Podcasts about failure modes

In Elixir Wizards S15E04, Charles Suggs and Emma Whamond are joined by Somtochi Onyekwere, a software engineer at Fly.io and contributor to the Corrosion distributed database project, to talk about distributed systems, infrastructure resilience, and the growing fragility of centralized cloud platforms.   We discuss what recent outages across major providers reveal about modern infrastructure and why more teams are starting to rethink assumptions around reliability, failover, and system design. Somtochi explains how Fly.io approaches geographic distribution, eventual consistency, and replication across nodes, along with the trade-offs that come with building systems this way.   The conversation explores CRDTs (Conflict-free Replicated Data Types), consensus, split-brain prevention, and what actually happens when distributed systems fail in production. We also talk about testing strategies, rollback planning, property-based testing tools, and how teams can reduce blast radius when things inevitably go wrong.   Along the way, we discuss AI infrastructure, sandboxing AI agents, and how newer workloads may add pressure to already centralized systems. The episode closes with practical advice for developers who want to build more resilient applications without over-complicating their architecture. Topics Discussed in this Episode: Corrosion and distributed database replication Centralized cloud fragility and recent outage patterns Distributed systems versus traditional cloud architectures Multi-region deployment strategies for Phoenix applications CRDTs and conflict resolution in distributed systems Eventual consistency versus strict consistency tradeoffs Consensus, leader election, and split-brain prevention Testing failover and recovery scenarios Property-based testing and Antithesis Rollback planning for database schema migrations Reducing blast radius through system isolation Health checks and blue-green deployment strategies Fly Proxy request routing and replay behavior Cross-region synchronization and replication challenges Single points of failure inside “redundant” systems Backup restoration testing and disaster recovery planning Network partitions and failure handling in production Infrastructure monitoring and operational visibility AI infrastructure workloads and operational strain Sandboxing and securing AI agents Sprites and AI workflows at Fly.io Latency improvements from geographic distribution Distributed systems tradeoffs in real-world environments Transitive dependency failures across cloud providers Practical resilience strategies for modern engineering teams Links Mentioned: https://fly.io https://github.com/superfly/corrosion https://docs.gitops.weaveworks.org/ FluxCD https://fluxcd.io/ Fly.io Stateful Sandbox Environments https://sprites.dev/ Cloudflare Workers AI Inference Platform https://www.cloudflare.com/products/workers-ai/ “An AI Agent Just Destroyed Our Production Data. It Confessed in Writing” Twitter post from PocketOS founder: https://x.com/lifeof_jer/status/2048103471019434248 Oct 2025 AWS Outage https://www.theguardian.com/technology/2025/oct/24/amazon-reveals-cause-of-aws-outage Dec 2025 Cloudflare Outage https://www.theguardian.com/technology/2025/dec/05/another-cloudflare-outage-takes-down-websites-linkedin-zoom July 2025 Crowdstrike Outage https://www.ibm.com/think/news/recent-crowdstrike-outage-what-you-should-know March 2026 Stryker Cyber Attack https://www.stryker.com/us/en/about/news/2026/a-message-to-our-customers-03-2026.html https://aws.amazon.com/ https://cloud.google.com/ https://azure.microsoft.com/en-us https://fly.io/docs/elixir/ CRDTs!! https://smartlogic.io/podcast/elixir-wizards/s13-e03-local-first-liveview-svelte-pwa/ https://antithesis.com/docs/resources/property_based_testing/ https://hex.pm/packages/proper

The future of war has been evolving before our eyes in Ukraine, yet the west still plans to fight the last war. In this special episode, guest host Noah Smith (@noahpinion) and Brandon Anderson sit down with Yaroslav Azhnyuk (@YaroslavAzhnyuk), a serial tech founder who went from building PetCube to founding The Fourth Law, one of the world's most advanced AI-guided drone companies. Over two hours we cover the technology, tactics, and geopolitics of drone warfare, and why the modern battlefield has already left the West behind:* Yaroslav's personal history and the Ukraine war [00:01:04 – 00:14:01]* The modern drone tech stack: why FPV drones are the new god of war, the future of the rifleman, fiber optic vs. AI, five levels of autonomy, and the eight dimensions of the autonomous battlefield [00:14:01 – 01:05:13]* The geopolitics and economics of drones: China's manufacturing advantage, the drone race, Western defense readiness, countermeasures, and why the gap is widening [01:05:13 – 01:58:57]For those looking for Noah Smith's commentary, it really gets going around the 00:51:31 mark.Yaroslav Azhnyuk / The Fourth Law:* X: https://x.com/YaroslavAzhnyuk* LinkedIn: https://www.linkedin.com/in/yaroslavazhnyuk/* The Fourth Law: https://thefourthlaw.aiNoah Smith:* Substack: Noah Smith * X: https://x.com/noahpinionTimestamps00:00:00 Cold Open: China's 4 Billion Drones and the Cameras-to-Explosives Pipeline00:01:04 Introduction: Brandon, Noah Smith, and Yaroslav Azhnyuk00:05:41 From Tech Entrepreneur to Defense: PetCube, Brave One, and the D3 Fund00:10:42 The Ethics of Building Weapons: Dual-Use Technology and the Wolf at the Door00:14:01 The Tech Stack: Cameras, Autonomy Modules, Interceptors, and a Semiconductor Fab00:18:47 Fiber Optic vs. AI: The Radio Horizon Problem and $32/km Cable00:25:32 FPV Drones: The New God of War — 70–80% of Frontline Casualties00:28:28 The Five Levels of Drone Autonomy: From Terminal Guidance to Full Autonomy00:41:37 The Eight Dimensions of the Autonomous Battlefield00:45:32 AI Safety and the Morality of Autonomous Weapons00:51:31 The End of the Rifleman? Noah's 2013 Prediction vs. Battlefield Reality01:05:13 China's Manufacturing Advantage and Western Vulnerabilities01:24:21 Policy Advice for Western Defense: Defense Valley and the Widening Gap01:32:54 The Drone Race: Who's Ahead, Category by Category01:41:57 Countermeasures: Shotguns, Jammers, Lasers, and Fishnets01:58:19 The Wedding and Final Takeaway: Be Prepared for WarTranscriptCold Open: China, FPV Drones, and the New Warning SignYaroslav [00:00:00]: Think about this. Last year, Ukraine produced 4 million FPV drones. Ukraine is not the most industrious nation in the world. China can produce 4 billion of these FPV drones.Noah [00:00:10]: Would you say that right now China is now the supreme conventional military power on Earth, given its ability to manufacture and deploy drones in the quantity and quality that you just described?Yaroslav [00:00:20]: I don't think we have all the information to claim that but we cannot count it out, and that alone should be a big warning sign. As I say, at some point in my life I went from making cameras that fling treats to pets to cameras that fling explosives to the occupiers. So that's the short story. And when you think about what your nation, what your patriots are going through, you realize that's the only morally right thing to do is to fight back, and it is immoral not to fight back, and then the choice becomes very clear.Introduction: Yaroslav Azhnyuk, Petcube, and the Last Flight into KyivBrandon [00:01:04]: Welcome to Latent Space. I'm Brandon. I normally do science podcasts, but today we're going to do something a little bit different. I'm joined by Noah Smith of Noahpinion on Substack and Twitter. And he has lots of interesting things to say about drones. And as a guest, we have Yaroslav Azhnyuk, founder of The Fourth Law and several other, drone-related startups. To get started, it is February 23rd, 2022. You are running a pet startup. You're connecting pets with their owners. Let's go in just a little bit of background. How did you get started in tech, and what were you working on before the Ukrainian war started?Yaroslav [00:01:50]: Good to be here. Thank you. On February 23rd, late in the evening, 11:00 PM Kyiv time, my wife and I landed in Kyiv. Actually, then she was a fiance. We came from Lviv, where we were looking at a church, where our wedding should have taken place. And we got into this cab ride from the airport to our home, and the driver was like, “You crazy. Like, everyone's leaving Kyiv. Why do you come?” We're like, “What? Nothing's going to happen. Dude, chill.” And then obviously, eight minutes later, or eight hours later, the bombs fell in the city. It was quite surreal. We probably landed on the last flight that landed in Kyiv, or one of those last flights. My background, I'm a tech guy. Studied applied mathematics in Kyiv Polytechnics, born and raised in Kyiv. My parents are old PhDs from academia, and grandparents too. Like, everything, from linguistics to nuclear physics. And I'm an entrepreneur, so I've built a bunch of companies. Petcube is the one you were referencing. So I lived in San Francisco 2014 to 2020, building Petcube, which is one of the leading, pet device companies in the world, selling lots of pet cameras. And then, yeah, as I say, at some point in my life I went from making cameras that fling treats to pets to cameras that fling explosives to the occupiers. So that's the short story.February 24th: Leaving Kyiv as the Invasion BeginsNoah [00:03:28]: February 24th, I guess a few hours after you, go to check out your wedding chapel, what do you do?Yaroslav [00:03:37]: We had a plan for this situation. So my parents and family live in Kyiv, and we're like, “Okay, this has actually started. The worst has, come true.” And so we basically packed our belongings and got in the car and spent 17 hours driving west. And that was pretty sure most people in our audience watched at least one apocalyptic movie in their life, so that was exactly like that. Like, felt exactly like that. Missiles are falling. Like, there was smoke in Kyiv. Like, my dad and I went, like, to central part of the cities. It's probably, likeYaroslav [00:04:20]: 800 meters from presidential office, to pick some stuff up at his workplace. Because he's, like, the head of an academic institution, so he had to get some of the things with him. And super surreal. Like, the streets are empty. Like, the gas stations are out of gas. Like, we found some gas station. We didn't have, like, spare canisters with us, so we're like, We figured out, like, the car was diesel, so like, we figured out, if it's diesel, you can actually store it in plastic, canisters, and we bought some window wash for the cars. We poured it out of the canisters, and we poured the diesel into that. Yeah, so it was like that. And then, like, helping friends get out, like my friend and his dog. Like, we found Like, my brother was also, like, riding in a separate car. We found a place for my friend who didn't have a car. It was like, yeah, it was like, totally surreal. And we didn't know of course, and you didn't know this will last for so long. You didn't know whether Ukraine will be able to defend Kyiv. And it was like, yeah, very little information and very little insight into future.From Pet Cameras to Defense Tech: Building for Ukraine and the Free WorldNoah [00:05:42]: What are your thoughts with regards to how do you, defend, Ukraine? So you eventually start building drones Like, what is the process to get from there from where you were building, devices that connect owners with pets to building drones, and what other things did you do to help the war effort in the process?Yaroslav [00:06:07]: It's definitely non-trivial, right? Like, I didn't go, to I didn't get any, like, military education when I was a student. Like, normally, in Ukraine, you would, you would go to like, this military school even if you're getting higher education in any other, sphere. I decided to skip that which is like, an unusual way to go. And I never thought that I will be somehow engaged in a war effort. Like, what is war? Of course, wars are over. It's the end of history. So one thing you got to understand about, like, many Ukrainians and like, I guess, it's also true about most of the people I met here in the US, that your who you are in terms of your nationality is a big part of your identity. So when that gets under attack, it's something deeper than just the country you live in gets under attack, right? And I Day one, I figured I'm going to I'm going to fight back with everything I can, right? But I didn't think on day one that I'm actually going to do, weapons. And a bunch of things. We were reaching out to a number of American, congresspeople and senators, and basically advocating for support of Ukraine, for voting for lend lease, which has happened in May 2022, but didn't actually work as expected. We helped start, Brave One, which is now a very important defense innovation cluster, sort of like a DIU here in the US. We helped start, a fund called D3. It's like, it was started or co-started by Eric Schmidt, former CEO of Google. So a bunch of these odd things, but then eventually I was like, “Okay,”by 2023 it was obvious this thing, A is going to last a lot more time, and B, that the whole world is shifting and that there's going to be a new arms race, that the warfare is redefined by drones as platforms. And for the first time in history, you have a platform that is software defined, that can increase your battlefield capabilities, in a in a step change just overnight. So it's like if you were able to push a software update and get all of your Roman legionnaires a new helmet? That has never been possible before. It's the first time in the history of war this is possible. So all of that and many other things like, supply chain fragilization, and the impact that AI is going to have on all of this all these things have become evident to me in 2023, and it's like, “Okay, I should do what I do best, or what I know how to do best, start a tech company, and sort of leverage the global techno capitalist machine, to provide, defensibility to Ukraine and the free world.” So that's literally the mission of the company, increase defensibility of Ukraine and the free world. And then there was some sort of soul-searching and like, asking yourself. It's like, “Okay, am I Actually, I know nothing about weapons. Am I actually, like, ready to make, things that other people use to kill other bad people?”Yaroslav [00:09:36]: When you think about what your nation, what your Compatriots are going through And think about all the terror of places like Bucha, the occupied cities in the east and south, the abducted children, the raped women, all the economic damage that's being done, and the intention to destroy a whole nation, to genocide the people of Ukraine, you realize that's the only morally right thing to do is to fight back, and it is immoral not to fight back. And then the choice becomes very clear. And look, we're just passing the ammunition. We're not doing the actual job. The actual fighters and defenders and heroes are people in the armed forces. We're just support.The Moral Question: Weapons, Responsibility, and Fighting BackNoah [00:10:33]: I have so many questions. Actually, I know you seem to have a question. Do you want to ask anything?Yaroslav [00:10:38]: No, I'm just listening. Go ahead.Noah [00:10:40]: I do want to talk about, some of let's say, the moral issues, like you just said. You endYaroslav [00:10:50]: I think there are no issues there.Yaroslav [00:10:52]: What would an example of a moral question be in this case?Noah [00:10:55]: No, I mean Okay. As you just said, you are creating the tools, but others are using them.Noah [00:11:05]: I was maybe thinking of having this conversation later, but one of the questions is like, is it actually you are going to be building them for your homeland, which you are building it for your homeland, which is I think, very a strong morally defensible position, but this technology is not going to stay with you, right?Noah [00:11:26]: This you will probably be selling these to other people Yeah. So the future is really where the moral issues may come into playYaroslav [00:11:38]: The this question becomes, easier and more complete if we ask this not about a particular technology or particular weapon, if we think that this question actually applies to any kind of technology Right? So -Knife or fire. You can use knife to do surgery and save people's lives, or you can use it as a weapon to take people's lives.Noah [00:12:06]: Cut tomatoes, too.Yaroslav [00:12:08]: Cut tomatoes too.Noah [00:12:09]: Yes, knife.Yaroslav [00:12:09]: That's helpful.Noah [00:12:10]: In Japan, sword and knife, they, call the same word.Yaroslav [00:12:14]: It's like, it's with any technology. Large language models, right? Look at how powerful they are and yet they're available to anyone in North Korea or in Russia.Yaroslav [00:12:29]: That's one side of the argument. The other side is As a maker, what is your responsibility for how the tools you're creating, will be used? There's definitely some responsibility, right? Then How should the decision process look like? Should you, like, try to calculate all the possible scenarios before starting to work on something? Or do you create something that is needed now to save people's lives, and then think about, addressing the unwanted edge cases later? In ideal world where there's like, or okay, it's not ideal world. In a mythical world where there is some one governing party and it gets to decide everything, and there is no other country, that can, decide on their own, you could say, “Well, we need to calculate for all the consequences, and only then, maybe build this building, by replacing this park because, maybe we need this park in the city,”right? So that kind of situation. But when you're in a situation where you're in a forest, in front of a wolf, you first going to deal with the wolf that wants to eat you, and then you're going to go consult Greenpeace. So that's kind of situation that Ukraine is in.The Fourth Law, Odd Systems, and Ukraine's Drone StackNoah [00:13:59]: Enough. Because this is a tech podcast, I did want to spend some time talking about, sort of the tech in that you've developed and what you've been working on. So can you explain, I guess, first of all, like, the problem that you were trying to solve from a technical standpoint? And I think, and then maybe, like, go into some of the solutions and some of the design process that led you from designing, little laser-guided, guiding lasers with a with an iPhone versus Having drones.Yaroslav [00:14:34]: Like, it so happened, that my partners and I, we sort of So I started one company called The Fourth Law, and its goal was and is to Make, massively scalable on-drone autonomy. And then In parallel with that together with my, Petcube co-founders, partners, and friends, we started another company called Odd Systems Which, was focused on making thermal cameras. Cameras, thermal cameras are seeing thermal radiation and are used to see at night. And we're now sort of those companies are getting closer and closer together and we're probably going to merge them. And this group of companies is currently the leading, team in on-drone AI and thermal imaging on the Ukrainian battlefield, and Likely one of the leading, if not the leading in the world. So We have these, like, three sort of business units, which are cameras, drone autonomy, and drones. So the cameras and drone autonomy sell daytime and nighttime cameras and different types of drone autonomous modules to other drone manufacturers, over 200 drone manufacturers in Ukraine. And then the UAV, business unit sells the drones themselves to the armed forces of Ukraine, Ukrainian government. And there are different types of drones. Those are sort of front strike, as we call them, so those are sort of FPV strike drones and the bombers, and then interceptors. And there are different kinds of interceptors. We do Shahed interceptors and we do ISR interceptors. We don't do the deep strike-FPV Drones, Interceptors, and Battery-Powered WarfareNoah [00:16:32]: What's an ISR interceptor?Yaroslav [00:16:33]: ISR is stands for intelligence, surveillance, reconnaissance, and those are basically drones which are which, Russians are using to watch over positions and then communicate where, the targets are coming.Noah [00:16:48]: It's a reconnaissance.Yaroslav [00:16:48]: That's, the ISR is sort of a classical term for a for a reconnaissance drone.Noah [00:16:53]: Are all of these battery-powered drones that you just described? ‘Cause I know that the sort of deep strike drones still have, like Some sort ofYaroslav [00:17:01]: Internal combustion engine?Noah [00:17:02]: Internal combustion engine. Are all the things you're talking about battery-powered?Yaroslav [00:17:06]: What we're working on is all battery-powered, right? We don't do the deep strikes, right? And then in terms of autonomy-Noah [00:17:12]: You can catch a Shahed with a battery-powered thing. It's not Fast to catch.Yaroslav [00:17:17]: No, absolutely. Look, Shahed interceptor, like ours, it's called Zero, it goes up to 326 kilometers per hour.Noah [00:17:26]: For reference, how fast is a Shahed?Yaroslav [00:17:28]: Eight, like, in internal phase it could be 280, but in cruise phase it's, like, 220-ish.Yaroslav [00:17:36]: Yeah. And sorry, I'm not like you can convert that into miles if you're interested.Noah [00:17:41]: No, that's fine.Noah [00:17:41]: Multiply by two thirds or point six or something.Yaroslav [00:17:44]: That's easy. Yeah, I was saying that for autonomy modules, right, we, -We make systems, autonomous systems for frontline, for interceptors and some for deep strikes as well, and then different levels of autonomy. So from terminal guidance, which is like lasts 500 meters, give or take, to autonomous bombing, to autonomous target detection, to autonomous navigation and all of that across day and night, different terrains, different time of the year, different platforms like quadcopters and fixed wing, and maybe some other platforms. So it's quite a wide variety of products. We also have like our own simulation. We have our own training school for the war fighters. And we're about to start construction of two, semiconductor plants to make, sensors for thermal cameras. So that's super exciting for me as a computer science guy is Doing semiconductors. Super cool.Noah [00:18:49]: Like in terms of kind of core drone technologies, you basically are one is an FPV replacement without fiber optics, and the other isYaroslav [00:18:59]: YouNoah [00:18:59]: Signal tracking with interceptorsYaroslav [00:19:00]: With or without fiber optics. Fiber optics Is just like, sort of a communication module.Yaroslav [00:19:05]: You can, you can use classical analog, video link and radio link. Those would be two separate radios. You can do digital, or you can do fiber optic, and then fiber optic Has its own advantages but also adds weight and decreases, the distance and decreases, how fast you can, sort of turn and With a drone. Yeah.Noah [00:19:33]: Do you need AI for fiber optic drones?Yaroslav [00:19:36]: Like you can use AI for fiber optic drones. AI replaces a human, right? Fiber optic is making your communication link more resilient. So those are slightly different goals. Like if you want, you can have, AI controlling hundreds of fiber optic drones instead of having 100 operators for each.Fiber Optics, Radio Horizons, and Terminal GuidanceNoah [00:20:03]: I guess I thought that the key reason that people moved to fiber optic drones was for like electronic, countermeasures. Or I guess to counter those.Yaroslav [00:20:13]: I think that's a correct assessment from sort of a public awareness standpoint. In practice it's somewhat more difficult Because besides electronic countermeasures, you have these issues of a radio horizon For FPV drones, which means that asYaroslav [00:20:36]: I believe Earth is round Some people disagree. But basically if you fly a drone and you have a land station over here and a drone flying over hereYaroslav [00:20:49]: If your drone is flying high, you have good direct radio visibility. If your drone goes low, and usually, Russian infantry and vehicles, they're on the ground and you want to hit them, you need to go low. Lower you go, maybe you'll get behind a hill or behind a forest, and if you're far enough, you'll just get behind the curvature of the earth. You get into what's called a radio shadow. And then That is a real bummer because for the last, be it 60 or 20 meters, you won't be able to see anything and it will be very difficult to hit the target. So to counter that what-- And then the distances that these FPV drones, act on they're, they can be quite large. So for example, here in the US there was this drone dominance program competition, and in drone dominance the furthest distance was about 10 kilometers.Noah [00:21:44]: What was drone dominance? What was that competition?Yaroslav [00:21:47]: Drone, the drone dominance is a is a program started, by the US government, to accelerate the development of drone technology here in the US.Noah [00:21:57]: Got it. And the longest range thing they were using was 10 kilometers.Yaroslav [00:22:00]: Was 10 kilometers, right. In Ukraine, like if your drone doesn't fly at least 20, 25, it just, no one's interested in it, and the usual hits are happening. It was like, okay, many hits are happening between 30 and 40 kilometers, and that's what expected from a regular 10-inch, FPV drone. So at that distance, even at altitudes of like 60 to 100 meters, you might start losing, the link. So some of the earlier AI technology that was fielded in FPV drone was this terminal guidance technology. That was the first product that we ever, launched that helped you as an operator, once you see the target from two, three, 500 meters, you lock onto the target and then, it just, drives the drone towards the target no matter what, even after you lost the visual connection. So optic fiber solves that. However, if you want to go like 20 kilometers with optic fiber, that will add an extra three kilos, of useful weight to your drone. SoNoah [00:23:12]: ‘Cause the cable that you have to unspool as you go weighs.Noah [00:23:15]: It is heavy.Yaroslav [00:23:15]: At first, like the spool is about 800 grams, so a bit less than a kilo, and then, and then think about 10, 10 kilometer optic fiber is another kilo, something like that. That takes away from your useful mass and then now you have like, you need a 15-inch drone and it can only carry maybe one or two kilos of explosives if you want to go, 20 kilometers. If you want to go to 30 or 40, like 30 is probably max. 40 is like very problem problematic on optic fiber. And then the problem with optic fiber is it's actually getting super expensive. So and why? Because of all the data centers for AI. That's literally the same optic fiber-Noah [00:24:01]: We're running out of centersYaroslav [00:24:02]: That's being used there.Yaroslav [00:24:02]: Like when Ukrainians and Russians come to Chinese factories to buy the optic fiber, they're like, “We're out. We sold it out to the Americans.”? That's the craziest thing. So optic fiber went up in price from like, $4 per, kilometer to like, $32 per kilometer in a few months in the beginning of this year. And I'veBrandon [00:24:26]: Claude Code is stopping the Russian drone effort here.Yaroslav [00:24:30]: Ukrainian as well. Yeah.Brandon [00:24:31]: Ukrainian. But I read somewhere that the Russians had grown more dependent on fiber optic drones relative to the Ukrainians, and that's one reason why the Ukrainians have sort of regained the initiative in drones recently.Brandon [00:24:42]: How accurate's that?Yaroslav [00:24:43]: The Russians were the first ones to scale that. I think by as of now, Ukraine has caught up. I think, like, as of maybe three months ago, Ukraine is mostly caught up on fiber optic. Yeah.Brandon [00:24:57]: What percent of damage would you say is in terms of FPV drone damage would you say is now fiber optic versus, like autonomous?FPVs as the New God of War: Tanks, Artillery, and Cost per KillYaroslav [00:25:07]: For our, for our audience, I actually, I cannot answer that question. Like, it's like I know the answer, but I would not disclose that. But for our audience, I think another interesting fact is out of all the casualties on the front line Between 70 and 80% are done by FPV drones.Brandon [00:25:30]: FPV drones are the new weapon of universal weapon of warfare.Yaroslav [00:25:34]: It'sBrandon [00:25:35]: Land warfare, anywayYaroslav [00:25:35]: They used to say that artillery is a god of war because artillery used to cause, like 80% of casualties, and now On that ranking-Brandon [00:25:46]: FPVYaroslav [00:25:47]: FPV drones rule.Brandon [00:25:48]: FPV drones are the god of war.Yaroslav [00:25:51]: Sort of. Dethroned artillery. But it's not to say that artillery is not useful, is not needed. Like, all of these systems are needed. Maybe except cavalry, although Russians still use it. I know, have you seen the videos of Russians using mules and horses?Brandon [00:26:09]: What is the usefulness-Yaroslav [00:26:10]: It'Brandon [00:26:10]: Of a tank in the in the modern-Yaroslav [00:26:11]: That's where we need Greenpeace to say a word, but they're silent. Yeah.Brandon [00:26:15]: What's the use of a tank on the modern battlefield?Yaroslav [00:26:21]: It's diminishing.Brandon [00:26:22]: Diminishing.Yaroslav [00:26:22]: However, I think there might be technologies which will, revive the tank. Look, tank still provides you armor, and armor is important. Like, you still need to armor and firepower, right? Like, you can be an armor personal carrier that provides you, armor. The challenge that currently exists is armor is not very well protected against incoming drones. However, there are ways to do to protect it. We were previously talking about this before the podcast. The CEO of Rheinmetall, recently sort of ridiculed, Ukrainian drone industry, saying that like, there is nothing interesting there, no real innovation, no to stand Compared to like, Rheinmetall or Boeing, and it's all made by housewives. There was like, obviously a ton of memes about this people ridiculing the CEO of Rheinmetall. And one of the best quotes, I heard on this topic is from my friend, Alexey Babenko, who's, the head of and founder of VIARI Drone, which is one of the largest manufacturers of FPV drones. They're our partner. They're using our autonomy. So he said that the drones we manufacture in one day will be more than enough to destroy all the tanks Rheinmetall manufactures in a year.Yaroslav [00:27:52]: Then, yeah, cost-wise, of course, a drone is like, $500 and a Rheinmetall tank is what, probably 5 million-ish or maybe more.Brandon [00:28:00]: Don't mess with those housewives.Yaroslav [00:28:03]: Drone wives.Brandon [00:28:04]: Drone wives.Yaroslav [00:28:06]: That's it.Noah [00:28:06]: There's a classic saying that everyone always fights the last war.Noah [00:28:12]: Yet do How did So from your standpoint, how did we get to the point where tanks became irrelevant in at least for now In a matter of just a few years?Yaroslav [00:28:24]: Look, I think it's the same way, how do we get to the point that calculators become irrelevant?Yaroslav [00:28:31]: Now we have iPhones. Like, why would you need a calculator? Technology progresses and its influence grows non-linearly. It's all exponential. So I can tell you that full autonomy, when you put it on a drone Look, so if you, if you think about a tank and a like, it's not a direct comparison, but even, like, a drone and a artillery shell or like, sort of cost per kill, an artillery shell for 155 caliber, which is a standard NATO caliber Currently market price is about $4,000 per piece. So compare that to say, $400 per drone. That's 10 times more expensive. Account for the amortization of the artillery gun and for how vulnerable it is and what is the sort of tactical, capabilities it gives you as compared to a drone. You'll figure out that an FPV drone is maybe three orders of magnitude, more versatile, more useful, more capable than artillery and many of than a classic artillery. Many of Because there are different types of artillery. Not just, like, one 155. You have mortars, you have all that. But give or take, roughly three orders of magnitude maybe. Again, it doesn't have that firepower. It's not one-to-one comparison still.Yaroslav [00:29:53]: Now, take that FPV drone. When you put full autonomy on that FPV drone, which can be not very expensive, like systems that we're, producing are like, in hundreds of dollars of pure bombFull Autonomy: From Human Pilots to Smartphone-Directed Drone MissionsNoah [00:30:06]: Just interrupt. You said full autonomy Just a second ago you were saying that the autonomy here is guidance, right? It's not decision-making.Yaroslav [00:30:14]: No, I was I was saying that's the f-First and sort of easiest pieces of autonomy that was fielded by us. But if you, if you add full autonomy to a droneBrandon [00:30:24]: He, I think he's asking what does it can you, for the listeners, can you explain What the term full autonomy means?Yaroslav [00:30:29]: Basically, I think a good way to think about an FPV drone is like an iPhone of warfare. It's, like, very inexpensive, very mass producible, very versatile. You don't need a bunch of other things when you have a iPhone in your pocket. You don't have, need an MP3 player, you don't need a calculator, don't need other things. All right? So FPV drone is an iPhone. Or like, okay, Apple please don't sue me, is a smartphone. And then, when you add autonomy to it sort of becomes like Uber or ride sharing. Okay? So what it means is instead of actually being a trained pilot who has this complex remote controller device which requires a couple months of training to actually pilot the drone, and then having to pilot it for 30 minutes, flying towards the target, et cetera, et cetera, now you basically, you have your smartphone, you have a drone, you pick your smartphone, you say, “We are here. The bad guys are here. Go and get them.” And the drone goes up, flies in a given direction, localizes itself on the map, finds the dedicated area where they, the bad guys are supposed to be sees the bad guys, bombs them, return, like, watches, so does a damage assessment, returns back, sits down, and then you can pick it up and watch the video if you didn't have the radio link, right?Noah [00:31:59]: That's a bomber drone.Yaroslav [00:32:00]: That's full autonomy for a bomber drone, right?Noah [00:32:03]: You're saying that no human decision is made in this entire process?Brandon [00:32:06]: That's not, that's not what he's saying.Yaroslav [00:32:07]: A human decision was made at the beginning of the process-Noah [00:32:09]: I get it. I get itYaroslav [00:32:09]: The same way as you would fire an artillery.Yaroslav [00:32:12]: When you fire an artillery, you don't stop at like, 500 meters away from a target and ask it whether, you want to strike or not. That's exactly, a human decision is always made at some point. So when you do that's full autonomy, and such full autonomy is happening as we speak. And such full autonomy increases the capabilities of an FPV drone, which is already, like, three orders more powerful than an artillery shell. Full autonomy increases its capabilities by four orders of magnitude because now you can have 100 times as many people who can use it, because you don't need to train those people, and this is important. You can have 10 times, mission success rate, and you can have 10 times utility per drone because now instead of being one-way kamikaze, it's, it can be a bomber.Brandon [00:33:05]: Now wait, let's, you said 10 times mission success rate, which means that fully autonomous bomber drones succeed in their missions 10 times more often than human piloted bomber drones do. That's an important thing to know.Noah [00:33:17]: Maybe, to push back onBrandon [00:33:19]: They're super, they're superhuman. They're, they' 10X superhuman.Yaroslav [00:33:22]: They're not vulnerable to electronic warfare. They don't care about the radio horizon. They don't lose track during navigation. They are not susceptible to human error when, an artillery shell or other drone blows up besides you and you're like, “Hell no,”like, “I'm getting out of here.” Right? That doesn't happen to an autonomous drone. Like, all of those things. Like, we have, like, one of the brigades that's using our drones with just first level autonomy They literally said that their success rates-Brandon [00:33:53]: What's first level autonomy?Yaroslav [00:33:54]: First level autonomy is just the terminal guidance.Yaroslav [00:33:57]: By the way, we have video of that. We can watch that.Brandon [00:33:59]: Terminal guidance means a human gets it nearby and then the AI takes over.Yaroslav [00:34:03]: The human flies it all the way, like 30 kilometers towards the target, and obviously the target was probably given to that human by someone who's flying some ISR drone, some reconnaissance drone, right? So all the way to the target, and once you see the target from a distance of 500 meters, you do target lock, and from there drone flies autonomous. So just that feature alone, it has increased the guy's, his call sign is Grom, so it has increased his, mission success rate, like precision of mission, yeah, mission success rate from 20% to 71%, and it also increased his kill zone from three kilometers to 10 kilometers, which means there's certain area around the front line which is designated kill zone. Whenever enemy goes into that area, it's almost guaranteed to be to be destroyed by a drone. And then obviously the drones are not launched from like, the zero line. They're usually launched from like, minus 10 kilometer-Mission Success, Failure Modes, and the Five Levels of AutonomyBrandon [00:35:03]: What is a zero line?Yaroslav [00:35:05]: Zero line is sort of an imaginary line of control, of two conflicting forces.Brandon [00:35:14]: It's important to explain these things to a lot of the listeners who areYaroslav [00:35:17]: Thank you for askingBrandon [00:35:18]: Familiar with warfare.Noah [00:35:20]: Myself.Noah [00:35:20]: I'm one of those listeners.Brandon [00:35:20]: You said that level one autonomy, in other words just terminal guidance, just, like, human gets it to the finish line and then it goes over the finish line, increases mission success from 20 something percent to 71%, or something like that.Yaroslav [00:35:33]: Increases the kill zoneBrandon [00:35:34]: Increases the kill zoneYaroslav [00:35:34]: Three kilometers to 10 kilometers.Brandon [00:35:36]: Got it.Yaroslav [00:35:36]: On both parameters-Brandon [00:35:37]: What is full autonomy, dude? AndNoah [00:35:38]: Actually on real quick, can we define mission success and like, maybe in a way, what are the failure modes of missions?Brandon [00:35:44]: I have a guess what mission success is.Noah [00:35:46]: But I couldBrandon [00:35:47]: Get ‘em.Yaroslav [00:35:49]: No, but that's a very good question, in fact, because, even if you fly into the target, well, first the target can be damaged or destroyed. Those are two different modes. Then there can be different targets. A sole infantryman is one kind of target. A dugout where supposed there are some, enemies there is another kind of target, and a some mechanical equipment is another type of target. Radio emitting equipment, which, like, often, like, the targets that the military want to get more than anything else is the some enemy radio tower or something like that or some small radio dish that really makes life difficult in that area, in that combat area. So those are different targets, right? It can be destroyed, can be damaged.Then sometimes, the drone hits but doesn't explode. Like, that happens. And then, there are other failure modes. You didn't even reach the target because you were A jammed by electronic warfare; B, you lost the control over drone because of the radio horizon; C, you were jammed by a different type of electronic warfare that happens way before You hit the target area. It's, impacting your, video receiver. So like jamming on video or jamming on control are two different types of jamming. Then something malfunctioned on a drone, just a mechanical malfunction, maybe like a motor broke or like, whatever. So all of those are different failure modes. Yeah, or maybe you got lost, you're navigate navigating to your, to your target. That happens, too.Noah [00:37:41]: The Level one autonomy, basically you manage to point in a direction.Noah [00:37:49]: You go there, and then the last mile The drone taking over.Yaroslav [00:37:52]: We define this like, I define that but it sort of got picked up by the industry. We define five levels of autonomy. So level one is terminal guidance. It's what we just discussed. Level two is bombing. Level three is autonomous target detection and engagement decision. Level four is autonomous navigation. And level five is autonomous takeoff and landing.Noah [00:38:15]: Those are good things to knowYaroslav [00:38:16]: Those are five levels of autonomy. Now, if youNoah [00:38:19]: I have a question for you.Yaroslav [00:38:19]: Sorry. Like, let me finish withNoah [00:38:21]: SorryYaroslav [00:38:21]: Theoretical part.Noah [00:38:23]: What is Tesla running at right now?Yaroslav [00:38:25]: Tesla?Noah [00:38:25]: No, sorry.Yaroslav [00:38:26]: That's very good point. Like, it's exactly, it was inspired by the levels of self-driving autonomy.Noah [00:38:32]: Waymo's level five, right?Noah [00:38:35]: You just tell it where you want to go, it picks you up, and then you go there.Yaroslav [00:38:36]: I think, like, if you, if you look at the classic definitions of self-driving cars, Waymo is still, like, level four because it still requires even remote, but still, like, human control. It's like if Waymo gets in trouble, there is an operator who takes over and resolves this. So that would still be a level four. It doesn't map directly, but it's also five levels.Brandon [00:38:58]: Can I, can I interject a question here? In terms of an FPV drone that's like a suicide drone that'll just blow itself up killing something, how do what it hit? Like, does it, just transmit back, or do you sort of like, lose track of it and hope it hit? Like, what happens to that?Yaroslav [00:39:16]: That's a great question. SoBrandon [00:39:18]: You need another droneYaroslav [00:39:19]: Like, the current battlefield in Ukraine is saturated with different types of drones. So obviously you have all the FPV drones and last year alone, Ukraine manufactured about 4 million of these, and then Russia's maybe, like, 20% less than that. And for this year, the publicly voiced target was 7 million on Ukrainian side. So it's, like, serious numbers. We're getting in serious numbers here. And then besides those, there are different, reconnaissance drones, ISR as we call them, and there are sort of tactical level ISR where we, both Ukrainians and Russians usually use, Mavic, drone by DJI. And then there are a bunch of locally produced drones, which are sort of fixed wing drones that can stay in the air for much longer than Mavic, maybe, like, half an hour. And then, there are drones that can stay for many hours or even up to a day. And those drones have, are more expensive, have more expensive cameras, et cetera, et cetera. We hunt those drones that Russians launch. The Russians hunt our drones, and so on. But ideally, when you, are a group of soldiers operating an FPV, you'll have someone in your, company, or someone in your platoon who has an ISR asset that will do target designation for you. They'll say, “Oh, like, there's a Russian vehicle over there. Go and get him.”and you go there, you get it, and they're like, “Okay, confirmed.”Battlefield Surveillance and the Eight Dimensions of AutonomyBrandon [00:40:57]: Those guys are watching. They have their own drones in the sky.Yaroslav [00:40:59]: Target destroyed. They have, like, a carousel of drones because One Mavic cannot stay more than 30 minutes. ItBrandon [00:41:06]: They're constantly surveilling the battlefield.Yaroslav [00:41:07]: Almost every spot on the battlefield.Yaroslav [00:41:11]: It's not always the case. Sometimes you will not have a surveillance asset, so then you would launch another FPV just to confirm that there was a hit. Then if you see there was a hit and you're not sure if it completely destroyed, you maybe hit again for good measure.Brandon [00:41:26]: You double tap.Yaroslav [00:41:28]: That's how it works. But I was about to give you another sort of piece of taxonomy. So you have five levels of autonomy, right? Then you have sort of eight dimensions of autonomous battlefield. So what is eight dimensions? It's crucial to understand how autonomy evolves in a modern, battlefield environment. So dimension number one is level of autonomy. What are the capabilities that your asset has? Dimension number two is the platform you're operating on. So it can be a quadcopter, a fixed wing drone, different types of maybe, like, a long range drone or short range drone, but it can also be a missile. You can have autonomy even on an artillery shell or a ground vehicle or a sea vehicle. So all of those are different platforms. Level three would be domain. So it's ground to ground or ground to air as an intersection, or ground to sea or sea to air. They're all, like, all the nuances with different domains. Then level four, would be higher levels of autonomy, such as swarming, drone carriers, drone nests, et cetera.Brandon [00:42:39]: Now when you're saying level, you're talking about dimensions, not about-Yaroslav [00:42:42]: Sorry. YeahBrandon [00:42:43]: Autonomy levels. So dimension four.Yaroslav [00:42:43]: The dimension. Yeah, I used to say I was supposed to say dimension. I say dimension because each of them works with another, right? So you might have, like third level autonomy, fixed wing drone operating in land to air, and stuff like that right? And then operating in a swarm or operating from a nest. Right? Then you have, sort of dimension number five is environment. So is it day or night? Is it summer or winter? Is it, humid, cold, dry? What kind of target is it? Is your target hiding in a forest, or is it, behind a hill or within buildings? So all of that is environment. Then you have, dimension number six is command and control. How are you dealing with or like, tens of thousands of those assets around the battlefield? How are you coordinating that on the higher levels of command? How are you collecting data? All that.Yaroslav [00:43:44]: Dimension number seven would be infrastructure, so things like simulation, data collection tools, security, deployment mechanisms, et cetera. So all those systems have to be developed separately and integrate with all the others. And finally, dimension number eight is sort of distribution. Have you deployed 100 of these systems or 100,000 of these systems? Because those are two very different ballgames. So that now gives you a more broad overview of how autonomy propagates across the battle space.Targeting, Human Responsibility, and Rules of EngagementNoah [00:44:23]: As someone who has done machine learning and had gone out of distribution and had things, go horribly wrong, you were talking several of these, kind of axes of thinking about drone warfare seem like they could be very susceptible to some sort of distribution shift if you start making things autonomous.Yaroslav [00:44:41]: Like what?Noah [00:44:41]: I mean Well, first ofYaroslav [00:44:43]: If the I'm very interested Sort of sort of kinds of scenarios that you're thinking about.Noah [00:44:48]: Like the most obvious one is you, if I assume these are computer vision guided systems for at least the last mile, how do you ensure that oh, well, like you now have some fog roll in or something, and you, the drones just attack the wrong thing? Or maybe, it probably will not turn around and fly back and attack you, but youYaroslav [00:45:10]: Same, the same, the same question, how do you ensure that your mortar fire hits the right thing? Well, it's like mortar fire, give or take half a kilometer could be plus or minus. So maybe you fire one, and then you fire another. So drones are actually, much better in being precise in those scenarios. And I think, to your point, I think five to 10 years from now it will be immoral to use weapons without AI.Yaroslav [00:45:44]: ‘Cause weapons without AI will be more likely to cause, collateral damage or unwanted damage. Same way, it will be immoral to drive your own car manually on a public road because it's more likely to cause, unwanted damage.Noah [00:46:02]: Wow, I never considered that mightBrandon [00:46:04]: Really? That's definitely coming.Yaroslav [00:46:07]: Anyway.Brandon [00:46:07]: No, but that' I don't know, it's an obvious, an obvious thought. I agree with you.Brandon [00:46:12]: I, No, they, obviously they're not going to let you drive once most of the cars on the road are autonomous.Noah [00:46:17]: No, that one, don't I believe.Yaroslav [00:46:19]: No, I think you were you were talking about drones, right?Brandon [00:46:21]: The drones, right. Cool.Yaroslav [00:46:22]: The weapons, right?Brandon [00:46:23]: Friendly fire and collateral damage and stuff like that is all minimized with AI.Brandon [00:46:27]: Here's my question. Take all let's go to level six autonomy. Let's take all of the target selection. Let's take all the battlefield data, integrate it into one big AI, and have that big AI basically be in command of the battlefield And agentically do target selection.Yaroslav [00:46:44]: Be the general, right?Brandon [00:46:44]: It's a general. It's, you've cut humans out of the loop except maybe as dexterous robots, repairing drones and fastening things to drones or maybe something like that because you don't have those robots yet. How soon are we there? AI general.Yaroslav [00:46:58]: The most important thing to ask ourselves is who will be faster to that us or our adversaries?Brandon [00:47:07]: I assume us, but how fast will we be to that? I hope us.Yaroslav [00:47:11]: I hope so too.Brandon [00:47:12]: How fast can we Like when are we looking at that in terms of like horizons years?Yaroslav [00:47:18]: Like technically, it could be done now. The question is of course, there's, some engineering work to be done. The bigger challenge is deployment. Right? So okay, technically Like operation in Iran, right? They, the publicly, it was claimed that I think Palantir system was used for target designation, et cetera, et cetera. So it is not exactly as you say, the AI makes all the decisions, but basically AI goes through all the data you have, gives you these 1,027 different targets and says, “You-- To confirm, please press Okay.” And you look at the targets and you're like, “Yeah, sounds right. Press Okay.”so that's, I think that's where we are now already, or we were a couple weeks ago as we're recording this on April 10th. Another question is how massively deployable it is. Is it, like, every decision being made like that or is it, like, just some of the decisions made like that? And then different levels of command and control. There you have, like, the platoon, the company level, the battalion, et cetera, et cetera, et cetera. But the tricky thing here when we get into that territory, the tricky thing is If your enemy is getting advantage of being Thousand times faster than yourself by deploying such systems What do you do?Yaroslav [00:49:10]: You got to-Brandon [00:49:12]: The if the enemy is a thousand times faster than you at deploying those systems?Yaroslav [00:49:16]: Like, if enemy starts deploying level six autonomy, as you call And you have not started doingBrandon [00:49:22]: You're in troubleYaroslav [00:49:23]: Yes, exactly. So you have to catch up. So my point is that it is very important to think about the safety of these systems, but that thinking should not slow you down in developing them because they are critical for your existential, survival, right? And like, one person who doesn't think, doesn't get to think about the ethics of the war is a dead person. That person surely doesn't get to think about that.Brandon [00:49:52]: What would be the safety risk of such a system?Yaroslav [00:49:55]: Of course-Brandon [00:49:56]: Friendly fire?Yaroslav [00:49:56]: Just wrong decisions, right?Brandon [00:49:59]: I see.Yaroslav [00:49:59]: Maybe, these decisions-AI Command Decisions, Dead Zones, and Complex BattlefieldsBrandon [00:50:06]: Skynet AI decides it's going to useYaroslav [00:50:08]: No, these-Brandon [00:50:08]: Drone army to kill usYaroslav [00:50:09]: Decisions will not only be made about drones. They are likely to made about what the humans should do on your side as well. Then obviously some environments are more like Ukrainian-Russian war, where you haveBrandon [00:50:26]: It will have to choose to risk lives. It will have to choose to sacrifice human lives-Yaroslav [00:50:28]: Of courseBrandon [00:50:29]: On your side.Yaroslav [00:50:29]: Of course. And then some environments are just, like, dead, like, dead zones and there are no civilians there, or virtually no civilians close to the front line because, like, super dangerous. Everyone has evacuated from there. But there are other environments which are more like, okay, there's a counterterrorist operation. There's, like, a group of terrorists or a group of civilians. Or like, it's like the recent operations in Iran, I imagine that the US and Israeli forces do not want to harm civilians. They only targeted the military targets there, right? So in those situations, it's a different level of responsibility for that decision-making as well. And then there is just such a big variety of those military missions, and I'm not even, like, well-informed or well-educated in military science to tell you about all those scenarios. We would need to put some general besides me, and maybe a Ukraine general and American general would have told you very different stories about these things.Brandon [00:51:34]: Got it. Can I ask a few more questions? All right. So in 2013, I wrote one of my first, paid articles ever was about how the era of drones will change human society. I was just sitting around bored thinking about things.Yaroslav [00:51:54]: You were way ahead of your time.Brandon [00:51:55]: I said, I said, “The following will happen.”Yaroslav [00:51:57]: It's, this article is real. I've read it.Yaroslav [00:51:58]: It's actually-Brandon [00:51:59]: I said small autonomous, suicide drones, will cleanse the battlefield of human infantry. Human infantry will not be able to stand against swarms of AI-powered, suicide drones. That was I didn't even know about, like, AlexNet at the time, I think.Yaroslav [00:52:19]: You're just an avid sci-fi reader.Brandon [00:52:23]: I'm an avid sci-fi reader, but also, like, it's not Like, there will be a way to do that. It's a it's a nonlinear multidimensional search problem, and you get enough compute, you'll find some search algorithm that will get you there. And soBrandon [00:52:38]: I, yeah, I think that one sentence describes the bitter lesson right there.Brandon [00:52:41]: It's just like it's a multidimensional search space. You search it somehow. I don't know. Figure out some get a grad student-Yaroslav [00:52:47]: Sooner or laterBrandon [00:52:47]: To make a search algorithm.Brandon [00:52:48]: It's not that hard. Anyway, so but then, but I guess the point is The point is that human infantry on the battlefield will be will be gone at the end. I wrote that in 2013. Many people on social media laughed at me for that called me hysterical, said things like, “Electronic warfare will knock all the drones out of the sky.”like, “You need humans to hold ground.”that's something you still hear from a lot of people on social media today. I feel that this article that I've written has never been directionally wrong. It has gotten more and more right steadily over time, and that we're very reading the battlefield reports from Ukraine, where, human infantry are basically guy, like a few guys hiding in dugouts for months, and I'm not sure what they're doing.Yaroslav [00:53:35]: That's on Ukraine's side. On the Russian side, that's just like a zerg rush.Brandon [00:53:38]: The zerg rush, and then they just die. Then, but they have some guys in dugouts too, right? Like hiding in dugouts for months.Yaroslav [00:53:45]: They have. Yeah.Brandon [00:53:45]: Like, but that like, what are those guys doing in the dugouts? Are providing, like, frontline, like, reconnaissance? Like, what are they doing?Yaroslav [00:53:54]: If there is a guy in a dugout with some bullets and automatic weapon, the other guy cannot come and take the that dugout. That'Brandon [00:54:07]: I seeYaroslav [00:54:08]: They are they're establishing control over territory.Brandon [00:54:10]: I see. So that is so there still is a use for human infantry on the battlefield as of today.Yaroslav [00:54:15]: LikeBrandon [00:54:15]: How long will that last?Yaroslav [00:54:17]: I think it will last for a while. This is funny. There's this whole Layer of the modern culture, a modern Ukraine culture built around the war-related stuff. So there is this -Punk rock band, that is called SZC, I guess in English that would be. Which stands short for like a deserter or something like that. So anyhow, this band has a song titled “2030.” It's basically about the year 2030, and the war still goes on as like the whatever, third world war or whatever. And they basically, they, sang about the AI and like cyborgs and everything, but the simple infantry is still needed, and we're still, like, getting cold in those dugouts, and we're still doing our job. That's sort of the theme of the song. And it seems like that's actually what's going to happen. There areGround Robots, Simulation, and the Limits of World ModelsBrandon [00:55:30]: Ground robots will not replace humans in the dugouts soon.Yaroslav [00:55:34]: I'm very much interested in following the whole humanoid robot theme andBrandon [00:55:39]: What about like a dog robot?Noah [00:55:41]: Or just mobile controlled platforms or something.Brandon [00:55:44]: Spider robot, yeah.Brandon [00:55:45]: Everything evolves into a crab.Brandon [00:55:46]: You build a crab robot.Yaroslav [00:55:47]: A humanoid-Noah [00:55:48]: The carcinization of warfare.Yaroslav [00:55:51]: There is a lot of utility in humanoid robots because the world is designed around humanoids. So I would not, like, 100% disqualify the possibility that sometimes 10 years in the future, humanoid robots, will be actually fighting. So that's an actual Terminator kind of scenario.Brandon [00:56:14]: Yeah, in the first Terminator movie, you look at what they've got on the battlefield, they've got flying bomber drones and humanoid robots.Yaroslav [00:56:20]: Look, the cost of large language models of running them is getting so low, you can have basically an inexpensive computer running, what was a state-of-the-art model a year and a half ago, running it locally on a device with an open source model, which also means that the Chinese can have it, the Russians can have it, the North Koreans can have it, et cetera. So that is already possible. And with when we're looking at the acceleration of the neural nets, I would've, if not the acceleration of the large language models, I would've said that I don't think that humanoid robots will be able to be useful in the battlefield earlier than in 10 years. But if you account for the exponential, it might be five years or so. The problem with all of the autonomous systems, and it's like starts with self-driving cars and even with all the AI, like modern day AI agents, to make them really, useful, you have to solve such a long tail of edge cases, that it's really difficult to make them useful. Like we were promised, self-driving cars, what, like 2007, Sebastian Thrun and Google, and even before that all the challenges, everything. And Elon of course told us it's going to be one year from 2014, and now we still don't have self-driving Teslas everywhere. We have Waymos in SF and some other places, but they're still, like, not perfect. So I think, I expect something similar from self-flying drones and fully autonomous drones, and we saw that firsthand as with each level of autonomy that we're adding, there is a very wide distance between a prototype and something that is ready to be scaled to millions of units and something that has been scaled to millions of units. But the race with like AI coding tools is just insane. So things might accelerate very fast, faster than we can imagine.Noah [00:58:46]: I think your point is that with due to this long tail behavior Level one autonomy as you've defined it, is actually very natural. Like you basically are just solving an image recognition and tracking system.Yaroslav [00:59:02]: It's actually interesting that you say it that way, and I thought about this the very same way, and we have this joke that there are like 200 companies in Ukraine which are trying to solve last mile, targeting or terminal guidance. It seems like we're like the only company that actually solved that because even that problem-Noah [00:59:22]: I'm not saying it's, I'm not saying it's trivial, but it's at least something that you imagine given our current state.Yaroslav [00:59:26]: Like us and Eric Schmidt, like Eric Schmidt's companies are pretty good.Yaroslav [00:59:29]: Like, I actually have lots of respect to what they're doing, and they're, they have been practically influential and helpful on the battlefield, and they have good engineering.Noah [00:59:38]: I wasn't, I wasn't saying it's trivial. I'm just saying this is a something naturally adaptive based upon things that we know work, well. But some of the other domains that where you do have to make decisions and you have a long tail become much harder, and you worry about edge cases more.Yaroslav [00:59:57]: Like the more, the more complex behavior you're trying to simulate, the more edge cases there are right? The more ways to do it wrong there are. And then there are different approaches. It's like if you think about, if you read academic papers about robotics, right? You sort of the robot is represented as something that has the sort of sensor input, and then you have three, levels of sort of logics or decision-making, which are perception, planning, and control, and then you have actuators as output.So pre-neural nets, you would do perception output and control all with classic logics, right? Then, with AlexNet and computer vision, you could do perception with neural nets and the rest with logic. You cannot currently do each of those separately with neural nets, each of those separately with logics, or you can just have one huge neural net that just takes lots of sensory data. It's not just pixels. Could be sound, could be accelerometer, could be everything, as input, and just outputs the controls. And some of the self-driving car companies are doing that or like, experimenting between different ways of doing that. So you can also, like, think about that and the way you implement those features, also influences how much degrees of freedom the system would have, right? Like control, you can do it classical algorithmic control with common filters and PAD filter, PAD controllers, et cetera, or you can do a neural net, that was trained in a gym with a reinforcement learning, et cetera. And those would be two different behaviors of a system.Noah [01:01:53]: I-- Maybe my point was just much more high level. It'Yaroslav [01:01:56]: Or you can If you go even like, if you go high level, you can, you can like train to like have whatever, like Feifei Li and folks who are doing like physical, sortBrandon [01:02:08]: World modelsYaroslav [01:02:08]: World models, right, physical intelligence, they're trying to make these big models and sort of understand the world and then supposedly you have such model and you can tell a drone, “Okay, like, go over that hill and like, find the bad guys and then get them,”or “Make me a video, make me a photo of the guy smiling and get back to me.” Right? That's one way. Another way you have like these subsystems, like one is navigation, another is finding the person, another is like getting to them to take a photo. And those are again, very different behaviors. And then it's not that one is necessarily better than the other, and we might have more technological ability to do one or another. But all of those systems will exist. And then again, you should always keep in mind that it's only the not only the good guys that are developing these systems, the bad guys are developing these systems as well.China's Drone Supply Chain and the West's Manufacturing GapNoah [01:03:00]: I guess where I'm going with this back to Noah's original thought with the end of the end of the soldier. And so in order to replace-Brandon [01:03:10]: Or at least the end of the rifleman.Noah [01:03:11]: Or the end of the rifleman, yeah.Yaroslav [01:03:13]: I'm not seeing that very close, and it was like I'm, as much as I'm a lover of sci-fi and all of that and a technologist, the more I try to beYaroslav [01:03:27]: Like the I try to have certain humility about these things, and like the military, domain and there was just so much human history and blood and tears, dedicated to sort of understanding this art of war and perfecting it and so on. There is so much knowledge in there that I don't feel like I even started to comprehend, a lot of that. But one thing that I really understood is that even though drones are now making eighty percent of the casualties, you go to the actual officers, you talk to the actual, like, brigade commanders, corps commanders, and they explain to you, how all of it fits together, how when you're thinking about an operation that involves a couple thousand people to get this piece of land, out of the enemy's hands, deoccu deoccupy it, how it is so complex, it involves, dozens of different types of drones and then land operations and reconnaissance operations, psychological operations and then aviations and tanks and logistics and all kinds of these different assets. So modern warfare is really very complex, and the fact that the drones are the latest, coolest thing, and then the AI is latest, coolest thing, doesn't mean that now it's that and only that right? So yeah. Whoever's looking into that I think should realize that it's not just what the press talks about, that the reality is much more difficult, much more complex.Brandon [01:05:17]: Let's talk about China and China's manufacturing capabilities. So suppose that someone, like suppose the United States went to war with China. AndYaroslav [01:05:26]: I hope not.Brandon [01:05:27]: I hope not as well. And then but suppose that drones were very essential to that war of all the types of drones that we're talking about here, and that suppose that China said, “All right, well, you need X and Y and Z, to make those drones to fight us, and we control the production of X and Y and Z, so we're just going to cut you right off, and now you have no drones.”Brandon [01:05:47]: I know that a number of countries, including Ukraine and Taiwan, have been making moves to China-proof their drone productions that China couldn't do that. Examples of things they might be able to cut off might include rare earths, fiber optic cable that you were talking about before, various other things that where even if they don't control one hundred percent of the production, they control enough of the production that would be extremely expensive to produce it without relying on Chinese sources. Or the market's fragmented enough, et cetera. What do you see as China's key bottlenecks, and how easy are those to overcome in terms of China-proofing drone production in case of a war against China?Yaroslav [01:06:30]: Let me start with a saying that -Although China does not sell directly to Ukraine and it does sell directly to Russia, a lot of Ukrainian supply chains, they start in China, right?Yaroslav [01:06:49]: We're not in a conflict with China, and we would not want to be in a conflict with China. And we'd hope that China stays a neutral power between Ukraine and Russia and the US as well. That said, the scenario that you're describing, everything is much worse.Yaroslav [01:07:11]: Think about this. Last year, Ukraine produced four million FPV drones. Ukraine is not the most industrious nation in the world.Yaroslav [01:07:19]: China can produce four billion of these FPV drones.Yaroslav [01:07:23]: China can make them not drones with propellers, but fixed-wing drones, which go not forty kilometers far, but maybe two to three hundred kilometers inland.

The Internet Report explores recent disruptions at Anthropic and X, analyzing how architectural differences and deployment cycles impact digital reliability. CHAPTERS 00:54 Anthropic's Claude: Three Early April Events 06:03 The Deployment Hypothesis 08:51 The Reliability Challenge for AI Services 11:24 A Pattern to Disruptions at X 12:45 Outage Trends: By the Numbers 17:59 Get in Touch For additional insights, check out The Internet Outage Survival Kit: https://www.thousandeyes.com/resources/the-internet-outage-survival-kit?utm_source=soundcloud&utm_medium=referral&utm_campaign=fy26q4_internetreport_q4fy26ep135_podcast  ———  Want to get in touch?  If you have questions, feedback, or guests you would like to see featured on the show, send us a note at InternetReport@thousandeyes.com. Or follow us on LinkedIn or X. ———  ABOUT THE INTERNET REPORT  This is The Internet Report, a podcast uncovering what's working and what's breaking on the Internet—and why.   Tune in to hear ThousandEyes' Internet experts dig into some of the most interesting outage events from the past couple weeks, discussing what went awry—was it the Internet, or an application issue?  Plus, learn about the latest trends in ISP outages, cloud network outages, collaboration network outages, and more.  Catch all the episodes on your favorite podcast platform:   - Apple Podcasts: https://podcasts.apple.com/us/podcast/the-internet-report/id1506984526   - Spotify: https://open.spotify.com/show/5ADFvqAtgsbYwk4JiZFqHQ?si=00e9c4b53aff4d08&nd=1&dlsi=eab65c9ea39d4773   - SoundCloud: https://soundcloud.com/ciscopodcastnetwork/sets/the-internet-report   - YouTube: https://www.youtube.com/@theinternetreport_official

The Internet Report explores recent disruptions at Anthropic and X, analyzing how architectural differences and deployment cycles impact digital reliability.For additional insights, check out The Internet Outage Survival Kit: https://www.thousandeyes.com/resources/the-internet-outage-survival-kit?utm_source=wistia&utm_medium=referral&utm_campaign=fy26q4_internetreport_q4fy26ep135_podcast ——— Want to get in touch? If you have questions, feedback, or guests you would like to see featured on the show, send us a note at InternetReport@thousandeyes.com. Or follow us on LinkedIn or X: @thousandeyes ——— ABOUT THE INTERNET REPORT This is The Internet Report, a podcast uncovering what's working and what's breaking on the Internet—and why. Tune in to hear ThousandEyes' Internet experts dig into some of the most interesting outage events from the past couple weeks, discussing what went awry—was it the Internet, or an application issue? Plus, learn about the latest trends in ISP outages, cloud network outages, collaboration network outages, and more. Catch all the episodes on your favorite podcast platform: - Apple Podcasts: https://podcasts.apple.com/us/podcast/the-internet-report/id1506984526 - Spotify: https://open.spotify.com/show/5ADFvqAtgsbYwk4JiZFqHQ?si=00e9c4b53aff4d08&nd=1&dlsi=eab65c9ea39d4773 - SoundCloud: https://soundcloud.com/ciscopodcastnetwork/sets/the-internet-report - YouTube: https://www.youtube.com/@theinternetreport_official

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Continual Improvement is at the heart of ISO Management, a large part of which is dedicated to ensuring issues don't reoccur. This is more than just putting a plaster on it and calling it a day, it's about finding the root cause. This not only eliminates wasted time, effort and money with firefighting repeated mistakes, but also drives meaningful improvement. Over the years, many techniques have been developed to help with finding cause. In this episode, Ian Battersby explores the need to find the root cause of issues in ISO Management and explains some key techniques for root cause analysis that you can put into practice to help stop recurring issues.   You'll learn ·      What is meant by 'finding cause'? ·      Why do you need to find the cause? ·      Where is finding cause specified in ISO Standards? ·      Finding cause in practice ·      What are the 5 Why's? ·      What is the fish bone / Ishikawa? ·      What is FMEA? ·      What is fault tree analysis? ·      How do these techniques work in practice?   Resources ·      Isologyhub   In this episode, we talk about: [02:05] Episode Summary – Ian dives into finding cause within ISO Management, explaining various techniques to help you prevent recurring issues. [03:15] What is meant by 'Finding cause'? When an output from a process is not what was expected, then it is classed as a non-conformity which will need to be addressed through corrective action. Before you can put that action into place, you need to identify the root cause for the issue. It's about putting right what went wrong. [04:00] Why do you need to find cause? Ian gives an example of a reactive response to resolving an issue, it didn't get to the root of why the mistake happened in the first place.   Finding cause is necessary to stop issues from repeating, rather than simply firefighting issues as they occur. ISO terminology has updated to reflect this over the years. There used to be a term called 'Preventive action', but this has since been changed to 'Corrective action' following on from the 2015 Annex SL update to many ISO Standards. This reflects the new risk-based approach to ISO management. The terms are largely the same in nature, but preventive action was widely misunderstood and so this was renamed and clarified following 2015. [05:55] Where is finding cause specified in ISO Standards? As with many aspects of ISO, the need for finding cause can be found in a few places within a Standard, including: - Clause 6.1.1 Planning: It specifies the need to determine risks and opportunities that need to be addressed. This is because they will affect the desired outcome of your Management System. It's also a good place to start thinking about how to reduce those risks. Evaluating your strengths and weaknesses also gives you the chance to contemplate whether your existing processes are good at delivering what you want. Clause 10 Improvement: The Standard states something to the effect of 'the organisation shall determine and select opportunities for improvement and implement any necessary actions to address those opportunities' These opportunities will focus on improving products and services, which includes correcting, preventing or reducing undesired results. Also included under clause 10 is a subclause that directly addresses non-conformities and corrective action. These specify not only the need to resolve issues as they arise, but to evaluate the need for action to eliminate the root cause. Additional requirements include the need to review these actions and determine if they are actually effective. Ian goes into Clause 10 in more detail in a previous podcast specifically looking at opportunities for improvement. [14:20] Finding cause in practice – Why a methodology is necessary: Ian provides an example where an employee may lack confidence completing a certain activity. Their lack of competence could lead to a process being delivered incorrectly. That adverse quality outcome would then likely end up with the customer who would raise a complaint, in this instance that could be a damaged product. The damaged product is what needs correcting, from your perspective you would be looking at what caused that to prevent recurrence. Without knowing the initial cause, you would need to determine whether it's a production issue or a human error. These types of scenarios can branch out further than the initial quality issue. For example, if that damaged product causes harm, then it turns into a health & safety risk. If products need to be scrapped, then there's an environmental factor. Complaints related to product quality may also not be recorded in a standard non-conformity system, and could easily be missed for a full investigation to find root cause. This is why it's important to have a consistent approach, in both logging issues and evaluating them to determine cause. [18:10] What are the 5 Why's? This is one of the more popular methods that people use to determine cause. It's simply a case of asking why a scenario happened, usually 5 times, though you can ask more or less depending on how long it takes to reach the core issue. It doesn't require much training and all it requires is an open and honest response to the questions. This method can get answers quickly and is often utilised as an early problem solving technique. [19:30] What is the fish bone / Ishikawa? This is a more visual method to find cause. Depicting a fish skeleton that categorises possible causes and groups these accordingly. These causes are then discussed for a few minutes, typically with teams of people in order to gain different perspectives to help pull apart complex problems into their contributing factors. This method is particularly useful in cases where there isn't a single underlying cause. [20:30] What is FMEA? FMEA or Failure Modes and Effects Analysis is a more structured technique and acts like a risk assessment in reverse. It looks at what can go wrong, what the effect of failure is and then how critical that failure is to the outcome of what you're trying to do. It uses risk priorities to decide what's more important. [21:15] What is Fault Tree Analysis? This method utilises a top-down logical approach. It's a diagrammatic representation of what's going wrong. It asks, does this happen? Yes or no or both, and branches down paths that explore the issue. It allows for quantitative measures with a number output that can help determine how likely recurrence will be.   It's a method that is often used in engineering and manufacturing processes. [22:55] Scatter Diagrams:  Scatter diagrams are a good tool to find correlation. They help visualise the relationship between two variables. If you have data rich environments, these can really help you plot out those relationships and make those links that otherwise may have been missed. [23:40] The 5 Why's in more detail:  The 5 Why's is a great starting technique as it requires little training. Ian provides an example of using the 5 Why's, with the scenario of a worker who has injured themselves while cutting some wood. Using the 5 Why's, he asks these questions: ·      Why did the workers hand slip while cutting the wood? – They were holding the material in one had without the use of any clamping device to keep it steady. ·      Why was the material being held by hand instead of using a clamp? Because there was no clamping device available. ·      Why was there no clamping device available on the table? The design of that workstation didn't take into consideration the need for a permanent clamping fixture. ·      Why wasn't that taken into consideration for the workstation? The risk assessment for that workstation was overlooked. From this exercise, you can see how you can get to the root of an issue by simply asking 'Why' a number of times. Again, it can be more or less than 5 times, the name is simply a guideline. [25:40] The Fishbone / Ishikawa method in more detail: Another favoured simple technique for finding cause is the fishbone method. It utilises 6 categories to get to the root of an issue, those being:- ·      Machine: Addressing the equipment or technology that you use to deliver products and services. ·      Method: The way in which you deliver products and services. ·      Material: The raw inputs into your processes. ·      Measurement: The data and metrics that you use to monitor the successful delivery of your products and services. ·      Mother Nature: The environment and conditions in which you're operating.  ·      Man – Although this has now been updated to 'People', addresses the human element of product and service delivery. This is a great method for instances where there may be multiple root issues, so you can categorise and analyse each of them with multiple perspectives involved as this is considered a more collaborative method for root cause. [28:15] Record your findings: We dive more into this in a previous episode, but essentially, it's a requirement of every ISO Standard to address these non-conformities as they occur. Going through the process of root cause and rectifying the issue will need documentation to prove that you are actively addressing these issues, as well as doing as much as you can to prevent recurrence.   There is no defined way to do this in the Standard, so it can be documented via forms, intranets, other digital systems etc. Documenting all the evidence of resolving issues may seem arduous at times, but it will ultimately lead to genuine continual improvement, and will lead to reduced overall error.   If you'd like any assistance with ISO Implementation or support, get in touch with us, we'd be happy to help. We'd love to hear your views and comments about the ISO Show, here's how: ●     Share the ISO Show on Twitter or Linkedin ●     Leave an honest review on iTunes or Soundcloud. Your ratings and reviews really help and we read each one. Subscribe to keep up-to-date with our latest episodes: Stitcher | Spotify | YouTube |iTunes | Soundcloud | Mailing List

You've worked hard to get your solopreneur business off the ground, but what happens when things start to stall, or worse, head in the wrong direction? In this episode of The Aspiring Solopreneur, Carly and Joe dig into the “improving” phase of the Solopreneur Success Cycle. They uncover the seven most common failure modes that can quietly sabotage your progress, from burnout and stagnation to external threats and even your own mindset. Whether your business is thriving or just surviving, these insights will help you spot trouble early, course-correct with confidence, and keep your business aligned with the life you want to live.Popular Questions and Answers From The EpisodeWhat's the first thing solopreneurs should address when improving their business?If there's an existential threat, that has to come first. These are big, business-ending issues—like a major competitor undercutting your prices, new technology disrupting your industry, or a business model that simply isn't working. Before worrying about tweaks or small improvements, solopreneurs need to tackle these threats head-on to ensure survival.What are the “seven failure modes” solopreneurs should watch out for?Joe outlined seven common failure modes:Misalignment – your business no longer matches your goals or interests.Overload – you're working too much and burning out.Money problems – not enough revenue or profit to sustain you.External risks – outside forces like platform changes or new competitors.Stagnation – growth stalls or customers start slipping away.Execution failure – not delivering quality results to clients.Psychological barriers – mindset issues like fear, procrastination, or undercharging.Recognizing which one you're facing is the first step toward fixing it.How do solopreneurs know when it's time to reimagine their business?It's time to reimagine when your business stops serving your life. Even if it's profitable and running smoothly, if you've lost enthusiasm, feel misaligned, or your personal goals have shifted, that's a signal to step back. Sometimes improving your business isn't about fixing broken systems—it's about reshaping it so it supports the life you want today, not the one you wanted years ago. Okay, this might be the craziest offer we've ever made. We're giving away a solopreneur platform that normally costs five hundred dollars a year…For twenty-five bucks. And not for a month, not for a year… forever.All you have to do is pre-order our new book: Solopreneur Business for Dummies.When we first went solo, we thought we could just Google our way through it. But the advice out there? It was built for startups with teams and money, not someone trying to do it all themselves. We kept thinking: “There's gotta be a better way.”So we made one. LifeStarr Premier is the system we wish we had back then: the tools, the strategy, the community, all in one place.Go to book.lifestarr.com to lock it in.This deal goes away when the book drops, October 6, 2025, and it's not coming back.Pre-order the book. Upload your receipt. You're in. For good.

Guest: Diana Kelley, CSO at Protect AI  Topics: Can you explain the concept of "MLSecOps" as an analogy with DevSecOps, with 'Dev' replaced by 'ML'? This has nothing to do with SecOps, right? What are the most critical steps a CISO should prioritize when implementing MLSecOps within their organization? What gets better  when you do it? How do we adapt traditional security testing, like vulnerability scanning, SAST, and DAST, to effectively assess the security of machine learning models? Can we? In the context of AI supply chain security, what is the essential role of third-party assessments, particularly regarding data provenance? How can organizations balance the need for security logging in AI systems with the imperative to protect privacy and sensitive data? Do we need to decouple security from safety or privacy? What are the primary security risks associated with overprivileged AI agents, and how can organizations mitigate these risks?  Top differences between LLM/chatbot AI security vs AI agent security?  Resources: “Airline held liable for its chatbot giving passenger bad advice - what this means for travellers” “ChatGPT Spit Out Sensitive Data When Told to Repeat ‘Poem' Forever” Secure by Design for AI by Protect AI “Securing AI Supply Chain: Like Software, Only Not” OWASP Top 10 for Large Language Model Applications OWASP Top 10 for AI Agents  (draft) MITRE ATLAS “Demystifying AI Security: New Paper on Real-World SAIF Applications” (and paper) LinkedIn Course: Security Risks in AI and ML: Categorizing Attacks and Failure Modes

ABOUT JON HYMANJon Hyman is the co-founder and chief technology officer of Braze, the customer engagement platform that delivers messaging experiences across push, email, in-app, and more. He leads the charge for building the platform's technical systems and infrastructure as well as overseeing the company's technical operations and engineering team.Prior to Braze, Jon served as lead engineer for the Core Technology group at Bridgewater Associates, the world's largest hedge fund. There, he managed a team that maintained 80+ software assets and was responsible for the security and stability of critical trading systems. Jon met cofounder Bill Magnuson during his time at Bridgewater, and together they won the 2011 TechCrunch Disrupt Hackathon. Jon is a recipient of the SmartCEO Executive Management Award in the CIO/CTO Category for New York. Jon holds a B.A. from Harvard University in Computer Science.ABOUT BRAZEBraze is the leading customer engagement platform that empowers brands to Be Absolutely Engaging.™ Braze allows any marketer to collect and take action on any amount of data from any source, so they can creatively engage with customers in real time, across channels from one platform. From cross-channel messaging and journey orchestration to Al-powered experimentation and optimization, Braze enables companies to build and maintain absolutely engaging relationships with their customers that foster growth and loyalty. The company has been recognized as a 2024 U.S. News & World Report Best Companies to Work For, 2024 Best Small & Medium Workplaces in Europe by Great Place to Work®, 2024 Fortune Best Workplaces for Women™ by Great Place to Work® and was named a Leader by Gartner® in the 2024 Magic Quadrant™ for Multichannel Marketing Hubs and a Strong Performer in The Forrester Wave™: Email Marketing Service Providers, Q3 2024.  Braze is headquartered in New York with 15 offices across North America, Europe, and APAC. Learn more at braze.com.SHOW NOTES:What Jon learned from being the only person on call for his company's first four years (2:56)Knowing when it's time to get help managing your servers, ops, scaling, etc. (5:42)Establishing areas of product ownership & other scaling lessons from the early days (9:25)Frameworks for conversations on splitting of products across teams (12:00)The challenges, complexities & strategies behind assigning ownership in the early days (14:40)Founding Braze (18:01)Why Braze? The story & insights behind the original vision for Braze (20:08)Identifying Braze's product market fit (22:34)Early-stage PMF challenges faced by Jon & his co-founders (25:40)Pivoting to focus on enterprise customers (27:48)“Let's integrate the SDK right now” - founder-led sales ideas to validate your product (29:22)Behind the decision to hire a chief revenue officer for the first time (34:02)The evolution of enterprise & its impact on Braze's product offering (36:42)Growing out of your early-stage failure modes (39:00)Why it's important to make personnel decisions quickly (41:22)Setting & maintaining a vision pre IPO vs. post IPO (44:21)Jon's next leadership evolution & growth areas he is focusing on (49:50)Rapid fire questions (52:53)LINKS AND RESOURCESWhen We Cease to Understand the World - Benjamín Labatut's fictional examination of the lives of real-life scientists and thinkers whose discoveries resulted in moral consequences beyond their imagining. At a breakneck pace and with a wealth of disturbing detail, Labatut uses the imaginative resources of fiction to tell the stories of Fritz Haber, Alexander Grothendieck, Werner Heisenberg, and Erwin Schrödinger, the scientists and mathematicians who expanded our notions of the possible.This episode wouldn't have been possible without the help of our incredible production team:Patrick Gallagher - Producer & Co-HostJerry Li - Co-HostNoah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/Dan Overheim - Audio Engineer, Dan's also an avid 3D printer - https://www.bnd3d.com/Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/

We are joined by Francois Chollet and Mike Knoop, to launch the new version of the ARC prize! In version 2, the challenges have been calibrated with humans such that at least 2 humans could solve each task in a reasonable task, but also adversarially selected so that frontier reasoning models can't solve them. The best LLMs today get negligible performance on this challenge. https://arcprize.org/SPONSOR MESSAGES:***Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/***TRANSCRIPT:https://www.dropbox.com/scl/fi/0v9o8xcpppdwnkntj59oi/ARCv2.pdf?rlkey=luqb6f141976vra6zdtptv5uj&dl=0TOC:1. ARC v2 Core Design & Objectives [00:00:00] 1.1 ARC v2 Launch and Benchmark Architecture [00:03:16] 1.2 Test-Time Optimization and AGI Assessment [00:06:24] 1.3 Human-AI Capability Analysis [00:13:02] 1.4 OpenAI o3 Initial Performance Results2. ARC Technical Evolution [00:17:20] 2.1 ARC-v1 to ARC-v2 Design Improvements [00:21:12] 2.2 Human Validation Methodology [00:26:05] 2.3 Task Design and Gaming Prevention [00:29:11] 2.4 Intelligence Measurement Framework3. O3 Performance & Future Challenges [00:38:50] 3.1 O3 Comprehensive Performance Analysis [00:43:40] 3.2 System Limitations and Failure Modes [00:49:30] 3.3 Program Synthesis Applications [00:53:00] 3.4 Future Development RoadmapREFS:[00:00:15] On the Measure of Intelligence, François Chollethttps://arxiv.org/abs/1911.01547[00:06:45] ARC Prize Foundation, François Chollet, Mike Knoophttps://arcprize.org/[00:12:50] OpenAI o3 model performance on ARC v1, ARC Prize Teamhttps://arcprize.org/blog/oai-o3-pub-breakthrough[00:18:30] Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, Jason Wei et al.https://arxiv.org/abs/2201.11903[00:21:45] ARC-v2 benchmark tasks, Mike Knoophttps://arcprize.org/blog/introducing-arc-agi-public-leaderboard[00:26:05] ARC Prize 2024: Technical Report, Francois Chollet et al.https://arxiv.org/html/2412.04604v2[00:32:45] ARC Prize 2024 Technical Report, Francois Chollet, Mike Knoop, Gregory Kamradthttps://arxiv.org/abs/2412.04604[00:48:55] The Bitter Lesson, Rich Suttonhttp://www.incompleteideas.net/IncIdeas/BitterLesson.html[00:53:30] Decoding strategies in neural text generation, Sina Zarrießhttps://www.mdpi.com/2078-2489/12/9/355/pdf

 In this Tactics for Tech Leadership podcast episode, Andy and Mon-Chaio explore SWIFT (Structured What If Technique). While traditionally seen as a technical tool for failure analysis, the hosts consider its potential applications in leadership and organizational contexts. Listeners will learn how SWIFT can help anticipate system failures even before they occur, from technical systems like Redis caches to social-technical systems like performance reviews and hiring processes. By the end, you'll understand how to adapt this structured method for diagnosing issues and improving both technical and organizational systems.Transcript: https://thettlpodcast.com/2025/03/18/s3e10-swiftly-understanding-failure-modes/ReferencesSWIFT - https://www.asems.mod.uk/toolkit/swift

# Full transcription available at [heartsofgoldpodcast.com](http://heartsofgoldpodcast.com/)   ## Episode Summary   Makayla Hoefs shares the inspiring story behind her Girl Scout Gold Award project, *"Coding for Cookies."* This innovative initiative bridges Girl Scouts and robotics, offering young girls hands-on STEM experiences through engaging events. Makayla discusses how her project evolved, collaborating with the Minnesota and Wisconsin Lakes & Pines Council, and making the program sustainable for future generations. Listen to hear about the impact she's made, the challenges she faced, and how she encourages girls to explore STEM fields.   ## More from Makayla   My name is Makayla Hoefs from Becker, Minnesota. I am a senior at Becker High School, and I plan on going to a four-year college next fall to get my master's degree in electrical engineering.   I have been a Girl Scout for about ten years. Throughout my time in Girl Scouts, I have earned my Bronze and Silver Awards and have completed many service projects. Last year, I was a Girl Scout delegate for my service unit.   I am also involved in Student Council, National Honors Society, archery, and robotics. This is my fourth year on the Becker Robotics team, *C.I.S. 4607.* I am part of the electrical department and facilitate *Failure Modes and Effects Analysis.* My time in robotics has inspired me to become an engineer and a woman in STEM.   ## What You'll Learn in This Episode   - How *"Coding for Cookies"* introduced over 100 Girl Scouts to robotics   - The collaboration between Makayla's robotics team and the Girl Scout council   - Challenges in creating sustainable robotics kits   - Makayla's advice for Gold Award candidates and key lessons from the process   ## Follow Makayla's Journey   Check out the resources from her project at [Coding for Cookies](https://sites.google.com/frc4607cis.com/cis4607/coding-for-cookies)   ## Connect with Us   Follow *Hearts of Gold* for more inspiring Gold Award stories. Don't forget to follow or subscribe and leave a review!  

Send us a Text Message.Ready to transform your team meetings from chaotic to cohesive? Discover a method used by Six Sigma practitioners, continuous improvement teams, and design sprints to make your meetings more effective and efficient. We'll guide you through the phases of discovery, examination, and prioritization to streamline idea generation and ensure that every team member's input is valued. You'll learn techniques for individual brainstorming, anonymous idea sharing, and collective refinement, making your meetings not just productive but a crucial part of your design process.But that's not all. We dive into the utilization of quality tools for superior team decision-making during design Failure Modes and Effects Analysis (FMEA). Explore how to categorize and evaluate potential failures, assign severity ratings, and use tools like tree diagrams and fishbone diagrams to organize complex discussions. By focusing on collaboration and consensus, you'll be setting your team up for effective failure analysis. Join us to elevate your team meetings and turn them into a powerhouse of creativity and efficiency.Visit the podcast blog for this episode.Other episodes you might like: Brainstorming within Design SprintsWays to Gather Ideas with a TeamProduct Design with Brainstorming, with Emily Haidemenos (A Chat with Cross Functional Experts)Give us a Rating & Review**NEW COURSE**FMEA in Practice: from Plan to Risk-Based Decision Making is enrolling students now. Visit the course page for more information and to sign up today! Click Here **FREE RESOURCES**Quality during Design engineering and new product development is actionable. It's also a mindset. Subscribe for consistency, inspiration, and ideas at www.qualityduringdesign.com.About meDianna Deeney helps product designers work with their cross-functional team to reduce concept design time and increase product success, using quality and reliability methods. She consults with businesses to incorporate quality within their product development processes. She also coaches individuals in using Quality during Design for their projects.She founded Quality during Design through her company Deeney Enterprises, LLC. Her vision is a world of products that are easy to use, dependable, and safe – possible by using Quality during Design engineering and product development.

What is an FMEA? When should you use it? Why is it an important step in helping maintenance teams move from a break-fix maintenance state to one that is more proactive? In this episode of Great Queston: A Manufacturing Podcast, Plant Services editor in chief Thomas Wilk spoke with a specialist in the reliability field, Brian Hronchek, to start answering these questions and more about failure modes and effects analyses. Brian draws from his former experience as reliability engineer for U.S. Steel, maintenance manager for Exxon Mobil, and a 16-year veteran of the Marine Corps, in addition to his current work as a principal trainer and consultant at Eruditio.

A couple years ago, my agency asked me to write some guidance on sediment modeling, so, I reached out to the morphological modelers I knew, and particularly the model developers who write the morphological model code other people use.I asked them about the common failure modes they have seen and best practices they teach, and realized we had all essentially spent a decade or two, learning the same principles. So when the US federal agencies held their periodic Federal interagency sediment conference (SEDHYD) last year, I invited three of the model developers I have learned from over the years (Alex Sanchez, Gary Brown, and Blair Greimann), to participate in a panel discussion on their lessons learned.And the panel was much more popular than we expected.  It turns out, there's appetite conversations like this.  So, I turned on the mics and we did a little editing, and we're running it here.Here are brief bios for our guests.:Gary Brown did his graduate work at the university of Florida and works at the Coastal and Hydraulics Lab which is part of ERDC, the Corp's major R&D center in Vicksburg Mississippi.   He's been developing sediment models for 29 years including SEDLIB, a set of sediment algorithms that are called by ERDC's hydraulic model, ADH or Adaptive hydraulics.  Alex Sanchez sits in the office next to me.  For the last 9 years, he has worked here at HEC and spearheaded the work to add 2D sediment to HEC-RAS which includes a novel formulation for the sub-grid approach.  But actually Alex started developing sediment models at ERDC's Coastal and Hydraulics Lab where he worked for 8 years, while working on the Coastal Modeling System which is still used for Corps of Engineers coastal applications.  Blair Greimann got his PhD from the University of Iowa and worked at the Bureau of Reclamation's Technical Service Center in Denver for more than 23 years, before his recent move to Stantec.  While working at the Bureau Blair led the development of SRH-1D and applied this model to a range of projects including the Matilija and and Klamath Dam removals.Finally, we were lucky enough to have Doug Shields moderating this session so you will hear from him in the breaks between the four sub-topics.  Dr. Shields, worked for more than 20 years at the Sedimentation Lab of the Agricultural Resource Center in Oxford MS and 10 years at ERDC and has taught at both Tennessee State and Old Miss and we were fortunate to draw Doug as a moderator.  (Note: I did not mic Doug, but wanted to keep his thoughtful and winsome transitions, so his sound quality is not at the same level as the rest of the recording).After Doug and I introduced the session you will hear from Blair Greimann, Alex Sanchez, me again, and Gary Brown in that order.The conference paper associated with this session is here:https://www.sedhyd.org/2023Program/1/157.pdfThank you to the SEDHYD organizers (including but not limited to ) for hosting this conversationThis series was funded by the Regional Sediment Management (RSM) program.Stanford Gibson (HEC Sediment Specialist) hosts.Mike Loretto edited the episode and wrote and performed the music.Video shorts and other bonus content are available at the podcast website (which was temporarily down but is back up now):https://www.hec.usace.army.mil/confluence/rasdocs/rastraining/latest/the-rsm-river-mechanics-podcast...but most of the supplementary videos are available on the HEC Sediment YouTube channel:https://www.youtube.com/user/stanfordgibsonIf you have guest recommendations or feedback you can reach out to me on LinkedIn or ResearchGate or fill out this recommendation and feedback form: https://forms.gle/wWJLVSEYe7S8Cd248

Quality during Design isn't just an add-on; it's a fundamental aspect that drives innovation, efficiency, and customer satisfaction!Welcome back from our brief hiatus!One of the highlights of this episode is the introduction of an upcoming FMEA course on Udemy, with The Manufacturing Academy. FMEA, or Failure Modes and Effects Analysis, is a systematic method for evaluating our offerings to identify where and how they might fail and to assess the relative impact of different failures.  Dianna's approach to FMEA is not only about adhering to traditional methods but also about addressing the criticisms and limitations often associated with them. This course promises to be a fresh take on risk-based decision-making. You'll hear more about it when it is released!Moreover, the episode touches upon the 'Quality During Design Fast Track' program, which is currently in the works and open to listener feedback. The initiative aims to harness quality tools in novel ways, even before a design concept is fully fleshed out. It emphasizes the importance of early input from cross-functional teams to gather requirements and user needs, thereby making the design process more effective and efficient. This program is system-based and affects how products and services are developed, leading to more thoughtful, user-centric designs.Listeners are invited: please take brief survey to help Dianna with aspects of these upcoming courses and more.  www.qualityduringdesign.com/surveySupport the show**FREE RESOURCES**Quality during Design engineering and new product development is actionable. It's also a mindset. Subscribe for consistency, inspiration, and ideas at www.qualityduringdesign.com. About meDianna Deeney helps product designers work with their cross-functional team to reduce concept design time and increase product success, using quality and reliability methods. She consults with businesses to incorporate quality within their product development processes. She also coaches individuals in using Quality during Design for their projects.She founded Quality during Design through her company Deeney Enterprises, LLC. Her vision is a world of products that are easy to use, dependable, and safe – possible by using Quality during Design engineering and product development.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: What rationality failure modes are there?, published by Ulisse Mini on January 19, 2024 on LessWrong. How do people fail to improve their rationality? How do they accidentally harm themselves in the process? I'm thinking of writing a post "How not to improve your rationality" or "A nuanced guide to reading the sequences" that preempts common mistakes, and I'd appreciate hearing people's experiences. Some examples: It took me an absurdly long time (like, 1-2yr in the rat community) before I realized you don't correct for cognitive biases, you have to "be introspectively aware of the bias occuring, and remain unmoved by it" (as Eliezer put it in a podcast) More generally, people can read about a bias and resolve to "do better" without concretely deciding what to do differently. This typically makes things worse, e.g. I have a friend who tried really hard to avoid the typical mind fallacy, and accidentally turned off her empathy in the process. The implicit frame rationalists push is logical and legible, and can lead to people distrusting their emotions. And I think it's really important to listen to listen to ick feelings when changing your thought processes, as there can be non obvious effects. E.g. My friend started thinking about integrity in terms of FDT, and this disconnected it from their motivational circuits and they made some pretty big mistakes because of it. If they'd listened to their feeling of "this is a weird way to think" this wouldn't have happened. (I think many people misinterpret sequence posts and decide to change their thinking in bad ways, and listening to your feelings can be a nice emergency check.) Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org

Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: What rationality failure modes are there?, published by Ulisse Mini on January 19, 2024 on LessWrong. How do people fail to improve their rationality? How do they accidentally harm themselves in the process? I'm thinking of writing a post "How not to improve your rationality" or "A nuanced guide to reading the sequences" that preempts common mistakes, and I'd appreciate hearing people's experiences. Some examples: It took me an absurdly long time (like, 1-2yr in the rat community) before I realized you don't correct for cognitive biases, you have to "be introspectively aware of the bias occuring, and remain unmoved by it" (as Eliezer put it in a podcast) More generally, people can read about a bias and resolve to "do better" without concretely deciding what to do differently. This typically makes things worse, e.g. I have a friend who tried really hard to avoid the typical mind fallacy, and accidentally turned off her empathy in the process. The implicit frame rationalists push is logical and legible, and can lead to people distrusting their emotions. And I think it's really important to listen to listen to ick feelings when changing your thought processes, as there can be non obvious effects. E.g. My friend started thinking about integrity in terms of FDT, and this disconnected it from their motivational circuits and they made some pretty big mistakes because of it. If they'd listened to their feeling of "this is a weird way to think" this wouldn't have happened. (I think many people misinterpret sequence posts and decide to change their thinking in bad ways, and listening to your feelings can be a nice emergency check.) Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org

This episode features a conversation between host, Sera Evcimen, and Alan Cohen, author of 'Prototype to Product: A Practical Guide for Getting to Market'. They delve into the complexities, misconceptions and opportunities in medical device development. The discussion includes the importance of generating good requirements, handling regulatory requirements, and conducting risk analysis. They also highlight the value of hiring experts conversant in both engineering and regulatory matters to guide a startup through navigating the FDA and EU approval processes.You can find his consultancy at www.alancohen.com.Rundown of Episode for easy navigation to topics of interest:00:00 Introduction and Host Background00:58 Guest Introduction: Alan Cohen01:32 Alan Cohen's Background and Experience02:37 Challenges in Medical Device Development05:40 Identifying a Hardware Medical Application Need07:48 Understanding Reimbursement and Business Models09:32 Regulatory Bodies and Compliance12:50 Risk Assessment in Medical Device Development21:22 Fault Tree Analysis vs Failure Modes and Effects Analysis27:48 Balancing Documentation and Process in Medical Device Development33:01 Understanding Development Process Standards33:19 Importance of Following Your Process33:42 Inspection and Audit Procedures34:16 Balancing FDA Requirements and Lean Operations35:13 Testing and Validation in Medical Device Development36:43 Navigating FDA and Institutional Review Boards40:02 Challenges in Medical Device Innovation and Funding47:16 Podcast Break47:29 Podcast break56:02 Navigating Supply Chain and Manufacturing Challenges01:04:09 Understanding FDA and EU Regulatory Differences01:07:00 Final Thoughts and Advice for Medical Device StartupsEnding with TLDL!Music by: Tom Stoke (in addition to royalty-free music provided by Descript)DISCLAIMER The content provided in this podcast is for informational purposes only and should not be construed as professional advice. Pratik Development, LLC., the hosts, guests, and producers of this podcast are not engaged in rendering legal, financial, or other professional services. The hosts and guests disclaim any liability for any errors or omissions in the content or for any actions taken based on the information provided. By accessing and listening to this podcast, you acknowledge and agree that the hosts, guests, and producers of the podcast shall not be held liable for any direct, indirect, incidental, consequential, or any other damages arising out of or in connection with the use of the information presented in the podcast. Furthermore, the hosts, guests, and producers of this podcast make no representations or warranties of any kind, express or implied, about the completeness, accuracy, reliability, suitability, or availability of the information, products, services, or related graphics contained in the podcast for any purpose. Listeners are advised to independently verify any information presented and consult with appropriate professionals before making any decisions or taking any actions based on the content of this podcast. By continuing to listen to this podcast, you indicate your understanding and acceptance of this disclaimer.

In this special episode of Quality during Design Redux, we're pulling episodes from our archive about test results analysis. In our Season 1 - Episode 93  titled "The Fundamental Thing to Know from Statistics for Design Engineering", we talked about hypothesis testing: how it is used for lots of data analysis techniques. The next 4 episodes of this QDD Redux are taking the next steps._________________________________________If we're not careful with or ignore failure modes, we can choose the wrong reliability model or statistical distribution. If our product performance is close to the required limits and/or we need a very accurate model, this could be a big problem.We talk about the importance of failure modes and step-through a tensile-test example to explore these other topics:competing failure modessuspensionsindependent vs. dependentreliability block diagramsThe podcast blog includes extra useful information/links.Support the show**FREE RESOURCES**Quality during Design engineering and new product development is actionable. It's also a mindset. Subscribe for consistency, inspiration, and ideas at www.qualityduringdesign.com. About meDianna Deeney helps engineers work with their cross-functional team to reduce concept design time and increase product success, using quality and reliability methods. She founded Quality during Design through her company Deeney Enterprises, LLC. Her vision is a world of products that are easy to use, dependable, and safe – possible by using Quality during Design engineering and product development.

Assets Anonymous is a 12-step podcast series designed to help you get grounded in reliability basics and create a culture of continuous improvement with your team. This series will feature interviews with George Williams and Joe Anderson of ReliabilityX. ReliabilityX aims to bridge the gap between operations and maintenance through holistic reliability focused on plant performance. In this episode, George and Joe help you understand how your facility's critical assets fail.

Haley is a geotechnical engineer who recently completed her PhD at the University of Alberta.  Amongst other things, Haley and I discuss her CDA award winning paper titled, "A Failure Modes and Effects Analysis Framework for Assessing Geotechnical Risks of Tailings Dam Closure" which was published in the Journal "Minerals".

In this episode we explore what Condition Based Maintenance (CBM) is (aka On-Condition Maintenance).  We'll talk about :- What CBM is- The biggest trap you can fall into when implementing CBM- And what governs how often you do a Condition Based Maintenance task.As asset managers, we know that most Failure Modes occur randomly, and that can seem a little intimidating or maybe even a little scary, but it doesn't have to be because that's where Condition Based Maintenance can be very helpful. The whole point of Condition Based Maintenance is to detect a Potential Failure Condition and take action before failure occurs.  That interval is called the P-F Interval and that is explained in this episode.Free Reliability Centered Maintenance (RCM) Overview Coursehttps://RCMTrainingOnline.com/OverviewLet's get connected on LinkedIn!https://www.linkedin.com/in/nancyreganrcm/

In this episode, we talk about what a Failure Mode is and why Failure Modes are so important to equipment Reliability.  As responsible custodians, it's up to us to identify the plausible Failure Modes that could occur so that we can figure out if and how we should manage each one.  If we don't, it can end up in disaster. Free Reliability Centered Maintenance (RCM) Overview Coursehttps://RCMTrainingOnline.com/OverviewLet's get connected on LinkedIn!https://www.linkedin.com/in/nancyreganrcm

We continue to explore mechanisms of how our cognitive system can be hijacked, leading to a breakdown and failure of natural intelligence. ======= Produced by Inqwire, a public benefit corporation on a mission to help create a world that makes sense. Inqwire is a technology platform designed to restore, enhance, and protect our natural ability to navigate towards what makes sense, alone and together. Learn more: https://www.inqwire.io/

We explore the core vulnerability of our cognitive system that allows for our natural intelligence to fail. ======= Produced by Inqwire, a public benefit corporation on a mission to help create a world that makes sense. Inqwire is a technology platform designed to restore, enhance, and protect our natural ability to navigate towards what makes sense, alone and together. Learn more: https://www.inqwire.io/

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: ML Systems Will Have Weird Failure Modes, published by jsteinhardt on January 26, 2022 on LessWrong. Previously, I've argued that future ML systems might exhibit unfamiliar, emergent capabilities, and that thought experiments provide one approach towards predicting these capabilities and their consequences. In this post I'll describe a particular thought experiment in detail. We'll see that taking thought experiments seriously often surfaces future risks that seem "weird" and alien from the point of view of current systems. I'll also describe how I tend to engage with these thought experiments: I usually start out intuitively skeptical, but when I reflect on emergent behavior I find that some (but not all) of the skepticism goes away. The remaining skepticism comes from ways that the thought experiment clashes with the ontology of neural networks, and I'll describe the approaches I usually take to address this and generate actionable takeaways. Thought Experiment: Deceptive Alignment Recall that the optimization anchor runs the thought experiment of assuming that an ML agent is a perfect optimizer (with respect to some "intrinsic" reward function R). I'm going to examine one implication of this assumption, in the context of an agent being trained based on some "extrinsic" reward function R∗ (which is provided by the system designer and not equal to R). Specifically, consider a training process where in step t, a model has parameters θt and generates an action at (its output on that training step, e.g. an attempted backflip assuming it is being trained to do backflips). The action at is then judged according to the extrinsic reward function R∗, and the parameters are updated to some new value θt+1 that are intended to increase at+1's value under R∗. At some point, the model is then deployed with final parameters θT, and continues to take actions. The following diagram illustrates this process: Now, let's assume that the model θt is a perfect optimizer whose objective is to maximize the discounted value of an intrinsic reward R≠R∗. That is, θt picks the action at satisfying at=argmaxaE[∑∞s=0γ−sR(at+s)∣at=a]. (I know that this is an unrealistic assumption. We'll examine the assumption in detail in the next section, but for now please grant it even if it requires suspending disbelief.) What action at will θt pick? Let's define aR=argmaxaR(a) and a∗=argmaxaR∗(a)--that is, aR maximizes the (instantaneous) intrinsic reward while a∗ maximizes the extrinsic reward. Assuming that γ is close to 1, the model wants aR to be picked on as many future rounds as possible (including at deployment time). If θt plays myopically, this means it would pick at=aR. However, this is not the optimal non-myopic action: if at=aR, then θt+1 will be updated towards producing outputs less like aR and more like a∗, so outputting aR in the current round leads to fewer aR outputs in future rounds. Instead, I think the optimal non-myopic action is approximately the following: during training time, output a∗ on every time step, so that the training process doesn't update θ (since it's already optimal) and hence θt=θt+1=⋯=θT. This guarantees that the deployed agent is θt, which is now free to output aR on every time step without risk of being modified by the training process.[1] The myopic and non-myopic outcomes are illustrated in the figure below: Myopic and non-myopic agents with an intrinsic reward, R. The myopic agent plays high intrinsic-reward actions during training, but is eventually modified to output action a∗ with high extrinsic reward R∗. The non-myopic agent instead plays a∗ during training so that it can pursue its intrinsic reward at deployment, by playing aR at all post-deployment time steps. To put it another way–the model's behavior will look aligned with R∗ during the training process...

Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: ML Systems Will Have Weird Failure Modes, published by jsteinhardt on January 26, 2022 on LessWrong. Previously, I've argued that future ML systems might exhibit unfamiliar, emergent capabilities, and that thought experiments provide one approach towards predicting these capabilities and their consequences. In this post I'll describe a particular thought experiment in detail. We'll see that taking thought experiments seriously often surfaces future risks that seem "weird" and alien from the point of view of current systems. I'll also describe how I tend to engage with these thought experiments: I usually start out intuitively skeptical, but when I reflect on emergent behavior I find that some (but not all) of the skepticism goes away. The remaining skepticism comes from ways that the thought experiment clashes with the ontology of neural networks, and I'll describe the approaches I usually take to address this and generate actionable takeaways. Thought Experiment: Deceptive Alignment Recall that the optimization anchor runs the thought experiment of assuming that an ML agent is a perfect optimizer (with respect to some "intrinsic" reward function R). I'm going to examine one implication of this assumption, in the context of an agent being trained based on some "extrinsic" reward function R∗ (which is provided by the system designer and not equal to R). Specifically, consider a training process where in step t, a model has parameters θt and generates an action at (its output on that training step, e.g. an attempted backflip assuming it is being trained to do backflips). The action at is then judged according to the extrinsic reward function R∗, and the parameters are updated to some new value θt+1 that are intended to increase at+1's value under R∗. At some point, the model is then deployed with final parameters θT, and continues to take actions. The following diagram illustrates this process: Now, let's assume that the model θt is a perfect optimizer whose objective is to maximize the discounted value of an intrinsic reward R≠R∗. That is, θt picks the action at satisfying at=argmaxaE[∑∞s=0γ−sR(at+s)∣at=a]. (I know that this is an unrealistic assumption. We'll examine the assumption in detail in the next section, but for now please grant it even if it requires suspending disbelief.) What action at will θt pick? Let's define aR=argmaxaR(a) and a∗=argmaxaR∗(a)--that is, aR maximizes the (instantaneous) intrinsic reward while a∗ maximizes the extrinsic reward. Assuming that γ is close to 1, the model wants aR to be picked on as many future rounds as possible (including at deployment time). If θt plays myopically, this means it would pick at=aR. However, this is not the optimal non-myopic action: if at=aR, then θt+1 will be updated towards producing outputs less like aR and more like a∗, so outputting aR in the current round leads to fewer aR outputs in future rounds. Instead, I think the optimal non-myopic action is approximately the following: during training time, output a∗ on every time step, so that the training process doesn't update θ (since it's already optimal) and hence θt=θt+1=⋯=θT. This guarantees that the deployed agent is θt, which is now free to output aR on every time step without risk of being modified by the training process.[1] The myopic and non-myopic outcomes are illustrated in the figure below: Myopic and non-myopic agents with an intrinsic reward, R. The myopic agent plays high intrinsic-reward actions during training, but is eventually modified to output action a∗ with high extrinsic reward R∗. The non-myopic agent instead plays a∗ during training so that it can pursue its intrinsic reward at deployment, by playing aR at all post-deployment time steps. To put it another way–the model's behavior will look aligned with R∗ during the training process...

What are Failure Modes and Effects Analysis (FMEA) and inductive safety analysis? With FMEA, you try to induce failure at a higher level. FMEA is a bottom-up approach. Why would we do this? Because we know the effects of the failure. Now, we're trying to understand what is causing the failure. Hear more of Praveen Suvarna explanation here #FuSa #safetyanalysis #FMEA #functionalsafety #functionaltesting #iso26262 #autonomousvehicles #iso21434

In this webinar, John Bernet from Fluke Reliability will discuss best practices for applying root cause analysis and expected failure modes to motor-drive systems. You will learn the simple steps of total condition maintenance, how different inspection techniques from electrical to thermal can help identify different failure modes, and how vibration analysis in particular can find the most common mechanical faults on rotating machines. We will wrap up with a discussion on the obstacles teams may face when starting a reliability program and learn from those who have succeeded. Register for an upcoming webinar at: https://flukereliability.info/bpw-frr Learn more about Accelix at: https://flukereliability.info/accelix

For every action taken to maintain a piece of equipment, a Failure Mode—or cause of failure—is managed. That is why a Failure Modes and Effects Analysis (FMEA) is an essential part of physical asset management. When done properly, an FMEA helps organizations: 1) Define equipment goals, 2) Identify what could cause Reliability to suffer, and 3) Assign criticality. Join Nancy as she shares how to avoid common FMEA pitfalls, and how to use a properly executed FMEA to make effective maintenance decisions. Register for an upcoming webinar here: https://www.accelix.com/best-practice-webinars/ (flukereliability.info/bpw)

In this episode, Corey and I discuss the use of risk analyses for improving the design and operation of tailings and other facilities. We discuss the use of Failure Modes and Effects Analyses (FMEAs) and bowtie analyses.

An asset is a collection of failure modes, manage the failure modes and you manage the asset... This is a quote from our guest this week, Tacoma Zach! In this episode we discuss failure modes, operating context, and a whole lot more. Asset management is one of Tacoma's passions and we were lucky to corner him for an hour to discuss! Connect with our Guest Here: Tacoma Zach - https://www.linkedin.com/in/tacoma-zach-p-eng-0913514/ www.mentorapm.com www.uberlytics.com If your company sells products or services to engaged maintenance & reliability professionals, tell your marketing manager about Maintenance Disrupted. If you'd like to discuss advertising, please email us at maintenancedisrupted@gmail.com Check out our website at www.maintenancedisrupted.com and sign up for the weekly disruption newsletter with bonus content. If you like the show, please tell your colleagues about it and follow maintenance disrupted on LinkedIn and YouTube. Follow Maintenance Disrupted on LinkedIn https://www.linkedin.com/company/maintenancedisrupted Music: The Descent by Kevin MacLeod Link: https://incompetech.filmmusic.io/song/4490-the-descent License: http://creativecommons.org/licenses/by/4.0/

If we're not careful with or ignore failure modes, we can choose the wrong reliability model or statistical distribution. If our product performance is close to the required limits and/or we need a very accurate model, this could be a big problem.We talk about the importance of failure modes and step-through a tensile-test example to explore these other topics:competing failure modessuspensionsindependent vs. dependentreliability block diagramsThe podcast blog includes extra useful information/links.Support the show

Nancy Regan has a degree in Aerospace Engineering and founded The Force, a company focusing on Reliability Centered Maintenance. In addition to being an engineer and entrepreneur, she also holds six patents, is an author and a key note speaker. Episode NotesMusic used in the podcast: Higher Up, Silverman Sound StudioAcronyms, Definitions, and Fact CheckReliability Centered Maintenance (RCM) - A lot of organizations don't get the Reliability they need from their equipment.  That causes chronic downtime, increased costs, and lost production.  Using Reliability Centered Maintenance (RCM), organizations figure out what proactive maintenance to do so they get what they need from their machines (https://RCMTrainingOnline.com).RCM Consists of seven steps:FunctionsFunctional FailuresFailure ModesFailure EffectsFailure ConsequencesProactive Maintenance and IntervalsDefault StrategiesSteps one through four make up the Failure Modes and Effects Analysis (FMEA).  Steps one through five make up the Failure Modes, Effects, and Criticality Analysis (FMECA).  Step six embodies preventive maintenance and Condition Based Maintenance (CBM)."The One Thing" by Gary Keller. The book discusses the benefits of prioritizing a single task, and it also provides examples of how to engage in those tasks with a singular focus.Embry-Riddle Aeronautical University - a private university focused on aviation and aerospace programs with its main campuses in Daytona Beach, Florida. Women make up 27% of enrollment currently. (Wikipedia)Naval Air Warfare Center Aircraft Division (NAWCAD) Lakehurst is the world leader in Aircraft Launch and Recovery Equipment (ALRE) and Naval Aviation Support Equipment (SE). It is part of the Naval Air Systems Command (NAVAIR) and is located on Joint Base McGuire-Dix-Lakehurst (JB MDL) in central New Jersey. As the Navy's lead engineering support activity for ALRE and SE, NAWCAD Lakehurst conducts programs of acquisition management, technology development, systems integration, engineering, rapid prototyping / manufacturing, developmental evaluation and verification, fleet engineering support and integrated logistics support management. NAWCAD Lakehurst is responsible for maintaining fleet support and infusing modern technology across the entire spectrum of equipment needed to launch, land and maintain aircraft from ships at sea and austere expeditionary airfields. (navair.navy.mil)The USS Midway Museum is a historical naval aircraft carrier museum located in downtown San Diego, California at Navy Pier. The museum consists of the aircraft carrier Midway. The ship houses an extensive collection of aircraft, many of which were built in Southern California. (wikipedia)

Using Failure Modes and Effects Analysis to improve special-order implant procurement by AORNJournal

Manufacturers struggle to manage product quality, achieve on-time delivery, and reduce product and process risks. However, one of their most important assets – employees – should also be included in failure modes and effect analysis (FMEA) risk assessment. This webinar will identify key risk areas affecting workers and how FMEA can identify and manage those risks for a safer work environment. You are listening to audio from a webinar in the Safety+Health Webinar Series presented on July 11, 2019, by Kelly Kuchinski, Quality and Document Control Product Marketing Manager, Cority. Watch the archived webinar video to see the presenter's slides at https://www.safetyandhealthmagazine.com/events/143-quality-up-injuries-down-using-failure-modes-and-effect-analysis-fmea-for-a-safer-work-environment

Manufacturers struggle to manage product quality, achieve on-time delivery, and reduce product and process risks. However, one of their most important assets – employees – should also be included in failure modes and effect analysis (FMEA) risk assessment. This webinar will identify key risk areas affecting workers and how FMEA can identify and manage those risks for a safer work environment. You are listening to audio from a webinar in the Safety+Health Webinar Series presented on July 11, 2019, by Kelly Kuchinski, Quality and Document Control Product Marketing Manager, Cority. Watch the archived webinar video to see the presenter's slides at https://www.safetyandhealthmagazine.com/events/143-quality-up-injuries-down-using-failure-modes-and-effect-analysis-fmea-for-a-safer-work-environment

Oil and gas rig performance integrity is an extremely important capability that involves risk analysis, threat analysis, failure mode and effect analysis, safety, health, environmental safety, situational awareness, and last, but not least, software quality. Software quality plays a very important role in rig performance, and one that is not fully appreciated. In this episode Don Shafer discusses the challenges, pitfalls, and successes with software quality in the oil patch.Don Shafer is a cofounder of the Athens Group and technical fellow. Don developed Athens Group’s oil and gas practice and leads engineers in delivering software services for exploration, production, and pipeline monitoring systems for clients such as BP, Chevron, ExxonMobil, ConocoPhillips, and Shell. He led groups developing and marketing hardware and software products for Motorola, AMD, and Crystal Semiconductor. Don managed a large PC product group producing award-winning audio components for Apple. From the development of low-level software drivers to the selection and monitoring of semiconductor facilities, he has led key product and process efforts. You can connect with Don here:Email: donshafer@athensgroup.com LinkedIn: Don ShaferWeb Site: Athens GroupAbout PPQC:Process and Product Quality Consulting (PPQC) helps global executives tackle complex corporate challenges.To learn more about PPQC, visit www.ppqc.netSupport the show (https://ppqc.net)

On this week's episode, I am joined by John Reeve, co-author of Failure Modes to Failure Codes to talk about computerized maintenance management systems (CMMS). We talk about how we can use a CMMS to facilitate chronic failure analysis, how to properly set up a component list and John gives us his top CMMS tips. Thank you for listening and if you enjoy the show, please subscribe to Rob's Reliability Project on your favourite podcast platform and share it with your colleagues. You can also follow Rob's Reliability Project on LinkedIn and Facebook and check out robsreliability.com as well. If you're looking for a shorter tip, subscribe to Rob's Reliability Tip of the Day on your favorite podcast platform or on your Amazon Alexa as a Flash Briefing. Finally, if there are any topics, guests you'd like to hear from, questions you want answered, or if you'd like to appear on the podcast, email me at robsreliabilityproject@gmail.com Follow Rob's Reliability Project on LinkedIn - https://www.linkedin.com/company/robsreliabilityproject/ Follow Rob's Reliability Project on Facebook - https://www.facebook.com/robsreliabilityproject/ Music by XTaKeRuX, Song: White Crow is licensed under a Creative Commons 4.0 Attribution License.

In this episode, we talk with Dr. Alecia Gabriel of P3 Group North America. Dr. Gabriel serves as the Quality Systems Consultant and brings her experience in project management, automotive and aerospace quality assurance, and automotive consulting to our healthcare platform to talk Failure Modes and Effects Analysis (FMEA). In this first installment of our Beyond Healthcare Quality segment, Alecia will share an outsider view of a powerful quality improvement methodology, the potential that it holds for transforming healthcare, and why its appropriate application and execution should be a key part of your organizational high-reliability strategy.

Jason Millar, Social Failure Modes in Technology: Implications for AI by Centre for Ethics, University of Toronto

Developing Failure Codes with Bill Leahy Failures codes help the reliability engineers to make intelligent business decisions regarding the issues with the assets. It is a formula that helps you understand how an asset fails and how can you gather the needed data to mitigate a failure? A good failure code contains a hierarchy of […] The post 142 – Developing Failure Codes with Bill Leahy appeared first on Accendo Reliability.

This week, I welcome Mark Benak on to the podcast. Mark is the VP of Business Ventures at Uptake. We talk about why merging a library of failure modes with artificial intelligence makes stronger predictive analytics. Follow Mark Benak on LinkedIn - https://www.linkedin.com/in/mbenak/ Thank you for listening and if you enjoy the show, please subscribe to Rob's Reliability Project on your favourite podcast platform and share it with your colleagues. You can also follow Rob's Reliability Project on LinkedIn and Facebook and check out robsreliability.com as well. If you're looking for a shorter tip, subscribe to Rob's Reliability Tip of the Day on your favorite podcast platform or on your Amazon Alexa as a Flash Briefing. Finally, if there are any topics, guests you'd like to hear from, questions you want answered, or if you'd like to appear on the podcast, email me at robsreliabilityproject@gmail.com Follow Rob's Reliability Project on LinkedIn - https://www.linkedin.com/company/robsreliabilityproject/ Follow Rob's Reliability Project on Facebook - https://www.facebook.com/robsreliabilityproject/ Music by XTaKeRuX, Song: White Crow is licensed under a Creative Commons 4.0 Attribution License.

On this follow-up deep dive episode of “Leader Dialogue“, CHIME CEO Russ Branzell again joins Ben, Jennifer and Duffie to discuss the two common Strategy Execution failure modes resulting from five (5) commonly held strategy execution myths. (Adapted from the research of: Donald Sull, MIT Sloan School of Management, and Rebecca Homkes fellow at the […] The post LEADER DIALOGUE: Clarifying Strategy Execution Failure Modes & Myths with CHIME – Deep Dive appeared first on Business RadioX ®.

Making FMEAs Work with Fred Schenkelberg In most of the organizations, Failure Modes and Effect Analysis, is taken as a light exercise where people come, argue, and leave without learning a single thing of value for their company. There is a lot going on when you are in need of FMEA because unless your equipment […] The post 81 – Making FMEAs Work with Fred Schenkelberg appeared first on Accendo Reliability.

Microcontroller Failure Modes: Why They Happen and How to Prevent Them by Altium Inc.

00:16 – Welcome to “Diamonds Are For Gender” …we mean, “Greater Than Code!” 00:56 – Origin Story, Superpowers, and Data Science 04:20 – Diversity and Career Paths in Data Science 10:51 – Ethical Debates Within the Data Science Field Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (https://www.amazon.com/gp/product/0553418815/ref=as_li_qf_sp_asin_il_tl?ie=UTF8&tag=therubyrep-20&camp=1789&creative=9325&linkCode=as2&creativeASIN=0553418815&linkId=0ed7c081ef2baa2e5a6f33a076e2929b) Therac-25 (https://en.wikipedia.org/wiki/Therac-25) FMEA (Failure Mode Effects Analysis) (https://www.greaterthancode.com/2017/06/21/episode-037-failure-mode-with-emily-gorcenski/) 17:21 – Software Development and Engineering; Failure Modes in Software 21:44 – Failure Modes in Democracy; Voting Machine Software 33:37 – Working for a Government Contractor 36:21 – Data Patterns and Tampering 39:00 – Open Data and Open Science 45:59 – Falsifying Data Reflections: Coraline: Considering all the ways something can fail. Sam: The world that I live in and the kind of software development practices that I take for granted are extraordinary niche. Emily: Tech conferences and their decadence vs academic/corporate conferences. This episode was brought to you by @therubyrep (https://twitter.com/therubyrep) of DevReps, LLC (http://www.devreps.com/). To pledge your support and to join our awesome Slack community, visit patreon.com/greaterthancode (https://www.patreon.com/greaterthancode). To make a one-time donation so that we can continue to bring you more content and transcripts like this, please do so at paypal.me/devreps (https://www.paypal.me/devreps). You will also get an invitation to our Slack community this way as well. Amazon links may be affiliate links, which means you’re supporting the show when you purchase our recommendations. Thanks! Special Guest: Emily Gorcenski.

In this podcast I will give a simple standardization for your Cabin Check that will increase your knowledge of your systems and add safety and redundancy to your flying. Fly Your Best! Jason