Podcasts about chinchillas

Alex's family chinchilla, Doug, passed away over the weekend — and the story behind it involves frozen hamburger and is somehow both heartbreaking and hilarious. We talk about navigating pet deaths with your kids as we remember the life of Doug.  Then, a Nantucket woman offered brownies to the School Committee — before casually revealing they contained trace amounts of Ex-Lax. No one ate them. It was a protest against PFAS testing standards for a proposed turf field, and now there's a police report. Alex and Ethan react to the video.  Plus, viral footage out of China shows a humanoid robot in a clown wig roundhouse-kicking a child in the stomach during a martial arts demo at an amusement park. Was this an issue of robot safety or poor planning of event boundaries?    Stream KSL NewsRadio LIVE: kslnewsradio.com/listen Watch on YouTube: https://www.youtube.com/@KSLBrightside Facebook: https://www.facebook.com/KSLBrightside Instagram: https://www.instagram.com/KSL_Brightside TikTok: https://www.tiktok.com/@ksl.brightside

La mejor música vuelve a sonar con fuerza en una nueva edición de Estación GNG, tu cita diaria con los éxitos del ayer, del hoy y de todo aquello que merece seguir sonando. En el programa de hoy disfrutamos de una selección musical tan variada como apasionante, con artistas que han marcado generaciones y nombres que están conquistando el presente. Sonarán temas de Estopa, Fangoria, Bebe Rexha, David Guetta, Miguel Chinchilla, Javi Medina, Maka, Mónica Naranjo, La Plazuela y muchos más artistas que forman parte de la banda sonora de nuestras vidas. Como siempre, en Estación GNG apostamos por una radio musical libre, cercana y sin etiquetas, mezclando pop, rock, indie, flamenco, remember, música internacional y grandes descubrimientos para que cada programa sea diferente y especial. Gracias por acompañarnos cada día y por hacer posible que este proyecto siga creciendo. Si te gusta lo que escuchas, suscríbete, comparte el programa y ayúdanos a seguir llevando paz y música a miles de oyentes en toda España y más allá. Estación GNG: la música que te acompaña, te emociona y te descubre nuevas canciones cada día. ¡Sube el volumen y disfruta del viaje musical! ✨ Etiquetas: Estopa, Fangoria, Bebe Rexha, David Guetta, Miguel Chinchilla, Javi Medina, Maka, Mónica Naranjo, La Plazuela, música, podcast musical, radio online, éxitos del momento, pop español, indie español, música internacional, Estación GNG, Guillermo Nieto, paz y música, iVoox, podcast España, novedades musicales, mejores canciones, música 2026, temazos, radio musical.

Bevor unser Gewissen an uns nagt, erfüllen wir lieber schnell einen Themenwunsch von euch! In dieser Woche feiern wir die artenreichste Ordnung aller Säugetiere: Nagetiere! Mit über 2.500 Arten gehört fast jede zweite Säugetierart auf der Erde zu ihnen. Sie leben in der Wüste, auf hohen Bergen, im Regenwald, unter der Erde, auf Bäumen und in unseren Städten. Einige haben das flauschigste Fell der Welt, andere tragen Stacheln, können durch die Luft segeln, speichern Vorräte in Backentaschen oder helfen sogar dabei, Landminen in Kriegsgebieten zu räumen. In dieser Folge von „tierisch!” tauchen wir ein in die erstaunliche Welt dieser kleinen Großmacht der Evolution.Dies ist eine rein Community finanzierte Folge! Wir sind euch sehr dankbar, wenn auch ihr uns unterstützt. Zum Beispiel bei Steady: https://steady.page/de/tierisch/aboutWeiterführende Links:Übersicht Nagetiere: https://www.cell.com/action/showPdf?pii=S0960-9822%2808%2900319-9Mehr über den Feldhamster: https://www.bund-naturschutz.de/tiere-in-bayern/feldhamsterChinchillaschutz vs. Goldabbau: https://www.science.org/doi/10.1126/science.add7709Ratten als Räumkommando: https://apopo.org/herorats/Doku „Räumungskommando Riesenratte“: https://www.amazon.de/gp/video/detail/0P52WNJ7IV7BMJX6PK60HMH64G/ref=atv_dl_rdrStudie zu wildlebenden Goldhamstern: https://www.spektrum.de/news/ueberraschung-wild-lebende-goldhamster-sind-tagaktiv/948800 Hosted on Acast. See acast.com/privacy for more information.

Did a very different format with Reiner Pope - a blackboard lecture where he walks through how frontier LLMs are trained and served.It's shocking how much you can deduce about what the labs are doing from a handful of equations, public API prices, and some chalk.It's a bit technical, but I encourage you to hang in there – it's really worth it.There are less than a handful of people who understand the full stack of AI, from chip design to model architecture, as well as Reiner. It was a real delight to learn from him.Recommend watching this one on YouTube so you can see the chalkboard.Reiner is CEO of MatX, a new chip startup (full disclosure - I'm an angel investor). He was previously at Google, where he worked on software efficiency, compilers, and TPU architecture.Download markdown of transcript here to chat with an LLM.Wrote up some flashcards and practice problems to help myself retain what Reiner taught. Hope it's helpful to you too!Sponsors* Jane Street needs constant access to incredibly low-latency compute. I recently asked one of their engineers, Clark, to talk me through how they meet these demands. Our conversation—which touched on everything from FPGAs to liquid cooling—was extremely helpful as I prepped to interview Reiner. You can watch the full discussion and explore Jane Street's open roles at janestreet.com/dwarkesh* Google's Gemma 4 is the first open model that's let me shut off the internet and create a fully disconnected “focus machine”. This is because Gemma is small enough to run on my laptop, but powerful enough to actually be useful. So, to prep for this interview, I downloaded Reiner's scaling book, disconnected from wifi, and used Gemma to help me break down the material. Check it out at goo.gle/Gemma4* Cursor helped me turn some notes I took on how gradients flow during large-scale pretraining into a great animation. At first, I wasn't sure the best way to visualize the concept, but Cursor's Composer 2 Fast model let me iterate on different ideas almost instantaneously. You can check out the animation in my recent blog post. And if you have something to visualize yourself, go to cursor.com/dwarkeshTimestamps(00:00:00) – How batch size affects token cost and speed(00:32:09) – How MoE models are laid out across GPU racks(00:47:12) – How pipeline parallelism spreads model layers across racks(01:03:37) – Why Ilya said, “As we now know, pipelining is not wise.”(01:18:59) – Because of RL, models may be 100x over-trained beyond Chinchilla-optimal(01:33:02) – Deducing long context memory costs from API pricing(02:04:02) – Convergent evolution between neural nets and cryptography Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

Join Jordan, Commish, Pitt Girl, Big Sky Brigit and our VP of Podcast Production Arthur. Hey y'all UFL is on, Jordan attends a game, a flag lands on the sky cam, a weird Walkoff Penalty win for Orlando, FROZEN FOUR, Denver Wins but is outshot on doubled up on shots, Frozen Four Hot Dogs, we retract our prior support of North Dakota, Jax State Women's Bowling Champs, Rory's sickos Masters win, Masters menus from Western Michigan strength coach and Kent State's O Line, HUNGRY HOWIE'S GIVE RICO STEELE AN NIL DEAL, RIP to a legend of the New Mexico Bowl Marcellus Medina, ARTEMIS II HOT SAUCE UPDATE, Heinz Hot Taco Sauce really??? We would love a White Sox Pope Hat too, Ichiro's broken bat statue, Memphis gets a jersey patch, no spring game for Chicago State but could they even have one and are their players enrolled? today in weird box scores St. Scholastica vs Carleton 42 runs on 19 hits? THE LEGEND OF COSTA RICA'S PRISCILA CHINCHILLA and a 21-0 win over the Cayman Islands, and oh so much, much more!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

We ask the rockaholics a Simple Question.

D20 Deathmatch season 7 episode 3 Akumu and Jynx slice, dice, and punt chinchillas?... in the Grandmaster's arena. Season 7 of D20 Deathmatch brings you the best of the best DnD PvP battles in our cult classic actual play series! Support us on Patreon: ⁠⁠https://www.patreon.com/D20Deathmatch⁠⁠️‍️ Stay connected with us:Connect with the talented creators behind our show:MUDCAT:⁠⁠ https://twitter.com/MudcatTV⁠⁠⁠⁠ ⁠⁠CAUSTIC PHOENIX: ⁠⁠https://twitter.com/BeccaGodsey ⁠⁠NATE: ⁠⁠https://www.instagram.com/maritimegamer⁠PETER: ⁠⁠https://www.instagram.com/champsofthetable⁠⁠⁠⁠EXPLORE exclusive D20 MERCH: ⁠https://d20deathmatch.com/Shop⁠⁠, Play Our TTRPG PvP Game 'Champions of Chaos' for Free: ⁠⁠⁠⁠⁠https://D20Deathmatch.com/Play⁠⁠ , ⁠⁠⁠️‍️‍️‍and become part of our vibrant community on Discord: ⁠⁠https://discord.gg/BadGuys⁠⁠. Connect with us across various platforms:Twitch: ⁠⁠https://twitch.tv/D20Deathmatch⁠⁠Twitter: ⁠⁠https://twitter.com/D20Deathmatch⁠⁠Podcast: https://anchor.fm/D20DeathmatchInstagram: ⁠⁠https://instagram.com/D20Deathmatch⁠⁠TikTok: ⁠⁠https://tiktok.com/@D20Deathmatch⁠⁠Facebook: ⁠⁠https://facebook.com/D20Deathmatch⁠⁠ Tune in for a monthly deathmatch at 9PM EST on Twitch for the next exhilarating installment, where champions rise and fall in their quest for supremacy in the Grandmaster's arena. Immerse yourself in the rich ambiance, enhanced by dynamic music sourced by Epidemic Sound, elevating every moment of this thrilling encounter.#DnD5e #TabletopRPG #TTRPG

Chad Peters of Ridgeline Minerals joins MSD today to provide his comments on this morning's new drill results from the Chinchilla Sulfide CRD discovery at the Selena Project in Nevada. Core hole SE25-054 was a 700 meter step-out northeast of discovery hole SE25-053 and intersected an "upper" oxide mineralized intercept of 3.1m grading 86.5 g/t silver ("Ag"), 0.3% zinc ("Zn"), 3.8% lead ("Pb), 2.9 g/t gold ("Au"), and 0.07% antimony ("Sb") (353.1 g/t AgEq or 14.7% ZnEq).

The world's wealthiest nations have agreed to release a record number of barrels of oil from emergency reserves, to ease major price rises coming out of the US and Israeli war with Iran. An emergency alert for flooding is in place this morning for residents of Chinchilla in Queensland's west. News from today's live program (1-2pm). - イランが原油価格の大幅な上昇を警告したことを受け、先進７カ国（G７）は足並みをそろえ、石油備蓄を緊急放出することで合意しました。クイーンズランド州の洪水で、西部チンチラの住民に対して今朝、緊急警報が発令されています。国民党の新代表にマット・キャナヴァン議員が選ばれたことを受け、無所属のモニーク・ライアン議員は、オーストラリアの中道派の有権者に魅力的な選択肢ではないとの考えを示しました。2026年３月12日放送。

LNP Member for Callide Bryson Head joined Dean & Sofie on 4BC Breakfast to share an update on the Chinchilla floods, revealing that while Charlie's Creek hit record highs, the town avoided a major disaster thanks to drainage into the Condamine River. As the clean-up begins, local farmers now face urgent fuel shortages ahead of harvest, while further west, Longreach prepares for impending floodwaters.See omnystudio.com/listener for privacy information.

Fermín Monteagudo habla esta semana del acto institucional del Gobierno Regional celebrado en Chinchilla con  motivo del Día Internacional de las Mujeres 

Programación regional de la SER, con el patrocinio de la Junta de Comunidades, desde Chinchilla por la celebración del 8M

In this bonus episode of Inside Politics, we’re joined by the Nationals leader David Littleproud.He’s a man under pressure – commentators, Liberal MPs and some of his own colleagues blamed January’s split on the 49-year-old from Chinchilla in regional Queensland.Today, chief political correspondent Paul Sakkal and Littleproud discuss the Coalition rupture and what’s next under the leadership of Angus Taylor.Subscribe to The Age & SMH: https://subscribe.smh.com.au/See omnystudio.com/listener for privacy information.

In this bonus episode of Inside Politics, we’re joined by the Nationals leader David Littleproud.He’s a man under pressure – commentators, Liberal MPs and some of his own colleagues blamed January’s split on the 49-year-old from Chinchilla in regional Queensland.Today, chief political correspondent Paul Sakkal and Littleproud discuss the Coalition rupture and what’s next under the leadership of Angus Taylor.Subscribe to The Age & SMH: https://subscribe.smh.com.au/See omnystudio.com/listener for privacy information.

Las fuentes revelan una Murcia históricamente mucho más extensa que la actual, destacando un mapa del año 1270 donde el territorio abarcaba zonas de las provincias de Albacete, Alicante y Jaén, incluyendo localidades como Almansa, Chinchilla, Albacete, Elda y la Sierra del Segura. Esta antigua configuración territorial se redujo, en parte, debido a la falta de acuerdos sobre condiciones institucionales —como la presencia de juzgados, una universidad o incluso el Corte Inglés— que Albacete solicitó para permanecer vinculada a la Región de Murcia antes de integrarse en Castilla-La Mancha. Más allá de lo geográfico, la identidad regional se ha visto reforzada recientemente en el ámbito gastronómico por la publicación en "El Comidista" de la receta de las "matasuegras", galletas fritas rellenas de natillas típicas de la huerta murciana. Finalmente, se subraya una conexión cultural sorprendente con Irlanda a través de San Patricio, quien es el patrón oficial de la ciudad de Murcia desde 1452 tras la Batalla de los Alporchones, compartiendo con la isla el simbolismo del trébol de tres hojas.

ｎｏｎｏｋａ, galaxy chinchilla & Ryota Nakamura 02.13.26 | VISLA FM by VISLA

Trevor discusses the latest updates from Ridgeline Minerals with CEO Chad Peters. The conversation covers recent drill results from the Swift project and the company's plans for 2026 drilling with Nevada Gold Mines. Additionally, they delve into the exploration target at the Selena project and its Oxide mineralization. Chad also discusses the progress on the Chinchilla sulfide area with partners South32.

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]:

This is a massive one! Nearly two hours, but you know what they say: Too much of a good thing can be wonderful!My friend MBI (

Former Costa Rican President Laura Chinchilla joins us for a conversation on global democratic backsliding, the role of the international community, and youth civic engagement. As a distinguished leader with experience at the highest level of national and global political affairs, President Chinchilla brings distinctive viewpoints to our conversation to foster democracy through democratic practices, public policy, and civil discourse. She currently serves as co-chair of the Inter-American Dialogue think tank, the newly inducted president of Club de Madrid, an independent, non-partisan organization created to promote democracy, and member of international initiatives like the United Nations Human Development Report and the International Olympic Committee. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

Former Costa Rican President Laura Chinchilla joins us for a conversation on global democratic backsliding, the role of the international community, and youth civic engagement. As a distinguished leader with experience at the highest level of national and global political affairs, President Chinchilla brings distinctive viewpoints to our conversation to foster democracy through democratic practices, public policy, and civil discourse. She currently serves as co-chair of the Inter-American Dialogue think tank, the newly inducted president of Club de Madrid, an independent, non-partisan organization created to promote democracy, and member of international initiatives like the United Nations Human Development Report and the International Olympic Committee. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/political-science

Former Costa Rican President Laura Chinchilla joins us for a conversation on global democratic backsliding, the role of the international community, and youth civic engagement. As a distinguished leader with experience at the highest level of national and global political affairs, President Chinchilla brings distinctive viewpoints to our conversation to foster democracy through democratic practices, public policy, and civil discourse. She currently serves as co-chair of the Inter-American Dialogue think tank, the newly inducted president of Club de Madrid, an independent, non-partisan organization created to promote democracy, and member of international initiatives like the United Nations Human Development Report and the International Olympic Committee. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/public-policy

In this episode of the Weinberg in the World podcast, Olyvia Chinchilla '18 shares how early experiences (from studying abroad in Poland to working with a Stanford-affiliated nonprofit) shaped her passion for economics, social justice, and empathy-driven research. Olyvia reflects on the challenges and rewards of creating change, emphasizing the importance of adaptability, framing conversations, and seeing people beyond statistics. Her career journey spans teaching, global research, and policy analysis. Transcript: Leonie: How did your career begin, and what were your career goals coming out of undergrad? Olyvia: Well, I believe I mentioned while I was at Northwestern, I had been in the reserve officer training course, I had been planning to go into the Navy, and that ended up not panning out because of a few athletic injuries. But earlier in my time at Northwestern, I believe it was the summer of my sophomore year, I studied abroad. And I was studying in Poland and it was my first time actually being out of the country, so it was super exciting. And I was just super excited also to explore Poland because my mom is originally from Poland. So it was just this really amazing moment. And I remember going into that program not being able to fully form sentences, to then leaving the program a few weeks later and literally just talking to my friends in the program in Polish as we tried to navigate the city. And I owed a lot of that to one of the instructors there at the program. So she spoke Polish the whole time, but she was so patient whenever we couldn't get it or we didn't understand or there's a translation error. And I think in that moment, my passion for learning was melded with my... I had this vision of perhaps using that to then also teach. So I had this idea, it stuck in the back of my mind, "Oh, maybe I could take a year off and teach or teach down the road." I did not take a year off, I went back to Northwestern, but I was at a career fair later. And one of the first people I ran into was a national teaching organization. And so that, again, kind of stoked that thought in my mind. And I had planned to actually teach for that program I got accepted, but then I took a year off actually to have a medical procedure following one of the athletic injuries. And when I took that year off, I'm like, "I'll just go to San Francisco for a few months and then Australia for the rest of that year." That was my plan to travel as I recovered. But when I got to San Francisco, I ended up getting in touch through the Northwestern network with a nonprofit based out of Stanford University, so they were sponsored by Stanford. And it was perfect because it melded my interest in teaching because they had a large educational component in the program for fifth through 12th grade students. And then there was also a significant amount of research being conducted by all of the people at the institution. So it was such, I think, a perfect blend for me because then I got to teach as well as do a lot of research for the program. And I actually ended up researching five continents, or I should say four. I didn't go to the last one, but I definitely traveled the whole world doing that research. So it was quite exciting. Leonie: Wow. What subjects were you teaching? Olyvia: So the program was structured so that people could focus on their specialties in teaching and research. So I was focused on economic and social policy, and I had colleagues that worked on immigration more specifically, more specifically on cybersecurity, technology issues. I did cover some of technology like AI issues where it met with economics. So I say I covered a lot of issues, but my specialty was always coming at it from an economic and social perspective. Leonie: And then I assume the research you were doing was related to economic policy? Olyvia: Yes, yes. Policy, but I would say also some of the societal and ethical questions that come up along with policymaking as well as just how communities work and operate. So for example, when we were studying immigration, we were also interviewing a lot of local businesses on the US-Mexico border. We talked with a lot of locals, nonprofits, immigration advocates. So it was kind of like a 360-degree look, but I was always the one who brought that economic knowledge and thought a lot about a lot of social issues too. So that's why several years later, I actually went to London to get a master's in political sociology. So that politics and society part, I think was definitely an element along with the economics. Leonie: Yeah. And you kind of touched on this earlier when you were speaking, but you can expand on it now. What was your motivation for going into this field? Olyvia: So I think I've always just been really fascinated with how people think and how people also are affected by different structures. And that's where the economics piece comes in because economics, of course, impacts different people differently, impacts different communities differently based on how the policies are structured. So I, for almost as long as I can remember, have been interested in economics. I remember as a 12-year-old, I read Adam Smith's The Wealth of Nations, and it was a really long book, but at that age, I was still really fascinated by it. So I've always just had that interest, how money interacts with people. But as I've went along, and definitely the role I was in really opened myself to thinking even more about a lot of the ways in which money and economics also creates wealth disparities, racial disparities, disparities for other minorities, like gender groups. So I feel like all of that, my thought and motivation has evolved quite a bit, I will say, but I think that as I've come along in my career, I've really, as I've just talked to so many people across the entire world, whether it's the communities in Colombia that are still recovering from drug trafficking or whether it's in Iceland talking to different police and then social groups or Portugal as they're working on drug decriminalization, and then seeing that in San Francisco as well in the criminal justice system, just having all of these conversations has really made me really just be motivated to see how we can create the best world for marginalized groups. Leonie: Yeah. And then along those lines, what has been the most rewarding and the most challenging aspect of your job? Olyvia: So for one, for teaching, it was incredibly rewarding to be able to work with students and to see them understand something. And I think it felt to me a little bit of a puzzle when they didn't as well, how's the best way you can communicate, what's the best way to present a topic? And what I found actually, which was interesting to me with teaching was that oftentimes the way I would structure a lesson to start would actually be the exact opposite of how I would end up teaching it. So I would perhaps structure it linearly, and then I compared it to pulling a plastic bag inside out. You would take something from the middle of that linear story and move it to the beginning and switch things around because I think the way that we actually think is often different than the way that we're tempted to explain things. So working with that jigsaw puzzle of how to best explain things was also very fascinating. And I think it's also inspired a lot of my thought process about even as I move forward with potentially moving more into policy implementation or other work and policy, definitely that experience will shape that moving forward because I find how we frame conversations around social policymaking really makes a big difference in terms of how it's understood, how it's received, even how people access the program, for example. There was a study I found very fascinating that was conducted, I believe it was by the University of Minnesota economists. And what they found was that even if they sent a letter in the mail to underprivileged students saying, "Hey, you qualify for this scholarship," but it was actually the same sort of funding they would get anyway just based on their need status, students were more likely to apply for that program. And so I think things like that are really fascinating where we're framing and conversation, thinking about how people think, not just students, but people broadly really does have a big impact on policy. So I think it was very just amazing and fulfilling to actually have that opportunity to grapple with that. But I think also even just the idea of stepping into a space where you're actively envisioning, researching, working towards creating a better world was very fulfilling. On the flip side, I think it is the same thing that's rewarding in that regards is also sometimes one of the challenges that I think definitely as someone who I really aspire to be a changemaker, and I think that that's sometimes you're constantly learning, constantly thinking. And I think sometimes it's easy to... It's challenging, I should say. It's easy to get burnt out or it's easy to perhaps work too hard maybe. But I would say, for example, even just some of the interviews that we conducted with people were challenging even to talk to unhoused people in the city or to talk to people in poverty in Aspen, Colorado when we were studying poverty there, or to interview people that had fled from Gaza and to hear their stories, all of those were definitely very emotionally challenging stories. And I think to meet people in that space, you have to give out a certain amount of empathy and understanding, even if it's for research purposes. And I think just navigating that balance was challenging in its own way. Leonie: Definitely. I think the point about balance is a really good one. I think being able to incorporate empathy into research is a very critical skill that I think sometimes is lacking. So I really do appreciate that point. And based on your vast experiences, how has your mindset towards your career evolved over time? Olyvia: It's interesting because I guess when I was younger, maybe a teenager, I was like, "Oh, A, B, C." And then when I was at Northwestern and it was like, "Well, you do this, and then that, and then this, and it's maybe not quite as linear as the alphabet, but it's point A, B, C." And so you kind of move forward. I think more recently I've come to realize that there's, and this has been a recurring lesson actually. It's not just now, but I would say when I left the military, for example, that was a lesson I was like, "There's many routes to the same destination. If service is one of my goals, there's many ways to serve, even if it's not in the military." Nowadays, I'm thinking about the fact that I stayed in San Francisco many years longer than I had expected. I was supposed to stay for three months, it became three years, six years, and counting at the moment. So I think one of the things I really realized is that sometimes life takes you in very different directions than you would've expected, and that's the same with your career. I think that definitely can be a challenge to be patient and understanding with the evolution that takes place, but definitely the experiences may be just as fulfilling on a very different route than what you had envisioned. Leonie: Yeah. Bringing us back to our Northwestern connection, are there any moments in your career that reminded you of CORE connected you to a lesson you learned at Northwestern? Olyvia: What I mentioned earlier about having the capacity to balance your own emotional needs as well as create change and serve, all of those lessons were lessons that I really learned at Northwestern. I remember when I was in ROTC, we watched this video that the Cleveland Clinic had put out, and it was a video that just shows different scenes in the hospital, but then it has thought bubbles next to the people. So for example, the girl petting the dog, it's like, "Well, her dad's dying of cancer. Or the woman sitting in the waiting room, she saw something on her mammogram." So all of these different thoughts and emotions people are experiencing, but you don't really know anything about it. And having such a diverse community at Northwestern as well as just thinking about that practice of empathy every day really helped me to see that even at Northwestern and since then, is that you might run into someone and think, "Oh, I don't know what to think about this person," or, "Oh, they're frustrating me at work," or, "Oh, this is happening." But a lot of times there's a lot more beneath the surface than we expect. So I think that lesson of empathy as well as humility is definitely... And I'd say empathy, humility, as well as endless possibilities for different lives of different people that all came together and sent me on a passion for learning and understanding people because I've come full circle, but I would say all of those lessons have really stuck with me throughout my work. And I'd say along those lines, in research, everyone that you're interviewing is more than a statistic. Leonie: Absolutely. Olyvia: A lot of times it's really hard to quantify things and we do our best as researchers, but sometimes what doesn't go into the research is actually sometimes the most impactful in many ways. Leonie: Yeah. Thank you for that answer. I'm a philosophy major and we've been talking a lot about character virtues, and so empathy comes up a lot in our classes. And yeah, seeing how you're able to use empathy in your research and looking at people's more than a statistic, I remember saying it before. Yeah, I think that's really touching and it gives me faith in the further research world and what people are able to do when they look at people beyond just their statistical measurements and whatnot. Yeah. Is there- Olyvia: Well, and I think to that point though, I think even if we think about ways that we've began to see different characteristics that have been left out of research, for example, even if we think about rates of death among African-American women during childbirth, or if we think about maybe other environmental effects of certain policies on particular communities that live by highways, for example, and low-income communities, all of that, if you don't look at the bigger picture, might go unnoticed, but definitely if you bring in those larger stories to individual people, you can understand a situation better. Leonie: Absolutely. Thank you for that.  

Ridgeline Minerals said new assay results from its Selena Project in Nevada exceeded expectations, further confirming the strength of the Chinchilla sulfide CRD discovery.In this episode of Mining Stock Daily, Michael McCrae is joined by Chad Peters, President and CEO of Ridgeline Minerals (TSX-V: RDG), to break down the latest results and what they mean for the broader system at Selena.Chad walks through why the company is highlighting discovery hole SE25-053, which returned multiple stacked sulfide horizons with strong zinc-silver-lead-gold grades, including a newly reported lower sulfide zone beneath the original discovery interval. The results reinforce the interpretation of a large, vertically extensive carbonate replacement deposit (CRD) system, rather than a single isolated intercept.The discussion covers how these new assays change Ridgeline's geological understanding of Chinchilla, how they fit with geophysics and alteration seen across the project, and why the presence of stacked mineralized horizons is critical for scale potential. Chad also outlines next steps for drilling, ongoing work under the South32 earn-in, and what investors should be watching for as follow-up holes are completed.

Gemini 3 was a landmark frontier model launch in AI this year — but the story behind its performance isn't just about adding more compute. In this episode, I sit down with Sebastian Bourgeaud, a pre-training lead for Gemini 3 at Google DeepMind and co-author of the seminal RETRO paper. In his first-ever podcast interview, Sebastian takes us inside the lab mindset behind Google's most powerful model — what actually changed, and why the real work today is no longer “training a model,” but building a full system.We unpack the “secret recipe” idea — the notion that big leaps come from better pre-training and better post-training — and use it to explore a deeper shift in the industry: moving from an “infinite data” era to a data-limited regime, where curation, proxies, and measurement matter as much as web-scale volume. Sebastian explains why scaling laws aren't dead, but evolving, why evals have become one of the hardest and most underrated problems (including benchmark contamination), and why frontier research is increasingly a full-stack discipline that spans data, infrastructure, and engineering as much as algorithms.From the intuition behind Deep Think, to the rise (and risks) of synthetic data loops, to the future of long-context and retrieval, this is a technical deep dive into the physics of frontier AI. We also get into continual learning — what it would take for models to keep updating with new knowledge over time, whether via tools, expanding context, or new training paradigms — and what that implies for where foundation models are headed next. If you want a grounded view of pre-training in late 2025 beyond the marketing layer, this conversation is a blueprint.Google DeepMindWebsite - https://deepmind.googleX/Twitter - https://x.com/GoogleDeepMindSebastian BorgeaudLinkedIn - https://www.linkedin.com/in/sebastian-borgeaud-8648a5aa/X/Twitter - https://x.com/borgeaud_sFIRSTMARKWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCapMatt Turck (Managing Director)Blog - https://mattturck.comLinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturck(00:00) – Cold intro: “We're ahead of schedule” + AI is now a system(00:58) – Oriol's “secret recipe”: better pre- + post-training(02:09) – Why AI progress still isn't slowing down(03:04) – Are models actually getting smarter?(04:36) – Two–three years out: what changes first?(06:34) – AI doing AI research: faster, not automated(07:45) – Frontier labs: same playbook or different bets?(10:19) – Post-transformers: will a disruption happen?(10:51) – DeepMind's advantage: research × engineering × infra(12:26) – What a Gemini 3 pre-training lead actually does(13:59) – From Europe to Cambridge to DeepMind(18:06) – Why he left RL for real-world data(20:05) – From Gopher to Chinchilla to RETRO (and why it matters)(20:28) – “Research taste”: integrate or slow everyone down(23:00) – Fixes vs moonshots: how they balance the pipeline(24:37) – Research vs product pressure (and org structure)(26:24) – Gemini 3 under the hood: MoE in plain English(28:30) – Native multimodality: the hidden costs(30:03) – Scaling laws aren't dead (but scale isn't everything)(33:07) – Synthetic data: powerful, dangerous(35:00) – Reasoning traces: what he can't say (and why)(37:18) – Long context + attention: what's next(38:40) – Retrieval vs RAG vs long context(41:49) – The real boss fight: evals (and contamination)(42:28) – Alignment: pre-training vs post-training(43:32) – Deep Think + agents + “vibe coding”(46:34) – Continual learning: updating models over time(49:35) – Advice for researchers + founders(53:35) – “No end in sight” for progress + closing

A caterpillar that looks like poop sends Dr. Isabel the Chinchilla on the silliest butterfly adventure ever!

In this episode, Honey German sits down with Calixto Chinchilla, founder and executive producer of the New York Latino Film Festival, to talk about how he brought the nation’s premier Latino film festival to life and what it means for our culture today. Joining the convo is acclaimed actor Manny Perez, who has two films screening at this year’s festival: 'A Tiro Limpio' & 'Tiguere' both shot in DR. Together, we dive deep into the world of Latino cinema from creating spaces that elevate our stories to the importance of showing up and supporting movies made for us, by us. From behind-the-scenes hustle to the joy of seeing ourselves on the big screen, this episode is all about celebrating Latino creatives and shifting the way we think about film.

Chinchilla chinchilla chinchilla Approachability: 7/10 (Super-stylized art film with lots of gross body things) Content Warnings: Body Horror; Sexual assault; Medical experimentation; Gore/Ooze Next Week's Film RandomHorror9 T-Shirts Hosts: Jeffrey Cranor & Cecil Baldwin (Find more of our work on Welcome to Night Vale) Logo: David Baldwin Random Horror 9 Patreon YouTube, Bluesky, Letterboxd, & Instagram: @RandomHorror9 We are part of Night Vale Presents Learn more about your ad choices. Visit megaphone.fm/adchoices

When her boyfriend left town and put her in charge of his beloved pet chinchilla, everything seemed fine—until Herman vanished. What happened next involves a cracked window, a panicked pet store visit, and one very confused boyfriend who now believes something scientifically impossible. In this hilarious and jaw-dropping episode of Dirty Little Secret, discover how one tiny mix-up turned into the greatest chinchilla cover-up of all time. The juiciest, most outrageous confession podcast from The Jubal Show! It's the Jubal Show's Dirty Little Secret! Listeners spill their wildest, weirdest, and most scandalous secrets anonymously—no judgment, just pure entertainment. From shocking revelations to hilarious mishaps, you never know what you'll hear next! Hosted by Jubal Fresh and the team, every episode is packed with jaw-dropping confessions, witty reactions, and unexpected twists. Got a secret? Share it with us… we promise we won’t tell!➡︎ Get on The Jubal Show with your story - https://thejubalshow.com This is just a tiny piece of The Jubal Show. You can find every podcast we have, including the full show every weekday right here…➡︎ https://thejubalshow.com/podcasts The Jubal Show is everywhere, and also these places: Website ➡︎ https://thejubalshow.com Instagram ➡︎ https://instagram.com/thejubalshow X/Twitter ➡︎ https://twitter.com/thejubalshow Tiktok ➡︎ https://www.tiktok.com/@the.jubal.show Facebook ➡︎ https://facebook.com/thejubalshow YouTube ➡︎ https://www.youtube.com/@JubalFresh Support the show: https://the-jubal-show.beehiiv.com/subscribeSee omnystudio.com/listener for privacy information.

Get access to over $1,000 worth of retention and gamification strategies, templates, and live sessions—for free. All inside our Professor Game community:

On this week's episode of the #NZXT Podcast... We have the legendary NZXTChinchilla joining us to rate the setups YOU submitted to us! Follow the Chinchilla: https://instagram.com/nzxtchinchilla

Anti-bullying Dog Therapy Fido is now being used to stop bullying. Rachel McPherson is introducing canines to both the bully victim as well as the bully-er. The transformation is astonishing. Find out what the Good Dog Foundation has planned. Listen Now Allergy Season Animal Radio is getting more and more calls from pet guardians whose furry friends are suffering from Summer allergies. It's essential to identify the type of allergy, whether it's food-related, environmental, or caused by a pest. For some pets, it may be a Miliary Flea Dermatitis - an acute reaction to fleas, pollen in the air, or simply a food allergy. Your vet has the tools to decipher and treat. Listen Now Courthouse Dogs Former Prosecutor Ellen O'Neill-Stephens thinks dogs can be extremely helpful to those testifying in a courtroom. And, in fact, she may be right. Over 30 dogs are currently tucked away next to the defendant as they deliver their sworn statements. It's all part of a program of the Courthouse Dogs Foundation. And while most people think this is a fine idea, it does have its detractors. Listen Now Seeing-Eye Dogs Bring Blind Couple Together A pair of seeing-eye dogs is getting credit for leading their owners to love.  Claire Johnson and Mark Gaffey first met at a course for their guide dogs, Rodd and Venice, last year in Shrewsbury, England. Apparently, the guide dogs were inseparable and seemed to be falling in love. Their visually impaired owners soon followed suit. Listen Now It's A Zoo! Dr. Debbie is prepared to answer all of your questions about almost every kind of animal. That's why she's at the top of her game. Today, it's all about Chinchillas. And that's because more and more pet guardians are choosing this furry critter over a cat or dog. Listen Now Cow Spa Produces More Milk We found a dairy farmer who's pampering his cows in order to produce more milk. Farmer Smith is using waterbeds for his cows to lounge in while they watch a giant flat-screen TV. And believe it or not, the cows seem to be reacting by making more milk than those moo-cows without TV privileges. Listen Now Read more about this week's show.

You can find our The Lost Biker Stories book, tool rolls, stickers and prints here: https://www.thelibertatia.com ______________ Please do leave a comment and share your thoughts. If you've got a story, insight or pictures to share, you can also email hi@tuesdayatdobbs.com Instagram: www.instagram.com/@tuesday_at_dobbs My other YouTube channel: @FreddieDobbs ______________ Time Stamps: 0:00: The Entry Level Cruiser You've Been Waiting For: Benda Chinchilla 500 3:42: Honda Goldwings with the Highest Mileage (Million-Mile" Phil Steiner, Allan Zarht, Daniel Wallender) 9:15: Australia's Biggest Selling Motorcycle (Honda CT110, Honda Cub Hunter, Honda Hunter) 16:18: Across The Line: War Heroes of the TT (with Charley Boorman). Kickstarter Campaign 18:00: Bike of the Week:

How has the history of AI been shaped by the "bitter lesson" that simple scaling beats complex algorithms, and what comes next? In this talk, Irina Rish traces AI's evolution from rule-based systems to today's foundation models, exploring how scaling laws predicted performance improvements and recent shifts toward more efficient approaches. She covers the progression from GPT scaling laws to Chinchilla's compute-optimal training, the rise of inference-time computation with models like OpenAI's o1, and why we might need to move beyond transformers to truly brain-inspired dynamical systems.Irina Rish is a professor at the University of Montreal and Mila Quebec AI Institute. She also co-founded a startup focused on developing more efficient foundation models and recently released a suite of open-source compressed models.This talk was recorded at Vision Weekend Puerto Rico 2025. To see the slides and more talks from the event, please visit our YouTube channel. Hosted on Acast. See acast.com/privacy for more information.

This week Jim is off with the lurgy but eltons woes with Facebook Marketplace continues with his newly acquired "Pppppffffftttpppllpppppp" guitar pedal. Darren doubles down on zombie action playing Last of Us and Dead Island. While Lee finishes Andor, finally catches up with Thunderbolts* and binge watches the Disney+ show Paradise.  After that we deal with A LOT of Last Voyage of the Demeter feedback and take time to answer (in a roundabout way) what we think our favourite post credits scene is. Then finally we review this weeks film. A dark quirkily humourous biopic / fantasy based around Frank Sidebottom or a "mental illness is a superpower" movie? We shout CHINCHILLA! as we review Michael Fassbender as Frank.  Media discussed this week Thunderbolts* - Cinema Release Dead Island 2 - PC Last of us - TV and PC versions Paradise - Disney+ Andor - Disney+ Frank - Channel 4 on demand

Explains language models (LLMs) advancements. Scaling laws - the relationships among model size, data size, and compute - and how emergent abilities such as in-context learning, multi-step reasoning, and instruction following arise once certain scaling thresholds are crossed. The evolution of the transformer architecture with Mixture of Experts (MoE), describes the three-phase training process culminating in Reinforcement Learning from Human Feedback (RLHF) for model alignment, and explores advanced reasoning techniques such as chain-of-thought prompting which significantly improve complex task performance. Links Notes and resources at ocdevel.com/mlg/mlg34 Build the future of multi-agent software with AGNTCY Try a walking desk stay healthy & sharp while you learn & code Transformer Foundations and Scaling Laws Transformers: Introduced by the 2017 "Attention is All You Need" paper, transformers allow for parallel training and inference of sequences using self-attention, in contrast to the sequential nature of RNNs. Scaling Laws: Empirical research revealed that LLM performance improves predictably as model size (parameters), data size (training tokens), and compute are increased together, with diminishing returns if only one variable is scaled disproportionately. The "Chinchilla scaling law" (DeepMind, 2022) established the optimal model/data/compute ratio for efficient model performance: earlier large models like GPT-3 were undertrained relative to their size, whereas right-sized models with more training data (e.g., Chinchilla, LLaMA series) proved more compute and inference efficient. Emergent Abilities in LLMs Emergence: When trained beyond a certain scale, LLMs display abilities not present in smaller models, including: In-Context Learning (ICL): Performing new tasks based solely on prompt examples at inference time. Instruction Following: Executing natural language tasks not seen during training. Multi-Step Reasoning & Chain of Thought (CoT): Solving arithmetic, logic, or symbolic reasoning by generating intermediate reasoning steps. Discontinuity & Debate: These abilities appear abruptly in larger models, though recent research suggests that this could result from non-linearities in evaluation metrics rather than innate model properties. Architectural Evolutions: Mixture of Experts (MoE) MoE Layers: Modern LLMs often replace standard feed-forward layers with MoE structures. Composed of many independent "expert" networks specializing in different subdomains or latent structures. A gating network routes tokens to the most relevant experts per input, activating only a subset of parameters—this is called "sparse activation." Enables much larger overall models without proportional increases in compute per inference, but requires the entire model in memory and introduces new challenges like load balancing and communication overhead. Specialization & Efficiency: Experts learn different data/knowledge types, boosting model specialization and throughput, though care is needed to avoid overfitting and underutilization of specialists. The Three-Phase Training Process 1. Unsupervised Pre-Training: Next-token prediction on massive datasets—builds a foundation model capturing general language patterns. 2. Supervised Fine Tuning (SFT): Training on labeled prompt-response pairs to teach the model how to perform specific tasks (e.g., question answering, summarization, code generation). Overfitting and "catastrophic forgetting" are risks if not carefully managed. 3. Reinforcement Learning from Human Feedback (RLHF): Collects human preference data by generating multiple responses to prompts and then having annotators rank them. Builds a reward model (often PPO) based on these rankings, then updates the LLM to maximize alignment with human preferences (helpfulness, harmlessness, truthfulness). Introduces complexity and risk of reward hacking (specification gaming), where the model may exploit the reward system in unanticipated ways. Advanced Reasoning Techniques Prompt Engineering: The art/science of crafting prompts that elicit better model responses, shown to dramatically affect model output quality. Chain of Thought (CoT) Prompting: Guides models to elaborate step-by-step reasoning before arriving at final answers—demonstrably improves results on complex tasks. Variants include zero-shot CoT ("let's think step by step"), few-shot CoT with worked examples, self-consistency (voting among multiple reasoning chains), and Tree of Thought (explores multiple reasoning branches in parallel). Automated Reasoning Optimization: Frontier models selectively apply these advanced reasoning techniques, balancing compute costs with gains in accuracy and transparency. Optimization for Training and Inference Tradeoffs: The optimal balance between model size, data, and compute is determined not only for pretraining but also for inference efficiency, as lifetime inference costs may exceed initial training costs. Current Trends: Efficient scaling, model specialization (MoE), careful fine-tuning, RLHF alignment, and automated reasoning techniques define state-of-the-art LLM development.

Dynasty may have brought your girls down, but Dynamite this week brought them WAY WAY UP. We may be the only people on the entire internet who felt that way but we're bold truth tellers. We talk through Swerve's loss, the Young Bucks' return and what it might mean for the Death Riders' storyline, and what we want from pro wrestling overall. Plus, we're not animals - we talk about the Young Bucks's outfits, Kenny and the cuck chair, "dookie", Anthony Bowens's insane nipple shirt, and all the other events of the week. Enjoy!(00:00) Chitchat Time and News(12:18) Dynasty and Dynamite Vibe Overview(16:06) Death Riders, CopeTR, the Elite(1:08:06) Don Callis Family(1:13:15) Ospreay, Speedball, etc(1:19:17) Toni Storm and Megan Bayne(1:24:39) Hurt People and MJF(1:27:39) Chris Jericho, Bandido, and the Learning Tree(1:42:35) Max Caster(1:46:51) Adam Cole and the ParagonSupport this podcast at — https://redcircle.com/social-suplex-podcast-network/donationsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Chad Peters, CEO of Ridgeline Minerals, discusses the company's recent corporate update on the Salena project and its exploration budget approval with partners South32. He highlights the drilling and the significance of the Chinchilla sulfide target area. The conversation also touches on the potential for critical minerals and updates on other projects like Big Blue and Atlas, emphasizing the company's largest budget in history and the excitement surrounding their exploration efforts.

There is a hamster, or, I mean, Chinchilla problem on today's Make Up or Break Up!Leave a rating and review wherever you listen, it helps us out a lot! Also follow us on social @joeyandlaurenshow Hosted on Acast. See acast.com/privacy for more information.

Chad Peters from Ridgeline Minerals discusses the significant developments at the Selena project, particularly the Chinchilla sulfide target. With tje partnership with South32, new data from an MT survey has opened up exciting opportunities for exploration. The conversation delves into the scale of the Chinchilla sulfide target, the structural controls influencing mineralization, and the next steps for drilling. Additionally, Peters highlights the company's new Atlas project and the financial backing that will support ongoing exploration efforts.

Lots of drill results out this morning. MSD reports the latest from Collective Mining, Arizona Sonoran Copper, Rua Gold and Southern Cross Gold. Ridgeline Minerals received MT geophysical data from Selena where the Chinchilla sulfide zone shows a large anomaly. This episode of Mining Stock Daily is brought to you by... Vizsla Silver is focused on becoming one of the world's largest single-asset silver producers through the exploration and development of the 100% owned Panuco-Copala silver-gold district in Sinaloa, Mexico. The company consolidated this historic district in 2019 and has now completed over 325,000 meters of drilling. The company has the world's largest, undeveloped high-grade silver resource. Learn more at⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠https://vizslasilvercorp.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Calibre Mining is a Canadian-listed, Americas focused, growing mid-tier gold producer with a strong pipeline of development and exploration opportunities across Newfoundland & Labrador in Canada, Nevada and Washington in the USA, and Nicaragua. With a strong balance sheet, a proven management team, strong operating cash flow, accretive development projects and district-scale exploration opportunities Calibre will unlock significant value.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.calibremining.com/⁠

La expresidenta de Costa Rica, Laura Chinchilla, habló en Conclusiones sobre lo que no puede descuidar la oposición en Venezuela para continuar con su plan de lograr la transición política en el país. Asegura que un logro es haber conseguido la movilización ciudadana, pero una tarea pendiente es la relación con las Fuerzas Armadas. Learn more about your ad choices. Visit podcastchoices.com/adchoices

La expresidenta de Costa Rica Laura Chinchilla analizó en entrevista con Fernando del Rincón cuáles son los apoyos internacionales al régimen de Nicolás Maduro en Venezuela. ¿Está aislado o aún cuenta con el respaldo de China, Rusia e Irán? Según Chinchilla, las imágenes de la caída de Bashar al-Assad deben estar muy presentes en Nicolás Maduro "porque ni Rusia, ni Irán, ni China, llegaron en su ayuda". Learn more about your ad choices. Visit podcastchoices.com/adchoices

Peggy Hopkins Joyce (1893-1957) was an American actress, model and socialite known for her multiple marriages to millionaires. For Further Reading: The Iron Forger and the Gold Digger The Legend of Peggy Hopkins Joyce: She Collected Men, Chinchilla, Diamonds Peggy Hopkins Joyce Dies at 63; Showgirl of ‘20’s Gold Digger: The Outrageous Life and Times of Peggy Hopkins Joyce This month we're talking about Go-Getters. Women who purposefully—or accidentally!—acquired life-changing wealth, good fortune, or influence. History classes can get a bad rap, and sometimes for good reason. When we were students, we couldn’t help wondering... where were all the ladies at? Why were so many incredible stories missing from the typical curriculum? Enter, Womanica. On this Wonder Media Network podcast we explore the lives of inspiring women in history you may not know about, but definitely should. Every weekday, listeners explore the trials, tragedies, and triumphs of groundbreaking women throughout history who have dramatically shaped the world around us. In each 5 minute episode, we’ll dive into the story behind one woman listeners may or may not know–but definitely should. These diverse women from across space and time are grouped into easily accessible and engaging monthly themes like Educators, Villains, Indigenous Storytellers, Activists, and many more. Womanica is hosted by WMN co-founder and award-winning journalist Jenny Kaplan. The bite-sized episodes pack painstakingly researched content into fun, entertaining, and addictive daily adventures. Womanica was created by Liz Kaplan and Jenny Kaplan, executive produced by Jenny Kaplan, and produced by Grace Lynch, Maddy Foley, Brittany Martinez, Edie Allard, Lindsey Kratochwill, Adesuwa Agbonile, Carmen Borca-Carrillo, Taylor Williamson, Sara Schleede, Paloma Moreno Jimenez, Luci Jones, Abbey Delk, Hannah Bottum, Adrien Behn, Alyia Yates, and Vanessa Handy. Special thanks to Shira Atkins. Original theme music composed by Miles Moran. Follow Wonder Media Network: Website Instagram Twitter See omnystudio.com/listener for privacy information.

➡︎ Jubal Phone Pranks on The Jubal ShowNeed someone to feel the wrath of a Jubal Fresh character? He'll call whoever you want and prank them... so hard. It's funny. Submit yours here: https://forms.gle/mgACgtLBP3SPcyRR7======This is just a tiny piece of The Jubal Show. You can find every podcast we have, including the full show every weekday right here…➡︎ https://thejubalshow.com/podcasts======The Jubal Show is everywhere, and also these places: Website ➡︎ https://thejubalshow.com  Instagram ➡︎ https://instagram.com/thejubalshow  X/Twitter ➡︎ https://twitter.com/thejubalshow  Tiktok ➡︎ https://www.tiktok.com/@the.jubal.show YouTube ➡︎ https://www.youtube.com/@JubalFresh  ======Meet The Jubal Show Cast:====== Jubal Fresh - https://jubalshow.com/featured/jubal-fresh/  Nina - https://thejubalshow.com/featured/ninaontheair/ Victoria - https://jubalshow.com/featured/victoria-ramirez/  Brad Nolan - https://jubalshow.com/featured/brad-nolan/  Sharkey - https://jubalshow.com/featured/richard-sharkey/ See omnystudio.com/listener for privacy information.

It's a weird one in this Midweek Mini as the boys wrap up 2023! Want Minisodes AS THEY RELEASE? Then head over to Patreon and enjoy 50+ more episodes! MERCH - http://www.theyetee.com/collections/chilluminati Special thanks to our sponsors this episode - All you lovely people at Patreon! HTTP://PATREON.COM/CHILLUMINATIPOD Jesse Cox - http://www.youtube.com/jessecox Alex Faciane - http://www.youtube.com/user/superbeardbros Editor - DeanCutty http://www.twitter.com/deancutty Art Commissioned by - http://www.mollyheadycarroll.com

Maddie, a 19-year-old college student with PCOS, acid reflux, low B12, low iron, and reactive hypoglycemia. Screen It Like You Mean It Eversense CGM Learn about the Medtronic Champions This BetterHelp link saves 10% on your first month of therapy Try delicious AG1 - Drink AG1.com/Juicebox I Have Vision Use code JUICEBOX to save 40% at Cozy Earth  JUICE CRUISE 2025 Eat Hungryroot Get Gvoke HypoPen CONTOUR NextGen smart meter and CONTOUR DIABETES app Learn about the Dexcom G6 and G7 CGM Go tubeless with Omnipod 5 or Omnipod DASH * Get your supplies from US MED  or call 888-721-1514 Learn about Touched By Type 1 Take the T1DExchange survey *The Pod has an IP28 rating for up to 25 feet for 60 minutes. The Omnipod 5 Controller is not waterproof.  How to listen, disclaimer and more Apple Podcasts> Subscribe to the podcast today! The podcast is available on Spotify, Google Play, iHeartRadio, Radio Public, Amazon Music and all Android devices The Juicebox Podcast is a free show, but if you'd like to support the podcast directly, you can make a gift here or buy me a coffee. Thank you! Disclaimer - Nothing you hear on the Juicebox Podcast or read on Arden's Day is intended as medical advice. You should always consult a physician before making changes to your health plan.  If the podcast has helped you to live better with type 1 please tell someone else how to find the show and consider leaving a rating and review on Apple Podcasts. Thank you! The Juicebox Podcast is not a charitable organization.

Live from the Nerdland Festival, Andrew, James, Dan and Lieven Scheire discuss coughing crocs, cunning computers, testing toilets, and tall tales about tails. Visit nosuchthingasafish.com for news about live shows, merchandise and more episodes. Join Club Fish for ad-free episodes and exclusive bonus content at apple.co/nosuchthingasafish or nosuchthingasafish.com/patreon