Podcasts about eecs

Take the 2026 AI Engineering Survey and get >$2k in credits and AIE WF tickets!This was recorded before Railway suffered a major GCP outage on May 19, despite being a multi-AZ, multi-zone mesh ring, with HA fiber interconnects between their Metal GCP AWS, because workload discoverability was unintentionally still tied to GCP. All has been resolved with a post-mortem.Railway did not start as an AI infrastructure company.It was founded in 2020 years before agents became the default way people thought about deploying software. Jake Cooper, formerly at Bloomberg and Uber, started Railway with a simple obsession: the activation energy to ship something to production should be near zero. Push code, get a URL, iterate. No Docker files, no Kubernetes manifests, no Ansible scripts stacked on Ansible scripts.For years, this was a slow grind. Railway spent its first 18 months hand-acquiring its first 100 users with Jake personally greeting every Discord signup on a second monitor.Today, Railway has raised $124m and is growing very fast. A 35-person team supports 3 million users, adding roughly 100,000 signups a week. Their bare metal data centers have a 3-month payback period vs. renting in the cloud, with 70% margins funding aggressive cloud bursting when needed. The servers they own have actually appreciated in value as RAM prices have climbed basically meaning the value of their hardware now exceeds the capital they've raised.From rebuilding Railway's network overlay over a weekend to moving the vast majority of workloads onto its own bare metal data centers, Jake Cooper is trying to build a new cloud for an agent-native world. In this episode, Railway's founder and “conductor” joins swyx and Alessio to unpack why the next era of software infrastructure is not just “Heroku but newer,” what agents need that humans did not, and why the old deployment loop of Git, PRs, CI/CD, and static cloud resources may be heading for a rewrite.We go deep on Railway's infrastructure stack: own-metal data centers, three-month cloud payback periods, cloud bursting, data center debt, Railpack, Nixpacks, Temporal, feature flags, Central Station, content-addressable filesystems, agent-safe production forks, and why the CLI may become more important than the canvas in an agent world. Jake also shares the founder journey behind Railway, how the company survived losing $500K/month, why it now serves millions of users with only 35 people, and why he believes the pull request is dying.We discuss:* How Railway went from a slow six-year grind to adding 100,000 users a week* How Railway thinks about agents as the next dominant software species* Why agents need version control, observability, compute, storage, and orchestration at 1000x scale* The economics of Railway's own-metal data centers and three-month payback* How Railway uses cloud bursting while scaling its own infrastructure* Why data center debt can be a better tool than venture debt for infra startups* Central Station, Railway's internal system for clustering customer feedback and incidents* Why responsible disclosure and over-communication matter for platforms* Why feature flags, progressive rollouts, and shadow traffic are essential for agents* Temporal's strengths, pain points, and why workflows matter for agents* Railpack, Nixpacks, Nix, and lazy-loaded content-addressable filesystems* Why “cattle, not pets” may change if you can clone the pets* Why Railway is building a new cloud from scratch instead of copying hyperscalers* The solo founder path, focus, writing, and how Jake thinks about company buildingRailway:* Website: https://railway.com/* X: https://x.com/RailwayJake Cooper:* LinkedIn: https://www.linkedin.com/in/thejakecooper/* X: https://x.com/JustJakeTimestamps00:00:00 Introduction: What Is Railway?00:02:07 Jake's Path to Railway00:06:13 Railway's Six-Year Growth Story00:08:52 Rebuilding the Business After the Free Tier00:11:17 Agents as the Next Software Platform00:13:29 Railway's Infrastructure Philosophy00:15:42 Bare Metal, Cloud Economics, and the Compute Crunch00:17:22 Cloud Bursting and Five-Cloud Networking00:20:20 Data Center Debt and Infra Financing00:23:31 Data Centers in Space00:25:24 What Agents Need From Infrastructure00:28:24 CLIs, Canvas, and Agent-Native UX00:35:15 Central Station, Incidents, and Responsible Disclosure00:40:30 Safe Rollouts, SRE Agents, and Production Forks00:45:00 AI SRE, Specs, Code, and Tests00:48:24 Self-Replicating Infrastructure and the New Serverless00:53:18 Heroku, Temporal, and Workflow Engines01:04:07 Railpack, Nixpacks, and Lazy-Loaded Filesystems01:06:01 Coding Agents, Token Spend, and Roadmap Acceleration01:10:56 The Pull Request Is Dying01:12:28 Feature Flags and the Agent-Era SDLC01:16:15 Cattle, Pets, and Cloning Machines01:19:29 Solo Founder Lessons01:24:12 Focus, GPUs, and Building a New Cloud01:28:20 Closing ThoughtsTranscriptAlessio [00:00:00]: Hey, everyone. Welcome to the Latent Space Podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space.Swyx [00:00:10]: Hey, hey, hey. Today we're in the studio with Jake Cooper of Railway.Alessio [00:00:14]: Conductor of Railway.Swyx [00:00:15]: Conductor at Railway. Yeah.Alessio [00:00:16]: Choo-choo.Swyx [00:00:17]: Do you actually have that anywhere, like on your business card?Jake [00:00:20]: We call some of our volunteer moderators conductors. I don't have a business card. We're not that big yet. At some point I will. I got handed a nice business card from the Supermicro folks, and I was like, “Damn, this is pretty official.”Swyx [00:00:30]: Business cards are coming back.Jake [00:00:32]: They're cool. They're hip. The conductor thing is good. We're trying to figure out what we want to call each other internally. Some people think it's super cringe and say, “You don't need a name for people internally.” Some people want to call each other something. We still don't have a really good one.Jake [00:00:55]: We've got New Railcrews, Trainiacs. Nothing has stuck yet.Swyx [00:01:00]: I like Trainiac. Trainiac sounds good. Railwayians. For those who don't know, what is Railway? Let's give people a crisp definition up front.Jake [00:01:09]: Railway is the easiest way to ship anything. You go to the canvas, or you talk with Claude, and you say, “Deploy a Postgres instance, deploy my GitHub repository, run this code,” and you're off to the races.Swyx [00:01:22]: You've got a nice animation on the landing page.Jake [00:01:24]: Thank you. None of my work, by the way. They don't let me touch the design stuff anymore.Jake [00:01:25]: We want to make it trivially easy not just to deploy things, but to evolve applications over time. Most tooling right now stacks entropy on top of entropy: Docker, Kubernetes, Ansible scripts, and all these other things. If we can version all of your software and keep track of all the changes, then we can make it trivial to clone environments, fork into a parallel universe, get copies of production data, get copies of any services, make changes, validate them, and collapse them back in without reproducing everything across a staging environment.The Railway Origin Story: From Uber Systems to a New CloudSwyx [00:02:07]: I was looking at your background: Bloomberg, Uber. Nothing immediately stands out as, “This guy is going to found the next great platform as a service.” What prepared you for Railway?Jake [00:02:21]: It was curiosity to keep going deeper. I started out on front-end stuff, working on Wolfram Mathematica and porting it over. Then I briefly moved to Bloomberg, then toward Uber and distributed systems, taking the Jump Bikes systems and moving them to a distributed system built on top of Cadence, the pre-Temporal Temporal.Swyx [00:02:44]: Which, by the way, I'm happy to talk about, pros and cons.Jake [00:02:48]: Totally.Swyx [00:02:51]: But let's do the Railway story.Jake [00:02:52]: It has been a continual step of wanting an experience. Whether it's walking up to a bike, unlocking it, and having it work frictionlessly, or something else, the depth required to make that happen follows from the experience. A lot of the work I do, and a lot of the team does, is in service of that experience. We fundamentally don't care how deep we have to go. We will swim to the bottom of the swimming pool to get the experience.Jake [00:03:17]: I don't have a physics PhD. I did an EECS degree. It has always been about figuring out the next step: how do we get there? That's what led to starting Railway for that experience and then moving all the way to bare metal data centers. I was adding patches to the kernel this week to get the experience there because I can see how much better it can be.Swyx [00:03:49]: Other patches to the Linux kernel this week?Jake [00:03:51]: Yeah. Not upstream. Our fork.Swyx [00:03:52]: That's a flex. Railpack? No, this is different. This is the OS on top of Railpack?Jake [00:03:57]: No, this is an actual kernel patch. It's always literally: what do we have to do to get that experience? Then figure it out. Anything is figureoutable.Swyx [00:04:10]: Would you send the patch upstream, or does it not fit other use cases?Jake [00:04:13]: Maybe. We have to work out the experience internally. It has to do with the storage layer we're building for some of the agentic stuff. Maybe it'll be useful upstream, but it's deeply useful for us internally.Open Source, Forks, and Non-Deterministic VersioningSwyx [00:04:29]: You mentioned open source before. How do you think about starting from open source, and then coding agents letting you do a lot more from forks of it?Jake [00:04:38]: GitHub's original sin is that it's almost a series of broken pointers. You have this thing, then you clone it, and now you've lost the whole upstream. How do we make it trivial for people to modify really small pieces of it?Jake [00:04:51]: We think of Git in a discrete sense: I've either made a change and merged upstream, or I haven't. What would it look like if it were percentage-based, a little more non-deterministic, or a stream of changes that users traverse as a percentage rolled out in general and then rolled all the way up?Jake [00:05:13]: We have the open-source kickback program and let you deploy templates because we want to make it trivial for people to version these shards over time. It solves a large problem around authentication, authorization, and security. NPM has a way to define, “Don't take any new packages.” The ideal end state is that you roll out progressively to users with the minimum impact zone and continue rolling up. JPMorgan should probably be the last one on the patch line, for all our sakes, because our money and livelihoods are there.Jake [00:05:53]: It's okay if Johnny Vibe Coder gets a broken patch because there's so much entropy in the system that the rubber has to meet the road at some point. You have to test at varying levels.The Long Grind: First Users, Free Tier, and Making the Business WorkSwyx [00:06:13]: I wanted to pull up this glorious chart, which is your usage or number of daily signups?Jake [00:06:22]: Daily signups, I think.Swyx [00:06:24]: You started six years ago. It was a slow grind, and now you're on a rocket ship. You say, “Don't doubt your fight and don't quit.” Maybe pick out certain points that were key inflections for the company.Jake [00:06:40]: At the start, it's about getting your first 100 users, hell or high water. We had a website and a support link. The support link was the Discord channel. I had notifications on with two monitors: the monitor I was working on and the other monitor with Discord. If anybody came in, I was immediately like, “Hey, how's it going?” It was rare, so getting those first 100 users to come back was the start.Jake [00:07:14]: Then you build a consultancy factory because users want all these things. You have to go back to the board and ask, “What is the actual product offering I want to build on top of this?”Jake [00:07:28]: VCs want charts that always go up and to the right, but in reality you don't necessarily want charts that look like that. For us, there have been periods of expansion where we add features to test use cases, and periods of compaction where we ask, “If the experience we have is good, how do we make it significantly better?” Maybe we strip out features that don't fit our ICP anymore.Jake [00:07:57]: The boom from 2022 to 2023 came from the free tier. Everybody under the sun was using it.Swyx [00:08:09]: A lot of Reddit bots and Discord bots.Jake [00:08:12]: And crypto miners. When you build an open product on the internet where anybody can sign up, the internet is a horrible place with so many things. You go through periods of asking, “How do I reach as many people as possible?” Then, “How do I fit the exact use case for the people who really matter and are really excited about this specific thing?”Jake [00:08:39]: Then there was a two-year period of making the actual business work. During the free-tier era, we were losing about half a million dollars a month.Swyx [00:08:59]: On a $20 million bank account.Jake [00:09:02]: On a $20 million bank account with maybe $50,000 a month in revenue. That's a horrible business. I don't know how anybody invested. But you have to go through it and say, “We have an experience people love, but the business has to work.”Jake [00:09:17]: There are two schools of thought. You can run the horrible business all the way up with bad margins, or you can go back and make it work. We've always wanted a super lean team. We're 35 people right now. It's very small.Swyx [00:09:36]: Supporting three million already?Jake [00:09:38]: Yeah. We're adding 100,000 users a week right now, so it's growing fast. We don't want to add headcount for the sake of headcount or throw bodies at problems. We want to build systems. It's hard to build systems during expansion because you're adding things to the system because people are asking for them or things are breaking.Jake [00:10:00]: We had to cut off the free users for a little while, rebuild the business, and make sure it worked. We want to reach as many people as possible because software is important. It's become difficult to create things in the physical world, so it's important to make it easy for people to build in the virtual world and have access to creation. But there are legs to that journey.Jake [00:10:30]: You can see divots in the charts. If you follow between 2025 and 2026, it's either summer or winter. People go on holiday with family.Swyx [00:10:50]: It affects that much?Jake [00:10:51]: Yeah. It's kind of B2C and kind of B2B. People are shipping constantly, then they stop. Our activation curve now shows more people activating on weekdays because we have more business users, so it smooths out over time.Agents as the New Interface to DeploymentSwyx [00:11:17]: Was there a point where you started prioritizing AI development or agent development?Jake [00:11:24]: We've prioritized agentic as a top-of-funnel thing. Over the last six months, we've deeply prioritized agentic as a mechanism to build and deploy things because we believe the curve is so steep and that is how people will build and deploy software.Jake [00:11:42]: It almost fundamentally doesn't matter whether this is dot-com or not because we're all on the internet anyway. If agents are going to deploy a bunch of things and we hit an inference wall at some point, we'll fix those problems. The dominant species over the next 10 years is that we've moved from assembly to C to C++ to JavaScript to words. You're going to need to close that loop.Swyx [00:12:13]: When you say this is dot-com, did you mean buying the domain, or the general case?Jake [00:12:17]: I mean the dot-com era, when companies had a huge run-up because people understood the internet was important. Then they hit bottlenecks, fundamental laws of physics, math didn't work, and everybody came back down to earth. But it didn't matter because the internet became so impactful. If you operate on a long enough time horizon, you should build these things anyway because you can see where it's going.Jake [00:12:45]: That's where I think a lot of agent stuff is. You get to a point where you're running thousands of agents in parallel. What is the inference cost? What is the compute cost? How do you make that efficient? How do you coordinate all this? We have issues coordinating humans; we don't even have good tooling for that. Now we have to figure out how to get agents to coordinate, safely version changes, and know when to raise their hand for someone to intervene. Otherwise it becomes an interrupt factory.Railway's Infrastructure Thesis: Network, Compute, Storage, and MetalSwyx [00:13:19]: Let's go right into the technical side. What are the core infrastructure or architectural beliefs of Railway that allow you to do what you do?Jake [00:13:29]: The primitives matter a lot for us. We need network, compute, storage, and orchestration around it. You need control over a lot of those things. We've talked a lot about how we don't really use Kubernetes because we want higher-order control to place workloads in very specific places.Jake [00:13:48]: The reason is that you have to be very efficient with agents: memory reuse and all these other things, or you're going to massively blow up your cost structure. Being able to rack and stack your own servers and build your own metal unlocks performance and cost. Experiences where you're running 1,000 agents in parallel are not massively cost prohibitive.Jake [00:14:13]: Token use and compute use are blowing up. Over time, those things have to get a lot more efficient. You can get a lot of margin to make those experiences solid by building your own metal. That's all in service of offering a differentiated experience to as many people as humanly possible.Swyx [00:14:51]: You have a data center in Singapore.Jake [00:14:53]: Yeah. We have two in every other region now. In Singapore, we're adding a second one in Q3.Swyx [00:14:58]: What's it like? I've never built a data center. Do you go to Equinix and say, “I want some slots?”Jake [00:15:05]: Yeah. Equinix. You basically go and say, “I want power and I want a cage.” They say, “Great, here's what it's going to be.” You rent the cage for a period of time, fill it with racks and servers, and hook up internet to it. That's all the pieces.Swyx [00:15:36]: Then you handle everything else.Jake [00:15:37]: You handle everything else.Swyx [00:15:39]: What's the math versus clouds doing it for you?Jake [00:15:43]: If we rented in the cloud, our payback period when we go to metal is about three months.Swyx [00:15:50]: Which is crazy.Jake [00:15:51]: It's nuts. That's four years of depreciated hardware. You're going to see a lot of this compute crunch because hyperscalers are buying up a lot of stuff. We're working directly with OEMs, resellers, and people building these machines: Supermicro, Dell, and others.Jake [00:16:11]: Upstream, there's a bunch of supply pressure. When we raised our last round, between deploying capital for servers and now, the amount of money we've raised is less than the amount of money we have in the bank plus the value of the servers because the servers have appreciated as RAM has gone up. It's nuts how valuable hardware has become.Jake [00:16:50]: If you look at hyperscalers, they deployed around $80 billion of capital expenditures this year, and next year will be more. That's a massive infrastructure build-out. You look at that and think it's crazy that they're spending way more than the Manhattan Project. But if every person is going to run dozens or hundreds of agents in parallel, you have no conceptual idea how much compute is required to make that experience happen, even if you're deeply efficient and sharing resources. And that doesn't even count inference.Swyx [00:17:22]: How do you plan the build-out? The growth chart is so vertical. Are you usually at 100% utilization as soon as racks are live? How far ahead are you planning?Jake [00:17:33]: We still maintain cloud presence for bursting. We work with AWS, GCP, and a few other clouds. We can rent, and then the moment we get space or power, we compact those workloads off the cloud. We started on the clouds, then built a system to migrate to our own metal. There's nothing that says you can't continually do that again, and that's exactly what we do. We never want to be compute constrained.Jake [00:18:09]: At the start of the year, we actually became compute constrained because one upstream provider wasn't able to give us quota at the rate we needed, and the hardware was slower. I spent a weekend rebuilding our entire network overlay so we could straddle five clouds: Oracle, AWS, ourselves, GCP, and one other one. We can do more than that now.Jake [00:18:38]: We got into a spot where we were trying to pack instances tight because we couldn't get enough compute. That led to a few reliability issues, which are now past us. I made a tweet pointing out that it's becoming harder and harder to acquire compute at the rate these models need to acquire compute. We got bit by it.Swyx [00:19:15]: How do you think about pricing knowing you might not have your own metal available at all times? Are you pricing assuming you need extra margin if you end up going into the cloud?Jake [00:19:26]: Because we've built out our metal data centers, our margins on metal are around 70%. We can deeply subsidize the cloud business if we want to scale at a reasonable rate. We have a few levers: metal, which makes the margins; cloud burst; debt to buy servers; and venture capital. It's an interesting operational problem: how much cash do we have, how much should we raise, how quickly can we deploy it, and can we scale revenue as quickly as we scale compute?Jake [00:20:05]: If we continue making it trivially easy for people to build and deploy, then the faster we close that loop and the more operationally excellent we are with capital, the faster the business can scale. It's almost a straight linear deployment rate.Financing Infrastructure: Hardware Debt, VC, and Operational LeverageSwyx [00:20:20]: I think infra startups raising debt is a tool people don't utilize enough or know enough about. What can you tell us about that? Is it secured against your CPUs?Jake [00:20:32]: It's secured against our hardware.Swyx [00:20:37]: What rates do you get? Who are the lenders?Jake [00:20:39]: We pay prime plus a spread, and we can refinance any of the debt as rates go down. The terms are pretty good. The unfortunate thing is that Twitter has no nuance, so people say, “Venture debt bad.” But as with all things, there are specific tools and areas where you can be deliberate instead of using one tool as a hammer. Venture capital is not the hammer for everything. You have to explore and figure out what works.Swyx [00:21:12]: VC is usually the most expensive financing you can get.Jake [00:21:15]: Yeah. I also think people think about VC incorrectly from a capital-raising perspective. Most people think, “How do I raise as much money as possible from whoever is probably the best I can get at that time?” That's close to right, but what we've tried to do is figure out what unfair advantage we can buy with that equity.Jake [00:21:34]: It's the most expensive equity you're going to give away at that point in time, assuming the company keeps getting better. How do you use it to work with someone stellar who complements you? In the seed stage, I had never started a company. Ray Tonsing had good advice, and I could text him all the time. He was really fast. Awesome.Jake [00:22:01]: Then with John and Erica at Unusual, they said, “You roughly know what you're doing building a product. We'll mostly leave you alone and be available for advice.” Amazing. Then we got to Series A and the business was an operational tire fire because we didn't know how to scale a business. Work with Erica, and Jordan is over at Redpoint, so bonus.Jake [00:22:28]: Now we've raised from TQ and FPV as we're moving into enterprises. Every step of the way, we've asked: who can we partner with at this specific time to unlock the next section of the journey? I don't know enterprise sales. As an engineer, I can eyeball what features we might need, and we have wonderful people internally who can help. But you want boardroom dynamics where everyone is aligned and asking, “How do we win this?” instead of bickering about strategy.Data Centers in Space and the Physics of ComputeSwyx [00:23:31]: You had a tweet about data centers in space. Why no data centers in space?Jake [00:23:37]: It's not “no data centers in space.” My hot take is that I think it is solvable. I've just never seen anybody solve it.Swyx [00:23:49]: You said, “How are you going to dissipate that much heat in a vacuum?” You're making a physics claim.Jake [00:23:55]: I haven't seen anybody prove how you're going to dissipate that much heat in a vacuum. It doesn't mean it's not possible. It just means nobody has brought it up yet.Swyx [00:24:05]: Astrophage.Jake [00:24:06]: I don't know what that is.Swyx [00:24:07]: The Martian thing. Okay, you're very logical.Jake [00:24:09]: It could work. A lot of people are putting the cart before the horse. They say, “We're going to put data centers in space.” Okay, but how? “We have time to figure it out.” It's like in The Martian where they ask how they're going to intercept something and say, “We'll figure it out.”Swyx [00:24:36]: Making a bet on human invention is weird because you blind trust that it can be solved. But with physics, there are first-principles bounds you can put on it. Maybe not. Maybe you're asking to travel time or break a fundamental thermodynamic law.Jake [00:24:57]: I don't know how VCs do this either. How do you know what's not possible and a grift versus what's possible but sounds completely insane? “We're going to put data centers in space.” Coin flip as to which it is, and I guess you'll know in 10 years. That's one cycle.What Agents Need: Versioning, Observability, and 1,000x ScaleSwyx [00:25:23]: Moving back to agents. The branching, fast spin-up, and orchestration you do feels like pre-work that happened to be exactly what agents want. What do agents want differently than humans?Jake [00:25:37]: They want the ability to version things. It's not that different; it materializes slightly differently. Agents want a way to test changes incrementally. Engineers have feature flags. Is there a reason agents can't use feature flags? I don't think so.Jake [00:25:54]: They want version control. Can we use Git or not Git? That one is up in the air. I think something outside Git will emerge for how we version these things over time. They need observability. You need to query what happened, when it happened, which steps failed, traces, logs, metrics, and all the rest. They need network, compute, and storage. They need to write files, save files, iterate on files, and snapshot file systems.Jake [00:26:25]: A lot of what humans needed is in line with what agents need. Branching and forking are not different; we're just moving 1,000 times quicker. It can look like you need something massively different, but what you need is something massively better than what existed. You need orchestration massively better than Kubernetes. You need networking probably better than Envoy. It goes all the way down the stack.Jake [00:26:55]: If the workload profile doesn't change so much as it gets massively compressed because you need thousands of these things, what assumptions change? etcd is going to melt. You need to replace it with something. You can go all the way down the stack and say, “That part has to change, that part has to change, and that part has to change.”Jake [00:27:19]: The interesting thing about the super-exponential curve is that you have to build systems where you can rip out those parts at any time because a new bottleneck might emerge. You get good at parallel agents, and a different part of the system breaks. So it's similar to what humans needed, but at 1,000x scale.Jake [00:27:55]: How do you do code review in the age of agents?Swyx [00:28:00]: You throw more agents at it.Jake [00:28:01]: You don't. But then who reviews for CVEs and all these other things?Swyx [00:28:07]: More agents.Jake [00:28:08]: And that's how we hit the inference wall. You can continually throw agents at the problem, but I think there's a limit to the number of agents you can throw at a problem.CLI, Agent Handles, and Closing the LoopSwyx [00:28:24]: You already had a CLI before it was cool. How is the shape of what you're exposing changing, if at all?Jake [00:28:28]: CLIs have always been cool. The CLI changes because we think about how to give Claude, Codex, ChatGPT, or any model a handhold.Jake [00:28:50]: A CLI is a single command: deploy, get logs, and so on. Things that were prohibitively annoying to humans are not annoying to agents. They're nice. If I handed you a CLI with 40 arguments and 600 flags, you'd think, “I'm never going to use all of this.” But if you hand it to an agent, it says, “This is excellent. I have so many handles to work with.”Jake [00:29:24]: If you're going to expose things to agents that way, you want as many handles as possible where they can get information, query dynamic information, and close the loop quickly. Most problems right now are about how to close the loop as quickly as possible. Where does the agent get stuck, and how can you remove that?Jake [00:29:49]: Telemetry is important. If you can tell where the agent gets stuck from the CLI and say, “12% of people deviate from the happy path because of this, and now I add this argument and drive it down to 2%,” you massively increase the rate of loop closure.Jake [00:30:03]: That's how we think about not just the CLI, but every point in the dashboard. It's a user journey: I hear about Railway. I get something deployed. I get my first green build or aha moment. I see an endpoint, logs, whatever. Then I iterate. The iteration loop is indefinite. The user wants to deploy a new thing, a Postgres instance, change code, and keep iterating.Jake [00:30:36]: If you focus on the iteration loops and what's blocking them from closing quickly, one thing we say internally is: you never want to be waiting on compute anymore. You always want to be waiting on intelligence. If you're waiting on compute, there's a bottleneck that needs to be destroyed because eventually that bottleneck becomes so large that another workflow emerges to change it.Jake [00:31:04]: We've built a product where you push code, build it, and so on. But I fundamentally believe the push-pull loop is going away. We'll get to a point where you make a small change in production, that change is versioned across your infrastructure, you're working alongside copy-on-write versions of your database and infrastructure, and then you merge it in and it's instantaneously live. That's the holy grail of loops. The push-pull-rebuild thing is a point of friction that we're removing entirely.Canvas as Output: Dashboards, Context Anchors, and HyperstructuresSwyx [00:31:43]: It's incredibly fast. If anyone hasn't tried it, that fast feedback is great. My hot take is that Railway was famous for its canvas, which visualizes your infrastructure and lets you manipulate it visually. But that was for humans. For the next phase of growth, Railway CLI is more important than canvas.Jake [00:32:05]: The canvas is funny because it's a mechanism to show changes over time. You're right that previously we used it a lot as an input. Moving forward, its goal is more like an output. You would go to the canvas, make changes, see them, and watch your infrastructure evolve. Now agents have access to the CLI and can make those changes. So the canvas becomes an output: what information does the human need at this moment to make suitable decisions about control requests? Do I approve this or not?Jake [00:32:57]: It also has to be an anchor for your context, a port in the storm. Think of it like layers in a file system. You start with a project, then drill down into services, then into a function or code, because you want to represent the entire thing not just in your head, but in the canvas. Other people can share that representation, think on the same wavelength, and move quickly.Jake [00:33:33]: A lot of organizations get in trouble as they scale because all the context lives in someone's head. “How does this microservice work?” “I have no idea; go ask this person.” Then you have whole categories of products built around context discovery. A lot of that melts away if you have a solid hierarchy and can infinitely nest services, code, context, and everything else all the way down. That's what lets you build these structures over time.Jake [00:34:18]: It's also what lets us build what I've called hyperstructures: things that are way bigger. You look at the Golden Gate Bridge and ask, “How did we build that?” There's a meme that we lost the technology. To some extent, yes, because the coordination that built those things evolved and changed. We lost some of the art of building structure as we jammed everything into Slack.Swyx [00:34:52]: But you jam everything in Discord.Jake [00:34:53]: Same point. It doesn't matter. It's message passing and interrupts, message passing and interrupts.Swyx [00:35:00]: So you're arguing there should be something better and more structured than Slack?Jake [00:35:04]: Yeah. For sure. I think Slack is awful, and Discord is awful too.Central Station: Context Routing, Support, and Incident ClustersSwyx [00:35:09]: This is the equivalent of my mom test. What have you done that has your solution to this?Jake [00:35:15]: Internally, we've built a tool called Central Station that aggregates all the context from our users. Every piece of feedback, every customer support item, everything gets aggregated into clusters. If an incident is brewing, we can determine how many users are affected and break off a discussion based on that.Jake [00:35:40]: That is more helpful than long-running channels where you're trying to decide which channel to put something in. If you can dynamically aggregate information and dynamically route it to the right person based on context, it works better. We know internally that these four people are close to networking. If we see a networking thing, we can drill it down to those four people. If it's with this part, we can look at the commits. This is no longer a manual process internally.Jake [00:36:13]: If you go to station or help.railway.com, that's why we built it. We wanted to scale with a massive amount of leverage by aggregating feedback.Swyx [00:36:27]: This is built in-house?Jake [00:36:28]: Yep.Swyx [00:36:29]: I remember helping out on this one with Angelo in 2023. You scale a lot with a very small team.Jake [00:36:38]: Yeah. We're about 10 times bigger now.Swyx [00:36:40]: You have your full developer code here? Very cool.Jake [00:36:44]: If you go to railway.com/stats, we expose this as a pub-sub-able thing. It's all real-time metrics. There's a way to get it as JSON somewhere if you care.Jake [00:37:01]: We're big on trying to build everything in public and talk about what we're working on. We've had issues in the past, and we'll say, “Here's how we're fixing these things.” We've gotten compliments and flak for incident reports. We're always trying to make them better and talk with people.Incidents, Disclosure, and Progressive RolloutsSwyx [00:37:20]: You had a big one recently. I liked that it was scoped to 3,000. You presumably used Central Station. Talk through what happened and how you address it internally as a team.Jake [00:37:38]: Internally, this one really sucked. It had to do with an upstream provider that didn't do the behavior it said it documented, which is unfortunate given they wrote the RFC for how the behavior should work. We rolled those things out, and Central Station caught it initially when a couple users said caches weren't invalidating. We turned it off immediately.Jake [00:38:03]: When you roll out to a large user base of three million people, you get a lot of disparate behaviors. We tested in staging and had tests, but we hit an edge case. We've hardened those systems, and now we can make that better. But it was a tough one.Swyx [00:38:39]: I always wonder how private disclosure is supposed to work if people find an issue. Are they supposed to contact you first? When you run a platform, these things will happen. What channels should people pursue to quietly resolve it before it becomes a bigger incident?Jake [00:38:59]: There's responsible disclosure. We err on the side of over-disclosing and letting you know something is wrong versus having your provider gaslight you. We've erred on sharing those things more publicly, even if they impact a small subset of users. That's a decision we've made internally. We have four values. One is honor. The honorable thing is to notify people to the widest degree at which they may have been affected or there was an issue, and then confront it head-on: why did it happen, what can we do better?Swyx [00:39:45]: Not the whole user base. That's because of incremental rollouts and other things?Jake [00:39:50]: Yeah. Progressive rollouts.Swyx [00:39:54]: That should be the norm at all large platforms.Jake [00:39:58]: It should. A variety of companies do this. There's the quote that Meta runs 10,000 different versions of Meta. To our earlier point about agents, they need the same thing. They need shadow traffic and all these other things. We've built so much ceremony around production being sacred that we need to make it trivially easy to test different behaviors in a safe environment. Then you can make mistakes in a safe environment.Safe AI SRE: Customer Agents, Forked Environments, and Production ParityAlessio [00:40:30]: Do you see a world where these things get automatically caught, not necessarily by your agent, but by your customer's agent? The cache invalidation issue seems easy to check if you know to look for it.Jake [00:40:44]: It's hard because to determine it, we almost need to hook into your observability infrastructure. That's why we have the template loop on the platform: so you can roll things out progressively. You can roll out to Johnny Vibe Coder initially, or push a shard that someone consumes at their own leisure. Or you can roll it out over weeks: 0.1% of people, 1% of people, early adopters, then all the way up. That's the non-deterministic version control we talked about earlier.Jake [00:41:30]: I believe that's where most things should go, because most companies end up building staged rollout systems in-house. It's the same thing built again and again at every company. There's a massive opportunity to consolidate developer debt.Alessio [00:41:45]: You should have a free tier. Model providers give free tokens if you let them use the data. You could give free compute if someone is the number-one shard that goes out and lets you plug into their observability.Jake [00:41:55]: We do that. That's why we talked about the impact on 3,000 people. We start with lower-impact people. Larger companies on the platform are last to receive those rollouts so they have a version of the platform that's deeply stable.Alessio [00:42:16]: I have three services, so I'm sure I get the first rollout. You can nuke my thing at any time. There are all these SRE agent companies. Observability people also want agents that fix upstream problems. You have your own agent in the canvas now. How do you see that playing out?Jake [00:42:39]: It's the stacking entropy problem. If you don't have primitives to make iteration in production safe, it becomes difficult. If you're an observability provider saying, “Here's the fix to this error,” assume 80% are good and make sense. But in the last 20% long tail of complex issues, if you let somebody stamp it, you create an opportunity for an incident.Jake [00:43:08]: That's why forked environments are important. People have staging, but it always drifts from production. You need primitives, workflows, and experience built first-party on the platform so you can fork any service at any point in time.Jake [00:43:33]: I think of the canvas as a sheet of transparency paper. The agent is a little guy you push up into the canvas. It should say, “I need to copy that service and that service so I can test these two things.” It gets a read-only copy of production. Anything that's PII gets marked as a transform when we clone the database, create a copy-on-write version, or read from it. Then the agent makes changes and asks, “Does this actually work?” as close to production as possible.Jake [00:44:22]: That's how close you have to be, or you get massive drift. The system becomes unstable. You see this with massive systems built on Docker for local, Kubernetes for production, and a specific thing for something else. That complexity slows developers and becomes unstable at scale, making it hard to iterate. We want to compress that way down and say, “As close to prod as possible is where we want to be.”From AISRE Skeptic to Agent BelieverSwyx [00:45:00]: I was texting Erica for questions, and she says you were originally not a believer in AISRE. Have you come around on it?Jake [00:45:10]: I flipped, but I'm still not a believer in AISRE if you don't have the primitives to make it safe. If you unleash AISRE on production infrastructure without safe primitives for copying volumes and making sure things are fine, it's going to nuke your production database. It's not a matter of if, but when. I'm a big believer in making those loops safe.Jake [00:45:33]: I was a deep AI skeptic until 2023. In 2024, I thought, “Maybe I can roughly make this thing do it.” In 2025, I thought, “Now I can hold this.” Over winter break, everybody came back saying, “It's almost impossible to hold this.”Swyx [00:46:01]: Did you see this on the Claude docs? CloudBot? OpenCloud?Jake [00:46:06]: It's gotten to a point where it's harder to hold it wrong than to hold it right. There's a scene in Avengers where Vision picks up Thor's hammer and says it's terribly well-balanced. It self-balances and works well. I'm a deep believer at this point that this will be the dominant species: assembly, C, C++, JavaScript, words.Swyx [00:46:35]: It feels like a big jump.Jake [00:46:37]: It is. But it's not like you abandon CPU-based discrete logic and move straight to fuzzy logic. You need both. Your skills should call code or applications or some static structure. You can use skills to distill what the procedure should be or how the code should act.Jake [00:47:02]: I'm coming to a thesis: you need three points. You need a clear spec defining the system, the code, and the tests. When you say it out loud, if you've been in engineering long enough, you're like, “Of course. That's an RFC, tests, and code.” But they all matter. Having them together lets them reinforce each other: the spec and tests match, but the code doesn't, so reconcile it. Or the tests and code match but the spec doesn't, so reconcile that. That's the iteration loop.Jake [00:47:41]: That's why you're seeing people talk about software factories, docs, and reconciliation. Some of that is architectural astronomy if you don't implement it, but that loop is where most things will end up.Swyx [00:48:07]: For listeners, we've been talking about this on the pod for three years: the holy trinity of specs and tests. Itamar Friedman from Qodo is the reference if people want to look it up.Self-Modifying Infrastructure and the End of Push-Pull-RebuildSwyx [00:48:18]: One thing I want to mention on the OpenCloud idea is self-modification. I don't know how Railway would support it, but I have my OpenClaw, and I just tell it it has the Railway CLI and can do whatever. In theory, whatever capabilities or new infra it needs, it can call the Railway CLI, provision it, and add it to itself. The agent can modify its own infra.Jake [00:48:45]: It's nuts. I have a loop set up where you put the Railway CLI on top of something that runs on Railway. You're authenticated as whatever the current box is, and you can make any changes to it. Then you call Railway deploy, and it deploys itself.Jake [00:49:04]: It's like: “I need to spin up this instance of this environment. I already exist in this environment. Excellent, I have access to a Postgres instance now.” That's where we want to go with agentic, self-replicating infrastructure. That's your loop: iterate in production. You continue making changes. If it works, merge it upstream. If it doesn't, throw it away.Jake [00:49:37]: How do you make throwaway copies trivial to spin up and super cheap? The era of “I have an AWS instance with four vCPU and 16 gigs of RAM” is going to get destroyed. If you do that for agents, you need a thousand of those machines. It's prohibitively expensive compared with what we've spent a ton of time figuring out: the atomic unit of deploy, whether you call it isolates, sandboxes, or something else. Only pay for what you use, spin up instantaneously, and close the loop as quickly as possible.Jake [00:50:15]: If the system can self-replicate safely and say, “This is my environment, I'm making these changes,” it can come back with, “Does this look good? This is a new state of infrastructure given this prompt. I think I've solved it.” Then you go back and say, “Actually, it looks different.” It does the loop again. Then you say, “Cool. Apply.”Swyx [00:50:38]: That's retroactively obvious, which is the most useful kind. Any other comments on agent deployment on Railway?Jake [00:50:51]: It's getting better every day. I'm on X or Twitter. You can always yell at me about the parts not working as well as they should, because plenty of things should work way better.The New Serverless: Stateful, Long-Running, Pay-for-What-You-Use LinuxSwyx [00:51:04]: At this stage, when people want massively or embarrassingly parallel compute, they usually talk serverless. I feel like there's a new serverless compared to the previous five years of serverless. You're in that new bucket. Do you have comparisons or philosophical differences you want to call out?Jake [00:51:31]: It's somewhere in between. It's the ability to run stateful, long-running workflows or executions.Swyx [00:51:42]: Vercel has Fluid Compute, Cloudflare has some container thing, Google has App Runner and others.Jake [00:51:55]: That's where everything is roughly going, and it's why we've been working on this for six years. We believe users need access to a computer: a box that speaks Linux. They need to deploy what they want. Other systems change the surface area of what you can build. For us, users need a computer and need to deploy anything they truly want. That's why we've focused on the primitives: network, compute, storage. If we give you those and expose them so you can run things indefinitely, that's where we believe it's going.Jake [00:52:43]: Twitter has no nuance, so everyone says “servers” or “serverless.” It's always somewhere in the middle: I want to run it for a long time, but I don't want to provision the resource statically or pay for things I'm not using. That's been our thesis from day one: pay only for what you use, run it indefinitely, and it is full Linux.Swyx [00:53:12]: That's why I like the naming of Fluid. It's fluid. Flexible.Heroku, Focus, and Carrying the Torch Without Becoming the PastSwyx [00:53:18]: Another milestone is the Heroku official deprecation. You're one of the presumptive new Herokus. “New Heroku” has been a category for as long as I've been in developer tooling. It's finally happening. What was that like? Any behind-the-scenes of, “This is the moment”?Jake [00:53:42]: You have people where you're like, “You were running stuff on here? You, as this company?” It's crazy that names you would know are running on it and now coming to us saying, “We want to move a lot of this off.”Swyx [00:54:00]: Any behind-the-scenes on why Salesforce let Heroku stagnate?Jake [00:54:05]: I can only guess. It's hard when it's not your business. Salesforce's business is to build a great CRM. That's their focus. Then you acquire a compute business as an offshoot. A lot of early Meta people talk about focus. Boz has a write-up about how in the early days of Meta they had no money, so they were forced to focus. Then they turned on the money tree and had no reason not to split their focus.Jake [00:54:52]: But that dilutes your product. You get offshoots where you ask, “Is this the focus of the business?” If it's not core, it languishes. A lot of companies get in trouble when they split focus because they're fighting a multi-front war, not just externally but internally for alignment. Where are we going? What are we doing? What is our purpose?Jake [00:55:24]: If you're Salesforce-built and mission-driven, you want to work on Salesforce. Heroku is off to the side. It's not core to the business. Getting resources, budget, focus, and alignment internally becomes hard. It was a matter of time.Swyx [00:56:06]: Kudos for them to call it out instead of leaving it unknown.Jake [00:56:12]: Their release was a little odd. They called it out, but they didn't say they were shutting it down. Behind the scenes, I think they issued messages to people saying they should close accounts and that they were going to deprecate and remove things over time.Jake [00:56:30]: It's crazy because some of my first deployment experiences were on Heroku. You start with dragging things into an FTP server, then you try to get a deploy working, and then it's Heroku. It was the on-ramp for us. But the wheel turns. New things emerge. We're happy to carry the torch for a lot of that. But we don't want to be the new Heroku. We want to be the way people build and deploy software, and ultimately the way people monetize software over time.Swyx [00:57:19]: It's still a big crown to be the new Heroku. There are 50 companies that fought for that.Jake [00:57:23]: Everybody is holding some portion of it. We're happy to support people and companies. The platform works differently. The game loop is similar, but we've been dogmatic about where these things are going: primitives, agents, fan-out. Some things fit; some workflows need to change. We have an approximation of Heroku pipelines with the environment system. It's exciting. We've got a ton of people we can support, and it's growing a lot.Temporal, Workflow Engines, and State MachinesSwyx [00:58:12]: I have one more technical question about Temporal. I've sold my shares. You're a power user and one of our earliest customers. I met you through Temporal. You built on Temporal. You have complaints. This may be the most neutral and informed conversation anyone will hear about Temporal without someone working at the company.Jake [00:58:39]: That's fair. I've used Temporal for almost 10 years because of Cadence at Uber.Swyx [00:58:52]: Give people a sense of what Cadence was at Uber.Jake [00:58:57]: Cadence was the precursor to Temporal. It powers trip actions, rides, when you rent a Jump bike or scooter or car. You're running workflows for a period of time and saying, “This ride will run indefinitely until it finishes.” You attach information: you paused in this zone, so add this charge to the bill. When you end the trip, the workflow is done. That experience was powered by Cadence at the time.Swyx [00:59:34]: I used to say it's like programming the entire user journey top-down as one function.Jake [00:59:39]: It's a powerful idea and important. It's also important for the next phase of the agentic journey. You want an agent to do a specific task, be complete or incomplete on that task, and move on to the next thing. You need a way to manage workflows dynamically.Jake [00:59:59]: Temporal was always great in theory, and great when you got it working the way you wanted in production. But it required you to model the entire journey in your head. If you didn't, you could cause issues where replaying the state of the workflow causes non-determinism.Swyx [01:00:25]: Because it works on deterministic workflow history.Jake [01:00:28]: Exactly. I describe it as a jet engine. If you know how to operate it and run it, it's great. But you can't hand it to people trying to build complicated things if they don't have the whole state in their head.Jake [01:00:48]: We run our whole deployment pipeline on top of it. That's a reasonably complicated workflow: pre-commit hooks, signaling, queuing, and all the rest. We ran into the same thing at Uber. As you express a large workflow, it gets more complicated, with more states in the state machine that you have to map back to the workflow.Swyx [01:01:15]: It's a lot of ifs.Jake [01:01:16]: Exactly. At Uber, we built a system for doing the state machine and testing it. We've started to build some of those things here because it's grown heavily. It's not quite love-hate. When it works well, it works super well. But if someone who doesn't have full context puts something into the system that invalidates state or causes non-determinism, or spins off a ton of activities, you have to keep track of underlying SRE knobs like activity slots. Those should scale with memory, vCPU, and so on. It becomes a bear to scale.Swyx [01:02:10]: You need a capable sysadmin running things behind the scenes. If you moved off, what would you do?Jake [01:02:19]: We'd build our own workflow engine. We have a few internally that we've worked on.Swyx [01:02:27]: This is one of those classes of things you typically wouldn't vibe code, but I'm wondering if you can.Jake [01:02:33]: I still don't think you should vibe code it. You still want to run decent tests to make sure it works.Swyx [01:02:39]: Timo didn't invent that from scratch either. There are libraries you can run. On top of that, it's just a state machine that you have to map out. Ultimately, you define the instructions you want and run them through a state machine.Jake [01:03:00]: It's very doable. Workflow stuff is interesting. Restate is doing neat stuff here.Swyx [01:03:10]: You're tied into JavaScript. Are you a JavaScript maxi?Jake [01:03:13]: Internally, we have TypeScript, Rust, and Go. We don't add more languages. Actually, we have a little C because we write BPF code and hooks. But those are the languages.Swyx [01:03:28]: Is this for sidecars?Jake [01:03:32]: No. It's for the networking stack, volumes, and things like that. We use TypeScript a lot because it powers the dashboard, but we're moving a lot of workflow stuff off the dashboard stack and into the infrastructure stack.Railpack, Nixpacks, and Content-Addressable FilesystemsSwyx [01:04:00]: Cool. Any other technical infrastructure stuff? Railpacks?Jake [01:04:07]: We built an engine for determining dependencies based on source code. It's called Railpack. We built the first version, Nixpacks, on top of Nix, and then we moved.Swyx [01:04:17]: People have been trying to get me to adopt Nix and NixOS for four years. Is it ever going to be a thing?Jake [01:04:23]: I don't know. We're excited about it, but it has pain points. Think of it as a stack of versioned binaries at specific slices in time. If you want version X and version Y, you bloat the package space, which blows up image size and makes real-world workloads difficult.Swyx [01:04:53]: But you content-address it and cache it. In theory, there are optimizations.Jake [01:05:00]: In theory, yes. But with a large enough user base and disparate enough machines, you run into a problem Meta described in the XFAAS paper, their internal serverless system. It becomes difficult at scale unless you break out specific runtimes.Jake [01:05:24]: We didn't want to do that because we wanted to truly allow you to deploy anything. That was our initial thing with Nix. But we've moved toward interesting work around content-addressable file systems that can lazy-load anything from any point and page it into memory.Swyx [01:05:48]: Amazing.Jake [01:05:49]: The future is very bright. It's crazy, and it's going to be nuts.Coding Agent Spend, Roadmaps, and Token ROISwyx [01:05:54]: Founder journey stuff?Alessio [01:05:56]: Your cloud usage: you tweeted you're going to spend $300K this month?Jake [01:06:01]: I think we got to $200K.Alessio [01:06:02]: Coding agents?Jake [01:06:03]: Yeah.Swyx [01:06:04]: Across the company?Alessio [01:06:05]: You only have 35 people, so I'm sure they're not all spending $10K a month. What's the distribution?Jake [01:06:10]: I think I'm at about $25K. We have power users all the way down. We came back from winter break, and I basically said, “If you're writing code by hand, you're doing this wrong.” The tools are good enough now that you can move extremely quickly. There are issues and pain points, but you should be reviewing the code you are writing instead of writing it by hand.Jake [01:06:40]: Architectural patterns matter more now than ever, but you shouldn't spend your time generating code you would write. If you know how to write it, ask the agent to write it and reconcile it until it looks like you would have written it yourself.Jake [01:06:58]: People misconstrue my propensity to push people toward agents as connected to our growth and some reliability bumps. They're not necessarily related. The tools are good enough to move extremely quickly and build things way larger than you could before.Jake [01:07:19]: To the earlier point about cooling data centers in space: I don't know. But with software, you can ask, “How would I build block storage from scratch? How would I do these things?” I have ideas because I have history and have read papers. Let me work them out and build massive test benches with thousands of tests, because those are now free to author. If you're not using AI systems to speed-run your roadmap and reconcile your existing system onto the future, you're missing a large point of what's happening.Alessio [01:08:12]: What's the path to spending $3 million a month? Is it bound by ideas and things customers can absorb?Jake [01:08:19]: For most companies, it's bound by deployment at this point. That's why we've seen a massive boom in users and companies, from Fortune 50s down, asking how to get developers to move faster. You'll probably hit your CFO before any technical limits because they'll look at the eye-watering amount of money spent on tokens. Inference costs have to come down, but we're inference constrained now. There will be price discovery around what makes sense for an org to adopt.Jake [01:09:06]: I think you'll end up with the F1 driver concept. If someone is really adept at these things, it makes sense to put them in a $3 million car. If they're not, it probably doesn't make sense. You'll take a few people and say, “You can drive the F1 car. We need to go in this direction. Figure out if it works and prototype it.”Jake [01:09:33]: We've done some of that and vastly accelerated our roadmap. We thought we'd ship something in a few years; now we can probably ship it in a few months because we validated it and don't have to build it incrementally. We can skip steps and move toward our vision.Alessio [01:09:58]: A lot of people are realizing the roadmap doesn't always have a business impact, so they say tokens are too expensive. But if your roadmap were built to make more money by the time you built it, you'd have token pricing for it, the same way you do with sales. You'd spend a billion dollars on sales if you knew you would get $2 billion of revenue.Jake [01:10:19]: Exactly. A naive way to measure this is the percentage of tokens that end up in production. If you can measure impact because those tokens end up in production, that's awesome. But the burden of proof will rise. Internally, we have a growing number of pull requests that haven't merged. The question becomes: how do you get this into production? It's about how quickly you can build and deploy software, which is exciting because that's our whole thing.The SDLC Shift: Prompt Requests, Feature Flags, and Safe RolloutsSwyx [01:10:56]: The SDLC is changing. One thesis is that the pull request is dying. It's going to be the prompt request. Beyond that, code review is also kind of dying if you have all the other systems in place. What else is changing about the SDLC?Jake [01:11:19]: The AISRE and the tools to make it happen. AISRE is pie-in-the-sky aspirational. What does it take to get an AISRE? What tools do you need to build?Swyx [01:11:32]: You should expose your tooling to customers at some point. The Central Station command center.Jake [01:11:39]: We have it for template maintainers. Template maintainers can deploy and maintain templates, and they get feedback. We're going to expose those things incrementally.Swyx [01:11:51]: Clustering around incidents. Everyone has a version of that, but I don't think anyone has solved it.Jake [01:11:56]: I won't say we've solved it internally, but it's gotten so good that we can see incidents forming pretty quickly. At some point, those will be things either someone else builds or we build. We've always built things purpose-built for us. If it makes sense to make it useful for users, monetize it, or turn that loop into a profit center instead of a cost center, we want to do that.Jake [01:12:28]: Pull request is definitely dying.Swyx [01:12:29]: Do you do first-party feature flagging and incremental rollout stuff?Jake [01:12:34]: We have a feature-flagging engine we built internally and will eventually roll out.Swyx [01:12:38]: I don't see it as a user. How come you didn't give us what you have?Jake [01:12:43]: We have to beta test it. We care a lot about the quality of the things. There's plenty we've used internally that doesn't make it all the way through the journey because it fails. It works for one service but not multiple services. We'd have to build it for multiple services and know that if we released it, we'd rebuild it again and again. Some things are worth that, but many inform the roadmap.Jake [01:13:18]: We don't want to dilute the experience by saying, “This works, but only for this service,” unless it's a core initiative. Over the next few months, we'll roll out things that work for a single service, then multiple services, then multiple services across the environment. You have to be deliberate. Otherwise you create broken disparate experiences and support load because people ask how to use the feature.Jake [01:13:52]: It's the earlier expansion and compaction pattern. You expand the company to get features, then compact and smooth them out so the experience is stellar. You told me in the hallway, “It's gotten so much better.” Internally we're saying, “This part really sucks. We need to make it significantly better.”Swyx [01:14:11]: I can attest to that over the last three years watching you build Railway. For listeners, feature flagging is a huge part of Uber culture. So much so that they have too many feature flags and another thing to remove feature flags. Facebook has Gatekeeper. Agents are going to need this. It's fundamental to incremental rollouts. OpenAI acquired Statsig. GPT-5 is routing and flagging through different models.Jake [01:14:56]: It's super important. If the software development lifecycle is going to change because we're doing things 1,000 times faster and 1,000 times more concurrently, what becomes important at scale?Jake [01:15:16]: Before I started Railway, I built a feature-flagging product and tried to sell it. It was an easier version of LaunchDarkly. I ran into a problem: anyone small enough to adopt your technology doesn't care about feature flags, and anyone large enough to need feature flags needs so much scale that you have to build out all the infrastructure. I scrapped it.Jake [01:15:42]: But what is old is new again. Companies are trying to move quickly, but you can't YOLO a vibe-coded thing straight into production. You need to say, “Here's my blast radius, my impact, and I want to shadow it for these users.” Feature flags. You're going to need the tools larger companies built to maintain their structures. Everything gets compressed by 1,000x so everybody can build those structures quickly.Jake [01:16:07]: That's exactly where we are: compressing the software development lifecycle, then expanding it and adding more new things.Cattle, Pets, and Clonable InfrastructureSwyx [01:16:15]: Another term that comes to mind for newer developers is “cattle, not pets.” People treat production like a pet. It has a name. You baby it and keep it alive. With cattle, you can mass farm, roll out, portion parts out, and kill them.Jake [01:16:37]: I think that might change. You can move toward having pets as long as you have a cloning machine for your pets.Swyx [01:16:52]: Yeah.Jake [01:16:52]: If you can snapshot every single thing at every frame, it doesn't matter if something gets obliterated because you have a snapshot of it. The things we've built right now are designed to block changes from the hermetically sealed DevOps line. You have to write a Dockerfile because you nee

If you’ve ever wondered why your email marketing isn’t converting, or why “join our newsletter” doesn’t inspire anyone to join your newsletter, this episode will change how you think about email completely. Today, I’m joined by Australian copywriter and ghostwriter Katia Spies, who’s built a highly profitable niche by creating a sequence known as Educational Email Courses. These are not your typical lead magnets. They’re structured, strategic, and designed to turn cold subscribers into qualified buyers. Katia shares how she’s evolved from traditional copywriting into a more personalised, results-driven approach that blends strategy, storytelling, and email automation; why ghost-writing is having a moment, and how businesses can replace tired lead magnets with something far more engaging and effective. What we cover: The difference between copywriting and ghost-writing (and why it matters more than ever). Why personal brands are outperforming traditional brand messaging. What an Educational Email Course is and how it works. Why “join our newsletter” is no longer enough. How a 5-day email course builds trust and warms leads before the ‘sell’. Real examples of EECs in action, including high-ticket services. The structure of a high-converting email course (and what to include each day). Ideal email length, format, and delivery timing. Where to promote your email course for maximum sign-ups. Why segmentation is the secret to better email performance. How to clean your email list (and why it actually improves results). The truth about ‘email is dead’ and what’s really happening instead. Katia’s take on SEO and what still works. How AI is changing copywriting and why human editing still wins. Smart ways to use AI as a copywriter without losing your voice. Read the show notes This podcast is brought to you by the Australian Writers' Centre. WritersCentre.com.au Join our community of copywriters at CopyClub.com.au.See omnystudio.com/listener for privacy information.

In this episode of The Effortless Podcast, Amit Prakash and Dheeraj Pandey are joined by Alex Dimakis for a wide-ranging, systems-first discussion on the future of long-horizon AI agents that can operate over time, learn from feedback, adapt to users, and function reliably inside real-world environments.The conversation spans research and industry, unpacking why prompt engineering alone collapses at scale; how advisor models, reward-driven learning, and environment-based evaluation enable continual improvement without retraining frontier models; and why memory in AI systems is as much about forgetting as it is about recall. Drawing from distributed systems, reinforcement learning, and cognitive science, the trio explores how personalization, benchmarks, and context engineering are becoming the foundation of AI-native software.Alex, Dheeraj, and Amit also examine the evolution from SFT to RL to JEPA-style world models, the role of harnesses and benchmarks in measuring real progress, and why enterprise AI has moved decisively from research into engineering. The result is a candid, deeply technical conversation about what it will actually take to move beyond demos and build agents that work over long horizons.Key Topics & Timestamps 00:00 – Introduction, context, and holiday catch-up04:00 – Teaching in the age of AI and why cognitive “exercise” still matters08:00 – Industry sentiment: fear, trust, and skepticism around LLMs12:00  – Memory in AI systems: documents, transcripts, and limits of recall17:00  – Why forgetting is a feature, not a bug22:00 – Advisor models and dynamic prompt augmentation27:00 – Data vs metadata: control planes vs data planes in AI systems32:00 – Personalization, rewards, and learning user preferences implicitly37:00 – Why prompt-only workflows break down at scale41:00 – RAG, advice, and moving beyond retrieval-centric systems46:00 – Long-horizon agents and the limits of reflection-based prompting51:00 – Environments, rewards, and agent-centric evaluation56:00 – From Q&A benchmarks to agents that act in the world1:01:00 – Terminal Bench, harnesses, and measuring real agent progress1:06:00 – Frontier labs, open source, and the pace of change1:11:00 – Context engineering as infrastructure (“the train tracks” analogy)1:16:00 – Organizing agents: permissions, visibility, and enterprise structure1:20:00 – SFT vs RL: imitation first, reinforcement last1:25:00 – Anti-fragility, trial-and-error, and unsolved problems in continual learning1:28:00 – Closing reflections on the future of long-horizon AI agentsHosts:Amit PrakashCEO & Founder at AmpUp, Former engineer at Google AdSense and Microsoft Bing, with deep expertise in distributed systems, data platforms, and machine learning.Dheeraj PandeyCo-founder & CEO at DevRev, Former Co-founder & CEO of Nutanix. A systems thinker and product visionary focused on AI, software architecture, and the future of work.Guest:Alex DimakisAlex Dimakis is a Professor in UC Berkeley in the EECS department. He received his Ph.D. from UC Berkeley and the Diploma degree from NTU in Athens, Greece. He has published more than 150 papers and received several awards including the James Massey Award, NSF Career, a Google research award, the UC Berkeley Eli Jury dissertation award, and several best paper awards. He is an IEEE Fellow for contributions to distributed coding and learning. His research interests include Generative AI, Information Theory and Machine Learning. He co-founded Bespoke Labs, a startup focusing on data curation for specialized agents.Follow the Hosts and the Guest: Dheeraj Pandey:LinkedIn - https://www.linkedin.com/in/dpandeyTwitter - https://x.com/dheerajAmit Prakash:LinkedIn - https://www.linkedin.com/in/amit-prak...Twitter - https://x.com/amitp42Alex Dimakis:LinkedIn - https://www.linkedin.com/in/alex-dima...Twitter - https://x.com/AlexGDimakis           Share Your Thoughts                                                                                          Have questions, comments, or ideas for future episodes?

Claire chatted to Heather Knight from Oregon State University about applying methods from the performing arts to robotics. Heather Knight runs the CHARISMA Robotics research group. Her education includes a PhD on Expressive Motion for Low Degree of Freedom Robots from Carnegie Mellon University, and M.S. and B.S. degrees in EECS from the Massachusetts Institute of Technology. She has worked at NASA's Jet Propulsion Laboratory and Aldebaran Robotics, and produced the Robot Film Festival, a Cyberflora robot flower garden, robot comedy on TED.com, and a two-floor Rube Goldberg machine for OK Go that won a British Video Music Award. This episode is sponsored by Soft Robotics for Healthcare, a national platform for accelerating the clinical adoption of soft robotic technologies. Their upcoming event: SoRoH 2026 'Shaping the Future of Soft Robotics in Health' is coming to Bristol on the 19th and 20th of January. Register at softroboticshealth.org.uk

In this episode, Alisa Cohn interviews Isaac Evans, co-founder and CEO of Semgrep, a startup giving security tools directly to developers. Isaac shares his journey from conducting research at the U.S. Defense Department and MIT Lincoln Laboratory, where he explored binary exploitation bypasses, control-flow integrity, and novel hardware defenses on architectures like RISC-V, to founding and leading a fast-growing company at the forefront of developer security. A graduate of MIT with BS and MS degrees in EECS, Isaac also brings a deep curiosity for next-generation programming languages, secure-by-design frameworks, and the intersection of cryptography and public policy.Together, Alisa and Isaac dive into the realities of startup leadership, the evolution of Semgrep's business model, the value of feedback, and the transition from founder to CEO. Isaac offers candid insights on managing a growing team, navigating change, and staying grounded through self-awareness. The conversation also explores how AI is reshaping software development, concluding with advice and reflections for aspiring founders building companies in today's fast-moving world.Where to find Isaac:SemgrepXLinkedInTimestamps:(00:00) Introduction to Deep Conversations(01:55) Exploring Love Languages in Relationships(06:00) The Founding Insight of Semgrep(10:06) Navigating Early Startup Challenges(13:45) The Evolution of Semgrep's Business Model(17:53) Handling Community Feedback and Criticism(21:54) Crisis Management and Personal Growth(25:46) The Importance of Feedback Culture(33:20) Embracing Feedback as a Gift(35:45) Shifting Leadership Styles(38:32) The A-Plus Responsibilities of a CEO(42:34) Navigating the Founder to CEO Transition(46:46) Learning Through Experience(50:32) The Challenge of Team Dynamics(54:31) The Future of AI and Security(59:28) Imposter Syndrome and Self-Awareness(01:03) 15 Advice for Aspiring FoundersConnect with Alisa! Follow Alisa Cohn on Instagram: @alisacohn Twitter: @alisacohn Facebook: facebook.com/alisa.cohn LinkedIn: https://www.linkedin.com/in/alisacohn/ Website: http://www.alisacohn.com Download her 5 scripts for delicate conversations (and 1 to make your life better) Grab a copy of From Start-Up to Grown-Up by Alisa Cohn from Amazon

Derek Champagne talks with Sramana Mitra. Sramana is the founder and CEO of One Million by One Million (1Mby1M), the world's first and only global virtual incubator/accelerator. Its goal is to help a million entrepreneurs globally reach a million dollars in annual revenue, build a trillion dollars in global GDP, and create 10 million jobs.Since its founding in 2010, 1Mby1M has become a powerful platform for democratization of entrepreneurship acceleration.Sramana also developed 1Mby1M's Incubator-in-a-Box methodology for Corporate Incubation that is used by enterprises to manage internal and external innovation endeavors.In 2015, LinkedIn named Sramana one of their Top 10 Influencers alongside Bill Gates and Richard Branson.Sramana has been an entrepreneur and a strategy consultant in Silicon Valley since 1994. Her fields of experience span from hardcore technology disciplines like Artificial Intelligence, Cloud Computing and Semiconductors, to sophisticated consumer marketing industries including e-commerce, fashion and education.As an entrepreneur CEO, Sramana founded three companies: Dais (off-shore software services), Intarka (sales lead generation and qualification software using Artificial Intelligence algorithms; VC: NEA) and Uuma (online personalized store for selling clothes using Expert Systems software; VC: Redwood). Two of these were acquired, while the third received an acquisition offer from Ralph Lauren which the company did not accept.As strategy consultant, Sramana has consulted with over 80 companies, including public companies such as SAP, Cadence Design Systems, Webex, KLA-Tencor, Best Buy, MercadoLibre and Tessera among others. Her work has also included numerous startups and VCs.Sramana has a Masters degree in EECS from MIT and a Bachelors degree in Computer Science and Economics from Smith College.From 2000 to 2004, Sramana chaired the MIT Club of Northern California's entrepreneurship program in Silicon Valley.Learn more at www.1Mby1M.comBusiness Leadership Series Intro and Outro music provided by Just Off Turner: https://music.apple.com/za/album/the-long-walk-back/268386576

In this episode of Leading Human, host Chad interviews Radhika Dutt, author of 'Radical Product Thinking' and the forthcoming book 'Escaping the Performance Trap.' Dutt challenges the conventional wisdom of goal-setting in performance management, arguing that tools like OKRs and KPIs often backfire. Instead, she introduces an alternative framework focused on detailed vision statements, puzzle setting, and puzzle solving, which encourages continuous learning and adaptation. The conversation explores the history of goal-setting, its limitations in complex environments, and practical steps for implementing Dutt's approach. Author of Radical Product Thinking, a speaker and entrepreneur, Radhika Dutt has participated in 5 acquisitions, 2 of which were companies she founded. She's an advisor on Product Thinking to the Monetary Authority of Singapore. She's also an MIT grad with SB and M.Eng in EECS, and speaks nine languages.01:12 Challenging Traditional Management03:38 The History and Problems with Goal Setting07:25 Alternative Approaches to Alignment14:56 Implementing Puzzle Setting and Solving20:30 Overcoming Resistance to Change26:23 Lightning Round and ConclusionRadhika's LinkedIn profileRadhika's WebsiteRadical Product Thinking (website for the methodology and Radhika's first book)Speaker reelLinks to the OHL framework/ toolkitWant a communication and wellbeing workshop that actually sticks? Whether you're building trust or leveling up team accountability, we've got you. Book a call to ask questions and learn more about improving how your team communicates here.

Send us a textJake shares his engineering journey, insights into technical interviews, and strategies for young engineers to succeed in the hardware industry. He discusses his experiences at companies like Tesla and Relativity Space and how he created Hardware is Hard to help mechanical engineers land top-tier jobs.Main Topics:Importance of internships in engineering career developmentTechnical interview preparation strategiesEmerging trends in hardware engineering (AR/VR, robotics, US manufacturing)Balancing productivity and personal growthBuilding engineering resources for students and early-career professionalsAbout the guest: Jake Whinnery is a mechanical engineer and hardware leader at Apple, driving innovation and empowering fellow engineers. A UC Berkeley graduate (B.S. in Mechanical Engineering, minor in EECS, 2023), he has contributed to cutting-edge hardware at Tesla, Meta, Relativity Space, and NASA Ames. In 2022, he co-founded Hardware Is Hard with Wilder Buchanan, a platform supporting 13,000+ engineers with tools like interview guides and technical resources. At Apple since August 2023, Jake works on camera hardware, integrating optics, design, and manufacturing. His blend of technical excellence and community leadership makes him a rising force in hardware engineering.LINKS:Jake Whinnery LinkedInJackson Wilder Buchanan LinkedInHardware Is Hard Website Aaron Moncur, host Click here to learn more about simulation solutions from Simutech Group.

Shreya Shankar is a  PhD student at UC Berkeley in the EECS department. This episode explores how Large Language Models (LLMs) are revolutionizing the processing of unstructured enterprise data like text documents and PDFs. It introduces DocETL, a framework using a MapReduce approach with LLMs for semantic extraction, thematic analysis, and summarization at scale.Subscribe to the Gradient Flow Newsletter

Arlindo Oliveira was born in Angola and lived in numerous other countries. He obtained his BSc and MSc degrees in Electrical Engineering and Computer Science (EECS) from Instituto Superior Técnico (IST) and his PhD degree, also in EECS, from the University of California at Berkeley, where he was a Fulbright fellow. He was an invited professor at MIT and a researcher at INESC, CERN, the Electronics Research Laboratory of UC Berkeley, the Berkeley Cadence Laboratories, and the University of Tokyo. He was a member of the National Council for Science Technology and Innovation and of the Advisory Board of the Science and Technology Options Assessment (STOA) Panel of the European Parliament. He is a distinguished professor of IST, president of INESC, distinguished visiting professor at Macau University of Science and Technology, member of the board of Caixa Geral de Depósitos and a researcher at INESC-ID. He authored five books and hundreds of articles in international conferences and journals in the areas of algorithms, artificial intelligence, machine learning, bioinformatics, and computer architecture. He also received several prizes and distinctionsHe has been on the boards of several companies and institutions and is a past president of Instituto Superior Técnico, of INESC-ID and of the Portuguese Association for Artificial Intelligence. He is a member of the Lisbon Academy of Sciences, of the Portuguese Academy of Engineering, of IEEE and of ACM.

"I had to make a decision because I knew that if nothing changed, I was just gonna fail out of EECS. I remember EECS actually sent me a letter where they were like, 'Hey, if your GPA continues this low, you will be kicked out next semester.'" In part one of our conversation with Alfredo Andere, we delve into his journey from Mexico to the prestigious EECS program at UC Berkeley. Alfredo shares his early fascination with technology, the challenges of adapting to a new educational system, and the pivotal moments that shaped his academic and personal growth. Raised in various cities across Mexico, Alfredo's passion for technology was sparked by reading biographies of tech leaders like Steve Jobs and Elon Musk. His entrepreneurial spirit emerged early when he organized a forum that attracted high-profile speakers, including former Mexican presidents, to his all-boys Catholic high school. Despite initial struggles with calculus at Berkeley, Alfredo's determination and commitment to his studies ultimately led to his success in the competitive EECS program.

How do we figure out whether interpretability is doing its job? One way is to see if it helps us prove things about models that we care about knowing. In this episode, I speak with Jason Gross about his agenda to benchmark interpretability in this way, and his exploration of the intersection of proofs and modern machine learning. Patreon: https://www.patreon.com/axrpodcast Ko-fi: https://ko-fi.com/axrpodcast Transcript: https://axrp.net/episode/2025/03/28/episode-40-jason-gross-compact-proofs-interpretability.html   Topics we discuss, and timestamps: 0:00:40 - Why compact proofs 0:07:25 - Compact Proofs of Model Performance via Mechanistic Interpretability 0:14:19 - What compact proofs look like 0:32:43 - Structureless noise, and why proofs 0:48:23 - What we've learned about compact proofs in general 0:59:02 - Generalizing 'symmetry' 1:11:24 - Grading mechanistic interpretability 1:43:34 - What helps compact proofs 1:51:08 - The limits of compact proofs 2:07:33 - Guaranteed safe AI, and AI for guaranteed safety 2:27:44 - Jason and Rajashree's start-up 2:34:19 - Following Jason's work   Links to Jason: Github: https://github.com/jasongross Website: https://jasongross.github.io Alignment Forum: https://www.alignmentforum.org/users/jason-gross   Links to work we discuss: Compact Proofs of Model Performance via Mechanistic Interpretability: https://arxiv.org/abs/2406.11779 Unifying and Verifying Mechanistic Interpretability: A Case Study with Group Operations: https://arxiv.org/abs/2410.07476 Modular addition without black-boxes: Compressing explanations of MLPs that compute numerical integration: https://arxiv.org/abs/2412.03773 Stage-Wise Model Diffing: https://transformer-circuits.pub/2024/model-diffing/index.html Causal Scrubbing: a method for rigorously testing interpretability hypotheses: https://www.lesswrong.com/posts/JvZhhzycHu2Yd57RN/causal-scrubbing-a-method-for-rigorously-testing Interpretability in Parameter Space: Minimizing Mechanistic Description Length with Attribution-based Parameter Decomposition (aka the Apollo paper on APD): https://arxiv.org/abs/2501.14926 Towards Guaranteed Safe AI: https://www2.eecs.berkeley.edu/Pubs/TechRpts/2024/EECS-2024-45.pdf     Episode art by Hamish Doodles: hamishdoodles.com

In this episode, we explore the intricate role of the vagus nerve as a central regulator within the microbiota-gut-brain (MGB) axis, examining its neuroanatomical structure, signaling mechanisms, and interactions with microbial metabolites and immune pathways. We discuss how vagal afferent fibers relay sensory input from the gut to the brain, including signals mediated by short-chain fatty acids (SCFAs) and gut-derived hormones, and how efferent fibers modulate gut motility, intestinal barrier integrity, and inflammation through the cholinergic anti-inflammatory pathway. Finally, we explore vagal dysfunction as well as associated conditions and symptoms, and we touch on just a few potential root causes. Topics: 1. Introduction Focus on the vagus nerve's role in the microbiota-gut-brain (MGB) axis. Bidirectional communication between the brain and microbiota. Overview of communication pathways: neural (e.g., vagus nerve), endocrine (e.g., HPA axis), immune (e.g., cytokines), and metabolic (e.g., SCFAs). 2. Overview of the Nervous System The CNS includes the brain and spinal cord - control centers for the body. The peripheral nervous system extends beyond the CNS The peripheral nervous system is divided into the somatic nervous system and the autonomic nervous system. 3. Autonomic Nervous System (ANS) and Subdivisions Sympathetic Nervous System (SNS) Parasympathetic Nervous System (PNS) Enteric Nervous System (ENS) 4. The Vagus Nerve and Role in the PNS Principal component of the parasympathetic nervous system. Governs "rest-and-digest" activities Contains both afferent (80%) and efferent (20%) fibers. 5. Vagus Nerve Anatomy Fibers originate at the base of the skull and extend into the gut wall. Fibers distributed throughout the mucosa, submucosa, and beyond. Interact indirectly with gut luminal contents via specialized gut cells, including EECs and immune cells. 6. Interaction with Intestinal Cells Enteroendocrine cells (EECs) release gut hormones in response to microbial metabolites. SCFAs, such as butyrate, activate free fatty acid receptors on EECs, stimulating vagal afferents. Immune cells within the gut wall modulate vagal signals during inflammatory responses. 7. Review of Functions Sensory input (afferent fibers): Detects gut-derived signals like microbial metabolites and mechanical stretch. Motor output (efferent fibers): Regulates gut motility, secretion, immune responses, and more. 8. Impact of a Diverse Microbiome on Vagal Activity Enhanced SCFA production boosts vagal activity. SCFAs improve gut barrier integrity, reduce systemic inflammation, and assist in regulating stress responses. 9. Examples: Intestinal Barrier Function Releases acetylcholine (ACh) to modulate inflammatory pathways. Helps enhance tight junction protein expression, preserving gut barrier integrity. Helps prevent the translocation of microbial endotoxins like LPS into systemic circulation. 10. Dysfunction of the Vagus Nerve Reduced vagal tone disrupts gut homeostasis. Conditions such as IBS, IBD, chronic fatigue syndrome, anxiety, depression, and POTS. Chronic stress, infections, and dysbiosis are common contributors. 11. Root Causes 12. Tying Back to the HPA Axis Low vagal tone is associated with increased HPA axis activity. Highlighting the interplay between the gut, brain, and stress response systems. 13. Conclusion Identifying potential root causes. Contributing lifestyle factors. "⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠75 Gut-Healing Strategies & Biohacks⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠" Follow Chloe on Instagram ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@synthesisofwellness⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Follow Chloe on TikTok @chloe_c_porter Visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠synthesisofwellness.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ --- Support this podcast: https://podcasters.spotify.com/pod/show/chloe-porter6/support

Graph learning has gained prominent traction from the academia and industry as a solution to detect complex cyber-attack campaigns. By constructing a graph that connects various network/host entities and modeling the benign/malicious patterns, threat-hunting tasks like data provenance and entity classification can be automated. We term the systems under this theme as Graph-based Security Analytics (GSAs). In this talk, we first provide a cursory view of GSA research in the recent decade, focusing on the academic side. Then, we elaborate a few GSAs developed in our lab, which are designed for edge-level intrusion detection (Argus), subgraph-level attack reconstruction (ProGrapher) and storage reduction (SEAL). In the end of the talk, we will review the progress and pitfalls along the development of GSA research, and highlight some research opportunities. About the speaker: Zhou Li is an Assistant Professor at UC Irvine, EECS department, leading the Data-driven Security and Privacy Lab. Before joining UC Irvine, he worked as Principal Research Scientist at RSA Labs from 2014 to 2018. His research interests include Internet Security, Organizational network security, Privacy Enhancement Technologies, and Security and privacy for machine learning. He received the NSF CAREER award, Amazon Research Award, Microsoft Security AI award and IRTF Applied Networking Research Prize.

All Things TechIE Podcast, Episode 104 where Justin is joined by a special guest, Jack McCauley. Jack designed and built the Oculus DK1 and DK2 virtual reality headsets, prior to the acquisition of the company in 2014 by Facebook for $2 billion. He holds numerous U.S. patents for inventions in software, audio effects, virtual reality, motion control, computer peripherals, and video game hardware and controllers. Jack was awarded a full scholarship to attend University of California, Berkeley where he earned a BSc., EECS in Electrical Engineering and Computer Science in 1986. He currently serves as an Innovator in Residence at Jacobs Institute for Design Innovation at UC Berkeley.

Episode 132I spoke with Manuel and Lenore Blum about:* Their early influences and mentors* The Conscious Turing Machine and what theoretical computer science can tell us about consciousnessEnjoy—and let me know what you think!Manuel is a pioneer in the field of theoretical computer science and the winner of the 1995 Turing Award in recognition of his contributions to the foundations of computational complexity theory and its applications to cryptography and program checking, a mathematical approach to writing programs that check their work. He worked as a professor of computer science at the University of California, Berkeley until 2001. From 2001 to 2018, he was the Bruce Nelson Professor of Computer Science at Carnegie Mellon University.Lenore is a Distinguished Career Professor of Computer Science, Emeritus at Carnegie Mellon University and former Professor-in-Residence in EECS at UC Berkeley. She is president of the Association for Mathematical Consciousness Science and newly elected member of the American Academy of Arts and Sciences. Lenore is internationally recognized for her work in increasing the participation of girls and women in Science, Technology, Engineering, and Math (STEM) fields. She was a founder of the Association for Women in Mathematics, and founding Co-Director (with Nancy Kreinberg) of the Math/Science Network and its Expanding Your Horizons conferences for middle- and high-school girls.Find me on Twitter for updates on new episodes, and reach me at editor@thegradient.pub for feedback, ideas, guest suggestions. I spend a lot of time on this podcast—if you like my work, you can support me on Patreon :) You can also support upkeep for the full Gradient team/project through a paid subscription on Substack!Subscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Intro* (03:09) Manuel's interest in consciousness* (05:55) More of the story — from memorization to derivation* (11:15) Warren McCulloch's mentorship* (14:00) McCulloch's anti-Freudianism* (15:57) More on McCulloch's influence* (27:10) On McCulloch and telling stories* (32:35) The Conscious Turing Machine (CTM)* (33:55) A last word on McCulloch* (35:20) Components of the CTM* (39:55) Advantages of the CTM model* (50:20) The problem of free will* (52:20) On pain* (1:01:10) Brainish / CTM's multimodal inner language, language and thinking* (1:13:55) The CTM's lack of a “central executive”* (1:18:10) Empiricism and a self, tournaments in the CTM* (1:26:30) Mental causation* (1:36:20) Expertise and the CTM model, role of TCS* (1:46:30) Dreams and dream experience* (1:50:15) Disentangling components of experience from multimodal language* (1:56:10) CTM Robot, meaning and symbols, embodiment and consciousness* (2:00:35) AGI, CTM and AI processors, capabilities* (2:09:30) CTM implications, potential worries* (2:17:15) Advice for younger (computer) scientists* (2:22:57) OutroLinks:* Manuel's homepage* Lenore's homepage; find Lenore on Twitter (https://x.com/blumlenore) and Linkedin (https://www.linkedin.com/in/lenore-blum-1a47224)* Articles* “The ‘Accidental Activist' Who Changed the Face of Mathematics” — Ben Brubaker's Q&A with Lenore* “How this Turing-Award-winning researcher became a legendary academic advisor” — Sheon Han's profile of Manuel* Papers (Manuel and Lenore)* AI Consciousness is Inevitable: A Theoretical Computer Science Perspective* A Theory of Consciousness from a Theoretical Computer Science Perspective: Insights from the Conscious Turing Machine* A Theoretical Computer Science Perspective on Consciousness and Artificial General Intelligence* References (McCulloch)* Embodiments of Mind* Rebel Genius Get full access to The Gradient at thegradientpub.substack.com/subscribe

Karl Sun is co-founder and chairman at Lucid Software, a leading provider of visual collaboration software. With Lucid's products—Lucidchart, Lucidspark and Lucidscale—teams can turn ideas into reality, clarify complexity, and collaborate visually, no matter where they're located. Prior to Lucid, Karl spent several years at Google, starting and leading business development at Google's China office, opening Google's patent department and setting patent strategy, and leading Google's investments in advanced wind and battery technologies. Karl holds a B.S. and M.S. in EECS from MIT, an M.S. from MIT in Technology and Policy, and a J.D. from Harvard Law School. He has been honored as a Utah Business CEO of the Year and EY Entrepreneur of the Year. Connect with Karl at https://www.linkedin.com/in/karlsun/ This week's episode is brought to you with the support of Netsuite. --- Support this podcast: https://podcasters.spotify.com/pod/show/uncharted1/support

Sramana Mitra is the founder and CEO of One Million by One Million (1Mby1M), the world's first and only global virtual incubator/accelerator. Its goal is to help a million entrepreneurs globally reach a million dollars in annual revenue, build a trillion dollars in global GDP, and create 10 million jobs.Since its founding in 2010, 1Mby1M has become a powerful platform for democratization of entrepreneurship acceleration.Sramana also developed 1Mby1M's Incubator-in-a-Box methodology for Corporate Incubation that is used by enterprises to manage internal and external innovation endeavors.In 2015, LinkedIn named Sramana one of their Top 10 Influencers alongside Bill Gates and Richard Branson.Sramana has been an entrepreneur and a strategy consultant in Silicon Valley since 1994. Her fields of experience span from hardcore technology disciplines like Artificial Intelligence, Cloud Computing and Semiconductors, to sophisticated consumer marketing industries including e-commerce, fashion and education.As an entrepreneur CEO, Sramana founded three companies: Dais (off-shore software services), Intarka (sales lead generation and qualification software using Artificial Intelligence algorithms; VC: NEA) and Uuma (online personalized store for selling clothes using Expert Systems software; VC: Redwood). Two of these were acquired, while the third received an acquisition offer from Ralph Lauren which the company did not accept.As strategy consultant, Sramana has consulted with over 80 companies, including public companies such as SAP, Cadence Design Systems, Webex, KLA-Tencor, Best Buy, MercadoLibre and Tessera among others. Her work has also included numerous startups and VCs.Sramana has a Masters degree in EECS from MIT and a Bachelors degree in Computer Science and Economics from Smith College.From 2000 to 2004, Sramana chaired the MIT Club of Northern California's entrepreneurship program in Silicon Valley.Learn more at www.1Mby1M.com

This episode continues the conversation with Jason Corso - Professor of Robotics and EECS at University of Michigan and Co-Founder/Chief Science Officer at Voxel51 - a Series A AI software company that enables machine learning and computer vision scientists to rapidly curate and experiment with their datasets in order to build higher performing machine learning systems.AI platforms have been a game changer for productivity and mapping, but it's not yet at the level of flexibility that would eliminate most of the grunt work. Eric and Jason speak on how they'd like to see AI platforms collaborate and get to a point where they not only map out your tasks, but also do a lot of the preliminary work - just like an assistant would. Generalized and personalized AI assistants are the next big step that would vastly increase the value that executives get from utilizing AI in their daily workflow. Eric speaks on how he's currently using ChatGPT to some level of success, but also mentions something we've all felt - the need for a system that compartmentalizes workflows, remembers information and draws custom next steps from previously established context.Everyone's excited for what's next for generative AI. Even though closed models are currently performing better than open-source, this situation will most likely change with time as more tools become available. Jason breaks down annotation strategies and speaks on how the idea of just taking data and training your own models might not be the most efficient way to drive innovation as an AI startup. There's a lot of work in understanding how to create good datasets, which is something that most companies are missing.Tune into the full episode to learn more about the potential of AI!HIGHLIGHTS:00:00 Utilizing Microsoft AutoGen04:04 What's next for AI assistant platforms?  09:03 The problem with ChatGPT 12:47 What's the next step in AI tool development?17:31 The problem with questionable datasetsConnect with Jason - https://www.linkedin.com/in/jason-corso/ Check out Voxel51 - https://voxel51.com/ Don't forget to subscribe to the Chaos to Clarity Podcast for more invaluable episodes to help you grow your business and stay ahead of the curve!To reach out to Eric, visit https://chaostoclarity.io/

Today's episode welcomes Jason Corso - Professor of Robotics and EECS at University of Michigan and Co-Founder/Chief Science Officer at Voxel51 - a Series A AI software company that enables machine learning and computer vision scientists to rapidly curate and experiment with their datasets in order to build higher performing machine learning systems.Jason is a builder by nature. During his academic career he started to ask himself - “can I have a bigger impact on the world?”, which led him to meet his co-founder and start Voxel 51. Both of them had a passion for machine learning and an ability to write code - which is how his transition from academia to consulting began. In the early days they received a grant which worked out well and funded them through the process of building their first service. Building a service, however, was never part of the plan. Once they realized that the limitations of the grant won't enable building a product, they sought out venture capital. This venture capital boost allowed them to build out their open-source platform which has more than 2 million downloads as of this episode. Jason speaks on the early success stories of Voxel51. One of their earliest clients took just 1% of their data and in a few hours they were able to look at the data visually, which enabled them to figure out where mistakes were made. Data quality is more important than the sophistication of the system, and Voxel51 enables their clients to find where inefficiencies live.Even with a great system - human input is still needed when it comes to quality control. Jason speaks on how humans filling in the gaps when the system is less certain is highly effective - even though it doesn't scale. Eric speaks on how experienced data scientists develop an almost 6th sense when looking at data and understanding that something is wrong and a change is in order. Check out the full episode to learn more on how to approach utilizing data! HIGHLIGHTS:03:41 Building a service-based business05:21 When do you utilize Voxel51 in the development process?09:52 Quality or quantity - what's more important for your data?15:05 The importance of human pattern recognition 18:03 New applications of AI toolsConnect with Jason - https://www.linkedin.com/in/jason-corso/ Check out Voxel51 - https://voxel51.com/ Don't forget to subscribe to the Chaos to Clarity Podcast for more invaluable episodes to help you grow your business and stay ahead of the curve!To reach out to Eric, visit https://chaostoclarity.io/

In episode 119 of The Gradient Podcast, Daniel Bashir speaks to Professor Michael Sipser.Professor Sipser is the Donner Professor of Mathematics and member of the Computer Science and Artificial Intelligence Laboratory at MIT.He received his PhD from UC Berkeley in 1980 and joined the MIT faculty that same year. He was Chairman of Applied Mathematics from 1998 to 2000 and served as Head of the Mathematics Department 2004-2014. He served as interim Dean of Science 2013-2014 and then as Dean of Science 2014-2020.He was a research staff member at IBM Research in 1980, spent the 1985-86 academic year on the faculty of the EECS department at Berkeley and at MSRI, and was a Lady Davis Fellow at Hebrew University in 1988. His research areas are in algorithms and complexity theory, specifically efficient error correcting codes, interactive proof systems, randomness, quantum computation, and establishing the inherent computational difficulty of problems. He is the author of the widely used textbook, Introduction to the Theory of Computation (Third Edition, Cengage, 2012).Have suggestions for future podcast guests (or other feedback)? Let us know here or reach Daniel at editor@thegradient.pubSubscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Intro* (01:40) Professor Sipser's background* (04:35) On interesting questions* (09:00) Different kinds of research problems* (13:00) What makes certain problems difficult* (18:48) Nature of the P vs NP problem* (24:42) Identifying interesting problems* (28:50) Lower bounds on the size of sweeping automata* (29:50) Why sweeping automata + headway to P vs. NP* (36:40) Insights from sweeping automata, infinite analogues to finite automata problems* (40:45) Parity circuits* (43:20) Probabilistic restriction method* (47:20) Relativization and the polynomial time hierarchy* (55:10) P vs. NP* (57:23) The non-connection between GO's polynomial space hardness and AlphaGo* (1:00:40) On handicapping Turing Machines vs. oracle strategies* (1:04:25) The Natural Proofs Barrier and approaches to P vs. NP* (1:11:05) Debates on methods for P vs. NP* (1:15:04) On the possibility of solving P vs. NP* (1:18:20) On academia and its role* (1:27:51) OutroLinks:* Professor Sipser's homepage* Papers discussed/read* Halting space-bounded computations (1978)* Lower bounds on the size of sweeping automata (1979)* GO is Polynomial-Space Hard (1980)* A complexity theoretic approach to randomness (1983)* Parity, circuits, and the polynomial-time hierarchy (1984)* A follow-up to Furst-Saxe-Sipser* The Complexity of Finite Functions (1991) Get full access to The Gradient at thegradientpub.substack.com/subscribe

In this rebroadcast episode of the IJGC podcast, Editor-in-Chief Dr. Pedro Ramirez is joined by Drs. Jenny Mueller and Bill Zammarrelli to discuss molecular classification and risk stratification in endometrial cancer. Jenny Mueller, MD, is a gynecologic oncologist and assistant attending in the department of surgery at Memorial Sloan Kettering Cancer Center. She leads the endometrial cancer research team at MSKCC with an emphasis on prospective, translational, and collaborative efforts within and across institutions. Bill Zammarrelli, MD, currently works as a gynecologic oncology fellow at Memorial Sloan Kettering Cancer Center. He is a commissioned officer in the US Army and completed his residency at Walter Reed National Military Medical Center. His current research focuses on the genetics of endometrial cancer.    Highlights: --PORTEC-1 and GOG-99 risk classifications are discordant for stage I grade 3 endometrioid endometrial carcinoma (EEC). --Stage I grade 3 EECs of CN-high molecular subtype have a worse 3-year progression-free survival compared to non-CN-high molecular subtypes. --Molecular classification in combination with clinicopathologic factors may provide improved prognostic information.

Signs Of Life Radio is Tonight! Hosted by Bob Ginsberg. Special Guest Dr. Christopher Roe TUNE IN TONIGHT! Thursday, November 30th 8:00 PM Eastern (check your time zone) Host: Bob Ginsberg Guest: Dr. Chris Roe Tonight on Signs of Life Radio, Host Bob Ginsberg will be speaking with special guest, Dr. Chris Roe! Chris Roe is professor of psychology at the University of Northampton and director of its Centre for Psychology and Social Sciences, which includes a research group devoted to Exceptional Experience and Consciousness Studies (EECS). Roe's research interests are focused around understanding the nature of anomalous experiences, including the psychology of belief and self-deception. He has utilized experimental approaches to test psi abilities, in particular those involving psychological factors. Most recently, his focus has shifted to investigating unconscious measures of psi and correlating these with behavioral and personality measures.

Anurupa Ganguly is the founder & CEO of Prisms. She founded Prisms to scale a new world teaching model where students learn core math and science concepts spatially, physically and steeped in important real world contexts. Prisms targets bottleneck topics that are often memorized in lieu of deeply understood -- leading to huge gaps and drops offs over time in STEM. Anurupa and her team are transforming math education in the US by rapidly deploying the next generation of spatial computing devices across US K-12 districts, training teachers to integrate problem-driven learning with VR into core curriculum, and working with district leadership to rapidly improve joy, confidence and proficiencies in the modern math and science classrooms. She began her career as a math & physics teacher and later served in leadership roles across the Boston Public Schools, NYC DOE, and Success Academies. She holds degrees in EECS from MIT, and an EdM from BU. Her life's mission is to empower an equitable workforce in STEM.Recommended Resource:Experience on Demand by Jeremy Bailenson

Dr. Vivienne Sze is an associate professor in the EECS department at MIT. Vivienne is recognized for her leading work on energy-efficient computing systems spanning a wide range of domains: from video compression, to machine learning, robotics and digital health. She received the DARPA Young Faculty Award, Edgerton Faculty Award, faculty grants from Google, Facebook and Qualcomm, and a Primetime Engineering Emmy as a member of the team that developed the High-Efficiency Video Coding standard.

Jitendra Malik, Professor of EECS at UC Berkeley discusses with host Pieter Abbeel building AI from the ground-up and sensorimotor before language. Subscribe to the Robot Brains Podcast today | Visit therobotbrains.ai and follow us on YouTube at TheRobotBrainsPodcast and Twitter at @pabbeel. Hosted on Acast. See acast.com/privacy for more information.

In this episode Lauren Hawker Zafer is joined by Dr. Christopher Nguyen Who is Dr. Christopher Nguyen? Dr. Christopher Nguyen, CEO and Cofounder of Aitomatic, has an extensive career in Silicon Valley and global industries. He served as the first engineering director for Gmail at Google and led Global Industrial AI at Panasonic for four years. His current work in Industrial AI is exemplified by the "Industrial Mind," a System-2 AI solution designed for problem-solving in the industrial sector.Dr. Nguyen combines human knowledge with transparent AI, focusing on maximizing Industrial AI's potential for societal impact. He holds a BS in EECS from UC Berkeley, PhD Stanford, and co-founded the Computer Engineering department at HKUST. With his latest company, ⁠Aitomatic⁠, he's hoping to redefine how companies approach AI in the context of life-critical, industrial applications. Why this Episode?

Spotlight Thirteen is a snippet from our upcoming episode: Dr. Christopher Nguyen - Why AI Needs a Human Eye! Listen to the full episode, as soon as it comes out by subscribing to Redefining AI. Who is Dr. Christopher Nguyen? Dr. Christopher Nguyen, CEO and Cofounder of Aitomatic, has an extensive career in Silicon Valley and global industries. He served as the first engineering director for Gmail at Google and led Global Industrial AI at Panasonic for four years. His current work in Industrial AI is exemplified by the "Industrial Mind," a System-2 AI solution designed for problem-solving in the industrial sector. Dr. Nguyen combines human knowledge with transparent AI, focusing on maximizing Industrial AI's potential for societal impact. He holds a BS in EECS from UC Berkeley, PhD Stanford, and co-founded the Computer Engineering department at HKUST. With his latest company, ⁠⁠Aitomatic⁠⁠, he's hoping to redefine how companies approach AI in the context of life-critical, industrial applications. Why this Episode?

00:00 - Intro 00:24 - How did you make a decision about hosting Ukrainian civil society here, in the comittee? What was your opinion about helping Ukrainians from the start of the war? 02:58 - Do you see Ukraine as a member of the #EU? How is the EECS responding to this topic? 03:57 - Do you feel any intensifications of support of Ukrainian civil society? 04:38 - How do European Institutions help Ukrainian people in Brussels? What is the main idea of this help? 06:27 - What is the future of the resolutions in support of Ukraine that were adopted last year? Does it also involve blocked Russian assets? 07:40 - What were your expectations from your presidency? Was it distorted by what was constantly happening around? 09:16 - Has the approach of the European Committee towards citizens changed during the COVID period and what exactly has changed since then? 11:09 - Do the citizens of European Union have enough political power to put pressure on European Institutions for changing their decisions? 12:03 - Looking back at your past experience, would you like to make a difference in the past or even pursue a career other than becoming #EESC President? Is there anything you would do more if you had enough time? 14:12 - Are you traveling to meet other committee members? If not, what are the main reasons for your business trips? Are you doing this to see what the real situation is in other countries? 15:50 - What challenges did you face on your path to leadership? Were there any problems because you are a woman? 17:43 - Do you have any messages or wishes for Ukrainians? Would you like to inspire them for a future? 18:31 - What do you think about citizens involvement into making democracy package legitimate in terms of defending from foreign interference? Should the citizens being empowered for that? 21:23 - Do you plan to visit #Ukraine after the victory? 21:42 - What are your plans for the future as a member of committee?

#oculus #oculusvr #podcast #toctw #virtualreality -OCULUS VR GARAGE TO A GLOBAL PHENOMENON Jack McCauley an Innovator in Residence at Jacobs Institute for Design Innovation at UC Berkeley, a Professor at UC Berkeley, Co-Founder of Oculus, an American engineer, hardware designer, inventor, video game developer, and philanthropist. Jack is best known for designing the guitars and drums for the Guitar Hero video game series, and as a co-founder and former chief engineer at Oculus VR. At Oculus, Jack designed and built the Oculus DK1 and DK2 virtual reality headsets. Oculus was acquired by Facebook for$2 Billion. McCauley holds numerous U.S. patents for inventions in software, audio effects, virtual reality, motion control, computer peripherals, and video game hardware and controllers. Jack was awarded a full scholarship to attend the University of California, Berkeley where he earned a BSc., EECS in Electrical Engineering and Computer Science in 1986. Jack has authored numerous research papers in the field of artificial intelligence (AI) and mathematical modeling of AI-based systems and is currently pursuing new projects at his private R&D facility and hardware incubator in Livermore, California. https://jackmccauley.com/https://www.linkedin.com/in/jack-j-mccauley-9237bb5https://twitter.com/jackmccauley1 Connect & Follow us at: https://in.linkedin.com/in/eddieavil https://in.linkedin.com/company/change-transform-india https://www.facebook.com/changetransformindia/ https://twitter.com/intothechange https://www.instagram.com/changetransformindia/ Listen to the Audio Podcast at: https://anchor.fm/transform-impossible https://podcasts.apple.com/us/podcast/change-i-m-possibleid1497201007?uo=4 https://open.spotify.com/show/56IZXdzH7M0OZUIZDb5mUZ https://www.breaker.audio/change-i-m-possible https://www.google.com/podcasts?feed=aHR0cHM6Ly9hbmNob3IuZm0vcy8xMjg4YzRmMC9wb2RjYXN0L3Jzcw Dont Forget to Subscribe www.youtube.com/ctipodcast

In this week's episode Anna Rose (https://twitter.com/annarrose) and Tarun Chitra (https://twitter.com/tarunchitra) explore the topic of auctions with guests Kshitij Kulkarni (https://twitter.com/ks_kulk), PHD student at Berkeley's EECS department (https://eecs.berkeley.edu/) and Matheus V. X. Ferreira (https://twitter.com/MatheusVXF), Postdoctoral Fellow in Computer Science at Harvard John A. Paulson School of Engineering and Applied Sciences (https://www.seas.harvard.edu/). They discuss the history of auctions, both in the real world and in blockchain, and go on to cover more recent blockchain uses, such as MEV and NFT auctions. They review the incentives of both auction holders and the participants as well as how this incentive design can influence the effectiveness of the auctions themselves. Here are some additional links for this episode: Credibility and Incentives in Gradual Dutch Auctions by Kulkarni, Ferreira and Chitra (https://people.eecs.berkeley.edu/~ksk/files/GDA.pdf) Credible, Optimal Auctions via Blockchains by Kulkarni, Ferreira and Chitra (https://arxiv.org/abs/2301.12532) Optimal Strategic Mining Against Cryptographic Self-Selection in Proof-of-Stake by Kulkarni, Ferreira and Chitra (https://arxiv.org/abs/2207.07996) Credible Auctions: A Trilemma by Akbarpour and Li (https://web.stanford.edu/~mohamwad/Credible.pdf) Credible, Truthful, and Two-Round (Optimal) Auctions via Cryptographic Commitments by Ferreira and Weinberg (https://arxiv.org/abs/2004.01598) Credible, Strategyproof, Optimal, and Bounded Expected-Round Single-Item Auctions for all Distributions by Essaidi, Ferreira and Weinberg (https://arxiv.org/abs/2205.14758) Dynamic Posted-Price Mechanisms for the Blockchain Transaction Fee Market by Ferreira, Moroz, Parkes and Stern (https://arxiv.org/abs/2103.14144) Proof-of-Stake Mining Games with Perfect Randomness by Ferreira and Weinberg (https://arxiv.org/abs/2107.04069) Optimal Strategic Mining Against Cryptographic Self-Selection in Proof-of-Stake by Ferreira, Hahn, Weinberg, Yu (https://arxiv.org/abs/2207.07996) Credible Decentralized Exchange Design via Verifiable Sequencing Rules by Ferreira and Parkes (https://arxiv.org/abs/2209.15569) https://jumpcrypto.com/thepit/zkweek/ (https://jumpcrypto.com/thepit/zkweek/) Find out more about zkSummit9 here: zksummit.com (https://www.zksummit.com/). Apply for ZK Hack Lisbon here: ZK Hack application (https://xng1lsio92y.typeform.com/zkhacklisbon?typeform-source=zkhack.dev) Aleo (https://www.aleo.org/) is a new Layer-1 blockchain that achieves the programmability of Ethereum, the privacy of Zcash, and the scalability of a rollup. Interested in building private applications? Check out Aleo's programming language called Leo by visiting http://developer.aleo.org (https://developer.aleo.org/getting_started/). You can also participate in Aleo's incentivized testnet3 by downloading and running a snarkOS node. No sign-up is necessary to participate. For questions, join their Discord at aleo.org/discord (https://discord.com/invite/aleohq). If you like what we do: * Find all our links here! @ZeroKnowledge | Linktree (https://linktr.ee/zeroknowledge) * Subscribe to our podcast newsletter (https://zeroknowledge.substack.com) * Follow us on Twitter @zeroknowledgefm (https://twitter.com/zeroknowledgefm) * Join us on Telegram (https://zeroknowledge.fm/telegram) * Catch us on YouTube (https://zeroknowledge.fm/)

Sergey Levine, an assistant professor of EECS at UC Berkeley, is one of the pioneers of modern deep reinforcement learning. His research focuses on developing general-purpose algorithms for autonomous agents to learn how to solve any task. In this episode, we talk about the bottlenecks to generalization in reinforcement learning, why simulation is doomed to succeed, and how to pick good research problems.

Anything that's worth it takes time. "My children now as you say, young adults, they've had more than enough of that, with me telling to them, just be patient. For all the progress that we make, the younger generation, whether it be millennials or anything else, they want stuff today." - Rob Neil OBE (Want to read a full written for reading transcript version of this episode? Download it here.) Who is Rob Neil? Rob was born and raised in the London Borough of Brent and began his career in 1983 at Willesden County Court for the Ministry of Justice (MoJ). During his time there, he advanced to Deputy Court Manager before joining the South Eastern I.T. Team. In 1998, he acquired his dream job with MoJ's Corporate HR as a Development Trainer. During his two years of study at the Civil Service College, he earned a Certificate in Training Practice (CTP) and is now a member of CIPD. In 2001, Rob was one of the first people to join the Ministry of Justice's BAME Staff network, referred to as P.R.O.U.D. He then went on to become the first elected Chair of the CSREN (now known as the Civil Service Race Forum) that same year. The Civil Service Race Forum was established then. Rob Neil's legacy in civil service Rob developed his career in the civil service at the MoJ in HR and became a founding member of the Employee Engagement Team. He was in charge of creating the Engagement Champions Network in the year 2008. He was also responsible for the growth of the Employee Engagement Champions across the MoJ, eventually reaching 1000 people in Courts, Tribunals and Legal Aid Agency. He's worked with other Government departments to help them set up their own EECs, including HMRC, DWP, and MoD. In the summer of 2015, Rob was responsible for steering the Diversity & Inclusion Department at the Ministry of Justice. He was instrumental in achieving essential goals, such as the implementation of the Civil Service Talent Action Plan [T.A.P.] and re-negotiating arrangements with all of the Department's Diversity Staff Networks. In April of 2016 Rob was noted as one of the 50 influential figures of BAME (Black Asian Minority Ethnic) background in the public sector, as part of the 'New View 50' You can see the list here. About Krystal Alliance The Krystal Alliance™ team consists of incredibly talented and devoted members who strive to collaborate with committed entities that desire to promote equity in the workplace, encourage and motivate others. The individuals of Krystal Alliance are all specialists in their field, whether devising plans to foster transformation in culture, running interactive sessions to promote inclusive leadership, or imagining ways to build a stronger sense of community. So how did Rob come to get involved with creating this powerhouse team? You'll need to listen to find out! What are you waiting for? Click play, and immerse yourself in another fascinating conversation with another wonderful guest on the Everyday Leadership podcast.   Key leadership learning moments (timestamps)   0:00 - Intro 03:55 - The importance of offering our best 08:47 - Career in policing 16:44 - The power of working alongside others 21:28 - What's good for one... 25:53 - Letting go of the civil service 33:11 - What is 'cultural intelligence'? 37:32 - Linking with people who want to be more inclusive  42:33 - Becoming a granddad  46:55 - Rob's Definition of Leadership Rob's links: LinkedIn Krystal Alliance   Follow the podcast   If you've just stumbled across this podcast episode by chance, please do click here to follow it so you never miss a future episode. If you want to learn more about this podcast, and myself, Sope Agbelusi, you can do so using any of the below links. Connect with Me Website Instagram  LinkedIn  Twitter  Email: hello@mindsetshift.co.uk I am always keen to hear your thoughts and connect with the community of listeners. If you have any comments, feedback or thoughts, please drop me an email at https://mindsetshift.co.uk/#ask-me-anything  

Anything that's worth it takes time. "My children now as you say, young adults, they've had more than enough of that, with me telling to them, just be patient. For all the progress that we make, the younger generation, whether it be millennials or anything else, they want stuff today." - Rob Neil OBE (Want to read a full written for reading transcript version of this episode? Download it here.) Who is Rob Neil? Rob was born and raised in the London Borough of Brent and began his career in 1983 at Willesden County Court for the Ministry of Justice (MoJ). During his time there, he advanced to Deputy Court Manager before joining the South Eastern I.T. Team. In 1998, he acquired his dream job with MoJ's Corporate HR as a Development Trainer. During his two years of study at the Civil Service College, he earned a Certificate in Training Practice (CTP) and is now a member of CIPD. In 2001, Rob was one of the first people to join the Ministry of Justice's BAME Staff network, referred to as P.R.O.U.D. He then went on to become the first elected Chair of the CSREN (now known as the Civil Service Race Forum) that same year. The Civil Service Race Forum was established then. Rob Neil's legacy in civil service Rob developed his career in the civil service at the MoJ in HR and became a founding member of the Employee Engagement Team. He was in charge of creating the Engagement Champions Network in the year 2008. He was also responsible for the growth of the Employee Engagement Champions across the MoJ, eventually reaching 1000 people in Courts, Tribunals and Legal Aid Agency. He's worked with other Government departments to help them set up their own EECs, including HMRC, DWP, and MoD. In the summer of 2015, Rob was responsible for steering the Diversity & Inclusion Department at the Ministry of Justice. He was instrumental in achieving essential goals, such as the implementation of the Civil Service Talent Action Plan [T.A.P.] and re-negotiating arrangements with all of the Department's Diversity Staff Networks. In April of 2016 Rob was noted as one of the 50 influential figures of BAME (Black Asian Minority Ethnic) background in the public sector, as part of the 'New View 50' You can see the list here. About Krystal Alliance The Krystal Alliance™ team consists of incredibly talented and devoted members who strive to collaborate with committed entities that desire to promote equity in the workplace, encourage and motivate others. The individuals of Krystal Alliance are all specialists in their field, whether devising plans to foster transformation in culture, running interactive sessions to promote inclusive leadership, or imagining ways to build a stronger sense of community. So how did Rob come to get involved with creating this powerhouse team? You'll need to listen to find out! What are you waiting for? Click play, and immerse yourself in another fascinating conversation with another wonderful guest on the Everyday Leadership podcast.   Key leadership learning moments (timestamps)   0:00 - Intro 03:55 - The importance of offering our best 08:47 - Career in policing 16:44 - The power of working alongside others 21:28 - What's good for one... 25:53 - Letting go of the civil service 33:11 - What is 'cultural intelligence'? 37:32 - Linking with people who want to be more inclusive  42:33 - Becoming a granddad  46:55 - Rob's Definition of Leadership Rob's links: LinkedIn Krystal Alliance   Follow the podcast   If you've just stumbled across this podcast episode by chance, please do click here to follow it so you never miss a future episode. If you want to learn more about this podcast, and myself, Sope Agbelusi, you can do so using any of the below links. Connect with Me Website Instagram  LinkedIn  Twitter  Email: hello@mindsetshift.co.uk I am always keen to hear your thoughts and connect with the community of listeners. If you have any comments, feedback or thoughts, please drop me an email at https://mindsetshift.co.uk/#ask-me-anything  

Anything that's worth it takes time. "My children now as you say, young adults, they've had more than enough of that, with me telling to them, just be patient. For all the progress that we make, the younger generation, whether it be millennials or anything else, they want stuff today." - Rob Neil OBE (Want to read a full written for reading transcript version of this episode? Download it here.) Who is Rob Neil? Rob was born and raised in the London Borough of Brent and began his career in 1983 at Willesden County Court for the Ministry of Justice (MoJ). During his time there, he advanced to Deputy Court Manager before joining the South Eastern I.T. Team. In 1998, he acquired his dream job with MoJ's Corporate HR as a Development Trainer. During his two years of study at the Civil Service College, he earned a Certificate in Training Practice (CTP) and is now a member of CIPD. In 2001, Rob was one of the first people to join the Ministry of Justice's BAME Staff network, referred to as P.R.O.U.D. He then went on to become the first elected Chair of the CSREN (now known as the Civil Service Race Forum) that same year. The Civil Service Race Forum was established then. Rob Neil's legacy in civil service Rob developed his career in the civil service at the MoJ in HR and became a founding member of the Employee Engagement Team. He was in charge of creating the Engagement Champions Network in the year 2008. He was also responsible for the growth of the Employee Engagement Champions across the MoJ, eventually reaching 1000 people in Courts, Tribunals and Legal Aid Agency. He's worked with other Government departments to help them set up their own EECs, including HMRC, DWP, and MoD. In the summer of2015, Rob was responsible for steering the Diversity & Inclusion Department at the Ministry of Justice. He was instrumental in achieving essential goals, such as the implementation of the Civil Service Talent Action Plan [T.A.P.] and re-negotiating arrangements with all of the Department's Diversity Staff Networks. In April of2016 Rob was noted as one of the 50 influential figures of BAME (Black Asian Minority Ethnic) background in the public sector, as part of the 'New View 50' You can see the list here. About Krystal Alliance The Krystal Alliance team consists of incredibly talented and devoted members who strive to collaborate with committed entities that desire to promote equity in the workplace, encourage and motivate others. The individuals of Krystal Alliance are all specialists in their field, whether devising plans to foster transformation in culture, running interactive sessions to promote inclusive leadership, or imagining ways to build a stronger sense of community. So how did Rob come to get involved with creating this powerhouse team? You'll need to listen to find out! What are you waiting for? Click play, and immerse yourself in another fascinating conversation with another wonderful guest on the Everyday Leadership podcast. Key leadership learning moments (timestamps) 0:00 - Intro 03:55 - The importance of offering our best 08:47 - Career in policing 16:44 - The power of working alongside others 21:28 - What's good for one... 25:53 - Letting go of the civil service 33:11 - What is 'cultural intelligence'? 37:32 - Linking with people who want to be more inclusive 42:33 - Becoming a granddad 46:55 - Rob's Definition of Leadership Rob's links: LinkedIn Krystal Alliance Follow the podcast If you've just stumbled across this podcast episode by chance, please do click here to follow it so you never miss a future episode. If you want to learn more about this podcast, and myself, Sope Agbelusi, you can do so using any of the below links. Connect with Me Website Instagram LinkedIn Twitter Email: hello@mindsetshift.co.uk I am always keen to hear your thoughts and connect with the community of listeners. If you have any comments, feedback or thoughts, please drop me an email at https://mindsetshift.co.uk/#ask-me-anything

Counselling Psychologist & Systemic & Family Practitioner Paula St Ange and Trainee Clinical Psychologist Cassia Keziah Maximen join me in this episode to discuss Emotional Emancipation Circles. Aka as EECs are evidence-informed, psychologically sound, culturally grounded, and community-defined self-help support groups designed to help heal, and end, the trauma caused by the root cause of anti-Black racism. These two women have been group participants and are now trained as EEC group facilitators.  They share how and why these groups were started, the concept is an import from the US which has been adapted to the UK context.  In the early evaluations, EEC participants reported notable improvements in their mental health. Facilitators have been trained in nearly 50 states in the US, Cuba, South Africa and of course the UK. For more information about EECs contact: Malcom_phillip@outlook.com My email is: adosylv@gmail.com Join our Facebook community at: Social Workers MatterSee omnystudio.com/listener for privacy information.

In this episode of the IJGC podcast, Editor-in-Chief Dr. Pedro Ramirez is joined by Drs. Jenny Mueller and Bill Zammarrelli to discuss molecular classification and risk stratification in endometrial cancer. Jenny Mueller, MD, is a gynecologic oncologist and assistant attending in the department of surgery at Memorial Sloan Kettering Cancer Center. She leads the endometrial cancer research team at MSKCC with an emphasis on prospective, translational, and collaborative efforts within and across institutions. Bill Zammarrelli, MD, currently works as a gynecologic oncology fellow at Memorial Sloan Kettering Cancer Center. He is a commissioned officer in the US Army and completed his residency at Walter Reed National Military Medical Center. His current research focuses on the genetics of endometrial cancer. Highlights: - PORTEC-1 and GOG-99 risk classifications are discordant for stage I grade 3 endometrioid endometrial carcinoma (EEC). - Stage I grade 3 EECs of CN-high molecular subtype have a worse 3-year progression-free survival compared to non-CN-high molecular subtypes. - Molecular classification in combination with clinicopathologic factors may provide improved prognostic information.

Tim Barat, CEO & Co-Founder, Gridware, an ex-power pole field worker, named in Forbes' 30 under 30 innovator list for developing a 24/7 monitoring system increasing visibility along power distribution lines - after getting his EECS degree at UC Berkeley.

As we discussed back in Episode 41, business partnerships can be a difficult path to navigate. They are notorious for being massive accelerants to success - or a quick path to failure. Today, we are ecstatic to host Dave Farrow on the podcast. After 23 years in software development and leadership roles, Dave moved into security. Over the past ten years he has founded and run security programs at Barracuda Networks and Veza where he was responsible for GRC, product and corporate security, and incident response. Dave currently serves as VP, CISO at Red Canary. He holds a BS, EECS from UC Berkeley. Dave got started in real estate investing a few years ago through the more unconventional route - private money lending. After getting his feet wet with lending on projects to local, reputable investors, this year he decided he wanted to make the leap on taking a more active role in real estate investing. Throughout the episode, Dave and Scott talk about their most recent flip - breaking down the numbers, their partnership structure, and much more.

Welcome back to Season 2, Episode 181 of the Asian Hustle Network Podcast! We are very excited to have James Vuong on this week's show. We interview Asian entrepreneurs around the world to amplify their voices and empower Asians to pursue their dreams and goals. We believe that each person has a message and a unique story from their entrepreneurial journey that they can share with all of us. Check us out on Anchor, iTunes, Stitcher, Google Play Music, TuneIn, Spotify, and more. If you enjoyed this episode, please subscribe and leave us a positive 5-star review. This is our opportunity to use the voices of the Asian community and share these incredible stories with the world. We release a new episode every Wednesday and Saturday, so stay tuned! James is a serial tech entrepreneur, angel investor, and former VC. He has broad experience ranging from engineering to building products, from investing to founding startups, and from Silicon Valley to SE Asia. James is currently the founder and CEO of Infina.vn, the leading investment and wealth management app in Vietnam, backed by Sequoia, Y Combinator, and many reputable global VCs. Prior to Infina, James was CEO of Lana group, a digital media startup acquired by LINE Corp, the listed Japanese chat app. Prior to that, James was a VC at IDG Ventures, Vietnam's first VC fund with $100M USD. James was a Kauffman Fellow (the first in SE Asia) selected by the Kauffman Foundation in the US in 2008. Prior to coming back to Asia, James held positions as product director and tech lead (in chip logic design) in different companies in Silicon Valley. He received his MBA from Berkeley Haas School of Business in 2006 and BS in EECS at UC Berkeley in 1997. --- Support this podcast: https://anchor.fm/asianhustlenetwork/support

Show Notes(01:35) Ville recalled his education getting degrees in Computer Science from the University of Helsinki in Finland.(04:35) Ville walked over his time working at a startup called Gurusoft that planned to commercialize self-organizing maps, a peculiar artificial neural network.(07:17) Ville reflected on his four years as a researcher at Nokia — working on big data infrastructure, analytics, and ML open-source projects (such as Disco and Ringo).(11:56) Ville shared the story of co-founding a startup that built a novel scriptable data platform called Bitdeli with his brother and not finding a product-market fit.(13:58) Ville walked through AdRoll's acquisition of Bitdeli in June 2013.(15:49) Ville discussed the engineering challenges associated with his work at AdRoll — AdRoll Prospecting and traildb.io.(19:33) Ville mentioned the product and leadership/management lessons during his time being AdRoll's Head of Data and leading various data/ML efforts.(24:43) Ville rationalized his decision to join the ML Infrastructure team at Netflix in 2017.(27:26) Ville discussed the motivation behind the creation of Netflix's human-centric ML infrastructure, Metaflow, later open-sourced in 2019.(30:21) Ville unpacked the key design principles that summarize the philosophy of Metaflow, which is influenced by the unique culture at Netflix.(35:00) Ville talked about his well-known diagram on the data infrastructure's hierarchy of needs.(37:33) Ville examined the technical details behind Metaflow's integration with AWS to make it easy for users to move back and forth between their local and remote modes of development and execution.(40:58) Ville expressed the challenges of finding Metaflow's early adopters internally at Netflix and externally later on at other companies.(45:13) Ville went over the strategy around prioritizing features for Metaflow's future roadmap.(52:22) Ville shared the story behind the founding of Outerbounds, which he co-founded with Savin Goyal and Oleg Avdeev.(55:03) Ville provided his thoughts behind Metaflow's contributors in a way that can generate valuable product feedback for Outerbounds.(58:30) Ville shared valuable hiring lessons to attract the right people who are excited about Outerbounds' mission.(01:01:28) Ville shared upcoming initiatives that he is most excited about for Outerbounds.(01:04:05) Ville walked through his writing process for an upcoming technical book with Manning called “Effective Data Science Infrastructure,” a hands-on guide to assembling infrastructure for data science and machine learning applications.(01:06:34) Ville unpacked his great O'Reilly article that digs deep into the fundamentals of ML as an engineering discipline.(01:11:03) Closing segment.Ville's Contact InfoLinkedInTwitterGitHubOuterboundsWebsite | Twitter | LinkedIn | GitHub | YouTubeMetaflow GitHub | Metaflow DocsSlack CommunityCareersMetaflow Resources for Data ScienceMetaflow Resources for EngineeringMentioned ContentTalksSF Data Mining Meetup: TrailDB — Processing Trillions of Events at AdRoll (July 2016)QConSF 2018: Human-Centric Machine Learning Infrastructure @Netflix (Feb 2019)AWS re:Invent 2019: More Data Science with Less Engineering — ML Infrastructure at Netflix (Dec 2019)Scale By The Bay 2019: Human-Centric ML Infrastructure at Netflix (Jan 2020)AICamp: Metaflow — The ML Infrastructure at Netflix (Aug 2021)ArticlesOpen-Sourcing Metaflow, a Human-Centric Framework for Data Science (Netflix Tech Blog, Dec 2019)Unbundling Data Science Workflows with Metaflow and AWS Step Functions (Netflix Tech Blog, July 2020)MLOps and DevOps: Why Data Makes It Different (O'Reilly, Oct 2021)PeopleMichael Jordan (Distinguished Professor in EECS and Statistics at UC Berkeley)Matthew Honnibal and Ines Montani (Creators of open-source NLP library spaCy)Hadley Wickham (Chief Scientist at RStudio and Adjunct Professor of Statistics at Rice University)Book“The Mom Test” (by Rob Fitzpatrick)NotesMy conversation with Ville was recorded back in October 2021. Since then, many things have happened at Outerbounds. I'd recommend:Visiting Outerbounds' new website with Metaflow resources for Data Science and EngineeringWatching Ville's recent talk at Data Council Austin about the Modern Stack for ML InfrastructureBuying Ville's newly released book “Effective Data Science Infrastructure”About the showDatacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.Datacast is produced and edited by James Le. Get in touch with feedback or guest suggestions by emailing khanhle.1013@gmail.com.Subscribe by searching for Datacast wherever you get podcasts or click one of the links below:Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf you're new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.

This is an Instagram Live on Sat March 12, 2022 1:30pm EDT (in English) of Professor Mohammad Hajiaghayi with Professor Ming Lin of UMD. We talk about life, US vs Taiwan education, pros and cons of being department chair, hiring process, robotics, Virtual Reality, Virtual Try-On, startup, autonomous driving, and many more. Join future lives if you can at @mhajiaghayi and subscribe at YouTube channel @hajiaghayi.#robotics#autonomousdriving#VirtualReality#VirtualTryOn#startup#Taiwanv.s.US#cs#departmentchair#instagramBio of Prof. Ming C. Lin: She received her B.S., M.S., Ph.D. in EECS from University of California, Berkeley. She is a Distinguished University Professor, Barry Mersky and Capital One Endowed Professor, and former Elizabeth Stevinson Iribe Chair of Computer Science at University of Maryland at College Park, as well as John & Louise Parker Distinguished Professor Emerita at University of North Carolina at Chapel Hill. She is also an Amazon Scholar. She has received several honors and awards, including NSF Young Faculty Career Award, UNC Hettleman Award for Scholarly Achievements, Beverly W. Long Distinguished Term Professor, IEEE VGTC VR Technical Achievement Award, Washington Academy of Sciences Distinguished Career Award and several best paper awards. She is a Fellow of ACM, IEEE, Eurographics, and SIGGRAPH Academy. She is an elected Fellow by the National Academy of Inventors (NAI).

Welcome to the Counter Narrative Podcast, a show designed to change the way we talk, and think, about education. By sharing stories of successes and triumphs, we aim to challenge the dominant narrative that often negatively portrays our disenfranchised populations. I'm your host, Charles Williams. An urban educator for more than 15 years, a current school principal in Chicago, an educational consultant, an equity advocate, and the co-host of Inside The Principal's Office. -- In this episode, I chat with Anurupa Ganguly, the Founder & CEO of Prisms, a VR learning platform that teaches foundational secondary math and science concepts spatially and intuitively, before building up to symbolic notation. She began her career as a math & physics teacher and later served in leadership roles across the Boston Public Schools, NYC DOE, and Success Academies. She holds a BS and MEng in EECS from MIT, and an EdM from BU. During our conversation, Anurupa explained her journey through education and how that inspired her to start a company that allows students to digitally immerse themselves into mathematical concepts through VR. Imagine how impactful and powerful it would be to shift from learning exponential rate of change on worksheets to being involved in a pandemic simulator calculating the spread of an unknown disease. Anurupa also pushed back on the idea that the limited access we often associate with such products is due to budgets and funding (consider the mass amounts of funding schools have received during the past two years) and other excuses that do little more than maintain the gaps that we see in opportunities. This is a powerful conversation that I wish I could have released immediately after recording it so be sure to check out Prisms and see how you can bring this innovative technology to your own space. -- Prisms Website Prisms Twitter Anurupa Twitter Prisms LinkedIn Anurupa LinkedIn -- I want to thank you for listening to The Counter Narrative Podcast. If you like what you are hearing, please be sure to like, subscribe, and of course share it with friends and family. I'd also love to hear your thoughts about the show so please leave a comment or two as well. I'm not sure what platform you're using but the show can be found on Anchor, Google Podcasts, Apple Podcasts, Spotify, and plenty of other platforms. If the show isn't on your preferred site, let me know and I'll be sure to get it up and running. This podcast is also featured on SchoolRubric.com, where you can find educational articles, videos, and interviews with educators from around the globe. Be sure to connect with me and other listeners by following the show on Twitter at @theCNpodcast and joining the show's Facebook Group. --- Support this podcast: https://anchor.fm/thecounternarrative/support

Karl Sun is co-founder and CEO at Lucid Software, a leading provider of visual collaboration software. With Lucid's products—Lucidchart, Lucidspark and Lucidscale—teams can turn ideas into reality, clarify complexity, and collaborate visually, no matter where they're located. Prior to Lucid, Karl spent several years at Google, starting and leading business development at Google's China office, opening Google's patent department and setting patent strategy, and leading Google's investments in advanced wind and battery technologies. Karl holds a B.S. and M.S. in EECS from MIT, an M.S. from MIT in Technology and Policy, and a J.D. from Harvard Law School. He has been honored as a Utah Business CEO of the Year and EY Entrepreneur of the Year. Connect with Karl at https://www.linkedin.com/in/karlsun/ This week's episode is brought to you with the support of Indeed. Grab your special offer at indeed.com/scale --- Support this podcast: https://anchor.fm/uncharted1/support

In Episode One of Season Two, Host Pieter Abbeel is joined by guest (and close collaborator) Sergey Levine, professor at UC Berkeley, EECS. Sergey discusses the early years of his career, how Andrew Ng influenced him to become interested in machine learning, his current projects, and his lab's recent accomplishments.The conversation concludes with Sergey's view on the dangers of machines not being intelligent enough and his advice for students seeking a career in robotic.| SUBSCRIBE TO THE ROBOT BRAINS PODCAST TODAY | Visit therobotbrains.ai and follow us on YouTube TheRobotBrainsPodcast, Twitter @therobotbrains, and Instagram @therobotbrains. | Host: Pieter Abbeel | Executive Producers: Alice Patel & Henry Tobias Jones | Audio Production: Kieron Matthew Banerji | Title Music: Alejandro Del Pozo See acast.com/privacy for privacy and opt-out information.

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: Jeff Hawkins on neuromorphic AGI within 20 years, published by Steven Byrnes on the LessWrong. I just listened to AI podcast: Jeff Hawkins on the Thousand Brain Theory of Intelligence, and read some of the related papers. Jeff Hawkins is a theoretical neuroscientist; you may have heard of his 2004 book On Intelligence. Earlier, he had an illustrious career in EECS, including inventing the Palm Pilot. He now runs the company Numenta, which is dedicated to understanding how the human brain works (especially the neocortex), and using that knowledge to develop bio-inspired AI algorithms. In no particular order, here are some highlights and commentary from the podcast and associated papers. Every part of the neocortex is running the same algorithm The neocortex is the outermost and most evolutionarily-recent layer of the mammalian brain. In humans, it is about the size and shape of a dinner napkin (maybe 1500cm²×3mm), and constitutes 75% of the entire brain. Jeff wants us to think of it like 150,000 side-by-side "cortical columns", each of which is a little 1mm²×3mm tube, although I don't think we're supposed to the "column" thing too literally (there's no sharp demarcation between neighboring columns). When you look at a diagram of the brain, the neocortex has loads of different parts that do different things—motor, sensory, visual, language, cognition, planning, and more. But Jeff says that all 150,000 of these cortical columns are virtually identical! Not only do they each have the same types of neurons, but they're laid out into the same configuration and wiring and larger-scale structures. In other words, there seems to be "general-purpose neocortical tissue", and if you dump visual information into it, it does visual processing, and if you connect it to motor control pathways, it does motor control, etc. He said that this theory originated with Vernon Mountcastle in the 1970s, and is now widely (but not universally) accepted in neuroscience. The theory is supported both by examining different parts of the brain under the microscope, and also by experiments, e.g. the fact that congenitally blind people can use their visual cortex for non-visual things, and conversely he mentioned in passing some old experiment where a scientist attached the optic nerve of a lemur to a different part of the cortex and it was able to see (or something like that). Anyway, if you accept that premise, then there is one type of computation that the neocortex does, and if we can figure it out, we'll understand everything from how the brain does visual processing to how Einstein's brain invented General Relativity. To me, cortical uniformity seems slightly at odds with the wide variety of instincts we have, like intuitive physics, intuitive biology, language, and so on. Are those not implemented in the neocortex? Are they implemented as connections between (rather than within) cortical columns? Or what? This didn't come up in the podcast. (ETA: I tried to answer this question in my later post, Human instincts, Symbol grounding, and the blank-slate neocortex.) (See also previous LW discussion at: The brain as a universal learning machine, 2015) Grid cells and displacement cells Background: Grid cells for maps in the hippocampus Grid cells, discovered in 2005, help animals build mental maps of physical spaces. (Grid cells are just one piece of a complicated machinery, along with "place cells" and other things, more on which shortly.) Grid cells are not traditionally associated with the neocortex, but rather the entorhinal cortex and hippocampus. But Jeff says that there's some experimental evidence that they're also in the neocortex, and proposes that this is very important. What are grid cells? Numenta has an educational video here. Here's my oversimplified 1D toy example (the modules can als...

Today on Power Lunch Live we talk about collaboration at work and what that means in 2022 and beyond. I have Karl Sun is co-founder and CEO at Lucid Software, a leading provider of visual collaboration software. With Lucid's products—Lucidchart, Lucidspark and Lucidscale—teams can turn ideas into reality, clarify complexity, and collaborate visually, no matter where they're located. Prior to Lucid, Karl spent several years at Google, starting and leading business development at Google's China office, opening Google's patent department and setting patent strategy, and leading Google's investments in advanced wind and battery technologies. Karl holds a B.S. and M.S. in EECS from MIT, an M.S. from MIT in Technology and Policy, and a J.D. from Harvard Law School. He has been honored as a Utah Business CEO of the Year and EY Entrepreneur of the Year. #Lucid #collaboration #futureofwork #powerlunchlive #LinkedInLive www.powerlunch.live

Episode 13: Interview with Jelani Nelson, Professor of EECS at UC Berkeley. Grew up in US Virgin Islands, where an overwhelming majority of population is Black and the educational ethos is very different from the mainland US 50 states. He also created two successful international CS programs for high schoolers: AddisCoder and USVICoder,  in Ethiopia and US Virgin Islands. MIT PhD 2011.  

Enola G. Aird is an activist mother. A former corporate lawyer. She was born in the Republic of Panama, of Caribbean heritage, and attributes much of her vision and passion for the movement for emotional emancipation to stories passed down in her family about her great-grandfather, Samuel Alleyne, a loyal follower of Marcus Garvey. Enola shares the framework of the Emotional Emancipation Circles (EE Circles, EECs) are evidence-informed, psychologically sound, culturally grounded, and community-defined self-help support groups designed to help heal, and end, the trauma caused by the root cause of anti-Black racism: the centuries-old lie of White superiority and Black inferiority. Emotional Emancipation Circles – Community Healing Network Interruptions: Disrupting The Silence | Facebook Interruption Disrupt Silence - YouTube 

The team sits down with  Douglas Mughan - the NSF office head for the convergence accelerator, Chris Sanford -  a program director with the accelerator, and Clea Harrelson -  2021 Knauss Marine policy fellow, to talk about what the National Science Foundation is hoping to achieve with this new program. We also speak with Fadel Adib - Doherty Chair of Ocean Utilization and Associate Professor at the MIT Media Lab and EECS and Seth Zippel - an assistant scientist at the Woods Hole Oceanographic Institution who generated the idea. Letters of intent are due May 5th for this huge opportunity, learn more here.

In this episode, I converse with Bharath Ramsundar, the creator of The DeepChem Project. Bharath received a BA and BS from UC Berkeley in EECS and Mathematics and was valedictorian of his graduating class in mathematics. He went on to finish a PhD in computer science at Stanford University where he studied the application of deep-learning to problems in drug-discovery, and was sponsored by the very prestigious Hertz Fellowship and advised by Prof. Vijay Pande. After his PhD, Bharath co-founded Computable, a startup that built better tools for collaborative dataset management. At Stanford Bharath created The DeepChem Project that seeks to create high quality, open source tools for drug discovery, materials science, quantum chemistry, and biology. We indulge in a fantastic conversation on his early fascination with quantum computing and artificial intelligence that inspired him to embark on his incredible journey in science; starting grad school as the deep learning revolution took off; how both academia and English monarchy are in need of urgent reforms; prescient insights on global manufacturing, geopolitics, and immigration; the revolutionary aspects of technologies like artificial intelligence, quantum computing, and the need to mitigate bias in these systems; and many more things!!

Episode summary introduction: Professor Dutt believes exciting research comes from a combination of serendipity, bright students and good, collaborative relationships. There is more. Professor Nikil Dutt is the Chancellor's Professor of Computer Science, EECS and Cognitive Science at the University of California Irvine. In particular, we discuss the following with him: Pursuing Graduate Programs in the US Joining the Academia The Pursuit of Research UG Students at UC Irvine Advice for Applicants Topics discussed in this episode: Introducing Prof Nikil Dutt, UCI [1:08] Professional Journey so far [3:11] Grad Study at Penn State and UI Urbana-Champaign [6:59] Penn State versus UI Urbana-Champaign [9:14] Research thru Serendipity [11:28] On Choosing Academia [13:24] UC Irvine: Rising thru the Ranks [15:46] On Staying Excited about Research [18:24] UG Students at Irvine [21:32] Research to be Proud of [23:32] Computation Self Awareness Systems [25:25] Tips for Aspiring Students [27:51] UG Students and Research [31:00] Advice for Budding Researchers [34:27] Close: Follow your passion [37:04] Our Guest: Professor Nikil Dutt is the Chancellor's Professor at University of California Irvine with appointments in Computer Science, EECS and Cognitive Science Departments. Professor Dutt is a graduate of Birla Institute of Technology Pilani with a degree in Mechanical Engineering. He then received his MS in Computer Science from Penn State University and a PhD in Computer Science from the University of Illinois at Urbana-Champaign. Memorable Quote: Prof Dutt: “Academia is not for the faint of heart”. Episode Transcript: Please visit Episode's Transcript. Calls-to-action: To Ask the Guest a question, or to comment on this episode, email podcast@almamatters.io. Subscribe or Follow our podcasts at any of these locations:, Apple Podcasts, Google Podcasts, Spotify, RadioPublic, Breaker, Anchor. For Transcripts of all our podcasts, visit almamatters.io/podcasts.

You can find John on Twitter at [twitter.com/jgrahamc](https://twitter.com/jgrahamc).- Babbage overview and the Difference Engine:    https://www.computerhistory.org/babbage/overview/- Difference Engine No. 2 at the London Science Museum:    https://collection.sciencemuseumgroup.org.uk/objects/co526657/difference-engine-no-2-designed-by-charles-babbage-built-by-science-museum-difference-engine- BBC Micro: https://en.wikipedia.org/wiki/BBC_Micro- Sinclair ZX81: https://en.wikipedia.org/wiki/ZX81- BBC Micro Advanced User Guide:    http://stardot.org.uk/mirrors/www.bbcdocs.com/filebase/essentials/BBC%20Microcomputer%20Advanced%20User%20Guide.pdf- Sharp MZ-80K: https://en.wikipedia.org/wiki/Sharp_MZ- John's TED Talk, The greatest machine that never was: https://www.ted.com/talks/john_graham_cumming_the_greatest_machine_that_never_was- Hilbert's Problems: https://mathworld.wolfram.com/HilbertsProblems.html- Gödel's incompleteness theorems: https://plato.stanford.edu/entries/goedel-incompleteness/- The Lovelace–De Morgan mathematical correspondence - A critical re-appraisal: https://www.sciencedirect.com/science/article/pii/S0315086017300319- The mathematical correspondence of Ada Lovelace and Augustus De Morgan:    https://dl.acm.org/doi/10.1145/2867731.2867738- Douglas Engelbart: https://www.britannica.com/biography/Douglas-Engelbart- "Mother of all demos": https://www.youtube.com/watch?v=yJDv-zdhzMY- John's OSCON talk "Turing's Curse": https://www.youtube.com/watch?v=hVZxkFAIziA- Design of the RISC-V Instruction Set Architecture:    https://people.eecs.berkeley.edu/~krste/papers/EECS-2016-1.pdf- Engines of Creation - The Coming Era of Nanotechnology: https://www.amazon.com/Engines-Creation-Nanotechnology-Scientific-Revolution/dp/1872180469/ref=tmm_hrd_swatch_0?_encoding=UTF8&qid=&sr=

Mehdi Maghsoodnia is the CEO of 1health, the leading testing as a service (TaaS) company. He is passionate about personalized healthcare and increasing access to testing in the U.S.  1health launched TaaS earlier this year by bringing to market the first FDA Emergency Use Authorized COVID-19 saliva test, in partnership with IBX (Previously RUCDR). Through these at-home saliva tests, 1health is responsible for the deployment of 1% of COVID-19 testing across the United States.  Instead of asking people to go get tested in laboratories that are often crowded and have delayed results, Mehdi is driving his businesses to bring affordable, simple testing to the people, wherever they are. 1health partners with hospital systems, telehealth companies, corporations and government agencies, school systems, consumer brands, as well as public health departments around the country. Through its lab network, centralized platform, and health partnerships, now more than seven million Americans have access to the 1health COVID-19 saliva test.  Mehdi is on the board of SpineZone and MedCorder, and previously served as CEO of Rafter, BookRenter.com, SVP of products at CafePress, COO at Actiance and SVP at Intellisync. He is also an industry fellow at UC Berkeley, where he teaches innovation and entrepreneurship. He holds a Master’s of Science in EECS from Stanford, and a Bachelor of Science from UC Berkeley. Some of the Topics Covered by Mehdi Maghsoodnia in this Episode What 1health is doing and how it got started Why Mehdi chose to work on this particular problem and get involved in the healthcare industry What the initial product at 1health consisted of How Mehdi funding 1health Mehdi's thoughts around building a team Acquiring customers for 1health and how Mehdi looks at testing different channels How Mehdi thinks about pricing and GTM strategy The competitive landscape 1health is operating in Educating the market when you're creating a whole new market The ideal customer for 1health today Mehdi's experience investing in companies and some of the things he's looking for in companies he invests in How Vitagene came about from 1health What Mehdi's schedule looks like today Learning to say no and how to delegate The magic of exercise to improve your thinking Sign up for The Weekly Grind, for actionable insights and stories from successful entrepreneurs delivered to your inbox once per week: https://www.justgogrind.com/newsletter/ Listen to all episodes of the Just Go Grind Podcast: https://www.justgogrind.com/podcast/ Follow Justin Gordon on Twitter: https://twitter.com/justingordon212 Follow Justin Gordon on Instagram: https://www.instagram.com/justingordon8/

Jitendra Malik is the Arthur J. Chick Professor of EECS at University of California at Berkeley and a Research Scientist at Facebook AI Research (FAIR). Find out more about him on his homepage. Humans of AI: Stories, Not Stats is an interview series with AI researchers to get to know them better as people. We don't talk about AI or their work or the stats of their life like what college they went to. They share what they think about, what they are insecure about, what they get excited about. They share the stories of their day-to-day life. Videos of these interviews are available at humanstories.ai. The host is Devi Parikh, an Associate Professor in the School of Interactive Computing at Georgia Tech and a Research Scientist at Facebook AI Research (FAIR). Find out more about her at her homepage or follow her on Twitter. This interview was recorded on September 22, 2020. --- Send in a voice message: https://anchor.fm/humanstoriesai/message

Sramana Mitra is the founder and CEO of One Million by One Million (1Mby1M), the world’s first and only global virtual incubator/accelerator. Its goal is to help a million entrepreneurs globally reach a million dollars in annual revenue, build a trillion dollars in global GDP, and create 10 million jobs. Since its founding in 2010, 1Mby1M has become a powerful platform for democratization of entrepreneurship acceleration. Sramana also developed 1Mby1M’s Incubator-in-a-Box methodology for Corporate Incubation that is used by enterprises to manage internal and external innovation endeavors. In 2015, LinkedIn named Sramana one of their Top 10 Influencers alongside Bill Gates and Richard Branson. Sramana has been an entrepreneur and a strategy consultant in Silicon Valley since 1994. Her fields of experience span from hardcore technology disciplines like Artificial Intelligence, Cloud Computing and Semiconductors, to sophisticated consumer marketing industries including e-commerce, fashion and education. As an entrepreneur CEO, Sramana founded three companies: Dais (off-shore software services), Intarka (sales lead generation and qualification software using Artificial Intelligence algorithms; VC: NEA) and Uuma (online personalized store for selling clothes using Expert Systems software; VC: Redwood). Two of these were acquired, while the third received an acquisition offer from Ralph Lauren which the company did not accept. As strategy consultant, Sramana has consulted with over 80 companies, including public companies such as SAP, Cadence Design Systems, Webex, KLA-Tencor, Best Buy, MercadoLibre and Tessera among others. Her work has also included numerous startups and VCs. Sramana has a Masters degree in EECS from MIT and a Bachelors degree in Computer Science and Economics from Smith College. From 2000 to 2004, Sramana chaired the MIT Club of Northern California’s entrepreneurship program in Silicon Valley. Learn more at www.1Mby1M.com

Josh Tobin holds a CS PhD from UC Berkeley, which he completed in four years while also working at OpenAI as a research scientist. His focus was on robotic perception and control, and contributed to the famous Rubik's cube robot hand video. He co-organizes the phenomenal Full Stack Deep Learning course and is now working on a new stealth startup. Learn more about Josh: http://josh-tobin.com/ (http://josh-tobin.com/) https://twitter.com/josh_tobin_ (https://twitter.com/josh_tobin_) Want to level-up your skills in machine learning and software engineering? Join the ML Engineered Newsletter: https://mlengineered.ck.page/943aa3fd46 (https://mlengineered.ck.page/943aa3fd46) Comments? Questions? Submit them here: https://charlie266.typeform.com/to/DA2j9Md9 (https://charlie266.typeform.com/to/DA2j9Md9) Follow Charlie on Twitter: https://twitter.com/CharlieYouAI (https://twitter.com/CharlieYouAI) Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/ (https://www.givingwhatwecan.org/) Subscribe to ML Engineered: https://mlengineered.com/listen (https://mlengineered.com/listen) Timestamps: 01:32 Follow Charlie on Twitter (http://twitter.com/charlieyouai (twitter.com/charlieyouai)) 02:43 How Josh got started in CS and ML 11:05 Why Josh worked on ML for robotics 15:03 ML for Robotics research at OpenAI 28:20 Josh's research process 34:56 Why putting ML into production is so difficult 44:46 What Josh thinks the ML Ops landscape will look like 49:49 Common mistakes that production ML teams and companies make 53:11 How ML systems will be built in the future 59:37 The most valuable skills that ML engineers should develop 01:03:50 Rapid Fire Questions Links https://course.fullstackdeeplearning.com/ (Full Stack Deep Learning) https://arxiv.org/abs/1703.06907 (Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World) https://arxiv.org/abs/1710.06425 (Domain Randomization and Generative Models for Robotic Grasping) https://deepmind.com/blog/article/neural-scene-representation-and-rendering (DeepMind Generative Query Network (GQN) paper) https://arxiv.org/abs/1911.04554 (Geometry Aware Neural Rendering) https://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-104.pdf (Josh's PhD Thesis) https://www.youtube.com/watch?v=x4O8pojMF0w (OpenAI Rubik's Cube Robot Hand video) https://www.wandb.com/podcast/josh-tobin (Weights and Biases interview with Josh) https://www.oreilly.com/library/view/designing-data-intensive-applications/9781491903063/ (Building Data Intensive Applications) http://creativeselection.io/ (Creative Selection)

https://charlesxjyang.github.io/ (Charles Yang) is an EECS masters student at UC Berkeley focusing on AI and dynamical systems. He writes the excellent https://ml4sci.substack.com/ (Machine Learning For Science newsletter) where he showcases a wide range of use cases for machine learning in scientific research and engineering. Learn more about Charles: Website: https://charlesxjyang.github.io/ (https://charlesxjyang.github.io/) Google Scholar: https://scholar.google.com/citations?user=BYOREdwAAAAJ&hl=en (https://scholar.google.com/citations?user=BYOREdwAAAAJ&hl=en) ML4Sci Newsletter (Highly Recommended!): https://ml4sci.substack.com/ (https://ml4sci.substack.com/) Want to level-up your skills in machine learning and software engineering? Subscribe to our newsletter: https://mlengineered.ck.page/943aa3fd46 (https://mlengineered.ck.page/943aa3fd46) Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/ (https://www.givingwhatwecan.org/) Subscribe to ML Engineered: https://www.mlengineered.com/listen (https://www.mlengineered.com/listen) Follow Charlie on Twitter: https://twitter.com/CharlieYouAI (https://twitter.com/CharlieYouAI) Timestamps: (02:08) Getting started in material science and machine learning (08:58) "ImageNet moment" for ML in science (13:20) Model explainability and transparency (17:06) Charles' Current Research (18:40) Embedding existing knowledge into ML models (22:26) "Bilingual Scientists" (24:46) Learning ML as a traditional scientist (28:22) Private vs Public ML Research (32:42) Rise of open-access research (35:22) "SOTA chasing" in ML research (38:10) Scientific ML research processes (44:34) Applying ML knowledge to a scientific problem (48:00) Biggest opportunities for ML in science (51:18) Diversity in the research community (54:24) Writing the ML4Sci newsletter (56:20) Keeping up with new research (01:05:30) Rapid Fire Questions Links: https://ml4sci.substack.com/ (Charles' ML4Sci newsletter) https://ml4sci.substack.com/p/ml4sci-8-defining-the-new-saas-science (Charles' article on AI-powered Science as a Service) https://towardsdatascience.com/deep-learning-in-science-fd614bb3f3ce (Charles' article on Deep Learning in Science) https://ml4sci.substack.com/p/ml4sci-12-thoughts-on-covid-19-scientific (Charles' article on Scientific Gatekeeping) https://ml4sci.substack.com/p/ml4sci-15-news-from-the-world-of (Charles' article on Open Access Research) https://arxiv.org/abs/1912.12132 (Google Weather Forecasting paper) https://ai.googleblog.com/2020/03/a-neural-weather-model-for-eight-hour.html?m=1 (Google 2nd Weather Forecasting paper ) https://deepmind.com/blog/article/AlphaFold-Using-AI-for-scientific-discovery (DeepMind Protein Folding paper) https://www.biorxiv.org/content/10.1101/2020.03.07.982272v1.full.pdf (SalesForce Protein Folding paper) https://www.sciencemag.org/news/2020/02/models-galaxies-atoms-simple-ai-shortcuts-speed-simulations-billions-times (ML speeding up simulations by 9+ orders of magnitude (!)) https://www.anl.gov/ai-for-science-report (Oak Ridge AI for Science Report) https://www.nature.com/articles/s41586-019-1335-8 (Nature paper using word2vec on MatSci papers) https://arxiv.org/abs/2006.11287 (Paper using Graph NNs to find dark matter concentrations) https://www.amazon.com/Power-Broker-Robert-Moses-Fall/dp/0394720245/ (Robert Caro - The Power Broker) https://www.amazon.com/Golden-Gates-Fighting-Housing-America/dp/0525560211/ (Conor Dougherty - Golden Gates)

Today we’re joined by the legendary Michael I. Jordan, Distinguished Professor in the Departments of EECS and Statistics at UC Berkeley.  Michael was gracious enough to connect us all the way from Italy after being named IEEE’s 2020 John von Neumann Medal recipient. In our conversation with Michael, we explore his career path, and how his influence from other fields like philosophy shaped his path.  We spend quite a bit of time discussing his current exploration into the intersection of economics and AI, and how machine learning systems could be used to create value and empowerment across many industries through “markets.” We also touch on the potential of “interacting learning systems” at scale, the valuation of data, the commoditization of human knowledge into computational systems, and much, much more. The complete show notes for this episode can be found at. twimlai.com/go/407.

Bharath created the deepchem.io open-source project to grow the deep drug discovery open source community, co-created the moleculenet.ai benchmark suite to facilitate development of molecular algorithms, and more. Bharath’s graduate education was supported by a Hertz Fellowship, the most selective graduate fellowship in the sciences. Bharath is the lead author of “TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning”, a developer’s introduction to modern machine learning, with O’Reilly Media. Today, Bharath is focused on designing the decentralized protocols that will unlock data and AI to create the next stage of the internet. He received a BA and BS from UC Berkeley in EECS and Mathematics and was valedictorian of his graduating class in mathematics. He did his PhD in computer science at Stanford University where he studied the application of deep-learning to problems in drug-discovery. Follow Bharath on Twitter and Github https://twitter.com/rbhar90 rbharath.github.io Check out some of his projects: https://deepchem.io/ https://moleculenet.ai/ https://scholar.google.com/citations?user=LOdVDNYAAAAJ&hl=en&oi=ao Visit our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast

Welcome to the first podcast of our weekly series! For our first episode we interview soon-to-be EECS legend, Mr. Jake Bringetto.

Isaac Evans is the leader of r2c (https://r2c.dev/), a small startup working on giving security tools directly to developers. Previously, he conducted research into binary exploitation bypasses for techniques like control-flow integrity and novel hardware defenses on new architectures like RISC-V as a researcher at the US Defense Department under a SFS program and at MIT Lincoln Laboratory. Isaac received his BS/MS degrees in EECS from MIT. Other interests include next-generation programming languages, secure-by-design frameworks, software-defined radio, and the intersection of cryptography and public policy.Isaac spoke about semgrem and its capabilities in this episode. - Source code: https://github.com/returntocorp/semgrep- Test in your browser: https://semgrep.live/

Prof. Michael Sung is a technology venture builder and investor, having founded various companies in diverse high-tech industries ranging from AI, blockchain, semiconductor, and new materials industries. In this new interview series, host Dinis Guarda and Michael Sung go through worldwide challenges and opportunities in the Fintech space and how new 4IR technologies like blockchain and AI can be seen as drivers of innovation for the future. Michael Sung also tells us about his CarbonBlue project, a tech-transfer and commercialisation platform that is focused on rapidly commercialising and scaling internationally-sourced high-tech innovation. INTERVIEW FOCUS1. Biography, professional background2. Academic background and research focus as Director of the Fintech Research Center at the Fanhai International School of Finance at Fudan University3. About Fintech: Trends, Challenges and Opportunities4. Dynamics of innovation in the Fintech space5. 4IR Technologies: blockchain - decentralized systems6. 4IR Technologies: blockchain and AI applied to cities and smart city projects7. About CarbonBlue projectBIOGRAPHYProf. Sung is an expert on blockchain and crypto-finance innovation, as founding co-director of the Fintech Research Center at the Fanhai International School of Finance at Fudan University. In addition, Prof. Sung is faculty at the Chinese Institute of Economics and Finance, a national-level think tank focused on developing finance innovation policy for central government. He is also a venture partner for FinNX, cross-border investment bank platform for Fundamental Labs, one of Asia's top blockchain funds.He has a strong background in cross-border technology transfer, strategic industry engagement, commercialisation strategy, business model innovation, and hi-tech entrepreneurship.Prof. Sung has served in numerous advisory roles over the years for the HK, Taiwan, and China governments on international tech transfer, innovation ecosystem building, AI, blockchain, and Fintech policy for various top city and ministry-level officials. Prof. Sung was also the chairman of the steering committee for MIT Tech Review's Emtech HK Conference. He has received various awards for technology entrepreneurship, including MIT Enterprise Forum's Most Visionary Technology Award and Google's Solve for X Prize. Prof. Sung received his Ph.D. in EECS at the MIT Media Lab/Computer Science and Artificial Intelligence Laboratory as well as a graduate financial engineering degree from MIT Sloan Business School. https://www.citiesabc.com/https://twitter.com/citiesabc_https://www.instagram.com/citiesabc/

Today we’re joined by Joseph Gonzalez, Assistant Professor in the EECS department at UC Berkeley.  Our main focus in the conversation is Joseph’s paper “Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers,” which explores compute-efficient training strategies, based on model size. We discuss the two main problems being solved; 1) How can we rapidly iterate on variations in architecture? And 2) If we make models bigger, is it really improving any efficiency? We also discuss the parallels between computer vision and NLP tasks, how he characterizes both “larger” and “faster” in the paper. Check out the complete show notes for this episode at twimlai.com/talk/378.

Pedro Pachuca, an ambitious entrepreneur passionately intentioned towards making an impact. Since the age of 11, Pedro has been enamored by Computer Science. His curiosity and willingness to learn has driven him to explore new opportunities. Not only is Pedro an exceptional learner, but also a teacher, and startup business leader. At just the age of 19, Pedro's body of work is already quite extensive. There is a substantial good list of challenges Pedro has already accomplished. As a member apart of the exclusive M.E.T. program at UC Berkeley, Pedro strongly believes the program has exceptionally facilitated him with strong interpersonal relationships with influential enterprises and influential people in the Silicon Valley. You can check out more information about Pedro Pachuca on LinkedIn.

On this episode of Silicon Valley Insider, Keith Koo's special guest is Silicon Valley native Aparna Dhinkaran, Co-Founder and Chief Product Officer of Arizen AI, a company focused on bringing infrastructure tooling to Artificial Intelligence (A.I.) and Machine Learning (ML). Aparna discusses her rise in the tech world having graduated in EECS at UC Berkeley and then on to start her PhD program at Cornell before pausing everything to start her previous company MonitorML a Y Combinator backed company. After MonitorMl was acquired, Aparna talks about joining forces with her co-founder to start Arize.ai On this week's Cyber-tip, Keith discusses gives another warning on scams related to the Coronavirus. Tune in to hear more! www.svin.biz Listen Saturdays 10-11am 860 KTRB Silicon Valley | San Francisco Listen and subscribe to the "Silicon Valley Insider" Podcast ahead of time to make sure you don't miss this show. Download the podcast at 11:00am on Saturdays. For questions or comments, email: info@svin.biz Be sure to subscribe and listen to the podcast. You can also listen to past podcasts here: Castbox: https://castbox.fm/channel/The-Silicon-Valley-Insider-Show-with-Keith-Koo-id1100209?country=us iTunes: https://itunes.apple.com/us/podcast/the-silicon-valley-insider-show/id1282637717?mt=2 Android, Spotify (and iTunes): https://omny.fm/shows/the-silicon-valley-insider-show Email us at info@svin.biz or find us here: www.svin.biz Artificial Intelligence, AI, Blockchain, Big Data, Data Analytics, Cyberrisk, Information security, VC, Venture Capital, Angel Investments, Fundraising, Capital Raising, Investor, Human Rights, Technology for Good, UN SDGs, Emerging Technology, #Patreon

Liwei Wang is a professor in the Department of Computer Science and Technology, School of EECS at Peking University. In this episode, Professor Wang highlighted his research papers accepted at NeurIPS 2019, shared his perspectives on data privacy, and the differences in the trends and challenges of AI research between China and the US. Learn more at Robin.ly: http://bit.ly/2wzlCgu Prof. Wang’s main research interest is machine learning theory and has published more than 100 papers on top conferences including NeurIPS. He was the first Asian researcher who was named among “AI’s 10 to Watch”. He served as the Area Chair of NeurIPS and the Associate Editor of PAMI.

Tragedy tomorrow (or at least next week). Comedy tonight! But first you might want to know what to expect because Old Comedy has some things that we don't really see in modern theatre. To join the discussion, visit the blog at Triumvir Clio's School of Classical Civilization. References Cartwright, Mark. "Ancient Greek Comedy." Ancient History Encyclopedia. Ancient History Encyclopedia, 25 Mar 2013. Web. 12 Dec 2019. MacLennan, Bruce. "Typical Structure Of A Greek Play". Web.Eecs.Utk.Edu, 1999, http://web.eecs.utk.edu/~bmaclenn/Classes/US210/Greek-play.html. --- Send in a voice message: https://anchor.fm/bethany-banner/message Support this podcast: https://anchor.fm/bethany-banner/support

In this episode of the Data Exchange I speak with Nir Shavit, Professor of EECS at MIT, and cofounder and CEO of Neural Magic, a startup that is creating software to enable deep neural networks to run on commodity CPUs (at GPU speeds or faster). Their initial products are focused on model inference, but they are also working on similar software for model training.Our conversation spanned many topics, including:Neurobiology, in particular the combination of Nir's research areas of multicore software and connectomics – a branch of neurobiology.Why he believes the combination of the right software and CPUs will prove capable of handling many deep learning tasks.Speed is not the only factor: the “unlimited memory” of CPUs are able to unlock larger problems and architectures.Neural Magic's initial offering is in inference, model training using CPUs is also on the horizon.Detailed show notes can be found on The Data Exchange web site.

Vikash Gilja is an assistant professor at UCSD, where he researchers brain-machine interfaces. Dr. Gilja is an advisor to Paradromics. He holds a Ph.D. in computer science from Stanford University, an M.Eng and B.S. in EECS from MIT, and a...

------------------Support the channel------------ Patreon: https://www.patreon.com/thedissenter SubscribeStar: https://www.subscribestar.com/the-dissenter PayPal: paypal.me/thedissenter PayPal Subscription 1 Dollar: https://tinyurl.com/yb3acuuy PayPal Subscription 3 Dollars: https://tinyurl.com/ybn6bg9l PayPal Subscription 5 Dollars: https://tinyurl.com/ycmr9gpz PayPal Subscription 10 Dollars: https://tinyurl.com/y9r3fc9m PayPal Subscription 20 Dollars: https://tinyurl.com/y95uvkao ------------------Follow me on--------------------- Facebook: https://www.facebook.com/thedissenteryt/ Twitter: https://twitter.com/TheDissenterYT Anchor (podcast): https://anchor.fm/thedissenter O Dr. Arlindo Oliveira é Presidente do Instituto Superior Técnico e Professor do Departamento de Engenharia Informática, onde tem desenvolvido trabalho no contexto de sistemas digitais, síntese lógica, algoritmia, aprendizagem automática e bioinformática. Doutorou-se em Engenharia Electrotécnica e Ciências da Computação (EECS) pela Universidade da Califórnia em Berkeley, em 1994. É um dos maiores especialistas portugueses em Inteligência Artificial, e publicou em 2017 o livro “The Digital Mind: How Science Is Redefining Humanity”, pela MIT Press, existindo também a sua versão em português, pela IST Press. Neste episódio, focamo-nos em alguns dos principais tópicos do livro “The Digital Mind” (ou “Mentes Digitais”, na versão em português). Falamos sobre sistemas de inteligência artificial (IA) especializados e gerais e a forma como a ciência cognitiva e a inteligência artificial se informam uma à outra. Depois, abordamos as várias maneiras possíveis de reproduzir um cérebro humano artificialmente, e da possibilidade de atingir a imortalidade. Também falamos sobre a relação entre mente e cérebro, o que é uma mente, o teste de Turing, as diferenças entre mentes naturais e mentes digitais, e vários tipos de tecnologia ao nosso dispor para tentar desenvolver sistemas de inteligência artificial mais avançados e melhorar/superar a condição humana. Finalmente, discutimos a ética por detrás de IA, tanto em relação à maneira como os devemos tratar como à maneira como eles nos devem tratar a nós. -- Sigam o trabalho do Dr. Arlindo Oliveira: Website (IST): https://bit.ly/2VpdDcD Blogue Digital Minds: https://bit.ly/30lsgkY Livro “The Digital Mind”: https://bit.ly/2Q0yJNF “Mentes Digitais” (livro em português): https://bit.ly/2E9mlGp Livro “Inteligência Artificial”: https://bit.ly/2WRlKjG Artificial Intelligence Podcast (podcast do MIT): https://bit.ly/2UbbDFa -- A HUGE THANK YOU TO MY PATRONS: KARIN LIETZCKE, ANN BLANCHETTE, SCIMED, PER HELGE HAAKSTD LARSEN, LAU GUERREIRO, RUI BELEZA, MIGUEL ESTRADA, ANTÓNIO CUNHA, CHANTEL GELINAS, JIM FRANK, JERRY MULLER, FRANCIS FORD, HANS FREDRIK SUNDE, BRIAN RIVERA, ADRIANO ANDRADE, YEVHEN BODRENKO, SERGIU CODREANU, ADAM BJERRE, ŁUKASZ STAFINIAK, AIRES ALMEIDA, BERNARDO SEIXAS, AND HERBERT GINTIS! A SPECIAL THANKS TO MY PRODUCERS, YZAR WEHBE and ROSEY!

This week, Jon Foust and Michelle Casbon bring you another fascinating interview from our time at Next! Michelle and special guest Amanda were able to catch up with Paco Nathan of Derwen AI to talk about his experience at Next and learn what Derwen is doing to advance AI. Paco and Derwen have been working extensively on ways developer relations can be enhanced by machine learning. Along with O’Reilly Media, Derwen just completed three surveys, called ABC (AI, Big Data, and Cloud), to look at the adoption of AI and the cloud around the world. The particular interest in these studies is a comparison between countries who have been using AI, Big Data, and Cloud for years and countries who are just beginning to get involved. One of the most interesting things they learned is how much budget companies are allocating to machine learning projects. They also noticed that more and more large enterprises are moving, at least partially, to the cloud. One of the challenges Paco noticed was the difference between machine learning projects in testing versus how they act once they go live. Here, developers come across bias, ethical, and safety issues. Good data governance polices can help minimize these problems. Developing good data governance policies is complex, especially with security issues, but it’s an important conversation to have. In the process of computing the survey data, Paco discovered many big companies spend a lot of time with this issue and even employ checklists of requirements before projects can be made live. In his research, Paco also discovered that about 54% of companies are non-starters. Usually, their problems stem from tech debt and issues with company personnel who do not recognize the need for machine learning. The companies working toward integrating machine learning tend to have issues finding good staff. Berkeley is working to solve this problem by requiring data science classes of all students. But as Paco says, data science is a team sport that works well with a team of people from different disciplines. Paco is an advocate of mentoring, to help the next generation of data scientists learn and grow, and of unbundling corporate decision making to help advance AI. Amanda, Michelle, and Paco wrap up their discussion with a look toward how to change ML biases. People tend to blame ML for bias outcomes, but models are subject to data we feed in. Humans have to make decisions to work around that by looking at things from a different perspective and taking steps to avoid as much bias as we can. ML and humans can work together to find these biases and help remove them. Paco Nathan Paco Nathan is the Managing Parter at Derwen. He has 35+ years tech industry experience, ranging from Bell Labs to early-stage start-ups. Paco is also the Co-chair Rev. Advisor for Amplify Partners, Recognai, Primer AI, and Data Spartan. He was formerly the Director of Community Evangelism for Databricks and Apache Spark. Cool things of the week CERN recreated the Higgs discovery on GCP video To discover the Higgs yourself, check out the CERN open data portal site Fun facts from Michelle’s visit: Seven total, four main experiments ATLAS (largest, general-purpose) site CMS (prettiest, general-purpose) site ALICE (heavy-ion) site LHCb (interactions of b-hadrons, matter/antimatter asymmetry) site The French/Swiss border runs across the CERN property Streetview of CERN control center site CERN is the birthplace of the web Where the protons come from site Watch Particle Fever movie Interview Derwen, Inc. site Derwen, Inc. Blog blog Cloud Programming Simplified: A Berkeley View on Serverless Computing paper Apache Spark site Google Cloud Storage site Datastore site Kubeflow site Quicksilver site O’Reilly Media site Google Knowledge Graph site Jupyter site JupyterCon site The Economics of Artificial Intelligence site “Why Do Businesses Fail At Machine Learning?” by Cassie Kozyrkov video The Gutenberg Galaxy site Programmed Inequality site Question of the week Stadia Connect occurred last Thursday. What are some of the biggest announcements that came out of it? Where can you find us next? Jon is in New York for Games for Change. Michelle and Mark Mirchandani are back in San Francisco. Brian & Aja are at home in Seattle. Gabi is in Brazil. Sound Effect Attribution “Crowd laugh.wav” by tom_woysky of Freesound.org

Today we’re joined by Genevera Allen, associate professor of statistics in the EECS Department at Rice University, Founder and Director of the Rice Center for Transforming Data to Knowledge and Investigator with the Neurological Research Institute with the Baylor College of Medicine. Genevera caused quite the stir at the American Association for the Advancement of Science meeting earlier this year with her presentation “Can We Trust Data-Driven Discoveries?" In our conversation we cover: • The goal of Genevera's talk, and what was lost in translation. • Use cases outlining the shortcomings of current machine learning techniques. • Reproducibility, including inference vs discovery, and the lack of terminology for many of the various reproducibility issues, & much more! The complete show notes for this episode can be found at twimlai.com/talk/266.  

How can the humble zebrafish teach us about the human microbiome? John Rawls discusses the benefits of using animal models Take the MTM Listener Survey  Julie’s Biggest Takeaways:   Zebrafish and other model animals provide opportunities to understand host-microbe interactions. Zebrafish are particularly useful for imaging studies, due to their translucent skin and the ease of in vivo microscopy. This allows zebrafish to be used to in studies of spatial architecture or longitudinal studies (imaging the same fish specimen over time) in ways that other model organisms can’t be.   Zebrafish get their first microbes from their mother, just like mammals! The chorion, a protective coating that surrounds the zebrafish embryo, is seeded with microbes from passing through the cloaca of the female zebrafish. Surface-sterilizing this chorion allows researchers to generate germ-free animals that are very useful for microbiome studies.   A gut epithelial transcription factor is regulated by a signal from the gut microbiota, and this signaling interaction is conserved among all vertebrates. The transcription factor itself, HNF4, is found in both complex and simple animals, like the sea sponge, and may serve a long-conserved function in regulating interactions between animals and their microbiota.   Enteroendocrine cells release hormones based on specific chemical cues, but they can also interact with the nervous system. This makes them an important part of the gut-brain system, and the power of in vivo imaging has made zebrafish a great model for better understanding their function. Specific members of the microbiome specifically stimulate these EECs, sending signals up the vagus nerve to the brain.   Featured Quotes:   “We know that the zebrafish functionality of its intestine is very similar to what one encounters in the mouse or human intestine and we and others have been able to translate our findings from zebrafish studies into human biology.”   On genomic studies that have found similar transcription profiles in zebrafish, stickleback fish, mice, and humans: “This suggested that there is a core transcriptome that gut epithelial cell use in different vertebrate species that haven’t shared an ancestor in 420 million years!”   Comparing fish and mouse: “Genes regulated by microbiota in these respective hosts display a lot of overlap. Many of the same signaling pathways and metabolic processes are affected by microbiotas in different hosts in similar ways.”   “There’s been a lot of interesting research documenting the role of the intestinal microbiome in promoting harvest of dietary nutrients we consume. Much of that literature has been focused on the events that occur in the distal intestine, in the colon, where recalcitrant carbohydrates and proteins that make it that far, many of which we are unable to digest, are made available to the colonic microbiome, members of which are able to digest and degrade them to things such as short chain fatty acids, which we can consume.”   “Eventually, we’ll have some strong candidates in terms of specific bacterial strains or communities or factors or pharmacologic agents that could be used to affect dietary fat absorption or metabolism. We’re still a long ways away from that.”   “One of the fascinating things about developmental biology is that the only way you get a viable animal is if the different tissues and the different cells within the body are coordinating amongst themselves for energy, for nutrients, for oxygen, et cetera. As you’re building an animal and as you’re sustaining an animal, the different tissues have to cooperate. When that doesn’t happen, when tissues or cells become selfish or don’t play by the rules, you get things like cancer and other diseases as well...when I began learning about the field of microbiome science and some of the work that was coming out from that field, it sounded to me like the microbiome was going to be a really important part of that. Not only can we think of the microbiome as a ‘microbial organ,’ as it is sometimes called, and therefore worthy of consideration within the context of developmental biology, but also the influence of the microbiome on any one tissue is going to modify its need and its ability to cooperate within the integrated system.”   Links for this Episode:   John Rawls’ lab website More amazing zebrafish images from the Rawls lab Duke University Microbiome Center Genome Research article on HNF4 regulation Cell Host and Microbe article on microbial influence on fatty acid absorption  

State of the Art Podcast was invited to attend and speak with participants in CODAME's Art + Tech Festival, ARTOBOTS at The Midway earlier this month. Part 1 features one-on-one on-site conversations with artists Alexander Reben and Meredith Tromble on art and AI. We conclude the episode with a fascinating conversation with UC Berkeley artist and professor, Ken Goldberg, on the "uncanny valley."Thank you CODAME for inviting us to cover this awesome event, and a special shoutout to Vanessa Chang, CODAME curator, for personally extending the invitation to us. You can listen to our interview with Vanessa Chang here.-About Alexander Reben-Alexander Reben is an artist and roboticist who explores humanity through the lens of art and technology. His work probes the inherently human nature of the artificial. Using tools such as artificial philosophy, synthetic psychology, perceptual manipulation and technological magic, he brings to light our inseparable evolutionary entanglement to invention which has unarguably shaped our way of being. This is done to not only help understand who we are, but to consider who we will become in our continued codevelopment with our artificial creations.Projects referred to in this episode: Boxie, Headgasmatron, and Pulse MachineLearn more at http://areben.com/-About Meredith Tromble-Meredith Tromble is a multimedia artist, writer, performer, and teacher at the San Francisco Art Institute. Learn more about Meredith at http://meredithtromble.net/-About Ken Goldberg-Ken Goldberg is an artist, inventor, and UC Berkeley Professor focusing on robotics. He was appointed the William S. Floyd Jr Distinguished Chair in Engineering and serves as Chair of the Industrial Engineering and Operations Research Department. He has secondary appointments in EECS, Art Practice, the School of Information, and Radiation Oncology at the UCSF Medical School. Ken is Director of the CITRIS "People and Robots" Initiative and the UC Berkeley AUTOLAB where he and his students pursue research in machine learning for robotics and automation in warehouses, homes, and operating rooms. Ken developed the first provably complete algorithms for part feeding and part fixturing and the first robot on the Internet. Despite agonizingly slow progress, he persists in trying to make robots less clumsy. He has over 250 peer-reviewed publications and 8 U.S. Patents. He co-founded and served as Editor-in-Chief of the IEEE Transactions on Automation Science and Engineering. Ken's artwork has appeared in 70 exhibits including the Whitney Biennial and films he has co-written have been selected for Sundance and nominated for an Emmy Award. Ken was awarded the NSF PECASE (Presidential Faculty Fellowship) from President Bill Clinton in 1995, elected IEEE Fellow in 2005 and selected by the IEEE Robotics and Automation Society for the George Saridis Leadership Award in 2016. He lives in the Bay Area and is madly in love with his wife, filmmaker and Webby Awards founder Tiffany Shlain, and their two daughters. Tweet him @Ken_Goldberg-About CODAME-Sparked by the network of creative coders, designers, and artists that Bruno Fonzi and Jordan Gray knew from around the world, CODAME was founded to celebrate their passion for art and technology. The CODAME brand of immersive, engaging, and out of the ordinary experiences was coined at the inaugural CODAME ART+TECH Festival in 2010 on a foggy rooftop in downtown San Francisco. CODAME builds ART+TECH projects and nonprofit events to inspire through experience.Follow them @codameTweet them @codameLearn more here-About ARTOBOTS-June 4-7, 2018 @ The Midway, San FranciscoThe annual CODAME ART+TECH Festival is a four-day conference with workshops, talks and nightlife events with immersive, engaging, out of the ordinary experiences. The festival features gallery installations, screenings, and performances.This year’s ART+TECH Festival, codenamed #ARTOBOTS, examines the sphere of robotics, automation, and artificial intelligence. Through art, discussion, play and performance, CODAME probes these potentials.

On today’s show I chat with Song Han, assistant professor in MIT’s EECS department, about his research on Deep Gradient Compression. In our conversation, we explore the challenge of distributed training for deep neural networks and the idea of compressing the gradient exchange to allow it to be done more efficiently. Song details the evolution of distributed training systems based on this idea, and provides a few examples of centralized and decentralized distributed training architectures such as Uber’s Horovod, as well as the approaches native to Pytorch and Tensorflow. Song also addresses potential issues that arise when considering distributed training, such as loss of accuracy and generalizability, and much more. The notes for this show can be found at twimlai.com/talk/146.

In this episode, I’m joined by Ian Goodfellow, Staff Research Scientist at Google Brain and Sandy Huang, Phd Student in the EECS department at UC Berkeley, to discuss their work on the paper Adversarial Attacks on Neural Network Policies. If you’re a regular listener here you’ve probably heard of adversarial attacks, and have seen examples of deep learning based object detectors that can be fooled into thinking that, for example, a giraffe is actually a school bus, by injecting some imperceptible noise into the image. Well, Sandy and Ian’s paper sits at the intersection of adversarial attacks and reinforcement learning, another area we’ve discussed quite a bit on the podcast. In their paper, they describe how adversarial attacks can also be effective at targeting neural network policies in reinforcement learning. Sandy gives us an overview of the paper, including how changing a single pixel value can throw off performance of a model trained to play Atari games. We also cover a lot of interesting topics relating to adversarial attacks and RL individually, and some related areas such as hierarchical reward functions and transfer learning. This was a great conversation that I’m really excited to bring to you! For complete show notes, head over to twimlai.com/talk/119

In this episode of the Data Show, I spoke with Kristian Hammond, chief scientist of Narrative Science and professor of EECS at Northwestern University. He has been at the forefront of helping companies understand the power, limitations, and disruptive potential of AI technologies and tools. In a previous post on machine learning, I listed types […]

Arlindo Oliveira é presidente do Instituto Superior Técnico e autor do livro The Digital Mind, lançado este ano e cuja edição em português, com o título Mentes Digitais, foi lançada na semana passada. Foi uma conversa cheia, em que falámos de vários temas ligados ao futuro da Inteligência Artificial, ou, nas palavras do convidado, ao surgimento das 'mentes digitais'. Ao usar esta palavra - 'mente' - Arlindo Oliveira transporta deliberadamente a discussão da Inteligência Artificial actual, independentemente dos seus avanços inegáveis, para um futuro mais ou menos longínquo, em que poderemos ter Inteligência Artificial equivalente - e, portanto, superior - à humana Algumas das questões de que falámos são, naturalmente, técnicas, outras - em maior número do que poderia parecer à partida - são mais filosóficas, levando-nos a questionar o que é ser Humano e como pode vir a ser o convívio com estas 'mentes digitais'. Arlindo Oliveira é um dos maiores especialistas portugueses em Inteligência Artificial e alguém numa posição privilegiada para prever o futuro neste campo, uma vez que junta interesse e formação académica em computação e neurologia.  Doutorou-se em Engenharia Electrotécnica e Ciências da Computação (EECS) pela Universidade da Califórnia em Berkeley, em 1994. Desde então, é Professor do Departamento de Engenharia Informática do Instituto Superior Técnico, onde tem desenvolvido trabalho no contexto de sistemas digitais, síntese lógica, algoritmia, aprendizagem automática e bioinformática. O livro 'The Digital Mind' foi lançado este ano pela prestigiada MIT Press. A tradução em português, editada pela IST Press, acaba de ser lançada no mercado nacional. (sonoplastia com a colaboração de Luís Ferreira) Clip do filme Maria Papoila retirado de: https://youtu.be/Mn90o038ZLE

Margaret Burnett is a professor of Computer Science in the School of EECS at Oregon State University. She is a pioneer woman in computer science whose work has been honoured with numerous awards, including ACM Distinguished Scientist. Her passion is to change the world by designing more gender-inclusive software. In this conversation, she shares experiences being the first woman software developer at Proctor & Gamble Ivorydale in the 1970s, and creating two start-ups as well as a women’s business network in the 1980s. She also talks about her work in academia, in particular about her GenderMag project, as well as practical experiences including mentoring and management using dove-tailing strategies as well as managing family life by drawing fences. She also tries to do one thing every day to make the world a better place. An inspirational person in so many ways! See http://www.changingacademiclife.com/blog/ 2017/7/5/margaret-burnett for a time-stamped overview of the conversation and related links.

Meeting for 6.172 industry mentors. Description of mentorship role, expectations, overview of course and how it fits into EECS curriculum.

Klothoiden sind Kurven, die 1794 von Jakob I. Bernoulli zuerst beschrieben wurden. Er hatte die Form eines Metallstreifens untersucht, der von einem Gewicht an einem Ende verbogen wird, während das andere Ende eingespannt ist. Als Resultat des elastischen Verhaltens ist dann die Krümmung proportional zur Kurvenlänge. Viele weitere Eigenschaften von Kurven mit dIeser Eigenschaft wurden dann von Leonhard Euler über den Rahmen des Gedankenexperiments von Bernoulli hinaus bewiesen , wie zum Beispiel die Position der asymptotischen Endpunkte. Im Straßen- und Schienenbau sind Klothoiden ausgezeichnete Übergangsbögen zwischen geraden Strecken und Kurven, da diese Kurve die Krümmung zwischen den beiden Abschnitten gleichmäßig anpasst. Bei der Planung von Schienentrassen wurde diese Eigenschaft vor etwa 100 Jahren schon ausgenutzt. Eine aktuelle wichtige Anwendung ist die Abbildung der Straßen in Fahrassistenzsystemen, wo passend parametrisierte Klothoiden große Vorteile gegenüber Splines besitzen, wie Gotami Heller im Gespräch mit Gudrun Thäter erklärt. Um Klothoiden tatsächlich im Computermodell benutzen zu können, muss eine möglichst adäquate Approximation gesucht und implementiert werden, die die nötige Glattheit in der Kurve erhält. Literatur und Zusatzinformationen M. Bäumker: Rechenverfahren der Ingenieurvermessung http://www.eecs.berkeley.edu/Pubs/TechRpts/2008/EECS-2008-111.pdf: R. Levien: The Euler spiral, a mathematical history, Technical Report University of Carlifornia at Berkeley, UCB/EECS-2008-111, 2008. D. Khosla: Accurate estimation of forward path geometry using two-clothoid road model, Intelligent Vehicle Symposium, 2002. IEEE. Vol. 1. IEEE, 2002.

The desire to compute on sensitive data without revealing it has led to several decades of research in the area of secure multi-party computation. Today, cloud computing serves as a major motivation for the development of secure data processing techniques suitable for use in outsourced environments for computing with private or sensitive data. Despite much attention, most of the available techniques focused on a rather narrow domain of integer arithmetic. In this talk, we describe our work on other types of computation and algorithms suitable for secure computation and outsourcing with the goal of enabling secure and efficient distributed implementation of a general-purpose program. This, in particular, includes a compiler that transforms a program written in C extension, where variables to be protected are marked as private, into its secure distributed implementation suitable for execution in the cloud. About the speaker: Marina Blanton is an assistant professor in the Department of ComputerScience and Engineering at the University of Notre Dame. She received her MS in EECS from Ohio University in 2002, MS in CS from Purdue University in2004, and PhD in CS from Purdue University in 2007. Dr. Blanton's researchinterests are centrally in information security, privacy, and appliedcryptography. Recent projects span across areas such as secure computationand outsourcing, integrity of outsourced computation and storage, privatebiometric and genomic computation, privacy-preserving systems for medicaland social networks, authentication, anonymity, and key management. Dr.Blanton has served on technical program committees for top conferences andworkshops and journal editorial boards. Her research is supported by NSF,AFOSR, and AFRL.

Lada Adamic is a visiting scholar at the UC Berkeley School of Information and an associate professor at the University of Michigan School of Information and Center for the Study of Complex Systems. She is also affiliated with EECS. Her research interests center on information dynamics in networks: how information diffuses, how it can be found, and how it influences the evolution of a network's structure. Her projects have included identifying expertise in online question and answer forums, studying the dynamics of viral marketing, and characterizing the structure in blogs and other online communities. She has received an NSF CAREER award, and best paper awards from Hypertext'08, ICWSM'10 and '11, and the most influential paper of the decade award from Web Intelligence'11.