Podcasts about SAE

DTI - FING Hydrogen Racing, estudiantes de ingeniería representarán a Uruguay en la fórmula SAE by En Perspectiva

Editor's note: In our first BioHub pod with Priscilla and Mark they discussed their acquisition of EvoScale, led by Alex Rives, who is now Head of Science at BioHub. With ESM-1 they trained language models on millions of protein sequences drawn from across life, with a simple “next token” objective: predict the amino acids that have been randomly masked out, based on the context of the rest of the sequence. But they soon found that these models also learned biological structure and function, including properties the model had never been explicitly shown AND that this ability scales predictably with compute, leading to ESM2 and ESM3.Today, Alex announced ESMFold 2, an open scientific engine to power prediction, design, and discovery across protein biology.Building on Cryo-EM data (discussed in the CZI pod), ESMFold2 reports state of the art performance on protein interactions, especially antibodies, a critical modality for therapeutics, and evidence that inference time scaling is also working across five targets in cancer and immunology.In a nod to that other famous AI x protein folding project, they are also releasing an atlas of 6.8 billion proteins, and 1.1 billion predicted structures, which you can play around with on their website. We are honored to work with them for this huge release!One of the refrains we've heard on the Science pod has been that protein folding, materials design, cellular biology, etc. are very different problems from Language Modeling. They definitely are. Yet Alex Rives and the ESM team at BioHub just released a preprint and model, demonstrating that vanilla BERT-like transformer models trained on sufficiently large and diverse data sets can beat specialized models like AlphaFold3 on some of the hardest protein-related problems. Andrew White had a great segment in our first LS-Science episode that explained how mind blowing AlphaFold2 was when it was released in 2020: it suddenly solved problems on a GPU on your desktop that DESRes had built custom-ASIC supercomputer clusters to solve. John Jumper and Demmis Hassabis received the Nobel Prize in Chemistry for this work.AlphaFold2 took advantage of an very clever observation: if multiple species co-evolve pairs of mutations, this implies that the mutations correspond to parts of the protein that are close in 3d space. This is usually shorthanded as MSAs (multi-sequence alignments), and is the key insight which makes AlphaFold2 so effective.Like other inductive biases, however, it hurts generalization.Scale-pilled before it was coolIf you take a look at the timeline for scaling laws for LLMs and release of structure prediction models, the ESM team notably doubled down on their MSAs-be-damned approach after AlphaFold2 released. This obviously requires a great deal of belief in the scale hypothesis.Why the conviction?ESM developed at a time when many of the scaling laws and the “Bitter Lesson” were proving increasingly correct. AlphaFold2's wild success must have been both exciting and bitterly disappointing. But using MSAs mean that the model is is dependent on training data that contains MSAs in order to be accurate in a given domain. For things like antibodies that don't have MSAs to train on, AlphaFold tends to do poorly.ESM takes a different approach: learn the relationship between different proteins by unsupervised training on as much diversity as you can find (sound familiar?) and then correlate that back to structures know from the Protein Data Bank (PDB) and other sources. In other words, a World Model.World Model for proteins“World Model” is a hype term that I define like this:Use unsupervised training to learn abstract patterns from the data:* The abstraction should be semantic - novel constructions represent things that obey the rules of the real world* The abstraction should be compositional - recombining different patterns leads to novel and often valid constructions* The abstraction should support generalization - it predicts things in the real world it wasn't trained on Once you have a world model, you can attach “heads” to it for downstream tasks: predict properties of a protein, decompose its functional features, or search the representation for proteins that meet design criteria. The two big models BioHub just released under MIT license map directly onto this:* World model → ESMC (a model trained on 2.8 billion sequences)* Structure-prediction head → ESMFold2One of the interesting ways the world model can “predict things” is to generate proteins sequences and then measure the predicted properties, such as binding affinity, in the lab. Alex talks in the episode about validating some of the harder molecules they predicted in the wet-lab. Very cool!Another way is to use mech-interp techniques such as Sparse Auto Encoders (SAEs) to extract semantic features from your model, and then find novel features that predict unknown biology. I won't spoil this part for you: it was one of the highlights of the episode for me!A cell is a computerWe have all heard that genes are like computer programs, but usually the analogy fizzles after that. Of course genes are transcribed into RNA and RNA is translated into proteins, so genes are programs for building proteins, but that carries the analogy only to “binary digits are programs.” Here's a better analogy: you can think of the cell nucleus as a storage device / storage controller, the ribosome as a JIT-compiler and runtime, and the semantic features that we learn from our world model via SAEs as functions, proteins as processes that interact together in workflows (signalling pathways) to produce behaviors and outputs (phenotypes). Like functions, the SAE features have a hierarchical composition from local, secondary and tertiary structures (mimicing protein structure), but also motifs that are conceptual, such as membrane integrations, disordered regions and disulfide bonds. As we learn to compose these features we into novel protein designs, we move further towards programmable biology. Alex goes into much more detail about this in the episode, as well as:* Principles for new data collection* BioHub's vision* Modeling the cellEnjoy!Full Video podcastplease like and subscribe!* X: https://x.com/alexrives* LinkedIn: This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

Hello and Welcome to the DX Corner for yourweekly Dose of DX. I'm Bill, AJ8B.The following DX information comes from Bernie, W3UR, editor of the DailyDX, the WeeklyDX, and the How's DXcolumn in QST. If you would like a free 2-week trial of the DailyDX, your only source of real-time DX information, just drop me a note at thedxmentor@gmail.comXT - Burkina Faso – Harald, DF2SWO,goes again to Burkina Faso using the callsign XT2AW, until May 19.  Harald plans to be on HF and the QO-100 satellite and he welcomes skeds.   CN – Morocco - CN2NQV is the call for F8NQV who is QRV until July 11.  The QTH is the town of Sidi Rahal Chatai, on the Atlantic Ocean, 70 kilometers south of Casablanca.   Pascal's gear runs 100 watts to a Diamond vertical on the rooftop, about 15 meters above ground level. 5Z - Kenya  - 5Z4/MM0ZBH is QRV Holiday Style until June 15, with 100 watts and wire antennas.  QSL via the MM0ZBH home QTH, but his first choice is Logbook of the World foryour request.  Direct is SAE, no USD or IRC needed.  Paul says"I am happy to pay return postage." A6 - United Arab Emirates (UAE) - Many A60PE/##calls will be on the air as part of a national campaign of pride,"Proud of the Emirates."  Flag Day and Union Day (National Day) are popular national pride days.  The current event goes through May 31.  A3 – Tonga - JH3QFL, Takio, will operate as A31AA from Tongatapu Island, Tonga between May 14–22, 2026, onthe 80m–6m bands. QSL cards are available via SASE, and QSOs will be uploaded to LoTW. T8 – Palau - T88IL, T88JH and T88KY will be an operation May 21-24, ops JF3PLF, JR3QFB and JA1MFR, from Koror.  Masa, Yoshi, and Masa will be on 160-6M SSB, CW and digital. QSL details are on QRZ.com.  ZC4 - UK Sovereign Base Areas on Cyprus - G4WXJ, Dave, will operate as ZC4RH from Dhekelia (KM64ux) between May 24 and 30, using 100 watts with Yaesu 857D and Xiegu X6100 radios. He will be active on CW, SSB, FT8, and FT4 modes across 40 to 6 meters, using dipoles and EFHW antennas. 3B9 - Rodrigues I - UR9IDX, Ivan, is QRV until June 1st, as 3B9IDX from Rodrigues Island. His operations will focus on HF bands, primarily using CW and some SSB, but not FT8. QSLdirect only to his address in Madeira Island, Portugal. JW – Svalbard - G1VAQ, Tom, will be briefly operating as JW/G1VAQ from Svalbard in May, using portable QRP (5W)CW on 20 meters. He asks for patience with his CW and notes that QSOs will be confirmed via LoTW and QRZ.com after his return to the UK. OX – Gree nland - OZ1DJJ, Bo, will be active as OX3LX from Aasiaat Island until May 22nd. This activity is part of a work trip, not a DXpedition, so limited radio contacts are expected. 6Y – Jamaica - KQ4PGV, Bill, is traveling to Jamaica from May 31 to June 8 for an anniversary trip and will operate as KQ4PGV/6Y on the radio when possible. Although experienced with POTA and SOTA, he is new to DXing and will be using an IC-705, tuner, and an amp (either 100W or 50W). He plans to activate parks for POTA using FT8 and Ham2kPortable Logger. CP – Bolivia - Team CP7DX has released some details of the upcoming DXpedition. They plan to be QRV from Tarija May 26 to June 6, including the CQ WW WPX CW weekend. The rest of the time they will do SSB, CW and FT8, 160-6M and EME on 144 and 432 MHz. QSL direct to LU1FM and Club Log OQRS too. PJ4 – Bonaire - WA7RAR, Chris, as PJ4CB will be there again May 27 to June 8, SSB and CW, 20-10M and from POTAsites on the island.  4K – Azerbaijan - The first ever POTA activation from Absheron National Park, AZ-0004 is May 28. The 4K0T“DXpedition and Contest Team” is going, joined by the ARAS, the Azerbaijan Radio Amateurs Society. They say the park is remarkable, on the Caspian Sea. It is grid LN50eg. They plan HF SSB and will have live updates, photos, logs and QSL info as things unfold.

Rusko pokračuje v masívnych útokoch na ukrajinské mestá a civilnú infraštruktúru, zatiaľ čo Ukrajina rozbieha vlastnú strategickú kampaň proti ruskej ekonomike a logistike. Generál Pavel Macko analyzuje vývoj na bojisku, meniacu sa ruskú taktiku aj otázku, či Ukrajina preberá iniciatívu. V druhej časti sa venujeme napätiu v Hormúzskom prielive, tichej angažovanosti Saudskej Arábie a SAE v konflikte s Iránom, situácii v Izraeli, Gaze a Libanone či možným predčasným voľbám v Izraeli. Rozoberieme aj Ficovu cestu do Ruska, zákulisie samitu NATO a významnú štátnu návštevu Donalda Trumpa v Číne, kde sa rokovalo o Iráne aj Taiwane.

Hello and Welcome to the DX Corner for yourweekly Dose of DX. I'm Bill, AJ8B.The following DX information comes from Bernie, W3UR, editor of the DailyDX, the WeeklyDX, and the How's DXcolumn in QST. If you would like a free 2-week trial of the DailyDX, your only source of real-time DX information, just drop me a note at thedxmentor@gmail.comXT - Burkina Faso – Harald, DF2SWO, goes again to Burkina Faso using the callsign XT2AW, until May 19. Harald plans to be on HF and the QO-100 satellite and he welcomesskeds.  CE0 - Juan Fernandez - 3G0Z is the call for XQ7IR, Felipe, when he goes later this month.  His call will be XR0Z when he's on Alejandro Selkirk Island, SA-101, a possible side trip for 24-36 hours.  His gear has been sent ahead successfully, from Valparaiso to Juan Fernandez Island. CN – Morocco - CN2NQV is the call for F8NQV who is QRV until July 11.  The QTH is the town of Sidi Rahal Chatai, on the Atlantic Ocean, 70 kilometers south of Casablanca.Pascal's gear runs 100 watts to a Diamond vertical on the rooftop, about 15 meters above ground level. 5Z - Kenya  - 5Z4/MM0ZBH is QRV Holiday Style until June 15, with 100 watts and wire antennas.  QSL via the MM0ZBH home QTH, but his first choice is Logbook of the World foryour request.  Direct is SAE, no USD or IRC needed.  Paul says"I am happy to pay return postage." A6 - United Arab Emirates (UAE) - Many A60PE/##calls will be on the air as part of a national campaign of pride,"Proud of the Emirates."  Flag Day and Union Day (National Day) are popular national pride days.  The current event goes through May 31.  TF – Iceland - TF/WE9G, Rikk,will again be traveling, this time to Borg, Iceland, May 10-19, IOTA EU-021 and grid HP94ob.  He will have three radios on, a pair of IC-7300 radios and an IC-705, to a homebrew vertical, a tunable vertical, and a G5RV-E.  He will do 160-6, mostly FT8/4/2 "with some SSB and CW."  A Park on the Air, POTA, is a possibility, depending on his local transportation there.  QSL direct or bureau to WE9G and TF/WE9G on Club Log OQRS, QRZ and LoTW.  He plans real time log uploads and also Club Log livestream.    T8 – Palau - T88IL, T88JH and T88KY will be an operation May 21-24, ops JF3PLF, JR3QFB and JA1MFR, from Koror.  Masa, Yoshi, and Masa will be on 160-6M SSB, CW and digital. QSL details are on QRZ.com.  ET – Ethiopia - DL9WVM, Ulli, says he has two more weeks in Addis, and is QRV on CW from ET3AA as time permits.  He is there visiting family.  W9XY, Bob, say he may do some remote operating from that station, when DL9WVM is not operating.  K4ZW, Ken, will also be there, next weekend.  QSLs for K4ZW operations will go to N2OO as usual. ZC4 - UK Sovereign Base Areas on Cyprus - G4WXJ, Dave, will operate as ZC4RH from Dhekelia (KM64ux) between May 24 and 30, using 100 watts with Yaesu 857D and Xiegu X6100 radios. He will be active on CW, SSB, FT8, and FT4 modes across 40 to 6 meters, using dipoles and EFHW antennas. TK – Corsica - F4FTV, Fabrice, will operate as TK/F4FTV from Porto-Vecchio, Corsica, from May 9 to 16, using SSB and digital modes. QSL is available via F4FTV and LoTW after three months.OX – Greenland - TF1OL, Olafur, plans to be QRV from Nuuk, Greenland from May 10 to 17. 3B8 - Mauritius & 3B9 - Rodrigues I - UR9IDX, Ivan, recently operated from Reunion Island (FR/UR9IDX, Mayotte (FH/UR9IDX)  and Comoros (D60DX), is QRV as 3B8IDX until May 16 from Mauritius and as 3B9IDX (May 18-June 1) from Rodrigues Island. His operations will focus on HF bands, primarily using CW and some SSB, but not FT8. QSL direct only to his address in Madeira Island, Portugal.

From building Applied Intuition from YC-era autonomy tooling into a $15B physical AI company, Qasar Younis and Peter Ludwig have spent the last decade living through the full arc of autonomy: from simulation and data infrastructure for robotaxi companies, to operating systems for safety-critical machines, to deploying AI onto cars, trucks, mining equipment, construction vehicles, agriculture, defense systems, and driverless L4 trucks running in Japan today. They join us to explain why “physical AI” is not just LLMs on wheels, why the real bottleneck is no longer model intelligence but deployment onto constrained hardware, and why the future of autonomy may look less like one-off demos and more like Android for every moving machine.We discuss:* Applied Intuition's mission: building physical AI for a safer, more prosperous world, powering cars, trucks, construction and mining equipment, agriculture, defense, and other moving machines* Why physical AI is different from screen-based AI: learned systems can make mistakes in chat or coding, but safety-critical machines like driverless trucks, autonomous vehicles, and robots need much higher reliability* The evolution from autonomy tooling to a broad physical AI platform: starting with simulation and data infrastructure for robotaxi companies, then expanding into 30+ products across simulation, operating systems, autonomy, and AI models* Why tooling companies came back into fashion: Qasar on why developer tooling looked unfashionable in 2016, why Applied Intuition still bet on it, and how the AI boom made workflows and tools central again* The three core buckets of Applied Intuition's technology: simulation and RL infrastructure, true operating systems for vehicles and machines, and fundamental AI models for autonomy and world understanding* Why vehicles need a real AI operating system: real-time control, sensor streaming, latency, memory management, fail-safes, reliable updates, and why “bricking a car” is much worse than bricking an iPad* Physical machines as “phones before Android and iOS”: Peter explains why today's vehicle and machine software stack is fragmented across many operating systems, and why Applied Intuition wants to consolidate the platform layer* Coding agents inside Applied Intuition: Cursor, Claude Code, internal adoption leaderboards, and how AI tools are changing engineering workflows even in embedded systems and safety-critical software* Verification and validation for physical AI: why evals get harder as models improve, how end-to-end autonomy changes simulation requirements, and why neural simulation has to be fast and cheap enough to make RL practical* From deterministic tests to statistical safety: why autonomy validation is shifting from binary pass/fail requirements toward “how many nines” of reliability and mean time between failures* Cruise, Waymo, and public trust: Qasar and Peter discuss why autonomy failures are not just technical issues, how companies interact with regulators, and why Waymo is setting a high bar for the industry* Simulation vs. reality: why no simulator perfectly represents the real world, how sim-to-real validation works, and why real-world testing will never disappear* World models for physical AI: hydroplaning, construction equipment, visual cues, cause-and-effect learning, and where world models help versus where they are not enough* Onboard vs. offboard AI: why data-center models can be huge and slow, but onboard vehicle models need millisecond-level latency, low power, small size, and distillation-like efficiency* Why physical AI is not constrained by model intelligence alone: the hard part is deploying models onto real hardware, under safety, latency, power, cost, and reliability constraints* Legacy autonomy vs. intelligent autonomy: RTK GPS in mining and agriculture, why hand-coded path-following worked for decades, and why modern systems need perception and dynamic intelligence* Planning for physical systems: how “plan mode” applies to robotaxis, mining, defense, and multi-step physical tasks where actions change the state of the world* Why robotics demos are not production: the brittle last 1%, humanoid reliability, DARPA Grand Challenge-style prize policy, and the advanced engineering gap between research and deployment* Applied Intuition's hard-earned lessons: after nearly a decade, Peter says they can look at a robotics demo and predict the next 20 problems the company will hit* Qasar's advice to founders: constrain the commercial problem, avoid copying mature-company strategies too early, and remember that compounding technology only matters if you survive long enough to see it compound* Why 2014 YC advice may not apply in 2026: capital markets, AI company dynamics, and the difference between building in stealth with a deep network versus building as a new founder today* What Applied is hiring for: operating systems, autonomy, dev tooling, model performance, evals, safety-critical systems, hardware/software boundaries, and engineers with deep curiosity about how things workApplied Intuition:* YouTube: https://www.youtube.com/@AppliedIntuitionInc* X: https://x.com/AppliedInt* LinkedIn: https://www.linkedin.com/company/applied-intuition-incQasar Younis:* X: https://x.com/qasar* LinkedIn: https://www.linkedin.com/in/qasar/Peter Ludwig:* LinkedIn: https://www.linkedin.com/in/peterwludwig/Timestamps00:00:00 Introduction: Applied Intuition, Physical AI, and 10 Years of Building00:01:37 Physical AI vs. Screen AI: Why Safety-Critical Changes Everything00:02:51 The Origin Story: Tooling, YC, and the Scale AI Comparison00:05:41 The Three Buckets: Simulation, Operating Systems, and Autonomy Models00:11:10 Hardware, Sensors, and the LiDAR Question00:14:26 The Operating System Layer: Why Vehicles Are Like Pre-Android Phones00:19:13 Customers, Licensing, and the Better-Together Stack00:21:19 AI Coding Adoption: Cursor, Claude Code, and the Bimodal Engineer00:26:41 Verifiable Rewards, Evals, and Neural Simulation00:31:04 Statistical Validation, Regulators, and the Cruise Lesson00:40:25 World Models, Hydroplaning, and Cause-Effect Learning00:43:34 Onboard vs. Offboard: Latency, Embedded ML, and Distillation00:50:57 Plan Mode for Physical Systems and Next-Token Prediction Universally00:53:04 Productionization: The 20 Problems Every Robotics Demo Will Hit00:58:00 Founder Advice: Constraints, Compounding Tech, and Mature-Company Mimicry01:05:41 Hiring Philosophy: Hardware/Software Boundary and Engineering Mindset01:08:50 General Motors Institute, Education, and the Curiosity MindsetTranscriptIntroduction: Applied Intuition, Physical AI, and 10 Years of BuildingAlessio [00:00:00]: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space.Swyx [00:00:10]: And today we're very honored to have the founders of Applied Intuition, Qasar and Peter. Welcome.Qasar [00:00:17]: You guys really know how to turn it on to podcast mode. That was, you guys are real pros at this.Qasar [00:00:23]: They were just joking around right before this, and then they flipped it pretty quick.Alessio [00:00:29]: Oh, yeah, it's good to have you guys. Maybe you just wanna introduce yourself so people know the voice on the mic and they'll know what they're hearing.Peter [00:00:33]: Oh, sure. Yeah, I'm Peter Ludwig. I'm the co-founder and CTO of Applied Intuition.Qasar [00:00:38]: And my name is Qasar Younis. I am the CEO and co-founder with Peter.Alessio [00:00:42]: Nice. Can you guys give the high-level overview of what Applied Intuition is? And I was reading through some of the Congress files, when you went out there, Peter, and eighteen of the top twenty global non-Chinese automakers, you two guys, you have customers in agriculture, defense, construction. I think most people have heard of Applied Intuition tied to YC when it was first started, and then you were kinda in stealth for a long time, so maybe just give people the high-level overview of what it is today, and then we'll dive into the different pieces.Peter [00:01:10]: Yeah. So at Applied Intuition, our mission is to build physical AI for a safer, more prosperous world. And so we work on physical AI for all different types of moving systems, everything from cars to trucks to construction and mining equipment, to defense technologies. And we're a true technology company, so we build and sell the technology, and we sell it to the companies that make the machines. We sell it to the government, really anyone that wants to buy a technology to make machines smart.Physical AI vs. Screen AI: Why Safety-Critical Changes EverythingQasar [00:01:38]: Yeah. And I think in the broader AI landscape, a lot of the focus, rightfully so in the last, three years has been on large language models, and so everything fits in a screen. Like, whether it's code complete products or things like that. And what's different about us is we're deploying intelligence onto a lot of things that don't have screens. they're physical machines. There are sometimes screens within the cabin or for example of a car or a truck or something like that, but most of the value we provide is putting intelligence that is in safety critical environments. So that those two words are really important because learn systems can make mistakes if you're asking for, like, some, so something like, “Tell me about these podcast hostsQasar [00:02:28]: that I'm about to go meet.” But you can't do that obviously when you run, like, as an example, we run driverless trucks in Japan right now, as we speak. We can't have errors. Those are L4 trucks. Yeah.Alessio [00:02:40]: Yeah. Was that always the mission? I remember initially, I think people put you and Scale AI very similarly for some things about being kinda like on the data infrastructure side of things. What was the evolution of the company?The Origin Story: Tooling, YC, and the Scale AI ComparisonPeter [00:02:51]: Well, from the very beginning, we always wanted to, really be a technology company that helped generally push forward the industrial sector. And so we started off working in autonomy. Our very first customers were robotaxi companies. And we started off doing a lot of work in simulation and data infrastructure. And then over the years, we've expanded our portfolios. Now we have, over thirty products, and it's a pretty broad technology play within the landscape of physical AI.Qasar [00:03:19]: Yeah, I think the Scale reason is because we're all YC Universe companies. But it was a very different company. Scale, was, is more of a services company, data labeling company fundamentally. We started and still are, do a lot of tooling. So like, you think developer tooling is now in vogue again, thanks to the AI boom. But honestly, ten years ago, it was out of vogue. It w Like, doing a tooling company in 2016, 2017 was not, like, the thing to do because, I don't know if you remember, the VCs generally, their views was that toolings are They're just workflows, and workflows ultimately are not really interesting. And we've gone and come, full circle with that. But when we started the company, our kind of it's kinda like in the periphery of what the company wants to be. It was like, from our earliest days, like, we wanna deploy software on physical machines, like on cars and on trucks and things like that. And obviously, we didn't know that the transformer boom was gonna happen. We didn't know that autonomy systems would become end-to-end. Those things we didn't know. And why that's important when autonomy systems become end-to-end, it is just now those models can be generalized to, multiple form factors. And so back nine, ten years ago, tooling was a great way, and still is a great way to, build the technology and sell technology to our end customers, a lot of them who wanna build this stuff themselves. And so we just offer like a spectrum of solutions from you can just use like one part of a development suite of tools all the way to buying the full thing. The way to think about the company, or at least the way we think about the company is, as Peter said, a technology provider. It's kinda like, what NVIDIA does or what an AMD, but we just don't do chips.Qasar [00:05:06]: We don't do silicon. But we're a technology provider fundamentally. And I think even, we used to joke when we started the company, like, we're not the guys to build, like, Instagram. Like that was just towards That's not our That's just not us in a most fundamental way. IAlessio [00:05:20]: You have thoughts.Qasar [00:05:21]: Yes.Qasar [00:05:22]: Well, it's, it's I mean, I think it's just like what And I mean, we worked on Maps and stuff, Google Maps. Consumer products are extremely difficult for a lot of different reasons. It just, I think doesn't scratch the itch. I think we're like Michigan guys who are kind of more of that traditional engineering kind of a realm, or lineage. we used to jokeThe Three Buckets: Simulation, Operating Systems, and Autonomy ModelsPeter [00:05:41]: I gotta say, though, what was clear ten years ago was that there was so much more that was possible with software and AI in vehiclesPeter [00:05:47]: and that was generally the space that we started in ten years ago.Peter [00:05:51]: And the precise path that we've taken over the years, I think we've been strategic, and we've adjusted to make sure that we're actually building stuff that's valuable to the market. And like, the technology has changed so much. Like our own technology stack has completely changed, I would say, roughly every two years. And so now we've probably done, let's say, four complete evolutions of our own technology stack. And I sort of see that cadence roughly keeping up.Peter [00:06:13]: And so the way even we think about engineering is almost on this two-year horizon, we're preparing ourselves that, hey, like, we wanna invest the appropriate amount, but then also be very dynamic as the research gets published and as our research team figures out new advancements and adapting to that.Qasar [00:06:27]: Yeah. One thing that has been consistent is the type of people we've, we've recruited. It's engineers who are fall into the sometimes very traditional, like, GoogleQasar [00:06:38]: -gen suite, but way different from, other companies. We are hiring folks who really know the intersection of hardware and software, who know really low-level systems. Obviously, traditional ML researchers and folks who've, actually, put ML systems into production. That's been pretty consistent. I think that, like, you look at the mix of our engineering, eighty-three percent of the company is engineering, so it's, like, a giant list.Qasar [00:07:05]: A lot of engineers.Alessio [00:07:06]: Which, by the way, a thousand engineersQasar [00:07:07]: Yeah. A thousand engineers.Alessio [00:07:08]: that's on your website, so I imagine it's up to date.Qasar [00:07:11]: It is, it is up to date, yes. Yes.Alessio [00:07:12]: okay. And then forty-plus founders.Qasar [00:07:15]: Yeah. We would tend to also, This was more luck than strategy. But we've recruited a lot of ex-founders. It's been a great place for founders, YC and non, ‘cause obviously I know a lot of the YC folks. It's kind of like we recruit a lot of Google people.Qasar [00:07:33]: For them to exercise both their technical and non-technical skills because, we're, we're, we're on the applied side. We have a research team that we do fundamental research, we publish, and we've, we've had great traction there. But fundamentally, the business wants to take this intelligence and deploy it into production and there's, like, a certain type of person that's more interested in that.Alessio [00:07:54]: Yeah. You mentioned the tech stack, Peter, so I just wanted to give you some rein to just go into it. I'm interested in where Wayve Nutrition, starts and ends in some sense, what won't you do? What, do you do that's common among all the verticals that you cover?Peter [00:08:10]: There's a few buckets of work that we do, and we've been at this for almost ten years now, so the technology's pretty broad. But we got startedQasar [00:08:17]: Yeah, with a thousand engineers, like, you could work on lots of things.Peter [00:08:19]: There's lots of stuff, yeah, espe-especially with AI tools to help.Peter [00:08:22]: So we got our start in simulation and simulation tooling and infrastructure. And so generally, if you're trying to build a very complex software system that involves moving machines, you need to test that, and the best way to test it is it's a combination of virtual developments, a simulation, and then also obviously real world testing.Peter [00:08:39]: And then there's a very careful process of that correlation between the simulation results and the real world results and ensuring that the simulator is in fact accurate to that. Simulation's a very deep topic.Peter [00:08:49]: We have a whole suite of products in that, and we could talk for many hours about that specifically. But that is one part of what we do as a company. Reinforcement learning as a subpart of that is also super critical. I think a lot of the a lot of the best advancements happening in a lot of these AI systems right now in some way relate to reinforcement learning, and with now we have lots of compute, and you can do tons of interesting things for reinforcement learning. The second bucket of work that we do is on operating systems technology. true operating systems. Like, think about, schedulers and memory management and middleware and message passing and highly reliable networking and data links. Like, the reality is, if you want to deploy AI onto vehicles, you need a really good operating system. And when we were getting deeper into that space, there wasn't really anything that we were happy with.Peter [00:09:39]: Like, things existed, absolutely, and we were using what was available in the market, and as an engineering organization, we roughly realized these things aren't great. We think we can do this better, and so let's, let's build something. And that was then the that was the moment of inspiration that started our operating systems business, which is now a very real business for us. And in order to write and run great AI, you need a great operating system, and so that-that's what got us into that. And then the third bucket that we work on, it's, it's true fundamental AI technology. Models, we do a lot of work in, as mentioned, the foundational research, but then the also the world models and the actual autonomy models that are running on these physical machines, and that's across cars, trucks, mining, construction, agriculture, and defense, and so that's both land, air, and sea.Qasar [00:10:31]: And also, a smaller subsector of that third bucket is the interaction of humans with those machines.Qasar [00:10:38]: So that's a multimodal, experience. Historically, if you're moving a dirt mover or any of these machines, there are, like, buttons you press, whether they're actual physical tactile buttons or something like a touch screen. That's just That fundamentally is changing to where you're just talking to the machine and the machine and you're teaming with the machine.Alessio [00:10:58]: Voice?Qasar [00:10:59]: Yeah, voice, absolutely, yeah.Alessio [00:11:00]: Oh.Qasar [00:11:00]: And also the machine just being aware of who is in the cabin, what their state is. you can think from a safety systems perspective, the most simple version of this is, like, the driver is tired, right? They're, they're if you get those alerts when you're driving your car and saysHardware, Sensors, and the LiDAR QuestionQasar [00:11:15]: -maybe take a coffee break, that take that times, a couple of order of magnitudes up. But this concept of teaming man and machine is important. When you think about running agents or just running, different instances of, Claude and doing work for you in the background, you can take that analogy out, almost copy and paste and put it into, like, a farm, where you have a farmer who's running a number of machines. So where they interact with the machine is where there's maybe a critical decision or a disengagement or something like that, but generally speaking, the agent on the physical machine is running and making decisions on the behalf of the farmer until there's something maybe critical. And that's also what we work on. So that's not pure autonomy. It's a little bit of a mix, but it falls under, autonomy. In the automotive sense, that's typically defined in SAE levels as an L2++ systemQasar [00:12:05]: -with a human in the loop. But just take that idea, to other verticals.Alessio [00:12:09]: Yeah. You've not mentioned hardware at all, like sensors or obviously we you mentioned you don't do chips. I think even in AV there's, like, a big, cameras versus lidars. Like, what are, like, in your space maybe some of those design decisions that you made, and are they driven by the OEM's ability to put things on the machinery? And like, how much influence do you guys have on co-designing those?Peter [00:12:32]: Yeah. So we don't make sensors. Like, we're, we're not a manufacturer. Obviously, we use a lot of sensors in our autonomy products. in terms of what actually goes on the vehicles, we have a preferred set of sensors that we, let's say fully support, and then our customers, they can sort of choose from those. And obviously if there's a very strong opinion on supporting something else, we'll add that to the platform as well. And the lidar question is at this point sort of the age-old,Peter [00:12:59]: topic in autonomy, and the state of the industry right now is lidar is hands down a useful sensor, specifically for data collection and the R&D phase of autonomy development. if you see, for example, a Tesla R&D vehicle, it actually has lidar on itPeter [00:13:17]: to this day, right? In the Bay Area we see these. you'll see, like, Model Ys or Cybercab that have lidars on them just driving around. So it's, it's useful because it gives you per pixel depth information. So if you can pair a lidar with a camerand you can say that, well, this camera's looking this direction, this lidar's looking this direction, and now for each pixel of the camera I can see how far away is that pixel. you can actually then use that as a part of your model training, and then the that depth information then becomes a learned, a learned state of the camera data. And then when you're doing the production system, you can now remove the lidarPeter [00:13:52]: and now you can actually get depth with just the camera. And so that difference between, like, a highly sensored R&D vehicle and then the down-costed production vehicle, we use that across our whole portfolio of products. And of course the end goal is you want super low cost and super reliable.Peter [00:14:08]: And then in certain use cases you have some more, bespoke things. Like in defense as an example, you do things at night oftentimes, and so you care about sensors like infrared, more so than And you don't, you don't wanna be putting energy out, so you don't wanna use lidar or radar.Peter [00:14:23]: but you still need to be able to see at nighttime. So yeah, we work the whole gamut.The Operating System Layer: Why Vehicles Are Like Pre-Android PhonesAlessio [00:14:27]: Cool. So that's kinda like on the hardware level. Then on the OS level, how does that look like? What is, like, unique? my drive- I drive a Tesla. Whenever I drive some other car that has a screen, it always sucks.Alessio [00:14:38]: It's on, like, cheap Android tablet. It's like, it's laggy and all of that. What does the OS of, like, the autonomy future look like?Peter [00:14:46]: When most people, it's really what you just described. When you think about operating system in a vehicle, you're thinking about the HMI, right? The human machine interface, and absolutely that's a an important part of it, but that's actually only one thin layer on top. So when we talk about operating systems for, like, AI in vehicles, there's many layers that go deep into the CPU critical realm and embedded systems, and you're talking about the real time control ofPeter [00:15:13]: let's say the electric motors or the engine and the actuators, and you have different redundancies for different, let's say, the steering actuation in the vehicle. And all of these things, need very core support in the in the operating system. And then of course for autonomy you have real time sensor data that's streaming in, and the latencies there are really important, right? If you try to Imagine you try to run Microsoft WindowsPeter [00:15:35]: like streaming your sensor data in or controlling the vehicle. Like, the latencies are gonna be absurd. Like, you can never do that. And so what's special about what we do is we really have this system level thinking, right? So we're looking at, we care about every performance characteristics of the entire system, and then we also, because we're doing a lot of the software or all of that software, we can fine-tune and control all of those things. So we can very carefully tune in the latencies for every aspect of the system. We can carefully tune in the memory management. We can have the right, fail-safes and fallbacks, for different things. ‘Cause you have to account for what if, what if there is a critical failure? What if there's a cosmic ray that flipsPeter [00:16:14]: a bit in the middle of the processor that causes some, malfunction? And you have to have a fail-safe to all of that, and so the core operating system is a part of that. And then the one last thing, which is a lot less exciting but is, actually a very big topic, is reliability of updates.Peter [00:16:30]: so the I have a Tesla and you get updates fairly frequently, right?Peter [00:16:36]: Once a month. Most companies that are making vehiclesPeter [00:16:40]: are basically never doing updates, and they're And even if they are doing updates, they're usually only updating maybe one module. Maybe they're updating the HMI module. But they're not able to update, let's say, the CPU critical parts of the system.Peter [00:16:51]: You have to go into the dealer for that. And so with our operating system now we can actually enable highly reliable updates of any system in the vehicle, and that's way easier said than done. Like, there's lots of technical, technically deep stuff, in the tech stack to do that in a way that you're not going to accidentally brick a vehicle.Peter [00:17:08]: And right? If, imagine yourAlessio [00:17:10]: That would be bad.Alessio [00:17:11]: Bad.Peter [00:17:11]: Bricking a car is a very expensivePeter [00:17:13]: and honestly, like across the industry maybe one of the most just pure impactful things that we've done is we've just, we're, we're now enabling the industry to actually do software updates.Alessio [00:17:22]: Just to clarify as well, who is the customer for this? Like, I assume a lot of hardware manufacturers have their own firmware, and I'm sure some of them would just have you write it for them because you're experts. And others would have their own. Like, who pays for this? Who invites you into the house? Is it, is it the end user, or is it, is it the manufacturer?Peter [00:17:41]: Yeah. So let me make an analogy firstly on the on the fragmentation of software. So physical machines today are more akin to the state of the phone market before Android and iOS existed, right? So I worked on Android at Google by the way many years ago, and part of the reason that Larry at Google decided to get into Android was they wanted to run Google products on a bunch of phones, and they bought all of these phones from the industry, and it turned out they had like 50 different operating systems on these phones. And it was virtually impossiblePeter [00:18:17]: for Google to make their app run on all 50 devices equally well. And so the solution was, well, actually what if, what if they created-A really great operating system and made it attractive to all of these phone makers, and that was sort of the genesis for what Android was and why Android existed. It was a way for Google to get their products onto really wide diversity of devices. The state of the physical, industry right now, it's a little bit like that. Like, there's yes, these companies have firmware, but they have so many different operating systems, it's so fragmented, and to actually get a modern AI application to run on these vehicles, you actually, you first have to consolidate the operating system, and so that's, that's why we've done that. And then, your specific question was who are our customers? It's, it's, generally it's the companies that are making these machines.Peter [00:19:06]: And we're, we're, we're selling our technology to them to really simplify the architecture and then enable these AI applications to run on them.Customers, Licensing, and the Better-Together StackSwyx [00:19:13]: How much is reusable across? Like, do you have, like, one OS that is just configured for everything, or is there some more customization that is needed?Peter [00:19:22]: Yeah, highly reusable. So the fundamental technology is quite universal, right? So things that we do have to think about though are, like, chipset support. And so if you're, if you're coding, let's say, an LLM and you have start with an assumption that, “Hey, oh, I'm gonna, I'm gonna use CUDA, and I'm gonna run this, on an NVIDIA chip,” then you don't really have to think about the hardware in that sense. Like, you're just, “Okay, I'm just I'm in the CUDA/NVIDIA ecosystem, and I'm, I'm going to use that.” But the hardware, especially in safety critical systems, it's a lot more diverse. There's not one or one or two players. There's a bunch of different chipsets that we have to support. And so our operating system doesn't just run on, like, the equivalent of X86. It has to, it has to run on a number of different architectures from chips from a bunch of different companies. But again, we've been working on this for a long time now, so we have, we have support for all of those chipsets. And then when you want to then run the AI applications, we can then do that reliably across now a variety of providers.Qasar [00:20:19]: And I think that is, like, heavily inspired by Android, right? Android has a huge suite of testing and it's a reliable operating system that runs on thousands of devices. And we think we can, we can do the same in all these physical moving machines, with the difference that we're really in a safety critical realm. Android isn't.Alessio [00:20:40]: So on Android, I don't need to use Gmail, I can use Superhuman. Like, what about your machinery? Like, can people bring somebody else's automation to it, or is it kinda like all-in-one?Qasar [00:20:50]: You have to use us. No. Yeah. we're If, Yeah. Yeah, it's totally open. Yeah.Peter [00:20:56]: Yeah. our philosophy is that we are a technology company, and so we license our technology to customers to use how they want. And so if a customer wants to If they wanna license our autonomy tech and our operating system, then great, we'll license those. If they just wanna license the operating system and then use different autonomy tech, that's fine also, and we have great documentation andSwyx [00:21:17]: Or if they wanna use developer tooling.Peter [00:21:18]: Yeah, exactly.AI Coding Adoption: Cursor, Claude Code, and the Bimodal EngineerSwyx [00:21:19]: It's, like, a better together if, obviously, if you, if they work together. Is it all C++ I assume is with different compile targets?Peter [00:21:27]: We use a lot of C++.Peter [00:21:28]: Rust is sort of a hot, the new hot kid on the blockPeter [00:21:32]: for a bunch of things as well. But yeah, the lower level you get, especially when you get to real-time constraints, you hit C++ at some point, and at some point maybe you work your way into assembly when needed.Swyx [00:21:44]: Oh, damn.Alessio [00:21:46]: I'm curious about the coding agent adoption, just, like, since you're mentioning more esoteric languages. Like, what's the adoption internally? What have you learned?Peter [00:21:55]: Yeah. We use everything. So Cursor was, I think the hottest tool in the company for a good while. Now Claude Code, I think has taken the reign on that. We have a internal leader, leaderboard that we use just to sort of encourage adoptionPeter [00:22:09]: with-within the company. And yeah, it's, they're phenomenally useful. it's, Honestly, we take inspiration from some of those tools also in how we're adapting some of that mindset of thinking to the physical realm. Like if it's so easy to build an app for this or that thing that lives just on a screen, we can We're taking now a lot of the same ideas and applying that to, “Okay, well, if you wanted a physical machine to do something, how easy can we make that, using our own tooling and platform as well?”Alessio [00:22:40]: Are you changing any of, like, the OS architecture, kinda like the way you expose services to, like, be more AI friendly or?Peter [00:22:48]: Yeah, absolutely. The in the early days of our tools infrastructure work, it was a lot about, You had engineers that were experts in certain topics, but the things that you're dealing with, they're oftentimes more mathematical or more abstract, where actually GUI tools are very useful for certain things. Like as an example, we have a product we call Sensor Studio, which is, it helps you design the sensor suite for your autonomous vehicle, whether, again, it could be a car, it could be a drone, could be a mining equipment, could be a robot. And you place sensors in different places. You There's different, There's a library. You can understand what are the trade-offs that you're making in the design of that system, and that was, like, a very, a very GUI intensive, thing ‘cause it's a little more like a CAD tool in that senseSwyx [00:23:37]: YepPeter [00:23:37]: if you've seen CAD tools. Nowadays, though, right, we expose all of the underlying APIs for that and now using, AI agents, you can actually configure a sensor suite with just text and likely reach a better result than you could've through the GUI in the past, and we're taking that thinking now through the whole product portfolio.Swyx [00:23:57]: Another thing I was thinking about is just in terms of, like, AI, adoption, does it change your hiring at least a little bit, or how do you, how do you sort of manage engineers, differently?Peter [00:24:08]: Yeah. absolutely, it does. we, I think like every company in the Valley right now, are evolving our hiring practicesPeter [00:24:16]: because the skills required to be effective are changing so fast, right? you used to really select for just rote implementation ability and now it is more the AI engineer skill set, right? Where it's like, yeah, how to implement, but actually-Just banging out code is no longer the core job, right? It's, it's actually knowing what questions to ask, knowing how to tie, how to tie together these different AI tools. And so the interviews that we give now I think are way harder than they've ever been.Peter [00:24:46]: But we also allow, right, selective use of AI tools to solve the problems. And I think in that you start to see more of a bimodal distribution of engineers, right? You start to see like wow, there's, there's this subset of people that they really get it. Like they're, they're all in and they've, they've clearly invested the hours needed to learn these tools and how to be effective.Peter [00:25:09]: And then there's sort of the group of people that haven't done that, and that the productivity gap is just enormous. And so we're, we're trying to obviously select for the people that are really into this.Qasar [00:25:20]: I first wrote the my AI engineer piece three years ago, and when I first wrote about it, I was like, “Actually, not everyone should be an AI engineer,” ‘cause I think there's a there's an extremist stance where well, every software is an engineer is an AI engineer. And my actual example of people who should not be adopting AI was embedded systems and operating systems, and database people. Are they adopting AI?Peter [00:25:41]: I think it's the classic bitter lesson, topic, which is the Six months ago I would've said the same thing, but it's, it's becoming super useful for every domain.Qasar [00:25:53]: I'm sure.Peter [00:25:54]: Right? Like,Peter [00:25:56]: there was, I think six months ago, or maybe a year ago, if you tried to use, let's say the latest Claude model for writing shaders, GPU shaders, the results were probably underwhelming. And if you use the latest model now to do that kind of task, you're a little bit blown away, like, “Wow, that actually worked. That's amazing.” And we see the same thing in the embedded realm. No question though, especially when you get into safety critical systems, the human validation isPeter [00:26:25]: is 100% key. Like I You're not gonna trust your life to a an AI written software that's, that's not been very carefully, checked by humans. And so I think now the really the challenge is about that appropriate level of human validation for these safety critical systems.Verifiable Rewards, Evals, and Neural SimulationAlessio [00:26:41]: How do you think about, yeah, touching on the simulation side, I think verifiable reward and reinforcement learning is, like, the hottest thing. What have you done internally to build around that? And like, what gives you What makes you sleep at night? Like, if somebody's like, just web coding something or likeAlessio [00:26:57]: wants to try something new, you have like a good enough system. Because I think the opposite is also true, is like if it's super easy to write anythingAlessio [00:27:04]: then it puts a lot of work on like the verifiableAlessio [00:27:07]: side of it. Like, what does that look like for people?Peter [00:27:10]: Yeah. So verifiability, a broader bucket of like evaluations, right? Like how do you evaluate the results that you're, you're getting? I think this is probably the hardest problem right now, because the As the models get better, it can be harder and harder to find the faults on the system.Peter [00:27:29]: And so like the problem of doing proper eval to find those faults, like that problem also keeps getting harder as the models get better. But it's no less important than it's ever been, right? You still there are still going to be edge cases that are not met and whatnot. And so it's, it's a big area of investment for us. On the reinforcement learning topic, the key thing is there's all these new requirements that come to be in the latest generation of these technologies. So for example, end-to-end is the big thing right now in autonomy and physical AI, which is you can now train these models that can effectively take sensor data in and then put control signals out, and get really good results out of that. But the way that you train and improve those models is really different from the previous generations. And so to do reinforcement learning on an end-to-end model, you now need to actually simulate all the sensor data, right? So then this becomes a we call our, work in this neural simulation, but it'sPeter [00:28:26]: think of it like a hybrid of Gaussian, splatting and diffusion methods, and where you really care about performance. Like performance is everything. If you can't do enough simulation fast enough and cheap enough, you actually can't get results that are worthwhile, in the end. It also gets to a lot of our work in embedded systems, which is like performance critical work, and that performance optimization, performance criticality, it carries over to a lot of the model training work. because, like, the only way to make it affordable is it has to be really fast.Qasar [00:28:58]: I think it's worth a few minutes talking about our own, evolving thoughts on verification and validation withinQasar [00:29:05]: kind of, traditional simulators, which are, you can think of like vehicle dynamics or something like that, which you're just taking textbooks and taking those formulasQasar [00:29:13]: and putting them into software, to like now this neural sim/world model universe. I think that's an interesting topic.Peter [00:29:20]: Yeah. So in more traditional development, right, you oftentimes would have, more black-and-white answers to questions.Peter [00:29:28]: And so the in Europe as an example, there's, a regulatory, system, it's called Euro NCAP. It's the European New Car Assessment Program, and as part of that, the vehicles have to pass a bunch of tests, and those tests actually, include, safety systems. So automatic emergency braking for a child that runs in front of a carPeter [00:29:51]: or let's say an occluded child that runs out and you hit it. And so you have You end up with sort of these binary answers of like, well, did the car under test pass this specific test? And there's a very well-known set of test casesPeter [00:30:05]: that the vehicle has to pass. And that was how the industry worked, let's say, until 10-ish years ago. But what's changed now is with these models, everything is statistics, right? Like you no longer have a black-and-white answer, but it's like, well, how many orders of magnitude or how many nines of reliability can I get in the system, and how can I, how can I prove that to be true? And the big unlock honestly for physical AI as an industry is that these models are just becoming much more reliable. Right? Things like things actually work a lot better. It's like the number of nines you can get out of these systems are now good enough that it actually becomes cost effective to really deploy these things. And so the big shift in, so verification and validation has been from a little bit more of a Again the past it was strictly requirements, and are you meeting or not? And now it's more of a statistical, verification and validation case where it's all about how many nines of reliability and meantime between failures, that sort of thing.Statistical Validation, Regulators, and the Cruise LessonSwyx [00:31:04]: And is the target audience regulators or even the customers are yeah, if you I imagine the customers are bought in, and it's mostly regulators that need to be satisfied.Peter [00:31:15]: We do work with the US government, we do work of course with the European governments and the government of Japan, and the government is not like an AI lab by any means.Peter [00:31:25]: So Swyx [00:31:26]: They just care about the outcome.Peter [00:31:27]: They care about the outcome.Peter [00:31:28]: And so we do education, in that regard, and like so sort of teaching about, “Hey, this is how we think validation should be done, and this is an approach that we think is reasonable,” and how to think about like when is a driverless system actually safe enough to go on the roads and that sort of thing. But I wouldn't say that the government is asking for it. It's like we're more teaching the government in that, in that sense. It's honestly, it's more so for our own, our own comfort, right? Like, we want to build very safe systems, and then of course our customers care deeply about that as well. But in that context we're also typically educating our customers.Qasar [00:32:01]: Yeah. Our first, our first core value is on round safety. So I think we can't underline enough that, us also verifying and validating that the systems that we're deploying are safe to us is probably as important as, like, some regulator or a customer saying,Swyx [00:32:19]: Of course. Okay. Yeah.Swyx [00:32:20]: You have to satisfy yourselves.Peter [00:32:22]: As I say, as a whole across the world, regulation oftentimes it's like a almost lowest common denominator. But like, you really have to substantially exceed what the regulators are expecting to make good products.Swyx [00:32:33]: Yeah. One thing I often talk about, I think and I try to make this relatable to the audience also, is Cruise, where they had an accident that basically ended the company. I wonder if people overreact to single incidents, because incidents are going to happen regardless, right? ‘Cause it's a statistical thing, but as long I don't know if regulators understand that, you cannot extrapolate from a single incident, but we do because that's all we have to go on. And your sample sizes are necessarily gonna be lower than, I don't knowSwyx [00:33:00]: consumer driving.Qasar [00:33:01]: Yeah. I think the Cruise example wasn't a technology failure. there was The real, compounding issue there was just how did the company talk to the regulators and what was their kind of behavior, and I think that became more of the issue. If you look,Peter [00:33:19]: It isn't It definitely was a technology failure, but it was made much worse by theSwyx [00:33:23]: Put the car back on the woman.Qasar [00:33:25]: Yeah. And let me put it another way. There is a version where Cruise still exists.Swyx [00:33:29]: right. Right.Qasar [00:33:30]: Right. It'sSwyx [00:33:30]: It was like the last strawQasar [00:33:31]: ItSwyx [00:33:31]: in like a long chain ofSwyx [00:33:33]: like issues.Qasar [00:33:33]: So do you feel like ATG had that horrific accident or someone actually dying, because, that was a homeless person crossing the street? So yeah, I think we can't understate enough that ultimately, like, statistical validation of something, that's one part of it, but it's not the only part of it. Like, consumer and let's say, mainstream adoption of these technologies is also gonna be part of that conversation. I think companies like Waymo are doing a lot of service positively to the industry in the sense of they're, they're setting a high benchmark and they're showing, kind of in a very responsible way how to, how to deal with these. There have been Waymo incidences as well. They've just not been as significant as the Cruise one that you mentioned. But yeah, so I think you'll just continue to see that. I think probably the long term question is really gonna be, again, around Like it is very clear humans are way worse drivers statistically.Qasar [00:34:29]: Like, there's no, there's no debate. And so at what point But we're emotional animals.Swyx [00:34:34]: Yeah. So my thing is, like, we have to get to a point as a society where we accept horrific accidents that would never happen by a human because statistically we understand that it is safer overall. In the same way that planes, they're safer, than I think they're the safest mode of transport that we have.Qasar [00:34:50]: Yeah. it's more dangerous to drive to the airport than it is to get on a flight.Qasar [00:34:53]: So if you're everQasar [00:34:54]: if you're ever getting nervous about getting on a plane, just think “I just gotta get to the airport.”Swyx [00:34:58]: Yes, we're flying.Qasar [00:34:59]: If I get to the airportQasar [00:35:00]: I'll be good.Swyx [00:35:00]: But then it's, planes also concentrate the tail risk if planesQasar [00:35:03]: Yeah. AndPeter [00:35:04]: And I was, I don't think we honestly have to worry about there ever being, accidents from these systems that are like much worse than what humans would cause, ‘cause humans do terrible things.Peter [00:35:14]: Like, people fall asleep at the wheel all the time.Swyx [00:35:16]: I have.Swyx [00:35:17]: Like, I'll call, I've been a drowsy driver.Peter [00:35:19]: Kinda drunk drivers, and that'sPeter [00:35:20]: that's the extreme end of the example. But these AI systems, you have redundancies, you have fallbacks. Like, there's many things have to go wrong for there to actually be a something catastrophic because there's, there's so many, fallbacks that these systems have.Alessio [00:35:36]: your simulation is like so vast because there's so many use cases. What are, like, maybe things that worked in a simulation and then you put it out and it's like, “F**k, this isAlessio [00:35:45]: this just did not work at all?”Peter [00:35:47]: Yes.Alessio [00:35:47]: IsPeter [00:35:47]: That's maybe a bit of a misconception, about simulation there. So let me go a little bit, more technical on this. So at first go, no simulation is going to represent the real world. There's always a process of this, sim to real matchingPeter [00:36:02]: where you actually, you need the real world feedback to basically feed into the parameters that are being used in the simulator, and you have to do that, it's like this validation flow, a number of times until you can get some confidence that, like I think the simulator is now accurately representingPeter [00:36:19]: what's gonna happen in the real world. Now, if you have a situation where you've done that full validation and you thought that it was accurate and then there's something different, those are much trickier cases, and that's, that absolutely can happen, but really I think the validation process is a really important part. You can never skip the simulation validation process, like where you're actually ensuring that, hey, the actual, my sim to real gap here is small enough that I can trust these simulation results. And there's, there's so many fun things that you can do when you get into it. Like, I'll, I'll give one fun example that came up recently is like in these humanoid robotics, systemsOverheating actuators is a real problem, right? So obviously phenomenal demos. IPeter [00:37:01]: The most amazingAlessio [00:37:02]: For 10 minutes.Peter [00:37:03]: The most amazing I can get. I love, I love watching robots do acrobatics like everybody but the these systems actually overheat, right? If, like, And one of the ways you can use simulation though is you can actually have that, the temperature of those actuators be one of the parameters that's representedPeter [00:37:18]: in the simulation. And if you're doing reinforcement learning over a certain task, then the robot can actually adjust its motions in the simulation to account for the fact that, oh, it knows that as it's moving, it's actually beginning to overheat this motor. But if you didn't have that parameter of, let's say, the heat of that motor represented in the simulation initially, then your RL policy might It will disregard that. And now you run that on the robot and the robot will overheat and fail.Alessio [00:37:43]: I guess the question is, like, how do you have all of these parameters taken care of while also understanding the deployment environment? Like, temperature is like a great example, right? WellAlessio [00:37:53]: why did you make my robot worse when it runs in like a freezer?Alessio [00:37:57]: So it actually shouldn't worry about that. it's like, yeah, how do you design these simulations?Peter [00:38:02]: This is honestly the This is what makes simulation so hard, right? it's because you Simulation is fundamentally about you're trying to optimize the development of a system, right? Like, how can I build this system faster and better and cheaper and what are all the levers that I have to actually accomplish that? And because simulation's just a software program, you can, you can change it a lot more easily than you can hardware systems. And then what's particularly awesome about the let's say, world models and using that as a part of simulation is now the simulation doesn't just scale with, let's say, adding new math equations inPeter [00:38:36]: but we can actually scale the simulation environment now with additional real world data and that also unlocks a whole new field of robotics.Qasar [00:38:46]: There is a meniscus line where you cross where still doing real world testing is better. there's, in this, sim-to-real gap, you can reproduce reality at exceedingly expensive costs and this So nothing is free. So really you have to you're finding that line where you're getting great performance, you're getting great feedback, whether it's on the training side or on the eval side, but it's way cheaper than doing it in the real world. At some point it, that doesn't make sense. And so even, from our earliest days in autonomy, our view was you're still gonna do real world testing. You There's, there's not, there's not this, magical land where you're not gonna do that. And maybe even like a more nuanced version of this in like traditional software development is, most of your testing for software in a vehicle, 95% of that can be like traditional CI/CD kind of, flows that you would have in traditional web development. But once you have Now you, let's say you have a truck. Well, you can do like 4% of those in like a rig which has all the components, the electrical and electronics of a truck, but doesn't have, it doesn't have the tires and it doesn't have the And then you have the 1%, which is actually the vehicle. There's something There's a similar analogy in terms of using simulation for intelligent systems. You can do a lot in a simulator, but in using world models, but ultimately it's, it's physical AI. So you're gonna deploy it on physical machines andQasar [00:40:17]: the freezer example comes to, comes to light.Alessio [00:40:20]: The world model thing has been to me the hardest thing toAlessio [00:40:22]: wrap my head around. Like we have Faith Eliyon on the podcast.World Models, Hydroplaning, and Cause-Effect LearningQasar [00:40:25]: We've been doing a small series with like another Intuition company, General Intuition as well.Qasar [00:40:31]: yeah, and I mean, lots of, lots of coverage on NeRFs and yes.Alessio [00:40:34]: Yeah. It feels like we talk with about, the heliocentric system, right? It's like in a world model, if you just feed visual data, the model might learn that the sun spins around the Earth. It makes sense, right? And it's like, well, not really. And I think what are like some of these other things that like hydroplaning is one thing I think about, is like can a world model understand hydroplaning and like what amount of water like causes it to happen? And it's like, yeah, to me it's like I don't understand how you guys do it. I guess it's like the real thing is like when you're doing both cars and the highway in Japan versus the excavator in a mine in,Qasar [00:41:13]: ArizonaAlessio [00:41:13]: wherever you're Arizona, wherever you're deploying them.Alessio [00:41:15]: How much of it are you relying on the world models to like generate the simulations for you and then try and close the gap after versus like giving the world models as a tool to your engineers to like curate the simulations if that makes sense?Peter [00:41:28]: Yeah, totally. So yeah, I can say at a pure engineering level, I think if you're hoping to do real world deploys and you're purely relying on a world model approach, you probably won't get to something that works, before you go bankrupt. So there is just a very practical mindset of like, world models are amazing and they're extremely useful for a lot of use cases, but there are a lot of other things that you need to do to actually get something started and something deployed and working. most fundamentally, world models are all about It's understanding the world, but also understanding what's going to happen. It's like the cause-effect relationship.Peter [00:42:01]: Right? And so like it, right, if you have a take some sort of construction tool, and that construction tool is gonna be doing some work on the Earth in some way, it's gonna be moving earth, the world model needs to understand that cause-effect relationship. Like, okay, when I, when I take this material from here and put it over there and now I have things that are over here and not over there anymore and that cause-effect, relationship. data obviously is a is a big problem. The hydroplaningPeter [00:42:26]: one is actually a really great example because it's actually quite non-obvious sometimes. Right? It's like, well, it's, it's raining and well this road, has, let's say the appropriate curvature to it so the water is running off the road and cars are driving faster here and then you approach a road that's very flat and water is now puddling on that road and all of a sudden cars are driving slower because when they were driving faster they were starting to lose control. And there are a lot of visual nuance, very nuanced visual cues in the scene and so I do think in the world model concept there's a good chance that the model actually would learn that you should just drive slower when these visual cues exist, and that's obviously the beautiful-The beauty of, these kinds of models where they just, they learn these non-obvious things.Swyx [00:43:14]: It doesn't need to know about hydroplaning to know that it needs to drive slower.Peter [00:43:17]: Yes.Swyx [00:43:17]: I guess it's Yeah. I wanna ask questions about, also deploying models. I presume, like, you use a lot of these world models for training data and simulation, but what about deploying it onto the systems in production? Presumably you have you have, like, GPUs on deviceOnboard vs. Offboard: Latency, Embedded ML, and DistillationSwyx [00:43:36]: but they're I keep saying on device. What's the what's the right term for that?Peter [00:43:40]: On machine.Swyx [00:43:41]: On machine.Peter [00:43:41]: Or embedded, yeah.Swyx [00:43:42]: Yeah. What is the embedded world like? because for people who are not used to that world, this is very alien.Peter [00:43:49]: Yeah. So it's actually We call it onboard and off board.Peter [00:43:52]: So like, onboard software and off board software.Peter [00:43:54]: And the great thing about off board software is you don't have to care about time, and you can run really large models, right? So you can, you can say, “Well, this model, I don't care if it takes one second for it to give me a result or 10 seconds for it to give me a result, because we have time.” And the models can be really big, and they can run, in a data center or on a on a huge GPU and you can obviously have distribute to compute, et cetera. But onboard you don't have any of those benefits. You're like, “Well, I need I have this many milliseconds where I need an answer from this model.” And so a lot more of the energy then is about, think of it more like distillation and it's like truly efficiency and like, literally every fraction of a millisecond counts. And you can't have a situation where the model takes too long because then the vehicle can't actually function.Peter [00:44:42]: And so you can, you can still use a lot of the same techniques, and the models themselves you can think of as like a derivative of larger models that you can run offline, and then you're, you're trying to just get a model that is still performs really well but it's, it's a it's smaller, small enough version that you can then run on this embedded system where you care about latency and power.Qasar [00:45:03]: Yeah. And I think like, the broader point I think which, maybe is not obvious but it's worth saying is in physical AI world, we're not really constrained right now by, like, the intelligence of the models. It's actually what Peter's talking about, it's actually deploying them inSwyx [00:45:19]: The hardware they give you.Qasar [00:45:21]: Yeah. On the hardware you give you.Qasar [00:45:22]: And so And there's just a reality is of safety critical systems. So those end up being the your limiting factorsQasar [00:45:29]: rather than, let's say, a limiting factor for, a foundation model companyQasar [00:45:34]: is gonna be just capital maybe or researchers.Qasar [00:45:38]: So we're, we're in that way dealing with, for us as people who kind of come in that realm with like a very interesting Those constraints force creativity.Swyx [00:45:47]: And I imagine, nobody was deploying or giving you the hardware for transformers back in 2018, whatever, but now they are. What's the evolution like? just peel back the curtains a little bit.Peter [00:45:59]: Yeah. Transformers first off, I think the paper was originally published in 2017.Swyx [00:46:02]: 2017.Swyx [00:46:02]: So there's no time.Peter [00:46:04]: And ISwyx [00:46:05]: But I'm just saying I guess I'm saying, like, embedded ML systems usually, like, a lot less parameters, a lot less compute, and now, like, orders of magnitude more.Peter [00:46:14]: Yeah. absolutely. what I was gonna say though was I think in the in the original paper in 2017, maybe it's in the last paragraph, somewhere in the paper they talk about, like, “Oh, by the way, this technique might be useful for, like, images and videos as well.”Peter [00:46:30]: These last subjects.Peter [00:46:31]: And it took a few years for that impact to really hit. But like, now, we're seeing transformers are everywhere.Swyx [00:46:39]: Yeah. Vision transformers.Peter [00:46:40]: And then then the compute just keeps getting better and better. But you do have this fundamental trade-off, right? It's like you have power, you have cost, and performance and like, getting the right, getting the right mix of those things in an embedded package that can also be, like, shaken and baked in all thePeter [00:47:00]: conditions that these things have to have to operate in. But yeah, I think that they're only going to keep getting better and so we also try to plan our strategy understanding that, we know the rate of improvements of these systems.Swyx [00:47:11]: Yeah. So like, Google just released the Gemma 2B modelSwyx [00:47:15]: that effective 2B model. Is that useful to you guys or is that too big?Peter [00:47:18]: You can run that model on an embedded system, definitely.Peter [00:47:21]: the So yes, it's, it's useful in that regard. The bigger question is, like, what do you use it for in an embedded system? Like, you actually need to customize it quite a bit to make it useful for something. But yeah, you could run a two billion parameter model, definitely.Swyx [00:47:35]: It also interesting, like, what percent is a custom ML model that only does that thing versus a generalist LLMSwyx [00:47:41]: which probably is not that useful actually for your context.Peter [00:47:46]: Like, you, like, you can imagine different use cases, right?Peter [00:47:48]: So theSwyx [00:47:49]: The voice stuff, yes.Peter [00:47:49]: Yeah, the voice test. Totally, yes.Peter [00:47:51]: So for the actual, autonomy elements, that's 100% in-house. We do every bit of that, the data simulation, the model, everything. But when you get into the more generic use cases like voice or voice assistant kind of thing, that's where these more generalist models like Gemma actually can be quite, can be quite useful.Swyx [00:48:09]: Yeah. And then there's also obviously a trade-off between, like, what percent must you do on machine, versus just call home.Peter [00:48:16]: Yeah. It's all about latency.Swyx [00:48:17]: Latency.Peter [00:48:17]: It's all about latency. Yeah.Swyx [00:48:18]: Yeah. Well, like, I think actually in a lot of contexts, especially in the US, you can just have a connection to the web.Qasar [00:48:26]: Yeah. I think though most of our universe is everything has to be fairly, embedded and local because just the nature of Even in the US there's a lot of likeSwyx [00:48:39]: PatchinessQasar [00:48:40]: don't haveQasar [00:48:41]: have coverage, right? And if you look at, like, the old world of autonomy within mining, which is, like, long before transformers and kind of, neural networks, in the like CNN and kind of a universe, they were really just hand-coded, systems. They were just like, this machine is gonna run to that place with thisPeter [00:49:03]: That was our GPS, like very accurate GPS.Qasar [00:49:05]: Yeah. And so that worked, and that worked for 20 years, so why would we actually need to use transformers or kind of more modern end-to-end systems? Mainly because you can only really run a path and run backwards. That provided a lot of value, but m-Not as much as you get when the machine is actually intelligent. It's, it's seeing, it's perceiving, it's acting in a dynamic world.Alessio [00:49:28]: I looked up RTK, real-time kinematic, one to two-centimeter accuracy.Qasar [00:49:32]: Yeah. Fantastic. But the and fantastic in faraway lands where there's not gonna be cell phone coverage.Peter [00:49:39]: Yeah, so it's widely used on the legacy mining and agricultural autonomy systems today. So like, for example, a combine that can be precise within one or two centimeters as it's driving down the field, they use RTK.Qasar [00:49:53]: Yes.Peter [00:49:53]: But it's, it's expensive.Qasar [00:49:54]: Yeah. And it's, it's, it's autonomy, but it's not intelligent in the way that I think all of usQasar [00:49:58]: if in twenty-six we'd be talking about intelligence.Alessio [00:50:00]: In one of your blog posts, you mentioned research on large scale transformers that are similar to those doing modern generative AI. What are, like, the big differences other than, “You're absolutely right. I should steer the car, so you probably wanna remove that?”Peter [00:50:14]: We have a diversified bet strategy internally, and the reason we've done that is because we operate in now a bunch of industries, a bunch of geographies, and each of the approaches has, obviously a different risk to them.Peter [00:50:27]: And so like, we're not going to put all of our eggs in a single basket for a single approach because that approach may no

Hello and Welcome to the DX Corner for your weekly Dose of DX. I'm Bill, AJ8B.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, just drop me a note at thedxmentor@gmail.comVK9/C - Cocos (Keeling) – Mark, VK9BSA, and Deena, VK9DEE, have received their radio equipment at Cocos (Keeling) and are now active on the air until May 17th, with operations mainly on weekends and after work, as they balance family life on the island. This Sunday will be a dedicated radio day, and Deena (VK9DEE) is interested in connecting with other women on air. Frequency and timing details will be shared via email, with SSB as the chosen mode and plans for regular after-work activity on the 20-meter band.CT3 - Madeira Island - CT9/DL1BU is QRV and continues until May 2. Marc says for his holiday he took his IC-7300, 10-meter-tall fiberglass mast, and an off center fed dipole, the "Aerial51." His first day was devoted to setting it all up and testing. CN – Morocco - CN2NQV is the call for F8NQV who is QRV until July 11. The QTH will be the town of Sidi Rahal Chatai, on the Atlantic, 70 kilometers south of Casablanca. He plans 40, 20, 17, 15, 12 and 10M, with target frequencies 7155, 14345, 18140, 21165 and 28575. Pascal's gear runs 100 watts to a Diamond vertical on the rooftop, about 15 meters above ground level.5Z - Kenya - 5Z4/MM0ZBH is QRV Holiday Style until June 15, with 100 watts and wire antennas. QSL via the MM0ZBH home QTH, but his first choice is Logbook of the World for your request. Direct is SAE, no USD or IRC needed. Paul says "I am happy to pay return postage."PJ4 – Bonaire - PJ4TB is QRV again by TJ, PE1OJR, TJ (short for Theerd), until May 4, holiday style, 40-6M SSB and FT8/FT4. TJ has an IC-7300, a "PAC-12" vertical that he's modified to cover 40-6, and an end fed wire antenna. He says he only uses LoTW (and Club Log, but he also mentions QRZ.com) for e-confirmations, no eQSL or traditional cards by mail. His LoTW and QRZ uploads are once a week.FO/M – Marquesas - TX9W, "Team Marquesas," arrived on Hiva Oa and made their way to their site to begin their setup. The team leader, K5WE, Jeff, had "a medical emergency" the night before the departure early Saturday, he spent the night in the hospital, and the decision is being made when and whether he can join the team. Setup is underway and they are QRV.Z6 – Kosovo - Z66SP with his Polish teammates will be QRV from near Pristina, April 23-28, CW, SSB and FT8, 160-10. They will be in the "SP DX RTTY Contest" weekend, and will also do some 6M and QO-100. QSL using Club Log OQRS and LoTW. https://z66sp.spdxc.org/7P, LESOTHO - 7P8WR will be QRV until May 1 by IZ0EVI, IZ0EWJ and IZ6DSQ. For antennas, they will have a spiderbeam covering 20, 17, 15, 12 and 10, a three-element "Skipper" for 10, loop for 20-10, another loop for 40-15, and a 40M vertical. For radios, it's three IC-7300s and an IC-706MKIIG, plus amplifiers. QSL via IZ0EWJ, bureau or direct, LoTW, QRZ.com, but no eQSLAll QSOs will be uploaded to LoTW, Club Log, and QRZ.com. https://www.mdxc.support/7p8wr/JT, MONGOLIA - Vladimir R9LR and Denis R8LCM will be QRV as JT0LR from rare grids NN49, NN48, NN58 and perhaps NN59. Activity between April 25 and April 30 on various bands using CW, SSB and digi. Satellite QO-100 also. QSL via R9LR. 4W - Timor-Leste - DX World reports 4W/EA2TA, Christian, has the licenses in hand now. He, 4W/EA3NT and 4W/IZ7ATN are now on the air from Timor Island. Their operation continues to April 28, 80-6M CW, SSB and FT8. 60M is not allowed in Timor-Leste, so no 60M for them. QSL all of them via IZ7ATN or use Logbook of the World.Until next week, this is Bill, AJ8B saying 73 and thanks to my XYL Karen for her love and support. I Hope to hear you in the pileups! Have a great DX week!

Galen Clavio previews the Little 500 races from Bill Armstrong Stadium as Bloomington gears up for the biggest weekend of the year — including the 75th running of the men's race. Galen is joined by two senior IU student broadcasters who will be on the call this weekend: Zach Browning (women's race play-by-play) and Nick Rodecap (men's race play-by-play). Zach breaks down the women's field, starting with Theta on the pole and the chase group behind them (including Teter, Alpha Chi Omega, Delta Gamma, Melanzana, CSF, and more), plus how weather could affect race-day strategy. Then Nick previews a loaded men's race, headlined by Cutters' dominant spring series and Black Key Bulls' quest for a three-peat — with SAE, Fiji, Sig Ep, and a deep group of contenders ready to make it a chess match over 200 laps.

Les Pays-Bas deviennent le premier terrain de jeu européen pour la conduite autonome de Tesla. Une avancée majeure, encore encadrée, qui pourrait accélérer l'adoption sur le continent.Depuis le 10 avril 2026, Tesla est autorisé à déployer son système de conduite autonome supervisée FSD (Full Self-Driving) aux Pays-Bas. Proposé sous forme d'achat ou d'abonnement, ce dispositif marque une première en Europe pour une utilisation relativement ouverte au grand public, même si elle reste strictement encadrée.Une conduite autonome… sous surveillanceLe FSD déployé repose sur un niveau 2+ selon la classification SAE, ce qui signifie que le conducteur doit rester vigilant à tout moment. Le véhicule peut gérer seul la navigation, les intersections ou les changements de voie, mais l'humain doit être prêt à intervenir immédiatement.Dans des environnements urbains complexes comme Amsterdam, les premiers retours sont positifs. Le système semble capable de cohabiter efficacement avec les cyclistes et de s'adapter à un trafic dense, alternant prudence et assertivité selon les situations.Une autorisation encore provisoireL'accord a été délivré par l'organisme néerlandais RDW après des tests approfondis. Selon les autorités, le système permettrait de réduire significativement les risques d'accident. Toutefois, cette autorisation reste temporaire et sous conditions strictes, notamment en matière de responsabilité qui incombe toujours au conducteur.Tesla a dû se conformer à la réglementation européenne R171, un cadre technique particulièrement exigeant comprenant des milliers de pages et des centaines de critères.Un premier pas vers une adoption européenneCe lancement pourrait faire jurisprudence. Le dossier a été transmis à la Commission européenne, ouvrant la voie à un possible effet domino dans d'autres pays. Toutefois, l'Europe reste prudente, avec une approche progressive et très réglementée.D'autres constructeurs comme Mercedes-Benz proposent déjà des systèmes de niveau 3, mais dans des conditions beaucoup plus limitées, par exemple sur autoroute uniquement.Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

Standards aren't flashy … but they make modern mobility possible by enabling emerging technologies to scale safely.   Listen in as we sit down with SAE International experts Christian Thiele, Senior Director of Global Vehicle Ground Standards, and David Franks, Standards Specialist Engineer for Aerospace, for a wide‑ranging conversation on how SAE standards quietly enable trust, interoperability, and scale across automotive and aerospace.   This discussion spans EV charging, wireless roads, automated driving, advanced air mobility, hydrogen propulsion, and the growing role of artificial intelligence. Go behind the scenes to learn how these standards are developed, the importance of industry consensus, and why they often exceed regulatory safety requirements.   Are you interested in shaping the standards behind next-gen mobility technology? Get involved at sae.org/standards/development.   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.

Our guest tonight went to a routine immigration check in last summer and his life changed forever! Sae Joon Park is a purple heart recipient and a combat veteran. You and I owe our freedom to men just like him. And yet, this evil regime swept him up in their dragnet and forced him to self deport over some harmless offenses from 20 years ago. Sae appeared in Kristi Noem's congressional hearings when Senator Magaziner represented Sae as an example that Trump was not just going after the bad element with his immigration policy. True patriots were getting targeted too. #trump #immigration #ice #kristinoem #purpleheart

Send us Fan MailCheck us out at:  https://www.cisspcybertraining.com/Get access to 360 FREE CISSP Questions:  https://www.cisspcybertraining.com/offers/dzHKVcDB/checkoutGet access to my FREE CISSP Self-Study Essentials Videos:  https://www.cisspcybertraining.com/offers/KzBKKouvA ransomware headline is easy to ignore until you realize it can shut down a factory line, break supplier networks, and trigger contract penalties that dwarf the original IT cleanup. We start with a real-world manufacturing case study from the UK where cyber incidents are becoming routine, then zoom in on why revenue hits are so brutal in an industry that often runs on tight margins. The Jaguar Land Rover disruption adds a sobering lesson: a single breach can ripple outward into suppliers, logistics, and even wider economic impact.From there, we switch into CISSP Question Thursday with Domain 4 focused practice that sharpens how you think under exam pressure. We walk through a zero trust private cloud scenario and explain why microsegmentation with software-defined networking gives the most granular workload-to-workload control for stopping east-west lateral movement after a compromised web server. We also tackle the split tunnel VPN tradeoff that can turn an endpoint into a bridge for attackers, plus a legacy ARP weakness that opens the door to ARP spoofing and man-in-the-middle attacks.We round it out with high-value protocols and technologies you're likely to see on the CISSP exam: DKIM for cryptographic email integrity and domain validation, WPA3's SAE for stronger protection against offline dictionary attacks, and VXLAN in shared infrastructure where encryption is not provided by default and must be layered in with controls like IPsec or MACsec. If you're studying communications and network security, this one connects technical decisions to real business risk. Subscribe, share with a study partner, and leave a review so more CISSP candidates can find the show.Gain exclusive access to 360 FREE CISSP Practice Questions at FreeCISSPQuestions.com and have them delivered directly to your inbox!  Don't miss this valuable opportunity to strengthen your CISSP exam preparation and boost your chances of certification success. Join now and start your journey toward CISSP mastery today!

Ready to dive into the world of middle school ag ed and supercharge your SAE program? Jacob Englin digests two powerful research articles! First, we explore trends, gaps, and teacher needs in U.S. middle school agricultural education, revealing where we need more up-to-date research. Then, we dive into how top teachers create high-quality SAE experiences by focusing on student growth, leveraging competition, and fostering accountability. Get ready for practical takeaways on scaffolding SAEs for independence, maintaining high expectations, and tailoring projects to keep all students engaged!   Journal Articles: https://jae-online.org/index.php/jae/article/view/2782 https://jae-online.org/index.php/jae/article/view/2783

Hoy recordaremos las hazañas del guionista Luis Peñafiel aunque era más conocido como director de cine fantástico y de terror o realizador de entretenidos programas televisivos. Hoy hablaremos de Narciso Ibáñez Serrador. El pequeño Chicho nació el 4 de julio de 1935 en Montevideo. Hijo único del director teatral, Narciso Ibánez Menta, y la actriz, Pepita Serrador, que era guapa de cartel de clínica privada pero más seca que julio en Sevilla. Sus abuelos paternos eran españoles y los dueños de una compañía de teatro que emigraron a Argentina en 1920 por eso Chicho dio más vueltas por Latinoamérica que Fernando Esteso persiguiendo una Sueca. Chicho padecía de Púrpura Hemorrágica, que es como la hemofilia pero pa los pobres. Esta condición hizo que, por seguridad, pasara sus primeros años alejado de sus padres cuando se iban de gira, sin poder jugar con otros niños ni practicar deporte, porque si se hacía un rasguño convertía el patio del colegio en una escena de holocausto caníbal. Cuando cumplió 5 años sus padres se separaron y Chicho se quedó con su madre que mu buena, mu santa pero más estricta que el padre de Michael Jackson. Su primer papel en la industria audiovisual fue en 1943 en la película de Walt Disney Bambi, con tan sólo 8 años, siendo para toda hispanoamérica la voz de Tambor, el conejito que parecía que iba siempre hasta arriba de Colacao. En 1947, con 12 años, se mudó a España con su madre. Como el niño había estado aburrido tanto tiempo se había leído hasta el manuscrito Voynich; el niño era más culto que el mayordomo de Batman así que la madre lo metió en el instituto de La Salle. Pero para ella eso no era suficiente, así que se lo llevaba al teatro y hacía como Amancio con la hija, lo ponía de acomodador, de taquillero, de árbol, antes de llegar a realizador. A principios de la década de los 50 le dijo a la madre que se comprara un poto que él se iba a Egipto detrás de una chavalita, que no se preocupara, que el comía de tó. Su carrera profesional empezó cuando volvió a Argentina a finales de los 50, aprovechando su talento como escritor de guiones y que su padre ya era como el Estiven Espilber de allí. En 1960 destacó con su primera serie de terror Obras Maestras de Terror pero la tele Argentina no le gustaba porque tenía menos calidad que los efectos especiales de Flash Gordon. Así que a los 28 años regresó a España y se apuntó al INEM. A la semana lo llamaron y dijeron que había un puesto de trabajo en TVE y ya no lo vieron más ni por el SAE ni por el SEPE. Empezó adaptando el teatro a la TV con Estudio3 y siguió escribiendo series de terror y reciclando algunas de las que hizo en Argentina porque en 1965 a ver quién se iba a dar cuenta en España de que iba a cobrar dos veces por lo mismo. En 1966 fue cuando dio el salto al éxito con una serie que daba más miedo que la dictadura, Historias para no dormir, aunque su consagración llegaría en 1972 no con el teatro, ni como guionista sino como creador de un espacio televisivo capaz de no chocar con la censura. Nacía el Un, Dos, Tres. Pero como su padre confiaba menos en el proyecto que James Cameron en Titanic, Chicho no puso al principio su nombre en los créditos, por lo que pudiera pasar. Cuando se afianzó el éxito del programa añadió un “Y si algo falla el responsable es…Narciso Ibáñez Serrador, porque a toro pasao hasta el aguador es torero. En esta época ya tenía papada pa 3 tortillas y las gafas de pasta gorda de quien se ha leído el manuscrito Voynich que luego cambiaría por unas finitas doradas, un fular y un puro. Chicho siguió triunfando con otros programas como Waku, Waku, Hablemos de Sexo o el Semáforo aunque también es verdad que tampoco había mucho más en la tele para elegir. Se casó, se rejuntó, se divorció, se separó y tuvo hijos con unas pocas, aunque a lo mejor no en el orden más respetuoso para sus parejas. En 2012 le sacaron una foto que han puesto en Wikipedia que parece Papá Noel cuando le dicen lo que tiene que pagar ese trimestre de IVA. Desgraciadamente, nuestro protagonista dejó de crear sueños el 7 de enero de 2019, a los 83 años, aunque ustedes siempre podrán recordarlo cada vez que conozcan a alguien que coma de tó o cuando alguien confíen menos en ustedes que James Cameron en Titanic.

Accessibility is often cited as a key benefit of automated vehicles and emerging mobility technologies, but designing vehicles for wheelchair users is complex. The SAE CivicProgress Challenge was created to help address this gap by empowering college students to develop innovative, real-world solutions for accessible mobility. Established in collaboration with General Motors, the multi-year program challenges students to tackle accessibility barriers while gaining hands-on engineering and design experience. Join us for a conversation exploring current accessibility challenges and the impact of the CivicProgress Challenge on the future of equitable mobility. Guests include Alicia Bott and David Sander of SAE, Michele Lee of Serve Robotics, and Jessica Swan of GM.

In dieser Episode des Life After SAE Podcasts haben wir Thorsten Schmidel zu Gast.Thorsten ist Senior Gameplay Programmer bei Ubisoft und erzählt uns über seinen Werdegang in der Gaming-Industrie.

From lifelong hobbies to deep nostalgia, there is no doubt that personal connections with our vehicles are real. But true car lovers know that the future is built on what we've learned from the past.   Listen in as we sit down with SAE History of Ground Vehicle Systems Committee members Terry Mueller, George Nicols, and Jeff Singer to explore what makes cars so special. As historical engineering storytellers, these automotive enthusiasts are creating a library within SAE that includes the evolution of vehicle systems and subsystems, making knowledge accessible for enthusiasts, engineers, and educators alike.   If you're an industry professional, retired engineer, or automotive enthusiast with experience in the automotive or aerospace sectors, learn how you can join and help us preserve history.   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.

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

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

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

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."

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.

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

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 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

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.

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 »