Podcasts about gaussian

Today's clip is from Episode 151 of the podcast, with Jonas ArrudaIn this conversation, Jonas Arruda explains how diffusion models generate data by learning to reverse a noise process. The idea is to start from a simple distribution like Gaussian noise and gradually remove noise until the target distribution emerges. This is done through a forward process that adds noise to clean parameters and a backward process that learns how to undo that corruption. A noise schedule controls how much noise is added or removed at each step, guiding the transformation from pure randomness back to meaningful structure.Get the full discussion here• Join this channel to get access to perks:https://www.patreon.com/c/learnbayesstats• Intro to Bayes Course (first 2 lessons free): https://topmate.io/alex_andorra/503302• Advanced Regression Course (first 2 lessons free): https://topmate.io/alex_andorra/1011122Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Tickets for AIEi Miami and AIE Europe are live, with first wave speakers announced!From pioneering software-defined networking to backing many of the most aggressive AI model companies of this cycle, Martin Casado and Sarah Wang sit at the center of the capital, compute, and talent arms race reshaping the tech industry. As partners at a16z investing across infrastructure and growth, they've watched venture and growth blur, model labs turn dollars into capability at unprecedented speed, and startups raise nine-figure rounds before monetization.Martin and Sarah join us to unpack the new financing playbook for AI: why today's rounds are really compute contracts in disguise, how the “raise → train → ship → raise bigger” flywheel works, and whether foundation model companies can outspend the entire app ecosystem built on top of them. They also share what's underhyped (boring enterprise software), what's overheated (talent wars and compensation spirals), and the two radically different futures they see for AI's market structure.We discuss:* Martin's “two futures” fork: infinite fragmentation and new software categories vs. a small oligopoly of general models that consume everything above them* The capital flywheel: how model labs translate funding directly into capability gains, then into revenue growth measured in weeks, not years* Why venture and growth have merged: $100M–$1B hybrid rounds, strategic investors, compute negotiations, and complex deal structures* The AGI vs. product tension: allocating scarce GPUs between long-term research and near-term revenue flywheels* Whether frontier labs can out-raise and outspend the entire app ecosystem built on top of their APIs* Why today's talent wars ($10M+ comp packages, $B acqui-hires) are breaking early-stage founder math* Cursor as a case study: building up from the app layer while training down into your own models* Why “boring” enterprise software may be the most underinvested opportunity in the AI mania* Hardware and robotics: why the ChatGPT moment hasn't yet arrived for robots and what would need to change* World Labs and generative 3D: bringing the marginal cost of 3D scene creation down by orders of magnitude* Why public AI discourse is often wildly disconnected from boardroom reality and how founders should navigate the noiseShow Notes:* “Where Value Will Accrue in AI: Martin Casado & Sarah Wang” - a16z show* “Jack Altman & Martin Casado on the Future of Venture Capital”* World Labs—Martin Casado• LinkedIn: https://www.linkedin.com/in/martincasado/• X: https://x.com/martin_casadoSarah Wang• LinkedIn: https://www.linkedin.com/in/sarah-wang-59b96a7• X: https://x.com/sarahdingwanga16z• https://a16z.com/Timestamps00:00:00 – Intro: Live from a16z00:01:20 – The New AI Funding Model: Venture + Growth Collide00:03:19 – Circular Funding, Demand & “No Dark GPUs”00:05:24 – Infrastructure vs Apps: The Lines Blur00:06:24 – The Capital Flywheel: Raise → Train → Ship → Raise Bigger00:09:39 – Can Frontier Labs Outspend the Entire App Ecosystem?00:11:24 – Character AI & The AGI vs Product Dilemma00:14:39 – Talent Wars, $10M Engineers & Founder Anxiety00:17:33 – What's Underinvested? The Case for “Boring” Software00:19:29 – Robotics, Hardware & Why It's Hard to Win00:22:42 – Custom ASICs & The $1B Training Run Economics00:24:23 – American Dynamism, Geography & AI Power Centers00:26:48 – How AI Is Changing the Investor Workflow (Claude Cowork)00:29:12 – Two Futures of AI: Infinite Expansion or Oligopoly?00:32:48 – If You Can Raise More Than Your Ecosystem, You Win00:34:27 – Are All Tasks AGI-Complete? Coding as the Test Case00:38:55 – Cursor & The Power of the App Layer00:44:05 – World Labs, Spatial Intelligence & 3D Foundation Models00:47:20 – Thinking Machines, Founder Drama & Media Narratives00:52:30 – Where Long-Term Power Accrues in the AI StackTranscriptLatent.Space - Inside AI's $10B+ Capital Flywheel — Martin Casado & Sarah Wang of a16z[00:00:00] Welcome to Latent Space (Live from a16z) + Meet the Guests[00:00:00] Alessio: Hey everyone. Welcome to the Latent Space podcast, live from a 16 z. Uh, this is Alessio founder Kernel Lance, and I'm joined by Twix, editor of Latent Space.[00:00:08] swyx: Hey, hey, hey. Uh, and we're so glad to be on with you guys. Also a top AI podcast, uh, Martin Cado and Sarah Wang. Welcome, very[00:00:16] Martin Casado: happy to be here and welcome.[00:00:17] swyx: Yes, uh, we love this office. We love what you've done with the place. Uh, the new logo is everywhere now. It's, it's still getting, takes a while to get used to, but it reminds me of like sort of a callback to a more ambitious age, which I think is kind of[00:00:31] Martin Casado: definitely makes a statement.[00:00:33] swyx: Yeah.[00:00:34] Martin Casado: Not quite sure what that statement is, but it makes a statement.[00:00:37] swyx: Uh, Martin, I go back with you to Netlify.[00:00:40] Martin Casado: Yep.[00:00:40] swyx: Uh, and, uh, you know, you create a software defined networking and all, all that stuff people can read up on your background. Yep. Sarah, I'm newer to you. Uh, you, you sort of started working together on AI infrastructure stuff.[00:00:51] Sarah Wang: That's right. Yeah. Seven, seven years ago now.[00:00:53] Martin Casado: Best growth investor in the entire industry.[00:00:55] swyx: Oh, say[00:00:56] Martin Casado: more hands down there is, there is. [00:01:00] I mean, when it comes to AI companies, Sarah, I think has done the most kind of aggressive, um, investment thesis around AI models, right? So, worked for Nom Ja, Mira Ia, FEI Fey, and so just these frontier, kind of like large AI models.[00:01:15] I think, you know, Sarah's been the, the broadest investor. Is that fair?[00:01:20] Venture vs. Growth in the Frontier Model Era[00:01:20] Sarah Wang: No, I, well, I was gonna say, I think it's been a really interesting tag, tag team actually just ‘cause the, a lot of these big C deals, not only are they raising a lot of money, um, it's still a tech founder bet, which obviously is inherently early stage.[00:01:33] But the resources,[00:01:36] Martin Casado: so many, I[00:01:36] Sarah Wang: was gonna say the resources one, they just grow really quickly. But then two, the resources that they need day one are kind of growth scale. So I, the hybrid tag team that we have is. Quite effective, I think,[00:01:46] Martin Casado: what is growth these days? You know, you don't wake up if it's less than a billion or like, it's, it's actually, it's actually very like, like no, it's a very interesting time in investing because like, you know, take like the character around, right?[00:01:59] These tend to [00:02:00] be like pre monetization, but the dollars are large enough that you need to have a larger fund and the analysis. You know, because you've got lots of users. ‘cause this stuff has such high demand requires, you know, more of a number sophistication. And so most of these deals, whether it's US or other firms on these large model companies, are like this hybrid between venture growth.[00:02:18] Sarah Wang: Yeah. Total. And I think, you know, stuff like BD for example, you wouldn't usually need BD when you were seed stage trying to get market biz Devrel. Biz Devrel, exactly. Okay. But like now, sorry, I'm,[00:02:27] swyx: I'm not familiar. What, what, what does biz Devrel mean for a venture fund? Because I know what biz Devrel means for a company.[00:02:31] Sarah Wang: Yeah.[00:02:32] Compute Deals, Strategics, and the ‘Circular Funding' Question[00:02:32] Sarah Wang: You know, so a, a good example is, I mean, we talk about buying compute, but there's a huge negotiation involved there in terms of, okay, do you get equity for the compute? What, what sort of partner are you looking at? Is there a go-to market arm to that? Um, and these are just things on this scale, hundreds of millions, you know, maybe.[00:02:50] Six months into the inception of a company, you just wouldn't have to negotiate these deals before.[00:02:54] Martin Casado: Yeah. These large rounds are very complex now. Like in the past, if you did a series A [00:03:00] or a series B, like whatever, you're writing a 20 to a $60 million check and you call it a day. Now you normally have financial investors and strategic investors, and then the strategic portion always still goes with like these kind of large compute contracts, which can take months to do.[00:03:13] And so it's, it's very different ties. I've been doing this for 10 years. It's the, I've never seen anything like this.[00:03:19] swyx: Yeah. Do you have worries about the circular funding from so disease strategics?[00:03:24] Martin Casado: I mean, listen, as long as the demand is there, like the demand is there. Like the problem with the internet is the demand wasn't there.[00:03:29] swyx: Exactly. All right. This, this is like the, the whole pyramid scheme bubble thing, where like, as long as you mark to market on like the notional value of like, these deals, fine, but like once it starts to chip away, it really Well[00:03:41] Martin Casado: no, like as, as, as, as long as there's demand. I mean, you know, this, this is like a lot of these sound bites have already become kind of cliches, but they're worth saying it.[00:03:47] Right? Like during the internet days, like we were. Um, raising money to put fiber in the ground that wasn't used. And that's a problem, right? Because now you actually have a supply overhang.[00:03:58] swyx: Mm-hmm.[00:03:59] Martin Casado: And even in the, [00:04:00] the time of the, the internet, like the supply and, and bandwidth overhang, even as massive as it was in, as massive as the crash was only lasted about four years.[00:04:09] But we don't have a supply overhang. Like there's no dark GPUs, right? I mean, and so, you know, circular or not, I mean, you know, if, if someone invests in a company that, um. You know, they'll actually use the GPUs. And on the other side of it is the, is the ask for customer. So I I, I think it's a different time.[00:04:25] Sarah Wang: I think the other piece, maybe just to add onto this, and I'm gonna quote Martine in front of him, but this is probably also a unique time in that. For the first time, you can actually trace dollars to outcomes. Yeah, right. Provided that scaling laws are, are holding, um, and capabilities are actually moving forward.[00:04:40] Because if you can put translate dollars into capabilities, uh, a capability improvement, there's demand there to martine's point. But if that somehow breaks, you know, obviously that's an important assumption in this whole thing to make it work. But you know, instead of investing dollars into sales and marketing, you're, you're investing into r and d to get to the capability, um, you know, increase.[00:04:59] And [00:05:00] that's sort of been the demand driver because. Once there's an unlock there, people are willing to pay for it.[00:05:05] Alessio: Yeah.[00:05:06] Blurring Lines: Models as Infra + Apps, and the New Fundraising Flywheel[00:05:06] Alessio: Is there any difference in how you built the portfolio now that some of your growth companies are, like the infrastructure of the early stage companies, like, you know, OpenAI is now the same size as some of the cloud providers were early on.[00:05:16] Like what does that look like? Like how much information can you feed off each other between the, the two?[00:05:24] Martin Casado: There's so many lines that are being crossed right now, or blurred. Right. So we already talked about venture and growth. Another one that's being blurred is between infrastructure and apps, right? So like what is a model company?[00:05:35] Mm-hmm. Like, it's clearly infrastructure, right? Because it's like, you know, it's doing kind of core r and d. It's a horizontal platform, but it's also an app because it's um, uh, touches the users directly. And then of course. You know, the, the, the growth of these is just so high. And so I actually think you're just starting to see a, a, a new financing strategy emerge and, you know, we've had to adapt as a result of that.[00:05:59] And [00:06:00] so there's been a lot of changes. Um, you're right that these companies become platform companies very quickly. You've got ecosystem build out. So none of this is necessarily new, but the timescales of which it's happened is pretty phenomenal. And the way we'd normally cut lines before is blurred a little bit, but.[00:06:16] But that, that, that said, I mean, a lot of it also just does feel like things that we've seen in the past, like cloud build out the internet build out as well.[00:06:24] Sarah Wang: Yeah. Um, yeah, I think it's interesting, uh, I don't know if you guys would agree with this, but it feels like the emerging strategy is, and this builds off of your other question, um.[00:06:33] You raise money for compute, you pour that or you, you pour the money into compute, you get some sort of breakthrough. You funnel the breakthrough into your vertically integrated application. That could be chat GBT, that could be cloud code, you know, whatever it is. You massively gain share and get users.[00:06:49] Maybe you're even subsidizing at that point. Um, depending on your strategy. You raise money at the peak momentum and then you repeat, rinse and repeat. Um, and so. And that wasn't [00:07:00] true even two years ago, I think. Mm-hmm. And so it's sort of to your, just tying it to fundraising strategy, right? There's a, and hiring strategy.[00:07:07] All of these are tied, I think the lines are blurring even more today where everyone is, and they, but of course these companies all have API businesses and so they're these, these frenemy lines that are getting blurred in that a lot of, I mean, they have billions of dollars of API revenue, right? And so there are customers there.[00:07:23] But they're competing on the app layer.[00:07:24] Martin Casado: Yeah. So this is a really, really important point. So I, I would say for sure, venture and growth, that line is blurry app and infrastructure. That line is blurry. Um, but I don't think that that changes our practice so much. But like where the very open questions are like, does this layer in the same way.[00:07:43] Compute traditionally has like during the cloud is like, you know, like whatever, somebody wins one layer, but then another whole set of companies wins another layer. But that might not, might not be the case here. It may be the case that you actually can't verticalize on the token string. Like you can't build an app like it, it necessarily goes down just because there are no [00:08:00] abstractions.[00:08:00] So those are kinda the bigger existential questions we ask. Another thing that is very different this time than in the history of computer sciences is. In the past, if you raised money, then you basically had to wait for engineering to catch up. Which famously doesn't scale like the mythical mammoth. It take a very long time.[00:08:18] But like that's not the case here. Like a model company can raise money and drop a model in a, in a year, and it's better, right? And, and it does it with a team of 20 people or 10 people. So this type of like money entering a company and then producing something that has demand and growth right away and using that to raise more money is a very different capital flywheel than we've ever seen before.[00:08:39] And I think everybody's trying to understand what the consequences are. So I think it's less about like. Big companies and growth and this, and more about these more systemic questions that we actually don't have answers to.[00:08:49] Alessio: Yeah, like at Kernel Labs, one of our ideas is like if you had unlimited money to spend productively to turn tokens into products, like the whole early stage [00:09:00] market is very different because today you're investing X amount of capital to win a deal because of price structure and whatnot, and you're kind of pot committing.[00:09:07] Yeah. To a certain strategy for a certain amount of time. Yeah. But if you could like iteratively spin out companies and products and just throw, I, I wanna spend a million dollar of inference today and get a product out tomorrow.[00:09:18] swyx: Yeah.[00:09:19] Alessio: Like, we should get to the point where like the friction of like token to product is so low that you can do this and then you can change the Right, the early stage venture model to be much more iterative.[00:09:30] And then every round is like either 100 k of inference or like a hundred million from a 16 Z. There's no, there's no like $8 million C round anymore. Right.[00:09:38] When Frontier Labs Outspend the Entire App Ecosystem[00:09:38] Martin Casado: But, but, but, but there's a, there's a, the, an industry structural question that we don't know the answer to, which involves the frontier models, which is, let's take.[00:09:48] Anthropic it. Let's say Anthropic has a state-of-the-art model that has some large percentage of market share. And let's say that, uh, uh, uh, you know, uh, a company's building smaller models [00:10:00] that, you know, use the bigger model in the background, open 4.5, but they add value on top of that. Now, if Anthropic can raise three times more.[00:10:10] Every subsequent round, they probably can raise more money than the entire app ecosystem that's built on top of it. And if that's the case, they can expand beyond everything built on top of it. It's like imagine like a star that's just kind of expanding, so there could be a systemic. There could be a, a systemic situation where the soda models can raise so much money that they can out pay anybody that bills on top of ‘em, which would be something I don't think we've ever seen before just because we were so bottlenecked in engineering, and this is a very open question.[00:10:41] swyx: Yeah. It's, it is almost like bitter lesson applied to the startup industry.[00:10:45] Martin Casado: Yeah, a hundred percent. It literally becomes an issue of like raise capital, turn that directly into growth. Use that to raise three times more. Exactly. And if you can keep doing that, you literally can outspend any company that's built the, not any company.[00:10:57] You can outspend the aggregate of companies on top of [00:11:00] you and therefore you'll necessarily take their share, which is crazy.[00:11:02] swyx: Would you say that kind of happens in character? Is that the, the sort of postmortem on. What happened?[00:11:10] Sarah Wang: Um,[00:11:10] Martin Casado: no.[00:11:12] Sarah Wang: Yeah, because I think so,[00:11:13] swyx: I mean the actual postmortem is, he wanted to go back to Google.[00:11:15] Exactly. But like[00:11:18] Martin Casado: that's another difference that[00:11:19] Sarah Wang: you said[00:11:21] Martin Casado: it. We should talk, we should actually talk about that.[00:11:22] swyx: Yeah,[00:11:22] Sarah Wang: that's[00:11:23] swyx: Go for it. Take it. Take,[00:11:23] Sarah Wang: yeah.[00:11:24] Character.AI, Founder Goals (AGI vs Product), and GPU Allocation Tradeoffs[00:11:24] Sarah Wang: I was gonna say, I think, um. The, the, the character thing raises actually a different issue, which actually the Frontier Labs will face as well. So we'll see how they handle it.[00:11:34] But, um, so we invest in character in January, 2023, which feels like eons ago, I mean, three years ago. Feels like lifetimes ago. But, um, and then they, uh, did the IP licensing deal with Google in August, 2020. Uh, four. And so, um, you know, at the time, no, you know, he's talked publicly about this, right? He wanted to Google wouldn't let him put out products in the world.[00:11:56] That's obviously changed drastically. But, um, he went to go do [00:12:00] that. Um, but he had a product attached. The goal was, I mean, it's Nome Shair, he wanted to get to a GI. That was always his personal goal. But, you know, I think through collecting data, right, and this sort of very human use case, that the character product.[00:12:13] Originally was and still is, um, was one of the vehicles to do that. Um, I think the real reason that, you know. I if you think about the, the stress that any company feels before, um, you ultimately going one way or the other is sort of this a GI versus product. Um, and I think a lot of the big, I think, you know, opening eyes, feeling that, um, anthropic if they haven't started, you know, felt it, certainly given the success of their products, they may start to feel that soon.[00:12:39] And the real. I think there's real trade-offs, right? It's like how many, when you think about GPUs, that's a limited resource. Where do you allocate the GPUs? Is it toward the product? Is it toward new re research? Right? Is it, or long-term research, is it toward, um, n you know, near to midterm research? And so, um, in a case where you're resource constrained, um, [00:13:00] of course there's this fundraising game you can play, right?[00:13:01] But the fund, the market was very different back in 2023 too. Um. I think the best researchers in the world have this dilemma of, okay, I wanna go all in on a GI, but it's the product usage revenue flywheel that keeps the revenue in the house to power all the GPUs to get to a GI. And so it does make, um, you know, I think it sets up an interesting dilemma for any startup that has trouble raising up until that level, right?[00:13:27] And certainly if you don't have that progress, you can't continue this fly, you know, fundraising flywheel.[00:13:32] Martin Casado: I would say that because, ‘cause we're keeping track of all of the things that are different, right? Like, you know, venture growth and uh, app infra and one of the ones is definitely the personalities of the founders.[00:13:45] It's just very different this time I've been. Been doing this for a decade and I've been doing startups for 20 years. And so, um, I mean a lot of people start this to do a GI and we've never had like a unified North star that I recall in the same [00:14:00] way. Like people built companies to start companies in the past.[00:14:02] Like that was what it was. Like I would create an internet company, I would create infrastructure company, like it's kind of more engineering builders and this is kind of a different. You know, mentality. And some companies have harnessed that incredibly well because their direction is so obviously on the path to what somebody would consider a GI, but others have not.[00:14:20] And so like there is always this tension with personnel. And so I think we're seeing more kind of founder movement.[00:14:27] Sarah Wang: Yeah.[00:14:27] Martin Casado: You know, as a fraction of founders than we've ever seen. I mean, maybe since like, I don't know the time of like Shockly and the trade DUR aid or something like that. Way back in the beginning of the industry, I, it's a very, very.[00:14:38] Unusual time of personnel.[00:14:39] Sarah Wang: Totally.[00:14:40] Talent Wars, Mega-Comp, and the Rise of Acquihire M&A[00:14:40] Sarah Wang: And it, I think it's exacerbated by the fact that talent wars, I mean, every industry has talent wars, but not at this magnitude, right? No. Yeah. Very rarely can you see someone get poached for $5 billion. That's hard to compete with. And then secondly, if you're a founder in ai, you could fart and it would be on the front page of, you know, the information these days.[00:14:59] And so there's [00:15:00] sort of this fishbowl effect that I think adds to the deep anxiety that, that these AI founders are feeling.[00:15:06] Martin Casado: Hmm.[00:15:06] swyx: Uh, yes. I mean, just on, uh, briefly comment on the founder, uh, the sort of. Talent wars thing. I feel like 2025 was just like a blip. Like I, I don't know if we'll see that again.[00:15:17] ‘cause meta built the team. Like, I don't know if, I think, I think they're kind of done and like, who's gonna pay more than meta? I, I don't know.[00:15:23] Martin Casado: I, I agree. So it feels so, it feel, it feels this way to me too. It's like, it is like, basically Zuckerberg kind of came out swinging and then now he's kind of back to building.[00:15:30] Yeah,[00:15:31] swyx: yeah. You know, you gotta like pay up to like assemble team to rush the job, whatever. But then now, now you like you, you made your choices and now they got a ship.[00:15:38] Martin Casado: I mean, the, the o other side of that is like, you know, like we're, we're actually in the job hiring market. We've got 600 people here. I hire all the time.[00:15:44] I've got three open recs if anybody's interested, that's listening to this for investor. Yeah, on, on the team, like on the investing side of the team, like, and, um, a lot of the people we talk to have acting, you know, active, um, offers for 10 million a year or something like that. And like, you know, and we pay really, [00:16:00] really well.[00:16:00] And just to see what's out on the market is really, is really remarkable. And so I would just say it's actually, so you're right, like the really flashy one, like I will get someone for, you know, a billion dollars, but like the inflated, um, uh, trickles down. Yeah, it is still very active today. I mean,[00:16:18] Sarah Wang: yeah, you could be an L five and get an offer in the tens of millions.[00:16:22] Okay. Yeah. Easily. Yeah. It's so I think you're right that it felt like a blip. I hope you're right. Um, but I think it's been, the steady state is now, I think got pulled up. Yeah. Yeah. I'll pull up for[00:16:31] Martin Casado: sure. Yeah.[00:16:32] Alessio: Yeah. And I think that's breaking the early stage founder math too. I think before a lot of people would be like, well, maybe I should just go be a founder instead of like getting paid.[00:16:39] Yeah. 800 KA million at Google. But if I'm getting paid. Five, 6 million. That's different but[00:16:45] Martin Casado: on. But on the other hand, there's more strategic money than we've ever seen historically, right? Mm-hmm. And so, yep. The economics, the, the, the, the calculus on the economics is very different in a number of ways. And, uh, it's crazy.[00:16:58] It's cra it's causing like a, [00:17:00] a, a, a ton of change in confusion in the market. Some very positive, sub negative, like, so for example, the other side of the, um. The co-founder, like, um, acquisition, you know, mark Zuckerberg poaching someone for a lot of money is like, we were actually seeing historic amount of m and a for basically acquihires, right?[00:17:20] That you like, you know, really good outcomes from a venture perspective that are effective acquihires, right? So I would say it's probably net positive from the investment standpoint, even though it seems from the headlines to be very disruptive in a negative way.[00:17:33] Alessio: Yeah.[00:17:33] What's Underfunded: Boring Software, Robotics Skepticism, and Custom Silicon Economics[00:17:33] Alessio: Um, let's talk maybe about what's not being invested in, like maybe some interesting ideas that you would see more people build or it, it seems in a way, you know, as ycs getting more popular, it's like access getting more popular.[00:17:47] There's a startup school path that a lot of founders take and they know what's hot in the VC circles and they know what gets funded. Uh, and there's maybe not as much risk appetite for. Things outside of that. Um, I'm curious if you feel [00:18:00] like that's true and what are maybe, uh, some of the areas, uh, that you think are under discussed?[00:18:06] Martin Casado: I mean, I actually think that we've taken our eye off the ball in a lot of like, just traditional, you know, software companies. Um, so like, I mean. You know, I think right now there's almost a barbell, like you're like the hot thing on X, you're deep tech.[00:18:21] swyx: Mm-hmm.[00:18:22] Martin Casado: Right. But I, you know, I feel like there's just kind of a long, you know, list of like good.[00:18:28] Good companies that will be around for a long time in very large markets. Say you're building a database, you know, say you're building, um, you know, kind of monitoring or logging or tooling or whatever. There's some good companies out there right now, but like, they have a really hard time getting, um, the attention of investors.[00:18:43] And it's almost become a meme, right? Which is like, if you're not basically growing from zero to a hundred in a year, you're not interesting, which is just, is the silliest thing to say. I mean, think of yourself as like an introvert person, like, like your personal money, right? Mm-hmm. So. Your personal money, will you put it in the stock market at 7% or you put it in this company growing five x in a very large [00:19:00] market?[00:19:00] Of course you can put it in the company five x. So it's just like we say these stupid things, like if you're not going from zero to a hundred, but like those, like who knows what the margins of those are mean. Clearly these are good investments. True for anybody, right? True. Like our LPs want whatever.[00:19:12] Three x net over, you know, the life cycle of a fund, right? So a, a company in a big market growing five X is a great investment. We'd, everybody would be happy with these returns, but we've got this kind of mania on these, these strong growths. And so I would say that that's probably the most underinvested sector.[00:19:28] Right now.[00:19:29] swyx: Boring software, boring enterprise software.[00:19:31] Martin Casado: Traditional. Really good company.[00:19:33] swyx: No, no AI here.[00:19:34] Martin Casado: No. Like boring. Well, well, the AI of course is pulling them into use cases. Yeah, but that's not what they're, they're not on the token path, right? Yeah. Let's just say that like they're software, but they're not on the token path.[00:19:41] Like these are like they're great investments from any definition except for like random VC on Twitter saying VC on x, saying like, it's not growing fast enough. What do you[00:19:52] Sarah Wang: think? Yeah, maybe I'll answer a slightly different. Question, but adjacent to what you asked, um, which is maybe an area that we're not, uh, investing [00:20:00] right now that I think is a question and we're spending a lot of time in regardless of whether we pull the trigger or not.[00:20:05] Um, and it would probably be on the hardware side, actually. Robotics, right? And the robotics side. Robotics. Right. Which is, it's, I don't wanna say that it's not getting funding ‘cause it's clearly, uh, it's, it's sort of non-consensus to almost not invest in robotics at this point. But, um, we spent a lot of time in that space and I think for us, we just haven't seen the chat GPT moment.[00:20:22] Happen on the hardware side. Um, and the funding going into it feels like it's already. Taking that for granted.[00:20:30] Martin Casado: Yeah. Yeah. But we also went through the drone, you know, um, there's a zip line right, right out there. What's that? Oh yeah, there's a zip line. Yeah. What the drone, what the av And like one of the takeaways is when it comes to hardware, um, most companies will end up verticalizing.[00:20:46] Like if you're. If you're investing in a robot company for an A for agriculture, you're investing in an ag company. ‘cause that's the competition and that's surprising. And that's supply chain. And if you're doing it for mining, that's mining. And so the ad team does a lot of that type of stuff ‘cause they actually set up to [00:21:00] diligence that type of work.[00:21:01] But for like horizontal technology investing, there's very little when it comes to robots just because it's so fit for, for purpose. And so we kinda like to look at software. Solutions or horizontal solutions like applied intuition. Clearly from the AV wave deep map, clearly from the AV wave, I would say scale AI was actually a horizontal one for That's fair, you know, for robotics early on.[00:21:23] And so that sort of thing we're very, very interested. But the actual like robot interacting with the world is probably better for different team. Agree.[00:21:30] Alessio: Yeah, I'm curious who these teams are supposed to be that invest in them. I feel like everybody's like, yeah, robotics, it's important and like people should invest in it.[00:21:38] But then when you look at like the numbers, like the capital requirements early on versus like the moment of, okay, this is actually gonna work. Let's keep investing. That seems really hard to predict in a way that is not,[00:21:49] Martin Casado: I think co, CO two, kla, gc, I mean these are all invested in in Harvard companies. He just, you know, and [00:22:00] listen, I mean, it could work this time for sure.[00:22:01] Right? I mean if Elon's doing it, he's like, right. Just, just the fact that Elon's doing it means that there's gonna be a lot of capital and a lot of attempts for a long period of time. So that alone maybe suggests that we should just be investing in robotics just ‘cause you have this North star who's Elon with a humanoid and that's gonna like basically willing into being an industry.[00:22:17] Um, but we've just historically found like. We're a huge believer that this is gonna happen. We just don't feel like we're in a good position to diligence these things. ‘cause again, robotics companies tend to be vertical. You really have to understand the market they're being sold into. Like that's like that competitive equilibrium with a human being is what's important.[00:22:34] It's not like the core tech and like we're kind of more horizontal core tech type investors. And this is Sarah and I. Yeah, the ad team is different. They can actually do these types of things.[00:22:42] swyx: Uh, just to clarify, AD stands for[00:22:44] Martin Casado: American Dynamism.[00:22:45] swyx: Alright. Okay. Yeah, yeah, yeah. Uh, I actually, I do have a related question that, first of all, I wanna acknowledge also just on the, on the chip side.[00:22:51] Yeah. I, I recall a podcast that where you were on, i, I, I think it was the a CC podcast, uh, about two or three years ago where you, where you suddenly said [00:23:00] something, which really stuck in my head about how at some point, at some point kind of scale it makes sense to. Build a custom aic Yes. For per run.[00:23:07] Martin Casado: Yes.[00:23:07] It's crazy. Yeah.[00:23:09] swyx: We're here and I think you, you estimated 500 billion, uh, something.[00:23:12] Martin Casado: No, no, no. A billion, a billion dollar training run of $1 billion training run. It makes sense to actually do a custom meic if you can do it in time. The question now is timelines. Yeah, but not money because just, just, just rough math.[00:23:22] If it's a billion dollar training. Then the inference for that model has to be over a billion, otherwise it won't be solvent. So let's assume it's, if you could save 20%, which you could save much more than that with an ASIC 20%, that's $200 million. You can tape out a chip for $200 million. Right? So now you can literally like justify economically, not timeline wise.[00:23:41] That's a different issue. An ASIC per model, which[00:23:44] swyx: is because that, that's how much we leave on the table every single time. We, we, we do like generic Nvidia.[00:23:48] Martin Casado: Exactly. Exactly. No, it, it is actually much more than that. You could probably get, you know, a factor of two, which would be 500 million.[00:23:54] swyx: Typical MFU would be like 50.[00:23:55] Yeah, yeah. And that's good.[00:23:57] Martin Casado: Exactly. Yeah. Hundred[00:23:57] swyx: percent. Um, so, so, yeah, and I mean, and I [00:24:00] just wanna acknowledge like, here we are in, in, in 2025 and opening eyes confirming like Broadcom and all the other like custom silicon deals, which is incredible. I, I think that, uh, you know, speaking about ad there's, there's a really like interesting tie in that obviously you guys are hit on, which is like these sort, this sort of like America first movement or like sort of re industrialized here.[00:24:17] Yeah. Uh, move TSMC here, if that's possible. Um, how much overlap is there from ad[00:24:23] Martin Casado: Yeah.[00:24:23] swyx: To, I guess, growth and, uh, investing in particularly like, you know, US AI companies that are strongly bounded by their compute.[00:24:32] Martin Casado: Yeah. Yeah. So I mean, I, I would view, I would view AD as more as a market segmentation than like a mission, right?[00:24:37] So the market segmentation is, it has kind of regulatory compliance issues or government, you know, sale or it deals with like hardware. I mean, they're just set up to, to, to, to, to. To diligence those types of companies. So it's a more of a market segmentation thing. I would say the entire firm. You know, which has been since it is been intercepted, you know, has geographical biases, right?[00:24:58] I mean, for the longest time we're like, you [00:25:00] know, bay Area is gonna be like, great, where the majority of the dollars go. Yeah. And, and listen, there, there's actually a lot of compounding effects for having a geographic bias. Right. You know, everybody's in the same place. You've got an ecosystem, you're there, you've got presence, you've got a network.[00:25:12] Um, and, uh, I mean, I would say the Bay area's very much back. You know, like I, I remember during pre COVID, like it was like almost Crypto had kind of. Pulled startups away. Miami from the Bay Area. Miami, yeah. Yeah. New York was, you know, because it's so close to finance, came up like Los Angeles had a moment ‘cause it was so close to consumer, but now it's kind of come back here.[00:25:29] And so I would say, you know, we tend to be very Bay area focused historically, even though of course we've asked all over the world. And then I would say like, if you take the ring out, you know, one more, it's gonna be the US of course, because we know it very well. And then one more is gonna be getting us and its allies and Yeah.[00:25:44] And it goes from there.[00:25:45] Sarah Wang: Yeah,[00:25:45] Martin Casado: sorry.[00:25:46] Sarah Wang: No, no. I agree. I think from a, but I think from the intern that that's sort of like where the companies are headquartered. Maybe your questions on supply chain and customer base. Uh, I, I would say our customers are, are, our companies are fairly international from that perspective.[00:25:59] Like they're selling [00:26:00] globally, right? They have global supply chains in some cases.[00:26:03] Martin Casado: I would say also the stickiness is very different.[00:26:05] Sarah Wang: Yeah.[00:26:05] Martin Casado: Historically between venture and growth, like there's so much company building in venture, so much so like hiring the next PM. Introducing the customer, like all of that stuff.[00:26:15] Like of course we're just gonna be stronger where we have our network and we've been doing business for 20 years. I've been in the Bay Area for 25 years, so clearly I'm just more effective here than I would be somewhere else. Um, where I think, I think for some of the later stage rounds, the companies don't need that much help.[00:26:30] They're already kind of pretty mature historically, so like they can kind of be everywhere. So there's kind of less of that stickiness. This is different in the AI time. I mean, Sarah is now the, uh, chief of staff of like half the AI companies in, uh, in the Bay Area right now. She's like, ops Ninja Biz, Devrel, BizOps.[00:26:48] swyx: Are, are you, are you finding much AI automation in your work? Like what, what is your stack.[00:26:53] Sarah Wang: Oh my, in my personal stack.[00:26:54] swyx: I mean, because like, uh, by the way, it's the, the, the reason for this is it is triggering, uh, yeah. We, like, I'm hiring [00:27:00] ops, ops people. Um, a lot of ponders I know are also hiring ops people and I'm just, you know, it's opportunity Since you're, you're also like basically helping out with ops with a lot of companies.[00:27:09] What are people doing these days? Because it's still very manual as far as I can tell.[00:27:13] Sarah Wang: Hmm. Yeah. I think the things that we help with are pretty network based, um, in that. It's sort of like, Hey, how do do I shortcut this process? Well, let's connect you to the right person. So there's not quite an AI workflow for that.[00:27:26] I will say as a growth investor, Claude Cowork is pretty interesting. Yeah. Like for the first time, you can actually get one shot data analysis. Right. Which, you know, if you're gonna do a customer database, analyze a cohort retention, right? That's just stuff that you had to do by hand before. And our team, the other, it was like midnight and the three of us were playing with Claude Cowork.[00:27:47] We gave it a raw file. Boom. Perfectly accurate. We checked the numbers. It was amazing. That was my like, aha moment. That sounds so boring. But you know, that's, that's the kind of thing that a growth investor is like, [00:28:00] you know, slaving away on late at night. Um, done in a few seconds.[00:28:03] swyx: Yeah. You gotta wonder what the whole, like, philanthropic labs, which is like their new sort of products studio.[00:28:10] Yeah. What would that be worth as an independent, uh, startup? You know, like a[00:28:14] Martin Casado: lot.[00:28:14] Sarah Wang: Yeah, true.[00:28:16] swyx: Yeah. You[00:28:16] Martin Casado: gotta hand it to them. They've been executing incredibly well.[00:28:19] swyx: Yeah. I, I mean, to me, like, you know, philanthropic, like building on cloud code, I think, uh, it makes sense to me the, the real. Um, pedal to the metal, whatever the, the, the phrase is, is when they start coming after consumer with, uh, against OpenAI and like that is like red alert at Open ai.[00:28:35] Oh, I[00:28:35] Martin Casado: think they've been pretty clear. They're enterprise focused.[00:28:37] swyx: They have been, but like they've been free. Here's[00:28:40] Martin Casado: care publicly,[00:28:40] swyx: it's enterprise focused. It's coding. Right. Yeah.[00:28:43] AI Labs vs Startups: Disruption, Undercutting & the Innovator's Dilemma[00:28:43] swyx: And then, and, but here's cloud, cloud, cowork, and, and here's like, well, we, uh, they, apparently they're running Instagram ads for Claudia.[00:28:50] I, on, you know, for, for people on, I get them all the time. Right. And so, like,[00:28:54] Martin Casado: uh,[00:28:54] swyx: it, it's kind of like this, the disruption thing of, uh, you know. Mo Open has been doing, [00:29:00] consumer been doing the, just pursuing general intelligence in every mo modality, and here's a topic that only focus on this thing, but now they're sort of undercutting and doing the whole innovator's dilemma thing on like everything else.[00:29:11] Martin Casado: It's very[00:29:11] swyx: interesting.[00:29:12] Martin Casado: Yeah, I mean there's, there's a very open que so for me there's like, do you know that meme where there's like the guy in the path and there's like a path this way? There's a path this way. Like one which way Western man. Yeah. Yeah.[00:29:23] Two Futures for AI: Infinite Market vs AGI Oligopoly[00:29:23] Martin Casado: And for me, like, like all the entire industry kind of like hinges on like two potential futures.[00:29:29] So in, in one potential future, um, the market is infinitely large. There's perverse economies of scale. ‘cause as soon as you put a model out there, like it kind of sublimates and all the other models catch up and like, it's just like software's being rewritten and fractured all over the place and there's tons of upside and it just grows.[00:29:48] And then there's another path which is like, well. Maybe these models actually generalize really well, and all you have to do is train them with three times more money. That's all you have to [00:30:00] do, and it'll just consume everything beyond it. And if that's the case, like you end up with basically an oligopoly for everything, like, you know mm-hmm.[00:30:06] Because they're perfectly general and like, so this would be like the, the a GI path would be like, these are perfectly general. They can do everything. And this one is like, this is actually normal software. The universe is complicated. You've got, and nobody knows the answer.[00:30:18] The Economics Reality Check: Gross Margins, Training Costs & Borrowing Against the Future[00:30:18] Martin Casado: My belief is if you actually look at the numbers of these companies, so generally if you look at the numbers of these companies, if you look at like the amount they're making and how much they, they spent training the last model, they're gross margin positive.[00:30:30] You're like, oh, that's really working. But if you look at like. The current training that they're doing for the next model, their gross margin negative. So part of me thinks that a lot of ‘em are kind of borrowing against the future and that's gonna have to slow down. It's gonna catch up to them at some point in time, but we don't really know.[00:30:47] Sarah Wang: Yeah.[00:30:47] Martin Casado: Does that make sense? Like, I mean, it could be, it could be the case that the only reason this is working is ‘cause they can raise that next round and they can train that next model. ‘cause these models have such a short. Life. And so at some point in time, like, you know, they won't be able to [00:31:00] raise that next round for the next model and then things will kind of converge and fragment again.[00:31:03] But right now it's not.[00:31:04] Sarah Wang: Totally. I think the other, by the way, just, um, a meta point. I think the other lesson from the last three years is, and we talk about this all the time ‘cause we're on this. Twitter X bubble. Um, cool. But, you know, if you go back to, let's say March, 2024, that period, it felt like a, I think an open source model with an, like a, you know, benchmark leading capability was sort of launching on a daily basis at that point.[00:31:27] And, um, and so that, you know, that's one period. Suddenly it's sort of like open source takes over the world. There's gonna be a plethora. It's not an oligopoly, you know, if you fast, you know, if you, if you rewind time even before that GPT-4 was number one for. Nine months, 10 months. It's a long time. Right.[00:31:44] Um, and of course now we're in this era where it feels like an oligopoly, um, maybe some very steady state shifts and, and you know, it could look like this in the future too, but it just, it's so hard to call. And I think the thing that keeps, you know, us up at [00:32:00] night in, in a good way and bad way, is that the capability progress is actually not slowing down.[00:32:06] And so until that happens, right, like you don't know what's gonna look like.[00:32:09] Martin Casado: But I, I would, I would say for sure it's not converged, like for sure, like the systemic capital flows have not converged, meaning right now it's still borrowing against the future to subsidize growth currently, which you can do that for a period of time.[00:32:23] But, but you know, at the end, at some point the market will rationalize that and just nobody knows what that will look like.[00:32:29] Alessio: Yeah.[00:32:29] Martin Casado: Or, or like the drop in price of compute will, will, will save them. Who knows?[00:32:34] Alessio: Yeah. Yeah. I think the models need to ask them to, to specific tasks. You know? It's like, okay, now Opus 4.5 might be a GI at some specific task, and now you can like depreciate the model over a longer time.[00:32:45] I think now, now, right now there's like no old model.[00:32:47] Martin Casado: No, but let, but lemme just change that mental, that's, that used to be my mental model. Lemme just change it a little bit.[00:32:53] Capital as a Weapon vs Task Saturation: Where Real Enterprise Value Gets Built[00:32:53] Martin Casado: If you can raise three times, if you can raise more than the aggregate of anybody that uses your models, that doesn't even matter.[00:32:59] It doesn't [00:33:00] even matter. See what I'm saying? Like, yeah. Yeah. So, so I have an API Business. My API business is 60% margin, or 70% margin, or 80% margin is a high margin business. So I know what everybody is using. If I can raise more money than the aggregate of everybody that's using it, I will consume them whether I'm a GI or not.[00:33:14] And I will know if they're using it ‘cause they're using it. And like, unlike in the past where engineering stops me from doing that.[00:33:21] Alessio: Mm-hmm.[00:33:21] Martin Casado: It is very straightforward. You just train. So I also thought it was kind of like, you must ask the code a GI, general, general, general. But I think there's also just a possibility that the, that the capital markets will just give them the, the, the ammunition to just go after everybody on top of ‘em.[00:33:36] Sarah Wang: I, I do wonder though, to your point, um, if there's a certain task that. Getting marginally better isn't actually that much better. Like we've asked them to it, to, you know, we can call it a GI or whatever, you know, actually, Ali Goi talks about this, like we're already at a GI for a lot of functions in the enterprise.[00:33:50] Um. That's probably those for those tasks, you probably could build very specific companies that focus on just getting as much value out of that task that isn't [00:34:00] coming from the model itself. There's probably a rich enterprise business to be built there. I mean, could be wrong on that, but there's a lot of interesting examples.[00:34:08] So, right, if you're looking the legal profession or, or whatnot, and maybe that's not a great one ‘cause the models are getting better on that front too, but just something where it's a bit saturated, then the value comes from. Services. It comes from implementation, right? It comes from all these things that actually make it useful to the end customer.[00:34:24] Martin Casado: Sorry, what am I, one more thing I think is, is underused in all of this is like, to what extent every task is a GI complete.[00:34:31] Sarah Wang: Mm-hmm.[00:34:32] Martin Casado: Yeah. I code every day. It's so fun.[00:34:35] Sarah Wang: That's a core question. Yeah.[00:34:36] Martin Casado: And like. When I'm talking to these models, it's not just code. I mean, it's everything, right? Like I, you know, like it's,[00:34:43] swyx: it's healthcare.[00:34:44] It's,[00:34:44] Martin Casado: I mean, it's[00:34:44] swyx: Mele,[00:34:45] Martin Casado: but it's every, it is exactly that. Like, yeah, that's[00:34:47] Sarah Wang: great support. Yeah.[00:34:48] Martin Casado: It's everything. Like I'm asking these models to, yeah, to understand compliance. I'm asking these models to go search the web. I'm asking these models to talk about things I know in the history, like it's having a full conversation with me while I, I engineer, and so it could be [00:35:00] the case that like, mm-hmm.[00:35:01] The most a, you know, a GI complete, like I'm not an a GI guy. Like I think that's, you know, but like the most a GI complete model will is win independent of the task. And we don't know the answer to that one either.[00:35:11] swyx: Yeah.[00:35:12] Martin Casado: But it seems to me that like, listen, codex in my experience is for sure better than Opus 4.5 for coding.[00:35:18] Like it finds the hardest bugs that I work in with. Like, it is, you know. The smartest developers. I don't work on it. It's great. Um, but I think Opus 4.5 is actually very, it's got a great bedside manner and it really, and it, it really matters if you're building something very complex because like, it really, you know, like you're, you're, you're a partner and a brainstorming partner for somebody.[00:35:38] And I think we don't discuss enough how every task kind of has that quality.[00:35:42] swyx: Mm-hmm.[00:35:43] Martin Casado: And what does that mean to like capital investment and like frontier models and Submodels? Yeah.[00:35:47] Why “Coding Models” Keep Collapsing into Generalists (Reasoning vs Taste)[00:35:47] Martin Casado: Like what happened to all the special coding models? Like, none of ‘em worked right. So[00:35:51] Alessio: some of them, they didn't even get released.[00:35:53] Magical[00:35:54] Martin Casado: Devrel. There's a whole, there's a whole host. We saw a bunch of them and like there's this whole theory that like, there could be, and [00:36:00] I think one of the conclusions is, is like there's no such thing as a coding model,[00:36:04] Alessio: you know?[00:36:04] Martin Casado: Like, that's not a thing. Like you're talking to another human being and it's, it's good at coding, but like it's gotta be good at everything.[00:36:10] swyx: Uh, minor disagree only because I, I'm pretty like, have pretty high confidence that basically open eye will always release a GPT five and a GT five codex. Like that's the code's. Yeah. The way I call it is one for raisin, one for Tiz. Um, and, and then like someone internal open, it was like, yeah, that's a good way to frame it.[00:36:32] Martin Casado: That's so funny.[00:36:33] swyx: Uh, but maybe it, maybe it collapses down to reason and that's it. It's not like a hundred dimensions doesn't life. Yeah. It's two dimensions. Yeah, yeah, yeah, yeah. Like and exactly. Beside manner versus coding. Yeah.[00:36:43] Martin Casado: Yeah.[00:36:44] swyx: It's, yeah.[00:36:46] Martin Casado: I, I think for, for any, it's hilarious. For any, for anybody listening to this for, for, for, I mean, for you, like when, when you're like coding or using these models for something like that.[00:36:52] Like actually just like be aware of how much of the interaction has nothing to do with coding and it just turns out to be a large portion of it. And so like, you're, I [00:37:00] think like, like the best Soto ish model. You know, it is going to remain very important no matter what the task is.[00:37:06] swyx: Yeah.[00:37:07] What He's Actually Coding: Gaussian Splats, Spark.js & 3D Scene Rendering Demos[00:37:07] swyx: Uh, speaking of coding, uh, I, I'm gonna be cheeky and ask like, what actually are you coding?[00:37:11] Because obviously you, you could code anything and you are obviously a busy investor and a manager of the good. Giant team. Um, what are you calling?[00:37:18] Martin Casado: I help, um, uh, FEFA at World Labs. Uh, it's one of the investments and um, and they're building a foundation model that creates 3D scenes.[00:37:27] swyx: Yeah, we had it on the pod.[00:37:28] Yeah. Yeah,[00:37:28] Martin Casado: yeah. And so these 3D scenes are Gaussian splats, just by the way that kind of AI works. And so like, you can reconstruct a scene better with, with, with radiance feels than with meshes. ‘cause like they don't really have topology. So, so they, they, they produce each. Beautiful, you know, 3D rendered scenes that are Gaussian splats, but the actual industry support for Gaussian splats isn't great.[00:37:50] It's just never, you know, it's always been meshes and like, things like unreal use meshes. And so I work on a open source library called Spark js, which is a. Uh, [00:38:00] a JavaScript rendering layer ready for Gaussian splats. And it's just because, you know, um, you, you, you need that support and, and right now there's kind of a three js moment that's all meshes and so like, it's become kind of the default in three Js ecosystem.[00:38:13] As part of that to kind of exercise the library, I just build a whole bunch of cool demos. So if you see me on X, you see like all my demos and all the world building, but all of that is just to exercise this, this library that I work on. ‘cause it's actually a very tough algorithmics problem to actually scale a library that much.[00:38:29] And just so you know, this is ancient history now, but 30 years ago I paid for undergrad, you know, working on game engines in college in the late nineties. So I've got actually a back and it's very old background, but I actually have a background in this and so a lot of it's fun. You know, but, but the, the, the, the whole goal is just for this rendering library to, to,[00:38:47] Sarah Wang: are you one of the most active contributors?[00:38:49] The, their GitHub[00:38:50] Martin Casado: spark? Yes.[00:38:51] Sarah Wang: Yeah, yeah.[00:38:51] Martin Casado: There's only two of us there, so, yes. No, so by the way, so the, the pri The pri, yeah. Yeah. So the primary developer is a [00:39:00] guy named Andres Quist, who's an absolute genius. He and I did our, our PhDs together. And so like, um, we studied for constant Quas together. It was almost like hanging out with an old friend, you know?[00:39:09] And so like. So he, he's the core, core guy. I did mostly kind of, you know, the side I run venture fund.[00:39:14] swyx: It's amazing. Like five years ago you would not have done any of this. And it brought you back[00:39:19] Martin Casado: the act, the Activ energy, you're still back. Energy was so high because you had to learn all the framework b******t.[00:39:23] Man, I f*****g used to hate that. And so like, now I don't have to deal with that. I can like focus on the algorithmics so I can focus on the scaling and I,[00:39:29] swyx: yeah. Yeah.[00:39:29] LLMs vs Spatial Intelligence + How to Value World Labs' 3D Foundation Model[00:39:29] swyx: And then, uh, I'll observe one irony and then I'll ask a serious investor question, uh, which is like, the irony is FFE actually doesn't believe that LMS can lead us to spatial intelligence.[00:39:37] And here you are using LMS to like help like achieve spatial intelligence. I just see, I see some like disconnect in there.[00:39:45] Martin Casado: Yeah. Yeah. So I think, I think, you know, I think, I think what she would say is LLMs are great to help with coding.[00:39:51] swyx: Yes.[00:39:51] Martin Casado: But like, that's very different than a model that actually like provides, they, they'll never have the[00:39:56] swyx: spatial inte[00:39:56] Martin Casado: issues.[00:39:56] And listen, our brains clearly listen, our brains, brains clearly have [00:40:00] both our, our brains clearly have a language reasoning section and they clearly have a spatial reasoning section. I mean, it's just, you know, these are two pretty independent problems.[00:40:07] swyx: Okay. And you, you, like, I, I would say that the, the one data point I recently had, uh, against it is the DeepMind, uh, IMO Gold, where, so, uh, typically the, the typical answer is that this is where you start going down the neuros symbolic path, right?[00:40:21] Like one, uh, sort of very sort of abstract reasoning thing and one form, formal thing. Um, and that's what. DeepMind had in 2024 with alpha proof, alpha geometry, and now they just use deep think and just extended thinking tokens. And it's one model and it's, and it's in LM.[00:40:36] Martin Casado: Yeah, yeah, yeah, yeah, yeah.[00:40:37] swyx: And so that, that was my indication of like, maybe you don't need a separate system.[00:40:42] Martin Casado: Yeah. So, so let me step back. I mean, at the end of the day, at the end of the day, these things are like nodes in a graph with weights on them. Right. You know, like it can be modeled like if you, if you distill it down. But let me just talk about the two different substrates. Let's, let me put you in a dark room.[00:40:56] Like totally black room. And then let me just [00:41:00] describe how you exit it. Like to your left, there's a table like duck below this thing, right? I mean like the chances that you're gonna like not run into something are very low. Now let me like turn on the light and you actually see, and you can do distance and you know how far something away is and like where it is or whatever.[00:41:17] Then you can do it, right? Like language is not the right primitives to describe. The universe because it's not exact enough. So that's all Faye, Faye is talking about. When it comes to like spatial reasoning, it's like you actually have to know that this is three feet far, like that far away. It is curved.[00:41:37] You have to understand, you know, the, like the actual movement through space.[00:41:40] swyx: Yeah.[00:41:40] Martin Casado: So I do, I listen, I do think at the end of these models are definitely converging as far as models, but there's, there's, there's different representations of problems you're solving. One is language. Which, you know, that would be like describing to somebody like what to do.[00:41:51] And the other one is actually just showing them and the space reasoning is just showing them.[00:41:55] swyx: Yeah, yeah, yeah. Right. Got it, got it. Uh, the, in the investor question was on, on, well labs [00:42:00] is, well, like, how do I value something like this? What, what, what work does the, do you do? I'm just like, Fefe is awesome.[00:42:07] Justin's awesome. And you know, the other two co-founder, co-founders, but like the, the, the tech, everyone's building cool tech. But like, what's the value of the tech? And this is the fundamental question[00:42:16] Martin Casado: of, well, let, let, just like these, let me just maybe give you a rough sketch on the diffusion models. I actually love to hear Sarah because I'm a venture for, you know, so like, ventures always, always like kind of wild west type[00:42:24] swyx: stuff.[00:42:24] You, you, you, you paid a dream and she has to like, actually[00:42:28] Martin Casado: I'm gonna say I'm gonna mar to reality, so I'm gonna say the venture for you. And she can be like, okay, you a little kid. Yeah. So like, so, so these diffusion models literally. Create something for, for almost nothing. And something that the, the world has found to be very valuable in the past, in our real markets, right?[00:42:45] Like, like a 2D image. I mean, that's been an entire market. People value them. It takes a human being a long time to create it, right? I mean, to create a, you know, a, to turn me into a whatever, like an image would cost a hundred bucks in an hour. The inference cost [00:43:00] us a hundredth of a penny, right? So we've seen this with speech in very successful companies.[00:43:03] We've seen this with 2D image. We've seen this with movies. Right? Now, think about 3D scene. I mean, I mean, when's Grand Theft Auto coming out? It's been six, what? It's been 10 years. I mean, how, how like, but hasn't been 10 years.[00:43:14] Alessio: Yeah.[00:43:15] Martin Casado: How much would it cost to like, to reproduce this room in 3D? Right. If you, if you, if you hired somebody on fiber, like in, in any sort of quality, probably 4,000 to $10,000.[00:43:24] And then if you had a professional, probably $30,000. So if you could generate the exact same thing from a 2D image, and we know that these are used and they're using Unreal and they're using Blend, or they're using movies and they're using video games and they're using all. So if you could do that for.[00:43:36] You know, less than a dollar, that's four or five orders of magnitude cheaper. So you're bringing the marginal cost of something that's useful down by three orders of magnitude, which historically have created very large companies. So that would be like the venture kind of strategic dreaming map.[00:43:49] swyx: Yeah.[00:43:50] And, and for listeners, uh, you can do this yourself on your, on your own phone with like. Uh, the marble.[00:43:55] Martin Casado: Yeah. Marble.[00:43:55] swyx: Uh, or but also there's many Nerf apps where you just go on your iPhone and, and do this.[00:43:59] Martin Casado: Yeah. Yeah. [00:44:00] Yeah. And, and in the case of marble though, it would, what you do is you literally give it in.[00:44:03] So most Nerf apps you like kind of run around and take a whole bunch of pictures and then you kind of reconstruct it.[00:44:08] swyx: Yeah.[00:44:08] Martin Casado: Um, things like marble, just that the whole generative 3D space will just take a 2D image and it'll reconstruct all the like, like[00:44:16] swyx: meaning it has to fill in. Uh,[00:44:18] Martin Casado: stuff at the back of the table, under the table, the back, like, like the images, it doesn't see.[00:44:22] So the generator stuff is very different than reconstruction that it fills in the things that you can't see.[00:44:26] swyx: Yeah. Okay.[00:44:26] Sarah Wang: So,[00:44:27] Martin Casado: all right. So now the,[00:44:28] Sarah Wang: no, no. I mean I love that[00:44:29] Martin Casado: the adult[00:44:29] Sarah Wang: perspective. Um, well, no, I was gonna say these are very much a tag team. So we, we started this pod with that, um, premise. And I think this is a perfect question to even build on that further.[00:44:36] ‘cause it truly is, I mean, we're tag teaming all of these together.[00:44:39] Investing in Model Labs, Media Rumors, and the Cursor Playbook (Margins & Going Down-Stack)[00:44:39] Sarah Wang: Um, but I think every investment fundamentally starts with the same. Maybe the same two premises. One is, at this point in time, we actually believe that there are. And of one founders for their particular craft, and they have to be demonstrated in their prior careers, right?[00:44:56] So, uh, we're not investing in every, you know, now the term is NEO [00:45:00] lab, but every foundation model, uh, any, any company, any founder trying to build a foundation model, we're not, um, contrary to popular opinion, we're

Virtuix CEO Jan Goetgeluk joined Steve Darling from Proactive to highlight the company's integration of AI-driven Gaussian splatting technology into its Virtual Terrain Walk (VTW) system, significantly enhancing its training and simulation capabilities for defense and security applications. Gaussian splatting is an advanced 3D reconstruction technique that rapidly converts real-world environments captured with 360-degree cameras into highly detailed, photorealistic, and fully navigable virtual worlds. Goetgeluk explained that VTW is a multi-user immersive training platform purpose-built for simulations where realism, spatial awareness, and physical movement are critical to operational readiness. By combining Gaussian splatting with Virtuix's omni-directional treadmill technology, VTW enables soldiers to physically walk, run, turn, and crouch in 360 degrees within geo-specific virtual environments. This allows military personnel to “walk the battlefield before they fight on it,” improving mission planning, rehearsal, and situational awareness in complex combat scenarios. The system supports collaborative training across more than 12 stations, either co-located or distributed across different geographic locations, enabling multi-user mission planning and leader rehearsals. Trainers can dynamically modify environmental conditions, introduce adversary forces, and adjust scenario variables in real time to simulate realistic and evolving battlefield conditions. Virtuix also noted early adoption of its technology within defense organizations. Omni One test units have already been purchased by Yokota Air Force Base, the U.S. Air Force Academy, and the U.S. Military Academy at West Point, underscoring growing institutional interest in immersive, physically active virtual training solutions. Beyond defense applications, Virtuix sees significant commercial opportunities for its VTW and Gaussian splatting capabilities across industrial and safety training, real estate visualization, and law enforcement. The company believes these markets can benefit from the same high-fidelity, location-based simulation and physically immersive experiences that are redefining how defense organizations prepare personnel for real-world operations. #proactiveinvestors #virtuix #nasdaq #VTIX #Virtuix #VTIX #NasdaqDebut #VirtualTerrainWalk #GaussianSplatting #DefenseTech #MilitaryTraining #SimulationTechnology #ImmersiveTraining #VRTraining #SpatialComputing #AI3D #SecurityTechnology #OmniTreadmill #DigitalTwins #ExtendedReality

In this episode we talk with Thomas Flynn, a digital heritage specialist with deep experience in 3D digitisation, open access, and online publishing. Thomas has worked with UNESCO, Europeana, Oxford University, Creative Commons, and served as cultural heritage lead at Sketchfab, where he helped launch the British Museum's first open 3D collection. In this conversation, he explains how museums and cultural organisations think about 3D capture, what Gaussian splatting can and cannot do for heritage workflows, and why long term storage, metadata, and interoperability matter just as much as scanning quality. He breaks down real examples of 3D printing for visitor engagement, web based publishing options, VR use cases, and the growing challenge of managing massive data sets.Subscribe to XR AI Spotlight weekly newsletter

This interview is disseminated on behalf of Virtuix.Virtuix is redefining movement in virtual reality (VR) through its “Omni” omnidirectional treadmills, which allow individuals to walk and run in 360 degrees within video games, VR applications, and even AI-generated worlds.CEO Jan Goetgeluk discusses his company's NASDAQ uplisting, growth strategy, and the launch of the Omni One VR system for home consumers. He also explains how Gaussian splatting enables photorealistic virtual environments.Discover more: https://virtuix.com/Watch the full YouTube interview here: https://youtu.be/k-wxUiHXfCsAnd follow us to stay updated: https://www.youtube.com/@GlobalOneMedia

Virtuix Holding CEO Jan Goetgeluk joind Steve Darling from Proactive to discuss the company's milestone debut on the Nasdaq under the ticker symbol VTIX, marking a significant step in Virtuix's growth as it scales its proprietary virtual reality technology across both consumer and defense markets. Virtuix is best known for its Omni technology, which enables full 360-degree movement in virtual reality, allowing users to walk, run, and move naturally within immersive digital environments. Goetgeluk highlighted the strong early traction of Omni One, the company's latest system designed specifically for home use. “We recently launched Omni One, and we reported 138% year-over-year growth in our S-1 filing,” he said, pointing to rising consumer demand for more immersive VR experiences. Omni One delivers full physical immersion by allowing users to physically move while remaining safely in place, creating a more realistic and engaging experience for gaming and entertainment. Beyond gaming, Goetgeluk emphasized the system's fitness benefits, noting that one customer reportedly lost 40 pounds in just four months through regular use, underscoring the platform's potential as a gamified fitness solution. Virtuix is also gaining momentum in the defense sector with the introduction of its Virtual Terrain Walk system. Powered by artificial intelligence and Gaussian splatting technology, the platform enables soldiers to rehearse missions in highly realistic virtual environments generated from real-world locations in a matter of hours. The system allows for immersive, repeatable training scenarios that can be rapidly customized to operational needs. Goetgeluk noted that Virtual Terrain Walk units are already deployed at key military institutions, including the U.S. Air Force Academy and Yokota Air Force Base, demonstrating growing adoption within defense training environments. Following its Nasdaq listing, Virtuix has strengthened its financial position, securing $11 million in funding from Chicago Venture Partners and establishing a $50 million equity line. These resources are expected to support the company's plans to scale operations, expand product development, and accelerate growth across both its consumer and defense businesses. #proactiveinvestors #virtuix #nasdaq #VTIX #Virtuix #VTIX #NasdaqDebut #VirtualReality #ImmersiveTech #OmniOne #VRGaming #VRFitness #DefenseTechnology #MilitaryTraining #AI #GaussianSplatting #MetaverseTech #ConsumerTech #GrowthStory #ProactiveInvestors

I interviewed Anne Jeppesen & Omid Zarei about Reality Looks Back on Tuesday, November 18, 2025 at IDFA DocLab in Amsterdam, Netherlands. This is a listener-supported podcast through the Voices of VR Patreon. Music: Fatality

Fei-Fei Li and Justin Johnson are cofounders of World Labs, who have recently launched Marble (https://marble.worldlabs.ai/), a new kind of generative “world model” that can create editable 3D environments from text, images, and other spatial inputs. Marble lets creators generate persistent 3D worlds, precisely control cameras, and interactively edit scenes, making it a powerful tool for games, film, VR, robotics simulation, and more. In this episode, Fei-Fei and Justin share how their journey from ImageNet and Stanford research led to World Labs, why spatial intelligence is the next frontier after LLMs, and how world models could change how machines see, understand, and build in 3D.We discuss:* The massive compute scaling from AlexNet to today and why world models and spatial data are the most compelling way to “soak up” modern GPU clusters compared to language alone.* What Marble actually is: a generative model of 3D worlds that turns text and images into editable scenes using Gaussian splats, supports precise camera control and recording, and runs interactively on phones, laptops, and VR headsets.* Fei-fei's essay:on spatial intelligence as a distinct form of intelligence from language: from picking up a mug to inferring the 3D structure of DNA, and why language is a lossy, low-bandwidth channel for describing the rich 3D/4D world we live in.* Whether current models “understand” physics or just fit patterns: the gap between predicting orbits and discovering F=ma, and how attaching physical properties to splats and distilling physics engines into neural networks could lead to genuine causal reasoning.* The changing role of academia in AI, why Fei-Fei worries more about under-resourced universities than “open vs closed,” and how initiatives like national AI compute clouds and open benchmarks can rebalance the ecosystem.* Why transformers are fundamentally set models, not sequence models, and how that perspective opens up new architectures for world models, especially as hardware shifts from single GPUs to massive distributed clusters.* Real use cases for Marble today: previsualization and VFX, game environments, virtual production, interior and architectural design (including kitchen remodels), and generating synthetic simulation worlds for training embodied agents and robots.* How spatial intelligence and language intelligence will work together in multimodal systems, and why the goal isn't to throw away LLMs but to complement them with rich, embodied models of the world.* Fei-Fei and Justin's long-term vision for spatial intelligence: from creative tools for artists and game devs to broader applications in science, medicine, and real-world decision-making.—Fei-Fei Li* X: https://x.com/drfeifei* LinkedIn: https://www.linkedin.com/in/fei-fei-li-4541247Justin Johnson* X: https://x.com/jcjohnss* LinkedIn: https://www.linkedin.com/in/justin-johnson-41b43664Where to find Latent Space* X: https://x.com/latentspacepodFull Video EpisodeTimestamps00:00:00 Introduction and the Fei-Fei Li & Justin Johnson Partnership00:02:00 From ImageNet to World Models: The Evolution of Computer Vision00:12:42 Dense Captioning and Early Vision-Language Work00:19:57 Spatial Intelligence: Beyond Language Models00:28:46 Introducing Marble: World Labs' First Spatial Intelligence Model00:33:21 Gaussian Splats and the Technical Architecture of Marble00:22:10 Physics, Dynamics, and the Future of World Models00:41:09 Multimodality and the Interplay of Language and Space00:37:37 Use Cases: From Creative Industries to Robotics and Embodied AI00:56:58 Hiring, Research Directions, and the Future of World Labs Get full access to Latent.Space at www.latent.space/subscribe

Fei-Fei Li and Justin Johnson are cofounders of World Labs, who have recently launched Marble (https://marble.worldlabs.ai/), a new kind of generative “world model” that can create editable 3D environments from text, images, and other spatial inputs. Marble lets creators generate persistent 3D worlds, precisely control cameras, and interactively edit scenes, making it a powerful tool for games, film, VR, robotics simulation, and more. In this episode, Fei-Fei and Justin share how their journey from ImageNet and Stanford research led to World Labs, why spatial intelligence is the next frontier after LLMs, and how world models could change how machines see, understand, and build in 3D. We discuss: The massive compute scaling from AlexNet to today and why world models and spatial data are the most compelling way to “soak up” modern GPU clusters compared to language alone. What Marble actually is: a generative model of 3D worlds that turns text and images into editable scenes using Gaussian splats, supports precise camera control and recording, and runs interactively on phones, laptops, and VR headsets. Fei-fei's essay (https://drfeifei.substack.com/p/from-words-to-worlds-spatial-intelligence) on spatial intelligence as a distinct form of intelligence from language: from picking up a mug to inferring the 3D structure of DNA, and why language is a lossy, low-bandwidth channel for describing the rich 3D/4D world we live in. Whether current models “understand” physics or just fit patterns: the gap between predicting orbits and discovering F=ma, and how attaching physical properties to splats and distilling physics engines into neural networks could lead to genuine causal reasoning. The changing role of academia in AI, why Fei-Fei worries more about under-resourced universities than “open vs closed,” and how initiatives like national AI compute clouds and open benchmarks can rebalance the ecosystem. Why transformers are fundamentally set models, not sequence models, and how that perspective opens up new architectures for world models, especially as hardware shifts from single GPUs to massive distributed clusters. Real use cases for Marble today: previsualization and VFX, game environments, virtual production, interior and architectural design (including kitchen remodels), and generating synthetic simulation worlds for training embodied agents and robots. How spatial intelligence and language intelligence will work together in multimodal systems, and why the goal isn't to throw away LLMs but to complement them with rich, embodied models of the world. Fei-Fei and Justin's long-term vision for spatial intelligence: from creative tools for artists and game devs to broader applications in science, medicine, and real-world decision-making. — Fei-Fei Li X: https://x.com/drfeifei LinkedIn: https://www.linkedin.com/in/fei-fei-li-4541247 Justin Johnson X: https://x.com/jcjohnss LinkedIn: https://www.linkedin.com/in/justin-johnson-41b43664 Where to find Latent Space X: https://x.com/latentspacepod Substack: https://www.latent.space/ Chapters 00:00:00 Introduction and the Fei-Fei Li & Justin Johnson Partnership 00:02:00 From ImageNet to World Models: The Evolution of Computer Vision 00:12:42 Dense Captioning and Early Vision-Language Work 00:19:57 Spatial Intelligence: Beyond Language Models 00:28:46 Introducing Marble: World Labs' First Spatial Intelligence Model 00:33:21 Gaussian Splats and the Technical Architecture of Marble 00:22:10 Physics, Dynamics, and the Future of World Models 00:41:09 Multimodality and the Interplay of Language and Space 00:37:37 Use Cases: From Creative Industries to Robotics and Embodied AI 00:56:58 Hiring, Research Directions, and the Future of World Labs

Daniele is a professor in the Computer Science and Engineering department at the University of California, San Diego and, in 2019, he was elected as a Fellow of the IACR. His main focus is on the foundations of lattice-based cryptography and its advanced applications, including fully homomorphic encryption.  Overall, he has published many classic papers that relate to lattice methods, including working with Chris Peikert on "Trapdoors for lattices", and with Oded Regev on the "Worst-case to average-case reductions based on Gaussian methods".  Daniele has also contributed greatly to the advancement of bootstrapping methods, including defining the DM/FHEW method - along with Leo Ducas.    He published a book on the "Complexity of Lattice Problems - A cryptographic perspective" with Shafi Goldwasser, and his paper on "Generalized Compact Knapsacks, Cyclic Lattices, and Efficient One-Way Functions" was given a FOCS 20 years test of time award in 2022. This paper led to the theoretical foundation of efficient lattice based cryptography. His paper with Regev also received the FOCS ToT award in 2024.  

Ian Failes from befores & afters chats to Infinite Realities' Lee Perry-Smith and Henry Pearce about the company's journey into the world of scanning, photogrammetry, videogrammetry and now 4D gaussian splats, including for the holograms in 'Superman.'

Will Eastcott, CEO of PlayCanvas and veteran of EA, Sony, and Activision with credits on GTA, Call of Duty, and Max Payne, explains how Gaussian splatting is moving from experiments to production. He breaks down SuperSplat, an open source editor for cropping, recoloring, and optimizing splats for the web, and details a streaming level of detail system that scales from phones to desktops. Will shares how the new SOG format, built on lossless WebP, can cut files by up to 95 % while preserving quality. You will learn practical capture options like lidar based rigs, when to use streaming, how to ship WebXR viewers, and where splats are gaining traction in ecommerce, real estate, and cultural heritage. Subscribe to XR AI Spotlight weekly newsletter

Today's clip is from episode 144 of the podcast, with Maurizio Filippone.In this conversation, Alex and Maurizio delve into the intricacies of Gaussian processes and their deep learning counterparts. They explain the foundational concepts of Gaussian processes, the transition to deep Gaussian processes, and the advantages they offer in modeling complex data. The discussion also touches on practical applications, model selection, and the evolving landscape of machine learning, particularly in relation to transfer learning and the integration of deep learning techniques with Gaussian processes.Get the full discussion here.Intro to Bayes Course (first 2 lessons free)Advanced Regression Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)TranscriptThis is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

Richard Brennan returns this week to explore how markets truly move - not through randomness or rationality, but through impact, feedback, and memory. What begins with a single trade builds into structure, not pattern; alignment, not noise. Drawing from neuroscience and fractal geometry, Rich challenges the idea that markets can be understood without understanding interaction. The episode builds toward a pointed exchange on position sizing - closed equity versus dynamic exposure - not as a technical footnote, but as a reflection of first principles. In a system where the path shapes the outcome, how you define risk... often reveals how you think the world works.-----50 YEARS OF TREND FOLLOWING BOOK AND BEHIND-THE-SCENES VIDEO FOR ACCREDITED INVESTORS - CLICK HERE-----Follow Niels on Twitter, LinkedIn, YouTube or via the TTU website.IT's TRUE ? – most CIO's read 50+ books each year – get your FREE copy of the Ultimate Guide to the Best Investment Books ever written here.And you can get a free copy of my latest book “Ten Reasons to Add Trend Following to Your Portfolio” here.Learn more about the Trend Barometer here.Send your questions to info@toptradersunplugged.comAnd please share this episode with a like-minded friend and leave an honest Rating & Review on iTunes or Spotify so more people can discover the podcast.Follow Rich on Twitter.Episode TimeStamps:00:00:00 – Welcome to the Systematic Investor Series00:00:23 – Niels' intro, show setup, and warm welcome to Rich00:00:57 – Heatwave down under: context and small talk00:02:10 – Rich: divided brain, AI vs embodiment, and markets needing rules00:07:50 – AI's edge shrinks prediction windows; why that helps trend following00:10:35 – Gold's violent selloff; electricity vs oil as the new macro lens00:14:51 – “Trend heaven”: why the backdrop now looks robust00:18:12 – Post-GFC compression vs today's decoupling and trends00:22:43 – Impact and reflexivity: trades reshape the next trade00:28:23 – Non-ergodic markets: path dependence beats Gaussian assumptions00:35:48 – Volatility ≠...

Evénements France Quantum : les vidéos de la quatrième édition du 10 juin à Station F, Paris, sont disponibles. https://www.youtube.com/playlist?list=PLHy9A3t7TeES-rvwyIHcY8_d8tYIfCp4L Bratislavahttps://www.oezratty.net/Files/Conferences/Olivier%20Ezratty%20ESSAI%20QT+AI%20Jul2025.pdf Innsbruck Osakahttps://www.qi2025.jp/ SQA Conference à Delft, sur les qubits supraconducteurs. https://www.sqa-conference.org/A venir en septembre et après : ·       Q2B Paris les 24 et 25 septembre à Paris https://q2b.qcware.com/conference/2025-paris·       DPG Gottingen la seconde semaine de septembre https://quantum25.dpg-tagungen.de/programm/industrietag·       Quantum Effects https://www.messe-stuttgart.de/quantum-effects/en/·       Quantum.Tech fin septembre à Rotterdam https://www.alphaevents.com/events-quantumtech·       Quantum Munich Software Forum fin octobre https://conference-questis.org/quest-is-2025/program/program-at-a-glance/·       GDR TEQ du CNRS à Grenoble du 12 au 14 novembre https://gdr-teq.cnrs.fr/·       QUEST-IS début décembrehttps://conference-questis.org/quest-is-2025/program/program-at-a-glance/ France Vidéo de vulgarisation de la physique et des technologies quantiques avec deux chercheurs du CEA, Nicolas Sangouard (IPhT) et Emmanuel Flurin (CEA-Iramis), dans un débat animé par Marie Treibert, une spécialiste de la vulgarisation scientifique.https://www.youtube.com/watch?v=6p1vQVZ__ZY Interview avec Valerian Giesz de Quandela pour la Société Générale.https://www.privatebanking.societegenerale.com/fr/actualites/ordinateur-quantique-big-bang-venir/Le livre d'Olivier en LateX International Une nouvelle suprématie quantique chez les Chinois avec des photons. Il s'agit de Jiuzhang 4.0, une nouvelle génération d'échantillonneur gaussien de bosons. Robust quantum computational advantage with programmable 3050-photon Gaussian boson sampling by Hua-Liang Liu, Jian-Wei Pan et al, arXiv, August 2025 (7 pages). Blueprint sur le FTQC FBQC dans la photonique avec des boites quantiquesPractical blueprint for low-depth photonic quantum computing with quantum dots by Ming Lai Chan, Aliki Anna Capatos, Peter Lodahl, Anders Søndberg Sørensen, and Stefano Paesani, arXiv, July 2025 (23 pages). QuEra continue de produire plein de papiers sur le FTQC Above 99.9% Fidelity Single-Qubit Gates, Two-Qubit Gates, and Readout in a Single Superconducting Quantum Device by Fabian Marxer, Antti Vepsäläinen, arXiv, August 2025 (35 pages). Levée de fonds de QuamCoreQuamCore Secures $26 Million Series A to Build 1-Million-Qubit Quantum Computer in a Single Cryostat by Matt Swayne, The Quantum Insider, August 2025. Marco Pistoia quitte JPMorganChase et rejoint IonQhttps://www.linkedin.com/posts/pistoia_research-quantumcomputing-quantumcommunications-activity-7353793320927567872-fXi7https://www.linkedin.com/posts/ionq-co_quantumcomputing-quantumnetworking-activity-7355560180954140672-12U3/ Début juillet, l'UE annonçait le lancement du Quantum Act. L'objectif est de faire de l'UE un leader mondial des technologies quantiques à l'horizon 2030.  https://digital-strategy.ec.europa.eu/en/library/quantum-europe-strategy Réaction du consortium QuiC avec un position paper qui insiste sur plusieurs points dont le besoin d'avoir aussi une stratégie dans le logiciel, et sur la sustainability :QuIC's Recommendations for the EU Quantum Strategy by Andy Penfold, QuIC, August 2025 (28 pages). A peu près au même moment, la fondation Novo Nordisk qui est le premier pourvoyeur de fonds des investissements quantiques au Danemark annonçait l'acquisition d'un ordinateur quantique d'Atom Computing, en partenariat avec Microsoft, qui dispose d'un petit laboratoire de recherche à Lyngby dans la banlieue nord de Copenhague. https://novonordiskfonden.dk/en/news/eifo-and-the-novo-nordisk-foundation-acquire-the-worlds-most-powerful-quantum-computer/ Bullshit Le CEO d'IonQ roi de la survente en ce moment chez les industry vendors.https://www.linkedin.com/posts/keith-king-03a172128_ionq-ceo-aims-to-build-a-trillion-dollar-activity-7363224769653039104-4_cQ Encrypted Qubits can be Cloned by Koji Yamaguchi, and Achim Kempf, arXiv, January 2025 (13 pages). “A Manifesto for Ontological Quantum Computing (Year 3000)” by Gonzalo Florez Giraldo. Avec de la conscience Quantique au programme. htt...

Today's clip is from episode 139 of the podcast, with with Max Balandat.Alex and Max discuss the integration of BoTorch with PyTorch, exploring its applications in Bayesian optimization and Gaussian processes. They highlight the advantages of using GPyTorch for structured matrices and the flexibility it offers for research. The discussion also covers the motivations behind building BoTorch, the importance of open-source culture at Meta, and the role of PyTorch in modern machine learning.Get the full discussion here.Attend Alex's tutorial at PyData Berlin: A Beginner's Guide to State Space Modeling Intro to Bayes Course (first 2 lessons free)Advanced Regression Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)TranscriptThis is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

スパコンより“100恒河沙”倍（10の54乗倍）高速　量子コンピュータ「九章4.0」を中国チームが発表。　中国科学技術大学を中心とする研究チームが発表した論文「Robust quantum computational advantage with programmable 3050-photon Gaussian boson sampling」は、これまでで最大規模となるフォトニック量子コンピュータ「九章4.0」（Jiuzhang4.0）を開発し、従来の古典スーパーコンピュータでは事実上不可能な計算を瞬時に実行できることを実証した研究報告だ。

We sit down with CG Supervisor Kevin Sears and Animation Supervisor Loic Mireault to unpack their contributions to the film.

Jess Loren, CEO of Global Objects, joins Charlie, Ted, and Rony to talk about the company's work creating photoreal digital twins for film, television, games, and beyond. She explains how her team scans everything from bugs to stadiums using LiDAR, photogrammetry, drones, and Gaussian splats, and why she's building a “clean data” archive of the physical world. The conversation ranges from Hollywood's shifting economics to the role of tech giants, the future of synthetic media, and how 3D assets could train robots and preserve cultural history. Hosted on Acast. See acast.com/privacy for more information.

At SIGGRAPH 2025, Chaos unveils major updates to Vantage and Arena that significantly expand real-time ray tracing workflows. Product managers Simeon Balabanov and Georgi Zhekov join Chris to break down the new capabilities, including native USD and MaterialX support, Gaussian splats with ray-traced lighting, volumetric caches, and a streamlined pipeline that keeps the same asset across previs, virtual production, and post. This episode arrives just in time for SIGGRAPH, where these features are being officially announced, giving listeners an early look at what will be showcased in Vancouver. The conversation dives into key production tools like mimic lights for realistic set illumination, in-volume color correction, real-time depth of field, and live lighting adjustments. Simeon and Georgi explain how these innovations reduce conversion work, improve on-set flexibility, and allow for advanced asset previews even from a home studio using Vantage with camera tracking. They also highlight new camera tracking protocols, a standalone material editor, and Arena's watermark trial mode, showing how Chaos is making high-end virtual production more accessible and adaptable for filmmakers.

Lennard Wolff, holds a Master in Cinematography and he is an expert in volumetric capture technology. He is Former Senior Technical Director at Synthesia and now the CEO of AdventuryXR a London based a startup revolutionising corporate learning through photorealistic immersive experiences.Georgii Vysotskii the co-founder and CEO of Gracia.ai a deep tech company specializing in the visualization and distribution of Gaussian splatting even in VR.Subscribe to XR AI Spotlight weekly newsletter

Knut Nesheim has a background in large-scale machine learning systems and big data. He leads the engineering teams building Teleport, the Gaussian splatting tool created by Varjo, the Finnish hardware company renowned for the top-tier MR headset used by the most demanding businesses and enterprises worldwide. In this conversation we take a deep dive at the intersection of Gaussian splatting and VR.We look at the very technological foundation of TeleportUnique features it offers to usersThe obvious consequences of their unrelenting pursuit of photorealism… with 2 caveatsHow this all fit within the bigger vision of serving top-tier enterprises worldwideSubscribe to XR AI Spotlight weekly newsletter

In this episode of the CG Pro podcast, Edd Dawson-Taylor interviews Fernando Rivas-Manzaneque, CEO of VOLINGA AI. They discuss the evolution of 3D technology, particularly Gaussian splats, and their applications in filmmaking and virtual production. Fernando shares his journey from electrical engineering to leading a tech startup, emphasizing the importance of intuition and emotional connection in innovation. The conversation also touches on the future of art in the context of AI and technology, and concludes with a tribute to James Bellman, a significant figure in the community.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Intro to Bayes Course (first 2 lessons free)Advanced Regression Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Takeaways:INLA is a fast, deterministic method for Bayesian inference.INLA is particularly useful for large datasets and complex models.The R INLA package is widely used for implementing INLA methodology.INLA has been applied in various fields, including epidemiology and air quality control.Computational challenges in INLA are minimal compared to MCMC methods.The Smart Gradient method enhances the efficiency of INLA.INLA can handle various likelihoods, not just Gaussian.SPDs allow for more efficient computations in spatial modeling.The new INLA methodology scales better for large datasets, especially in medical imaging.Priors in Bayesian models can significantly impact the results and should be chosen carefully.Penalized complexity priors (PC priors) help prevent overfitting in models.Understanding the underlying mathematics of priors is crucial for effective modeling.The integration of GPUs in computational methods is a key future direction for INLA.The development of new sparse solvers is essential for handling larger models efficiently.Chapters:06:06 Understanding INLA: A Comparison with MCMC08:46 Applications of INLA in Real-World Scenarios11:58 Latent Gaussian Models and Their Importance15:12 Impactful Applications of INLA in Health and Environment18:09 Computational Challenges and Solutions in INLA21:06 Stochastic Partial Differential Equations in Spatial Modeling23:55 Future Directions and Innovations in INLA39:51 Exploring Stochastic Differential Equations43:02 Advancements in INLA Methodology50:40 Getting Started with INLA56:25 Understanding Priors in Bayesian ModelsThank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Intro to Bayes Course (first 2 lessons free)Advanced Regression Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Takeaways:Setting appropriate priors is crucial to avoid overfitting in models.R-squared can be used effectively in Bayesian frameworks for model evaluation.Dynamic regression can incorporate time-varying coefficients to capture changing relationships.Predictively consistent priors enhance model interpretability and performance.Identifiability is a challenge in time series models.State space models provide structure compared to Gaussian processes.Priors influence the model's ability to explain variance.Starting with simple models can reveal interesting dynamics.Understanding the relationship between states and variance is key.State-space models allow for dynamic analysis of time series data.AI can enhance the process of prior elicitation in statistical models.Chapters:10:09 Understanding State Space Models14:53 Predictively Consistent Priors20:02 Dynamic Regression and AR Models25:08 Inflation Forecasting50:49 Understanding Time Series Data and Economic Analysis57:04 Exploring Dynamic Regression Models01:05:52 The Role of Priors01:15:36 Future Trends in Probabilistic Programming01:20:05 Innovations in Bayesian Model SelectionThank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki...

KeenTools Gaussian splatting, доступность и развитие AI, точный трекинг когда он не нужен. by CG ПОДКАСТ №1

Lisa Maria Egger, Co-founder & COO at Arrival.Space, an award-winning immersive web-based platform developed by Stratum1 that empowers people to create, connect, share, and explore next-generation content wherever you want on VR headset, mobile or the browser. In this conversation, we will look at:The key feature that turns Gaussian Splatting into the next level web contentHow these spaces can be experienced on web, mobile, and in VRA storytelling feature  I have never seen in any other platformThe core reasons why this must be built on the open webSubscribe to XR AI Spotlight weekly newsletter

BoE quant discusses a top-down counterparty risk framework that uses Gaussian distributions and copulae

Jess Loren, CEO and co-founder of Global Objects, joins us for a wide-ranging conversation about the future of immersive content and the creative tech reshaping the industry. A force to be reckoned with, Jess has a sharp pulse on where things are headed, and she doesn't hold back when discussing the current state of Hollywood. She talks candidly about the challenges studios and creators face today, and how technology like digital scanning, virtual production, and Gaussian splats can embolden independent filmmakers. Jess also shares insight into how she builds meaningful partnerships across art, tech, and media. Her business and life partner, Erick Geisler, appeared back in episode 483, and together they've helped position Global Objects at the intersection of innovation and storytelling. In this episode, Jess dives into her own journey as an entrepreneur and explains how she identifies trends before they break, working with brands, creators, and studios to help them stay ahead. Whether you're building pipelines, pitching ideas, or just trying to understand where things are going, this episode offers a grounded, unfiltered look at the creative future.

Want to build a serious drone career in construction, VDC, or mapping? This episode explores how Brian Owens transformed construction workflows using drones and BIM models—and how you can profit from it. In this episode of Elevating Drone Life, Rob talks with Brian Owens, Field Solutions Lead at The Whites Company, about how high tech drones transform construction workflows, strengthen risk management, and elevate client communication. You'll learn how drone mapping, BIM drone integration, FPV flythroughs, and Gaussian splat models are setting a new industry standard.    From mechanical engineer to construction drone innovator, Brian shares practical insights on turning drone expertise into a full-time career. He explains: How drone mapping fits into Virtual Design and Construction (VDC) workflows Which deliverables: orthomosaics, Gaussian splats, and FPV flythroughs build client trust Why polished drone media = higher client confidence + bigger contracts How drone data supports construction site safety, QA/QC, and real-time site inspections Career tips for becoming a professional drone pilot in construction This episode offers a behind-the-scenes look at what it really takes to succeed in the construction drone industry.  ? Ready to Launch Your Construction Drone Career? Master construction drone mapping, build your business, and get certified to fly commercially—all in one place: ?? Explore Courses + Memberships ? https://www.thedroneu.com ?? Timestamps [00:00] Meet Brian Owens and his journey into construction tech [05:00] How FPV drones led to a career in construction VDC [10:00] What is Virtual Design and Construction (VDC)? [15:00] Essential tools: laser scanners, field printers, drones in construction [20:00] Using drones for site mapping, risk management, and QA/QC [28:00] Building a company-wide drone culture [35:00] Winning with better drone deliverables: orthomosaic mapping, Gaussian splats, and cinematic videos [45:00] Future of construction media: cinematic drone marketing + FPV flythroughs [52:00] Career advice: focus on client-ready deliverables, not just flying drones Resources & Links ? Drone U Membership – Join Here ? Drone Business Mastery Course – Explore Courses ?? Part 107 Certification Prep – Start Here ? Experience Drone Training Event – Learn More Want to land your first construction drone job? ? Get our Drone Pilot Starter Kit  Learn to Master the Skies and Build Your Confidence as a Drone Pilot. The Drone Starter Kit is a collection of 3 amazing courses worth $97 - all for free.  ?? https://learn.thedroneu.com/bundles/drone-pilot-starter-kit  Stay Connected ? Like this episode if it helped ? Subscribe and turn on notifications for weekly expert interviews and tutorials ? Share this video with someone dreaming of a career in drones and construction tech and drones!

Olli Huttunen is an entrepreneur, video editor, 3D specialist, software developer and a real authority when it comes to Gaussian Splatting. I got to know Olli thanks to his Youtube channel, a priceless resource for anybody who wants to dive into this new 3D paradigm and today we take a deep dive into the world of Gaussian splatting:Olli explains why PostShot is his favorite tool and how to make the most out of itHe shares best practices to minimize file sizesHe tells us about a Blender add-on he has developed to create virtual camera arrays and why turning a mesh into a splat is not as dumb as it soundsAnd how the capabilities of the same camera array tool can be pushed even further to create synthetic 4DGSSubscribe to XR AI Spotlight weekly newsletter

Joey breaks down his week at NAB while Addy provides perspective from afar, covering the unexpected surge in VP innovations, practical AI implementations in post-production tools, and notable hardware releases that dominated the show floor.

Blackmagic leaks a 12K PYXIS camera just before NAB as the industry prepares for a major trade show. In this episode, hosts Addy Ghani and Joey Daoud dive into pre-NAB announcements, examining Strada's peer-to-peer media sharing tool, Adobe's new AI features, and how Gaussian splats are quietly becoming the next significant technology evolution. Plus, they unpack OpenAI's strategic move into open weights and what it means for enterprise AI adoption.

Jack Wang is the CEO of Kiri engine a powerful 3D scanning app for Android, iOS (and web browsers!). I have experienced Kiri myself and trust me, there some key features that makes it different from anything else on the market. Today we will dive into the world of Gaussian splatting with Jack discussing:Some of the current limitations of Gaussian splatting- The benefits of Gaussian Splatting compared to photogrammetry and the verticals benefitting from it- Some of the unique features that make Kiri engine the must have scanning app for Gaussian Splatting- A new path the team has taken after making an unexpected discovery Subscribe to XR AI Spotlight weekly newsletter

Sam Hodge discusses why NeRFs and gaussian splatting are changing VFX, real-time, photoreal volume capture in an AI game-changing way.

In the digital networked age, people's attention often overlooks local problems in favour of global ones, which don't necessarily impact them in their daily lives, or over which they don't have a say due to the skewed Pareto distribution of power in modern day societies. Puja Ohlhaver, in her recent research paper ‘Community currencies', proposes a dual-currency model that prices attention and influence in each community, with the ultimate goal of creating a Gaussian distribution of power, either locally, or globally through the dynamic interaction of multiple local communities. This model allows community members to stake their currency to earn non-transferable governance rights, creating a substrate for decentralised societal coordination that favours social innovation.Topics covered in this episode:Puja's backgroundWeb3 research‘Community currencies'Pareto vs. Gaussian distributionsGlobal vs. local power distributionsThe community currencies modelMeritocracy vs. influenceQuadratic fundingGovernance, bribery and the crisis of legitimacyExperimenting with community currenciesEpisode links:Puja Ohlhaver on X'Community Currencies' Research Paper'Decentralized Society' Research PaperSponsors:Gnosis: Gnosis builds decentralized infrastructure for the Ethereum ecosystem, since 2015. This year marks the launch of Gnosis Pay— the world's first Decentralized Payment Network. Get started today at - gnosis.ioChorus One: one of the largest node operators worldwide, trusted by 175,000+ accounts across more than 60 networks, Chorus One combines institutional-grade security with the highest yields at - chorus.oneThis episode is hosted by Friederike Ernst.

Niantic launched their Into the Scaniverse application on Quest 3 on February 26th, 2025 that features over 50,000 Gaussian Spats from 120 different countries. They originally launched the WebXR version on December 10th, 2024 at IntoTheScaniverse.com, which was built using Niantic Studio (be sure to check out their comprehensive history of Gaussian Splats by Kirsten M. Johnson released at the same time). Users can use the Scaniverse mobile app on Android or iOS to capture, render, geotag, and upload their own Gaussian Splats onto the Into the Scaniverse mapps that can be viewed on either mobile phone or XR devices. I had a chance to speak more about Into the Scaniverse with Joel Udwin, who is Niantic's Director of Product for Niantic's AR, Research, Developer Platforms, and Scaniverse. Gaussian Splats are only about 1 year and a half old as the original "3D Gaussian Splatting for Real-Time Radiance Field Rendering" paper was presented at SIGGRAPH in August 2023, but it represents a new rendering pipeline for volumetrically captured content. Niantic's Into the Scaniverse apps are able to process and render these splats locally on the phone or Quest devices, and they have a lot of plans for how they will continue to utilize and develop this as a core part of their technology infrastructure and enabling new mixed reality applications. https://www.youtube.com/watch?v=NR51MrAtUM4 This is a listener-supported podcast through the Voices of VR Patreon. Music: Fatality

  On this episode of Unsupervised Learning Razib talks to Tade Souaiaia, a statistical geneticist at SUNY Downstate about his new preprint, Striking Departures from Polygenic Architecture in the Tails of Complex Traits. Souaiaia trained as a computational biologist at USC, but also has a background as a division I track and field athlete. Razib and Souaiaia discuss what “genetic architecture” means, and consider what we're finding when we look at extreme trait values in characteristics along a normal distribution. Though traits like height or risk for type II diabetes can be thought of as represented by an idealized Gaussian distribution, real molecular and cellular processes still underlie their phenotypic expression. Souaiaia talks about how genomics has resulted in an influx of data and allowed statistical geneticists with a theoretical bent to actually test some of the models that underpin our understanding of traits and examine how models like mutation-selection balance might differ from what we've long expected. After wading through the depths of genetic abstraction and how it intersects with the new age of big data, Razib and Souaiaia talk about race and sports, and whether there might be differences between groups in athletic ability. Souaiaia argues that the underlying historical track record is too variable to draw firm conclusions, while Razib argues that there are theoretical reasons that one should expect differences between groups at the tails and even around the memes.

Gaussian Splatting es una técnica que redefine la captura y visualización 3D, utilizando splats gaussianos para representar la realidad de forma eficiente y precisa. Una evolución que plantea interrogantes sobre el futuro de las nubes de puntos en el mundo BIM. ¿Un modelo 3D desde un vuelo dron con una calidad fotográfica? ¿Es el Gaussian Splatting el fin de las nubes de puntos? ¿Listos para descubrir cómo pintar la realidad en 3D de una forma sorprendente? Porque hoy nos zambullimos en el fascinante, caótico y, para qué negarlo, un poco mágico mundo del Gaussian Splatting, la tecnología que amenaza con convertirse en el fin de las nubes de puntos. ¡Bienvenido al episodio 175 de BIMrras!

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!My Intuitive Bayes Online Courses1:1 Mentorship with meOur theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Takeaways:Bayesian statistics offers a robust framework for econometric modeling.State space models provide a comprehensive way to understand time series data.Gaussian random walks serve as a foundational model in time series analysis.Innovations represent external shocks that can significantly impact forecasts.Understanding the assumptions behind models is key to effective forecasting.Complex models are not always better; simplicity can be powerful.Forecasting requires careful consideration of potential disruptions. Understanding observed and hidden states is crucial in modeling.Latent abilities can be modeled as Gaussian random walks.State space models can be highly flexible and diverse.Composability allows for the integration of different model components.Trends in time series should reflect real-world dynamics.Seasonality can be captured through Fourier bases.AR components help model residuals in time series data.Exogenous regression components can enhance state space models.Causal analysis in time series often involves interventions and counterfactuals.Time-varying regression allows for dynamic relationships between variables.Kalman filters were originally developed for tracking rockets in space.The Kalman filter iteratively updates beliefs based on new data.Missing data can be treated as hidden states in the Kalman filter framework.The Kalman filter is a practical application of Bayes' theorem in a sequential context.Understanding the dynamics of systems is crucial for effective modeling.The state space module in PyMC simplifies complex time series modeling tasks.Chapters:00:00 Introduction to Jesse Krabowski and Time Series Analysis04:33 Jesse's Journey into Bayesian Statistics10:51 Exploring State Space Models18:28 Understanding State Space Models and Their Components

Happy New Year's Eve! Before the clock strikes midnight I'm sharing a quick message of thanks and appreciation to all of my guests this year and to you, the listeners, who've downloaded episodes in 37 countries across the world.Our top listeners are from the U.S., Germany, Australia, Canada and the UK.Our Top podcast episode of 2024 is:Retail Therapy: My inner child said no to a $4500 Apple Vision ProFollowed by: Apple Vision Pro's paradigm shift with Ash Baccus-Clark, Speculative Researcher and Creative Strategist, ABC WorldwideLaunching JADU on mobile: a first-of-its-kind multiplayer AR fighting game, with Sarah Stumbo Huth, Gameplay Engineer, Jadu AROptimizing XR Development: The Power of Automated Testing with Shane Evans, Cofounder, GameDriverDECEMBER 2024 NEWSAndroid XR: The Gemini Era Comes to Headsets and Glasses - Google Android XR Resources Thread - @tom_krikorianHands on: Samsung's XR headset - Ben Lang, Road to VRInto the Scaniverse - YouTubeBuilding Into the Scaniverse in Niantic Studio - 8th Wall BlogImages & Ideas: A New Partnership With James Cameron's Lightstorm Vision - MetaAWE's The Future of VR Sports/Esports with Sonya Haskins - LinkedIn LivestreamQuest 2/3s/3 Software Update Issue - Meta Community ForumsNEW VR FITNESS WORKOUTS!  SynthRiders Experience - Barbie Dance'nDream DLCJane Fonda X Supernatural - YouTube trailerTaeBo Reboot - Billy Blanks Thanks for listening! Happy installing 2025. 

In this episode, Bill Bellows and Andrew Stotz explore the intersection of variation and quality through awareness of the "Paradigms of Variation.” In a progression from acceptability to desirability, Bill created this 4-part model to offer economic insights for differentiating “Zero Defect” quality from “Loss Function" quality," with the aim of avoiding confusion between precision and accuracy when desirability is the choice.   Learn how to decide which paradigm your quality management system fits into! TRANSCRIPT 0:00:02.5 Andrew Stotz: My name is Andrew Stotz and I'll be your host as we dive deeper into the teachings of Dr. W. Edwards Deming. Today I'm continuing my discussion with Bill Bellows, who has spent 31 years helping people apply Dr. Deming's ideas to become aware of how their thinking is holding them back from their biggest opportunities. This is episode 7, The Paradigms of Variation. Bill, take it away.   0:00:30.3 Bill Bellows: Thank you, Andrew, and welcome to our listeners, as well as viewers, if you have access to the viewing version. Yeah, so I went back and listened to Episode 6. I'm going out bike riding 2-3 hours a day, so I listened to the podcast, listened to other things, stop and write down. Let me go write that down. And, so, we're going to pick up today on some major themes. And, what I keep coming back to is, is I think the difference between acceptability and desirability is the difference between how most companies operate and how a company inspired by Dr. Deming would operate.   0:01:29.3 BB: And, I just think of, if there was no difference between the two, then... Well, lemme even back up. I mentioned last time we were talking about why my wife and I buy Toyotas. And, yes, we've had one terrible buy, which I continue to talk about. [laughter] And, it's fun because it's just a reminder that even a company like Toyota can deliver a really lousy product, which we were unfortunate to have purchased. And, we're not the only ones that, and they've rebounded and they've apologized, they've had issues. There's no doubt about that. They have issues, but they have notably been inspired by Dr. Deming.   0:02:30.6 BB: The one thing I brought up last time was relative on this thinking of acceptability, desirability, where acceptability is looking at things and saying it's a quality system of good and bad. It's acceptable, which is good and unacceptable is not good. And, that's how most organizations view quality. Again, the focus of this series is Misunderstanding Quality. Our previous series was broadly looking at implications for Dr. Deming's ideas. And, here our focus is quality. And, so what I'm trying to get across here is quality management, traditional quality management.   0:03:17.4 BB: In most organizations, in all organizations I've ever interacted with is acceptability basis, good parts and bad parts. It's a measurement system of it meets requirements, we ship it, if it meets requirements, we buy it. And, I'm not saying there's anything wrong with that, but I don't think a system focused on acceptability can explain... To me, it does not explain the incredible reliability I have personally experienced in Toyota products.   0:03:46.9 BB: Now, I'm working with a graduate student and I wanna pursue that as a research topic in the spring, 'cause for all I know, the reliability of components in all cars has improved. I don't know if it's, I only by Toyota, 'cause so this woman I've met recently and I'm mentoring her and we're working on a research project. And, I thought recently, I'd like... And, I'm not sure how to do this, but I just know, I think I've mentioned I worked at my father's gas station back in the '70s and I remember replacing water pumps and alternators and all this stuff. This was before Japanese cars were everywhere. There were Japanese cars, but not like you see today.   0:04:33.3 BB: And, so I'm just used to all those components being routinely replaced. And, all I know is I don't routinely replace anything but the battery and the tires and change the oil. I think that's about it. Everything else is pretty good. But, I do think the differentiation between Toyota and most other companies is their appreciation of desirability and how to manage desirability. And, that's why I keep coming back to this as a theme for these sessions. And, what I think is a differentiation between a Deming view of quality and all other views of quality. What I tried to say last time is I just give you indications of a focus on acceptability. It's a quality system which looks at things that are good or things that are bad. It's, last time we talked about category thinking. It's black and white thinking. If the parts are good, then the mindset, if they're good, then they fit.   0:05:38.4 BB: Well, with a focus on continuum thinking, then you have the understanding that there's variation in good. And, that leads to variation in fit and variation in performance. And, that's a sense of things are relatively good, not absolutely good, whereas black and white category thinking is acceptability. They're all good. And, if they're all good, then they should all fit. I was, when I was at Rocketdyne, met, and the one thing I wanted to point out is... Again, as I said in the past, so much of what I'm sharing with the audience and people I've met through these podcasts or people I'm mentoring, helping them bring these ideas to their respective organizations or their consultants, whatever it is.   0:06:29.0 BB: And, so I like to provide examples in here for things for them to go off and try. You at the end of each podcast encourage them to reach out to me, a number of them have, and from that I've learned a great deal. And, so one guy was... A guy I was working with at Rocketdyne, he was at a site that did final assembly of rocket engine components. And, so one thing I'd say is the people who... And for those listening, if you wanna find people in your organization that would really value the difference between an acceptability focus and a desirability focus, find the people that do assembly, find the people that put things together. 'Cause the ones that machine the holes, they think all the holes are good. People that make the tubes, all the tubes are good. But, find the people that are trying to put the tubes into the holes. Those are the people I loved working with because they were the ones that felt the difference every day.   0:07:31.1 BB: And, so I was in a workshop for a week or so. And there's two people ahead of me. They came from this final assembly operation of Rocketdyne. And, during a break, I was trying to clarify some of the things I had said and I used, I shared with them an example of how when we focused on not the tubes by themselves or the holes by themselves, that we focused on how well the tubes go into the holes, which has a lot to do with the clearance between them and the idea that nobody owns the clearance. One person owns one part, one owns another. And, what we realized is if we focused on the relationship, what a big difference it made. So I'm explaining it to him and he turns to me and he says, he's like, "Oh, my God," he says, "I've got hundreds of turbine blades and a bunch of turbine wheels and the blades slide into the wheel." And he says, "I can't get the blades onto the wheel."   0:08:31.0 BB: And I said, "But they're all good." He says, "They're all good." But he said, "Well, what you're now explaining to me is why they don't go together. Why I have this headache." So I said, "Well, do you know where the blades come from?" He says, "yeah". And I said, "Do you know where the wheels come from?" He says, "yeah". I said, "Well, why don't you call them up and talk to them?" He says, "There's no reason for a phone call 'cause all they're going to say is, "Why are you calling me? They're all good." So, he just walked away with his head exploding 'cause he's got all these things.   0:09:05.8 BB: And, so I use that for our listeners is if you want to find people that would really resonate with the difference between acceptable and desirable, talk to the people that have to put things together. There you will find... And, so my strategy was, get them smart. Now they have to be patient with the people upstream 'cause the people upstream are not deliberately doing what they're doing to them. So, what you don't want to do is have them get... You want their consciousness to go up but you now wanna use them to talk to the component people. Now you've got a conversation. Otherwise, the component people say, "Why are you talking to me? Everything I do is good."   0:09:51.6 BB: So, I just want to talk at this point, just to reinforce that I think there's something going on with Toyota that is very intentional about managing desirability when it makes sense using acceptability. So, it's a choice. And, so indications of a focus on desirability is when you look at options that are acceptable and you say, "Of all these apples, I want this one. It's the ripest. Of all these donuts, I want this one. It's got the most sprinkles. Of all these parking spots, I want this one. It's a little bit wider than the other. I want this surgeon. I want this professor for this course."   0:10:33.8 BB: All right. So, what we're saying "is of all the choices, I want this one". So, some new ideas I want to get into tonight are the Paradigms of Variation A, B, C, D, and E. Paradigm A we looked at in the past. That's just acceptability. Does it meet requirements or not? The quality focus is achieving zero defects. And tonight I want to get into B and C. The next time we'll look at D and E. In explaining these ideas recently to someone who listened to one of our previous podcasts and were focusing on, he started asking about decision making. And that got me thinking about, of course, I took years ago decision making with Kepner and Tregoe. And there they talk about decisions. We're gonna look, we're gonna go buy a car, go buy a house. We're gonna make a decision.   0:11:29.4 BB: And, once you decide on the decision, you then list the criteria of the decision. And you come up with all the things you want in this decision. And then you look at each of them and you say, "is it a must or a want"? And let's say you're looking at houses. It could be a lot of houses to go look at. What makes this focus on acceptability, it's musts and wants. And must is very much acceptability. So you say: "We're looking for a house that must be one story, it must be in the middle of the block. The house must be in the middle of the block. It must have four bedrooms, must have two bathrooms". So now when you're looking at all these houses, acceptability says "I'm only gonna look at the ones that meet those requirements". And, so now the strategy is to go from hundreds of options down to an order of magnitude less.   0:12:25.1 BB: Now we're going to get it down to maybe 20. Now you look at the wants. So you've got an original list of all the things, the criteria, and you look at each one and say, "is it a must, is it a want"? And what I've just said is the first screening is all the ones that pass the must get into the next category. Well, with the Kepner-Tregoe folks, they talk about must, which is acceptability, and the wants are about desirability.   0:12:51.4 BB: And then here it ties into Dr. Taguchi's mindset, and we'll look at Taguchi in a future session. Taguchi looks at a characteristic of quality, such as the diameter of a hole, the performance of an automobile, miles per gallon. And he says, in terms of desirability, there's three different targets. There is desirability, I want the smallest possible value. So, if you're buying a house, it could be, I want the lowest possible electric bills where zero is the goal. It's not gonna be zero, but I'm looking, of all the ones that pass the must, now I'm looking at all the houses, and I'm saying "I want the lowest possible electric bill". That's a Smaller-is-Best.   0:13:35.9 BB: Larger-is-Best is I want something which is as big as possible. It could be I want the most roof facing the sun, in case I put solar in. That's a Larger-is-Best characteristic, where Taguchi would say the ideal is infinity, but the bigger, the better, as opposed to Smaller-is-Better. And, the other characteristic is what Taguchi calls Nominal-is-Best, is I have an ideal single value in mind. And in each case, the reason I point that out is that desirability is about going past acceptability and saying amongst all the things that are acceptable, I want the smallest, I want the largest, or I want this. It is a preference for one of those.   0:14:19.4 BB: So, I thought... I was using that to explain to this friend the other day, and I thought that would be nice to tie in here. That desirability is a focus on of all the things that meet requirements, now I want to go one step further. That's just not enough. All right, so now let's get into Paradigms B and C. And I want to use an exercise we used in the first series. And, the idea for our audience is imagine a quality characteristic having a lower requirement, a minimum, otherwise known as the lower spec, the lower tolerance. So, there's a minimum value, and then there's a maximum value. And, when I do this in my classes, I say "let's say the quality characteristic is the outer diameter of a tube." And, then so what I'd like the audience to appreciate is we've got a min and a max.   0:15:18.9 BB: And, then imagine your job as listener is to make the decision as to who to buy from. And. let's say we've got two suppliers that are ready to provide us with their product, these tubes that we're gonna buy. And, your job as a listener is to make the decision as to who to buy from. Who are we going to buy from? And, so we go off and we tell them, "Here's the min, here's the max," and they come back. And, they each give us a distribution. And, so what I'd like the audience to think about is a distribution. Just think very simply of two normal distributions, two Gaussian distributions. And, let's say the first distribution goes all the way from the min to the max. It takes up the entire range.   0:16:08.5 AS: So wide and flat.   0:16:12.1 BB: Wide and flat. That's supplier one. And supplier two, let's say is maybe three quarters of the way over. It's incredibly uniform. It uses a very small fraction of the tolerance. So that's tall and narrow. That's distribution two as opposed to wide and flat. So, imagine we've got those two to buy from. But imagine also, and this is a highly idealized scenario. And, I use this and this is why I want to share it with our audience. Because it becomes a great way of diving into what I think is a lot of confusion about meeting requirements. And, so what I want you to imagine is that no matter who you buy from, they both promise that they will deliver at the same price per tube.   0:17:00.8 BB: So, no matter who you buy from, price-wise, they are identical. To which I'd say that's highly idealized, but that's a given. Criteria number two, the delivery rates are the same. So, we cannot differentiate on delivery. We cannot differentiate on price. The third condition we find out is that everything they deliver meets requirements, 100%. So, if there is any scrap and rework, they don't ship that to us. So, everything they deliver meets requirements. And, again, that's highly idealized.   0:17:41.6 BB: Number four is the distributions are in control. And, that means that the processes are predictable and stable. And, that's guaranteed. So, imagine these distributions day by day every order is the same shape, the same average, the same amount of variation. Also, it will never change. It will never change. And, the other thing I want to point out in this fourth point here is that your job as the buyer is to buy these. They are used as is within our organization. , 0:18:15.5 BB: And, the fifth point is that there's a min and a max. And, so I've been using this exercise for, gosh, going back to 1995, and I throw it out there and then I show them the distributions. I say "same price, same schedule, delivery rate, everything meets requirements, distributions never change shape or location. You're going to use as is. And there's the min, there's the max. Who do you buy from?" And, I give people not only do we buy from one or two, but I also say I'll give you a third option.   0:18:51.5 BB: The third option is it doesn't matter. It doesn't matter. So, what I find is that three quarters of the audience will take distribution two, the narrow one. And when I ask them, why do you like distribution two? They say, "because it has less variation". I then say, "From what?" Then they say, "From each other." And, that's what a standard deviation is, variation from each other. So roughly 75% plus and minus...   [overlapping conversation]   0:19:25.8 AS: When you say of each other, you're talking about each other curve or each other item in the...   0:19:31.3 BB: Each other tube.  So, the amount of variation from all the tubes are close together, so the variation from each other.   0:19:38.6 AS: Okay. Each item. Yeah, okay.   0:19:41.8 BB: Standard deviation is the average variation from the average value. So, when I ask them, why do you like two? Okay, and then I asked the ones who take the wide one in the middle, I say, "why do you like that one," and they say because... And, actually, we'll come back to that. This is pretty funny. They will take that, but a very small percent say it doesn't matter, and here's what's interesting, if I didn't show the distributions, if all I did was say there's two suppliers out there, the same price, same schedule, that guarantee zero defects, the results will never change. Here's the min, here's the max, I'm willing to bet if I didn't show the distributions, people would say "it doesn't matter, I'll take either one". But, as soon as I show them the distributions, they want the narrow one. And, I use this for our attendees, this is a great way to show people that they really don't believe in tolerances, 'cause as soon as you go past meeting requirements, what you're really saying is, there's a higher bar.   0:21:05.6 AS: Okay, so requirements would be... Or, tolerances would be the extremes of that flat, wide curve. And, any one of those outcomes meets the tolerance.   0:21:17.5 BB: Yes, and so for companies that are striving to meet requirements, why is it when I give you two distributions that meet requirements... Why is it when I show you the distributions, and I'm willing to bet if I don't show you the distributions and all you know is they're 100% good, then you say "well, it doesn't matter," Well then what changes when I show you the distributions?   0:21:43.6 AS: I know why I'd choose the narrow one.   0:21:48.1 BB: Go ahead.   0:21:49.1 AS: I know how damn hard it is to reduce variation and I forget about any tolerance of anything, if I have two companies that show me a wide distribution, and another one shows me a narrow one, and let's say it's accurate. I'm much more impressed with how a company can do the same exact output as another company, the same product that they're trying to deliver, but they are producing a much more narrow range of outcome, which could be that they just have automation in their production line and the other one has manual.   0:22:27.4 BB: And, I have seen that within Rocketdyne, I've seen processes do that. I have seen the wide become the narrow through automation. Yeah. Okay, so hold that thought then. So, what I do in my graduate classes is I show that... Not only do I give them two options, I give them four options. So, I throw in two other distributions, but really what it comes down to is the wide one versus the narrow one, and then the other two, I throw in there that usually aren't taken, they're distractions. All right, so what I'll do in a graduate class in quality management is to show that and get the results I just showed. If I present the same exercise and then say, "imagine the average value of distribution one, the middle of distribution one, imagine that is the ideal value".   0:23:24.7 AS: That, you're talking about the wide and flat.   0:23:28.4 BB: Yes. So, all I do is I go back to the entire exercise and now I add in a line at the average of the wide distribution, and then go through and ask one more time, who would you take.   0:23:46.3 AS: So, now the dilemma that the listener has is that now we have a, within limits, within tolerances, we have a wide but flat distribution that's centered on the middle point between the upper and lower tolerance.   0:24:06.4 BB: Yeah, yes.   0:24:08.8 AS: And, then we have... Go ahead.   0:24:11.7 BB: Well, yeah, that is distribution one, same as the first part, we went through this, and all I'm doing now is saying, "imagine the average value of the middle is said to be the ideal value".   0:24:29.4 AS: And, now you're gonna tell us that the narrow one is not on that central or ideal value.   0:24:36.2 BB: No, that is still where it is at the three-quarter point, all I've done is now said, this is desirability. I'm now saying "that is the ideal value, that is the target, that is the value we prefer". And, people still take the narrowest distribution number two.   0:24:58.8 AS: I wouldn't take the narrow one because I would think that the company would have to prove to me that they can shift that narrow curve.   0:25:06.6 BB: Well, okay, and I'm glad you brought that up because according to the explanation I gave of equal price, equal schedule, meets requirements. I deliberately put in the criteria that you have to use them as is. So, now I'm forcing people to choose between the narrowest one over there at the three-quarter point, and the wide one on target. And, there's no doubt if I gave them the option of taking the narrow distribution and sliding it over, they would. Every single person would do that. But, when I give you a choice of, okay, now what? So, two things here, one is, is it calling out the ideal of value, 'cause desirability is not just beyond acceptability, it is saying, "I desire this value, I want this parking spot, I want this apple, I want this value". And, that's something we've been alluding to earlier, but that's what I wanna call out today is that...   0:26:13.7 BB: So, in other words, when I presented the exercise of the two distributions, without calling out what's desirable, all I'm doing is saying they're both acceptable, which do you prefer? But, instead of saying it doesn't matter, I'd like the narrowest one, and it may well be what people are doing is exactly what you're saying is the narrowest one seems better and easily could be for what you explained.   0:26:40.8 BB: But, what's interesting is, even when I call out what's desirable as the value, people will take the narrowest distribution, and so now what I wanna add to our prior conversation is Paradigm A, acceptability, the Paradigm A response would be, it doesn't matter. Choosing the narrowest one, otherwise known as precision, we're very precisely hitting that value, small standard deviation, that's what I refer to as Paradigm B, piece-to -piece consistency. Paradigm C is desirability being on the ideal value, that's piece-to-target consistency. And, in Dr. Taguchi's work, what he's talking about is the impact downstream of not just looking at the tubes, but when you look at how the tubes are inserted into a hole, perhaps, then what he's saying is that the reason you would call out the desirable value is what you're saying is how this tube integrates in a bigger system matters, which is why I want this value.   0:27:54.2 AS: Okay, so let's go back, A, meet requirements, that's acceptability. Anything within those tolerances we can accept. B is a narrow distribution, what you called precision or piece-to -piece consistency. And what was C?   0:28:12.8 BB: C is, I'll take the wide distribution where the average value is on target, that's piece- to-target consistency. Otherwise known as accuracy.   0:28:27.3 AS: Okay. Target consistency, otherwise known as accuracy. All right, and then precision around D is precision around the ideal value.   0:28:37.7 BB: Well, for those that want to take the narrowest one and slide it over, what you're now doing is saying, "I'm gonna start with precision, and I'm going to focus on the ideal". Now, what you're doing is saying, "step one is precision, step two is accuracy".   0:28:56.4 AS: Okay. And step three or D?   0:29:00.9 BB: Paradigm D?   0:29:02.7 AS: Yeah.   0:29:02.7 BB: Is that what you're... Yeah. Paradigm D would be the ability to produce, to move the distribution as needed to different locations.   0:29:17.4 AS: The narrow distribution?   0:29:18.9 BB: Yes, and so I'll give you an example in terms of, let's say tennis, Paradigm A in tennis is just to get the ball across the net. I just wanna get it somewhere on the other side of the court, right. Now that may be okay if you and I are neighbors, but that doesn't get us into professional level. Paradigm B, is I can hit it consistently to one place on your side of the court. Now, I can't control that location, but boy, I can get that location every single time. Next thing you know, you know exactly where the ball is going, and that's Paradigm B.  Paradigm C is I can move it to where I want it to go, which you will eventually figure out, so I can control where it goes. Paradigm D is I can consistently hit any side of the court on the fly.   0:30:11.0 BB: So, Paradigm D is I can take that narrow distribution and move it around for different customers, different applications, and Dr. Taguchi refers to that as Technology Development, and what Taguchi is talking about is developing a technology which has incredible precision in providing your sales people the ability to move the next move it to accuracy and to sell that product by tuning it to different customers as you would in sports, move the ball around to the other side of the court. So now you're going to the point that you've got incredible precision, and now you've got “on demand accuracy,” that's Paradigm D.  Paradigm C is I can do one-size-fits-all which is, which may be all you need for the application.   0:31:06.9 AS: I wanna separate the Paradigm B, the narrow distribution and that's precision around some value versus Paradigm D is precision around the ideal value.   0:31:20.7 BB: And, the idea is that desirability is about an ideal value. And, so if we're talking piece-to-piece consistency, that means it's uniform, but I'm not paying attention to... I have a value in mind that I want. And that's the difference between Dr. Taguchi's work, I mean, it's the ability to be precise. Again, accuracy, desirability is I have an ideal value in mind. And acceptability is it doesn't really matter.  Precision is uniformity without accuracy. And so, if you are... What Dr. Taguchi is talking about is, is depending on how what you're delivering integrates, being consistent may cause the person downstream to consistently need a hammer to get the tube into the hole.   0:32:24.2 BB: So, it's consistent, but what you're now saying, what Taguchi is saying is, if you pay attention to where you are within requirement, which is desirability, then you can improve integration. And, that is my explanation for why Toyota's products have incredibly reliability, that they are focusing on integration, not just uniformity and precision by itself.   0:32:49.8 AS: I would love to put this in the context of a dart thrower. The Paradigm A meeting requirability or acceptability, they stand way behind and they throw and they hit the overall dart board.   0:33:04.3 BB: Dart board. It's on the board. Yes.   0:33:07.2 AS: And, the narrow distribution is, well, they hit the same spot over to the left, right towards the edge, they hit that spot consistently. And, then basically, I'm gonna jump to D just because I'm imagining that I'm just gonna ask the guy, Hey, can you just move over just a little bit, and I'mma move them over about a half a foot, and when I do, you're gonna start throwing that dart right at the same location, but over to the right, meaning at the target. The center of the dart...   0:33:43.9 BB: The bull's eye. Yeah. Yeah, well, that's... And you call that C or D?   0:33:47.6 AS: I call that D.   0:33:49.5 BB: No, I would say, let's call that C being on target, meaning that C is, for games of darts where the most points are being on the bull's eye, that's Paradigm C.   0:34:04.0 AS: So accuracy, yeah.   0:34:05.4 BB: Paradigm D would be a game in which the ideal value changes. So now, okay, now I watch the... When I play darts, I'm sure there's lots of darts games, but one game we used to play it in our cellar at home was baseball. So, the dart board is divided into has numbers one, two, three through, and you'd go to... There'd be a wedge number one, a wedge number two, a wedge number three, that's Paradigm D that I could hit the different wedges on demand. But that's what it is. So A is anywhere in. B is consistent, precision, but again, the idea is if you can move that, but now what we're talking about is, is there an impulse to move it or are we happy just being precise? What Taguchi's talking about is the value proposition of desirability is to take precision, take that uniformity and move it to the ideal value, and what you've just done and doing so, you're now focusing on not this characteristic in isolation, you're now focusing on how this characteristic meshes with another characteristic. And, it's not just one thing in isolation, one thing in isolation does not give you a highly reliable automobile.   0:35:38.9 AS: Is there anything you wanna add to that, or are you ready to sum it up?   0:35:45.0 BB: No, that's it. The big summation is, we've been building up to the contrast between acceptable and desirable. I just wanted to add some more fidelity. Desirable is I have a value in mind, which Dr. Taguchi referred to as a target. So, for people at home, in the kitchen, the target value could be exactly one cup of flour. We talked earlier about our daughter, when she worked in a coffee shop and then, and at home she'd give us these recipes for making coffee and it'd be dad, exactly this amount of coffee and exactly that. And, we had a scale, it wasn't just anywhere between. She'd say "dad, you have to get a scale." I mean she was... We started calling her the coffee snob, 'cause it was very, this amount, this amount. So, in the kitchen then it's about precisely one cup. Precisely one this. And that's desirability.   0:36:40.6 AS: And, I was just thinking, the best word for that is bull's eye!   0:36:48.3 BB: Yes.   0:36:48.8 AS: You hit it right on the spot.   0:36:50.6 BB: Yeah.   0:36:51.6 AS: Great. Well, Bill, on behalf of everyone at The Deming Institute, I wanna thank you again for this discussion. It was not only acceptable, it was desirable. For listeners, remember to go to deming.org to continue your journey. And, if you want to keep in touch with Bill, just find him on LinkedIn. He'll reply. This is your host, Andrew Stotz, and I leave you with one of my favorite quotes from Dr. Deming, "people are entitled to joy in work."

I interviewed Marcello Typrin Product Director at Reality Labs, at Meta Connect 2024 about the Hyperscape Demo. See more context in the rough transcript below. This is a listener-supported podcast through the Voices of VR Patreon. Music: Fatality

I interviewed Tipatat Chennavasin, General Partner and co-founder of Venture Reality Fund, at Meta Connect 2024 about why he has gone all-in with Gaussian Splatting by investing in the Gracia.ai and their viewer that's on the Quest store. We also talk about some of the news from Meta Connect, Quest 3S excitement, the merits of AAA games like Batman Arkham Shadow to bring more people into VR, and other some experiences and demos that caught his attention at Meta Connect 2024. See more context in the rough transcript below. This is a listener-supported podcast through the Voices of VR Patreon. Music: Fatality

Domna Ladopoulou, a researcher in the Department of Statistical Science at UCL, is working on improving the efficiency and reliability of wind energy production through statistical and machine learning modelling approaches. Her research focuses on developing a probabilistic condition monitoring system for wind farms using SCADA data to detect faults and failures early. This system aims to enhance the sustainability of wind farms by reducing maintenance costs and improving overall reliability. Donna's methodology involves non-parametric probabilistic methods like Gaussian processes and probabilistic neural networks, which offer flexibility and computational efficiency. She emphasizes the importance of informed decision-making in sustainability and the potential for her research to be scaled globally, particularly in regions with high wind power reliance. Date of episode recording: 2024-05-30T00:00:00Z Duration: 00:17:34 Language of episode: English Presenter:Stephanie Dickinson Guests: Domna Ladopoulou Producer: Nathan Green

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!My Intuitive Bayes Online Courses1:1 Mentorship with meOur theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Takeaways:Bayesian statistics is a powerful framework for handling complex problems, making use of prior knowledge, and excelling with limited data.Bayesian statistics provides a framework for updating beliefs and making predictions based on prior knowledge and observed data.Bayesian methods allow for the explicit incorporation of prior assumptions, which can provide structure and improve the reliability of the analysis.There are several Bayesian frameworks available, such as PyMC, Stan, and Bambi, each with its own strengths and features.PyMC is a powerful library for Bayesian modeling that allows for flexible and efficient computation.For beginners, it is recommended to start with introductory courses or resources that provide a step-by-step approach to learning Bayesian statistics.PyTensor leverages GPU acceleration and complex graph optimizations to improve the performance and scalability of Bayesian models.ArviZ is a library for post-modeling workflows in Bayesian statistics, providing tools for model diagnostics and result visualization.Gaussian processes are versatile non-parametric models that can be used for spatial and temporal data analysis in Bayesian statistics.Chapters:00:00 Introduction to Bayesian Statistics07:32 Advantages of Bayesian Methods16:22 Incorporating Priors in Models23:26 Modeling Causal Relationships30:03 Introduction to PyMC, Stan, and Bambi34:30 Choosing the Right Bayesian Framework39:20 Getting Started with Bayesian Statistics44:39 Understanding Bayesian Statistics and PyMC49:01 Leveraging PyTensor for Improved Performance and Scalability01:02:37 Exploring Post-Modeling Workflows with ArviZ01:08:30 The Power of Gaussian Processes in Bayesian ModelingThank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna,...

If you see this in time, join our emergency LLM paper club on the Llama 3 paper!For everyone else, join our special AI in Action club on the Latent Space Discord for a special feature with the Cursor cofounders on Composer, their newest coding agent!Today, Meta is officially releasing the largest and most capable open model to date, Llama3-405B, a dense transformer trained on 15T tokens that beats GPT-4 on all major benchmarks:The 8B and 70B models from the April Llama 3 release have also received serious spec bumps, warranting the new label of Llama 3.1.If you are curious about the infra / hardware side, go check out our episode with Soumith Chintala, one of the AI infra leads at Meta. Today we have Thomas Scialom, who led Llama2 and now Llama3 post-training, so we spent most of our time on pre-training (synthetic data, data pipelines, scaling laws, etc) and post-training (RLHF vs instruction tuning, evals, tool calling).Synthetic data is all you needLlama3 was trained on 15T tokens, 7x more than Llama2 and with 4 times as much code and 30 different languages represented. But as Thomas beautifully put it:“My intuition is that the web is full of s**t in terms of text, and training on those tokens is a waste of compute.” “Llama 3 post-training doesn't have any human written answers there basically… It's just leveraging pure synthetic data from Llama 2.”While it is well speculated that the 8B and 70B were "offline distillations" of the 405B, there are a good deal more synthetic data elements to Llama 3.1 than the expected. The paper explicitly calls out:* SFT for Code: 3 approaches for synthetic data for the 405B bootstrapping itself with code execution feedback, programming language translation, and docs backtranslation.* SFT for Math: The Llama 3 paper credits the Let's Verify Step By Step authors, who we interviewed at ICLR:* SFT for Multilinguality: "To collect higher quality human annotations in non-English languages, we train a multilingual expert by branching off the pre-training run and continuing to pre-train on a data mix that consists of 90% multilingualtokens."* SFT for Long Context: "It is largely impractical to get humans to annotate such examples due to the tedious and time-consuming nature of reading lengthy contexts, so we predominantly rely on synthetic data to fill this gap. We use earlier versions of Llama 3 to generate synthetic data based on the key long-context use-cases: (possibly multi-turn) question-answering, summarization for long documents, and reasoning over code repositories, and describe them in greater detail below"* SFT for Tool Use: trained for Brave Search, Wolfram Alpha, and a Python Interpreter (a special new ipython role) for single, nested, parallel, and multiturn function calling.* RLHF: DPO preference data was used extensively on Llama 2 generations. This is something we partially covered in RLHF 201: humans are often better at judging between two options (i.e. which of two poems they prefer) than creating one (writing one from scratch). Similarly, models might not be great at creating text but they can be good at classifying their quality.Last but not least, Llama 3.1 received a license update explicitly allowing its use for synthetic data generation.Llama2 was also used as a classifier for all pre-training data that went into the model. It both labelled it by quality so that bad tokens were removed, but also used type (i.e. science, law, politics) to achieve a balanced data mix. Tokenizer size mattersThe tokens vocab of a model is the collection of all tokens that the model uses. Llama2 had a 34,000 tokens vocab, GPT-4 has 100,000, and 4o went up to 200,000. Llama3 went up 4x to 128,000 tokens. You can find the GPT-4 vocab list on Github.This is something that people gloss over, but there are many reason why a large vocab matters:* More tokens allow it to represent more concepts, and then be better at understanding the nuances.* The larger the tokenizer, the less tokens you need for the same amount of text, extending the perceived context size. In Llama3's case, that's ~30% more text due to the tokenizer upgrade. * With the same amount of compute you can train more knowledge into the model as you need fewer steps.The smaller the model, the larger the impact that the tokenizer size will have on it. You can listen at 55:24 for a deeper explanation.Dense models = 1 Expert MoEsMany people on X asked “why not MoE?”, and Thomas' answer was pretty clever: dense models are just MoEs with 1 expert :)[00:28:06]: I heard that question a lot, different aspects there. Why not MoE in the future? The other thing is, I think a dense model is just one specific variation of the model for an hyperparameter for an MOE with basically one expert. So it's just an hyperparameter we haven't optimized a lot yet, but we have some stuff ongoing and that's an hyperparameter we'll explore in the future.Basically… wait and see!Llama4Meta already started training Llama4 in June, and it sounds like one of the big focuses will be around agents. Thomas was one of the authors behind GAIA (listen to our interview with Thomas in our ICLR recap) and has been working on agent tooling for a while with things like Toolformer. Current models have “a gap of intelligence” when it comes to agentic workflows, as they are unable to plan without the user relying on prompting techniques and loops like ReAct, Chain of Thought, or frameworks like Autogen and Crew. That may be fixed soon?

Bayesian methods take the spotlight in this episode with Alex Andorra, co-founder of PyMC Labs, and Jon Krohn. Learn how Bayesian techniques handle tough problems, make the most of prior knowledge, and work wonders with limited data. Alex and Jon break down essentials like PyMC, PyStan, and NumPyro libraries, show how to boost model efficiency with PyTensor, and talk about using ArviZ for top-notch diagnostics and visualizations. Plus, get into advanced modeling with Gaussian Processes. This episode is brought to you by Crawlbase (https://crawlbase.com), the ultimate data crawling platform. Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: • Practical introduction to Bayesian statistics [04:54] • Definition and significance of epistemology [17:52] • Explanation of PyMC and Monte Carlo methods [27:57] • How to get started with Bayesian modeling and PyMC [34:26] • PyMC Labs and its consulting services [50:50] • ArviZ for post-modeling diagnostics and visualization [01:02:23] • Gaussian processes and their applications [01:09:02] Additional materials: www.superdatascience.com/793

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!My Intuitive Bayes Online Courses1:1 Mentorship with meGPs are extremely powerful…. but hard to handle. One of the bottlenecks is learning the appropriate kernel. What if you could learn the structure of GP kernels automatically? Sounds really cool, but also a bit futuristic, doesn't it?Well, think again, because in this episode, Feras Saad will teach us how to do just that! Feras is an Assistant Professor in the Computer Science Department at Carnegie Mellon University. He received his PhD in Computer Science from MIT, and, most importantly for our conversation, he's the creator of AutoGP.jl, a Julia package for automatic Gaussian process modeling.Feras discusses the implementation of AutoGP, how it scales, what you can do with it, and how you can integrate its outputs in your models.Finally, Feras provides an overview of Sequential Monte Carlo and its usefulness in AutoGP, highlighting the ability of SMC to incorporate new data in a streaming fashion and explore multiple modes efficiently.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell and Gal Kampel.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Takeaways:- AutoGP is a Julia package for automatic Gaussian process modeling that learns the