches as limited edition, one of 250 — fine print discloses no connection to the Trump Organization. Knight: sold out — he has sold us out. ──────────────────────────────────────── [01:42:00] Pam Bondi Testified Under a Throat Bandage — She Recalled Nothing, Praised Blanche, and Refused to Mention Trump Bondi has cancer with a good prognosis — unlike the Trump administration she served. She answered every question by saying she did not recall or telling the committee to ask Blanche. ──────────────────────────────────────── Money should have intrinsic value AND transactional privacy: Go to https://davidknight.gold/ for great deals on physical gold/silver For 10% off Gerald Celente's prescient Trends Journal, go to https://trendsjournal.com/ and enter the code “KNIGHT” For high quality made in America products go to HomeSteadProducts.shop and use promo code “Knight” for 10% off your purchases Find out more about the show and where you can watch it at TheDavidKnightShow.com If you would like to support the show and our family please consider subscribing monthly here: SubscribeStar https://www.subscribestar.com/the-david-knight-show Or you can send a donation throughMail: David Knight POB 994 Kodak, TN 37764Zelle: @DavidKnightShow@protonmail.comCash App at: $davidknightshowBTC to: bc1qkuec29hkuye4xse9unh7nptvu3y9qmv24vanh7Become a supporter of this podcast: https://www.spreaker.com/podcast/the-real-david-knight-show--5282736/support.

Dringend gesucht und massiv unter Druck – Tierärztinnen und Tierärzte in der Schweiz. Und: Wie die iranischen Revolutionsgarden trotz Sanktionen im lukrativen Ölgeschäft mitmischen. Zudem: Die Recherche zur Mafia im Misox. Tierärzte am Limit: Vom Traumberuf zum Verschleissjob Viele Tierärztinnen und Tierärzte in der Schweiz stehen massiv unter Druck. Fachkräftemangel, extreme Arbeitsbelastung durch Notdienste und herausfordernde Tierhalter führen zu einer physischen und psychischen Überlastung. Die «Rundschau» hat einen Landtierarzt und eine junge Kleintierärztin in der Stadt begleitet. Die Ölgeschäfte der Revolutionsgarden: Spuren führen in die Schweiz Die iranischen Revolutionsgarden sichern mit Gewalt die Macht der Mullahs. Trotz internationaler Sanktionen mischen sie im lukrativen Erdölhandel mit: mit dem Verkauf und dem Schmuggel fossiler Energieträger, Öl und petrochemischer Produkte. Um den Handel abzuwickeln, kontrollieren die Revolutionsgarden eine Tanker-Schattenflotte. Recherchen zeigen: Die Spur der iranischen Ölgeschäfte führt auch in die Schweiz. Das Misox und die Mafia: Wer sind die Verhafteten? Im Februar geriet die Gemeinde Roveredo im Kanton Graubünden durch eine internationale Anti-Mafia-Operation in den Fokus. Im Dorf hatte sich eine Gruppe von Personen mit Verbindungen zu Clans der Camorra niedergelassen. Wer sind diese Personen, was wollten sie im Misox und warum hat sie niemand gestoppt?

Our 246th episode with a summary and discussion of last week's big AI news!Recorded on 05/22/2026Hosted by Andrey Kurenkov and Jeremie HarrisFeel free to email us your questions and feedback at andreyvkurenkov@gmail.com and/or hello@gladstone.aiRead out our text newsletter and comment on the podcast at https://lastweekin.ai/In this episode:Google I/O highlights included Gemini 3.5 (with 3.5 Flash emphasized for speed and benchmarks), the always-on agent Gemini Spark running on Google Cloud with MCP tool support, and Gemini Omni multimodal video generation/editing, plus updates like Anti-Gravity 2.0, Gemini for Science, and Genie world-model navigation using Street View and Waymo simulation.Coding-agent competition accelerated with Cursor Composer 2.5 (fine-tuned on Moonshot's Kimi K2.5) and xAI's early Grok Build release, alongside discussion of potential Cursor–xAI ties and xAI's talent churn and compute utilization concerns.Business and legal updates included Elon Musk losing his OpenAI lawsuit on statute-of-limitations grounds, reported OpenAI–Apple partnership tensions, Anthropic agreeing to a $30B funding round at a $900B valuation and projecting its first profitable quarter, and Cerebras' IPO surging about 90%. Research and safety stories covered OpenAI's result on an 80-year-old Erdős geometry problem, findings on “negation neglect” in training, interpretability work showing multiple redundant circuits per capability, agent benchmarks like Terminal World, new deepfake takedown enforcement under the Take It Down Act, demonstrations of autonomous hacking/self-replication, rapidly improving AI cyber capabilities, and steps toward image provenance metadata and watermarks.Timestamps:(00:00:10) Intro / Banter(00:01:15) News PreviewTools & Apps(00:05:05) Google unveils AI model Gemini 3.5 and AI agent Gemini Spark(00:11:43) Google's Gemini Omni turns images, audio, and text into video — and that's just the start | TechCrunch(00:17:27) Google launches Antigravity 2.0 with an updated desktop app and CLI tool at IO 2026 | TechCrunch(00:22:35) Google Debuts AI-Powered Tools To Optimize Scientific Research Workflows(00:27:20) Google's Genie world model can now simulate real streets with Street View | TechCrunch(00:29:51) Cursor's Composer 2.5 matches Opus 4.7 and GPT-5.5 benchmarks at a fraction of the cost(00:37:37) xAI Introduces Its Coding Agent Called Grok BuildApplications & Business(00:41:55) Musk loses OpenAI court battle as he waited too long to sue(00:48:08) Anthropic agrees terms of $30bn funding deal at $900bn valuation(00:53:12) OpenAI co-founder Andrej Karpathy joins Anthropic's pre-training team | TechCrunch(00:56:49) Greg Brockman Officially Takes Control of OpenAI's Products in Latest Shake-Up | WIRED(00:58:15) OpenAI-Apple Partnership Frays, Setting Up Possible Legal Fight - Bloomberg(01:01:13) AI chipmaker Cerebras soars 90% in year's biggest IPO so farResearch & Advancements(01:07:10) AI just solved an 80-year-old ‘Erdős problem,' and mathematicians are amazed | Scientific American(01:11:50) Negation Neglect: When models fail to learn negations in training(01:13:18) All Circuits Lead to Rome: Rethinking Functional Anisotropy in Circuit and Sheaf Discovery for LLMs(01:16:20) Autonomous AI research for nanogpt speedrun(01:21:59) TerminalWorld: Benchmarking Agents on Real-World Terminal TasksPolicy & Safety(01:23:15) America's dangerous, messy deepfakes crackdown is here | The Verge(01:25:17) Language Models Can Autonomously Hack and Self-Replicate(01:28:48) How fast is autonomous AI cyber capability advancing?(01:31:32) Positive Alignment: Artificial Intelligence for Human FlourishingSynthetic Media & Art(01:33:15) OpenAI is making it easier to check if an image was made by their models | TechCrunch(01:33:56) How Chinese short dramas became AI content machines | MIT Technology ReviewSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, the Moonshot mates discuss SpaceX's record-breaking IPO filing and its growing ties to Anthropic, OpenAI's AI model disproving a decades-old Erdős conjecture in mathematics, and GPT-5.5 beating prediction markets at forecasting. Get access to metatrends 10+ years before anyone else - ⁠https://qr.diamandis.com/metatrends⁠   Peter H. Diamandis, MD, is the Founder of XPRIZE, Singularity University, ZeroG, and A360 Salim Ismail is the founder of OpenExO Dave Blundin is the founder & GP of Link Ventures Dr. Alexander Wissner-Gross is a computer scientist and founder of Reified Apply for Salim's Pilot Program: https://openexo.com/organizational-singularity-pilot?podcast=23.5.26 Subscribe to Salim's channel: https://www.youtube.com/@salimismail – My companies: Apply to Dave's and my new fund: ⁠https://qr.diamandis.com/linkventures...⁠ Go to Blitzy to book a free demo and start building today: ⁠https://qr.diamandis.com/blitzy⁠ Your body is incredibly good at hiding disease. Schedule a call with Fountain Life to add healthy decades to your life, and to learn more about their Memberships: ⁠https://www.fountainlife.com/peter⁠ _ Connect with Peter: ⁠X⁠ ⁠Instagram⁠ ⁠Substack⁠ ⁠Website⁠ ⁠Xprize⁠ Connect with Dave: ⁠Web⁠ ⁠X⁠ ⁠LinkedIn⁠ ⁠Instagram⁠ ⁠TikTok⁠ Connect with Salim: ⁠X⁠ ⁠Join Salim's Workshop to build your ExO⁠  Connect with Alex ⁠Website⁠ ⁠LinkedIn⁠ ⁠X⁠ Email ⁠Substack⁠  ⁠Spotify⁠ ⁠Threads⁠ Listen to MOONSHOTS: ⁠Apple⁠ ⁠YouTube⁠ – *Recorded on May 21st, 2026 *The views expressed by me and all guests are personal opinions and do not constitute Financial, Medical, or Legal advice. Learn more about your ad choices. Visit megaphone.fm/adchoices

This episode explores how AI, specifically OpenAI's recent breakthrough in solving an 80-year-old math conjecture, is transforming the field of mathematics. Featuring insights from Professor Daniel Litt, the discussion covers the implications of AI in mathematical research, the value of human verification, and the future of mathematical practice.Key topicsAI solving long-standing mathematical problemsThe role of human verification in AI-generated proofsImplications of AI breakthroughs in discrete geometryThe future of mathematical research with AINumber theory and algebraic constructions in AI discoveriesChapters00:00 Introduction to the Conjecture and Its Significance01:15 Understanding the Erdős Problem04:34 The Role of AI in Solving Mathematical Problems09:17 The Implications of AI in Mathematics10:32 AI vs Human Mathematicians: A Comparative Analysis17:20 Standards for AI-Generated Proofs21:10 Corporate Interests in Mathematical Research24:42 The Future of Mathematics and AI27:50 Final Thoughts on AI and Mathematics31:37 Revolutionizing Mathematics: AI's Breakthrough in Discrete Geometry37:37 Exploring the Implications: AI and the Future of Mathematics38:03 The Role of AI in Mathematics39:23 Human Value in the Age of AIFollow Daniel Litt onX (https://x.com/maiasz) Website (https://daniellitt.com)Follow Breaking Math onSubstack (https://breakingmath.substack.com/)X (https://x.com/breakingmathpod)Instagram (https://www.instagram.com/breakingmathmedia/)Bluesky (https://bsky.app/profile/breakingmath.bsky.social)Website (https://www.breakingmath.io/)YouTube (https://www.youtube.com/@BreakingMathPod)Follow Noah onInstagram (https://www.instagram.com/profnoahgian/)X (https://x.com/ProfNoahGian)Bluesky (https://bsky.app/profile/profnoahgian.bsky.social)Follow Autumn onX (https://x.com/1autumn_leaf)Bluesky (https://bsky.app/profile/1autumnleaf.bsky.social)Instagram (https://www.instagram.com/1autumnleaf/)Substack (https://substack.com/@1autumnleaf)email: breakingmathpodcast@gmail.com

In Wien wird das Klima wie in Skopje (Mazedonien). Was machen wir? Das habe ich die Ökonomin Sigrid Stagl gefragt. Was der Klimawandel mit der österreichischen Wirtschaft macht – und wir uns an ihn anpassen und dadurch wohlhabender und unabhängiger werden.

What Your Doctor Isn't Telling You About Nutrition and Perimenopause Have you ever caught yourself thinking, "This is just how I am now — it's perimenopause, there's nothing I can do"? You're not alone. But today we're flipping that narrative. In this episode, I'm sharing what the research actually says about nutrition and perimenopause — and why this conversation isn't happening nearly enough in conventional medicine. Because yes, the hormonal shifts are real. The brain fog, the weight changes, the sleep disruption — all real. But so is your ability to influence how you feel through what you eat. In this episode: Why declining estrogen changes your metabolism (by more than you'd think) The dietary pattern with the strongest evidence base for perimenopausal symptoms 3 nutrition tools backed by peer-reviewed research that you can start using now The 3 changes we cover (and what to do about them): Metabolism changes — why your muscle is at risk and how much you actually need during this transition Gut health changes — the estrobolome, why your gut microbiome directly influences your estrogen levels, and the simple weekly goal that can shift everything Your Supplement Stack needs!  Resources mentioned: Erdélyi et al., 2024 — Nutrients — comprehensive review of nutrition priorities in perimenopause and menopause Grab your FREE Why Am I Tired Guide HERE! This includes a where to start quiz, a list of all the nutrition related labs I recommend to my clients, and a week's worth of energizing menus!! 

La tertulia semanal en la que repasamos las últimas noticias de la actualidad científica. En el episodio de hoy:Cara B:-¿Es ético que los médicos hablen públicamente sobre la salud mental de Trump? (07:00)-Problema número 1196 de Erdös (1:14:30)-El cometa R3/2025 (PANSTARRS) (1:47:50)-Señales de los oyentes (1:56:00)Este episodio es continuación de la Cara A.Contertulios: Juan Carlos Gil, Borja Tosar, Ignacio Crespo, Francis Villatoro, Héctor Socas. Imagen de portada realizada con Midjourney. Todos los comentarios vertidos durante la tertulia representan únicamente la opinión de quien los hace... y a veces ni eso Hosted on Acast. See acast.com/privacy for more information.