Podcasts about Multimodal

In the electric utility industry, lineworkers often retire with years of experience and take that knowledge with them. Eversource discovered a way to bridge the knowledge gap by overhauling its electric and gas training using multimodal digital tools, e-books and mobile apps. This episode of the Line Life Podcast features a narrated version of the article "Eversource Advances Workforce Training" from the November 2025 field-focused Electric Utility Operations section of T&D World magazine.  This audio story outlines the utility's plan to deploy roughly 85 custom training applications by 2026–27 across core programs like overhead and underground electric, substations, gas maintenance, corrosion and field communications. The episode highlights how tablet-delivered, interactive content preserves institutional knowledge, supports different learning styles and improves safety through virtual practice. It also provides scalable, just-in-time training to meet growing workforce demands and close the experience gap. For more information, read the article on T&D World's website. 

Karan Singhal, Head of Health AI at OpenAI, explains how ChatGPT Health is achieving attending-physician-level performance and already serving hundreds of millions of users. He details how OpenAI works with over 250 doctors, built the 49,000-criteria HealthBench evaluation, and ran one of the first randomized trials of AI copilots in clinical care. The conversation explores privacy and safety safeguards, medical multimodality, N-of-1 treatment plans, and how AI could become a standard part of global medical practice. Use the Granola Recipe Nathan relies on to identify blind spots across conversations, AI research, and decisions: https://bit.ly/granolablindspot LINKS: modeling human wellness Sponsors: Claude: Claude is the AI collaborator that understands your entire workflow, from drafting and research to coding and complex problem-solving. Start tackling bigger problems with Claude and unlock Claude Pro's full capabilities at https://claude.ai/tcr Serval: Serval uses AI-powered automations to cut IT help desk tickets by more than 50%, freeing your team from repetitive tasks like password resets and onboarding. Book your free pilot and guarantee 50% help desk automation by week 4 at https://serval.com/cognitive Framer: Framer is an enterprise-grade website builder that lets business teams design, launch, and optimize their.com with AI-powered wireframing, real-time collaboration, and built-in analytics. Start building for free and get 30% off a Framer Pro annual plan at https://framer.com/cognitive Tasklet: Tasklet is an AI agent that automates your work 24/7; just describe what you want in plain English and it gets the job done. Try it for free and use code COGREV for 50% off your first month at https://tasklet.ai CHAPTERS: (00:00) About the Episode (06:11) Cancer story and mission (11:46) Designing safe health AI (Part 1) (17:49) Sponsors: Claude | Serval (21:09) Designing safe health AI (Part 2) (26:48) Uncertainty, HealthBench and robustness (Part 1) (30:23) Sponsors: Framer | Tasklet (32:50) Uncertainty, HealthBench and robustness (Part 2) (38:11) Chain-of-thought and evaluation (46:49) Real-world performance and frontiers (55:35) Multimodal data and science (01:05:36) Personalization, privacy and monitoring (01:15:47) Models, data and incentives (01:29:31) Doctor adoption and workflows (01:38:13) Scalable oversight and alignment (01:51:06) Move 37 and future (02:00:50) Episode Outro (02:03:06) Outro PRODUCED BY: https://aipodcast.ing

The integration of Artificial Intelligence (AI) into post-injury rehabilitation is transforming recovery paradigms by enabling personalized, adaptive, and efficient rehabilitation pathways tailored to individual patient needs. This podcast reviews the current advances in AI applications that facilitate assessment, monitoring, and optimization of rehabilitation programs following injuries. Through machine learning algorithms, wearable sensors, and predictive analytics, AI enhances the precision of therapy plans, tracks patient progress in real-time, and predicts recovery trajectories. The discussion includes the benefits of AI-driven rehabilitation, including improved functional outcomes, reduced recovery times, and increased patient engagement. It also addresses challenges such as data privacy, algorithmic bias, and integration with clinical workflows.  1. Transforming recovery paradigms Traditional post‑injury rehab relies on periodic in‑person assessments, therapist intuition, and standardized protocols that only partially account for individual variability. AI is shifting this model toward: Continuous, data‑driven care: Instead of snapshots in clinic, rehab can be informed by near real‑time streams of kinematic, physiological, and behavioral data from wearables, smart devices, and robot interfaces. Dynamic adaptation: Therapy intensity, task difficulty, and exercise selection can be automatically adjusted based on ongoing performance, fatigue, and recovery trends, rather than fixed schedules. Precision rehabilitation: Algorithms can identify which patients are likely to respond to specific interventions (e.g., constraint‑induced movement therapy vs robotics) and tailor plans accordingly. This moves rehabilitation from a "one‑size‑fits‑many" paradigm toward precision, context‑aware therapy, analogous to precision oncology but focused on function and participation. 2. Assessment, monitoring, and optimization AI for assessment Sensor‑based movement analysis: Machine learning models process accelerometer, IMU, EMG, and pressure data to quantify gait symmetry, joint kinematics, balance, and fine motor control with higher resolution than visual observation alone. Automated scoring: AI can approximate or support standardized scales (e.g., Fugl‑Meyer, Berg Balance Scale) by mapping sensor features or video-derived pose estimates to clinical scores, reducing inter‑rater variability and saving clinician time. Continuous monitoring Home and community tracking: Wearable and ambient sensors enable monitoring of daily steps, walking speed, arm use, posture, and adherence to exercises outside the clinic, feeding rich longitudinal datasets into AI models. Real‑time alerts: Algorithms can detect abnormal patterns—such as increased fall risk, reduced limb use, or signs of over‑exertion—and flag the clinician or adjust digital therapy content automatically. Optimization and decision support Predictive models: Using historical data, AI can forecast functional gains, plateau points, or risk of complications (e.g., falls, readmission), supporting individualized goal‑setting and resource allocation. Reinforcement learning and "digital twins": Emerging work in neurorehabilitation treats rehab as a sequential decision problem, using model‑based reinforcement learning and patient "digital twins" to recommend optimal timing, dosing, and progression of interventions over weeks to months.​ 3. Technologies: ML, wearables, analytics Machine learning algorithms: Supervised ML classifies movement quality (normal vs compensatory), detects exercise type from sensor streams, and estimates clinical scores. Unsupervised learning clusters patients into phenotypes (e.g., gait patterns after stroke), revealing subgroups that respond differently to certain therapies. Reinforcement learning and contextual bandits explore which therapy adjustments yield the best long‑term functional outcomes for a given individual.​ Wearable sensors and robotics: Inertial sensors, EMG, pressure insoles, and exoskeleton sensors capture high‑frequency movement and muscle activity data during training. Robotic devices (upper‑limb exoskeletons, gait trainers) coupled with AI can modulate assistance, resistance, or task difficulty in real time based on performance and predicted fatigue. Predictive and prescriptive analytics: Predictive analytics estimate trajectories (e.g., time to independent walking, expected upper‑limb function) to inform shared decisions with patients and families. Prescriptive analytics recommend therapy intensity, modality mix, and scheduling to maximize functional gains under resource constraints. 4. Benefits: outcomes, efficiency, engagement Improved functional outcomes: Studies report better motor recovery, gait quality, and ADL performance when AI‑assisted training is used—especially when robotics and intelligent feedback are involved. Reduced recovery time and resource use: More precise dosing and earlier identification of non‑responders can reduce ineffective sessions, shorten time to key milestones, and support safe earlier discharge with robust remote follow‑up. Increased adherence and engagement: AI‑driven digital rehab platforms use gamification, adaptive difficulty, and personalized feedback to keep patients engaged in home programs, improving adherence compared to static paper instructions. Support for clinicians: Instead of replacing therapists, AI can offload repetitive measurement tasks, highlight concerning trends, and offer data‑driven suggestions, allowing clinicians to focus on relational, motivational, and complex decision‑making aspects of care. 5. Challenges and ethical considerations Data privacy and security: Rehab AI often relies on continuous collection of sensitive motion, physiological, and sometimes audio/video data, raising questions about consent, storage, secondary use, and breach risk. Approaches like federated learning and on‑device processing are being explored to reduce centralization of identifiable data while still enabling model training. Algorithmic bias and fairness: If training data under‑represent older adults, women, certain racial/ethnic groups, or people with severe disability, AI models may misestimate performance or risk for those groups, potentially widening disparities in rehab access and outcomes. Ongoing auditing, diverse datasets, and participatory design with patients and clinicians are needed to ensure equitable performance. Integration with clinical workflows: Many AI tools are developed in research settings and are not yet seamlessly integrated into EHRs, scheduling systems, or therapist documentation workflows. Poorly integrated tools risk adding documentation burden or "alert fatigue," reducing adoption. Successful implementations co‑design interfaces with frontline therapists and physicians. Regulation, liability, and trust: It remains unclear in many jurisdictions how to regulate adaptive rehab algorithms (as medical devices, clinical decision support, or wellness tools) and who is liable when AI‑informed plans cause harm.​ Transparent, explainable models and clear communication to patients about the role of AI are critical for maintaining trust. 6. Case studies and emerging trends Remote and hybrid digital rehabilitation: AI‑driven platforms providing home‑based stroke, orthopedic, or Parkinson's rehab with clinician dashboards are improving adherence and extending care beyond brick‑and‑mortar clinics. Collaborative AI for precision neurorehabilitation: Frameworks combining patient‑clinician goal setting, digital twins, and reinforcement learning exemplify "collaborative AI" that augments rather than replaces therapists.​ Multimodal personalization: Integration of movement data, EMG, heart rate, sleep, and self‑reported pain/fatigue is enabling more nuanced adaptation to daily fluctuations in capacity. Conversational AI for education and coaching: Early work is assessing tools like ChatGPT as low‑risk supports for exercise education and motivation, though they are not yet precise enough to replace professional plan design AI is moving rehab toward patient‑centered, continuously adapting, and data‑rich care, but realizing this promise depends on addressing privacy, bias, workflow, and regulatory challenges in partnership with clinicians and patients.

Dormir no es simplemente descansar, sino un proceso activo en el que el cerebro reorganiza información, regula emociones y consolida la memoria. Cuando este proceso se altera de manera crónica, aumenta el riesgo de desarrollar trastornos como ansiedad, depresión y dificultades en la regulación emocional, por lo que su abordaje preventivo resulta fundamental en salud mental. Abordar la conducta del dormir desde las neurociencias y mediante una atención multimodal implica reconocer que el sueño es un pilar fundamental del bienestar psicológico en este podcast de El Expresso de las 10 la Dra. Teresita Villaseñor Jefa del Servicio de Neuropsicología en el Antiguo Hospital Civil de Guadalajara Fray Antonio alcalde, nos habla de ello, parte de nuestra cobertura de CIAM 2026.

Send a textIs AI in pathology actually improving diagnosis — or just adding complexity?In DigiPath Digest #37, we reviewed four recent publications covering AI-based biomarker quantification in glioblastoma, real-world digital workflow integration in prostate cancer, multimodal AI combining histopathology and genomics, and patient perspectives on AI in cancer diagnostics.This episode connects technical performance with something equally important: trust.Episode Highlights[00:02] Community & updates Digital Pathology 101 free PDF, upcoming patient-focused book, and global attendance.[04:07] AI-based image analysis in glioblastoma AI showed strong consistency with pathologists when quantifying Ki-67, P53, and PHH3. Significant biological correlations (Ki-67 ↔ PHH3, PHH3 ↔ P53) were detected by AI — not by manual assessment. Takeaway: computational quantification improves precision.[09:28] Real-world digital workflow + AI in prostate cancer (France) AI-pathologist concordance: • 93.2% (high probability cancer detection) • 99.0% (low probability slides) Gleason concordance: 76.6% 10% failure rate due to pre-analytical artifacts. Takeaway: infrastructure and sample quality still matter.[15:58] Multimodal AI (MARBIX framework) Combines whole slide images + immunogenomic data in a shared latent space using binary “monograms.” Performance in lung cancer: 85–89% vs 69–76% unimodal models. Takeaway: integrated data improves case retrieval and similarity reasoning.[22:13] AI-powered paper summary subscription introduced Structured summaries for busy professionals who want more than abstracts.[26:17] Patient roundtable on AI in pathology (Belgium) Patients expect: • Better accuracy • Faster turnaround • Stronger collaborationTrust is high when: • Algorithms use diverse datasets • Pathologists retain final responsibilityClinical validity mattered more than full algorithm transparency. Privacy concerns focused more on insurer misuse than cloud transfer.Key TakeawaysAI improves biomarker precision in glioblastoma.Digital pathology implementation works — but pre-analytics can limit AI performance.Multimodal AI represents the next meaningful step in precision diagnostics.Patients are not afraid of AI — they want validation, oversight, and governance.Human–AI collaboration remains central.If you're working in digital pathology, computational pathology, or precision oncology, this episode connects evidence, implementation, and patient perspective.Support the showGet the "Digital Pathology 101" FREE E-book and join us!

Guest: Adil Harroud, MD Guest: Dylan Hamitouche Host: Ryan Quigley Multimodal aging signatures are reshaping our understanding of progression and prognosis in multiple sclerosis (MS). Host Ryan Quigley sits down with Dr. Adil Harroud and Mr. Dylan Hamitouche to learn more about implications for the future of risk stratification and personalized treatment in MS, a topic they presented on at the 2026 ACTRIMS Forum. Dr. Harroud is a neurologist and the co-leader of the Neuroimmunology Diseases Research Group at the Montreal Neurological Institute at McGill University. Mr. Hamitouche is a medical student at McGill University.

Guest: Adil Harroud, MD Guest: Dylan Hamitouche Host: Ryan Quigley Multimodal aging signatures are reshaping our understanding of progression and prognosis in multiple sclerosis (MS). Host Ryan Quigley sits down with Dr. Adil Harroud and Mr. Dylan Hamitouche to learn more about implications for the future of risk stratification and personalized treatment in MS, a topic they presented on at the 2026 ACTRIMS Forum. Dr. Harroud is a neurologist and the co-leader of the Neuroimmunology Diseases Research Group at the Montreal Neurological Institute at McGill University. Mr. Hamitouche is a medical student at McGill University.

CEO & President Kyle and Graphic Designer & Brand strategist Kelsey explore how prompting has evolved from using AI like a “smarter Google” to structured strategies that deliver sharper, less generic results.They break down the CRIT framework (Context, Role, Interview, Task), share why detailed context reduces hallucinations, and explain how prompt libraries and model memory speed up repeatable work. The conversation also dives into context engineering with tools like Microsoft 365 Copilot and Google Workspace Gemini to make AI outputs more relevant and secure.Plus: common prompting mistakes, model comparisons, multimodal inputs, and how to onboard teams without losing brand consistency.Listen now to level up how you work with AI.00:00 Prompting Then vs Now: From “Smarter Google” to Strategic Skill 00:39 Why AI Sounds Vanilla: Averages, Models & AI Slop 01:33 Prompt Engineering & the CRIT Framework 02:35 Interview-Style Prompts: Fewer Hallucinations, Better Results 04:10 Garbage In, Garbage Out: Treat AI Like a New Hire 05:04 Let AI Help Write Prompts + Tools & Libraries 07:08 Why One-Liners Fall Flat (Contractor Analogy) 07:55 From Prompts to Systems: Templates & Model Memory 11:21 Context Engineering: Files, Memory & Workplace Data (Copilot/Gemini) 13:27 Over-Prompting: Context Limits & When to Reset 16:26 Set Outcomes, Don't Micromanage 18:22 Smarter Models: Gemini & Claude Need Less Steering 19:06 Claude Opus vs ChatGPT: Speed vs Detail 20:27 Multi-Model Workflow: Use Each for Its Strength 21:20 Why New Models Feel Smarter 22:11 Ask AI to Improve Your Prompts 24:42 Planning Mode: Structured Builds & AI Interviews 26:13 Training Teams: Frameworks, SOPs & Safe Experimentation 31:47 Multimodal & Voice Prompting (Gemini's Edge) 33:15 Wrap-Up & What's Next

What happens when AI safety filters fail to catch harmful content hidden inside images? Alessandro Pignati, AI Security Researcher at NeuralTrust, joins Sean Martin to reveal a newly discovered vulnerability that affects some of the most widely used image-generation models on the market today. The technique, called semantic chaining, is an image-based jailbreak attack discovered by the NeuralTrust research team, and it raises important questions about how enterprises secure their multimodal AI deployments.How does semantic chaining work? Pignati explains that the attack uses a single prompt composed of several parts. It begins with a benign scenario, such as a historical or educational context. A second instruction asks the model to make an innocent modification, like changing the color of a background. The final, critical step introduces a malicious directive, instructing the model to embed harmful content directly into the generated image. Because image-generation models apply fewer safety filters than their text-based counterparts, the harmful instructions are rendered inside the image without triggering the usual safeguards.The NeuralTrust research team tested semantic chaining against prominent models including Gemini Nano Pro, Grok 4, and Seedream 4.5 by ByteDance, finding the attack effective across all of them. For enterprises, the implications extend well beyond consumer use cases. Pignati notes that if an AI agent or chatbot has access to a knowledge base containing sensitive information or personal data, a carefully structured semantic chaining prompt can force the model to generate that data directly into an image, bypassing text-based safety mechanisms entirely.Organizations looking to learn more about semantic chaining and the broader landscape of AI agent security can visit the NeuralTrust blog, where the research team publishes detailed breakdowns of their findings. NeuralTrust also offers a newsletter with regular updates on agent security research and newly discovered vulnerabilities.This is a Brand Highlight. A Brand Highlight is a ~5 minute introductory conversation designed to put a spotlight on the guest and their company. Learn more: https://www.studioc60.com/creation#highlightGUESTAlessandro Pignati, AI Security Researcher, NeuralTrustOn LinkedIn: https://www.linkedin.com/in/alessandro-pignati/RESOURCESLearn more about NeuralTrust: https://neuraltrust.ai/Are you interested in telling your story?▶︎ Full Length Brand Story: https://www.studioc60.com/content-creation#full▶︎ Brand Spotlight Story: https://www.studioc60.com/content-creation#spotlight▶︎ Brand Highlight Story: https://www.studioc60.com/content-creation#highlightKEYWORDSAlessandro Pignati, NeuralTrust, Sean Martin, brand story, brand marketing, marketing podcast, brand highlight, semantic chaining, image jailbreak, AI security, agentic AI, multimodal AI, LLM safety, AI red teaming, prompt injection, AI agent security, image-based attacks, enterprise AI security Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

What happens when AI safety filters fail to catch harmful content hidden inside images? Alessandro Pignati, AI Security Researcher at NeuralTrust, joins Sean Martin to reveal a newly discovered vulnerability that affects some of the most widely used image-generation models on the market today. The technique, called semantic chaining, is an image-based jailbreak attack discovered by the NeuralTrust research team, and it raises important questions about how enterprises secure their multimodal AI deployments.How does semantic chaining work? Pignati explains that the attack uses a single prompt composed of several parts. It begins with a benign scenario, such as a historical or educational context. A second instruction asks the model to make an innocent modification, like changing the color of a background. The final, critical step introduces a malicious directive, instructing the model to embed harmful content directly into the generated image. Because image-generation models apply fewer safety filters than their text-based counterparts, the harmful instructions are rendered inside the image without triggering the usual safeguards.The NeuralTrust research team tested semantic chaining against prominent models including Gemini Nano Pro, Grok 4, and Seedream 4.5 by ByteDance, finding the attack effective across all of them. For enterprises, the implications extend well beyond consumer use cases. Pignati notes that if an AI agent or chatbot has access to a knowledge base containing sensitive information or personal data, a carefully structured semantic chaining prompt can force the model to generate that data directly into an image, bypassing text-based safety mechanisms entirely.Organizations looking to learn more about semantic chaining and the broader landscape of AI agent security can visit the NeuralTrust blog, where the research team publishes detailed breakdowns of their findings. NeuralTrust also offers a newsletter with regular updates on agent security research and newly discovered vulnerabilities.This is a Brand Highlight. A Brand Highlight is a ~5 minute introductory conversation designed to put a spotlight on the guest and their company. Learn more: https://www.studioc60.com/creation#highlightGUESTAlessandro Pignati, AI Security Researcher, NeuralTrustOn LinkedIn: https://www.linkedin.com/in/alessandro-pignati/RESOURCESLearn more about NeuralTrust: https://neuraltrust.ai/Are you interested in telling your story?▶︎ Full Length Brand Story: https://www.studioc60.com/content-creation#full▶︎ Brand Spotlight Story: https://www.studioc60.com/content-creation#spotlight▶︎ Brand Highlight Story: https://www.studioc60.com/content-creation#highlightKEYWORDSAlessandro Pignati, NeuralTrust, Sean Martin, brand story, brand marketing, marketing podcast, brand highlight, semantic chaining, image jailbreak, AI security, agentic AI, multimodal AI, LLM safety, AI red teaming, prompt injection, AI agent security, image-based attacks, enterprise AI security Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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]:

In which the boys discuss Pynchon's delightful drug-haze California novel, Inherent Vice, and Paul Thomas Anderson's adaptation to film. Both are good hangs. And honestly, perhaps uniquely, are better for being read and watched together than on their own. 

Many of you know, because I mention it frequently, that I grew up in Memphis.  And if you are from Memphis,  because of its proximity, you might have been lucky enough to visit one of the most beautiful and most interesting of American cities - New Orleans. The history is so fascinating.And, if you are in the export or import business, you are going to be very excited to hear my conversation with today's guest, Beth Branch, President & CEO of the Port of New Orleans.  Not only does she bring us up to date on what is happening with this dynamic ocean port, but she is also a great storyteller in her own right.I hope you enjoy this episode. After you've listened, we would love to hear your thoughts and comments, which you can post at https://www.exportstoriespodcast.com/ or on our Facebook or LinkedIn pages.

Let's talk about an incredibly adaptable project in which students experiment with creative ideas across modes. It's easy to plug into a variety of units and times of year, and ready to tap at a moment's notice. It remixes easily for Valentine's Day on the horizon, but it could also work well at Halloween, or as part of a creative writing unit, or when you're reading any verse novel or graphic novel. This project starts with fiction, moves into verse, and lands in a multimodal combination of verse and imagery. I call it a multimodal flash verse project, informed along the way by the brilliant mode collaborations of Jason Reynolds. Let's dig into it. Links Mentioned: Jason Reynolds' Interview with the Kennedy Center: https://www.youtube.com/watch?v=cuXNsJvNaFs  Book Trailer for Ain't Burned all the Bright: https://www.youtube.com/watch?v=EjqvOyAh36Y  Reynolds on his collab with Novgorodoff: https://www.youtube.com/watch?v=0ErpAXd7Swg  There was a Party for Langston Read-Aloud: https://www.youtube.com/watch?v=m4MYO4WmR9s  Go Further:  Explore alllll the Episodes of The Spark Creativity Teacher Podcast. Get my popular free hexagonal thinking digital toolkit Join our community, Creative High School English, on Facebook. Come hang out on Instagram.  Enjoying the podcast? Please consider sharing it with a friend, snagging a screenshot to share on the 'gram, or tapping those ⭐⭐⭐⭐⭐ to help others discover the show. Thank you!   

What if your sales motion created real partnerships instead of fragile price wins? That's the thread we pull with Hans, CEO of Odyssey Logistics, as he maps a journey from Danish directness and early Maersk rotations to leading a global multimodal platform through a roll-up-to-one-brand transformation. The conversation is practical, candid, and loaded with moves you can copy tomorrow—whether you're running a desk or running a P&L.We start with the foundation: a value proposition built on facts, not slogans. Hans explains how probing, silence, and quarterly KPI reviews expose true customer pain, unlock share of wallet, and make relationships stick at multiple levels, including the C-suite. He shares why he spends heavy time in the field, what onsite town halls surface that email never will, and how a consistent cadence—global Q&A, divisional sessions, defined values—turns culture from posters into behavior.Then we dig into Odyssey's shift from 16+ legacy brands to One Odyssey. Hans breaks down the integration playbook: centralizing shared services, standardizing procurement, and rebranding fast without crushing entrepreneurial spirit. He's frank about PE carve-outs, IT risk, and why overcommunication beats overpromising during ownership changes. On growth, we get specific: three levers—share of wallet, new logos, and cross-sell—powered by a cross-trained sales force and subject matter experts. Multimodal strategy is the differentiator, with intermodal often beating truckload on cost and CO2 when planned well.Technology underpins the whole plan. A data lake fuels route optimization, predictive analytics, and automated bidding, while better systems lift both customer outcomes and employee satisfaction. Odyssey's rebranded brokerage in Atlanta becomes the easy entry point—truckload and LTL open the door to deeper multimodal solutions. Hans closes with career advice that never expires: choose training over titles, learn every job, stay humble, and remember the team is smarter than any one of us.If this resonates, follow the show, share it with a colleague who sells on price, and leave a quick review so more people can find conversations that move logistics forward.Follow The Freight Pod and host Andrew Silver on LinkedIn.Thanks to our sponsors:Stuut Technologies: Your AI coworker that collects your cash automatically.https://www.stuut.ai/Cloneops.ai: Not just AI. Industry-born AI.https://www.cloneops.ai/Rapido Solutions Group: Nearshore solutions for logistics companies.https://www.gorapido.com/GenLogs: Freight Intelligence on every carrier, shipper, and asset via a nationwide sensor networkhttps://www.genlogs.io/

With developments in generative AI progressing at such a furious pace, how can investors cut through the noise to identify the companies that will really matter? Baillie Gifford's Kyle McEnery shares his approach to meeting the entrepreneurs building the future – including his encounters with AppLovin, Anthropic, NVIDIA, Roblox and Reddit. Background:Kyle McEnery is an investment manager in our Long Term Global Growth Team (LTGG) and previously led Baillie Gifford's Artificial Intelligence Research Project. In this conversation, he tells host Leo Kelion why AI's ever-increasing capabilities make this one of the most exciting times to be a growth investor, and how leadership and culture act as signals in the noise to help identify companies with the greatest long-term growth potential. In addition to discussing which of the firms enabling and using today's language-based ‘frontier' AI models are leading the pack, he explains how efforts to understand and simulate real-world physics could unlock further progress. Portfolio companies discussed include:Anthropic – developer of the Claude AI models, which excel at coding, among other tasks.NVIDIA – the semiconductors firm whose accelerator chips are powering many of the advances in generative AI.Roblox – the video games platform whose Cube 3D technology allows creators to build objects and environments out of text-based descriptions.AppLovin – the ad-tech company whose AI-first strategy keeps the business lean and nimble.Reddit – the online discussion forum, whose authentic human conversations are gaining in value as a counterpoint to AI-generated output. Resources:AI and the future of everything: a long-term perspectiveAnthropic: why we are backing the AI frontrunnerLong Term Global Growth Strategy (institutional investors only)LTGG philosophy and process (institutional investors only)Private companies: from Anthropic to ZetwerkShort Briefings on Long Term Thinking hub Companies mentioned include:Alphabet/GoogleAmazonAnthropicAppLovinHorizon RoboticsNVIDIARedditRobloxTesla  Timecodes:00:00  Introduction – Dartmouth College's artificial intelligence workshop01:50   From quantum to AI via asset management02:50  Creating and then culling a machine-learning initiative08:05  ChatGPT's wake-up call10:35   Exceptional companies at the dawn of generative AI12:10   Anthropic's appeal to business customers14:55   A winner-takes-all opportunity?17:05   Dario Amodei and the scaling laws19:10   NVIDIA's foundational role in neural networks22:55  Making video game items in Roblox with AI25:00  AppLovin – a company built for the next era26:55  Reddit's valuable conversational communities29:35  World models, spatial AI and the physical world32:35  Staying open-minded and humble33:35  Book choice  Glossary of terms (in order of mention): Generative AI: AI systems that create new content such as text, images or code rather than just analysing data.Machine learning: AI techniques where systems learn patterns from data rather than being explicitly programmed.End-to-end, systematic (investment strategy): Fully automated, with decisions made by predefined rules rather than human judgement.Agentic AI: AI systems that can plan and carry out tasks autonomously rather than just responding to prompts.R&D: Research and development.GPT: OpenAI's models, which power its ChatGPT chatbot.Natural language processing: AI that enables computers to understand and generate human language.Token: A chunk of text, such as a word or part of a word, used by language models.Foundation models: Large AI models that can handle a wide variety of tasks.Know your customer (KYC): Financial checks used by banks to verify customers' identities and risks.Scaling laws: The idea that AI performance improves predictably as models, data and computing power increase.Compute: The processing power required to train and run AI models.Jevons' paradox: The counterintuitive idea that efficiency gains can increase, rather than reduce, overall usage.CUDA: NVIDIA's software platform for programming its chips for high-performance computing.Jensen: Jensen Huang, NVIDIA's co-founder and chief executive.Metaverse: Shared virtual worlds where people interact, create and play online.Large language models (LLMs): AI systems trained on vast amounts of text to understand and generate language.Multimodal models: AI systems that can process multiple types of data, such as text, images and video.World models: AI systems that learn how the physical world works in order to predict and simulate it.Embodied AI: AI that learns through physical interaction with the real world, such as robots or vehicles.Imitation learning: Training AI by having it copy actions demonstrated by humans.

In this Omni Talk Retail episode, recorded live from NRF 2026 in the Vusion podcast studio, Mark Propes from Vusion and Art Miller from Qualcomm reveal how their partnership is enabling "detect and connect" capabilities that transform physical retail into personalized experiences, and why retailers still testing need to operationalize now before the gap becomes permanent. From edge computing that processes 4K video locally instead of streaming to the cloud, to closed-loop attribution tracking customer intent in real-time physical space, Mark and Art break down the multimodal signal taxonomy (RFID, Wi-Fi, Bluetooth, vision) powering connected stores. They share insights on why scanning barcodes continuously creates data-poor environments, how agentic AI creates new doorways into physical stores, and the precision needed for sub-30 minute delivery promises. If you've wondered what detect and connect actually means beyond buzzwords, this conversation delivers the technical foundation and business applications.

ChatGPT Health explodes serving 230M weekly patients with clinical-grade conversational diagnostics instantly worldwide. Multimodal symptom reasoning integrates vitals medications history delivering personalized care plans seamlessly. OpenAI captures trillion-dollar healthcare market conversational medicine leadership disruptively.Get the top 40+ AI Models for $20 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustleSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Clinic worldwide ChatGPT Health handles 230M weekly conversations clinical-grade accuracy conversational format seamlessly. Multimodal symptom analysis accelerates care delivery rivaling specialist accuracy potently globally. OpenAI disrupts trillion-dollar healthcare delivery disruptively comprehensively.Get the top 40+ AI Models for $20 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustleSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

230M weekly patients ChatGPT Health debuts delivering physician-level diagnosis via conversational AI seamlessly. Multimodal reasoning analyzes symptoms vitals history generating personalized treatment plans instantly. Healthcare disruption begins trillion-parameter medical agent eclipses traditional gatekeepers disruptively.Get the top 40+ AI Models for $20 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustleSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Empire building ChatGPT Health processes 230M weekly conversations doctor-equivalent reasoning conversational format seamlessly. Multimodal reasoning analyzes symptom clusters vitals rivaling specialist accuracy worldwide potently. OpenAI captures trillion-dollar healthcare market disruptively comprehensively.Get the top 40+ AI Models for $20 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustleSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Weekly clinics ChatGPT Health serves 230M patients doctor-level conversational diagnostics potently worldwide. Multimodal symptom analysis integrates history medications providing triage recommendations comprehensively. Healthcare disruption trillion-parameter medical agent leadership established disruptively.Get the top 40+ AI Models for $20 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustleSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

We are reupping this episode after LMArena announced their fresh Series A (https://www.theinformation.com/articles/ai-evaluation-startup-lmarena-valued-1-7-billion-new-funding-round?rc=luxwz4), raising $150m at a $1.7B valuation, with $30M annualized consumption revenue (aka $2.5m MRR) after their September evals product launch.—-From building LMArena in a Berkeley basement to raising $100M and becoming the de facto leaderboard for frontier AI, Anastasios Angelopoulos returns to Latent Space to recap 2025 in one of the most influential platforms in AI—trusted by millions of users, every major lab, and the entire industry to answer one question: which model is actually best for real-world use cases? We caught up with Anastasios live at NeurIPS 2025 to dig into the origin story (spoiler: it started as an academic project incubated by Anjney Midha at a16z, who formed an entity and gave grants before they even committed to starting a company), why they decided to spin out instead of staying academic or nonprofit (the only way to scale was to build a company), how they're spending that $100M (inference costs, React migration off Gradio, and hiring world-class talent across ML, product, and go-to-market), the leaderboard delusion controversy and why their response demolished the paper's claims (factual errors, misrepresentation of open vs. closed source sampling, and ignoring the transparency of preview testing that the community loves), why platform integrity comes first (the public leaderboard is a charity, not a pay-to-play system—models can't pay to get on, can't pay to get off, and scores reflect millions of real votes), how they're expanding into occupational verticals (medicine, legal, finance, creative marketing) and multimodal arenas (video coming soon), why consumer retention is earned every single day (sign-in and persistent history were the unlock, but users are fickle and can leave at any moment), and his vision for Arena as the central evaluation platform that provides the North Star for the industry—constantly fresh, immune to overfitting, and grounded in millions of real-world conversations from real users.We discuss:* The $100M raise: use of funds is primarily inference costs (funding free usage for tens of millions of monthly conversations), React migration off Gradio (custom loading icons, better developer hiring, more flexibility), and hiring world-class talent* The scale: 250M+ conversations on the platform, tens of millions per month, 25% of users do software for a living, and half of users are now logged in* The leaderboard illusion controversy: Cohere researchers claimed undisclosed private testing created inequities, but Arena's response demolished the paper's factual errors (misrepresented open vs. closed source sampling, ignored transparency of preview testing that the community loves)* Why preview testing is loved by the community: secret codenames (Gemini Nano Banana, named after PM Naina's nickname), early access to unreleased models, and the thrill of being first to vote on frontier capabilities* The Nano Banana moment: changed Google's market share overnight, billions of dollars in stock movement, and validated that multimodal models (image generation, video) are economically critical for marketing, design, and AI-for-science* New categories: occupational and expert arenas (medicine, legal, finance, creative marketing), Code Arena, and video arena coming soonFull Video EpisodeTimestamps00:00:00 Introduction: Anastasios from Arena and the LM Arena Journey00:01:36 The Anjney Midha Incubation: From Berkeley Basement to Startup00:02:47 The Decision to Start a Company: Scaling Beyond Academia00:03:38 The $100M Raise: Use of Funds and Platform Economics00:05:10 Arena's User Base: 5M+ Users and Diverse Demographics00:06:02 The Competitive Landscape: Artificial Analysis, AI.xyz, and Arena's Differentiation00:08:12 Educational Value and Learning from the Community00:08:41 Technical Migration: From Gradio to React and Platform Evolution00:10:18 Leaderboard Delusion Paper: Addressing Critiques and Maintaining Integrity00:12:29 Nano Banana Moment: How Preview Models Create Market Impact00:13:41 Multimodal AI and Image Generation: From Skepticism to Economic Value00:15:37 Core Principles: Platform Integrity and the Public Leaderboard as Charity00:18:29 Future Roadmap: Expert Categories, Multimodal, Video, and Occupational Verticals00:19:10 API Strategy and Focus: Doing One Thing Well00:19:51 Community Management and Retention: Sign-In, History, and Daily Value00:22:21 Partnerships and Agent Evaluation: From Devon to Full-Featured Harnesses00:21:49 Hiring and Building a High-Performance Team Get full access to Latent.Space at www.latent.space/subscribe

Meta just made a multi-billion acquisition for AI agents.

From building LMArena in a Berkeley basement to raising $100M and becoming the de facto leaderboard for frontier AI, Anastasios Angelopoulos returns to Latent Space to recap 2025 in one of the most influential platforms in AI—trusted by millions of users, every major lab, and the entire industry to answer one question: which model is actually best for real-world use cases? We caught up with Anastasios live at NeurIPS 2025 to dig into the origin story (spoiler: it started as an academic project incubated by Anjney Midha at a16z, who formed an entity and gave grants before they even committed to starting a company), why they decided to spin out instead of staying academic or nonprofit (the only way to scale was to build a company), how they're spending that $100M (inference costs, React migration off Gradio, and hiring world-class talent across ML, product, and go-to-market), the leaderboard delusion controversy and why their response demolished the paper's claims (factual errors, misrepresentation of open vs. closed source sampling, and ignoring the transparency of preview testing that the community loves), why platform integrity comes first (the public leaderboard is a charity, not a pay-to-play system—models can't pay to get on, can't pay to get off, and scores reflect millions of real votes), how they're expanding into occupational verticals (medicine, legal, finance, creative marketing) and multimodal arenas (video coming soon), why consumer retention is earned every single day (sign-in and persistent history were the unlock, but users are fickle and can leave at any moment), the Gemini Nano Banana moment that changed Google's market share overnight (and why multimodal models are becoming economically critical for marketing, design, and AI-for-science), how they're thinking about agents and harnesses (Code Arena evaluates models, but maybe it should evaluate full agents like Devin), and his vision for Arena as the central evaluation platform that provides the North Star for the industry—constantly fresh, immune to overfitting, and grounded in millions of real-world conversations from real users. We discuss: The $100M raise: use of funds is primarily inference costs (funding free usage for tens of millions of monthly conversations), React migration off Gradio (custom loading icons, better developer hiring, more flexibility), and hiring world-class talent The scale: 250M+ conversations on the platform, tens of millions per month, 25% of users do software for a living, and half of users are now logged in The leaderboard illusion controversy: Cohere researchers claimed undisclosed private testing created inequities, but Arena's response demolished the paper's factual errors (misrepresented open vs. closed source sampling, ignored transparency of preview testing that the community loves) Why preview testing is loved by the community: secret codenames (Gemini Nano Banana, named after PM Naina's nickname), early access to unreleased models, and the thrill of being first to vote on frontier capabilities The Nano Banana moment: changed Google's market share overnight, billions of dollars in stock movement, and validated that multimodal models (image generation, video) are economically critical for marketing, design, and AI-for-science New categories: occupational and expert arenas (medicine, legal, finance, creative marketing), Code Arena, and video arena coming soon Consumer retention: sign-in and persistent history were the unlock, but users are fickle and earned every single day—"every user is earned, they can leave at any moment" — Anastasios Angelopoulos Arena: https://lmarena.ai X: https://x.com/arena Chapters 00:00:00 Introduction: Anastasios from Arena and the LM Arena Journey 00:01:36 The Anjney Midha Incubation: From Berkeley Basement to Startup 00:02:47 The Decision to Start a Company: Scaling Beyond Academia 00:03:38 The $100M Raise: Use of Funds and Platform Economics 00:05:10 Arena's User Base: 5M+ Users and Diverse Demographics 00:06:02 The Competitive Landscape: Artificial Analysis, AI.xyz, and Arena's Differentiation 00:08:12 Educational Value and Learning from the Community 00:08:41 Technical Migration: From Gradio to React and Platform Evolution 00:10:18 Leaderboard Delusion Paper: Addressing Critiques and Maintaining Integrity 00:12:29 Nano Banana Moment: How Preview Models Create Market Impact 00:13:41 Multimodal AI and Image Generation: From Skepticism to Economic Value 00:15:37 Core Principles: Platform Integrity and the Public Leaderboard as Charity 00:18:29 Future Roadmap: Expert Categories, Multimodal, Video, and Occupational Verticals 00:19:10 API Strategy and Focus: Doing One Thing Well 00:19:51 Community Management and Retention: Sign-In, History, and Daily Value 00:22:21 Partnerships and Agent Evaluation: From Devon to Full-Featured Harnesses 00:21:49 Hiring and Building a High-Performance Team

From creating SWE-bench in a Princeton basement to shipping CodeClash, SWE-bench Multimodal, and SWE-bench Multilingual, John Yang has spent the last year and a half watching his benchmark become the de facto standard for evaluating AI coding agents—trusted by Cognition (Devin), OpenAI, Anthropic, and every major lab racing to solve software engineering at scale. We caught up with John live at NeurIPS 2025 to dig into the state of code evals heading into 2026: why SWE-bench went from ignored (October 2023) to the industry standard after Devin's launch (and how Walden emailed him two weeks before the big reveal), how the benchmark evolved from Django-heavy to nine languages across 40 repos (JavaScript, Rust, Java, C, Ruby), why unit tests as verification are limiting and long-running agent tournaments might be the future (CodeClash: agents maintain codebases, compete in arenas, and iterate over multiple rounds), the proliferation of SWE-bench variants (SWE-bench Pro, SWE-bench Live, SWE-Efficiency, AlgoTune, SciCode) and how benchmark authors are now justifying their splits with curation techniques instead of just "more repos," why Tau-bench's "impossible tasks" controversy is actually a feature not a bug (intentionally including impossible tasks flags cheating), the tension between long autonomy (5-hour runs) vs. interactivity (Cognition's emphasis on fast back-and-forth), how Terminal-bench unlocked creativity by letting PhD students and non-coders design environments beyond GitHub issues and PRs, the academic data problem (companies like Cognition and Cursor have rich user interaction data, academics need user simulators or compelling products like LMArena to get similar signal), and his vision for CodeClash as a testbed for human-AI collaboration—freeze model capability, vary the collaboration setup (solo agent, multi-agent, human+agent), and measure how interaction patterns change as models climb the ladder from code completion to full codebase reasoning. We discuss: John's path: Princeton → SWE-bench (October 2023) → Stanford PhD with Diyi Yang and the Iris Group, focusing on code evals, human-AI collaboration, and long-running agent benchmarks The SWE-bench origin story: released October 2023, mostly ignored until Cognition's Devin launch kicked off the arms race (Walden emailed John two weeks before: "we have a good number") SWE-bench Verified: the curated, high-quality split that became the standard for serious evals SWE-bench Multimodal and Multilingual: nine languages (JavaScript, Rust, Java, C, Ruby) across 40 repos, moving beyond the Django-heavy original distribution The SWE-bench Pro controversy: independent authors used the "SWE-bench" name without John's blessing, but he's okay with it ("congrats to them, it's a great benchmark") CodeClash: John's new benchmark for long-horizon development—agents maintain their own codebases, edit and improve them each round, then compete in arenas (programming games like Halite, economic tasks like GDP optimization) SWE-Efficiency (Jeffrey Maugh, John's high school classmate): optimize code for speed without changing behavior (parallelization, SIMD operations) AlgoTune, SciCode, Terminal-bench, Tau-bench, SecBench, SRE-bench: the Cambrian explosion of code evals, each diving into different domains (security, SRE, science, user simulation) The Tau-bench "impossible tasks" debate: some tasks are underspecified or impossible, but John thinks that's actually a feature (flags cheating if you score above 75%) Cognition's research focus: codebase understanding (retrieval++), helping humans understand their own codebases, and automatic context engineering for LLMs (research sub-agents) The vision: CodeClash as a testbed for human-AI collaboration—vary the setup (solo agent, multi-agent, human+agent), freeze model capability, and measure how interaction changes as models improve — John Yang SWE-bench: https://www.swebench.com X: https://x.com/jyangballin Chapters 00:00:00 Introduction: John Yang on SWE-bench and Code Evaluations 00:00:31 SWE-bench Origins and Devon's Impact on the Coding Agent Arms Race 00:01:09 SWE-bench Ecosystem: Verified, Pro, Multimodal, and Multilingual Variants 00:02:17 Moving Beyond Django: Diversifying Code Evaluation Repositories 00:03:08 Code Clash: Long-Horizon Development Through Programming Tournaments 00:04:41 From Halite to Economic Value: Designing Competitive Coding Arenas 00:06:04 Ofir's Lab: SWE-ficiency, AlgoTune, and SciCode for Scientific Computing 00:07:52 The Benchmark Landscape: TAU-bench, Terminal-bench, and User Simulation 00:09:20 The Impossible Task Debate: Refusals, Ambiguity, and Benchmark Integrity 00:12:32 The Future of Code Evals: Long Autonomy vs Human-AI Collaboration 00:14:37 Call to Action: User Interaction Data and Codebase Understanding Research

From creating SWE-bench in a Princeton basement to shipping CodeClash, SWE-bench Multimodal, and SWE-bench Multilingual, John Yang has spent the last year and a half watching his benchmark become the de facto standard for evaluating AI coding agents—trusted by Cognition (Devin), OpenAI, Anthropic, and every major lab racing to solve software engineering at scale. We caught up with John live at NeurIPS 2025 to dig into the state of code evals heading into 2026: why SWE-bench went from ignored (October 2023) to the industry standard after Devin's launch (and how Walden emailed him two weeks before the big reveal), how the benchmark evolved from Django-heavy to nine languages across 40 repos (JavaScript, Rust, Java, C, Ruby), why unit tests as verification are limiting and long-running agent tournaments might be the future (CodeClash: agents maintain codebases, compete in arenas, and iterate over multiple rounds), the proliferation of SWE-bench variants (SWE-bench Pro, SWE-bench Live, SWE-Efficiency, AlgoTune, SciCode) and how benchmark authors are now justifying their splits with curation techniques instead of just “more repos,” why Tau-bench's “impossible tasks” controversy is actually a feature not a bug (intentionally including impossible tasks flags cheating), the tension between long autonomy (5-hour runs) vs. interactivity (Cognition's emphasis on fast back-and-forth), how Terminal-bench unlocked creativity by letting PhD students and non-coders design environments beyond GitHub issues and PRs, the academic data problem (companies like Cognition and Cursor have rich user interaction data, academics need user simulators or compelling products like LMArena to get similar signal), and his vision for CodeClash as a testbed for human-AI collaboration—freeze model capability, vary the collaboration setup (solo agent, multi-agent, human+agent), and measure how interaction patterns change as models climb the ladder from code completion to full codebase reasoning.We discuss:* John's path: Princeton → SWE-bench (October 2023) → Stanford PhD with Diyi Yang and the Iris Group, focusing on code evals, human-AI collaboration, and long-running agent benchmarks* The SWE-bench origin story: released October 2023, mostly ignored until Cognition's Devin launch kicked off the arms race (Walden emailed John two weeks before: “we have a good number”)* SWE-bench Verified: the curated, high-quality split that became the standard for serious evals* SWE-bench Multimodal and Multilingual: nine languages (JavaScript, Rust, Java, C, Ruby) across 40 repos, moving beyond the Django-heavy original distribution* The SWE-bench Pro controversy: independent authors used the “SWE-bench” name without John's blessing, but he's okay with it (”congrats to them, it's a great benchmark”)* CodeClash: John's new benchmark for long-horizon development—agents maintain their own codebases, edit and improve them each round, then compete in arenas (programming games like Halite, economic tasks like GDP optimization)* SWE-Efficiency (Jeffrey Maugh, John's high school classmate): optimize code for speed without changing behavior (parallelization, SIMD operations)* AlgoTune, SciCode, Terminal-bench, Tau-bench, SecBench, SRE-bench: the Cambrian explosion of code evals, each diving into different domains (security, SRE, science, user simulation)* The Tau-bench “impossible tasks” debate: some tasks are underspecified or impossible, but John thinks that's actually a feature (flags cheating if you score above 75%)* Cognition's research focus: codebase understanding (retrieval++), helping humans understand their own codebases, and automatic context engineering for LLMs (research sub-agents)* The vision: CodeClash as a testbed for human-AI collaboration—vary the setup (solo agent, multi-agent, human+agent), freeze model capability, and measure how interaction changes as models improve—John Yang* SWE-bench: https://www.swebench.com* X: https://x.com/jyangballinFull Video EpisodeTimestamps00:00:00 Introduction: John Yang on SWE-bench and Code Evaluations00:00:31 SWE-bench Origins and Devon's Impact on the Coding Agent Arms Race00:01:09 SWE-bench Ecosystem: Verified, Pro, Multimodal, and Multilingual Variants00:02:17 Moving Beyond Django: Diversifying Code Evaluation Repositories00:03:08 Code Clash: Long-Horizon Development Through Programming Tournaments00:04:41 From Halite to Economic Value: Designing Competitive Coding Arenas00:06:04 Ofir's Lab: SWE-ficiency, AlgoTune, and SciCode for Scientific Computing00:07:52 The Benchmark Landscape: TAU-bench, Terminal-bench, and User Simulation00:09:20 The Impossible Task Debate: Refusals, Ambiguity, and Benchmark Integrity00:12:32 The Future of Code Evals: Long Autonomy vs Human-AI Collaboration00:14:37 Call to Action: User Interaction Data and Codebase Understanding Research Get full access to Latent.Space at www.latent.space/subscribe

This month, we are presenting recordings of two events from the Academy of Management Annual Meeting 2025. The first event was Multimodal Impact: Translating Academic Knowledge via Contextual, Collaborative, and Collectivist Modes. This symposium brings together five presenters to explore diverse modes of translating academic expertise into practice. As management researchers increasingly strive to achieve societal impact, this event sought to understand how different communication modes can bridge the persistent research-practice divide.

What's on your mind? Let CX Passport know...Jon Deragon brings a global lens to AI product design as the Head of Design at FPT, the sponsor for today's episode. Thank you FPT for collaborating with CX Passport.Jon guides a 140+ person design org building everything from mobile apps to automotive interfaces while navigating the rapid shift into AI and multimodal experiences. This conversation gets into what modern design teams truly need to succeed and how respect transforms the design and development partnership.Here are five insights you'll hear in this episode: • How multimodal input changes the entire UX landscape • Why design literacy helps… but “everyone is a designer” does not • The real fix for design and development friction • Why centralizing design creates more meaningful output • How AI learning happens in layers and why that mattersCHAPTERS00:00 Welcome00:16 Jon's global path and design focus01:52 Designing for AI03:38 Multimodal input05:17 Keeping pace with AI11:12 Should everyone be a designer14:44 First Class Lounge21:01 Structuring a large design org24:15 Making design and development collaboration healthy28:03 Respect as a design principle29:04 Where to learn more about Jon and FPTGUEST LINKSFPT: https://fpt.com/ Jon's website: https://jonderagon.com/THREE WAYS TO KEEP EXPLORING CX PASSPORTListen: https://www.cxpassport.com Watch: https://www.youtube.com/@cxpassport Newsletter: https://cxpassport.kit.com/signupI'm Rick Denton and I believe the best meals are served outside and require a passport.Disclaimer: This podcast is for informational and entertainment purposes only. The views and opinions expressed are those of the hosts and guests and should not be taken as legal, financial, or professional advice. Always consult with a qualified attorney, financial advisor, or other professional regarding your specific situation. The opinions expressed by guests are solely theirs and do not necessarily represent the views or positions of the host(s).

There has been a lot of buzz about Gemini 3, Google's LLM. In this episode, I dig into Google's big announcement and try to get past the hype to what it really means for healthtech marketers. Google is positioning Gemini 3 as a highly multimodal, context-aware AI system that can handle text, images, data, and reasoning in one place. I will do my best to explain what that means in English and why you should care about that. I will also share how I benchmarked Gemini 3 vs the other guys to see if it lives up to the promise.I also cover what this all means for search. This is kind of a big deal - potentially. This may presage the likely evolution of search from text-only answers to rich, AI-generated “micro-sites” with visuals, maybe even video, built on the fly. I will wrap up with five key takeaways on when to use which tool (Gemini, ChatGPT, Claude, Perplexity), where Gemini really shines, and why Google's evolving ad model should be on every healthtech marketer's radar right now.Topics Covered:"(00:00)" – Introduction & setup"(01:10)" – What Gemini 3 actually is"(03:40)" – Nano Banana Pro for visuals"(05:30)" – Multimodal workflows & creative speed"(07:30)" – Deep integration with Google apps"(09:30)" – AI Overviews & the future of search"(12:30)" – Visual, interactive AI results & declining SEO value"(15:10)" – Rethinking Google Ads in an AI-first world"(17:00)" – Introducing the HLP BrAIn & benchmarking approach"(18:30)" – Benchmark results: BrAIn vs ChatGPT vs Gemini"(21:00)" – Script-writing test across four LLMs"(24:00)" – Strengths and weaknesses of each LLM"(26:00)" – Five key takeaways & closingIf you are interested in discussing this or any other topic, let's have a chat.  Reach out to me directly to schedule a no-obligation discussion. This isn't a sales call, but rather an opportunity to talk through your questions and challenges.Follow me on LinkedIn.Subscribe to The Healthtech Marketing Show on Spotify or watch us on YouTube for more insights into marketing, AI, ABM, buyer journeys, and beyond!Thank you to our presenting sponsors, HIMSS, a leader in advancing health equity, digital innovation, and data-driven care through technology, policy, and community collaboration. And also HealthcareNOW, 24/7 expert shows, interviews, and podcasts, powering healthcare leaders with innovation, policy, and strategy insights.

Program notes:0:35 Update on RSV, flu and COVID-19 vaccines1:35 500 studies included2:35 Rare myocarditis3:35 Flu vaccine in older adults4:30 Tai chi or CBT-I for chronic insomnia5:30 Trained in one or the other6:30 Inexpensive and accessible7:30 $150 billion cost of chronic insomnia7:45 GLP-1s and WHO guidance8:50 Multimodal approach required9:45 Prevention is important9:55 Corticosteroids in pregnancy10:50 1.3 million pregnancies11:50 Used for multiple indications12:46 End

If you love weaving books into speech and language therapy, this episode is absolutely your lane. In this conversation, Kelly breaks down a 2025 scoping review on early language development and reading aloud, then translates it into five practical literacy “hacks” you can use with preschool and early elementary students starting tomorrow. She pulls zero punches about the study design: you'll hear exactly what a scoping review is (and isn't), why it doesn't carry the same weight as a systematic review or meta-analysis, and how to use it wisely as an “idea generator” rather than gospel. From there, she layers in two decades of clinical experience and walks through the habits that actually move the needle in real therapy rooms. You'll hear about: Why this 2025 scoping review on reading aloud and early language is best viewed as an “idea article” How the authors used PCC (Population, Context, Concept) to narrow 1,000+ studies down to 106 Why repetitive, predictable books (like The Gingerbread Man or Brown Bear, Brown Bear) allow diverse learners to participate at a higher level How to rethink “social stories” using a Brown Bear-style repetitive frame and a child's favorite characters for more powerful behavior change What Universal Design for Learning actually looks like in speech therapy when you go all-in on multimodal cueing How multisensory, multimodal activities (print, props, movement, AAC, writing) especially support autistic students and kids with attention and motor planning challenges Why connecting books to real-world roles and prior knowledge (“You're the zookeeper…”) drives deeper language and thinking than fact-based WH questions Simple language shifts that move you away from quizzing (“What color is…?”) toward higher-level thinking (“I wonder why…”, “Tell me about a time…”) How predictable literacy routines reduce cognitive load and move kids out of fight/flight and into learning Why the interaction itself matters more than any single treatment target or book choice How prepping rich, ready-to-go materials frees you to be fully present in the interaction (where the real “magic” happens) By the end, you'll walk away with five concrete literacy routines you can plug into your week and a much clearer lens for judging research quality while still using it creatively. Want these literacy hacks done for you every week? If you're ready to stop reinventing the wheel and want literacy-based, movement-rich activities that already embed these principles, join the SIS Membership. Inside SIS, you get: Weekly Google Slides decks built around repetitive, predictable books Multimodal, multisensory activities (movement, props, print, AAC, writing) you can use with your entire caseload Treatment targets that are already leveled and ready to go, so you can focus on the interaction instead of scrambling for materials Join SIS here and grab everything instantly:

Tyler Duvall--currently CEO and co-founder of Cavnue, an infrastructure company dedicated to building safer, more efficient roadways while adapting today's transportation network to the next generation of vehicles--has spent most of his professional lifetime in transportation. Having served in both the public and private sectors, Mr. Duvall brings a unique expertise to solving challenges faced by all kinds of system users. He sits down with Allan to discuss his multifaceted career, as well as his take on the best approach to transforming the U.S. transportation system to meet the needs of the 21st century. | More on Transforming Roads Unleashing Smart Technologies (TRUST)

Send us a textThis special on-site episode of Edtech Insiders was recorded live at the Google AI for Learning Forum in London on November 14, 2024, where we sat down with leaders shaping Google's next generation of learning tools, including Shantanu Sinha, VP of Google for Education, Tal Oppenheimer, Product Management Director, Google Labs & Learning, Julia Wilkowski, Pedagogy & Learning Sciences Team Lead, Google, and Maureen Heymans, VP & GM, Learning, Google. Together, they share how Google is designing AI-powered tools grounded in learning science and built to scale across classrooms worldwide.

Nikola Todorovic is the Co-Founder of Wonder Dynamics, an Autodesk company, which is one of the most groundbreaking AI companies transforming the future of filmmaking.In this episode, Nikola opens up about building an AI startup from scratch, surviving the early chaos of generative AI, and why he believes the future belongs to creators. We go deep on AI, tech, creativity, entrepreneurship, mental resilience, and the future of storytelling; including how Nikola and Tye Sheridan built a product people thought was “impossible,” what really happened behind the scenes of their viral launch, and why democratizing filmmaking matters more today than ever.If you're a filmmaker, 3D artist, startup founder, or AI-curious entrepreneur, this is one of the most important conversations of 2025.Episode 75 Chapter:00:00:00 – Introduction00:03:50 – Early days of Wonder Dynamics & scaling challenges00:05:45 – The power of “Permissionless Storytelling”00:07:10 – Nikola's childhood, war, and escaping through film00:11:45 – The origins of Wonder Studio 00:13:25 – Pivoting from VR to AI filmmaking00:15:10 – Funding, team size & early startup struggles00:20:20 – The emotional rollercoaster of building something new00:22:40 – Why creators forget their darkest moments00:24:45 – Fear, discomfort & pushing through self-doubt00:28:10 – Perspective: comparing yourself to your past, not others00:30:10 – What truly brings happiness (scarcity & meaning)00:32:30 – Why hiring curious people beats hiring experts00:35:15 – The “say yes and figure it out” philosophy00:37:00 – How uncertainty becomes a competitive advantage00:38:45 – Nature vs nurture00:41:20 – What top creatives really struggle with00:49:35 – The problem with modern AI launches00:51:10 – Generative AI editability00:53:00 – Multimodal future of filmmaking00:56:20 – AI's irony: the first jobs it replaced were creative00:58:00 – Why AI won't kill filmmaking01:02:40 – Democratization of filmmaking in 202501:05:50 – Why now is the best time ever to be a creator01:07:10 – Final thoughts on storytelling, creativity & the futureLearn Unreal Engine in 14 Days - $300 OFF https://join.baddecisions.studio/c/podcast?discounts=PODCASTIf this podcast is helping you, please take 2 minutes to rate our podcast on Spotify or Apple Podcasts, It will help the Podcast reach and help more people! Spotify - https://open.spotify.com/show/12jUe4lIJgxE4yst7rrfmW?si=ab98994cf57541cfApple Podcasts (Scroll down to review)- https://podcasts.apple.com/us/podcast/bad-decisions-podcast/id1677462934Find out more about Brandon:https://www.instagram.com/lunchbqxJoin our discord server where we connect and share assets:https://discord.gg/zwycgqezfD Bad Decisions Audio Podcast

How do you redesign a newsroom's entire workflow when AI is no longer a single tool, but a collection of agents, voice interfaces, and ambient intelligence changing how journalism gets produced?This week on Newsroom Robots, host Nikita Roy is joined by Markus Franz, Chief Technology Officer at Ippen Digital, one of Germany's largest digital media networks with more than 80 online news and media portals. This episode was recorded live at the Digital Growth Summit in Stuttgart, where Markus shared how his team is building some of the most forward-looking AI experiments in European media.Markus leads Ippen Digital's Incubator Lab, an innovation unit focused on reimagining how publishing and AI-driven experiences will evolve. With 16 years inside the company, Markus has been central to Ippen's digital transformation and now leads efforts around multi-agent architectures and building adaptive workflows for the newsroom.In this conversation, Markus breaks down how his lab is experimenting with multi-agent “virtual teams,” voice-first newsroom interfaces, multimodal content production and an ambient AI-powered newsroom where intelligent systems support journalists in real time. He shares what his team has learned from early prototypes, why the biggest challenges are cultural rather than technical, and how news organizations should think about guardrails, platform dependency, and the rise of self-evolving models.This episode covers: 02:22 – Why Ippen Digital built an Incubator Lab and how it's structured as a future-focused R&D unit04:49 – What multi-agent systems look like inside a newsroom9:42 – The case for voice as the next major interface for both journalists and audiences14:41 – The shift from human-in-the-loop to human-on-the-loop workflows17:40 – Guardrails for agent systems: grounding, bounding, editorial policies19:33 – The vision for an ambient newsroom powered by AI companions and real-time intelligence27:31 – Why vendor lock-in and self-evolving LLMs pose new strategic risks30:08 – Multimodal personalization and rethinking how news is experienced34:27 – Why most AI pilots fail and what experimentation looks like in practice49:19 – Markus's personal AI stack and how he uses these tools day-to-daySign up for the Newsroom Robots newsletter for episode summaries and insights from host Nikita Roy. Hosted on Acast. See acast.com/privacy for more information.

On the latest Vet Blast Podcast presented by dvm360 episode, host Adam Christman, DVM, MBA sits down with Bonnie D. Wright DVM, DACVAA, to explore the cutting edge of multimodal analgesia. Wright dives into the critical need for integrating pharmacologic and non-pharmacologic techniques for optimal pain control. 

Wildest week in AI since December 2024.

In this episode, Tristan Handy sits down with Chang She — a co-creator of Pandas and now CEO of LanceDB — to explore the convergence of analytics and AI engineering. The team at LanceDB is rebuilding the data lake from the ground up with AI as a first principle, starting with a new AI-native file format called Lance. Tristan traces Chang's journey as one of the original contributors to the pandas library to building a new infrastructure layer for AI-native data. Learn why vector databases alone aren't enough, why agents require new architecture, and how LanceDB is building a AI lakehouse for the future. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

Most of us probably experienced a homogenous version of literacy in our English classes: read a book, answer a few questions along the way, and compose an essay at the end about how we viewed a key theme. Rinse and repeat. And in our current age of high-stakes testing and high-stakes literacy, some kids are lucky to ever encounter a book at all; however, those same students are also surrounded by the narratives and themes of English class - in the messages they send and receive and the virtual communities they participate in, the media they consume and discuss with their friends, and in the video games they play. The goal of my guests today is to expand our vision of what that English class could be and induct students into something of an animistic perspective of literacy, as you heard from one guest in the opening: that the narratives and themes of English class are everywhere for those equipped to see them as such. Their Reader-Player Interactivity Framework aims to give teachers and students the tools and confidence to do just that. Their paper, linked in the show notes, is a collaboration between Karis Jones, Brady Nash, Virginia Killian Lund, Scott Storm, Alex Corbitt, Beth Krone, and Trevor Aleo, of which Karis, Brady, Virginia, and Trevor joined me for this conversation.Article: The Reader-Player Interactivity Framework: How Do Readers Navigate Diverse Varieties of Narrative Texts?Unsilencing Gratia: a tabletop RPG book designed to be an easy introduction to collaborative storytelling, usable in a classroom setting.We Know Something You Don't Know: a tabletop RPG that invites you into the lives of students making their way day-by-day through the education system.You can reach any of our guests by email:Trevor Aleo: aleotc@gmail.comKaris Jones: karis.michelle.jones@gmail.comVirginia Killian Lund: vkillianlund@uri.eduBrady Nash: bradylnash@gmail.com

You ever see a new AI model drop and be like.... it's so good OMG how do I use it?

The world record for fastest pit stop—a mere 1.8 seconds—was set by the McLaren F1 Team at the Qatar Grand Prix in 2023. It's an incredible feat of speed and choreography; a pit stop that fast can't happen without a team of people operating at peak human performance. But as Dan Keyworth explains, AI plays a crucial role, too. As the Director of Business Technology at McLaren Racing, Dan is responsible for helping the whole team perform at their best—and that starts with having the right tools. Whether it's the firehose of sensor data coming off a race car, video analysis of the pit crew in action, or marketing analytics for the next Grand Prix, AI helps the McLaren F1 Team make the right decisions—and make them fast.On this episode, Dan talks about the importance of getting simple answers from complex data, how they use Dropbox Dash, and why we shouldn't think of AI as labor replacement so much as laborious replacement.You can learn more about the McLaren F1 Team at mclaren.com/racing/formula-1. And if you haven't already seen it, be sure to watch their world record pit stop at youtube.com/watch?v=tRBOiq-Q6_s. Seriously, it's blink-and-you'll-miss-it fast.~ ~ ~Working Smarter is brought to you by Dropbox Dash—the AI universal search and knowledge management tool from Dropbox. Learn more at workingsmarter.ai/dashYou can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube Music, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.aiThis show would not be possible without the talented team at Cosmic Standard: producer Dominic Girard, sound engineer Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to our illustrators Justin Tran and Fanny Luor, marketing consultant Meggan Ellingboe, and editorial support from Catie Keck. Our theme song was composed by Doug Stuart. Working Smarter is hosted by Matthew Braga. Thanks for listening!

Dr. Pedro Barata and Dr. Aditya Bagrodia discuss the evolving landscape of testicular cancer survivorship, the impact of treatment-related complications, and management strategies to optimize long-term outcomes and quality of life. TRANSCRIPT:  Dr. Pedro Barata: Hello and welcome to By the Book, a podcast series from ASCO that features engaging conversations between editors and authors of the ASCO Educational Book. I'm Dr. Pedro Barata. I'm a medical oncologist at University Hospitals Seidman Cancer Center and associate professor of medicine at Case Western Reserve University in Cleveland, Ohio. I'm also an associate editor of the ASCO Educational Book. We all know that testicular cancer is a rare but highly curable malignancy that mainly affects young men. Multimodal advances in therapy have resulted in excellent cancer specific survival, but testicular cancer survivors face significant long term treatment related toxicities which affect their quality of life and require surveillance and management. With that, I'm very happy today to be joined by Dr. Aditya Bagrodia, a urologic oncologist, professor, and the GU Disease Team lead at UC San Diego[KI1]  Health, and also the lead author of the recently published paper in the ASCO Educational Book titled, "Key Updates in Testicular Cancer: Optimizing Survivorship and Survival." And he's also the host of the world-renowned BackTable Urology Podcast. Dr. Bagrodia, I'm so happy that you're joining us today. Welcome. Dr. Aditya Bagrodia: Thanks, Pedro. Absolutely a pleasure to be here. Really appreciate the opportunity. Dr. Pedro Barata: Absolutely.  So, just to say that our full disclosures are available in the transcript of this episode.  Let's get things started. I'm really excited to talk about this. I'm biased, I do treat testicular cancer among other GU malignancies and so it's a really, really important topic that we face every day, right? Fortunately, for most of these patients, we're able to cure them. But it always comes up the question, "What now? You know, scans, management, cardio oncology, what survivorship programs we have in place? Are we addressing the different survivorship piece, psychology, fertility, et cetera?" So, we'll try to capture all of that today. Aditya, congrats again, you did a fantastic job putting together the insights and thoughts and what we know today about this important topic. And so, let's get focused specifically about what happens when patients get cured. So, many of us, in many centers, were fortunate enough to have these survivorship programs together, but I find that sometimes from talking to colleagues, they're not exactly the same thing and they don't mean the same thing to different people, to different institutions, right? So, first things first. What do you tell a patient perhaps when they ask you, "What can happen to me now that I'm done with treatment for testicular cancer?" Whether it's chemotherapy or just surgery or even radiation therapy? "So, what about the long term? What should I expect, Doctor, that might happen to me in the long run?" Dr. Aditya Bagrodia: Totally. I mean, I think that question's really front and center, Pedro, and really appreciate you all highlighting this topic. It was an absolute honor to work with true thought leaders and the survivorship bit of it is front and center, in my opinion. It's really the focus, you know, we, generally speaking should be able to cure these young men, but it's the 10, 15, 20 years down the way that they're going to largely contend with. The conversation really begins at diagnosis, pre-education. Fortunately, the bulk of patients that present are those with stage one disease, and even very basic things like before orchiectomy, talking about a prosthetic; we know that that can impact body image and self esteem, whether or not they decide to receive it or not. Actually, just being offered a prosthetic is important and this is something, you know, for any urologist, it's kind of critical. To discussing fertility elements to this, taking your time to examine the contralateral testicle, ask about fertility problems, issues, concerns, offer sperm banking, even in the context of a completely normal contralateral testicle, I think these things are quite important.  So if it's somebody with stage one disease, you know, without going too far down discussing adjuvant therapy and so forth, I will start the conversation with, "You know, the testes do largely two things. They make testosterone and they make sperm." By and large, patients are going to be able to have acceptable levels of testosterone, adequate sperm parameters to maintain kind of a normal gonadal state and to naturally conceive, should that be something they're interested in. However, there's still going to be, depending on what resource you look at, somewhere in the order of 10-30% that are going to have issues. Where I think for the stage one patients, it's really incumbent upon us is actually to not wait for them to discuss their concerns, particularly with testosterone, which many times can be a little bit vague, but to proactively ask about it every time. Libido, erectile quality, muscle mass maintenance, energy, fatigue. All of these are kind of associated symptoms of hypogonadism. But for a lot of kids 18-20 years old, it's going to be something insidious that they don't think about. So, for the stage one patients, it absolutely starts with gonadal function. If they are stage two getting surgery, I think the counseling really needs to center around a possibility for ejaculatory dysfunction. Now, for a chemotherapy-naive, nerve-sparing RPLND, generally these days we should be able to preserve ejaculatory function at high volume centers, but you still want to bring that up and again kind of touch base on thinking about sperm banking and so forth before the operation, scars, those are things I think worth talking about, small risk of ascites. Then, I think the intensity of potential long term adverse effects really ramps up when we're talking about systemic therapy, chemotherapy. And then there's of course some radiation therapy specific elements that come up. So, for the chemotherapy bits of it, I really think this is going to be something that can be a complete multi-system affected intervention. So, anxiety, depression, our group has actually shown using some population resources that even suicidality can be increased among patients that have been treated for germ cell tumor. You know, really from the top down, tinnitus, hearing changes, those are things that we need to ask about at every appointment. Neuropathy, sexual health, that we kind of talked about, including ED (erectile dysfunction), vertigo, dizziness, Raynaud's phenomenon, these are kind of more the symptoms that I think we need to inquire about every time. And what we do here and I think at a lot of survivorship programs is use kind of a battery of validated instruments, germ cell tumor specific, platinum treated patient specific. So we use a combination of EORTC questions and PROMIS questions, which actually serves as like a review of systems for the patient, also as a research element. We review that and then depending on what might be going on, we can dig into that further, get them over to colleagues in audiology or psychology, et cetera.  And then of course, screening for the hypertension, hyperlipidemia, metabolic syndrome with basically you or myself or somebody kind of like us serving, many times it's the role of the PCP, just making sure we're checking out, you know, CBC, CMP, et cetera, lipid parameters to screen for those kind of cardiac associated issues along with secondary malignancies. Dr. Pedro Barata: So that's super comprehensive and thorough. Thank you so much. Actually, I love how you break it down in a simple way. Two functions of the testes, produce testosterone and then, you know, the problem related to that is the hypogonadism, and then the second, as you mentioned, produce sperm and of course related to the fertility issues with that.  So, let's start with the first one that you mentioned. So, you do cite that in your paper, around 5-10% of men end up getting, developing hypogonadism, maybe clinical when they present with symptoms, maybe subclinical. So, I'm wondering, for our audience, what kind of recommendations we would give for addressing that or kind of thinking of that? How often are you ordering those tests? And then, when you're thinking about testosterone replacement therapy, is that something you do immediately or are there any guidelines into context that? How do you approach that? Dr. Aditya Bagrodia: So, just a bit more on digging into it even in terms of the questions to ask, you know, "Do you have any decrease in sexual drive? Any erectile dysfunction? Are your morning erections still taking place? Has the ejaculate volume changed? Physically, muscle mass, strength? Have you been putting on weight? Have you noticed increase in body fat?" And sometimes this is complicated because there's some anxiety that comes along with a cancer diagnosis when you're 20, 30 years old, multifactorial, hair loss, hot flashes, irritability. Sometimes they'll, you know, literally they'll say, "You know, my significant other or partners noticed that I'm really just a little bit labile." So I think, you know, there's the symptoms and then checking, usually kind of a gonadal panel, FSH, LH, free and total testosterone, sex hormone binding globulin, that's going to be typically pretty comprehensive. So if you've got symptoms plus some laboratory work, and ideally that pre-orchiectomy testosterone gives you some delta. If they started out at an 800, 900, now they're 400, that might be a big change for them. And then, when you talk about TRT (Testosterone Replacement Therapy) recommendations, you know, Pedro, yourself, myself, we're kind of lucky to be at academic centers and we've got men's health colleagues that are ultra experts, but at a high level, I would say that a lot of the TRT options center around fertility goals. Exogenous testosterone treats the low T, but it does suppress gonadal function, including spermatogenesis. So if that's not a priority, they can just get TRT. It should be done under the care of a urologist, a men's health, an endocrinologist, where we're checking liver chemistries and CBCs and a PSA and so forth. If they're interested in fertility preservation, then I would say engaging an endocrinologist, men's health expert is important. There's medications even like hCG, Clomid, which works centrally and stimulate the gonadal access. Niche scenarios where they might want standard TRT now, and then down the way, 5, 7 years, they're thinking about coming off of that for fertility purposes, I think that's really where you want to have an expert involved because there's quite a bit of nuance there in recovery of actual spermatogenesis and so forth.  To kind of summarize, you got to ask about it. Checking it is, is not overly complicated. We do a baseline pre-orchiectomy and at least once annually, you can tag it in with the tumor markers, so it's not an extra blood draw. And if they have symptoms of course, kind of developed, then we'll move that up in the evaluation. Dr. Pedro Barata: Got it. And you also touch base on the fertility angle, which is truly important. And I'm just curious, you know, a lot of times many of us might see one, two patients a year, right, and we forget these protocols and what we've got to do about that.  And so I'm interested to hear your thoughts about when you think about fertility, and how proactive you get. In other words, who do you refer for the fertility clinic, for a fertility preservation program? You know, do all cases despite getting through orchiectomy or just the cases that you're going to, you know you're going to seek chemotherapy at some point? What kind of selection or it depends on the chemo, like how do you do that assessment about the referral for preservation program that you might have available at UCSD? Dr. Aditya Bagrodia: Yeah, I mean I feel really fortunate to sit on the NCCN Testis Cancer Guidelines. It's in there that fertility counseling should be discussed prior to orchiectomy. So 100% bring it up. If there are risk factors, undescended testicles, previous history of fertility concerns, atrophic contralateral testicle, anything on the ultrasound like microlithiasis in the contralateral testicle, you kind of wanna get it there. And then again, there's kind of niche scenarios where you're really worried, maybe get a semen analysis and it doesn't look that good, arrange for the time of orchiectomy to have onco-testicular sperm extraction from the, quote unquote, "normal" testis parenchyma. You know, I think you have to be kind of prepared to go that route and really make sure you're doing this completely comprehensively.  So pre-orchiectomy all patients. Don't really push for it too hard if they've got a contralateral testicle, if they've had no issues having children. There's some cost associated with this, sperm banking still isn't kind of covered even in the context of men with cancer. If they've got risk factors, absolutely pre-orchiectomy. Pre-RPLND, even though the rates of ejaculatory dysfunction at a high-volume center should be low single digits, I'll still offer it. That'd be a real catastrophe if they were in that small proportion of patients and now they're going to be reliant on things like intrauterine insemination, where it becomes quite expensive.  Pre-chemo, everybody. That's basically a standard these days where it should be discussed and it's kind of amazing currently, even if you don't have an accessible men's health fertility clinic, there are actually companies, I have no vested interest, Fellow is one such company where you can actually create an account, receive a FedEx semen analysis and cryopreservation kit, send it back in, and all CLIA certified, it's based out of California. The gentleman that runs it, is a urologist and very, very bright guy who's done a lot of great stuff for testis cancer. So, even for patients that are kind of in extremis at the hospital that kind of need to get going like yesterday, we still discuss it. We've got some mechanisms in place to either have them take a semen analysis over to our Men's Health clinic or send it off to Fellow, which I think is pretty cool and that even extends to some of our younger adolescent patients where going to a clinic and providing a sample might be tricky.  So, I think bringing it up every stage, anytime there's an intervention that might be offered, orchiectomy, chemo, surgery, radiation, it's kind of incumbent on us to discuss it. Dr. Pedro Barata: Gotcha. That's super helpful. And you also touch base on another angle, which is the psychosocial angle around this. You mentioned suicidal rates, you mentioned anxiety, perhaps depression in some cases as well as chronic fatigue, not necessarily just because of the low testosterone that you can get, but also from a psychological perspective. I'm curious, what do the recommendations look like for that? Do these patients need to see a social worker or a psychologist, or do they need to answer a screening test every time they come to see us and then based on that, we kind of escalate, take the next steps according to that? Do they see a psychologist perhaps every so often? How should that be managed and addressed? Dr. Aditya Bagrodia: It's an excellent question and again, these can be rather insidious symptoms where if you don't really dig in and inquire, they can be glossed over. I mean, how easy to say, "Your markers look okay, your scans look okay. See you in six months," and keep it kind of brief. First off, I think bringing it up proactively and normalizing it, that, "This may be something that you experience. Many people do, you're not alone, there's nothing kind of wrong with you." I also think that this is an area where support groups can be incredibly useful. We host the Testicular Cancer Awareness Foundation support group here. They'll talk about chemo brain or just like a little bit of an adjustment disorder after their diagnosis. Support groups, I think are critical. As I mentioned, we have a survivorship program that's led by a combination of our med oncs, myself on the uro-onc side, as well as APPs, where we are systematically asking about essentially the whole litany of issues that may arise, including psychosocial, anxiety, depression, suicidality. And we've got a nice kind of fast path into our cancer center support services for these young men to meet with a psychologist. If that isn't going to be sufficient, they can actually see a psychiatrist to discuss medications and so forth. I do think that we've got to screen for these because, as anticipated from diagnosis, those first 2 years, we see a rise. But even 10, 15 years out, we note, compared to controls, that there is an increased level of anxiety, depression, suicidality that might not just take place at that initial acute period of diagnosis and treatment. Dr. Pedro Barata: Really well said. Super important.  So I guess if I were to put all these together, with these really amazing advances in technology, we all know AI, some of us might be more or less aware of biomarkers coming up, including microRNA for example, and others, like as I think of all these potential long term complications for these patients, look at the future, I guess, can we use this as a way to deescalate treatment where it's not really necessary, as a way to actually prevent some of these complications? Like, how do we see where we're heading? As we manage testicular cancer, let's say, within the next 5 or 10 years, do you think there's something coming up that's going to be different from what we're doing things today? Dr. Aditya Bagrodia: Totally. I mean, I think it's as exciting as a time as there's ever been, you know, maybe notwithstanding circa 1970s when platinum was discovered. So microRNAs, which you mentioned, you know, there's a new candidate biomarker, microRNA-371. We are super excited here at UCSD. We actually have it CLIA-certified available in our lab and are ordering these tests for patients kind of in their acute stage, you know, stage one and surveillance, stage two, post-RPLND, receiving chemotherapy. And essentially this is a universal germ cell tumor specific biomarker, except for teratoma, suffice it to say 90% sensitive and specific. And I think it's going to change the way that we diagnose and manage patients. You know, pre-orchiectomy, that's pretty straightforward. Post-orchiectomy, maybe we can really decrease the number of CT scans that are done. Maybe we can identify those patients that basically have occult disease where we can intervene early, either with RPLND or single cycle chemo. Post-RPLND, identify the patients who are at higher risk of relapse that may benefit from some adjuvant therapy. In the advanced setting, look at marker decline for patients in addition to standard tumor markers. Can we modulate their systemic therapy?  So, the international interest is largely on modifying things. There's really cool clinical trials that we have for stage one patients, that treatment would be prescribed based on a post-orchiectomy microRNA. I think the microRNAs are really exciting. Teratoma remains an outstanding question. I think this is where maybe ctDNA, perhaps some radiomics and advanced imaging processing and incorporating AI may allow us to safely avoid a lot of these post-chemo RPLNDs. And then identification using SNPs and so forth of who might be most susceptible to some of the cardiac toxicity, autotoxicity and personalizing things in that way as well. Dr. Pedro Barata: Super exciting, right, what's about to come? And I agree with you, I think it's going to change dramatically how we manage this disease.  This has been a pleasure sitting down with you. I guess before letting you go, anything else you'd like to add before we wrap it up? Dr. Aditya Bagrodia: Yeah, first off, again, just want to thank you and ASCO for the opportunity. And it's easy enough to, I think, approach a patient with the testicular germ cell tumor as, "This is an easy case. We're just going to do whatever we've done. Go to the guidelines that says do X, Y, or Z." But there's so much more nuance to it than that. Getting it done perfectly, I think, is mandatory. Whatever we do is an impact on them for the next 50, 60, 70 years of their life. And I found the germ cell tumor community, people are really passionate about it. If you're ever uncertain, there's experts throughout the country and internationally. Ask somebody before you do something that you can't undo. I think we owe it to them to get it perfect so that we can really maximize the survivorship and the survival like we've been talking about. Dr. Pedro Barata: Aditya, thanks for sharing your fantastic insights with us on this podcast. Dr. Aditya Bagrodia: All right, Pedro. Fantastic. Appreciate the opportunity. Dr. Pedro Barata: And also, thank you to our listeners for your time today. I actually encourage you to check out Dr. Bagrodia's article in the 2025 ASCO Educational Book. We'll post a link to the paper in the show notes. Remember, it's free access online, and you can actually download it as well as a PDF. You can also find on the website a wealth of other great papers from the ASCO Educational Book on key advances and novel approaches that are shaping modern oncology.  So with that, thank you everyone. Thank you, Aditya, one more time, for joining us. Thank you, have a good day. Disclaimer: The purpose of this podcast is to educate and to inform. This is not a substitute for professional medical care and is not intended for use in the diagnosis or treatment of individual conditions. Guests on this podcast express their own opinions, experience, and conclusions. Guest statements on the podcast do not express the opinions of ASCO. The mention of any product, service, organization, activity, or therapy should not be construed as an ASCO endorsement. Follow today's speakers:         Dr. Pedro Barata  @PBarataMD   Dr. Aditya Bagrodia @AdityaBagrodia Follow ASCO on social media:         @ASCO on X (formerly Twitter)         ASCO on Bluesky        ASCO on Facebook         ASCO on LinkedIn         Disclosures:      Dr. Pedro Barata:  Stock and Other Ownership Interests: Luminate Medical  Honoraria: UroToday  Consulting or Advisory Role: Bayer, BMS, Pfizer, EMD Serono, Eisai, Caris Life Sciences, AstraZeneca, Exelixis, AVEO, Merck, Ipson, Astellas Medivation, Novartis, Dendreon  Speakers' Bureau: AstraZeneca, Merck, Caris Life Sciences, Bayer, Pfizer/Astellas  Research Funding (Inst.): Exelixis, Blue Earth, AVEO, Pfizer, Merck   Dr. Aditya Bagrodia: Consulting or Advisory Role: Veracyte, Ferring  

This episode is sponsored by Vetnique On this special episode of The Vet Blast Podcast presented by dvm360, the tables have turned and Adam Christman, DVM, MBA, our usual host, is now the guest! Join Christman and interim host Matt Bubala, president at Black Dog Productions, Inc., as they explore the spectrum of care for the chronically itchy dog. 

Established in 1995 by the Texas Legislature, TTI's Center for Ports and Waterways (CPW) provides valuable applied research and expertise to the Texas Marine Transportation System. Over the past 30 years, CPW's experts have helped public- and private-sector stakeholders improve the efficiency, safety, and cost-effectiveness of waterborne freight at all operational levels. Recently, TTI Senior Research Scientist Jim Kruse, who led the center for 23 years, announced his retirement from TTI. To succeed him as director, the Institute named Vince Mantero, formerly director of the Office of Ports and Waterways Planning in the U.S. Department of Transportation Maritime Administration. Mantero brings to the job more than 25 years of experience in maritime and freight policy, planning and program management. Allan sits down with the CPW's captains, past and present, to discuss the transition, the importance of waterborne freight in the twenty-first century, and what lies ahead in the area of waterways research. | See the related story on the change in leadership

Iain Thomas is a poet, author, and the Chief Innovation Officer at Sounds Fun—an advertising and creative agency that he co-founded with the belief that human creativity could be enhanced, rather than diminished, with the help of AI. It's a realization that actually began to dawn on Iain a few years prior, after his mother died. He wasn't sure how to explain death to his children, so he turned to an early version of ChatGPT for help—and was so impressed by the poetry of its responses that he came away convinced of AI's immense potential as a thought partner for his creative work. On this episode, Iain talks about using AI to make more space for the creative parts of your work, and why, in a world where everyone has access to the same tools, it's never been more important to lean into the skills, context, and experiences that make each of us most unique—and most human.Learn more about Sounds Fun soundsfun.co~ ~ ~Working Smarter is brought to you by Dropbox Dash—the AI universal search and knowledge management tool from Dropbox. Learn more at workingsmarter.ai/dashYou can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube Music, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.aiThis show would not be possible without the talented team at Cosmic Standard: producer Dominic Girard, sound engineer Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to our illustrators Justin Tran and Fanny Luor, marketing consultant Meggan Ellingboe, and editorial support from Catie Keck. Our theme song was composed by Doug Stuart. Working Smarter is hosted by Matthew Braga. Thanks for listening!

Bespoken Spirits isn't your typical whiskey distillery. Yes, they're based in the American bourbon heartland of Lexington, Kentucky, and yes, they often make private label whiskeys for clients. But everything from how Bespoken Spirits distills their whiskey to how they market it is done with the help of AI. Jordan Spitzer, their head of flavor, can finish a whiskey in days instead of years—while precisely crafting its taste—using their machine-learning backed approach. And Wane Lindsey, their director of marketing, credits AI tools with helping his tiny team punch way above their weight.The result is a whiskey that may not be traditional, but still tastes great—and in a fraction of the time it would otherwise take. That's time they can spend on the creative side of their craft and the work that has the most meaning: building brands and bespoke spirits that people will want to drink.On this episode, Jordan and Wane share how AI has helped them explore creative new ways to make and market whiskey—and why, no matter how smart our tools get, there's still no substitute for human taste.You can learn more about Bespoken Spirits at bespokenspirits.com~ ~ ~Working Smarter is brought to you by Dropbox Dash—the AI universal search and knowledge management tool from Dropbox. Learn more at workingsmarter.ai/dashYou can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube Music, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.aiThis show would not be possible without the talented team at Cosmic Standard: producer Dominic Girard, sound engineer Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to our illustrators Justin Tran and Fanny Luor, marketing consultant Meggan Ellingboe, and editorial support from Catie Keck. Our theme song was composed by Doug Stuart. Working Smarter is hosted by Matthew Braga. Thanks for listening!

 In 1950, the Texas A&M Board of Directors charged the Texas Transportation Institute (now the Texas A&M Transportation Institute, or TTI) to enlist the broad resources of the college across the spectrum of transportation research to benefit Texas, while also providing unique educational opportunities for students to study and work in the field. This agreement solidified the Cooperative Research Program between the then-Texas Highway Department (now the Texas Department of Transportation) and TTI. For 75 years, these agencies have partnered to conduct applied research that benefits Texans and travelers worldwide by innovating and improving the safety, mobility, and resilience of our transportation network. Our host, Allan Rutter, talks about this longstanding relationship with TxDOT Executive Director Marc Williams and TTI Agency Director Greg Winfree.