Podcasts about SAE

5 emergencies The 5 Skills That Eliminate Most Emergencies | Episode 595 Good morning. It's 18 degrees. Tennessee decided to remind us who's in charge. This is James from SurvivalPunk.com. Today we're talking about something that doesn't get enough credit in prepping circles. Not gear. Not bunker fantasies. Skills. Five specific skills that eliminate most emergencies before they ever become emergencies. Let's get into it. 1. Preventative Maintenance There are two types of people. The proactive maintenance crowd. And the rest of us. I'll admit — I'm not perfect at it. But I know better. And knowing better already puts you ahead. Basic maintenance prevents most mechanical disasters: • Oil changes • Cleaning AC units • Replacing spark plugs • Checking filters • Roof inspections • HVAC servicing I clean our window units every year. Pull them out, dismantle them, clean the coils, clear the sludge. Since I started doing that, they've lasted years longer. Most people run things until they fail. Failure is expensive. Maintenance is cheap. Same goes for your car. Same goes for your house. Ignore it long enough and you're buying a new roof instead of patching a leak. Preventative maintenance turns “emergency repair” into “routine upkeep.” 2. Financial Awareness Most “emergencies” are just financial mismanagement. Overdraft fees. Late fees. Impulse spending. Untracked subscriptions. Lifestyle creep. You don't need to make more money. You need to control the money you already make. When my wife and I started actually tracking spending and living on a budget, we built savings fast. No magic. No lottery. No second job. Just awareness. Turn off overdraft protection so transactions decline instead of charging you $35 to be broke. Set alerts. Call and negotiate fees when they happen. Financial awareness eliminates overdraft emergencies, debt spirals, and panic purchases. Most financial disasters are preventable. 3. Cooking From Basic Ingredients If you can cook from scratch, shortages don't wreck you. Missing celery? Pivot. No carrots in the store? Make something else. Eggs gone? Mayo works in cornbread. If you rely on recipes as rigid law, you panic. If you understand ingredients and substitutions, you adapt. Cooking skill equals flexibility. Flexibility eliminates food stress. You don't need a fully stocked gourmet kitchen. You need knowledge. And honestly? AI is great for this. “Hey, I have chicken, rice, and canned tomatoes. What can I make?” Boom. Ideas. Over time, you build your own mental database. That eliminates grocery store drama. 4. Basic Health & First Aid Awareness Don't ignore your health. Monitor blood pressure. Watch blood sugar. Get basic labs done. Exercise. Eat like an adult. Letting your health degrade until you're dependent on emergency medicine is the opposite of preparedness. You don't have to become a biohacker. But you should know your numbers. You should understand symptoms. You should have basic first aid skills. Most long-term “health emergencies” are years in the making. Early action prevents crisis. 5. Calm Problem Solving This one is huge. When something goes wrong: Slow down. Assess. Act deliberately. Panicking compounds problems. Calm thinking: • Avoids dumb decisions • Reduces accidents • Keeps conflict small • Stops mistakes from stacking Most situations aren't life-or-death. They feel like it because people escalate emotionally. Calm problem solving turns chaos into steps. And steps are manageable. Final Thoughts Most disasters aren't hurricanes or EMPs. They're: • Neglected maintenance • Financial sloppiness • Poor health • Inability to cook • Emotional overreaction Master these five skills and you eliminate most emergencies before they begin. Prepping isn't about hoarding. It's about competence. This is James from SurvivalPunk.com. DIY to survive. Amazon Item OF The Day Amazon Basics 201-Piece Mechanic’s Socket Tool Set With Case, SAE and Metric Sizes, Chrome-Vanadium Steel, Portable Think this post was worth 20 cents? Consider joining The Survivalpunk Army and get access to exclusive content and discounts! Don't forget to join in on the road to 1k! Help James Survivalpunk Beat Couch Potato Mike to 1k subscribers on Youtube Want To help make sure there is a podcast Each and every week? Join us on Patreon Subscribe to the Survival Punk Survival Podcast. The most electrifying podcast on survival entertainment. Itunes Pandora RSS Spotify Like this post? Consider signing up for my email list here > Subscribe Join Our Exciting Facebook Group and get involved Survival Punk Punk's The post The 5 Skills That Eliminate Most Emergencies | Episode 595 appeared first on Survivalpunk.

Sorg and Podnar cover big and bizarre tech headlines: an alleged DJI robot vacuum security mess, AI-assisted “vibe coding,” and why camera-equipped home gadgets deserve extra caution. They also dig into the SAE Civic Progress Challenge (accessible mobility innovation), geek out over a playable Tetris magazine cover, and hit viral Winter Olympics moments—plus a Dunkin iced coffee mitten that's as ridiculous as it sounds. Includes Chachi's Video Game Minute and a Black History Month spotlight on Frederick McKinley Jones.

Sorg and Podnar cover big and bizarre tech headlines: an alleged DJI robot vacuum security mess, AI-assisted “vibe coding,” and why camera-equipped home gadgets deserve extra caution. They also dig into the SAE Civic Progress Challenge (accessible mobility innovation), geek out over a playable Tetris magazine cover, and hit viral Winter Olympics moments—plus a Dunkin iced coffee mitten that's as ridiculous as it sounds. Includes Chachi's Video Game Minute and a Black History Month spotlight on Frederick McKinley Jones.

HeroHero: https://herohero.co/dojetoczForendors: https://www.forendors.cz/dojetoRemco Evenepoel chtěl přidat další výhru ve svém parádním začátku sezóny, ale proti byly kopce v SAE. Mohla za to nefunkční klimatizace nebo je problém jinde? A může Isaac del Toro vyzvat Tadeje Pogačara k vnitrotýmovému souboji na Strade Bianche nebo Tour de France?Miniatura: Getty ImagesDejte nám odběr na Youtube: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://www.youtube.com/dojeto/noodlemx?sub_confirmation=1⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jsme i na Instagramu: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/dojetocz/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Twitteru: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://twitter.com/DOJETOcz⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Hablamos con Michel De L'Herbe, experto en gestión de emergencias, sobre la alerta de sismos de Google y la confusión con la alerta SAE durante el temblor registrado esta jornada en el norte del país, además de la confianza y conocimiento de la población en los sistemas de alerta y evacuación.

From Palantir and Two Sigma to building Goodfire into the poster-child for actionable mechanistic interpretability, Mark Bissell (Member of Technical Staff) and Myra Deng (Head of Product) are trying to turn “peeking inside the model” into a repeatable production workflow by shipping APIs, landing real enterprise deployments, and now scaling the bet with a recent $150M Series B funding round at a $1.25B valuation.In this episode, we go far beyond the usual “SAEs are cool” take. We talk about Goodfire's core bet: that the AI lifecycle is still fundamentally broken because the only reliable control we have is data and we post-train, RLHF, and fine-tune by “slurping supervision through a straw,” hoping the model picks up the right behaviors while quietly absorbing the wrong ones. Goodfire's answer is to build a bi-directional interface between humans and models: read what's happening inside, edit it surgically, and eventually use interpretability during training so customization isn't just brute-force guesswork.Mark and Myra walk through what that looks like when you stop treating interpretability like a lab demo and start treating it like infrastructure: lightweight probes that add near-zero latency, token-level safety filters that can run at inference time, and interpretability workflows that survive messy constraints (multilingual inputs, synthetic→real transfer, regulated domains, no access to sensitive data). We also get a live window into what “frontier-scale interp” means operationally (i.e. steering a trillion-parameter model in real time by targeting internal features) plus why the same tooling generalizes cleanly from language models to genomics, medical imaging, and “pixel-space” world models.We discuss:* Myra + Mark's path: Palantir (health systems, forward-deployed engineering) → Goodfire early team; Two Sigma → Head of Product, translating frontier interpretability research into a platform and real-world deployments* What “interpretability” actually means in practice: not just post-hoc poking, but a broader “science of deep learning” approach across the full AI lifecycle (data curation → post-training → internal representations → model design)* Why post-training is the first big wedge: “surgical edits” for unintended behaviors likereward hacking, sycophancy, noise learned during customization plus the dream of targeted unlearning and bias removal without wrecking capabilities* SAEs vs probes in the real world: why SAE feature spaces sometimes underperform classifiers trained on raw activations for downstream detection tasks (hallucination, harmful intent, PII), and what that implies about “clean concept spaces”* Rakuten in production: deploying interpretability-based token-level PII detection at inference time to prevent routing private data to downstream providers plus the gnarly constraints: no training on real customer PII, synthetic→real transfer, English + Japanese, and tokenization quirks* Why interp can be operationally cheaper than LLM-judge guardrails: probes are lightweight, low-latency, and don't require hosting a second large model in the loop* Real-time steering at frontier scale: a demo of steering Kimi K2 (~1T params) live and finding features via SAE pipelines, auto-labeling via LLMs, and toggling a “Gen-Z slang” feature across multiple layers without breaking tool use* Hallucinations as an internal signal: the case that models have latent uncertainty / “user-pleasing” circuitry you can detect and potentially mitigate more directly than black-box methods* Steering vs prompting: the emerging view that activation steering and in-context learning are more closely connected than people think, including work mapping between the two (even for jailbreak-style behaviors)* Interpretability for science: using the same tooling across domains (genomics, medical imaging, materials) to debug spurious correlations and extract new knowledge up to and including early biomarker discovery work with major partners* World models + “pixel-space” interpretability: why vision/video models make concepts easier to see, how that accelerates the feedback loop, and why robotics/world-model partners are especially interesting design partners* The north star: moving from “data in, weights out” to intentional model design where experts can impart goals and constraints directly, not just via reward signals and brute-force post-training—Goodfire AI* Website: https://goodfire.ai* LinkedIn: https://www.linkedin.com/company/goodfire-ai/* X: https://x.com/GoodfireAIMyra Deng* Website: https://myradeng.com/* LinkedIn: https://www.linkedin.com/in/myra-deng/* X: https://x.com/myra_dengMark Bissell* LinkedIn: https://www.linkedin.com/in/mark-bissell/* X: https://x.com/MarkMBissellFull Video EpisodeTimestamps00:00:00 Introduction00:00:05 Introduction to the Latent Space Podcast and Guests from Goodfire00:00:29 What is Goodfire? Mission and Focus on Interpretability00:01:01 Goodfire's Practical Approach to Interpretability00:01:37 Goodfire's Series B Fundraise Announcement00:02:04 Backgrounds of Mark and Myra from Goodfire00:02:51 Team Structure and Roles at Goodfire00:05:13 What is Interpretability? Definitions and Techniques00:05:30 Understanding Errors00:07:29 Post-training vs. Pre-training Interpretability Applications00:08:51 Using Interpretability to Remove Unwanted Behaviors00:10:09 Grokking, Double Descent, and Generalization in Models00:10:15 404 Not Found Explained00:12:06 Subliminal Learning and Hidden Biases in Models00:14:07 How Goodfire Chooses Research Directions and Projects00:15:00 Troubleshooting Errors00:16:04 Limitations of SAEs and Probes in Interpretability00:18:14 Rakuten Case Study: Production Deployment of Interpretability00:20:45 Conclusion00:21:12 Efficiency Benefits of Interpretability Techniques00:21:26 Live Demo: Real-Time Steering in a Trillion Parameter Model00:25:15 How Steering Features are Identified and Labeled00:26:51 Detecting and Mitigating Hallucinations Using Interpretability00:31:20 Equivalence of Activation Steering and Prompting00:34:06 Comparing Steering with Fine-Tuning and LoRA Techniques00:36:04 Model Design and the Future of Intentional AI Development00:38:09 Getting Started in Mechinterp: Resources, Programs, and Open Problems00:40:51 Industry Applications and the Rise of Mechinterp in Practice00:41:39 Interpretability for Code Models and Real-World Usage00:43:07 Making Steering Useful for More Than Stylistic Edits00:46:17 Applying Interpretability to Healthcare and Scientific Discovery00:49:15 Why Interpretability is Crucial in High-Stakes Domains like Healthcare00:52:03 Call for Design Partners Across Domains00:54:18 Interest in World Models and Visual Interpretability00:57:22 Sci-Fi Inspiration: Ted Chiang and Interpretability01:00:14 Interpretability, Safety, and Alignment Perspectives01:04:27 Weak-to-Strong Generalization and Future Alignment Challenges01:05:38 Final Thoughts and Hiring/Collaboration Opportunities at GoodfireTranscriptShawn Wang [00:00:05]: So welcome to the Latent Space pod. We're back in the studio with our special MechInterp co-host, Vibhu. Welcome. Mochi, Mochi's special co-host. And Mochi, the mechanistic interpretability doggo. We have with us Mark and Myra from Goodfire. Welcome. Thanks for having us on. Maybe we can sort of introduce Goodfire and then introduce you guys. How do you introduce Goodfire today?Myra Deng [00:00:29]: Yeah, it's a great question. So Goodfire, we like to say, is an AI research lab that focuses on using interpretability to understand, learn from, and design AI models. And we really believe that interpretability will unlock the new generation, next frontier of safe and powerful AI models. That's our description right now, and I'm excited to dive more into the work we're doing to make that happen.Shawn Wang [00:00:55]: Yeah. And there's always like the official description. Is there an understatement? Is there an unofficial one that sort of resonates more with a different audience?Mark Bissell [00:01:01]: Well, being an AI research lab that's focused on interpretability, there's obviously a lot of people have a lot that they think about when they think of interpretability. And I think we have a pretty broad definition of what that means and the types of places that can be applied. And in particular, applying it in production scenarios, in high stakes industries, and really taking it sort of from the research world into the real world. Which, you know. It's a new field, so that hasn't been done all that much. And we're excited about actually seeing that sort of put into practice.Shawn Wang [00:01:37]: Yeah, I would say it wasn't too long ago that Anthopic was like still putting out like toy models or superposition and that kind of stuff. And I wouldn't have pegged it to be this far along. When you and I talked at NeurIPS, you were talking a little bit about your production use cases and your customers. And then not to bury the lead, today we're also announcing the fundraise, your Series B. $150 million. $150 million at a 1.25B valuation. Congrats, Unicorn.Mark Bissell [00:02:02]: Thank you. Yeah, no, things move fast.Shawn Wang [00:02:04]: We were talking to you in December and already some big updates since then. Let's dive, I guess, into a bit of your backgrounds as well. Mark, you were at Palantir working on health stuff, which is really interesting because the Goodfire has some interesting like health use cases. I don't know how related they are in practice.Mark Bissell [00:02:22]: Yeah, not super related, but I don't know. It was helpful context to know what it's like. Just to work. Just to work with health systems and generally in that domain. Yeah.Shawn Wang [00:02:32]: And Mara, you were at Two Sigma, which actually I was also at Two Sigma back in the day. Wow, nice.Myra Deng [00:02:37]: Did we overlap at all?Shawn Wang [00:02:38]: No, this is when I was briefly a software engineer before I became a sort of developer relations person. And now you're head of product. What are your sort of respective roles, just to introduce people to like what all gets done in Goodfire?Mark Bissell [00:02:51]: Yeah, prior to Goodfire, I was at Palantir for about three years as a forward deployed engineer, now a hot term. Wasn't always that way. And as a technical lead on the health care team and at Goodfire, I'm a member of the technical staff. And honestly, that I think is about as specific as like as as I could describe myself because I've worked on a range of things. And, you know, it's it's a fun time to be at a team that's still reasonably small. I think when I joined one of the first like ten employees, now we're above 40, but still, it looks like there's always a mix of research and engineering and product and all of the above. That needs to get done. And I think everyone across the team is, you know, pretty, pretty switch hitter in the roles they do. So I think you've seen some of the stuff that I worked on related to image models, which was sort of like a research demo. More recently, I've been working on our scientific discovery team with some of our life sciences partners, but then also building out our core platform for more of like flexing some of the kind of MLE and developer skills as well.Shawn Wang [00:03:53]: Very generalist. And you also had like a very like a founding engineer type role.Myra Deng [00:03:58]: Yeah, yeah.Shawn Wang [00:03:59]: So I also started as I still am a member of technical staff, did a wide range of things from the very beginning, including like finding our office space and all of this, which is we both we both visited when you had that open house thing. It was really nice.Myra Deng [00:04:13]: Thank you. Thank you. Yeah. Plug to come visit our office.Shawn Wang [00:04:15]: It looked like it was like 200 people. It has room for 200 people. But you guys are like 10.Myra Deng [00:04:22]: For a while, it was very empty. But yeah, like like Mark, I spend. A lot of my time as as head of product, I think product is a bit of a weird role these days, but a lot of it is thinking about how do we take our frontier research and really apply it to the most important real world problems and how does that then translate into a platform that's repeatable or a product and working across, you know, the engineering and research teams to make that happen and also communicating to the world? Like, what is interpretability? What is it used for? What is it good for? Why is it so important? All of these things are part of my day-to-day as well.Shawn Wang [00:05:01]: I love like what is things because that's a very crisp like starting point for people like coming to a field. They all do a fun thing. Vibhu, why don't you want to try tackling what is interpretability and then they can correct us.Vibhu Sapra [00:05:13]: Okay, great. So I think like one, just to kick off, it's a very interesting role to be head of product, right? Because you guys, at least as a lab, you're more of an applied interp lab, right? Which is pretty different than just normal interp, like a lot of background research. But yeah. You guys actually ship an API to try these things. You have Ember, you have products around it, which not many do. Okay. What is interp? So basically you're trying to have an understanding of what's going on in model, like in the model, in the internal. So different approaches to do that. You can do probing, SAEs, transcoders, all this stuff. But basically you have an, you have a hypothesis. You have something that you want to learn about what's happening in a model internals. And then you're trying to solve that from there. You can do stuff like you can, you know, you can do activation mapping. You can try to do steering. There's a lot of stuff that you can do, but the key question is, you know, from input to output, we want to have a better understanding of what's happening and, you know, how can we, how can we adjust what's happening on the model internals? How'd I do?Mark Bissell [00:06:12]: That was really good. I think that was great. I think it's also a, it's kind of a minefield of a, if you ask 50 people who quote unquote work in interp, like what is interpretability, you'll probably get 50 different answers. And. Yeah. To some extent also like where, where good fire sits in the space. I think that we're an AI research company above all else. And interpretability is a, is a set of methods that we think are really useful and worth kind of specializing in, in order to accomplish the goals we want to accomplish. But I think we also sort of see some of the goals as even more broader as, as almost like the science of deep learning and just taking a not black box approach to kind of any part of the like AI development life cycle, whether that. That means using interp for like data curation while you're training your model or for understanding what happened during post-training or for the, you know, understanding activations and sort of internal representations, what is in there semantically. And then a lot of sort of exciting updates that were, you know, are sort of also part of the, the fundraise around bringing interpretability to training, which I don't think has been done all that much before. A lot of this stuff is sort of post-talk poking at models as opposed to. To actually using this to intentionally design them.Shawn Wang [00:07:29]: Is this post-training or pre-training or is that not a useful.Myra Deng [00:07:33]: Currently focused on post-training, but there's no reason the techniques wouldn't also work in pre-training.Shawn Wang [00:07:38]: Yeah. It seems like it would be more active, applicable post-training because basically I'm thinking like rollouts or like, you know, having different variations of a model that you can tweak with the, with your steering. Yeah.Myra Deng [00:07:50]: And I think in a lot of the news that you've seen in, in, on like Twitter or whatever, you've seen a lot of unintended. Side effects come out of post-training processes, you know, overly sycophantic models or models that exhibit strange reward hacking behavior. I think these are like extreme examples. There's also, you know, very, uh, mundane, more mundane, like enterprise use cases where, you know, they try to customize or post-train a model to do something and it learns some noise or it doesn't appropriately learn the target task. And a big question that we've always had is like, how do you use your understanding of what the model knows and what it's doing to actually guide the learning process?Shawn Wang [00:08:26]: Yeah, I mean, uh, you know, just to anchor this for people, uh, one of the biggest controversies of last year was 4.0 GlazeGate. I've never heard of GlazeGate. I didn't know that was what it was called. The other one, they called it that on the blog post and I was like, well, how did OpenAI call it? Like officially use that term. And I'm like, that's funny, but like, yeah, I guess it's the pitch that if they had worked a good fire, they wouldn't have avoided it. Like, you know what I'm saying?Myra Deng [00:08:51]: I think so. Yeah. Yeah.Mark Bissell [00:08:53]: I think that's certainly one of the use cases. I think. Yeah. Yeah. I think the reason why post-training is a place where this makes a lot of sense is a lot of what we're talking about is surgical edits. You know, you want to be able to have expert feedback, very surgically change how your model is doing, whether that is, you know, removing a certain behavior that it has. So, you know, one of the things that we've been looking at or is, is another like common area where you would want to make a somewhat surgical edit is some of the models that have say political bias. Like you look at Quen or, um, R1 and they have sort of like this CCP bias.Shawn Wang [00:09:27]: Is there a CCP vector?Mark Bissell [00:09:29]: Well, there's, there are certainly internal, yeah. Parts of the representation space where you can sort of see where that lives. Yeah. Um, and you want to kind of, you know, extract that piece out.Shawn Wang [00:09:40]: Well, I always say, you know, whenever you find a vector, a fun exercise is just like, make it very negative to see what the opposite of CCP is.Mark Bissell [00:09:47]: The super America, bald eagles flying everywhere. But yeah. So in general, like lots of post-training tasks where you'd want to be able to, to do that. Whether it's unlearning a certain behavior or, you know, some of the other kind of cases where this comes up is, are you familiar with like the, the grokking behavior? I mean, I know the machine learning term of grokking.Shawn Wang [00:10:09]: Yeah.Mark Bissell [00:10:09]: Sort of this like double descent idea of, of having a model that is able to learn a generalizing, a generalizing solution, as opposed to even if memorization of some task would suffice, you want it to learn the more general way of doing a thing. And so, you know, another. A way that you can think about having surgical access to a model's internals would be learn from this data, but learn in the right way. If there are many possible, you know, ways to, to do that. Can make interp solve the double descent problem?Shawn Wang [00:10:41]: Depends, I guess, on how you. Okay. So I, I, I viewed that double descent as a problem because then you're like, well, if the loss curves level out, then you're done, but maybe you're not done. Right. Right. But like, if you actually can interpret what is a generalizing or what you're doing. What is, what is still changing, even though the loss is not changing, then maybe you, you can actually not view it as a double descent problem. And actually you're just sort of translating the space in which you view loss and like, and then you have a smooth curve. Yeah.Mark Bissell [00:11:11]: I think that's certainly like the domain of, of problems that we're, that we're looking to get.Shawn Wang [00:11:15]: Yeah. To me, like double descent is like the biggest thing to like ML research where like, if you believe in scaling, then you don't need, you need to know where to scale. And. But if you believe in double descent, then you don't, you don't believe in anything where like anything levels off, like.Vibhu Sapra [00:11:30]: I mean, also tendentially there's like, okay, when you talk about the China vector, right. There's the subliminal learning work. It was from the anthropic fellows program where basically you can have hidden biases in a model. And as you distill down or, you know, as you train on distilled data, those biases always show up, even if like you explicitly try to not train on them. So, you know, it's just like another use case of. Okay. If we can interpret what's happening in post-training, you know, can we clear some of this? Can we even determine what's there? Because yeah, it's just like some worrying research that's out there that shows, you know, we really don't know what's going on.Mark Bissell [00:12:06]: That is. Yeah. I think that's the biggest sentiment that we're sort of hoping to tackle. Nobody knows what's going on. Right. Like subliminal learning is just an insane concept when you think about it. Right. Train a model on not even the logits, literally the output text of a bunch of random numbers. And now your model loves owls. And you see behaviors like that, that are just, they defy, they defy intuition. And, and there are mathematical explanations that you can get into, but. I mean.Shawn Wang [00:12:34]: It feels so early days. Objectively, there are a sequence of numbers that are more owl-like than others. There, there should be.Mark Bissell [00:12:40]: According to, according to certain models. Right. It's interesting. I think it only applies to models that were initialized from the same starting Z. Usually, yes.Shawn Wang [00:12:49]: But I mean, I think that's a, that's a cheat code because there's not enough compute. But like if you believe in like platonic representation, like probably it will transfer across different models as well. Oh, you think so?Mark Bissell [00:13:00]: I think of it more as a statistical artifact of models initialized from the same seed sort of. There's something that is like path dependent from that seed that might cause certain overlaps in the latent space and then sort of doing this distillation. Yeah. Like it pushes it towards having certain other tendencies.Vibhu Sapra [00:13:24]: Got it. I think there's like a bunch of these open-ended questions, right? Like you can't train in new stuff during the RL phase, right? RL only reorganizes weights and you can only do stuff that's somewhat there in your base model. You're not learning new stuff. You're just reordering chains and stuff. But okay. My broader question is when you guys work at an interp lab, how do you decide what to work on and what's kind of the thought process? Right. Because we can ramble for hours. Okay. I want to know this. I want to know that. But like, how do you concretely like, you know, what's the workflow? Okay. There's like approaches towards solving a problem, right? I can try prompting. I can look at chain of thought. I can train probes, SAEs. But how do you determine, you know, like, okay, is this going anywhere? Like, do we have set stuff? Just, you know, if you can help me with all that. Yeah.Myra Deng [00:14:07]: It's a really good question. I feel like we've always at the very beginning of the company thought about like, let's go and try to learn what isn't working in machine learning today. Whether that's talking to customers or talking to researchers at other labs, trying to understand both where the frontier is going and where things are really not falling apart today. And then developing a perspective on how we can push the frontier using interpretability methods. And so, you know, even our chief scientist, Tom, spends a lot of time talking to customers and trying to understand what real world problems are and then taking that back and trying to apply the current state of the art to those problems and then seeing where they fall down basically. And then using those failures or those shortcomings to understand what hills to climb when it comes to interpretability research. So like on the fundamental side, for instance, when we have done some work applying SAEs and probes, we've encountered, you know, some shortcomings in SAEs that we found a little bit surprising. And so have gone back to the drawing board and done work on that. And then, you know, we've done some work on better foundational interpreter models. And a lot of our team's research is focused on what is the next evolution beyond SAEs, for instance. And then when it comes to like control and design of models, you know, we tried steering with our first API and realized that it still fell short of black box techniques like prompting or fine tuning. And so went back to the drawing board and we're like, how do we make that not the case and how do we improve it beyond that? And one of our researchers, Ekdeep, who just joined is actually Ekdeep and Atticus are like steering experts and have spent a lot of time trying to figure out like, what is the research that enables us to actually do this in a much more powerful, robust way? So yeah, the answer is like, look at real world problems, try to translate that into a research agenda and then like hill climb on both of those at the same time.Shawn Wang [00:16:04]: Yeah. Mark has the steering CLI demo queued up, which we're going to go into in a sec. But I always want to double click on when you drop hints, like we found some problems with SAEs. Okay. What are they? You know, and then we can go into the demo. Yeah.Myra Deng [00:16:19]: I mean, I'm curious if you have more thoughts here as well, because you've done it in the healthcare domain. But I think like, for instance, when we do things like trying to detect behaviors within models that are harmful or like behaviors that a user might not want to have in their model. So hallucinations, for instance, harmful intent, PII, all of these things. We first tried using SAE probes for a lot of these tasks. So taking the feature activation space from SAEs and then training classifiers on top of that, and then seeing how well we can detect the properties that we might want to detect in model behavior. And we've seen in many cases that probes just trained on raw activations seem to perform better than SAE probes, which is a bit surprising if you think that SAEs are actually also capturing the concepts that you would want to capture cleanly and more surgically. And so that is an interesting observation. I don't think that is like, I'm not down on SAEs at all. I think there are many, many things they're useful for, but we have definitely run into cases where I think the concept space described by SAEs is not as clean and accurate as we would expect it to be for actual like real world downstream performance metrics.Mark Bissell [00:17:34]: Fair enough. Yeah. It's the blessing and the curse of unsupervised methods where you get to peek into the AI's mind. But sometimes you wish that you saw other things when you walked inside there. Although in the PII instance, I think weren't an SAE based approach actually did prove to be the most generalizable?Myra Deng [00:17:53]: It did work well in the case that we published with Rakuten. And I think a lot of the reasons it worked well was because we had a noisier data set. And so actually the blessing of unsupervised learning is that we actually got to get more meaningful, generalizable signal from SAEs when the data was noisy. But in other cases where we've had like good data sets, it hasn't been the case.Shawn Wang [00:18:14]: And just because you named Rakuten and I don't know if we'll get it another chance, like what is the overall, like what is Rakuten's usage or production usage? Yeah.Myra Deng [00:18:25]: So they are using us to essentially guardrail and inference time monitor their language model usage and their agent usage to detect things like PII so that they don't route private user information.Myra Deng [00:18:41]: And so that's, you know, going through all of their user queries every day. And that's something that we deployed with them a few months ago. And now we are actually exploring very early partnerships, not just with Rakuten, but with other people around how we can help with potentially training and customization use cases as well. Yeah.Shawn Wang [00:19:03]: And for those who don't know, like it's Rakuten is like, I think number one or number two e-commerce store in Japan. Yes. Yeah.Mark Bissell [00:19:10]: And I think that use case actually highlights a lot of like what it looks like to deploy things in practice that you don't always think about when you're doing sort of research tasks. So when you think about some of the stuff that came up there that's more complex than your idealized version of a problem, they were encountering things like synthetic to real transfer of methods. So they couldn't train probes, classifiers, things like that on actual customer data of PII. So what they had to do is use synthetic data sets. And then hope that that transfer is out of domain to real data sets. And so we can evaluate performance on the real data sets, but not train on customer PII. So that right off the bat is like a big challenge. You have multilingual requirements. So this needed to work for both English and Japanese text. Japanese text has all sorts of quirks, including tokenization behaviors that caused lots of bugs that caused us to be pulling our hair out. And then also a lot of tasks you'll see. You might make simplifying assumptions if you're sort of treating it as like the easiest version of the problem to just sort of get like general results where maybe you say you're classifying a sentence to say, does this contain PII? But the need that Rakuten had was token level classification so that you could precisely scrub out the PII. So as we learned more about the problem, you're sort of speaking about what that looks like in practice. Yeah. A lot of assumptions end up breaking. And that was just one instance where you. A problem that seems simple right off the bat ends up being more complex as you keep diving into it.Vibhu Sapra [00:20:41]: Excellent. One of the things that's also interesting with Interp is a lot of these methods are very efficient, right? So where you're just looking at a model's internals itself compared to a separate like guardrail, LLM as a judge, a separate model. One, you have to host it. Two, there's like a whole latency. So if you use like a big model, you have a second call. Some of the work around like self detection of hallucination, it's also deployed for efficiency, right? So if you have someone like Rakuten doing it in production live, you know, that's just another thing people should consider.Mark Bissell [00:21:12]: Yeah. And something like a probe is super lightweight. Yeah. It's no extra latency really. Excellent.Shawn Wang [00:21:17]: You have the steering demos lined up. So we were just kind of see what you got. I don't, I don't actually know if this is like the latest, latest or like alpha thing.Mark Bissell [00:21:26]: No, this is a pretty hacky demo from from a presentation that someone else on the team recently gave. So this will give a sense for, for technology. So you can see the steering and action. Honestly, I think the biggest thing that this highlights is that as we've been growing as a company and taking on kind of more and more ambitious versions of interpretability related problems, a lot of that comes to scaling up in various different forms. And so here you're going to see steering on a 1 trillion parameter model. This is Kimi K2. And so it's sort of fun that in addition to the research challenges, there are engineering challenges that we're now tackling. Cause for any of this to be sort of useful in production, you need to be thinking about what it looks like when you're using these methods on frontier models as opposed to sort of like toy kind of model organisms. So yeah, this was thrown together hastily, pretty fragile behind the scenes, but I think it's quite a fun demo. So screen sharing is on. So I've got two terminal sessions pulled up here. On the left is a forked version that we have of the Kimi CLI that we've got running to point at our custom hosted Kimi model. And then on the right is a set up that will allow us to steer on certain concepts. So I should be able to chat with Kimi over here. Tell it hello. This is running locally. So the CLI is running locally, but the Kimi server is running back to the office. Well, hopefully should be, um, that's too much to run on that Mac. Yeah. I think it's, uh, it takes a full, like each 100 node. I think it's like, you can. You can run it on eight GPUs, eight 100. So, so yeah, Kimi's running. We can ask it a prompt. It's got a forked version of our, uh, of the SG line code base that we've been working on. So I'm going to tell it, Hey, this SG line code base is slow. I think there's a bug. Can you try to figure it out? There's a big code base, so it'll, it'll spend some time doing this. And then on the right here, I'm going to initialize in real time. Some steering. Let's see here.Mark Bissell [00:23:33]: searching for any. Bugs. Feature ID 43205.Shawn Wang [00:23:38]: Yeah.Mark Bissell [00:23:38]: 20, 30, 40. So let me, uh, this is basically a feature that we found that inside Kimi seems to cause it to speak in Gen Z slang. And so on the left, it's still sort of thinking normally it might take, I don't know, 15 seconds for this to kick in, but then we're going to start hopefully seeing him do this code base is massive for real. So we're going to start. We're going to start seeing Kimi transition as the steering kicks in from normal Kimi to Gen Z Kimi and both in its chain of thought and its actual outputs.Mark Bissell [00:24:19]: And interestingly, you can see, you know, it's still able to call tools, uh, and stuff. It's um, it's purely sort of it's it's demeanor. And there are other features that we found for interesting things like concision. So that's more of a practical one. You can make it more concise. Um, the types of programs, uh, programming languages that uses, but yeah, as we're seeing it come in. Pretty good. Outputs.Shawn Wang [00:24:43]: Scheduler code is actually wild.Vibhu Sapra [00:24:46]: Yo, this code is actually insane, bro.Vibhu Sapra [00:24:53]: What's the process of training in SAE on this, or, you know, how do you label features? I know you guys put out a pretty cool blog post about, um, finding this like autonomous interp. Um, something. Something about how agents for interp is different than like coding agents. I don't know while this is spewing up, but how, how do we find feature 43, two Oh five. Yeah.Mark Bissell [00:25:15]: So in this case, um, we, our platform that we've been building out for a long time now supports all the sort of classic out of the box interp techniques that you might want to have like SAE training, probing things of that kind, I'd say the techniques for like vanilla SAEs are pretty well established now where. You take your model that you're interpreting, run a whole bunch of data through it, gather activations, and then yeah, pretty straightforward pipeline to train an SAE. There are a lot of different varieties. There's top KSAEs, batch top KSAEs, um, normal ReLU SAEs. And then once you have your sparse features to your point, assigning labels to them to actually understand that this is a gen Z feature, that's actually where a lot of the kind of magic happens. Yeah. And the most basic standard technique is look at all of your d input data set examples that cause this feature to fire most highly. And then you can usually pick out a pattern. So for this feature, If I've run a diverse enough data set through my model feature 43, two Oh five. Probably tends to fire on all the tokens that sounds like gen Z slang. You know, that's the, that's the time of year to be like, Oh, I'm in this, I'm in this Um, and, um, so, you know, you could have a human go through all 43,000 concepts andVibhu Sapra [00:26:34]: And I've got to ask the basic question, you know, can we get examples where it hallucinates, pass it through, see what feature activates for hallucinations? Can I just, you know, turn hallucination down?Myra Deng [00:26:51]: Oh, wow. You really predicted a project we're already working on right now, which is detecting hallucinations using interpretability techniques. And this is interesting because hallucinations is something that's very hard to detect. And it's like a kind of a hairy problem and something that black box methods really struggle with. Whereas like Gen Z, you could always train a simple classifier to detect that hallucinations is harder. But we've seen that models internally have some... Awareness of like uncertainty or some sort of like user pleasing behavior that leads to hallucinatory behavior. And so, yeah, we have a project that's trying to detect that accurately. And then also working on mitigating the hallucinatory behavior in the model itself as well.Shawn Wang [00:27:39]: Yeah, I would say most people are still at the level of like, oh, I would just turn temperature to zero and that turns off hallucination. And I'm like, well, that's a fundamental misunderstanding of how this works. Yeah.Mark Bissell [00:27:51]: Although, so part of what I like about that question is you, there are SAE based approaches that might like help you get at that. But oftentimes the beauty of SAEs and like we said, the curse is that they're unsupervised. So when you have a behavior that you deliberately would like to remove, and that's more of like a supervised task, often it is better to use something like probes and specifically target the thing that you're interested in reducing as opposed to sort of like hoping that when you fragment the latent space, one of the vectors that pops out.Vibhu Sapra [00:28:20]: And as much as we're training an autoencoder to be sparse, we're not like for sure certain that, you know, we will get something that just correlates to hallucination. You'll probably split that up into 20 other things and who knows what they'll be.Mark Bissell [00:28:36]: Of course. Right. Yeah. So there's no sort of problems with like feature splitting and feature absorption. And then there's the off target effects, right? Ideally, you would want to be very precise where if you reduce the hallucination feature, suddenly maybe your model can't write. Creatively anymore. And maybe you don't like that, but you want to still stop it from hallucinating facts and figures.Shawn Wang [00:28:55]: Good. So Vibhu has a paper to recommend there that we'll put in the show notes. But yeah, I mean, I guess just because your demo is done, any any other things that you want to highlight or any other interesting features you want to show?Mark Bissell [00:29:07]: I don't think so. Yeah. Like I said, this is a pretty small snippet. I think the main sort of point here that I think is exciting is that there's not a whole lot of inter being applied to models quite at this scale. You know, Anthropic certainly has some some. Research and yeah, other other teams as well. But it's it's nice to see these techniques, you know, being put into practice. I think not that long ago, the idea of real time steering of a trillion parameter model would have sounded.Shawn Wang [00:29:33]: Yeah. The fact that it's real time, like you started the thing and then you edited the steering vector.Vibhu Sapra [00:29:38]: I think it's it's an interesting one TBD of what the actual like production use case would be on that, like the real time editing. It's like that's the fun part of the demo, right? You can kind of see how this could be served behind an API, right? Like, yes, you're you only have so many knobs and you can just tweak it a bit more. And I don't know how it plays in. Like people haven't done that much with like, how does this work with or without prompting? Right. How does this work with fine tuning? Like, there's a whole hype of continual learning, right? So there's just so much to see. Like, is this another parameter? Like, is it like parameter? We just kind of leave it as a default. We don't use it. So I don't know. Maybe someone here wants to put out a guide on like how to use this with prompting when to do what?Mark Bissell [00:30:18]: Oh, well, I have a paper recommendation. I think you would love from Act Deep on our team, who is an amazing researcher, just can't say enough amazing things about Act Deep. But he actually has a paper that as well as some others from the team and elsewhere that go into the essentially equivalence of activation steering and in context learning and how those are from a he thinks of everything in a cognitive neuroscience Bayesian framework, but basically how you can precisely show how. Prompting in context, learning and steering exhibit similar behaviors and even like get quantitative about the like magnitude of steering you would need to do to induce a certain amount of behavior similar to certain prompting, even for things like jailbreaks and stuff. It's a really cool paper. Are you saying steering is less powerful than prompting? More like you can almost write a formula that tells you how to convert between the two of them.Myra Deng [00:31:20]: And so like formally equivalent actually in the in the limit. Right.Mark Bissell [00:31:24]: So like one case study of this is for jailbreaks there. I don't know. Have you seen the stuff where you can do like many shot jailbreaking? You like flood the context with examples of the behavior. And the topic put out that paper.Shawn Wang [00:31:38]: A lot of people were like, yeah, we've been doing this, guys.Mark Bissell [00:31:40]: Like, yeah, what's in this in context learning and activation steering equivalence paper is you can like predict the number. Number of examples that you will need to put in there in order to jailbreak the model. That's cool. By doing steering experiments and using this sort of like equivalence mapping. That's cool. That's really cool. It's very neat. Yeah.Shawn Wang [00:32:02]: I was going to say, like, you know, I can like back rationalize that this makes sense because, you know, what context is, is basically just, you know, it updates the KV cache kind of and like and then every next token inference is still like, you know, the sheer sum of everything all the way. It's plus all the context. It's up to date. And you could, I guess, theoretically steer that with you probably replace that with your steering. The only problem is steering typically is on one layer, maybe three layers like like you did. So it's like not exactly equivalent.Mark Bissell [00:32:33]: Right, right. There's sort of you need to get precise about, yeah, like how you sort of define steering and like what how you're modeling the setup. But yeah, I've got the paper pulled up here. Belief dynamics reveal the dual nature. Yeah. The title is Belief Dynamics Reveal the Dual Nature of Incompetence. And it's an exhibition of the practical context learning and activation steering. So Eric Bigelow, Dan Urgraft on the who are doing fellowships at Goodfire, Ekt Deep's the final author there.Myra Deng [00:32:59]: I think actually to your question of like, what is the production use case of steering? I think maybe if you just think like one level beyond steering as it is today. Like imagine if you could adapt your model to be, you know, an expert legal reasoner. Like in almost real time, like very quickly. efficiently using human feedback or using like your semantic understanding of what the model knows and where it knows that behavior. I think that while it's not clear what the product is at the end of the day, it's clearly very valuable. Thinking about like what's the next interface for model customization and adaptation is a really interesting problem for us. Like we have heard a lot of people actually interested in fine-tuning an RL for open weight models in production. And so people are using things like Tinker or kind of like open source libraries to do that, but it's still very difficult to get models fine-tuned and RL'd for exactly what you want them to do unless you're an expert at model training. And so that's like something we'reShawn Wang [00:34:06]: looking into. Yeah. I never thought so. Tinker from Thinking Machines famously uses rank one LoRa. Is that basically the same as steering? Like, you know, what's the comparison there?Mark Bissell [00:34:19]: Well, so in that case, you are still applying updates to the parameters, right?Shawn Wang [00:34:25]: Yeah. You're not touching a base model. You're touching an adapter. It's kind of, yeah.Mark Bissell [00:34:30]: Right. But I guess it still is like more in parameter space then. I guess it's maybe like, are you modifying the pipes or are you modifying the water flowing through the pipes to get what you're after? Yeah. Just maybe one way.Mark Bissell [00:34:44]: I like that analogy. That's my mental map of it at least, but it gets at this idea of model design and intentional design, which is something that we're, that we're very focused on. And just the fact that like, I hope that we look back at how we're currently training models and post-training models and just think what a primitive way of doing that right now. Like there's no intentionalityShawn Wang [00:35:06]: really in... It's just data, right? The only thing in control is what data we feed in.Mark Bissell [00:35:11]: So, so Dan from Goodfire likes to use this analogy of, you know, he has a couple of young kids and he talks about like, what if I could only teach my kids how to be good people by giving them cookies or like, you know, giving them a slap on the wrist if they do something wrong, like not telling them why it was wrong or like what they should have done differently or something like that. Just figure it out. Right. Exactly. So that's RL. Yeah. Right. And, and, you know, it's sample inefficient. There's, you know, what do they say? It's like slurping feedback. It's like, slurping supervision. Right. And so you'd like to get to the point where you can have experts giving feedback to their models that are, uh, internalized and, and, you know, steering is an inference time way of sort of getting that idea. But ideally you're moving to a world whereVibhu Sapra [00:36:04]: it is much more intentional design in perpetuity for these models. Okay. This is one of the questions we asked Emmanuel from Anthropic on the podcast a few months ago. Basically the question, was you're at a research lab that does model training, foundation models, and you're on an interp team. How does it tie back? Right? Like, does this, do ideas come from the pre-training team? Do they go back? Um, you know, so for those interested, you can, you can watch that. There wasn't too much of a connect there, but it's still something, you know, it's something they want toMark Bissell [00:36:33]: push for down the line. It can be useful for all of the above. Like there are certainly post-hocVibhu Sapra [00:36:39]: use cases where it doesn't need to touch that. I think the other thing a lot of people forget is this stuff isn't too computationally expensive, right? Like I would say, if you're interested in getting into research, MechInterp is one of the most approachable fields, right? A lot of this train an essay, train a probe, this stuff, like the budget for this one, there's already a lot done. There's a lot of open source work. You guys have done some too. Um, you know,Shawn Wang [00:37:04]: There's like notebooks from the Gemini team for Neil Nanda or like, this is how you do it. Just step through the notebook.Vibhu Sapra [00:37:09]: Even if you're like, not even technical with any of this, you can still make like progress. There, you can look at different activations, but, uh, if you do want to get into training, you know, training this stuff, correct me if I'm wrong is like in the thousands of dollars, not even like, it's not that high scale. And then same with like, you know, applying it, doing it for post-training or all this stuff is fairly cheap in scale of, okay. I want to get into like model training. I don't have compute for like, you know, pre-training stuff. So it's, it's a very nice field to get into. And also there's a lot of like open questions, right? Um, some of them have to go with, okay, I want a product. I want to solve this. Like there's also just a lot of open-ended stuff that people could work on. That's interesting. Right. I don't know if you guys have any calls for like, what's open questions, what's open work that you either open collaboration with, or like, you'd just like to see solved or just, you know, for people listening that want to get into McInturk because people always talk about it. What are, what are the things they should check out? Start, of course, you know, join you guys as well. I'm sure you're hiring.Myra Deng [00:38:09]: There's a paper, I think from, was it Lee, uh, Sharky? It's open problems and, uh, it's, it's a bit of interpretability, which I recommend everyone who's interested in the field. Read. I'm just like a really comprehensive overview of what are the things that experts in the field think are the most important problems to be solved. I also think to your point, it's been really, really inspiring to see, I think a lot of young people getting interested in interpretability, actually not just young people also like scientists to have been, you know, experts in physics for many years and in biology or things like this, um, transitioning into interp, because the barrier of, of what's now interp. So it's really cool to see a number to entry is, you know, in some ways low and there's a lot of information out there and ways to get started. There's this anecdote of like professors at universities saying that all of a sudden every incoming PhD student wants to study interpretability, which was not the case a few years ago. So it just goes to show how, I guess, like exciting the field is, how fast it's moving, how quick it is to get started and things like that.Mark Bissell [00:39:10]: And also just a very welcoming community. You know, there's an open source McInturk Slack channel. There are people are always posting questions and just folks in the space are always responsive if you ask things on various forums and stuff. But yeah, the open paper, open problems paper is a really good one.Myra Deng [00:39:28]: For other people who want to get started, I think, you know, MATS is a great program. What's the acronym for? Machine Learning and Alignment Theory Scholars? It's like the...Vibhu Sapra [00:39:40]: Normally summer internship style.Myra Deng [00:39:42]: Yeah, but they've been doing it year round now. And actually a lot of our full-time staff have come through that program or gone through that program. And it's great for anyone who is transitioning into interpretability. There's a couple other fellows programs. We do one as well as Anthropic. And so those are great places to get started if anyone is interested.Mark Bissell [00:40:03]: Also, I think been seen as a research field for a very long time. But I think engineering... I think engineers are sorely wanted for interpretability as well, especially at Goodfire, but elsewhere, as it does scale up.Shawn Wang [00:40:18]: I should mention that Lee actually works with you guys, right? And in the London office and I'm adding our first ever McInturk track at AI Europe because I see this industry applications now emerging. And I'm pretty excited to, you know, help push that along. Yeah, I was looking forward to that. It'll effectively be the first industry McInturk conference. Yeah. I'm so glad you added that. You know, it's still a little bit of a bet. It's not that widespread, but I can definitely see this is the time to really get into it. We want to be early on things.Mark Bissell [00:40:51]: For sure. And I think the field understands this, right? So at ICML, I think the title of the McInturk workshop this year was actionable interpretability. And there was a lot of discussion around bringing it to various domains. Everyone's adding pragmatic, actionable, whatever.Shawn Wang [00:41:10]: It's like, okay, well, we weren't actionable before, I guess. I don't know.Vibhu Sapra [00:41:13]: And I mean, like, just, you know, being in Europe, you see the Interp room. One, like old school conferences, like, I think they had a very tiny room till they got lucky and they got it doubled. But there's definitely a lot of interest, a lot of niche research. So you see a lot of research coming out of universities, students. We covered the paper last week. It's like two unknown authors, not many citations. But, you know, you can make a lot of meaningful work there. Yeah. Yeah. Yeah.Shawn Wang [00:41:39]: Yeah. I think people haven't really mentioned this yet. It's just Interp for code. I think it's like an abnormally important field. We haven't mentioned this yet. The conspiracy theory last two years ago was when the first SAE work came out of Anthropic was they would do like, oh, we just used SAEs to turn the bad code vector down and then turn up the good code. And I think like, isn't that the dream? Like, you know, like, but basically, I guess maybe, why is it funny? Like, it's... If it was realistic, it would not be funny. It would be like, no, actually, we should do this. But it's funny because we know there's like, we feel there's some limitations to what steering can do. And I think a lot of the public image of steering is like the Gen Z stuff. Like, oh, you can make it really love the Golden Gate Bridge, or you can make it speak like Gen Z. To like be a legal reasoner seems like a huge stretch. Yeah. And I don't know if that will get there this way. Yeah.Myra Deng [00:42:36]: I think, um, I will say we are announcing. Something very soon that I will not speak too much about. Um, but I think, yeah, this is like what we've run into again and again is like, we, we don't want to be in the world where steering is only useful for like stylistic things. That's definitely not, not what we're aiming for. But I think the types of interventions that you need to do to get to things like legal reasoning, um, are much more sophisticated and require breakthroughs in, in learning algorithms. And that's, um...Shawn Wang [00:43:07]: And is this an emergent property of scale as well?Myra Deng [00:43:10]: I think so. Yeah. I mean, I think scale definitely helps. I think scale allows you to learn a lot of information and, and reduce noise across, you know, large amounts of data. But I also think we think that there's ways to do things much more effectively, um, even, even at scale. So like actually learning exactly what you want from the data and not learning things that you do that you don't want exhibited in the data. So we're not like anti-scale, but we are also realizing that scale is not going to get us anywhere. It's not going to get us to the type of AI development that we want to be at in, in the future as these models get more powerful and get deployed in all these sorts of like mission critical contexts. Current life cycle of training and deploying and evaluations is, is to us like deeply broken and has opportunities to, to improve. So, um, more to come on that very, very soon.Mark Bissell [00:44:02]: And I think that that's a use basically, or maybe just like a proof point that these concepts do exist. Like if you can manipulate them in the precise best way, you can get the ideal combination of them that you desire. And steering is maybe the most coarse grained sort of peek at what that looks like. But I think it's evocative of what you could do if you had total surgical control over every concept, every parameter. Yeah, exactly.Myra Deng [00:44:30]: There were like bad code features. I've got it pulled up.Vibhu Sapra [00:44:33]: Yeah. Just coincidentally, as you guys are talking.Shawn Wang [00:44:35]: This is like, this is exactly.Vibhu Sapra [00:44:38]: There's like specifically a code error feature that activates and they show, you know, it's not, it's not typo detection. It's like, it's, it's typos in code. It's not typical typos. And, you know, you can, you can see it clearly activates where there's something wrong in code. And they have like malicious code, code error. They have a whole bunch of sub, you know, sub broken down little grain features. Yeah.Shawn Wang [00:45:02]: Yeah. So, so the, the rough intuition for me, the, why I talked about post-training was that, well, you just, you know, have a few different rollouts with all these things turned off and on and whatever. And then, you know, you can, that's, that's synthetic data you can kind of post-train on. Yeah.Vibhu Sapra [00:45:13]: And I think we make it sound easier than it is just saying, you know, they do the real hard work.Myra Deng [00:45:19]: I mean, you guys, you guys have the right idea. Exactly. Yeah. We replicated a lot of these features in, in our Lama models as well. I remember there was like.Vibhu Sapra [00:45:26]: And I think a lot of this stuff is open, right? Like, yeah, you guys opened yours. DeepMind has opened a lot of essays on Gemma. Even Anthropic has opened a lot of this. There's, there's a lot of resources that, you know, we can probably share of people that want to get involved.Shawn Wang [00:45:41]: Yeah. And special shout out to like Neuronpedia as well. Yes. Like, yeah, amazing piece of work to visualize those things.Myra Deng [00:45:49]: Yeah, exactly.Shawn Wang [00:45:50]: I guess I wanted to pivot a little bit on, onto the healthcare side, because I think that's a big use case for you guys. We haven't really talked about it yet. This is a bit of a crossover for me because we are, we are, we do have a separate science pod that we're starting up for AI, for AI for science, just because like, it's such a huge investment category and also I'm like less qualified to do it, but we actually have bio PhDs to cover that, which is great, but I need to just kind of recover, recap your work, maybe on the evil two stuff, but then, and then building forward.Mark Bissell [00:46:17]: Yeah, for sure. And maybe to frame up the conversation, I think another kind of interesting just lens on interpretability in general is a lot of the techniques that were described. are ways to solve the AI human interface problem. And it's sort of like bidirectional communication is the goal there. So what we've been talking about with intentional design of models and, you know, steering, but also more advanced techniques is having humans impart our desires and control into models and over models. And the reverse is also very interesting, especially as you get to superhuman models, whether that's narrow superintelligence, like these scientific models that work on genomics, data, medical imaging, things like that. But down the line, you know, superintelligence of other forms as well. What knowledge can the AIs teach us as sort of that, that the other direction in that? And so some of our life science work to date has been getting at exactly that question, which is, well, some of it does look like debugging these various life sciences models, understanding if they're actually performing well, on tasks, or if they're picking up on spurious correlations, for instance, genomics models, you would like to know whether they are sort of focusing on the biologically relevant things that you care about, or if it's using some simpler correlate, like the ancestry of the person that it's looking at. But then also in the instances where they are superhuman, and maybe they are understanding elements of the human genome that we don't have names for or specific, you know, yeah, discoveries that they've made that that we don't know about, that's, that's a big goal. And so we're already seeing that, right, we are partnered with organizations like Mayo Clinic, leading research health system in the United States, our Institute, as well as a startup called Prima Menta, which focuses on neurodegenerative disease. And in our partnership with them, we've used foundation models, they've been training and applied our interpretability techniques to find novel biomarkers for Alzheimer's disease. So I think this is just the tip of the iceberg. But it's, that's like a flavor of some of the things that we're working on.Shawn Wang [00:48:36]: Yeah, I think that's really fantastic. Obviously, we did the Chad Zuckerberg pod last year as well. And like, there's a plethora of these models coming out, because there's so much potential and research. And it's like, very interesting how it's basically the same as language models, but just with a different underlying data set. But it's like, it's the same exact techniques. Like, there's no change, basically.Mark Bissell [00:48:59]: Yeah. Well, and even in like other domains, right? Like, you know, robotics, I know, like a lot of the companies just use Gemma as like the like backbone, and then they like make it into a VLA that like takes these actions. It's, it's, it's transformers all the way down. So yeah.Vibhu Sapra [00:49:15]: Like we have Med Gemma now, right? Like this week, even there was Med Gemma 1.5. And they're training it on this stuff, like 3d scans, medical domain knowledge, and all that stuff, too. So there's a push from both sides. But I think the thing that, you know, one of the things about McInturpp is like, you're a little bit more cautious in some domains, right? So healthcare, mainly being one, like guardrails, understanding, you know, we're more risk adverse to something going wrong there. So even just from a basic understanding, like, if we're trusting these systems to make claims, we want to know why and what's going on.Myra Deng [00:49:51]: Yeah, I think there's totally a kind of like deployment bottleneck to actually using. foundation models for real patient usage or things like that. Like, say you're using a model for rare disease prediction, you probably want some explanation as to why your model predicted a certain outcome, and an interpretable explanation at that. So that's definitely a use case. But I also think like, being able to extract scientific information that no human knows to accelerate drug discovery and disease treatment and things like that actually is a really, really big unlock for science, like scientific discovery. And you've seen a lot of startups, like say that they're going to accelerate scientific discovery. And I feel like we actually are doing that through our interp techniques. And kind of like, almost by accident, like, I think we got reached out to very, very early on from these healthcare institutions. And none of us had healthcare.Shawn Wang [00:50:49]: How did they even hear of you? A podcast.Myra Deng [00:50:51]: Oh, okay. Yeah, podcast.Vibhu Sapra [00:50:53]: Okay, well, now's that time, you know.Myra Deng [00:50:55]: Everyone can call us.Shawn Wang [00:50:56]: Podcasts are the most important thing. Everyone should listen to podcasts.Myra Deng [00:50:59]: Yeah, they reached out. They were like, you know, we have these really smart models that we've trained, and we want to know what they're doing. And we were like, really early that time, like three months old, and it was a few of us. And we were like, oh, my God, we've never used these models. Let's figure it out. But it's also like, great proof that interp techniques scale pretty well across domains. We didn't really have to learn too much about.Shawn Wang [00:51:21]: Interp is a machine learning technique, machine learning skills everywhere, right? Yeah. And it's obviously, it's just like a general insight. Yeah. Probably to finance too, I think, which would be fun for our history. I don't know if you have anything to say there.Mark Bissell [00:51:34]: Yeah, well, just across the science. Like, we've also done work on material science. Yeah, it really runs the gamut.Vibhu Sapra [00:51:40]: Yeah. Awesome. And, you know, for those that should reach out, like, you're obviously experts in this, but like, is there a call out for people that you're looking to partner with, design partners, people to use your stuff outside of just, you know, the general developer that wants to. Plug and play steering stuff, like on the research side more so, like, are there ideal design partners, customers, stuff like that?Myra Deng [00:52:03]: Yeah, I can talk about maybe non-life sciences, and then I'm curious to hear from you on the life sciences side. But we're looking for design partners across many domains, language, anyone who's customizing language models or trying to push the frontier of code or reasoning models is really interesting to us. And then also interested in the frontier of modeling. There's a lot of models that work in, like, pixel space, as we call it. So if you're doing world models, video models, even robotics, where there's not a very clean natural language interface to interact with, I think we think that Interp can really help and are looking for a few partners in that space.Shawn Wang [00:52:43]: Just because you mentioned the keyword

TECH CLUBBERS PODCAST W/ SABINE HOFFMANN Sabine grew up in Berlin. Her first years of life she spend in East Berlin. Early 2000's She discovered her passion for spinning vinyl, when she was first exposed to electronic music. She plays a deep blend of techno and house music in all their different genres. Besides club music, she is also available for Ambient sets. But regardless of the style, you will always find Sabine playing vinyl only sets as she prefers the warmth of analog sounds and the classic feel of turntables. She has played at almost all the leading clubs in Berlin such as About Blank, Tresor, Heideglühen, Salon zur wilden Renate, Berghain Kantine, Suicide, Griessmühle, IPSE, Farbfernseher, Golden Gate, Arena Club and the list goes on. Her international profile has been steadily increasing, she already played in Tiflis, Paris, Baku, Yerevan, Krakow and Thessaloniki to name a few. Since 2016 Sabine works at the well known OYE record shop in Berlin. She is taking care of all OYE club events and also all Instores happening at one of the record shops. Since January 2023 she is hosting the OYE Podcast series. Sabine is the founder of "Frauengedeck". It´s an event series focusing on an all female line up. She wants to support and present female DJs. She has held many events at different clubs throughout Berlin like Wilde Renate, Griessmühle, Farbfernseher and Beate Uwe. The first international events outside of Germany, were held in Armenia, Azerbaijan and Georgia. Her newest project is "Dub Nation". An event series brought to life by good old colleagues and friends Sabine Hoffmann and Kenneth Christiansen. Both share the unconditional love for the deeper and more dubby side of Techno and House. She finished her Audio Engineering studies at SAE. Inspired by her gained knowledge, she started putting her focus on music production. The first result, a collaboration with No Mad Ronin is available on the label Zaijenroots. TRACKLIST: Psi Performer (Anthony Rother) ‎- 1948 [Kanzleramt] Jeff Mills - The Deep [Purpose Maker] Oliver Ho - The Link (1999) [Blueprint] Johannes Heil - 20.000 Leagues Under The Skin Pt. 2.1 [Kanzleramt] Joey Beltram - 5.7 Litre [Tresor] Surgeon - Floorshow Part 2. [Counterbalance] James Ruskin - Surfaced [Tresor] Deetron - Cone [Music Man Records] Technasia - Hydra [Technasia] Aural Emote (Ben Sims) - Theoretical [Tresor] The Horrorist - The Virus ( Ben Sims Remix ) [A45 Music] Ken Ishii - Extra ( Luke Slater Remix ) [R & S Records] Marco Bailey - The Ctila [Primate Recordings] Player - Respect Yourself [Player] Follow SABINE HOFFMANN here: Facebook: https://www.facebook.com/DjSabineHoffmann Instagram: https://www.instagram.com/sabine_hoffmann Soundcloud: https://soundcloud.com/sabine-hoffmann

La Región del Biobío enfrenta una crítica jornada este viernes debido a un sistema frontal que ha traído intensas precipitaciones y actividad eléctrica a las áreas recientemente afectadas por los incendios forestales. "Se declaró una alerta SAE preventiva para que las personas se pongan a resguardo a propósito de la tormenta eléctrica que se está sintiendo en varios sectores", dijo el delegado presidencial regional, Eduardo Pacheco, a El Diario de Cooperativa. Conduce Rodrigo Vergara.

Curious how SAE can open doors in the automotive industry and beyond?  Listen in as we sit down with Dean Case, Membership Director, SAE SoCal, whose 45-year journey in the automotive industry is packed with stories of racing, engineering, and community building. From joining SAE as a Cal Poly student to shaping the future of motorsports and electric vehicles at Mazda, Ford, and Nissan, learn how SAE International became a springboard to building lifelong connections and uncovering new opportunities.   Whether you're a student, engineering professional, or automotive enthusiast, this conversation is packed with inspiration, practical advice, and proof that SAE provides a firm foundation for meaningful relationships that last.   We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today—a podcast where we discuss emerging technology and trends in mobility with the leaders, innovators and strategists making it all happen—and give us a review on your preferred podcasting platform.   Follow SAE on LinkedIn, Instagram, Facebook, X, and YouTube. Follow host Grayson Brulte on LinkedIn, X, and Instagram.

In dieser Episode sprechen Glen, Kurt und ihr Gast Detlef Halaski über Detlefs beeindruckende Karriere im Bereich der Filmmischung und Synchronisation. Detlef teilt seine Erfahrungen aus über 30 Jahren in der Branche, seine Anfänge bei der SAE und seine Arbeit an großen Filmproduktionen wie Avatar, LaLaLand oder Die Tribute Von Panem. Er gibt Einblicke in die Herausforderungen und Entwicklungen der Synchronbranche und betont die Bedeutung von Leidenschaft und kontinuierlichem Lernen. Hört rein!IMDB:https://www.imdb.com/de/name/nm0354694/LinkedIn: https://www.linkedin.com/in/detlef-halaski-833439145/Instagram:https://www.instagram.com/deti_berlin/Life After SAE auf Instagram: https://www.instagram.com/lifeaftersae/Mehr zu Kurt gibt's hier:https://www.instagram.com/kurt_jonathan_engert/Mehr zu Glen gibt's hier:https://glenschaele.com/linktree

The following DX information comes from Bernie, W3UR, editor of the DailyDX, the WeeklyDX, and the How's DX column in QST. If you would like a free 2-week trial of the DailyDX, your only source of real-time DX information, justdrop me a note at thedxmentor@gmail.comVP2V - British Virgin Islands - W5GI, Jonathan,has returned to Anegada Island in the British Virgin Islands and is QRV as VP2V/W5GI until January 20th. He is hopeful to work 1000 stations from POTA VG-0021. Listen for him on SSB and FT8 from both the park and his living QTH. He will be mainly on 20 meters but can also operate on 40, 17, 15, 12and 10 meters.ZD7 - St. Helena Island - AC1GQ, Casey,will be on St. Helena Island from January 10-24. He plans to operate with a QRP rig (QMX from QRP Labs) and an end-fed antenna (QRP Guys) on the 40m and 20m bands, if possible. Casey  will bring a copy of his home amateur radio license and is seeking advice on applying for a ZD7 license.  This one is right around the corner. “In collaboration with the Vieques Island Amateur Radio Club (NP3VI) and theManyana DXFoundation, we are proud to announce KP5/NP3VI, a landmark DXpedition to Desecheo Island (KP5), currently ranked by Club Log as the 14th most wantedDXCC entity worldwide. Located approximately 13 miles off the west coast of Puerto Rico, Desecheo Island has not been activated since 2009. This operation represents the first Puerto Rican-led DXpedition to Desecheo in 48 years,following the historic KP4AM/D activation in 1978. The primary mission of this DXpedition is to provide an All-Time New One (ATNO) to as many amateur radio operators worldwide as possible. Operators from Puerto Rico and international locations will participate to maximize coverage, band availability, and global accessibility. To ensure continuous, global on-air presence, two self-sustainedRemote Deployable Units (RDUs) provided by the Manyana DXFoundation will be deployed on the island. These stations will operate 24 hours per day for 30 consecutive days,utilizing state-of-the-art remote operating infrastructure from Remote Ham Radio (RHR). Operations will be livestreamed, and there will be real-time activityupdates via Club Log. NP4G, Dr. Otis Vicens, is DXpedition leader, and N2AJ, Stephen Hass, is media officer and pilot. DK6SP, Philipp, and DJ4MX, Sven, have announced the next adventure of the Next Generation DX Club. “This time, ouryoung and ambitious team will travel to the People's Republic of Bangladesh, better known as S2 to the amateur radio community…After bringing you 8R7X, Guyana in 2024 and V73WW, Marshall Islands last year, we are ready to make waves from one  Asia's most exciting and under-activated locations.” More information about callsign, dates, andoperators will follow. XU - Cambodia - DL7BO, Tom, who is QRV until January 18, is using the callsign XU7O. He will be active on 160-6 meters using CW, SSB, and FT8, with a focus on the lower bands. QSLinformation remains direct to DJ4WK, or via LoTW, Club Log, or eQSL. FY - French Guiana - F4GPK, Peter, is QRV as TO2FY until January 15 from Kourou. C5YK, The Gambia – Andre, ON7YK, is QRV from The Gambia as C5YK until January 25. He is operating on SSB, RTTY, PSK,FT8, FT4, and some CW on 20, 17, 15, 12, and 10M. QSL only via LoTW, eQSL, or direct to ON7YK. He posts his logbook on his website.   Z6 – Kosovo - HB9TSW, Gab, isQRV as Z68BG from Slatina Air Base, Kosovo, until January 28 using CW only. For direct QSL, send an SAE  with 3 green stamps via HB9TSW.

In this podcast episode, I'm joined by Lurch and we discuss the Kraus EZ Shift and the Stealthport Motorcycle Battery and Accessory Port for Harley-Davidson.  These two seemingly small and simple items result in big benefits when installed on your Harley-Davidson motorcycle.  So, strap in and tune in as we talk about how these two items improved our riding enjoyment.  We don't just sell items in our store.  We test and review them.  SUPPORT US AND SHOP IN THE OFFICIAL LAW ABIDING BIKER STORE CHECK OUT OUR HUNDREDS OF FREE HELPFUL VIDEOS ON OUR YOUTUBE CHANNEL AND SUBSCRIBE! The Kraus EZ‑Shift of M8 and Twin Cam motors is a popular aftermarket shift assist for Harley-Davidson motorcycles designed to improve the feel and ease of gear changes. It works by altering the shift linkage's leverage ratio so that finding neutral — especially from a stop — becomes much easier and more positive, which can reduce clutch wear by allowing riders to drop into neutral quickly and release the clutch instead of dragging it at lights. Made in the USA from high-quality billet aluminum, the device bolts on in just a few minutes and is compatible with both stock and many aftermarket shift levers. Riders report smoother, more confident shifts across all gears, with up to about 20% less effort required compared to stock linkage feel. NEW FREE VIDEO RELEASED: Upgrade Your Motorcycle Helmet with a Quick Release Stainless Steel Helmet Chin Ratchet Strap Polaris Sells Indian Motorcycle?! What's REALLY Going On Behind the Scenes… The Stealthport Motorcycle Battery Charging & Accessory Port and related Stealthport adapters are aftermarket solutions for Harley-Davidson riders who want a clean, factory-style connection point for charging their bike's battery or powering accessories without dealing with the standard dangling SAE pigtail. Instead of fumbling with the loose cable under the bike, a Stealthport install mounts a low-profile port in a convenient location on the frame or bodywork, using existing threaded holes on many Softail, Touring, and other Harley models. Once installed, the port connects to your bike's OEM battery tender lead, giving you a weather-protected, easily accessible charging point that can also be used for heated gear or other 12 V accessories — all while keeping the look tidy and integrated. Installation is typically straightforward and reversible, and multiple mounting options let you choose how hidden or accessible you want the port to be. Sponsor-Ciro 3D CLICK HERE! Innovative products for Harley-Davidson & Goldwing Affordable chrome, lighting, and comfort products Ciro 3D has a passion for design and innovation Sponsor-Butt Buffer CLICK HERE Want to ride longer? Tired of a sore and achy ass? Then fix it with a high-quality Butt Buffer seat cushion? If you appreciate the content we put out and want to make sure it keeps on coming your way then become a Patron too! There are benefits and there is no risk. Thanks to the following bikers for supporting us via a flat donation: Joseph Ellis of Ira Township, Michigan Chuck Spencer of Twentynine Palms, California Richard Gundermann of Cudahy, Wisconsin

The Goodhue FFA catches us up on their SAE's - learn more about the Goodhue FFA branch here!

You think getting divorced at 23 is too young to learn anything valuable? Think again.Sade Mickelson, life coach and Chinese medicine expert, shares her story of marrying an Iranian restaurant owner who was arrested for federal drug charges while she was still in college. Instead of walking away, she doubled down—visiting him in prison, planning to move to Iran, and proving her capability at every turn.The wake-up call came on a broken Ferris wheel in Isfahan. Sae realized she was wishing her entire life away, rushing to reach the end just to prove she picked the right person. She discovered her best friend was having an affair with her husband. But the real revelation came when she told him: "I made you up."This episode explores how women use their professional strengths—resilience, capability, problem-solving—to stay trapped in relationships that drain them. Sade's story reveals the difference between proving your worth and protecting your peace. She learned that truth feels like freedom, even when it hurts.Her journey from codependence to self-befriending offers wisdom for any woman rebuilding after divorce. The answer is not figuring everything out tonight.Ready to stop proving yourself and start protecting your peace? Schedule a consultation call with Sade Curry at https://sadecurry.com/info.

Bob Moats and Mike Wiemuth continue their conversation with IU historian Bill Murphy, shifting from football glory to basketball history. This installment dives deep into the Branch McCracken era, revealing why Bill's favorite IU coach isn't who most fans would expect.Branch McCracken: The SheriffBill makes his case for Branch McCracken over Bob Knight, drawing fascinating parallels between the two legendary coaches. Branch coached 24 years (1938-1965, minus three years serving in WWII), finishing first or second in the Big Ten in 12 of those seasons with two national titles. Knight coached 29 years, finishing first or second in 16 seasons with three titles. Bill argues that had NCAA tournament rules been different, Branch might have won in 1960 when IU beat Ohio State by 16 in Bloomington after their last 12-game win streak, while Knight's 1987 title came when IU tied for the Big Ten title with three other teams. Bill recounts meeting Branch as an eighth grader in New Albany, a handshake he didn't want to wash for a week, and describes a six-foot-four presence who earned nicknames like "The Sheriff" and "The Bear" while drinking coffee at every shop on the Bloomington square to keep tabs on his players.The Van Arsdale Twins' Supernatural SymmetryThe conversation turns to Tom and Dick Van Arsdale, whose three-year careers produced jaw-dropping statistical similarities:Separated by just 12 points over 72 games (1,252 to 1,240)Only 10 rebounds apart (729 to 719)Both hit exactly 15 field goals in their career-high game against Notre DameConstantly pranked Branch by wearing mismatched socks after he tried to distinguish them by colorOfficials sometimes let the wrong twin shoot free throws because they couldn't tell them apartMike shares stories from his father, who lived in the SAE house with the twins and John McGlocklin—three of IU's seven all-time NBA All-Stars living in the same room.Chesty Chips and Television HistoryBill reveals how IU became the first university to televise basketball games in 1950 when radio announcer Paul Lennon convinced a Terre Haute potato chip company to sponsor games for $1,500 each. After one broadcast, Chesty Potato Chips went from one shift to three and sold out across the region, causing the price to jump to $5,000 per game the next year.Branch's BoysBill shares his favorite McCracken moments—from officials threatening a technical for every step back to the bench (so players carried him), to another ref getting him to sit down by saying "your fly is open," to Branch's simple philosophy: if he could only win one game all year, it would be against Purdue. That hatred paid off in 1940 when IU swept Purdue but finished second in the Big Ten, yet still received the NCAA tournament invitation over the conference champs.This episode brought to you by the Back Home Network and Homefield Apparel.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

My guest today is  Ravikiran Pothukuchi, the leader of Dassault Systèmes' Enterprise Portfolio business in India.In this conversation, Ravi shares his journey from his humble beginnings in a small village in India to becoming a key player in Dassault Systems' business landscape.Ravi dives deep into his upbringing, education, and multiple career transitions that shaped his professional life. Key highlights include his transition from an R&D role to a customer-facing role, the importance of building human connections, the value of curiosity, and how he integrated traditional knowledge with modern business strategies.Notable quotes and insights punctuate the narrative, offering valuable lessons on adaptability, resilience, and the power of networking. 00:00 Introduction and Welcome00:36 Early Life and Education03:26 Higher Education and Career Beginnings13:24 Transition to Business Development19:14 Leadership and Team Management26:39 Transitioning to Customer-Facing Roles27:33 The Challenges of Business Development31:30 The Importance of Networking35:21 Building Genuine Connections40:05 Navigating Career Transitions46:22 Personal Practices for Staying Grounded47:02 The Five Cs Framework for Success49:46 Conclusion and Final ThoughtsThe timestamps are approximate, and after the intro that is about 90 seconds.For more closer timestamps, add 90 seconds to the labels aboveRavikiran Pothukuchi is the leader of Dassault Systèmes' Enterprise Portfolio business in India. In this role, he is responsible for defining the business strategy to expand the company's Portfolio presence in India's rapidly growing economic sectors.Ravikiran began his career with Dassault Systèmes in 2004, initially working in various roles within the Research and Development (R&D) organization before transitioning to business development in 2011. In 2017, he assumed responsibility for increasing market share across the company's core industry vertical, achieving a year-over-year double-digit growth for five consecutive years. He is now entrusted with the responsibility of tapping the growth potential of Dassault Systèmes' Enterprise portfolio while diversifying into new industries and segments.Ravikiran holds degrees from prestigious institutions, IIT-Madras and IIM-Bangalore. He is also a DAAD scholar and a member of several industry organizations, including SAE and IFCCI.Ravi may be reached at: https://www.linkedin.com/in/ravikiran-pothukuchi-47750312/?originalSubdomain=in

Recent news about the Russian launch disaster is discussed as well as us venting about SAE vs Metric. Yes, Metric is better. They enjoy the Room 101 Breakfast in Portugal Maduro with the Lone Elm Single Barrel pick. https://www.cnn.com/2025/11/28/science/russia-space-launch-pad-damaged-intl-hnk  

When is self-driving not self-driving? How do the words we use for autonomous vehicles affect safety? Professor Bryant Walker Smith talks about how the SAE levels came to be, how he hopes to improve them, and his latest paper "Self-Driving Means Self-Driving."

Radio TRO is brought to you in part by:Twisted Road - Motorcycle Rental in the USAVisit Twisted.TRO.bike to get a FREE riding day!Robin gives a warm, drummer-to-drummer salute to Jack DeJohnette before slamming Cloudflare for breaking TRO's podcast feed. He's changing tires, tubes and all, on a '70 CB350 while considering cool SAE selector ideas (one battery tender for three bikes). Brian's battery-tender advice is met with Robin's half agreement and a bit of eye-rolling.Brian plans a winter of family road miles and rereading Moby-Dick along with Lord of the Rings. He's all cheers 'n' tears about Blackhawk Farms raceway getting a pavement makeover. Eventually he dives into the techno jargon of cylinder count.Both take aim at mushy moto-speak, mocking phrases like "hand tight" and clearing up foot-pounds versus pound-feet. Also, save your chicken strips and knee dragging for clear, actionable coaching. Let's have a sane talk about dual-sport difficulty so that we can all maintain our momentum.Speaker Entry:Robin Dean - 00:03:46Brian Wringer - 00:04:02Episode Page: https://tro.bike/podcast/2025e29/Music by Rabid Neon and Otis McDonald

Radio TRO is brought to you in part by:Twisted Road - Motorcycle Rental in the USAVisit Twisted.TRO.bike to get a FREE riding day!Robin gives a warm, drummer-to-drummer salute to Jack DeJohnette before slamming Cloudflare for breaking TRO's podcast feed. He's changing tires, tubes and all, on a '70 CB350 while considering cool SAE selector ideas (one battery tender for three bikes). Brian's battery-tender advice is met with Robin's half agreement and a bit of eye-rolling.Brian plans a winter of family road miles and rereading Moby-Dick along with Lord of the Rings. He's all cheers 'n' tears about Blackhawk Farms raceway getting a pavement makeover. Eventually he dives into the techno jargon of cylinder count.Both take aim at mushy moto-speak, mocking phrases like "hand tight" and clearing up foot-pounds versus pound-feet. Also, save your chicken strips and knee dragging for clear, actionable coaching. Let's have a sane talk about dual-sport difficulty so that we can all maintain our momentum.Episode Page: https://tro.bike/podcast/2025e29/Music by Rabid Neon and Otis McDonald

In dieser Episode des Life After SAE Podcasts spricht Anne Katrin Tausch über ihren Werdegang als Radiomoderatorin und Journalistin. Sie erzählt von ihren Anfängen im Radio, ihrem Studium an der SAE, ihren Erfahrungen im Öffentlich-Rechtlichen Rundfunk und ihrer Rückkehr zu Energy Sachsen. Zudem gibt sie Einblicke in ihre Tätigkeit als Traurednerin und teilt wertvolle Tipps für alle, die auch mal vor's Mikrofon wollen. LinkedIn: https://www.linkedin.com/in/anne-katrin-tausch-28527814b/Life After SAE auf Instagram: https://www.instagram.com/lifeaftersae/Mehr zu Kurt gibt's hier:https://www.instagram.com/kurt_jonathan_engert/Mehr zu Glen gibt's hier:https://glenschaele.com/linktree

Season 2.6 Episode 6, Chat with Sae the travel philosophy of staying in a safe zone is like a gift to oneself. Gathering with friends is the source of exploring new places第2.6季第6期（英文），和Sae聊稳在安全区的旅行观，如同给自己的礼物，和朋友相聚是探索新地点的源头For more information, you can follow the WeChat public account: willyi_You can also follow personal ins: willyi_更多内容，可以关注微信公众号：不著还可以关注个人ins：willyi_「This Season」I want to know,The role played by travelAnd our relationship with it【关于本季】我想知道，旅行所扮演的角色，以及我们与它的关系

In this episode, Neil Ashton discusses various conferences and workshops in the automotive, aerospace, and machine learning fields. He highlights the importance of these events for networking, education, and staying updated with industry trends. From the SAE and AIAA events to machine learning workshops, Neil provides insights into what attendees can expect and the value of participating in these gatherings.

Tu Le and Lei Xing dive into one of the busiest weeks yet in the global EV world — from corporate drama to policy blueprints shaping the next 15 years.

Amelia Pérez, directora de la SAE by Diario La república

Petro usará oro en la SAE para ayudar a niños en GazaPetro dice que ayudó en el tema Gaza - Israel Petro hablando de negar hijos al presidente de la Constitucional 

On this week's episode of Growing Kentucky's Leaders, we hear from 2025 Kentucky FFA State Star in Agricultural Placement Wade Moore. Through his SAE at Henry County Animal Clinic, Wade has gained real-world experience in animal care, from assisting in surgeries to helping on farm calls.Links:2025 State Star in Ag PlacementHenry County FFAKentucky Ag Development Fund

In this episode, we sit down with Jeremy McCool, founder and CEO of HEVO, a company building wireless charging systems for electric vehicles. Think of a garage-floor charging pad—pull in, align, and your car charges automatically. HEVO has been solving the physics, standards and automotive integration work for over a decade, and now stands at the front line of commercial adoption.HEVO is underway with two major global automakers, including Stellantis (Jeep, Dodge, Fiat, Peugeot, and more), to integrate wireless charging into up to seven EV platforms beginning 2027–2028. This isn't a small bolt-on—the company has achieved UL certification and alignment with SAE wireless charging standards, clearing essential hurdles for true automotive-grade integration.Beyond the OEM opportunity, HEVO is partnering with Steer Tech to enable autonomous parking + wireless charging for fleet yards—a use case that eliminates manual charging attendants and enables round-the-clock operation. Wireless charging isn't just convenient—it's the missing piece for scaling autonomous fleets.HEVO's cost and efficiency discipline makes this more than a vision. The company's target pricing for on-vehicle components aims to be competitive with plug-in equipment, while the 11 kW bidirectional home charger is priced at $1,200, enabling vehicle-to-home (V2H) power during outages. With grid-to-battery efficiency in the low-to-mid 90%, 85 kHz universality, and a 12-inch air gap tolerance, HEVO is designed for scale.The most striking part: once an OEM launches, the curve goes from flat to 50,000+ units in year one—across multiple vehicle programs. HEVO expects to be profitable on hardware and software at volume from day one of scaling production.

In this episode, Jonathan Puu sits down with Ognjen Topić (@topicfight), one of the most respected Muay Thai fighters to ever come out of the United States. Recorded during his visit to Ventura for a seminar, Ognjen shares his journey from graphic designer in New Jersey to Omnoi Stadium Champion in Bangkok, Thailand.We cover:His breakout fight against Neungsiam from Fairtex and why it was a turning point for him.What it was really like to fight legends like Saenchai and face off under the brutal stadium system in Thailand.The harsh realities of the gambling culture, gym politics, and the infamous “Round 6” treatment fighters endure.Why Ognjen quit his steady 9–5 career in design to go all-in on Muay Thai, and how branding, social media, and content creation became critical to sustaining his career.His perspective on ONE Championship's small-glove era, bonuses, and how the sport is changing for better or worse.What authenticity means in Muay Thai, why some fighters fall into “influencer fraud,” and how to truly stand out as a fighter.Reflections on his future as a coach, seminar leader, and what he believes is next for the sport.This is a must-listen for fighters, coaches, and anyone passionate about the culture and evolution of Muay Thai.

Le “syndrome de l'accent étranger” ou SAE est une condition neurologique dans laquelle une personne commence à parler avec un accent étranger qui n'est pas le sien. C'est un syndrome rare mais qui existe bel et bien ! Il peut survenir à la suite de certains types de lésions cérébrales, comme celles causées par un AVC ou des migraines sévères. Environ 15% des cas sont dus à des traumatismes crâniens, c'est ce que relate l'Académie nationale de médecine. Que se passe-t-il dans le cerveau ? Est-ce grave ? Ce syndrome est-il courant ? Écoutez la suite de cet épisode de "Maintenant vous savez". Un podcast Bababam Originals, écrit et réalisé par Laura Taouchanov. Date de la première diffusion : 16/01/2025 À écouter aussi : Qu'est que le Plan Madagascar du IIIe Reich ?  Réduire son empreinte carbone : quels sont les gestes qui comptent vraiment ? Faut-il couper l'électricité pendant un orage ? Suivez Bababam sur ⁠Instagram⁠. Learn more about your ad choices. Visit megaphone.fm/adchoices

Fuera Petro gritan a las afueras del congreso Largas filas para entrar a la cámara ardiente donde tenían a Miguel UribeEl show de Quintero en la Isla Santa Rosa de PerúPetro y su cuento sobre el subsecretario del Dpto de EstadoInvestigación en el caso de Miguel Uribe Petro amenaza con denuncias contra los que hablen de élAgarrón entre la SAE y el Min Educación en Cali Qué pasa con la Pensional 

We need to have a certain level of trust in our fellow man just for society to exist. We extend a deeper faith in our group of friends, but what happens when they don't warrant that trust? A Killer Among Friends explores this and starts off with the tragic death of Trent DeGiuro.Email us: KillerFunPodcast@gmail.comFollow us on Facebook: fb.me/KillerFunPodcastAll the Tweets, er, POSTS: https://x.com/KillerFunPodInstagram: killerfunpodcast

Catherine Anne Davies, also known as The Anchoress, joins Caro C to discuss her preferred recording methods, the decision to invest in her own studio and receiving three consecutive MPG Award nominations.Chapters00:00 - Introduction01:14 - From Multitrack To MPG Nominations04:09 - Balancing Locations And Condensed Hours 08:14 - Old School Recording Techniques11:20 - Recording Piano And Vocals12:51 - Piano Recording Technique14:09 - Re-releasing Communion 18:57 - Last Night From Glasgow21:39 - Favourite Synths24:41 - New Album Project27:20 - Recording Own Vocals32:24 - TeachingCatherine Anne Davies BiogCatherine Anne Davies is a producer, multi-instrumentalist and songwriter originally from Wales. Her artist project, The Anchoress, has seen her twice nominated for the Welsh Music Prize and named an Album of the Year by everyone from Elton John to The Sunday Times, to Record Collector.A three times nominee for the prestigious Music Producers Guild Awards, Catherine's early roots as a classically-trained flautist have seen her journey from her engineering and co-writing credits on Top 20 albums, to recently being appointed as Professor of Music Production and Songwriting at ICMP & SAE.Since winning a PRS Writer/Producer award in 2019,  she has spent much of the last six years on her current obsession with vintage synthesizers, as well as building a growing reputation as a go-to remixer for artists such Bishi, Thea Gilmore, and Roxanne DeBastion. Catherine's recent production projects include the lead radio single for Dot Allison's remix project, producing and mixing on the Hen Hoose Vol. 2 compilation, co-producing her BBC 6 Music supported collaboration with Band Spectra, and producing and mixing the forthcoming Liberty's Mother album.Catherine has previously co-written with and worked alongside a variety of prominent industry names including  Ed Harcourt (Paloma Faith & James Bay),  Liam Howe (Jessie Ware, Marina) and Paul Statham (Kylie, Dido). She has also co-written with artists outside of the alternative rock genre, including MOBO-nominated singer-songwriter/cellist  Ayanna Witter-Johnson  and British rapper  Riz MC .Catherine also joined the line-up of Simple Minds in 2014, appearing on the Big Music (2015)and Walk Between Worlds (2018) albums before departing in 2019.She released her debut solo album Confessions of a Romance Novelist in 2016, a collaborative album In Memory of My Feelings (2020) with Bernard Butler. The Art Of Losing (2021), and Versions (2023).http://theanchoress.co.uk/http://www.facebook.com/theanchoresshttp://instagram.com/theanchoressofficialhttps://www.youtube.com/channel/UCprifKdWotavrgXSy_qelEwCaro C BiogCaro C is an artist, engineer and teacher specialising in electronic music. Her self-produced fourth album 'Electric Mountain' is out now. Described as a "one-woman electronic avalanche" (BBC), Caro started making music thanks to being laid up whilst living in a double decker bus and listening to the likes of Warp Records in the late 1990's. This 'sonic enchantress' (BBC Radio 3) has now played in most of the cultural hotspots of her current hometown of Manchester, UK. Caro is also the instigator and project manager of electronic music charity Delia Derbyshire Day.URL: http://carocsound.com/Twitter: @carocsoundInst: @carocsoundFB: https://www.facebook.com/carocsound/Catch more shows on our other podcast channels: https://www.soundonsound.com/sos-podcasts

In Episode 740 of AwesomeCast, hosts Michael Sorg, Katie Dudas, and Dave Podnar go full geek across Pittsburgh happenings, practical gadgets, and a dash of nostalgia. From the chaotic charm of Picklesburgh to high-tech fire truck cameras, we spotlight local events, incredible DIY tools, and how Pokémon made its way to the Pope. Discover how furries raised nearly $90,000 for a cat rescue, explore Chuck E. Cheese's adult-only arcade concept, and find out if AirPods Pro can double as hearing aids. Plus, LEGO Babylon 5, TSA updates, and more tech-fueled fun.

In Episode 740 of AwesomeCast, hosts Michael Sorg, Katie Dudas, and Dave Podnar go full geek across Pittsburgh happenings, practical gadgets, and a dash of nostalgia. From the chaotic charm of Picklesburgh to high-tech fire truck cameras, we spotlight local events, incredible DIY tools, and how Pokémon made its way to the Pope. Discover how furries raised nearly $90,000 for a cat rescue, explore Chuck E. Cheese's adult-only arcade concept, and find out if AirPods Pro can double as hearing aids. Plus, LEGO Babylon 5, TSA updates, and more tech-fueled fun.

Thanks to our Partner, NAPA Autotech TrainingIn this technical deep-dive, Matt Fanslow tackles the misconceptions surrounding Exhaust Gas Recirculation (EGR) in modern engines. Far from just a NOx-reduction tool, EGR plays a critical role in thermal efficiency, throttling losses, and combustion control. Matt dismantles common myths (like "lean burns hotter") and explains why engineers use EGR—even as technology evolves.Key Topics CoveredEGR's Real PurposeBeyond NOx reduction: How inert exhaust gases slow flame fronts, improve thermal efficiency, and reduce throttling losses.Why lean air/fuel ratios don't burn hotter—but can still cook exhaust valves.Throttling Losses & EfficiencyHow EGR allows wider throttle openings, reducing engine workload and boosting fuel economy.The link between EGR, Atkinson/Miller cycles, and extended combustion push.Internal EGR & Valve TimingModern engines use cam phasing to trap exhaust gases, creating insulating "pockets" that reduce heat loss to cylinder walls.SAE paper highlights: HCCI engines, controlled auto-ignition, and residual gas effects.Why This Matters for TechniciansUnderstanding EGR helps diagnose drivability issues, software updates, and emission failures.Matt's rabbit-hole warning: Complexity is growing, but so are diagnostic opportunities.Notable Quotes"Lean air/fuel ratios burn longer, not hotter—that's why exhaust valves fry.""EGR isn't just about emissions; it's about making the engine work smarter, not harder.""The more you know why engineers do something, the better you'll diagnose it."Resources & ReferencesSAE Papers (Available at sae.org):Lean Burn SI Engines: NOx Control via Air/Fuel Ratio Modulation (2017)Impact of Valve Timing on Cold Start Emissions in GDI Engines (2019)Effects of Valve Timing on Residual Gas, Combustion, and Heat Transfer (2009)Thanks to our Partner, NAPA Autotech TrainingNAPA Autotech's team of ASE Master Certified Instructors are conducting over 1,200 classes covering 28 automotive topics. To see a selection, go to napaautotech.com for more details.Contact InformationEmail Matt: mattfanslowpodcast@gmail.comDiagnosing the Aftermarket A - Z YouTube Channel Subscribe & Review: Loved this episode? Leave a 5-star review on Apple Podcasts and SpotifyThe Aftermarket Radio Network: https://aftermarketradionetwork.com/Remarkable Results Radio Podcast with Carm Capriotto: Advancing the Aftermarket by Facilitating Wisdom Through Story Telling and Open Discussion. https://remarkableresults.biz/Diagnosing the Aftermarket A to Z with Matt Fanslow: From Diagnostics to Metallica and Mental Health, Matt Fanslow is Lifting the Hood on Life.

In this action-packed episode of AwesomeCast, hosts Michael Sorg and Katie Dudas recap their high-tech adventures at the Formula SAE EV event at Michigan International Speedway. From streaming challenges and tech problem-solving to a surprise appearance by a lizard named Hank, it's a wild ride through the world of engineering and media production. Plus, we get geeky with a new LEGO robotics set for kids, Dragon's Lair nostalgia on Netflix, cutting-edge photo apps, and a Godzilla-themed theater experience!

In this powerful episode, Jimmy sits down with former NFL linebacker and BYU standout Sae Tautu for one of the most vulnerable and insightful conversations we've had on the show. From playing in front of 65,000 fans to waking up to the realities of life after football, Sae opens up about the emotional toll of losing his lifelong dream, the struggle with identity, and how he rediscovered purpose through faith, family, and leadership.The two dive deep into topics like navigating depression, masculinity, marriage, grief, and the power of accountability. Sae shares how losing close family members, facing financial hardship, and being a young father forced him to grow in ways he never expected. This isn't just a story about football — it's about the kind of transformation that happens when life forces you to level up.If you're a man navigating tough transitions, searching for meaning, or looking for inspiration to own your truth, this episode will hit home.

Bob Bullock's mixed more hits than a jukebox, with 40+ years in the biz and 50+ gold & platinum credits. He's redesigned his studio three times with Carl Tatz, teaches at Belmont & SAE, and knows the magic of great acoustics, collaboration, and staying ahead of the mix! Get access to FREE mixing mini-course: https://MixMasterBundle.com My guest today is Bob Bullock, an acclaimed engineer and producer who began his career in Los Angeles training under legends like Humberto Gatica, Reggie Dozier, and Roger Nichols. He quickly worked with top acts such as The Tubes, Art Garfunkel, REO Speedwagon, and Chick Corea before moving to Nashville, where he earned over 50 gold and platinum credits with artists like Shania Twain, George Strait, Reba McEntire, and Hank Williams Jr. With a 40-year career spanning engineering, producing, and artist development, Bob has worked with legends like Kenny Chesney, Loretta Lynn, and Keith Urban while also focusing on independent artists worldwide. He now shares his expertise through teaching at Belmont University, SAE, and other institutions. Bob has been a guest on the podcast on episodes RSR055 and RSR369. THANKS TO OUR SPONSORS! http://UltimateMixingMasterclass.com https://usa.sae.edu/ https://www.izotope.com Use code ROCK10 to get 10% off! https://www.native-instruments.com Use code ROCK10 to get 10% off! https://www.adam-audio.com/ https://www.phantomfocus.com/category-s/149.htm https://www.makebelievestudio.com/mbsi Get your MBSI plugin here! https://RecordingStudioRockstars.com/Academy  https://www.thetoyboxstudio.com/ Listen to the podcast theme song “Skadoosh!” https://solo.to/lijshawmusic Listen to this guest's discography on Spotify: https://open.spotify.com/playlist/62mgdopY5MN7Gvta4TAeUK?si=ed9ccbe6500d4529 If you love the podcast, then please leave a review: https://RSRockstars.com/Review CLICK HERE FOR COMPLETE SHOW NOTES AT: https://RSRockstars.com/510

In this special on-location episode of AwesomeCast 735, recorded live from the Hilton Garden Inn in Ann Arbor during the SAE Autodrive Challenge, Michael Sorg, Katie Dudas, and Dave Podnar are joined by Pittsburgh-based cinematographer Scotty Swemba. The crew dives into new tech discoveries including the Bing AI video creator, Apple's latest game studio acquisition, and Katie's OmniBreeze fan obsession. They also explore how Scotty brings a cinematic eye to live productions, including their evolving video work for SAE events. Plus: robot droids that follow you, iPhone filmmaking in Hollywood, and the return of Cat Wars. It's geeky, techy, sweaty (thanks to the heat), and wonderfully weird — exactly what you expect from AwesomeCast. Find more at AwesomeCast.com and SorgatronMedia.com ⸻

Thanks to our Partner, NAPA Autotech TrainingIn this episode, Matt Fanslow dives into listener-submitted questions, covering a wide range of automotive diagnostic and repair topics. From personal influences in the industry to technical advice on exhaust gas analyzers, catalytic converter testing, and ADAS calibrations, Matt shares his insights and expertise.1. Who Do You Try to Emulate?Matt reflects on the mentors and industry leaders who have shaped his approach to diagnostics and repair.TV Doctors vs. Real Mentors: While he jokes about emulating fictional doctors like Hawkeye Pierce, Gregory House, and Perry Cox, Matt credits real-world experts like John Thornton, Randy Burkholder, Jim Kemper, Matthew Ragsdale, Harvey Chan, and John Riegel for their influence.The Value of Deep Research: Matt highlights the importance of studying SAE documents, technical manuals, and foundational books like Internal Combustion Engine Fundamentals by John B. Heywood.Thought Leaders in the Industry: He also mentions Jim Wilson (ScanShare.io), Scott Manna, and others for their diagnostic methodologies and problem-solving approaches.Takeaway: Success in automotive diagnostics comes from continuous learning, leveraging industry resources, and adopting best practices from experienced professionals.2. Exhaust Gas Analyzers – What to Look For?A listener asks about choosing the right exhaust gas analyzer for their shop. Matt breaks down the key features:PC/Android Interface: Essential for graphing gas readings (lambda, air-fuel ratio) over time.Portability: Needed for on-road testing to monitor performance under real driving conditions.Fast Sample Times: Look for analyzers with low transfer delays (under 5 seconds) for accurate real-time data.Cost Consideration: Expect to invest 5,000–5,000–7,000+ for a quality unit. Takeaway: A good exhaust gas analyzer should provide real-time data logging, lambda calculations, and portability for effective diagnostics.3. Testing Catalytic Converters – Temperature vs. PCM DiagnosticsA student questions the validity of using infrared thermometers to test catalytic converters after hearing conflicting advice.PCM Algorithms Are Superior: Modern vehicles use complex oxygen storage calculations—far more accurate than manual temperature checks.Why Temperature Testing Falls Short:A "bad" cat might still pass a temp test.A "good" cat might fail due to external factors (exhaust leaks, sensor issues).Best Practice: Trust OBD-II diagnostics, fuel control verification, and factory procedures over manual methods.Takeaway: Always verify fuel control, exhaust integrity, and PCM data before condemning a catalytic converter.4. ADAS Calibrations – Troubleshooting Static Windshield Camera IssuesA technician struggles with static calibrations for windshield-mounted cameras. Matt offers troubleshooting tips:Check the Windshield Glass: Aftermarket glass is a common culprit for calibration failures.Lighting Conditions:Too much LED glare? Try diffusers or dimming shop lights.Use shipping blankets to reduce reflections on the hood/dash.Target Placement: Ensure the target is positioned per OEM specs—avoid background interference.RTFM (Read the Factory Manual): Always follow OEM procedures for target setup.Takeaway: Calibration issues often stem from glass quality, lighting, or incorrect target alignment—double-check these factors first.Listener Q&A Submission: Have a question for Matt? Email: MattFanslowPodcast@gmail.comContact...

Thanks to our Partner, NAPA Autotech TrainingIn this episode, Matt Fanslow dives into listener-submitted questions, covering a wide range of automotive diagnostic and repair topics. From personal influences in the industry to technical advice on exhaust gas analyzers, catalytic converter testing, and ADAS calibrations, Matt shares his insights and expertise.1. Who Do You Try to Emulate?Matt reflects on the mentors and industry leaders who have shaped his approach to diagnostics and repair.TV Doctors vs. Real Mentors: While he jokes about emulating fictional doctors like Hawkeye Pierce, Gregory House, and Perry Cox, Matt credits real-world experts like John Thornton, Randy Burkholder, Jim Kemper, Matthew Ragsdale, Harvey Chan, and John Riegel for their influence.The Value of Deep Research: Matt highlights the importance of studying SAE documents, technical manuals, and foundational books like Internal Combustion Engine Fundamentals by John B. Heywood.Thought Leaders in the Industry: He also mentions Jim Wilson (ScanShare.io), Scott Manna, and others for their diagnostic methodologies and problem-solving approaches.Takeaway: Success in automotive diagnostics comes from continuous learning, leveraging industry resources, and adopting best practices from experienced professionals.2. Exhaust Gas Analyzers – What to Look For?A listener asks about choosing the right exhaust gas analyzer for their shop. Matt breaks down the key features:PC/Android Interface: Essential for graphing gas readings (lambda, air-fuel ratio) over time.Portability: Needed for on-road testing to monitor performance under real driving conditions.Fast Sample Times: Look for analyzers with low transfer delays (under 5 seconds) for accurate real-time data.Cost Consideration: Expect to invest 5,000–5,000–7,000+ for a quality unit. Takeaway: A good exhaust gas analyzer should provide real-time data logging, lambda calculations, and portability for effective diagnostics.3. Testing Catalytic Converters – Temperature vs. PCM DiagnosticsA student questions the validity of using infrared thermometers to test catalytic converters after hearing conflicting advice.PCM Algorithms Are Superior: Modern vehicles use complex oxygen storage calculations—far more accurate than manual temperature checks.Why Temperature Testing Falls Short:A "bad" cat might still pass a temp test.A "good" cat might fail due to external factors (exhaust leaks, sensor issues).Best Practice: Trust OBD-II diagnostics, fuel control verification, and factory procedures over manual methods.Takeaway: Always verify fuel control, exhaust integrity, and PCM data before condemning a catalytic converter.4. ADAS Calibrations – Troubleshooting Static Windshield Camera IssuesA technician struggles with static calibrations for windshield-mounted cameras. Matt offers troubleshooting tips:Check the Windshield Glass: Aftermarket glass is a common culprit for calibration failures.Lighting Conditions:Too much LED glare? Try diffusers or dimming shop lights.Use shipping blankets to reduce reflections on the hood/dash.Target Placement: Ensure the target is positioned per OEM specs—avoid background interference.RTFM (Read the Factory Manual): Always follow OEM procedures for target setup.Takeaway: Calibration issues often stem from glass quality, lighting, or incorrect target alignment—double-check these factors first.Listener Q&A Submission: Have a question for Matt? Email: MattFanslowPodcast@gmail.comContact...

Innovation isn't a solo act—it's a team sport. And if you're an engineer, the world needs your expertise now more than ever.   SAE International is a place where engineers, dreamers, and doers unite to build a safer, smarter future. Our lively community is made up of cross-disciplinary volunteers who develop life-saving standards, mentor the next generation, and work collaboratively to build a strong and ethical foundation for powerful emerging technologies.   Listen in as Dr. Jacqueline El-Sayed, CEO of SAE International, shares how SAE is setting the standard (literally) for tomorrow's mobility solutions while fostering professional growth and collaboration along the way.   Ready to make an impact and level up your career? Whether you're passionate about safety standards, emerging tech, or mentoring the next generation, volunteering with SAE connects you with a global network of innovators just like you. You'll gain leadership experience, grow your expertise, and help drive real change in mobility and beyond. Learn how you can join the SAE community today!   We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today—a podcast where we discuss emerging technology and trends in mobility with the leaders, innovators and strategists making it all happen—and give us a review on your preferred podcasting platform.   Follow SAE on LinkedIn, Instagram, Facebook, Twitter, and YouTube. Follow host Grayson Brulte on LinkedIn, Twitter, and Instagram.

In AwesomeCast 733, co-hosts Michael Sorg and Dave Podnar dive into a jam-packed week of tech, video games, and behind-the-scenes adventures. Sorg shares a wild look at Sidekick Media Services' most ambitious SAE live stream production yet—from setting up gear at Michigan International Speedway during a tornado watch to integrating wireless cameras and collaborating internationally with Brazilian media teams. The crew also explores Google I/O 2025's latest AI features, Google's new AR glasses prototype, and the surreal promise of Google's Flow video creation tool. Plus, Chachi delivers the latest in gaming news with Waffle House in Tekken, Krispy Kreme's Pac-Man collab, and Epic vs. Apple heating up again over Fortnite. We also get a warm update on Sesame Street's new deal with Netflix and celebrate the culture of motorsport engineering. Stick around for rainbow sightings, bookstore detours, and vintage arcades on the road home. Subscribe and explore more at AwesomeCast.com and SorgatronMedia.com. ⸻ Bulleted Summaries of Topics Covered:

Wireless security takes center stage in this episode of Packet Protector. Jennifer Minella and guests discuss “secure by default” efforts by WLAN vendors; the current state of PSK, SAE, and WPA3; NAC and zero trust; more WLAN vendors adding AI to their products (or at least their messaging); and more. Jennifer is joined by Jonathan... Read more »

Wireless security takes center stage in this episode of Packet Protector. Jennifer Minella and guests discuss “secure by default” efforts by WLAN vendors; the current state of PSK, SAE, and WPA3; NAC and zero trust; more WLAN vendors adding AI to their products (or at least their messaging); and more. Jennifer is joined by Jonathan... Read more »

On this episode of AwesomeCast, the crew returns after a stormy week in Pittsburgh that knocked out power citywide! Sorg, Katie, and Dave catch up on their tech-filled travels, including live streaming Baja SAE Arizona and rock shopping in Tucson. Katie dives into her Star Wars-themed social media marketing for Mancini's Bread on May the 4th, while Dave covers a landmark ruling that now allows Kindle and Epic Games to link to outside stores on iOS, changing Apple's walled garden. We highlight local innovations in 3D printing at Pittsburgh International Airport and discuss Slate's $20K no-frills electric truck. Plus, Chachi gives us the scoop on the GTA VI release date, Google Gemini's Pokémon Blue win, and a surprising new Call of Duty skin. Power Outages & Recovery: Last week's show canceled due to severe Pittsburgh storms and widespread outages. Pittsburgh Marathon: Katie and Dave recap their marathon adventures and Steel Challenge medals. Baja SAE Arizona Recap: Sorg's team live-streams 4-hour Baja Endurance event. Overcame dust storms and wireless interference. Intern John, student volunteers, and Enjoy Wrestling's Scotty Swemba helped make it happen. May the 4th at Mancini's Bread: Katie's Grogu marketing campaign and Star Wars-themed bread humor. Apple App Store Ruling: Kindle now links directly to outside purchases. Impacts Epic Games and Apple's 30% cut model. 3D-Printed Military Parts: Air Force and SAE engineers save costs by 3D printing aircraft drip pans. Local manufacturing efforts at Neighborhood 91 near Pittsburgh Airport. Slate's Minimalist Electric Truck: $20K US-made EV with no paint, touchscreen, or stereo. Ideal for DIY modders and city use. ChachiSays Video Game Minute Highlights : Call of Duty: Seth Rogen announced as new skin in Black Ops 6. AI vs. Pokémon: Google Gemini 2.5 beats Pokémon Blue, edging out Claude AI. GTA VI Release Date: Now officially May 26, 2026, disappointing fans

When Karim Ben Dhia founded Adveez in 2011, the company wasn't focused on airports at all - it was building hands-free access control systems for buildings. Today, with nearly 20,000 GSE units monitored worldwide, Adveez stands at the forefront of a technological revolution transforming ground operations at airports globally.Product and Customer Success Director Matthias Moulinier takes us through this remarkable journey, revealing how their first aviation client simply wanted to prevent competitors from using their equipment on the ramp. That single need quickly expanded into a comprehensive tracking system collecting everything from GPS coordinates to engine hours, shock detection, and battery management data.What makes GSE telematics fundamentally different from standard vehicle tracking? The lack of standardization. While passenger vehicles have universal OBD connections, every GSE manufacturer implements different systems requiring specialized hardware solutions. This technical challenge became Adveez's opportunity to develop purpose-built systems for the unique airport environment.Perhaps most revealing is what the data shows about equipment utilization. Despite ramp operators consistently claiming equipment shortages, the metrics tell a different story - no customer ever utilizes more than 80-85% of their equipment simultaneously. This insight allows procurement teams to make data-driven investments rather than reacting to perceived shortages.Looking forward, Adveez is pioneering innovations like charger management systems to optimize electric GSE infrastructure and camera monitoring to enhance safety. They're also developing AI algorithms that predict maintenance needs based on patterns detected across thousands of operating hours, moving from reactive to predictive operations.As the industry gradually moves toward factory installations rather than field retrofits, Mathias works closely with manufacturers like Oshkosh to integrate these systems during production. However, challenges remain, particularly the lack of standardized data protocols - a topic currently being addressed in IATA and SAE working groups.Curious about the future of GSE management or how these systems might benefit your operation? Visit www.adveez.com or connect with their team on LinkedIn to learn more about this rapidly evolving technology.Looking for reliable and flexible ground support equipment leasing solutions? Look no further than Xcēd! As your trusted partner, Xcēd specializes in tailored operating leases for ground handlers and airlines, offering top-notch equipment and flexible terms to suit your needs. Whether you're seeking the latest electric GSE or traditional equipment, Xcēd has you covered with competitive rates and exceptional customer service. Keep your operations running smoothly and efficiently with Xcēd. Visit xcedgse.com today and soar to new heights with Xcēd Ground Support Equipment Leasing!

Alethea Dunn of the Stewarts Creek FFA Chapter is the 2025 Tennessee FFA State Star in Agriscience. She was announced as the winner at the 97th annual Tennessee FFA Convention. Dunn explains what it means to win this award and describes her SAE on researching hypotonic produce.

In this podcast episode, we dive deep into motorcycle heated riding gear. Riding in cold weather can be uncomfortable without proper gear. Over the years, I've tested a range of heated gear—from budget to premium—and have real-world experience as a police motor officer and personal rider. In this guide, I'll walk you through heated gear options, share insights, and explain why Gerbing gear stands above the rest. 1. The Problem with Cheap Heated Gear White-Labeled & Generic Gear Many low-cost heated gear products, like Mobile Warming, are white-labeled items from platforms like Alibaba. These products appear across Amazon and eBay, rebranded and sold under different names. The Issue: Cheap materials lead to quick failure, poor battery performance, and safety concerns. Flimsy Batteries & Chargers Cheap heated gear often uses low-quality lithium-ion batteries. Problems include: Fire hazard concerns. Poor charge indication (no light or status). Chargers that break quickly. Batteries don't last and will have to be replaced often. Tip: Avoid relying on inexpensive gear for long-term riding needs. 2. Why Gerbing Sets the Standard Unmatched Quality & Reliability Gerbing heated gear is tested, durable, and built for serious riders. Their batteries, chargers, and construction far outperform budget brands. Gerbing 7V Soft Shell Vest Versatile & Stylish: Wearable on motorcycles, outdoor activities, or day-to-day tasks. Battery Performance: High: ~2 hours Medium: ~3-4 hours Design Note: Should fit snugly to maximize heat transfer. 3. My Favorite Heated Gear: Gerbing Heated Shirt The Gerbing heated shirt is my go-to for daily police motor duty and personal riding. Why I Love It: Thin material fits under any jacket.  Versatile enough for any outdoor tasks.  Battery Life: Up to 5 hours on low. Functionality: Easy controls with visible power indicators. SUPPORT US AND SHOP IN THE OFFICIAL LAW ABIDING BIKER STORE 4. Battery-Powered vs. Plug-In Gear Battery-Powered Gear Pros: Portable and versatile for short trips or off-bike use. Cons: Limited runtime; requires carrying extra batteries. Plug-In Gear (Gerbing Heated Jacket Liner) Why Plug-In? Unlimited runtime while riding. Always in my saddlebag, ready for unexpected cold weather. Setup: Plugs into your bike's power system, offering a reliable heat source for long rides. Pro Tip: Use both for maximum versatility! 5. Heated Gloves, Pants, and Socks: Do You Need Them? For riders or bikes without fairings or heated seats, heated gloves, pants, and socks are invaluable. My Setup: Heated jacket liner. Heated grips. Saddlemen Heated Seat. This combination is sufficient for even freezing temperatures. 6. First Gear vs. Gerbing: A Comparison Key Differences Feature Gerbing First Gear Construction Wind-resistant nylon; ripstop shell Standard nylon Redundancy Backup manual controls Limited to lowest heat if remote fails Wireless Control Redundant options available No manual fallback Verdict: Gerbing's durability and redundancy give it the edge for serious riders. CHECK OUT OUR HUNDREDS OF FREE HELPFUL VIDEOS ON OUR YOUTUBE CHANNEL AND SUBSCRIBE! 7. Wireless Controllers for Convenience Manual in pocket controllers can be awkward on the road. Solution: Gerbing's wireless controller. I mount it with velcro on my bike's dash. Setup: The manual controller offers redundant and  is needed to pair with the wireless remote  8. Wiring Your Bike for Heated Gear Option 1: Using OEM Accessory Plugs Newer Harleys (like my 2024 Street Glide) come with accessory plugs. Extension Needed: Use Gerbing's male-to-female extension to make connections accessible. Option 2: Direct Battery Connection Pigtail Setup: Attach Gerbing's pigtail connector to your battery terminals. Safety Tip: Choose the correct fuse size for your gear combination (see Gerbing's fuse chart/comes with your gear). 9. SAE Connections for Adventure Bikes Use an SAE-to-Gerbing adapter for bikes equipped with battery tender leads. This works seamlessly for adventure bikes like my KLR or any other bike for that matter.  10. Final Thoughts Heated gear transforms cold-weather riding, but quality matters. Gerbing delivers unmatched performance, durability, and comfort. Whether you prefer battery-powered or plug-in options, they have solutions for all riding styles. Benefits of Shopping Law Abiding Biker Store No sales tax (outside WA state). Free shipping on orders over $100. Expert customer support from riders who understand your needs. Explore Heated Gear Options Today Browse the Law Abiding Biker Store to find the best heated gear for your riding needs. Stay warm, ride longer, and keep enjoying the journey! [Shop Heated Gear Now] NEW FOR PURCHASE VIDEO RELEASED (TWO-PART DOCUMENTARY): Journey to the Hottest Place on Earth: Our Motorcycle Adventure to Death Valley-Part 1 of 2 California Dreams to Desert Extremes: Our Epic Motorcycle Journey-Part 2 of 2 Sponsor-Ciro 3D CLICK HERE! Innovative products for Harley-Davidson & Goldwing Affordable chrome, lighting, and comfort products Ciro 3D has a passion for design and innovation Sponsor-Butt Buffer CLICK HERE Want to ride longer? Tired of a sore and achy ass? Then fix it with a high-quality Butt Buffer seat cushion? New Patrons: Derrick Galan of Regina, Canada Scott Bendall of Garden City, Idaho Jeffrey Szarley of Kouts, Indiana If you appreciate the content we put out and want to make sure it keeps on coming your way then become a Patron too! There are benefits and there is no risk. Thanks to the following bikers for supporting us via a flat donation: Ryan Lambright of Shipshewana, Indiana Terry Graham of Culpeper, Virginia Quinn Smith of Albuquerque, New Mexico HELP SUPPORT US! JOIN THE BIKER REVOLUTION! #BikerRevolution #LawAbidingBiker #Bikaholics #RyanUrlacher