Podcasts about Eta

El presentador de Radioestadio reflexiona en Noticias fin de semana sobre las víctimas de ETA.

In this episode, the hosts break down a $1M EBITDA smart home and AV integration business in booming Charlotte, NC—debating whether the real opportunity lies in the electrical add-on and attached $3.5M showroom real estate.Business Listing – https://www.bizbuysell.com/business-opportunity/1-00mm-sde-smart-home-and-av-integration-company-in-charlotte/2365518/Welcome to Acquisitions Anonymous – the #1 podcast for small business M&A. Every week, we break down businesses for sale and talk about buying, operating, and growing them.Looking to build a professional website in minutes? Try Wix: https://wix.pxf.io/c/6898629/3115214/25616?trafcat=templateHubSpot is the backbone for how businesses scale without chaos. Try them out here: https://go.try-hubspot.com/OeG9Vr

Welcome to another episode of Fratello On Air. This week, we hop into our time machine and travel back 10 years to 2016. As we'll see, in some ways, this wasn't so long ago. Yet, in others, it was a different lifetime. Join us as we look back on fonder days.HandgelenkskontrolleIt's been a while since we've recorded, and much of this is down to Mike's travels in Germany. Sadly, on one of the trips, he had a very Balazs-like travel experience involving planes and trains. If he did have a time machine, he'd probably go back and choose to sleep through his alarm that day. We mention an upcoming Sotheby's auction featuring a pair of Michael Jordan's shoes from the Dream Team Barcelona Olympics. Balazs discusses the return of The Night Manager, a show that, coincidentally, has returned after its first season in 2016! For the Handgelenkskontrolle, Balazs is wearing an upcoming release, the Nivada Grenchen F77 MKII. Mike was wearing his Rolex Sea-Dweller 1665 "Great White" in Germany and still has it on his wrist.Taking our time machine back to 2016Yes, it's hard to believe that 2016 was 10 years ago! Time flies! In this episode, we're reminiscing about a year that brought some interesting releases and behavior. Specifically, vintage watches were within a boom period. In particular, a Patek Philippe 1518 sold at Phillips for over CHF 11 million! It was hard for us to believe that watches were achieving these sums a decade ago.Many new watches from back then still feel relatively modern, but there's no doubt that larger diameters and chunkier cases were in vogue. The Blancpain Bathyscaphe Blue and Ceramic is a good example of this, but to be fair, the same basic platform is still in the catalog today. Speaking of big and robust watches, Robert-Jan reviewed the Sinn U212, which still looks fresh. If we truly want to feel old, Tudor was already in its second year of offering non-ETA movements. Omega launched a rare box-office stinker with the blue and white CK2998, a piece that has aged quite well.Our time machine continues its journey and finds the wild Hublot Big Bang Berluti, which was available in gold or steel and showed off a leather dial. Seiko, announced a partnership with PADI. This caused a stir, and several of us on the team grabbed one of the new models. TAG Heuer announced a Monza PVD, a retro chronograph with a thick case. However, we save the most significant announcement for last. Rolex, after producing steel Daytona 116500LN models with a steel bezel for over 25 years, introduced pieces with ceramic bezels. Understandably, the excitement was palpable and hit Basel like a hurricane. We remark that these watches have been unavailable since that time!We hope you enjoy today's episode. Share your watch memories from 2016 in the comments below, and let us know if we forgot any momentous pieces.

Madrid multa con hasta 1000€ por residuos fuera de contenedores para concienciar. Trenes Madrid-Sevilla, retrasos mínimos; Alvia a Huelva, transbordo en Córdoba. Cinco jóvenes mueren en incendio en Cataluña; enfermera asesinada en Benicassim. El discurso político critica el "sanchismo" por señalar empresarios y por promesas de vivienda incumplidas. Escándalos de corrupción como Borja Cabezón (elusión fiscal) y Begoña Gómez (África Center, Air Europa, OMT, Koldo García) cuestionan a Sánchez. Fiscalía frena semilibertad de ETA. Congreso conmemora Constitución con ausencias. Gobierno sube el SMI 3.1% (1221€), beneficiando a 2.5M, sin acuerdo patronal. Esto genera debate sobre su impacto económico y función recaudatoria. Analistas vinculan más casos de corrupción (Air Europa, Begoña Gómez) al Presidente, critican la creación de "supervillanos" e ineficacia en vivienda y economía. Real Madrid lidera la liga.

In this episode, the hosts break down a $1M EBITDA smart home and AV integration business in booming Charlotte, NC—debating whether the real opportunity lies in the electrical add-on and attached $3.5M showroom real estate.Business Listing – https://www.bizbuysell.com/business-opportunity/1-00mm-sde-smart-home-and-av-integration-company-in-charlotte/2365518/Welcome to Acquisitions Anonymous – the #1 podcast for small business M&A. Every week, we break down businesses for sale and talk about buying, operating, and growing them.Looking to build a professional website in minutes? Try Wix: https://wix.pxf.io/c/6898629/3115214/25616?trafcat=templateHubSpot is the backbone for how businesses scale without chaos. Try them out here: https://go.try-hubspot.com/OeG9Vr

Graham Weihmiller's journey from Six Sigma black belt to acquiring Griswold Home Care during the Great Recession exemplifies resilience and strategic leadership. He shares his evolution through franchising, scaling Griswold from 100 to 250 locations, and later steering BNI—a global networking organization with 350,000+ members across 77 countries—through one of business history's most dramatic pivots during the COVID-19 pandemic. This episode unpacks the art of founder transitions, the undervalued potential of franchising in ETA, and why your family are your first customers.ChaptersEarly Signs of Entrepreneurship and Financial Distress (2:38)First Venture and Discovering Search Funds (6:45)Acquiring Griswold Home Care During the GFC (10:04)Why Franchising Deserves More Attention (15:00)Founder Transitions: Lessons from Griswold (19:31)Getting the Right People on the Bus (23:59)Acquiring BNI: A Different Kind of Transition (31:45)The Three Bucket Framework (36:11)COVID-19: Pivoting a Global Network Overnight (40:14)Endurance Sports, Burnout, and Family First (48:00)Some advice from Graham:"Your job is not to fix the processes. Your job is to get the right people in the right seats. And they will fix the processes in a much better way than you'll ever be able to.""I don't know if this organization is gonna survive this pivot that we're about to do, but I know it's the right thing to do. Nobody is gonna get hurt if I can help it. Nothing to me is worth somebody getting hurt or certainly worse."

durée : 00:58:51 - Erle Beltzaren elkarteak formakuntzak eta laguntza teknikoa proposatzen ditu Vous aimez ce podcast ? Pour écouter tous les autres épisodes sans limite, rendez-vous sur Radio France.

durée : 00:58:51 - Erle Beltzaren elkarteak formakuntzak eta laguntza teknikoa proposatzen ditu Vous aimez ce podcast ? Pour écouter tous les autres épisodes sans limite, rendez-vous sur Radio France.

El presidente murciano, Fernando López Miras, informa de la favorable evolución del incendio, con un centenar de profesionales y una línea eléctrica rota como posible origen. La crecida del Duero complica Castilla y León: desalojos, confinamientos y nueve avisos rojos.La semilibertad de miembros de ETA indigna a las víctimas, quienes denuncian prisa del PSOE e influencia política, tachando la medida de inmoral y sin apelación. La Guardia Civil de Sevilla detiene a un hombre por grabar en secreto a dos mujeres cercanas.La periodista María Estévez comparte vivencias de Hollywood: Clooney, profesional y simpático; Jolie, estratégica; Pitt, carismático, violencia que causó su divorcio. Tarantino admira el cine español. Penélope Cruz, reservada; Banderas, generoso. Roberts, poco simpática e intimidante. Pamela Anderson, sensible e inteligente, sufrió viralización de vídeo íntimo. Kim Kardashian, cercana. El "caso Epstein" transforma las élites de Hollywood, revelando poder y ...

Fernando Múgica, nieto del dirigente socialista asesinado por ETA (Fernando Múgica) denuncia la hipocresía del PSOE

Analizamos el acto de Isabel Díaz Ayuso y Miguel Ángel Rodríguez con Nuevas Generaciones del PP en Madrid, el mensaje lanzado a los jóvenes y la estrategia discursiva basada en la confrontación permanente, desde la apelación a “Bildu y ETA” hasta la denuncia de una supuesta ofensiva contra la Comunidad de Madrid. También abordamos el contexto mediático que rodea estos discursos, el papel de Telemadrid y determinados tertulianos afines, y las contradicciones del PP madrileño en plena polémica por el caso del alcalde de Móstoles. Mas vídeos de Pandemia Digital: https://www.youtube.com/c/PandemiaDigital1 Si quieres comprar buen aceite de primera prensada, sin intermediarios y ayudar de esa forma a los agricultores con salarios justos tenemos un código de promoción para ti: https://12coop.com/cupon/pandemiadigital/ Este video puede contener temas sensibles, así como discursos de odi*, ac*so, o discr*minación. El objetivo de abordar estos temas es exclusivamente informativo y busca concienciar a la audiencia sobre estos acontecimientos, y denunciar y señalar el origen de los mismos para crear consciencia y evitar su propagación. Si consideras que el contenido puede afectarte, te recomendamos proceder con precaución o evitar su visualización. ----------------------------------------------------------------------------------------------- Únete a nuestra comunidad de YouTube https://www.youtube.com/channel/UCFOwGZY-NTnctghtlHkj8BA/join Se mecenas de Patreon https://www.patreon.com/PandemiaDigital ----------------------------------------------------------------------------------------------- Súmate a la comunidad en Twitch - En vivo de Lunes a Jueves: https://www.twitch.tv/pandemiadigital Sigue nuestro Canal de Telegram: https://t.me/PandemiaDigital Suscríbete en nuestra web: https://PandemiaDigital.net Sigue nuestras redes: Twitter: https://twitter.com/PandemiaDigitaI Facebook: https://www.facebook.com/PandemiaDigitalObservatorio Instagram: https://www.instagram.com/pandemia_digital_twitch TikTok: https://www.tiktok.com/@pandemiadigital #PandemiaDigital

This episode is part of our Inside the Mind of an Acquirer series, and it unpacks the ETA (Entrepreneurship Through Acquisition) wave now flooding the market.  For business owners, ETA is a double-edged sword. On the upside, more buyers courting you means more choice, more urgency, and more liquidity. On the downside, many ETA buyers are first-timers who lean on heavy leverage and seller financing. If they misread your business or hit a snag they can't handle, the part of the deal you financed can quickly become the part you never collect. 

Las autoridades de Protección Civil de Cataluña piden prudencia por riesgos persistentes tras un temporal que dejó cinco heridos graves. España expresa malestar por su exclusión de una cumbre europea informal en Bélgica, organizada por Alemania, Bélgica e Italia, aunque el primer ministro belga suavizó las tensiones. El gobierno español reclamó a Italia, cuestionando la validez de encuentros previos excluyentes. Covite denuncia un preocupante aumento en la concesión de terceros grados a presos de ETA, con un caso semanal en 2026, calificando la situación de legal pero inmoral, dada la falta de arrepentimiento. En deportes, el Atlético de Madrid goleó al Alavés (4-0) y se acerca a la final de la Copa del Rey. El programa "Poniendo las Calles" celebra el Día Mundial de la Radio. La cartelera de cine de Jerónimo José Martín recomienda el thriller político "Ruta de escape" y la animación familiar "Como cabras"; critica el melodrama "Cumbres borrascosas", el thriller coreano "No hay otra ...

En Noruega, la confesión de infidelidad del atleta Sturla Holm en los JJ.OO. eclipsa logros del equipo. Óscar López acusa a Javier Lambán por malos resultados socialistas en Aragón, dividiendo al PSOE. Juez recibe confesiones sobre presunta financiación ilegal socialista. Conmemoran 30 años del asesinato de Francisco Tomás y Valiente por ETA. Iglesia de Madrid: 1200+ sacerdotes en asamblea sobre renovación ministerial, santidad, comunión; Cáritas e iniciativas matrimoniales. Previsión: fin de semana sin lluvias, anticiclón; alivio en Andalucía, aunque persisten desalojos. Grave deterioro de carreteras españolas con socavones por lluvias preocupa a transportistas, planteando responsabilidad administrativa. Salud: 84% supervivencia cáncer infantil en España gracias a investigación, que requiere más financiación. Deportes: Barcelona critica arbitraje tras derrota en Copa del Rey; Madrid se prepara para Carnaval.

From rewriting Google's search stack in the early 2000s to reviving sparse trillion-parameter models and co-designing TPUs with frontier ML research, Jeff Dean has quietly shaped nearly every layer of the modern AI stack. As Chief AI Scientist at Google and a driving force behind Gemini, Jeff has lived through multiple scaling revolutions from CPUs and sharded indices to multimodal models that reason across text, video, and code.Jeff joins us to unpack what it really means to “own the Pareto frontier,” why distillation is the engine behind every Flash model breakthrough, how energy (in picojoules) not FLOPs is becoming the true bottleneck, what it was like leading the charge to unify all of Google's AI teams, and why the next leap won't come from bigger context windows alone, but from systems that give the illusion of attending to trillions of tokens.We discuss:* Jeff's early neural net thesis in 1990: parallel training before it was cool, why he believed scaling would win decades early, and the “bigger model, more data, better results” mantra that held for 15 years* The evolution of Google Search: sharding, moving the entire index into memory in 2001, softening query semantics pre-LLMs, and why retrieval pipelines already resemble modern LLM systems* Pareto frontier strategy: why you need both frontier “Pro” models and low-latency “Flash” models, and how distillation lets smaller models surpass prior generations* Distillation deep dive: ensembles → compression → logits as soft supervision, and why you need the biggest model to make the smallest one good* Latency as a first-class objective: why 10–50x lower latency changes UX entirely, and how future reasoning workloads will demand 10,000 tokens/sec* Energy-based thinking: picojoules per bit, why moving data costs 1000x more than a multiply, batching through the lens of energy, and speculative decoding as amortization* TPU co-design: predicting ML workloads 2–6 years out, speculative hardware features, precision reduction, sparsity, and the constant feedback loop between model architecture and silicon* Sparse models and “outrageously large” networks: trillions of parameters with 1–5% activation, and why sparsity was always the right abstraction* Unified vs. specialized models: abandoning symbolic systems, why general multimodal models tend to dominate vertical silos, and when vertical fine-tuning still makes sense* Long context and the illusion of scale: beyond needle-in-a-haystack benchmarks toward systems that narrow trillions of tokens to 117 relevant documents* Personalized AI: attending to your emails, photos, and documents (with permission), and why retrieval + reasoning will unlock deeply personal assistants* Coding agents: 50 AI interns, crisp specifications as a new core skill, and how ultra-low latency will reshape human–agent collaboration* Why ideas still matter: transformers, sparsity, RL, hardware, systems — scaling wasn't blind; the pieces had to multiply togetherShow Notes:* Gemma 3 Paper* Gemma 3* Gemini 2.5 Report* Jeff Dean's “Software Engineering Advice fromBuilding Large-Scale Distributed Systems” Presentation (with Back of the Envelope Calculations)* Latency Numbers Every Programmer Should Know by Jeff Dean* The Jeff Dean Facts* Jeff Dean Google Bio* Jeff Dean on “Important AI Trends” @Stanford AI Club* Jeff Dean & Noam Shazeer — 25 years at Google (Dwarkesh)—Jeff Dean* LinkedIn: https://www.linkedin.com/in/jeff-dean-8b212555* X: https://x.com/jeffdeanGoogle* https://google.com* https://deepmind.googleFull Video EpisodeTimestamps00:00:04 — Introduction: Alessio & Swyx welcome Jeff Dean, chief AI scientist at Google, to the Latent Space podcast00:00:30 — Owning the Pareto Frontier & balancing frontier vs low-latency models00:01:31 — Frontier models vs Flash models + role of distillation00:03:52 — History of distillation and its original motivation00:05:09 — Distillation's role in modern model scaling00:07:02 — Model hierarchy (Flash, Pro, Ultra) and distillation sources00:07:46 — Flash model economics & wide deployment00:08:10 — Latency importance for complex tasks00:09:19 — Saturation of some tasks and future frontier tasks00:11:26 — On benchmarks, public vs internal00:12:53 — Example long-context benchmarks & limitations00:15:01 — Long-context goals: attending to trillions of tokens00:16:26 — Realistic use cases beyond pure language00:18:04 — Multimodal reasoning and non-text modalities00:19:05 — Importance of vision & motion modalities00:20:11 — Video understanding example (extracting structured info)00:20:47 — Search ranking analogy for LLM retrieval00:23:08 — LLM representations vs keyword search00:24:06 — Early Google search evolution & in-memory index00:26:47 — Design principles for scalable systems00:28:55 — Real-time index updates & recrawl strategies00:30:06 — Classic “Latency numbers every programmer should know”00:32:09 — Cost of memory vs compute and energy emphasis00:34:33 — TPUs & hardware trade-offs for serving models00:35:57 — TPU design decisions & co-design with ML00:38:06 — Adapting model architecture to hardware00:39:50 — Alternatives: energy-based models, speculative decoding00:42:21 — Open research directions: complex workflows, RL00:44:56 — Non-verifiable RL domains & model evaluation00:46:13 — Transition away from symbolic systems toward unified LLMs00:47:59 — Unified models vs specialized ones00:50:38 — Knowledge vs reasoning & retrieval + reasoning00:52:24 — Vertical model specialization & modules00:55:21 — Token count considerations for vertical domains00:56:09 — Low resource languages & contextual learning00:59:22 — Origins: Dean's early neural network work01:10:07 — AI for coding & human–model interaction styles01:15:52 — Importance of crisp specification for coding agents01:19:23 — Prediction: personalized models & state retrieval01:22:36 — Token-per-second targets (10k+) and reasoning throughput01:23:20 — Episode conclusion and thanksTranscriptAlessio Fanelli [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space. Shawn Wang [00:00:11]: Hello, hello. We're here in the studio with Jeff Dean, chief AI scientist at Google. Welcome. Thanks for having me. It's a bit surreal to have you in the studio. I've watched so many of your talks, and obviously your career has been super legendary. So, I mean, congrats. I think the first thing must be said, congrats on owning the Pareto Frontier.Jeff Dean [00:00:30]: Thank you, thank you. Pareto Frontiers are good. It's good to be out there.Shawn Wang [00:00:34]: Yeah, I mean, I think it's a combination of both. You have to own the Pareto Frontier. You have to have like frontier capability, but also efficiency, and then offer that range of models that people like to use. And, you know, some part of this was started because of your hardware work. Some part of that is your model work, and I'm sure there's lots of secret sauce that you guys have worked on cumulatively. But, like, it's really impressive to see it all come together in, like, this slittily advanced.Jeff Dean [00:01:04]: Yeah, yeah. I mean, I think, as you say, it's not just one thing. It's like a whole bunch of things up and down the stack. And, you know, all of those really combine to help make UNOS able to make highly capable large models, as well as, you know, software techniques to get those large model capabilities into much smaller, lighter weight models that are, you know, much more cost effective and lower latency, but still, you know, quite capable for their size. Yeah.Alessio Fanelli [00:01:31]: How much pressure do you have on, like, having the lower bound of the Pareto Frontier, too? I think, like, the new labs are always trying to push the top performance frontier because they need to raise more money and all of that. And you guys have billions of users. And I think initially when you worked on the CPU, you were thinking about, you know, if everybody that used Google, we use the voice model for, like, three minutes a day, they were like, you need to double your CPU number. Like, what's that discussion today at Google? Like, how do you prioritize frontier versus, like, we have to do this? How do we actually need to deploy it if we build it?Jeff Dean [00:02:03]: Yeah, I mean, I think we always want to have models that are at the frontier or pushing the frontier because I think that's where you see what capabilities now exist that didn't exist at the sort of slightly less capable last year's version or last six months ago version. At the same time, you know, we know those are going to be really useful for a bunch of use cases, but they're going to be a bit slower and a bit more expensive than people might like for a bunch of other broader models. So I think what we want to do is always have kind of a highly capable sort of affordable model that enables a whole bunch of, you know, lower latency use cases. People can use them for agentic coding much more readily and then have the high-end, you know, frontier model that is really useful for, you know, deep reasoning, you know, solving really complicated math problems, those kinds of things. And it's not that. One or the other is useful. They're both useful. So I think we'd like to do both. And also, you know, through distillation, which is a key technique for making the smaller models more capable, you know, you have to have the frontier model in order to then distill it into your smaller model. So it's not like an either or choice. You sort of need that in order to actually get a highly capable, more modest size model. Yeah.Alessio Fanelli [00:03:24]: I mean, you and Jeffrey came up with the solution in 2014.Jeff Dean [00:03:28]: Don't forget, L'Oreal Vinyls as well. Yeah, yeah.Alessio Fanelli [00:03:30]: A long time ago. But like, I'm curious how you think about the cycle of these ideas, even like, you know, sparse models and, you know, how do you reevaluate them? How do you think about in the next generation of model, what is worth revisiting? Like, yeah, they're just kind of like, you know, you worked on so many ideas that end up being influential, but like in the moment, they might not feel that way necessarily. Yeah.Jeff Dean [00:03:52]: I mean, I think distillation was originally motivated because we were seeing that we had a very large image data set at the time, you know, 300 million images that we could train on. And we were seeing that if you create specialists for different subsets of those image categories, you know, this one's going to be really good at sort of mammals, and this one's going to be really good at sort of indoor room scenes or whatever, and you can cluster those categories and train on an enriched stream of data after you do pre-training on a much broader set of images. You get much better performance. If you then treat that whole set of maybe 50 models you've trained as a large ensemble, but that's not a very practical thing to serve, right? So distillation really came about from the idea of, okay, what if we want to actually serve that and train all these independent sort of expert models and then squish it into something that actually fits in a form factor that you can actually serve? And that's, you know, not that different from what we're doing today. You know, often today we're instead of having an ensemble of 50 models. We're having a much larger scale model that we then distill into a much smaller scale model.Shawn Wang [00:05:09]: Yeah. A part of me also wonders if distillation also has a story with the RL revolution. So let me maybe try to articulate what I mean by that, which is you can, RL basically spikes models in a certain part of the distribution. And then you have to sort of, well, you can spike models, but usually sometimes... It might be lossy in other areas and it's kind of like an uneven technique, but you can probably distill it back and you can, I think that the sort of general dream is to be able to advance capabilities without regressing on anything else. And I think like that, that whole capability merging without loss, I feel like it's like, you know, some part of that should be a distillation process, but I can't quite articulate it. I haven't seen much papers about it.Jeff Dean [00:06:01]: Yeah, I mean, I tend to think of one of the key advantages of distillation is that you can have a much smaller model and you can have a very large, you know, training data set and you can get utility out of making many passes over that data set because you're now getting the logits from the much larger model in order to sort of coax the right behavior out of the smaller model that you wouldn't otherwise get with just the hard labels. And so, you know, I think that's what we've observed. Is you can get, you know, very close to your largest model performance with distillation approaches. And that seems to be, you know, a nice sweet spot for a lot of people because it enables us to kind of, for multiple Gemini generations now, we've been able to make the sort of flash version of the next generation as good or even substantially better than the previous generations pro. And I think we're going to keep trying to do that because that seems like a good trend to follow.Shawn Wang [00:07:02]: So, Dara asked, so it was the original map was Flash Pro and Ultra. Are you just sitting on Ultra and distilling from that? Is that like the mother load?Jeff Dean [00:07:12]: I mean, we have a lot of different kinds of models. Some are internal ones that are not necessarily meant to be released or served. Some are, you know, our pro scale model and we can distill from that as well into our Flash scale model. So I think, you know, it's an important set of capabilities to have and also inference time scaling. It can also be a useful thing to improve the capabilities of the model.Shawn Wang [00:07:35]: And yeah, yeah, cool. Yeah. And obviously, I think the economy of Flash is what led to the total dominance. I think the latest number is like 50 trillion tokens. I don't know. I mean, obviously, it's changing every day.Jeff Dean [00:07:46]: Yeah, yeah. But, you know, by market share, hopefully up.Shawn Wang [00:07:50]: No, I mean, there's no I mean, there's just the economics wise, like because Flash is so economical, like you can use it for everything. Like it's in Gmail now. It's in YouTube. Like it's yeah. It's in everything.Jeff Dean [00:08:02]: We're using it more in our search products of various AI mode reviews.Shawn Wang [00:08:05]: Oh, my God. Flash past the AI mode. Oh, my God. Yeah, that's yeah, I didn't even think about that.Jeff Dean [00:08:10]: I mean, I think one of the things that is quite nice about the Flash model is not only is it more affordable, it's also a lower latency. And I think latency is actually a pretty important characteristic for these models because we're going to want models to do much more complicated things that are going to involve, you know, generating many more tokens from when you ask the model to do so. So, you know, if you're going to ask the model to do something until it actually finishes what you ask it to do, because you're going to ask now, not just write me a for loop, but like write me a whole software package to do X or Y or Z. And so having low latency systems that can do that seems really important. And Flash is one direction, one way of doing that. You know, obviously our hardware platforms enable a bunch of interesting aspects of our, you know, serving stack as well, like TPUs, the interconnect between. Chips on the TPUs is actually quite, quite high performance and quite amenable to, for example, long context kind of attention operations, you know, having sparse models with lots of experts. These kinds of things really, really matter a lot in terms of how do you make them servable at scale.Alessio Fanelli [00:09:19]: Yeah. Does it feel like there's some breaking point for like the proto Flash distillation, kind of like one generation delayed? I almost think about almost like the capability as a. In certain tasks, like the pro model today is a saturated, some sort of task. So next generation, that same task will be saturated at the Flash price point. And I think for most of the things that people use models for at some point, the Flash model in two generation will be able to do basically everything. And how do you make it economical to like keep pushing the pro frontier when a lot of the population will be okay with the Flash model? I'm curious how you think about that.Jeff Dean [00:09:59]: I mean, I think that's true. If your distribution of what people are asking people, the models to do is stationary, right? But I think what often happens is as the models become more capable, people ask them to do more, right? So, I mean, I think this happens in my own usage. Like I used to try our models a year ago for some sort of coding task, and it was okay at some simpler things, but wouldn't do work very well for more complicated things. And since then, we've improved dramatically on the more complicated coding tasks. And now I'll ask it to do much more complicated things. And I think that's true, not just of coding, but of, you know, now, you know, can you analyze all the, you know, renewable energy deployments in the world and give me a report on solar panel deployment or whatever. That's a very complicated, you know, more complicated task than people would have asked a year ago. And so you are going to want more capable models to push the frontier in the absence of what people ask the models to do. And that also then gives us. Insight into, okay, where does the, where do things break down? How can we improve the model in these, these particular areas, uh, in order to sort of, um, make the next generation even better.Alessio Fanelli [00:11:11]: Yeah. Are there any benchmarks or like test sets they use internally? Because it's almost like the same benchmarks get reported every time. And it's like, all right, it's like 99 instead of 97. Like, how do you have to keep pushing the team internally to it? Or like, this is what we're building towards. Yeah.Jeff Dean [00:11:26]: I mean, I think. Benchmarks, particularly external ones that are publicly available. Have their utility, but they often kind of have a lifespan of utility where they're introduced and maybe they're quite hard for current models. You know, I, I like to think of the best kinds of benchmarks are ones where the initial scores are like 10 to 20 or 30%, maybe, but not higher. And then you can sort of work on improving that capability for, uh, whatever it is, the benchmark is trying to assess and get it up to like 80, 90%, whatever. I, I think once it hits kind of 95% or something, you get very diminishing returns from really focusing on that benchmark, cuz it's sort of, it's either the case that you've now achieved that capability, or there's also the issue of leakage in public data or very related kind of data being, being in your training data. Um, so we have a bunch of held out internal benchmarks that we really look at where we know that wasn't represented in the training data at all. There are capabilities that we want the model to have. Um, yeah. Yeah. Um, that it doesn't have now, and then we can work on, you know, assessing, you know, how do we make the model better at these kinds of things? Is it, we need different kind of data to train on that's more specialized for this particular kind of task. Do we need, um, you know, a bunch of, uh, you know, architectural improvements or some sort of, uh, model capability improvements, you know, what would help make that better?Shawn Wang [00:12:53]: Is there, is there such an example that you, uh, a benchmark inspired in architectural improvement? Like, uh, I'm just kind of. Jumping on that because you just.Jeff Dean [00:13:02]: Uh, I mean, I think some of the long context capability of the, of the Gemini models that came, I guess, first in 1.5 really were about looking at, okay, we want to have, um, you know,Shawn Wang [00:13:15]: immediately everyone jumped to like completely green charts of like, everyone had, I was like, how did everyone crack this at the same time? Right. Yeah. Yeah.Jeff Dean [00:13:23]: I mean, I think, um, and once you're set, I mean, as you say that needed single needle and a half. Hey, stack benchmark is really saturated for at least context links up to 1, 2 and K or something. Don't actually have, you know, much larger than 1, 2 and 8 K these days or two or something. We're trying to push the frontier of 1 million or 2 million context, which is good because I think there are a lot of use cases where. Yeah. You know, putting a thousand pages of text or putting, you know, multiple hour long videos and the context and then actually being able to make use of that as useful. Try to, to explore the über graduation are fairly large. But the single needle in a haystack benchmark is sort of saturated. So you really want more complicated, sort of multi-needle or more realistic, take all this content and produce this kind of answer from a long context that sort of better assesses what it is people really want to do with long context. Which is not just, you know, can you tell me the product number for this particular thing?Shawn Wang [00:14:31]: Yeah, it's retrieval. It's retrieval within machine learning. It's interesting because I think the more meta level I'm trying to operate at here is you have a benchmark. You're like, okay, I see the architectural thing I need to do in order to go fix that. But should you do it? Because sometimes that's an inductive bias, basically. It's what Jason Wei, who used to work at Google, would say. Exactly the kind of thing. Yeah, you're going to win. Short term. Longer term, I don't know if that's going to scale. You might have to undo that.Jeff Dean [00:15:01]: I mean, I like to sort of not focus on exactly what solution we're going to derive, but what capability would you want? And I think we're very convinced that, you know, long context is useful, but it's way too short today. Right? Like, I think what you would really want is, can I attend to the internet while I answer my question? Right? But that's not going to happen. I think that's going to be solved by purely scaling the existing solutions, which are quadratic. So a million tokens kind of pushes what you can do. You're not going to do that to a trillion tokens, let alone, you know, a billion tokens, let alone a trillion. But I think if you could give the illusion that you can attend to trillions of tokens, that would be amazing. You'd find all kinds of uses for that. You would have attend to the internet. You could attend to the pixels of YouTube and the sort of deeper representations that we can find. You could attend to the form for a single video, but across many videos, you know, on a personal Gemini level, you could attend to all of your personal state with your permission. So like your emails, your photos, your docs, your plane tickets you have. I think that would be really, really useful. And the question is, how do you get algorithmic improvements and system level improvements that get you to something where you actually can attend to trillions of tokens? Right. In a meaningful way. Yeah.Shawn Wang [00:16:26]: But by the way, I think I did some math and it's like, if you spoke all day, every day for eight hours a day, you only generate a maximum of like a hundred K tokens, which like very comfortably fits.Jeff Dean [00:16:38]: Right. But if you then say, okay, I want to be able to understand everything people are putting on videos.Shawn Wang [00:16:46]: Well, also, I think that the classic example is you start going beyond language into like proteins and whatever else is extremely information dense. Yeah. Yeah.Jeff Dean [00:16:55]: I mean, I think one of the things about Gemini's multimodal aspects is we've always wanted it to be multimodal from the start. And so, you know, that sometimes to people means text and images and video sort of human-like and audio, audio, human-like modalities. But I think it's also really useful to have Gemini know about non-human modalities. Yeah. Like LIDAR sensor data from. Yes. Say, Waymo vehicles or. Like robots or, you know, various kinds of health modalities, x-rays and MRIs and imaging and genomics information. And I think there's probably hundreds of modalities of data where you'd like the model to be able to at least be exposed to the fact that this is an interesting modality and has certain meaning in the world. Where even if you haven't trained on all the LIDAR data or MRI data, you could have, because maybe that's not, you know, it doesn't make sense in terms of trade-offs of. You know, what you include in your main pre-training data mix, at least including a little bit of it is actually quite useful. Yeah. Because it sort of tempts the model that this is a thing.Shawn Wang [00:18:04]: Yeah. Do you believe, I mean, since we're on this topic and something I just get to ask you all the questions I always wanted to ask, which is fantastic. Like, are there some king modalities, like modalities that supersede all the other modalities? So a simple example was Vision can, on a pixel level, encode text. And DeepSeq had this DeepSeq CR paper that did that. Vision. And Vision has also been shown to maybe incorporate audio because you can do audio spectrograms and that's, that's also like a Vision capable thing. Like, so, so maybe Vision is just the king modality and like. Yeah.Jeff Dean [00:18:36]: I mean, Vision and Motion are quite important things, right? Motion. Well, like video as opposed to static images, because I mean, there's a reason evolution has evolved eyes like 23 independent ways, because it's such a useful capability for sensing the world around you, which is really what we want these models to be. So I think the only thing that we can be able to do is interpret the things we're seeing or the things we're paying attention to and then help us in using that information to do things. Yeah.Shawn Wang [00:19:05]: I think motion, you know, I still want to shout out, I think Gemini, still the only native video understanding model that's out there. So I use it for YouTube all the time. Nice.Jeff Dean [00:19:15]: Yeah. Yeah. I mean, it's actually, I think people kind of are not necessarily aware of what the Gemini models can actually do. Yeah. Like I have an example I've used in one of my talks. It had like, it was like a YouTube highlight video of 18 memorable sports moments across the last 20 years or something. So it has like Michael Jordan hitting some jump shot at the end of the finals and, you know, some soccer goals and things like that. And you can literally just give it the video and say, can you please make me a table of what all these different events are? What when the date is when they happened? And a short description. And so you get like now an 18 row table of that information extracted from the video, which is, you know, not something most people think of as like a turn video into sequel like table.Alessio Fanelli [00:20:11]: Has there been any discussion inside of Google of like, you mentioned tending to the whole internet, right? Google, it's almost built because a human cannot tend to the whole internet and you need some sort of ranking to find what you need. Yep. That ranking is like much different for an LLM because you can expect a person to look at maybe the first five, six links in a Google search versus for an LLM. Should you expect to have 20 links that are highly relevant? Like how do you internally figure out, you know, how do we build the AI mode that is like maybe like much broader search and span versus like the more human one? Yeah.Jeff Dean [00:20:47]: I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. With a giant number of web pages in our index, many of them are not relevant. So you identify a subset of them that are relevant with very lightweight kinds of methods. You know, you're down to like 30,000 documents or something. And then you gradually refine that to apply more and more sophisticated algorithms and more and more sophisticated sort of signals of various kinds in order to get down to ultimately what you show, which is, you know, the final 10 results or, you know, 10 results plus. Other kinds of information. And I think an LLM based system is not going to be that dissimilar, right? You're going to attend to trillions of tokens, but you're going to want to identify, you know, what are the 30,000 ish documents that are with the, you know, maybe 30 million interesting tokens. And then how do you go from that into what are the 117 documents I really should be paying attention to in order to carry out the tasks that the user has asked? And I think, you know, you can imagine systems where you have, you know, a lot of highly parallel processing to identify those initial 30,000 candidates, maybe with very lightweight kinds of models. Then you have some system that sort of helps you narrow down from 30,000 to the 117 with maybe a little bit more sophisticated model or set of models. And then maybe the final model is the thing that looks. So the 117 things that might be your most capable model. So I think it has to, it's going to be some system like that, that is really enables you to give the illusion of attending to trillions of tokens. Sort of the way Google search gives you, you know, not the illusion, but you are searching the internet, but you're finding, you know, a very small subset of things that are, that are relevant.Shawn Wang [00:22:47]: Yeah. I often tell a lot of people that are not steeped in like Google search history that, well, you know, like Bert was. Like he was like basically immediately inside of Google search and that improves results a lot, right? Like I don't, I don't have any numbers off the top of my head, but like, I'm sure you guys, that's obviously the most important numbers to Google. Yeah.Jeff Dean [00:23:08]: I mean, I think going to an LLM based representation of text and words and so on enables you to get out of the explicit hard notion of, of particular words having to be on the page, but really getting at the notion of this topic of this page or this page. Paragraph is highly relevant to this query. Yeah.Shawn Wang [00:23:28]: I don't think people understand how much LLMs have taken over all these very high traffic system, very high traffic. Yeah. Like it's Google, it's YouTube. YouTube has this like semantics ID thing where it's just like every token or every item in the vocab is a YouTube video or something that predicts the video using a code book, which is absurd to me for YouTube size.Jeff Dean [00:23:50]: And then most recently GROK also for, for XAI, which is like, yeah. I mean, I'll call out even before LLMs were used extensively in search, we put a lot of emphasis on softening the notion of what the user actually entered into the query.Shawn Wang [00:24:06]: So do you have like a history of like, what's the progression? Oh yeah.Jeff Dean [00:24:09]: I mean, I actually gave a talk in, uh, I guess, uh, web search and data mining conference in 2009, uh, where we never actually published any papers about the origins of Google search, uh, sort of, but we went through sort of four or five or six. generations, four or five or six generations of, uh, redesigning of the search and retrieval system, uh, from about 1999 through 2004 or five. And that talk is really about that evolution. And one of the things that really happened in 2001 was we were sort of working to scale the system in multiple dimensions. So one is we wanted to make our index bigger, so we could retrieve from a larger index, which always helps your quality in general. Uh, because if you don't have the page in your index, you're going to not do well. Um, and then we also needed to scale our capacity because we were, our traffic was growing quite extensively. Um, and so we had, you know, a sharded system where you have more and more shards as the index grows, you have like 30 shards. And then if you want to double the index size, you make 60 shards so that you can bound the latency by which you respond for any particular user query. Um, and then as traffic grows, you add, you add more and more replicas of each of those. And so we eventually did the math that realized that in a data center where we had say 60 shards and, um, you know, 20 copies of each shard, we now had 1200 machines, uh, with disks. And we did the math and we're like, Hey, one copy of that index would actually fit in memory across 1200 machines. So in 2001, we introduced, uh, we put our entire index in memory and what that enabled from a quality perspective was amazing. Um, and so we had more and more replicas of each of those. Before you had to be really careful about, you know, how many different terms you looked at for a query, because every one of them would involve a disk seek on every one of the 60 shards. And so you, as you make your index bigger, that becomes even more inefficient. But once you have the whole index in memory, it's totally fine to have 50 terms you throw into the query from the user's original three or four word query, because now you can add synonyms like restaurant and restaurants and cafe and, uh, you know, things like that. Uh, bistro and all these things. And you can suddenly start, uh, sort of really, uh, getting at the meaning of the word as opposed to the exact semantic form the user typed in. And that was, you know, 2001, very much pre LLM, but really it was about softening the, the strict definition of what the user typed in order to get at the meaning.Alessio Fanelli [00:26:47]: What are like principles that you use to like design the systems, especially when you have, I mean, in 2001, the internet is like. Doubling, tripling every year in size is not like, uh, you know, and I think today you kind of see that with LLMs too, where like every year the jumps in size and like capabilities are just so big. Are there just any, you know, principles that you use to like, think about this? Yeah.Jeff Dean [00:27:08]: I mean, I think, uh, you know, first, whenever you're designing a system, you want to understand what are the sort of design parameters that are going to be most important in designing that, you know? So, you know, how many queries per second do you need to handle? How big is the internet? How big is the index you need to handle? How much data do you need to keep for every document in the index? How are you going to look at it when you retrieve things? Um, what happens if traffic were to double or triple, you know, will that system work well? And I think a good design principle is you're going to want to design a system so that the most important characteristics could scale by like factors of five or 10, but probably not beyond that because often what happens is if you design a system for X. And something suddenly becomes a hundred X, that would enable a very different point in the design space that would not make sense at X. But all of a sudden at a hundred X makes total sense. So like going from a disk space index to a in memory index makes a lot of sense once you have enough traffic, because now you have enough replicas of the sort of state on disk that those machines now actually can hold, uh, you know, a full copy of the, uh, index and memory. Yeah. And that all of a sudden enabled. A completely different design that wouldn't have been practical before. Yeah. Um, so I'm, I'm a big fan of thinking through designs in your head, just kind of playing with the design space a little before you actually do a lot of writing of code. But, you know, as you said, in the early days of Google, we were growing the index, uh, quite extensively. We were growing the update rate of the index. So the update rate actually is the parameter that changed the most. Surprising. So it used to be once a month.Shawn Wang [00:28:55]: Yeah.Jeff Dean [00:28:56]: And then we went to a system that could update any particular page in like sub one minute. Okay.Shawn Wang [00:29:02]: Yeah. Because this is a competitive advantage, right?Jeff Dean [00:29:04]: Because all of a sudden news related queries, you know, if you're, if you've got last month's news index, it's not actually that useful for.Shawn Wang [00:29:11]: News is a special beast. Was there any, like you could have split it onto a separate system.Jeff Dean [00:29:15]: Well, we did. We launched a Google news product, but you also want news related queries that people type into the main index to also be sort of updated.Shawn Wang [00:29:23]: So, yeah, it's interesting. And then you have to like classify whether the page is, you have to decide which pages should be updated and what frequency. Oh yeah.Jeff Dean [00:29:30]: There's a whole like, uh, system behind the scenes that's trying to decide update rates and importance of the pages. So even if the update rate seems low, you might still want to recrawl important pages quite often because, uh, the likelihood they change might be low, but the value of having updated is high.Shawn Wang [00:29:50]: Yeah, yeah, yeah, yeah. Uh, well, you know, yeah. This, uh, you know, mention of latency and, and saving things to this reminds me of one of your classics, which I have to bring up, which is latency numbers. Every programmer should know, uh, was there a, was it just a, just a general story behind that? Did you like just write it down?Jeff Dean [00:30:06]: I mean, this has like sort of eight or 10 different kinds of metrics that are like, how long does a cache mistake? How long does branch mispredict take? How long does a reference domain memory take? How long does it take to send, you know, a packet from the U S to the Netherlands or something? Um,Shawn Wang [00:30:21]: why Netherlands, by the way, or is it, is that because of Chrome?Jeff Dean [00:30:25]: Uh, we had a data center in the Netherlands, um, so, I mean, I think this gets to the point of being able to do the back of the envelope calculations. So these are sort of the raw ingredients of those, and you can use them to say, okay, well, if I need to design a system to do image search and thumb nailing or something of the result page, you know, how, what I do that I could pre-compute the image thumbnails. I could like. Try to thumbnail them on the fly from the larger images. What would that do? How much dis bandwidth than I need? How many des seeks would I do? Um, and you can sort of actually do thought experiments in, you know, 30 seconds or a minute with the sort of, uh, basic, uh, basic numbers at your fingertips. Uh, and then as you sort of build software using higher level libraries, you kind of want to develop the same intuitions for how long does it take to, you know, look up something in this particular kind of.Shawn Wang [00:31:21]: I'll see you next time.Shawn Wang [00:31:51]: Which is a simple byte conversion. That's nothing interesting. I wonder if you have any, if you were to update your...Jeff Dean [00:31:58]: I mean, I think it's really good to think about calculations you're doing in a model, either for training or inference.Jeff Dean [00:32:09]: Often a good way to view that is how much state will you need to bring in from memory, either like on-chip SRAM or HBM from the accelerator. Attached memory or DRAM or over the network. And then how expensive is that data motion relative to the cost of, say, an actual multiply in the matrix multiply unit? And that cost is actually really, really low, right? Because it's order, depending on your precision, I think it's like sub one picodule.Shawn Wang [00:32:50]: Oh, okay. You measure it by energy. Yeah. Yeah.Jeff Dean [00:32:52]: Yeah. I mean, it's all going to be about energy and how do you make the most energy efficient system. And then moving data from the SRAM on the other side of the chip, not even off the off chip, but on the other side of the same chip can be, you know, a thousand picodules. Oh, yeah. And so all of a sudden, this is why your accelerators require batching. Because if you move, like, say, the parameter of a model from SRAM on the, on the chip into the multiplier unit, that's going to cost you a thousand picodules. So you better make use of that, that thing that you moved many, many times with. So that's where the batch dimension comes in. Because all of a sudden, you know, if you have a batch of 256 or something, that's not so bad. But if you have a batch of one, that's really not good.Shawn Wang [00:33:40]: Yeah. Yeah. Right.Jeff Dean [00:33:41]: Because then you paid a thousand picodules in order to do your one picodule multiply.Shawn Wang [00:33:46]: I have never heard an energy-based analysis of batching.Jeff Dean [00:33:50]: Yeah. I mean, that's why people batch. Yeah. Ideally, you'd like to use batch size one because the latency would be great.Shawn Wang [00:33:56]: The best latency.Jeff Dean [00:33:56]: But the energy cost and the compute cost inefficiency that you get is quite large. So, yeah.Shawn Wang [00:34:04]: Is there a similar trick like, like, like you did with, you know, putting everything in memory? Like, you know, I think obviously NVIDIA has caused a lot of waves with betting very hard on SRAM with Grok. I wonder if, like, that's something that you already saw with, with the TPUs, right? Like that, that you had to. Uh, to serve at your scale, uh, you probably sort of saw that coming. Like what, what, what hardware, uh, innovations or insights were formed because of what you're seeing there?Jeff Dean [00:34:33]: Yeah. I mean, I think, you know, TPUs have this nice, uh, sort of regular structure of 2D or 3D meshes with a bunch of chips connected. Yeah. And each one of those has HBM attached. Um, I think for serving some kinds of models, uh, you know, you, you pay a lot higher cost. Uh, and time latency, um, bringing things in from HBM than you do bringing them in from, uh, SRAM on the chip. So if you have a small enough model, you can actually do model parallelism, spread it out over lots of chips and you actually get quite good throughput improvements and latency improvements from doing that. And so you're now sort of striping your smallish scale model over say 16 or 64 chips. Uh, but as if you do that and it all fits in. In SRAM, uh, that can be a big win. So yeah, that's not a surprise, but it is a good technique.Alessio Fanelli [00:35:27]: Yeah. What about the TPU design? Like how much do you decide where the improvements have to go? So like, this is like a good example of like, is there a way to bring the thousand picojoules down to 50? Like, is it worth designing a new chip to do that? The extreme is like when people say, oh, you should burn the model on the ASIC and that's kind of like the most extreme thing. How much of it? Is it worth doing an hardware when things change so quickly? Like what was the internal discussion? Yeah.Jeff Dean [00:35:57]: I mean, we, we have a lot of interaction between say the TPU chip design architecture team and the sort of higher level modeling, uh, experts, because you really want to take advantage of being able to co-design what should future TPUs look like based on where we think the sort of ML research puck is going, uh, in some sense, because, uh, you know, as a hardware designer for ML and in particular, you're trying to design a chip starting today and that design might take two years before it even lands in a data center. And then it has to sort of be a reasonable lifetime of the chip to take you three, four or five years. So you're trying to predict two to six years out where, what ML computations will people want to run two to six years out in a very fast changing field. And so having people with interest. Interesting ML research ideas of things we think will start to work in that timeframe or will be more important in that timeframe, uh, really enables us to then get, you know, interesting hardware features put into, you know, TPU N plus two, where TPU N is what we have today.Shawn Wang [00:37:10]: Oh, the cycle time is plus two.Jeff Dean [00:37:12]: Roughly. Wow. Because, uh, I mean, sometimes you can squeeze some changes into N plus one, but, you know, bigger changes are going to require the chip. Yeah. Design be earlier in its lifetime design process. Um, so whenever we can do that, it's generally good. And sometimes you can put in speculative features that maybe won't cost you much chip area, but if it works out, it would make something, you know, 10 times as fast. And if it doesn't work out, well, you burned a little bit of tiny amount of your chip area on that thing, but it's not that big a deal. Uh, sometimes it's a very big change and we want to be pretty sure this is going to work out. So we'll do like lots of carefulness. Uh, ML experimentation to show us, uh, this is actually the, the way we want to go. Yeah.Alessio Fanelli [00:37:58]: Is there a reverse of like, we already committed to this chip design so we can not take the model architecture that way because it doesn't quite fit?Jeff Dean [00:38:06]: Yeah. I mean, you, you definitely have things where you're going to adapt what the model architecture looks like so that they're efficient on the chips that you're going to have for both training and inference of that, of that, uh, generation of model. So I think it kind of goes both ways. Um, you know, sometimes you can take advantage of, you know, lower precision things that are coming in a future generation. So you can, might train it at that lower precision, even if the current generation doesn't quite do that. Mm.Shawn Wang [00:38:40]: Yeah. How low can we go in precision?Jeff Dean [00:38:43]: Because people are saying like ternary is like, uh, yeah, I mean, I'm a big fan of very low precision because I think that gets, that saves you a tremendous amount of time. Right. Because it's picojoules per bit that you're transferring and reducing the number of bits is a really good way to, to reduce that. Um, you know, I think people have gotten a lot of luck, uh, mileage out of having very low bit precision things, but then having scaling factors that apply to a whole bunch of, uh, those, those weights. Scaling. How does it, how does it, okay.Shawn Wang [00:39:15]: Interesting. You, so low, low precision, but scaled up weights. Yeah. Huh. Yeah. Never considered that. Yeah. Interesting. Uh, w w while we're on this topic, you know, I think there's a lot of, um, uh, this, the concept of precision at all is weird when we're sampling, you know, uh, we just, at the end of this, we're going to have all these like chips that I'll do like very good math. And then we're just going to throw a random number generator at the start. So, I mean, there's a movement towards, uh, energy based, uh, models and processors. I'm just curious if you've, obviously you've thought about it, but like, what's your commentary?Jeff Dean [00:39:50]: Yeah. I mean, I think. There's a bunch of interesting trends though. Energy based models is one, you know, diffusion based models, which don't sort of sequentially decode tokens is another, um, you know, speculative decoding is a way that you can get sort of an equivalent, very small.Shawn Wang [00:40:06]: Draft.Jeff Dean [00:40:07]: Batch factor, uh, for like you predict eight tokens out and that enables you to sort of increase the effective batch size of what you're doing by a factor of eight, even, and then you maybe accept five or six of those tokens. So you get. A five, a five X improvement in the amortization of moving weights, uh, into the multipliers to do the prediction for the, the tokens. So these are all really good techniques and I think it's really good to look at them from the lens of, uh, energy, real energy, not energy based models, um, and, and also latency and throughput, right? If you look at things from that lens, that sort of guides you to. Two solutions that are gonna be, uh, you know, better from, uh, you know, being able to serve larger models or, you know, equivalent size models more cheaply and with lower latency.Shawn Wang [00:41:03]: Yeah. Well, I think, I think I, um, it's appealing intellectually, uh, haven't seen it like really hit the mainstream, but, um, I do think that, uh, there's some poetry in the sense that, uh, you know, we don't have to do, uh, a lot of shenanigans if like we fundamentally. Design it into the hardware. Yeah, yeah.Jeff Dean [00:41:23]: I mean, I think there's still a, there's also sort of the more exotic things like analog based, uh, uh, computing substrates as opposed to digital ones. Uh, I'm, you know, I think those are super interesting cause they can be potentially low power. Uh, but I think you often end up wanting to interface that with digital systems and you end up losing a lot of the power advantages in the digital to analog and analog to digital conversions. You end up doing, uh, at the sort of boundaries. And periphery of that system. Um, I still think there's a tremendous distance we can go from where we are today in terms of energy efficiency with sort of, uh, much better and specialized hardware for the models we care about.Shawn Wang [00:42:05]: Yeah.Alessio Fanelli [00:42:06]: Um, any other interesting research ideas that you've seen, or like maybe things that you cannot pursue a Google that you would be interested in seeing researchers take a step at, I guess you have a lot of researchers. Yeah, I guess you have enough, but our, our research.Jeff Dean [00:42:21]: Our research portfolio is pretty broad. I would say, um, I mean, I think, uh, in terms of research directions, there's a whole bunch of, uh, you know, open problems and how do you make these models reliable and able to do much longer, kind of, uh, more complex tasks that have lots of subtasks. How do you orchestrate, you know, maybe one model that's using other models as tools in order to sort of build, uh, things that can accomplish, uh, you know, much more. Yeah. Significant pieces of work, uh, collectively, then you would ask a single model to do. Um, so that's super interesting. How do you get more verifiable, uh, you know, how do you get RL to work for non-verifiable domains? I think it's a pretty interesting open problem because I think that would broaden out the capabilities of the models, the improvements that you're seeing in both math and coding. Uh, if we could apply those to other less verifiable domains, because we've come up with RL techniques that actually enable us to do that. Uh, effectively, that would, that would really make the models improve quite a lot. I think.Alessio Fanelli [00:43:26]: I'm curious, like when we had Noam Brown on the podcast, he said, um, they already proved you can do it with deep research. Um, you kind of have it with AI mode in a way it's not verifiable. I'm curious if there's any thread that you think is interesting there. Like what is it? Both are like information retrieval of JSON. So I wonder if it's like the retrieval is like the verifiable part. That you can score or what are like, yeah, yeah. How, how would you model that, that problem?Jeff Dean [00:43:55]: Yeah. I mean, I think there are ways of having other models that can evaluate the results of what a first model did, maybe even retrieving. Can you have another model that says, is this things, are these things you retrieved relevant? Or can you rate these 2000 things you retrieved to assess which ones are the 50 most relevant or something? Um, I think those kinds of techniques are actually quite effective. Sometimes I can even be the same model, just prompted differently to be a, you know, a critic as opposed to a, uh, actual retrieval system. Yeah.Shawn Wang [00:44:28]: Um, I do think like there, there is that, that weird cliff where like, it feels like we've done the easy stuff and then now it's, but it always feels like that every year. It's like, oh, like we know, we know, and the next part is super hard and nobody's figured it out. And, uh, exactly with this RLVR thing where like everyone's talking about, well, okay, how do we. the next stage of the non-verifiable stuff. And everyone's like, I don't know, you know, Ellen judge.Jeff Dean [00:44:56]: I mean, I feel like the nice thing about this field is there's lots and lots of smart people thinking about creative solutions to some of the problems that we all see. Uh, because I think everyone sort of sees that the models, you know, are great at some things and they fall down around the edges of those things and, and are not as capable as we'd like in those areas. And then coming up with good techniques and trying those. And seeing which ones actually make a difference is sort of what the whole research aspect of this field is, is pushing forward. And I think that's why it's super interesting. You know, if you think about two years ago, we were struggling with GSM, eight K problems, right? Like, you know, Fred has two rabbits. He gets three more rabbits. How many rabbits does he have? That's a pretty far cry from the kinds of mathematics that the models can, and now you're doing IMO and Erdos problems in pure language. Yeah. Yeah. Pure language. So that is a really, really amazing jump in capabilities in, you know, in a year and a half or something. And I think, um, for other areas, it'd be great if we could make that kind of leap. Uh, and you know, we don't exactly see how to do it for some, some areas, but we do see it for some other areas and we're going to work hard on making that better. Yeah.Shawn Wang [00:46:13]: Yeah.Alessio Fanelli [00:46:14]: Like YouTube thumbnail generation. That would be very helpful. We need that. That would be AGI. We need that.Shawn Wang [00:46:20]: That would be. As far as content creators go.Jeff Dean [00:46:22]: I guess I'm not a YouTube creator, so I don't care that much about that problem, but I guess, uh, many people do.Shawn Wang [00:46:27]: It does. Yeah. It doesn't, it doesn't matter. People do judge books by their covers as it turns out. Um, uh, just to draw a bit on the IMO goal. Um, I'm still not over the fact that a year ago we had alpha proof and alpha geometry and all those things. And then this year we were like, screw that we'll just chuck it into Gemini. Yeah. What's your reflection? Like, I think this, this question about. Like the merger of like symbolic systems and like, and, and LMS, uh, was a very much core belief. And then somewhere along the line, people would just said, Nope, we'll just all do it in the LLM.Jeff Dean [00:47:02]: Yeah. I mean, I think it makes a lot of sense to me because, you know, humans manipulate symbols, but we probably don't have like a symbolic representation in our heads. Right. We have some distributed representation that is neural net, like in some way of lots of different neurons. And activation patterns firing when we see certain things and that enables us to reason and plan and, you know, do chains of thought and, you know, roll them back now that, that approach for solving the problem doesn't seem like it's going to work. I'm going to try this one. And, you know, in a lot of ways we're emulating what we intuitively think, uh, is happening inside real brains in neural net based models. So it never made sense to me to have like completely separate. Uh, discrete, uh, symbolic things, and then a completely different way of, of, uh, you know, thinking about those things.Shawn Wang [00:47:59]: Interesting. Yeah. Uh, I mean, it's maybe seems obvious to you, but it wasn't obvious to me a year ago. Yeah.Jeff Dean [00:48:06]: I mean, I do think like that IMO with, you know, translating to lean and using lean and then the next year and also a specialized geometry model. And then this year switching to a single unified model. That is roughly the production model with a little bit more inference budget, uh, is actually, you know, quite good because it shows you that the capabilities of that general model have improved dramatically and, and now you don't need the specialized model. This is actually sort of very similar to the 2013 to 16 era of machine learning, right? Like it used to be, people would train separate models for lots of different, each different problem, right? I have, I want to recognize street signs and something. So I train a street sign. Recognition recognition model, or I want to, you know, decode speech recognition. I have a speech model, right? I think now the era of unified models that do everything is really upon us. And the question is how well do those models generalize to new things they've never been asked to do and they're getting better and better.Shawn Wang [00:49:10]: And you don't need domain experts. Like one of my, uh, so I interviewed ETA who was on, who was on that team. Uh, and he was like, yeah, I, I don't know how they work. I don't know where the IMO competition was held. I don't know the rules of it. I just trained the models, the training models. Yeah. Yeah. And it's kind of interesting that like people with these, this like universal skill set of just like machine learning, you just give them data and give them enough compute and they can kind of tackle any task, which is the bitter lesson, I guess. I don't know. Yeah.Jeff Dean [00:49:39]: I mean, I think, uh, general models, uh, will win out over specialized ones in most cases.Shawn Wang [00:49:45]: Uh, so I want to push there a bit. I think there's one hole here, which is like, uh. There's this concept of like, uh, maybe capacity of a model, like abstractly a model can only contain the number of bits that it has. And, uh, and so it, you know, God knows like Gemini pro is like one to 10 trillion parameters. We don't know, but, uh, the Gemma models, for example, right? Like a lot of people want like the open source local models that are like that, that, that, and, and, uh, they have some knowledge, which is not necessary, right? Like they can't know everything like, like you have the. The luxury of you have the big model and big model should be able to capable of everything. But like when, when you're distilling and you're going down to the small models, you know, you're actually memorizing things that are not useful. Yeah. And so like, how do we, I guess, do we want to extract that? Can we, can we divorce knowledge from reasoning, you know?Jeff Dean [00:50:38]: Yeah. I mean, I think you do want the model to be most effective at reasoning if it can retrieve things, right? Because having the model devote precious parameter space. To remembering obscure facts that could be looked up is actually not the best use of that parameter space, right? Like you might prefer something that is more generally useful in more settings than this obscure fact that it has. Um, so I think that's always attention at the same time. You also don't want your model to be kind of completely detached from, you know, knowing stuff about the world, right? Like it's probably useful to know how long the golden gate be. Bridges just as a general sense of like how long are bridges, right? And, uh, it should have that kind of knowledge. It maybe doesn't need to know how long some teeny little bridge in some other more obscure part of the world is, but, uh, it does help it to have a fair bit of world knowledge and the bigger your model is, the more you can have. Uh, but I do think combining retrieval with sort of reasoning and making the model really good at doing multiple stages of retrieval. Yeah.Shawn Wang [00:51:49]: And reasoning through the intermediate retrieval results is going to be a, a pretty effective way of making the model seem much more capable, because if you think about, say, a personal Gemini, yeah, right?Jeff Dean [00:52:01]: Like we're not going to train Gemini on my email. Probably we'd rather have a single model that, uh, we can then use and use being able to retrieve from my email as a tool and have the model reason about it and retrieve from my photos or whatever, uh, and then make use of that and have multiple. Um, you know, uh, stages of interaction. that makes sense.Alessio Fanelli [00:52:24]: Do you think the vertical models are like, uh, interesting pursuit? Like when people are like, oh, we're building the best healthcare LLM, we're building the best law LLM, are those kind of like short-term stopgaps or?Jeff Dean [00:52:37]: No, I mean, I think, I think vertical models are interesting. Like you want them to start from a pretty good base model, but then you can sort of, uh, sort of viewing them, view them as enriching the data. Data distribution for that particular vertical domain for healthcare, say, um, we're probably not going to train or for say robotics. We're probably not going to train Gemini on all possible robotics data. We, you could train it on because we want it to have a balanced set of capabilities. Um, so we'll expose it to some robotics data, but if you're trying to build a really, really good robotics model, you're going to want to start with that and then train it on more robotics data. And then maybe that would. It's multilingual translation capability, but improve its robotics capabilities. And we're always making these kind of, uh, you know, trade-offs in the data mix that we train the base Gemini models on. You know, we'd love to include data from 200 more languages and as much data as we have for those languages, but that's going to displace some other capabilities of the model. It won't be as good at, um, you know, Pearl programming, you know, it'll still be good at Python programming. Cause we'll include it. Enough. Of that, but there's other long tail computer languages or coding capabilities that it may suffer on or multi, uh, multimodal reasoning capabilities may suffer. Cause we didn't get to expose it to as much data there, but it's really good at multilingual things. So I, I think some combination of specialized models, maybe more modular models. So it'd be nice to have the capability to have those 200 languages, plus this awesome robotics model, plus this awesome healthcare, uh, module that all can be knitted together to work in concert and called upon in different circumstances. Right? Like if I have a health related thing, then it should enable using this health module in conjunction with the main base model to be even better at those kinds of things. Yeah.Shawn Wang [00:54:36]: Installable knowledge. Yeah.Jeff Dean [00:54:37]: Right.Shawn Wang [00:54:38]: Just download as a, as a package.Jeff Dean [00:54:39]: And some of that installable stuff can come from retrieval, but some of it probably should come from preloaded training on, you know, uh, a hundred billion tokens or a trillion tokens of health data. Yeah.Shawn Wang [00:54:51]: And for listeners, I think, uh, I will highlight the Gemma three end paper where they, there was a little bit of that, I think. Yeah.Alessio Fanelli [00:54:56]: Yeah. I guess the question is like, how many billions of tokens do you need to outpace the frontier model improvements? You know, it's like, if I have to make this model better healthcare and the main. Gemini model is still improving. Do I need 50 billion tokens? Can I do it with a hundred, if I need a trillion healthcare tokens, it's like, they're probably not out there that you don't have, you know, I think that's really like the.Jeff Dean [00:55:21]: Well, I mean, I think healthcare is a particularly challenging domain, so there's a lot of healthcare data that, you know, we don't have access to appropriately, but there's a lot of, you know, uh, healthcare organizations that want to train models on their own data. That is not public healthcare data, uh, not public health. But public healthcare data. Um, so I think there are opportunities there to say, partner with a large healthcare organization and train models for their use that are going to be, you know, more bespoke, but probably, uh, might be better than a general model trained on say, public data. Yeah.Shawn Wang [00:55:58]: Yeah. I, I believe, uh, by the way, also this is like somewhat related to the language conversation. Uh, I think one of your, your favorite examples was you can put a low resource language in the context and it just learns. Yeah.Jeff Dean [00:56:09]: Oh, yeah, I think the example we used was Calamon, which is truly low resource because it's only spoken by, I think 120 people in the world and there's no written text.Shawn Wang [00:56:20]: So, yeah. So you can just do it that way. Just put it in the context. Yeah. Yeah. But I think your whole data set in the context, right.Jeff Dean [00:56:27]: If you, if you take a language like, uh, you know, Somali or something, there is a fair bit of Somali text in the world that, uh, or Ethiopian Amharic or something, um, you know, we probably. Yeah. Are not putting all the data from those languages into the Gemini based training. We put some of it, but if you put more of it, you'll improve the capabilities of those models.Shawn Wang [00:56:49]: Yeah.Jeff Dean [00:56:49]:

Estefanía era en 1996 director de Opinión en el periódico El País y el día después del asesinato de Tomás y Valiente, El País publicó un artículo suyo, un artículo que escribió antes de morir a raíz del asesinato de Fernando Múgica a manos de ETA. Fue Estefanía quien se lo pidió. 

El programa "Mediodía Cope" informa que maquinistas de Adif están inactivos, cobrando por trenes nuevos sin homologar desde 2021, aunque Adif garantiza la seguridad. El temporal "Neels" causa graves estragos en España: Cataluña sin clase ni vuelos, Galicia con vientos, Júcar en rojo y desalojos. Llega "Oriana" con más nevadas. En el Congreso, se aprueba la ley de multirreincidencia ampliamente, endureciendo penas por delitos menores, Sumar critica. Italia agiliza la deportación de inmigrantes. Sánchez se ausenta de reunión europea clave. El Supremo celebra audiencia del caso mascarillas. España supera 49,5 millones de habitantes, con escasez de vivienda y caída de construcción oficial. La recaudación fiscal 2025 excede 300.000 millones, impulsada por precios y crecimiento. El etarra Asier Arzayus obtiene el tercer grado, denunciándose más beneficios a miembros de ETA. Madrid lidera creación de empresas en 2025. El Círculo de Bellas Artes celebra su centenario. AVE Andalucía retrasado; ...

Santiago González comenta la pregunta que el líder de los socialistas vascos ha hecho a Feijóo a cuenta de la libertad de Txeroki.

How To Provide a Release Plan Without Losing Agility - Mike CohnStakeholders want to know what will be delivered, and when. Your team wants to stay agile. So how do you create a roadmap (aka release plan or milestone plan) without locking down every detail? I'm about to start on a road trip between Idaho and Colorado: a 16-hour drive. I know where I'm going, and my general route, but I don't know every turn I'll take — and that's fine.That's how agile teams should treat release plans and roadmaps.My route is a plan, not a promise. It's not set in stone. The turns I made and my ETA could change based on roadwork, traffic congestion, an opportunity for an exciting detour, or even a flat tire. The further the distance I have to travel, the more uncertainty I should expect.Agile plans are the same. We can't predict every eventuality, but we can provide a forecast. We can provide a general idea of where we are planning to go, a predicted range of when we will likely hit key milestones, and our confidence level in the plan. Most agile teams know there's too much uncertainty to make guarantees. At the same time, they feel like a guarantee is the only thing stakeholders will accept.Here's what agile teams might be missing: Stakeholders have their own plans to make. And they are just as worried about being held accountable to their predictions as teams are.Stakeholders need accurate delivery dates and milestones (note I didn't say precise). They crave predictability.Sometimes it might feel like they're asking for a guarantee. But in truth, the only way to give them absolute certainty is to Overpad your estimates (like me telling someone my 16-hour drive will take 24, just in case), orRefuse to adapt when conditions change. Neither is good for the product, or the team. So what can you do when a stakeholder seems to want a guarantee vs a forecast? Try this: Talk to stakeholders in terms they understand.Here's one technique I've found helpful:Compare their request to requests for similar forecasts in their own domain.For example: Ask a salesperson what their comfort level would be if they were asked to guarantee exactly how much they'll sell — and which customers they'll close — in each of the next six months, or in the first year of a product's release.Ask a marketing person what their concerns would be if asked to commit to specific campaign results with exact timelines.Don't be confrontational. The point isn't to trap them — it's to show that uncertainty exists everywhere, and that agility is a strength, not a weakness. Then, share my road trip analogy with your stakeholders. Tell them that you can't give them a guarantee, but you can present a roadmap that looks ahead 3-6 months. The roadmap will show the team's goal, how much progress you believe you can make by when (expressed as a range), and your team's confidence in the plan.  Need help communicating your plans? Try our Plan Visualizer Tool, free for all MGS Essentials members.   Remind stakeholders that, like suggested routes on a long trip, agile roadmaps provide visibility, align expectations, and help people plan — without pretending every turn is known in advance.Freeing your team from unrealistic expectations can accelerate their move from good to great.A roadmap is a plan, not a promise Why stakeholders push for guarantees  The path to alignment starts with empathy Give stakeholders what they need to succeed How to connect with AgileDad:- [website] ⁠https://www.agiledad.com/⁠- [instagram] ⁠https://www.instagram.com/agile_coach/⁠- [facebook] ⁠https://www.facebook.com/RealAgileDad/⁠- [Linkedin] ⁠https://www.linkedin.com/in/leehenson/

El exlíder de ETA, Garicoi Zaspiaz Urrubina, alias 'Txeroki', disfruta desde hace tres días de un régimen de semilibertad. Este beneficio, considerado un tercer grado encubierto, ha sido concedido por el Gobierno Vasco gracias a las competencias penitenciarias cedidas por Pedro Sánchez. La decisión permite al terrorista salir a diario de la cárcel para trabajar y realizar un voluntariado, regresando solo para dormir.Txeroki acumula condenas que suman casi 400 años de prisión por su brutal historial. Entre sus crímenes se encuentra el atentado contra Eduardo Madina, que resultó en la amputación de una pierna, y la orden del ataque en la T4 del aeropuerto de Barajas, donde murieron dos personas. Su mayor condena, de 377 años, fue por el intento de asesinato de la exteniente de alcalde de Portugalete, Esther Cabezudo.La periodista Marisa Guerrero, exdelegada de Antena 3 en el País Vasco, ha relatado en el programa 'Herrera en COPE' el intento de asesinato que sufrió por ...

durée : 00:56:47 - Kherau, Miren eta Roberto Etxebarria, Mirua Vous aimez ce podcast ? Pour écouter tous les autres épisodes sans limite, rendez-vous sur Radio France.

Un reportaje de Josema Jiménez, con la narración de Aimar Bretos, reconstruye el atentado con el que ETA acabó con la vida de Francisco Tomás y Valiente

Las elecciones aragonesas: PP gana, pero pierde 2 escaños, fortaleciendo a VOX. Azcón necesita su apoyo, lo que debilita su posición. VOX exige participación con peso. Feijóo pide responsabilidad a VOX; Abascal critica la ambigüedad del PP. Pilar Alegría (PSOE) logra un resultado bajo, que achaca a Sánchez, sin dimitir. Chunta dobla escaños; Podemos desaparece. Gobierno complejo, con implicaciones para las generales. El ascenso de VOX se atribuye al "sanchismo" y al descontento. Sánchez aspira a mantener su mandato hasta septiembre de 2027. Acuerdo ferroviario mejora inversión, empleo y pausas de maquinistas, evitando una huelga tras incidentes que generaron inseguridad ciudadana. Gobierno vasco da semilibertad al exjefe de ETA, Cheroki. Indignación por falta de arrepentimiento y percibida instrumentalización de la política penitenciaria por Bildu. Actriz Elisa Moulia mantiene denuncia por agresión sexual contra Íñigo Errejón, pese a petición fiscal de absolución. Venezuela: ...

durée : 00:59:19 - Ozaze-Zühara, Iruri, Mendikota eta Zalgize-Doneztebeko gazteek maskarada herriz herri aurkezten dute, giro alaiean - Aurten lau herrietako gazteak elkartu dira, maskaradarendako aski jende biltzeko. 35 gazteek 15 emanaldi herriz herri segurtatuko dituzte, Apirilaren 25a arte. Muskildin, Xexili Foix-k maskarada goizetik arrats segitu du, giroa Hemengo Magazinan partekatzeko. Vous aimez ce podcast ? Pour écouter tous les autres épisodes sans limite, rendez-vous sur Radio France.

durée : 00:59:19 - Ozaze-Zühara, Iruri, Mendikota eta Zalgize-Doneztebeko gazteek maskarada herriz herri aurkezten dute, giro alaiean - Aurten lau herrietako gazteak elkartu dira, maskaradarendako aski jende biltzeko. 35 gazteek 15 emanaldi herriz herri segurtatuko dituzte, Apirilaren 25a arte. Muskildin, Xexili Foix-k maskarada goizetik arrats segitu du, giroa Hemengo Magazinan partekatzeko. Vous aimez ce podcast ? Pour écouter tous les autres épisodes sans limite, rendez-vous sur Radio France.

Thinking about buying a business for the first time? Most people don't fail because they're lazy — they fail because they're alone. In this episode of the Franchise Fit Podcast, Lance Graulich explains the biggest advantage of buying a franchise: you're in business for yourself… but not by yourself.We break down franchising vs. buying a business through acquisition (ETA), the “support stack” that reduces risk, and why AI + franchise community is a major edge in 2026.Sponsored by SEO Samba (AI-driven franchise marketing).

FILMOTECAMURCIA.ESViernes 6 de febrero / 20:30 horas / Entrada libre hasta completar aforoLa película de su vida: Yayo Delgado. (Responsable de Comunicación y Relaciones Externas de Estrella de Levante) Evasión o victoria (John Huston, 1981). Estados Unidos. VOSESegunda Guerra Mundial, año 1943. El comandante del campo de concentración de Gensdorff, que antes de la guerra había formado parte de la selección alemana de fútbol, se interesa por un grupo de prisioneros que practica este deporte. Se le ocurre entonces la idea de organizar un partido en el que se enfrenten una selección alemana y una selección formada por prisioneros de guerra. Aunque al principio los aliados rechazan la propuesta, al final aceptan el desafío. Sábado 7 de febrero / 21:15 horas Premios Goya: El cine que nos uneUn fantasma en la batalla (Agustín Díaz Yanes, 2025). España. 105'.Amaia es una joven guardia civil que permanece más de una década trabajando como agente encubierta dentro de ETA, con el objetivo de localizar los zulos que la banda terrorista tenía escondidos en el sur de Francia... Inspirada en las vidas y experiencias de varios miembros de la Guardia Civil directamente involucrados en la lucha antiterrorista y cimentada en el contexto histórico, político y social de los años 90 y los 2000 en España. Martes 10 de febrero / 18:30 horasPremios Goya: El cine que nos uneSorda (Eva Libertad, 2025). España. 99'.Ángela, una mujer sorda, va a tener un bebé con Héctor, su pareja oyente. El embarazo hace aflorar sus miedos frente la maternidad y sobre cómo podrá comunicarse con su hija. La llegada de la niña genera una crisis en la pareja y lleva a Ángela a afrontar la crianza de su hija en un mundo que no está hecho para ella. Largometraje basado en el corto de 2021 del mismo nombre. ESTRENOS DE LA SEMANA Primate (Johannes Roberts, 89´)Johnny Sequoya, Jessica Alexander, Kevin McNallyTras regresar de la universidad, Lucy se reúne con su familia, incluido el chimpancé Ben. Pero el simio contrae la rabia durante una fiesta en la piscina y se vuelve muy agresivo. Lucy y sus amigos se atrincheran en la piscina e idean formas de sobrevivir a los ataques del feroz chimpancé. La fiera (Salvador calvo, 113´)Carlos Cuevas, Miguel Bernardeau, Miguel Ángel SilvestreCuenta la historia de Carlos Suárez, Darío Barrio y Álvaro Bultó, tres amigos a los que les unía su pasión por los deportes extremos, que descubren la experiencia más cercana a volar: el salto BASE con traje de alas.Hellboy: El hombre retorcido (Brian Tylor, 100´)Jack Kesy, Adeline Rudolph, Jefferson WhiteHellboy y un agente novato del BPRD se quedan varados en los Apalaches rurales de los años cincuenta. Allí descubren una pequeña comunidad embrujada, liderada por el Hombre Torcido.

durée : 00:55:48 - Bide Ertzean, Zea Mays, Anje Duhalde eta Benito Lertxundi Vous aimez ce podcast ? Pour écouter tous les autres épisodes sans limite, rendez-vous sur Radio France.

La falta de apoyos ha obligado al Gobierno a dividir el decreto ómnibus y presentar por separado la revalorización de las pensiones para asegurar la subida a los jubilados. La negociación se centra ahora en aprobar, por un lado, las pensiones y, por otro, el resto de iniciativas sociales, para evitar que el Congreso las tumbe de nuevo. Además, Feijóo defendió en la comisión de investigación de la dana el comportamiento del expresident el día de la tragedia. Comparó la gestión de Mazón con el ingreso hospitalario de Salvador Illa durante la crisis de Rodalies y recurrió a ETA para atacar a EH Bildu. El líder popular también incurrió en varios datos falsos, como la supuesta falta de información de la Confederación Hidrográfica del Júcar o una previsión incorrecta atribuida a la delegada del Gobierno, Pilar Bernabé.

In this episode of The Venture Capital Podcast (VC.fm), hosts Jon Bradshaw and Peter Harris sit down with Olivia Goldstein, General Partner at StartFast Ventures and CEO of Upstate Venture Connect, to discuss venture capital, startup ecosystems, and why the best founders and companies can be built far outside Silicon Valley.Olivia shares her founder journey (including building a startup that used influencer marketing and exiting in 2019), her thesis on investing in overlooked markets like Upstate New York, and practical advice for founders raising venture capital from non-traditional hubs.The conversation also explores emerging opportunities in entrepreneurship through acquisition (ETA), small business succession as baby boomers retire, and how AI and shifting labor markets may push more people toward owning real-world cash-flowing assets.Keywords and topics:Venture capital, StartFast Ventures, Upstate Venture Connect, Upstate New York startups, Buffalo startups, Rochester startups, Syracuse startups, B2B SaaS investing, startup ecosystem, founder-led companies, fundraising advice, entrepreneurship through acquisition, ETA, small business acquisition, SBA loans, VC opinions, investing outside Silicon Valley.Follow the PodcastInstagram: https://www.instagram.com/venturecapitalfm/Twitter: https://twitter.com/vcpodcastfmLinkedIn: https://www.linkedin.com/company/venturecapitalfm/Spotify: https://open.spotify.com/show/7BQimY8NJ6cr617lqtRr7N?si=ftylo2qHQiCgmT9dfloD_g&nd=1&dlsi=7b868f1b72094351Apple: https://podcasts.apple.com/us/podcast/venture-capital/id1575351789Website: https://www.venturecapital.fm/Follow Jon BradshawLinkedIn: https://www.linkedin.com/in/mrbradshaw/Instagram: https://www.instagram.com/mrjonbradshaw/Twitter: https://twitter.com/mrjonbradshawFollow Peter HarrisLinkedIn: https://www.linkedin.com/in/peterharris1Twitter: https://twitter.com/thevcstudentInstagram: https://instagram.com/shodanpeteYoutube: https://www.youtube.com/@peterharris2812

20 grudnia 1973 roku w zamachu na ulicach Madrytu ginie premier Hiszpanii, Luis Carrero Blanco - człowiek wyznaczony na następcę generała Franco. Zamachowcy podłożyli ładunki w tunelu pod jezdnią, a siła eksplozji wyrzuciła samochód premiera na 25 metrów w górę. Do ataku przyznała się nacjonalistyczna baskijska organizacja ETA. Czy za zaplanowaniem tej operacji mogło jednak stać CIA lub osoby z najbliższego otoczenia hiszpańskiego dyktatora? W Misji specjalnej odkrywamy kulisy zamachu na prawą rękę generała Franco.

In this episode of the ETA Insider Podcast, Alex Panosian, MBA '18, joins the podcast to discuss his path from the U.S. Army to ETA and operating small businesses. Alex reflects on how military leadership shaped his approach to ownership, discipline, and decision-making. He shares firsthand experiences operating acquired businesses, including the day-to-day reality of being both an owner and an operator, the “duality” of doing manual work while making strategic decisions, and what it looks like to involve family in the journey. The conversation also touches on evaluating acquisition opportunities, lessons learned from operating across different industries, and how his perspective on business ownership has evolved since leaving the Army and completing Booth.

In this episode, Meghan dives into the heart of the Equestrian Travel Association (ETA), tracing its journey from a big idea to a refined mission focused on ethics, credibility, and horse welfare. While the goal has always been to support outfitters and ranches, the ETA is getting more intentional about quality over quantity, prioritizing honest business practices and exceptional experiences. It's a candid look at why professional standards and thoughtful curation matter in the world of travel, setting the stage for what's to come this year on Galloping Getaways. If you're passionate about doing equestrian travel the right way, this conversation is the perfect place to start. Listen in...HORSES IN THE MORNING Episode 3873 – Show Notes & Links:Host: Meghan Brady of the Equestrian Travel AssociationPresenting Sponsor: Equestrian Travel Association | Facebook | InstagramGuest: Glenn the GeekTime Stamps:00:58 - ETA mission, standards, and updates02:23 - Glenn joins, ETA direction03:40 - Move from big directory to curated selection05:19 - ETA history, starting in 201107:09 - Megan's story and why ETA began08:19 - Trend toward unique travel experiences10:19 - Shift to “Michelin star” vetting12:33 - Staff care, customer service discussed13:38 - Plans for Portugal curated trips14:45 - Megan's travel bucket list16:42 - Seeking Asia travel recommendations17:40 - New website, personalized trip planning18:41 - Tips for first-time horse travelers20:54 - Podcast future, more trip stories21:43 - Responsible travel, ethics

In this episode, Meghan dives into the heart of the Equestrian Travel Association (ETA), tracing its journey from a big idea to a refined mission focused on ethics, credibility, and horse welfare. While the goal has always been to support outfitters and ranches, the ETA is getting more intentional about quality over quantity, prioritizing honest business practices and exceptional experiences. It's a candid look at why professional standards and thoughtful curation matter in the world of travel, setting the stage for what's to come this year on Galloping Getaways. If you're passionate about doing equestrian travel the right way, this conversation is the perfect place to start. Listen in...HORSES IN THE MORNING Episode 3873 – Show Notes & Links:Host: Meghan Brady of the Equestrian Travel AssociationPresenting Sponsor: Equestrian Travel Association | Facebook | InstagramGuest: Glenn the GeekTime Stamps:00:58 - ETA mission, standards, and updates02:23 - Glenn joins, ETA direction03:40 - Move from big directory to curated selection05:19 - ETA history, starting in 201107:09 - Megan's story and why ETA began08:19 - Trend toward unique travel experiences10:19 - Shift to “Michelin star” vetting12:33 - Staff care, customer service discussed13:38 - Plans for Portugal curated trips14:45 - Megan's travel bucket list16:42 - Seeking Asia travel recommendations17:40 - New website, personalized trip planning18:41 - Tips for first-time horse travelers20:54 - Podcast future, more trip stories21:43 - Responsible travel, ethics

Santi González comenta cómo para la hermana de Gregorio Ordóñez es más "cruel" es "odio" de la derecha que los asesinatos de ETA.

Tercer programa de ‘D.L. Confidential’, el nuevo podcast de la factoría ‘Dimensión Límite’ en el que el espionaje, la corrupción, el terrorismo de Estado, el crimen organizado o el periodismo de investigación serán los asuntos principales. Y en esta ocasión, toca sondear al siempre áspero y esquivo mundo de la política española. Lo hacemos, en su domicilio, con José Luis Corcuera, quien fuera Ministro del Interior socialista entre 1988 y 1993, en una extensa (y a ratos tensa) entrevista de dos horas y media de duración. Agarraos, que vienen curvas… Con el Sr. Corcuera hablamos de los polémicos diarios de Manglano, jefe de los espías españoles entre 1981 y 1995, en los que se relaciona a Corcuera con varias operaciones encubiertas de… ¿guerra sucia? contra ETA. También conversamos con él de su polémica ley “de la patada en la puerta”, de inmigración, su controvertida opinión sobre los GAL, el triple crimen de Alcàsser (ojo a lo que cuenta…) o la política actual (“algunas declaraciones de Pedro Sánchez son antidemocráticas y propias de un fascista”, llega a decir). De todos estos temas, y otros tantos, hablamos con el controvertido ex-ministro. Por último, si quieres colaborar con el programa, puedes hacerlo a través de nuestra cuenta de PayPal para que, libremente, quien así lo crea conveniente, pueda contribuir agradeciendo el trabajo realizado para que este pueda seguir adelante. Toma nota: - E-mail: Apoyodimensionlimite@gmail.com - Página: http://www.paypal.me/dimensionlimite -Voz corporativa: Ángel Ruiz -Apoyo técnico: Daniel Valcárcel -Dirige, presenta y produce: David Cuevas.

Lucien Besse of Shippeo talks about navigating supply chain chaos; data quality & continuous improvement; AI; and the keys to visibility project success.   IN THIS EPISODE WE DISCUSS:   [03.38] An introduction to Lucien and the journey that led him to co-found Shippeo. [07.07] The biggest sources of chaos for supply chain teams, and why they need flexibility and visibility. "Supply chain managers need to make hundreds of decisions every day, every hour, every minute – they need to navigate uncertainty every single day, and that hasn't changed… What has really changed is the number of disruptions." [09.28] Why supply chain professionals are 'the calm in the storm,' what even small problems amidst the backdrop of chaos mean for businesses, and how Shippeo help customers with both inbound and outbound challenges. [15.00] The big issues that sit behind supply chain chaos, and why trust in data is crucial. "Visibility is a commodity, everybody needs it. But accurate visibility is not a commodity. The reliability of the information you provide to the customer needs to be correct, and they need to have the ability to take action. Because the end goal is not just to look at an ETA on a platform, it's to take an action." [18.02] The importance of data quality and why it's a continuous improvement project. "When we talk to our customers, we tell the truth. There is work to be done, there's no magic wand. It's continuous improvement." "Visibility is about technology, but also about process and people." [23.47] The importance of honesty and setting clear expectations in communications with customers, and why three-way collaboration between vendors, carriers and customers is key. [28.37] How data issues impact the success of AI, and how Shippeo is leveraging AI for customers. "There are two main pillars when it comes to AI – data quality and automation of manual tasks, and making visibility actionable through workflows." [33.31] What success looks like, how organizations can ensure their visibility projects are a success, and why change management relies on understanding people and their daily habits. [40.13] The two core areas Shippeo will be focusing on in 2026.   RESOURCES AND LINKS MENTIONED:   Head over to Shippeo's website now to find out more and discover how they could help you too. You can also connect with Shippeo and keep up to date with the latest over on LinkedIn, Facebook, Instagram or X (Twitter), or you can connect with Lucien on LinkedIn. If you enjoyed this episode and want to hear more from Shippeo, check out: 494: The Digitization Dilemma: Overcoming Transformation Failures with Shippeo 443: Mastering Visibility: Insights from LogTech Live with Shippeo 486: Revealed – The Number One Way To Make Your Supply Chain Future-Proof 475: Leverage Real-Time Transportation Visibility, with Shippeo Check out our other podcasts HERE.

¡Bienvenidos a la nueva era! Hoy despedimos oficialmente a "El Podcast de Noruega" para dar nacimiento a Somos MAAS: Migrantes de Alta Ambición.En este episodio fundacional, Figo y Marcela explican por qué ya no basta con "sobrevivir" al migrar y lanzan la plataforma definitiva para acelerar tu carrera internacional. Además, estrenamos la sección de Oportunidades Globales con datos concretos para facturar más.En este episodio aprenderás:

Business turnaround strategies expert Micah Logan reveals his proven framework for transforming struggling companies into thriving enterprises. Forbes contributor shares how to diagnose financial problems, restructure operations, and build sustainable revenue streams. You'll learn: the STUPID framework identifying six fatal mistakes that kill companies, the TURNAROUND method for immediate recovery, ETA tactics transforming teams into revenue assets, and KPI systems creating predictable income. Perfect for business owners with 2-10 years of operation struggling to scale beyond six figures. Micah shares coaching insights from micro- Listen now to turn your struggling business around.

SEASON: 6 EPISODE: 9Episode Overview:Welcome back to Becoming Preferred, the podcast where we help you become preferred in the markets you serve. If you thought the flower business was quaint, slow, or low-tech, get ready for a wake-up call. Our guest today is Michael Jacobson, CEO of French Florist. He didn't just take over an old business; he engineered a dramatic transformation. Starting from a struggling $600,000 operation, he built a high-performance franchise model that now generates millions in annual revenue and triples the profitability of traditional flower shops. Michael's mastery is the fusion of timeless artistry with modern operational technology.In this episode, Michael is going to share his framework on how to spot a broken business model ready for 10x growth and how to design a high-tech performance franchise that protects quality while maximizing margins.If you're ready to learn how to apply disruptive tech and scalable systems to any industry, old or new, you've come to the right place. Join me for my conversation with Michael Jacobson.Guest Bio: What's possible in one of the world's oldest industries. Under Michael Jacobson's leadership, French Florist has transformed the traditional flower shop model by putting technology, innovation, and operational excellence at the heart of its franchise system. The results speak for themselves: French Florist franchise locations generate triple the revenue of standard flower shops—while operating at higher margins.With a career spanning entrepreneurship, investment, and leadership, Michael is passionate about helping the next generation of business owners succeed through entrepreneurship through acquisition (ETA). He brings a fresh perspective to how small business ownership can be reinvented for the modern age, leveraging digital tools, streamlined operations, and strong brand strategy to unlock sustainable growth.Michael's mission is simple: to empower franchise owners with a proven system that blends timeless beauty with cutting-edge innovation. Whether speaking about the future of franchising, how technology reshapes consumer-facing industries, or why flowers are a surprisingly powerful business opportunity, Michael shares candid insights that inspire entrepreneurs to think bigger and build smarter.Resource Links:Website: https://www.frenchflorist.comProduct Link: https://frenchfloristfranchise.com/Insight Gold Timestamps:03:53 I found that intersection between how to live a purposeful life and a life full of intentionality09:08 We're in the business of love10:02 You've coined a phrase, entrepreneurship through acquisition and creative playbook12:27 We want to build something for the world that's kind of fun and cool19:33 It was really cool, just solving actual real pain points20:01 The root of everything is how do we enhance the client experience?25:16 As a franchisor, our primary responsibility is creating a community of owners26:49 95% of startups fail27:43 What I look for is what's your why?29:23 How do you avoid getting distracted by shiny objects?30:35 Saying no is more important than saying yes31:39 Tom Peters, who's the management guru, who wrote In Search of Excellence34:56 What we want to do from a feelings...

durée : 00:13:50 - Lettre à Liza : quand l'ETA menaçait Bixente Lizarazu en 2000 - En décembre 2000, les parents de Bixente Lizarazu reçoivent à leur domicile d'Hendaye une lettre glaçante. L'organisation terroriste basque ETA réclame au footballeur le paiement de l'impôt révolutionnaire, sous peine de représailles. Vous aimez ce podcast ? Pour écouter tous les autres épisodes sans limite, rendez-vous sur Radio France.

Ángel Expósito y su equipo reflexionan sobre su viaje a Groenlandia, destacando la nieve acumulada y el interés de Trump por la isla para la seguridad nacional. María Corina Machado descarta acuerdos y confía en una transición venezolana apoyada por EE. UU. En Irán, el régimen cobra a las familias por las balas usadas contra sus seres queridos en protestas. La política española se centra en la "humillación" de María Jesús Montero ante independentistas por la financiación catalana, las investigaciones sobre el "caso Koldo" y la polémica dimisión de Miguel Ángel Gallardo. Se conmemoran 30 años del secuestro de Ortega Lara por ETA, el más largo de su historia. España lidera los trasplantes de órganos a nivel mundial, con más de 6300 en 2023. La Comunidad de Madrid afronta una denuncia por no crear una lista de médicos objetores al aborto. Preocupa el uso indebido de la IA, como GROK de Elon Musk, prohibida en varios países por generar imágenes de menores. En cultura, destaca el estreno ...

I've had the privilege to work in a business consulting capacity with Sammy and Erin Siegel, the masterminds behind Empire Training Academy in Raleigh, NC. In less than 10 months, this group training facility has hit nearly 550 members. Shit got real, real fast.Enjoy this very candid conversation with Sammy & Erin as we discuss their glow up and what's next for ETA. —-------------------------------------------------------------------------------------------------------------I solve problems in your business and make you more money.  Guaranteed. For over a decade I've been working with gym owners (via one-on-one consulting) to help create tailored solutions to solve their business problems, engineer the game plan and empower them to execute the strategy.Stop wishing your business problems are going to magically go away.  Invest in your business and let me solve your problems and optimize your business fast and efficiently. We'll work together daily/weekly, with a monthly call until the problem is solved and then I want you to fire me.  Because this is YOUR business, I'm just here to solve a specific problem and then get out of your way.⁠Learn more about what it's like for us to work together.⁠—-------------------------------------------------------------------------------------------------------------Want to increase your business IQ by 100x for only $50? Get enrolled in Microgym University - the only online business school that teaches you the best practices and business frameworks from some of the most successful brands in our industry and then lets you decide which ones to install in your business.New courses are added every month. ⁠⁠www.microgymuniversity.com⁠⁠ —-------------------------------------------------------------------------------------------------------------Need help leasing or buying a building?I created the Gym Real Estate Company so that gym owners had someone who could go beyond the duties of a typical real estate broker and actually advise them on business aspects as they relate to site selection, market location fit, operational capacity, facility layout, pre-sell marketing, and more.If you're looking for help with your next lease or if you want us to help you along the journey of buying a building -⁠ ⁠⁠⁠head over to www.gymrealestate.co and book a Discovery Call.⁠—--------------------------------------------------------------------------------------------------------------

If you're planning a trip to Ireland in 2026 these tips will make planning smoother, help you avoid crowds, and maybe even save you a bit of money. Probably the most important tips are when NOT to be in Dublin, but there are some updates to European entry & exit systems that you need to know, as well as really important advice for 2027. Jody Halsted – Ireland Family Vacations and the Traveling in Ireland podcast In case you're wondering why you would want to get your Ireland travel advice from me… My name is Jody Halsted, and I am an Ireland travel advisor. I have been traveling to and through Ireland for over 23 years (!!), publish Ireland Family Vacations, host the Traveling in Ireland podcast, assist hundreds of travelers with their Ireland vacations each year and, occasionally, I also host small group tours through Ireland (I have 2 this spring so you can look forward to some live from Ireland episodes in April and May). That's a lot! But Ireland travel is my passion; and my goal -100% – is to help you have an amazing Ireland vacation. And I have quite a few ways to do that. My website, Ireland Family Vacations, is a complete resource for Ireland vacation planning – filled with information on what to do & see as well as handy tips for planning your trip and free itineraries to get your plans in motion quickly. (Don't let the name fool you! I travel through Ireland solo, with my husband, with my family, and hosting small group tours. Whatever your group size or dynamic I am able to assist!) My itinerary personalization, vacation coaching, and custom itinerary creation services help you maximize your Ireland experience, and my small group guided tours are designed to not only deliver the ‘authentic' Ireland of your dreams but also introduce you to the people and hidden places that make the country so very magical. If you love the process of planning your trip the Ireland Travel Compass walks you step-by-step through my expert process, from when to visit and how long the perfect vacation lasts, to what to see, where to stay, and even what to eat. It's basically my entire Ireland vacation planning brain laid out. On a more personal note, I have 2 daughters, now in college, who have been traveling through Ireland with me since before they could toddle and one very supportive husband (who loves it when I plan other people's vacations because it means I'm not planning my own). At Malahide Castle (It's probably time to get new family photos done)By Aoife for Flytographer; Dublin, Ireland. All rights reserved. Now that you know all about me, let's jump into what you need to know as you consider planning an Ireland vacation in 2026! This article is based on Traveling in Ireland podcast episode 320. Use the player below to listen or scroll to continue reading the article and get resource links. 2026 Ireland Travel Tips You Need Ireland Travel Rules & Regulations There are currently no restrictions in place for North Americans traveling to Ireland.Ireland travel restrictions and other requirement are found here. In Case of Cancellation Read and understand cancellation policies on everything from flights and transportation to tours and accommodations before booking. If booking with a credit card be aware of the cancellation coverage you may have with that card. I highly recommend purchasing travel insurance if you book tickets our tours that don't allow 100% refund if cancelled. And always if you are booking a tour package (and not necessarily the one the company offers). 2026 Looks like Another Busy Year from Travel to Ireland from North America Irish tourism numbers showed that tourism from Europe into Ireland was down, but numbers from North America remained steady. From my perspective things are already looking busy, especially in the months of May, late August, and September. (The shoulder season is the new high season.) More Direct Flights to Ireland in 2026 Aer Lingus, Ireland's flagship airline is adding 2 new US routes in 2026, bringing the total to 18 routes from Dublin!This is important because Aer Lingus is considered a ‘budget' carrier. And when budget carriers enter a market, US based airlines will drop rates to compete.

In this episode, Eric Malzone sits down with Alyssa D'Aquino, Vice President of Group Fitness at CR Fitness, to discuss a major shift in the big-box gym landscape: the integration of boutique-style Pilates Reformer studios inside traditional gym clubs. Alyssa shares her journey from dancer and behavioral therapist to corporate fitness leader, and explains how CR Fitness is leading the way by making high-end Pilates more accessible through an innovative, inclusive membership model. Key Takeaways

ETA-Angeklagte drehen den Schauprozess um, prangern Folter an – und zwingen Franco, die Todesurteile vom 28.12.1970 unter weltweitem Druck zurückzunehmen. Von Thomas Pfaff.

Santiago González comenta cómo Otegi ha recordado al etarra Peixoto y otros comentarios de tonnntos patrios como Ione Belarra.

Robert Gayden worked for over a year to buy a home care business. Revenue kept growing but the price remained the same.Register for the webinar: What Killed Deals in 2025 - TOMORROW!! - https://bit.ly/44r1pH5Topics in Robert's interview:Influence of his late fatherThe “go bigger” search philosophyAppeal of the home health care industry17-month acquisition processChoosing to operate “in the weeds” of the businessLeading with high expectationsFocusing on increasing salesAchieving 15% growth in 8 monthsWorking capital dynamics in home careInvesting in employeesReferences and how to contact Robert:LinkedInAizik Zimerman on Acquiring Minds: Founder Mode for ETA $6m to $25m in 3 YearsMorgan McCauley on Acquiring Minds: How to Buy a $2.5m Home Care BusinessDevin Fitzgerald on Acquiring Minds: Buying $5m of Revenue with $50k of EquityRobert Graham & Aaron Blick on Acquiring Minds: How to Build a Roll-Up to $60m RevenueJérôme Bouillon on Acquiring Minds: How to Buy & Double a Home Care AgencyGet a free review of your books & financial ops from System Six (a $500 value):Book a call with Tim or hello@systemsix.com and mention Acquiring MindsDownload the New CEO's Guide to Human Resources from Aspen HR:From this page or contact mark@aspenhr.comGet complimentary due diligence on your acquisition's insurance & benefits program:Oberle Risk Strategies - Search Fund TeamConnect with Acquiring Minds:See past + future interviews on the YouTube channelConnect with host Will Smith on LinkedInFollow Will on TwitterEdited by Anton RohozovProduced by Pam Cameron