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Spectra drops Vae and Anima off so they can partner with one Zatea Skychaser to follow a possible lead on the mystery ship and its unsavory mercenaries.

We're announcing AIEWF speakers this week! Take the AI Engineering Survey!Today's guest Ethan first joined us for the LS Paper Club as the lead on NVIDIA Cosmos World Model, but then joined xAI and built Grok Imagine in 3 months:He comes back on Latent Space with some nuclear hot takes: that Video Models primarily get their intelligence from LLMs, not from training on video data, and that the next frontier for truly interactive, realtime, long-horizon world models is to work on LLMs (perhaps Interaction Models as well…)Put it this way: In the near term, the next Sora won't be a better video model, but a video agent.Generative Media may more closely follow the evolution of AI coding which went from focusing on one-shot output performance and cost, to multiturn reasoning and planning models for agents and systems that can plan, edit, test, debug, and submit PRs.At a certain point, coding models got so good that the only significant next step to improve performance was handling the orchestration of these models.Now as the performance of video models increases significantly across realism, consistency, & prompt adherence while becoming more cost efficient, the next evolution of video generation may also be systems that can plan, generate, edit, critique, and iterate across an entire creative task. In this episode, Ethan joins swyx and Vibhu to unpack what it actually takes to build frontier image and video systems: data, VAEs, diffusion transformers, audio-video alignment, inference speedups, and the hidden cost of storing and moving massive video datasets. From building NVIDIA's Cosmos world model to joining xAI as Grok Imagine was being built from zero to one, Ethan He has been at the center of some of the most important work in video generation, multimodal models, and real-time world models.We go deep on Grok Imagine, how a small xAI team shipped its first multimodal video model in three months, why iteration speed matters more than almost anything in model development, and why many of the biggest gains come from fixing tiny bugs in data and training pipelines. Flipbook: The future of VideomaxxingVideo agents are almost a sure bet to be the trend in the coming year. We end with a glance at what's beyond video agents:Flipbook caused a minor sensation this year when it was released, but most treat it as a fun demo. Ethan takes it very seriously — with the speed and cost of inference coming down every year, the future of custom video JIT UI is closer than you think. We talked about why videogen models may become the front end of AI, how generative UI could replace traditional HTML/CSS, why world models need to be real-time, interactive, and long-horizon, and why the future of video generation may depend more on language models and agents than on diffusion alone.We discuss:* Why fast iteration mattered more than meetings* Why small training bugs can drive huge model quality gains* Why coding models may make compute the bottleneck again* How image and video models are trained with synthetic captions* The role of VAEs and latent space in frontier video models* Why image models are the foundation for video models* The tradeoff between temporal compression and real-time interactivity* Flipbook, Neural OS, and the future of generative UI* Why future interfaces may go from user intent to pixels* The hidden cost of training video models: storage, egress, and GPU hours* How step distillation and consistency models (like OpenAI sCM) makes video inference orders of magnitude faster* Grok Imagine 0.9 and large-scale audio-video generation* Why audio-video alignment is harder than text-video alignment* Ethan's definition of world models* Reference-to-video, video extension, and long-context video generation* Why xAI's research communication undersells Grok Imagine* How xAI culture shaped the speed of development* AI watermarking, SynthID, and detecting generated media* Why prompt rewriting matters for video models* Grok Imagine Agent and the rise of video agents* Why language models may unlock better video generation* Robotics, physical AI, and embodied world models* Why Ethan left xAI and shifted focus toward LLMs* Self-managed context, memory, and the next frontier for language modelsEthan He* LinkedIn: https://www.linkedin.com/in/ethanhe42* X: https://x.com/EthanHe_42Timestamps00:00:00 Introduction00:01:25 From NVIDIA Cosmos to xAI00:03:24 Building Grok Imagine from Zero to One00:10:07 How Image and Video Models Are Trained00:18:53 Video Compression, VAEs, and Real-Time Tradeoffs00:22:10 Generative UI, Flipbook, and Neural OS00:32:10 The Cost of Training Large Video Models00:37:04 Distillation, GANs, and Fast Video Inference00:41:21 Audio-Video Generation and Grok Imagine 0.900:48:34 What Makes a World Model?00:55:51 Reference Videos, Long Context, and Video Memory01:00:11 xAI Culture, Research, and First-Principles Building01:09:45 AI Safety, Watermarking, and Prompt Rewriting01:13:10 Video Agents and AI-Assisted Creation01:27:32 Why Language Models Unlock Better Video01:31:15 Robotics, Physical AI, and Embodied World Models01:32:38 Why Ethan Left xAI01:34:16 Self-Managed Context and the Future of LLMs01:38:43 Ethan's Career Path and Closing ThoughtsTranscriptIntroduction: Ethan He, Latent Space, and the Path to xAISwyx [00:00:00]: We're here in the studio with Ethan He, most recently of xAI. Welcome.Ethan [00:00:10]: Thank you. Glad being here.Swyx [00:00:11]: We're also here with Vibhu. you were first coming to us or joining the latent space world because you were working on Kosmos at NVIDIA, and you did a paper. We loved it. you presented it as well, so thank you for doing that.Ethan [00:00:23]: I've actually, I also presented the MoEs twice at latent space.Swyx [00:00:29]: How did you actually hear about us? Did we reach out to you? Is that how it worked?Ethan [00:00:33]: No, actually, I-- the community. Like I realized, oh, there is this online community that people talk about AI and also learn from each other through papers every week through the Paperclip. It's very nice.Ethan [00:00:49]: I learned a lot.Swyx [00:00:49]: I think three years stop. We haven't stopped even on Christmas and New Years. many weeks I want to stop but it keeps going.Vibhu [00:00:58]: No, that was good. I think you had posted that you worked on a paper, and I was “Oh, very cool. We have Paperclip. Present then.”Vibhu [00:01:04]: But I might have reached out to you after.Swyx [00:01:05]: you-- because it's an amateur club, right?Swyx [00:01:08]: so it's very unusual and but we have sometimes paper authors come by and actually explain the paper. Today we just did, the poolside paper, which was apparently very good.Vibhu [00:01:18]: Came out yesterday.Vibhu [00:01:19]: pretty interesting, right? Fully open. They talk about everything, systems. So it's a good one. We'll, we'll recommend people to read it.Swyx [00:01:25]: Bring us up to speed on your transition to xAI, ‘cause I actually don't even know when you joined. just like tell the, tell the story about the sort of transition.From NVIDIA Cosmos to xAI: Scaling Video and World ModelsEthan [00:01:34]: Before xAI, I was working on Kosmos world model as in-- at NVIDIA. So Kosmos is, it's a giant video foundation models that can-- that aims to simulate the world and for-- it serves as a foundation of-- for all of the roboticists to build on top of. There, once I built the Kosmos one, I realized as this thing also has a scaling law similar to language model, we need to scale up the video models further. that's, that's why I realized I need to move to somewhere with much more compute resources. That's how ISwyx [00:02:13]: Than NVIDIA?Vibhu [00:02:14]: The GPU rich came themselves.Vibhu [00:02:19]: And timeline-wise, when was Kosmo? It was pretty early, right? It was open world model, open paper, everything.Ethan [00:02:25]: It was end of twenty-four.Vibhu [00:02:28]: End of twenty-four.Ethan [00:02:30]: Then at mid twenty-five, I moved to xAI. At that time-- I joined about the time when xAI was about to build video models and in multi-model models. There were no infra, no data, and no model, and it just-- as a few engineers, we built it in three months and released the first model, Grok Imagine zero point nine.Ethan [00:02:55]: And since then, I keep working on video models and move more from training and to post-training of the video models. For example, like a reference to videos, kind of like the cameo feature and, video extensions. And, before I left, I worked on a world model, leading a small team to focus on the real-time long horizon video generation.Building Grok Imagine From Scratch in Three MonthsSwyx [00:03:24]: Can you give like a rough roadmap of okay, you're on a brand-new team. Grok previously was only text, or they partnered with BFL for their image gen stuff. What do you-- what are the building blocks, right? You have compute, data you can procure somewhere. Like just what are like the sequence of things that people should think about when you're setting up a new team?Vibhu [00:03:43]: actually even deeper, not just data you can procure. You guys had to go through getting the data too, right? So you shipped it pretty fast, but yeahSwyx [00:03:51]: three months is likeVibhu [00:03:52]: From everythingSwyx [00:03:52]: actually like very surprisingly fast.Ethan [00:03:55]: One thing I say like thanks to my experience at NVIDIA, ‘cause first time when we were building Kosmos together, we built it, for about a year. So this is like the second time I do it. Roughly have an idea, what to do. I say the most important thing is the talent. Everyone were very strong and clever, very close with each other towards a common goal. So that speed up things a lot. So you reduce the communication bandwidth among people, and everyone can work towards the same goal. It's, it's like every day there's not that much meetings on the calendar, like maybe like a, like a sync a day, and after that it's, it's just all building. It was pretty fun at that time.Ethan [00:04:47]: And another thing is that xAI has very strong foundations of like data inference, model inference, and the supporting there can help the model develop a lot. When I look at, training models, I don't so actually the top important thing is like how many, how many iterations can you do, per day? and the more iteration can you do, you can, you can train the model much faster. So if you have very strong infra and you have a lot of compute, you can, you can train these models in very short period of time. That can give you a much larger buffer to, for errors, and it also gives you the opportunity to spot more bugs.Iteration Speed, Compute, and Debugging Model PipelinesSwyx [00:05:46]: What is an iteration? Is it like a few hundred steps or what are youEthan [00:05:50]: Let's say just the train-training the model, like from acquire new data and maybe design new algorithms and train a new model, maybe at smaller scale orSwyx [00:06:01]: So cycle time for like any hyperparam that you're searching.Ethan [00:06:04]: Cycle time and tune to like eval this model. Is this model better than my previous iteration?Ethan [00:06:11]: SoSwyx [00:06:11]: So it's like before you, someone had already set this up that you can iterate very quickly.Ethan [00:06:15]: I think the foundation there is extremely good forDeveloping and research models.Ethan [00:06:23]: And often I find is it-- this is kind of boring, but like a lot of the improvements does not come from new algorithms. It comes from finding small bugs here and there in the data pipeline, in the, in the model training pipeline. Those give, those give the biggest boost to the model quality.Vibhu [00:06:46]: It's interesting, right? So you say it's like small team, less communication bandwidth, but also a lot of quality is like find little bugs. It seems counterintuitive, right? You have a lot of people, you can iron out more of those, but it's interesting to see the other side, right?Swyx [00:07:00]: I also wonder, have you-- do you try using LLMs to look for bugs? I don't know.Ethan [00:07:05]: I remember at that time it was mid two thousand and twenty-five, so it's the coding model wasn't quite there yet. I remem- I remember like December two thousand and twenty-five, it was extremely good. Yeah, I've been, I've been using it at that time. It's, it's helpful. sometimes it produce codes that are kind of difficult to maintain, even though like the first time it built something extremely fast. But it gave the, like a spaghetti code, thousands of lines that I couldn't maintain, and the LLM itself couldn't figure out what's, what's wrong and how to improve on top of it. But now I find it much better. Yeah, I want to bring up another point here is now coding models are much more efficient and can help us implement stuff much faster. Compute might become a bottleneck again because previously, like if you want to train a new model, say you want to generate new synthetic data and then or write a new algorithm, it might take a few weeks. And during that period of time, you don't-- you might not have experiments to run. But now you can build that thing within a few hours, then you can immediately train a model.Ethan [00:08:24]: Now you have to have enough compute to try all of the ideas. So compute might be the bottleneck of iterating speed again.Swyx [00:08:36]: yeah, I actually, honestly, I think it's like kind of a stressful job because you're “Well, I should be trying everything, and if I'm not, then I'm not doing my job well.”Vibhu [00:08:48]: there's also the stress of you're eating thousands of GPUs per hour, which is very expensive and, compute can go to other researchers.Swyx [00:08:56]: You got the daddy Elon toVibhu [00:08:57]: You got daddy Elon.Ethan [00:08:59]: It wasVibhu [00:09:00]: But there's still finite amount of compute, like you want to use it, you want to use it well, you want more of it.Ethan [00:09:06]: That was quite stressful indeed. Yeah, I think one thing is the-- with coding models now, like a lot of these jobs can be automated, which is much better. A second, it's a, it's a marathon, so you got to maintain good health and, a regular schedule.Vibhu [00:09:28]: It's, it's hard to hear that when you shift from zero to nothing in two months.Swyx [00:09:32]: and, I think obviously the culture at xAI is very famously, people work very hard. one thing I did want to dive into, in our-- in the notes that you, that you sent ahead of time, you had specific comments about the cost of Video Gen training. presumably this is on the Colossus-1, right? the two hundred megawatt cluster. Any whatever you want to just share on that.Vibhu [00:09:54]: I think there's, there's three things we're talking about, right? So there's Video Gen, there's also the Image Gen model that you put out. Do you want to like complete the, okay, so zero to one, you have a few months. Just what are the stages of create Image Gen model?Swyx [00:10:06]: Oh, yeah, maybe I got distracted.How Image and Video Models Are Trained: Synthetic Captions, Tokenizers, and VAEsVibhu [00:10:07]: Sorry. and then, from there's Video Gen, there's Audio Gen. Would love to get into those next. But what is that first few months like? So small team, a lot of bugs, iterations, but what does it look like? Do we take something off the shelf? Do we just get data compute? What's, what's the few months like? How do you go to state-art Image Gen model? How do you just start?Ethan [00:10:28]: I cannot comment specifically how xAI did, but it's, it's a quite standard process. I can draw some, examples from Cosmos. So mainly it's building a video model, you actually need to build a image model first. And building these two models, the data you need is a hundred percent synthetic pair of language and image or language to video. Because on the, on the internet, actually, the videos don't naturally associate with text. So you can say, oh, like on YouTube, you have the title and you have the description and the commentsSwyx [00:11:11]: TitleEthan [00:11:11]: of a video, but usually they're not relevant to the video itself. And say maybe like the video is a natural scene of mountains or something, and the title is, I'm so happy today.Ethan [00:11:26]: So they have they have no correlation at all. So the first step is to, you have to generate synthetic pair of language with the videos. So you gather videos from the internet, and you use a VLM to caption the videos. So that part, here's a question, like how do you, how do you gather VLM to begin with? So if there's noSwyx [00:11:55]: You, so you fuse the model, right? LikeEthan [00:11:57]: Say if there's no like VLM exists, like how do you generate the text to the beginning, right? It's, it's impossible.Swyx [00:12:04]: I see.Ethan [00:12:05]: In the beginning, it's like you ask human to describe the video as detailed as possible.For example, you ask them to describe everything, like all objects, all characters, and all interaction and dialogues in the, in the videos. So that's in the protocol of Cosmos labeling. We require the objective we give to the labelers was that you have to describe the video as detailed as possible, such that a blind person hears a blob of text can reconstruct what the video is like from their head.Swyx [00:12:43]: Video or image? You're talking about images.Ethan [00:12:44]: Video or image, either one of them.Vibhu [00:12:47]: This was pretty common when we went from clip and DALL-E, right?Vibhu [00:12:51]: It's all training on really detailed captioning of images. So same is applied to video, but insteadEthan [00:12:57]: same appliedVibhu [00:12:57]: of using multimodal model to pass in video images and write rich descriptions, you can alsoSwyx [00:13:04]: I think there's this traditional perspective of supervised, or, very highly human curated thing. I feel like there's a unlock with unsupervised, right? Where like you have enough to bootstrap that you can just throw common corpus on it or, whatever. like unsupervised vision and language pairing, right? Like where you just have, interspersed image and text and it just learns. To me, that is the VLM breakthrough that is different from the clip, different from the LM era.Ethan [00:13:36]: It's interesting to see that you kind of need both data.Ethan [00:13:41]: For example, for theSwyx [00:13:41]: You need it to bootstrap it up. YeahEthan [00:13:43]: for the generative model training, there's also usually like a small percentage of unlabeled data. So the model is instructed to generate a video without any text instruction. That can also help the model generalize. So after this stage of generative synthetic pair, so, one important common step is to train a compressor or a tokenizer of the image or videos. So because, if you train-- If you can technically, theoretically train image or video models on pure pixels, but the problem is that the, it's, it's a lot of tokens. So like one image, it's, a thousand by a thousand, it's like one million tokens, one million pixels. It's impossible to train transformer on that. So it's, you need to train a tokenizer, which can go from image to latent space and latent space back to image.Swyx [00:14:45]: That's why we named the podcast.Swyx [00:14:48]: But, basically, you're talking about vocabulary science.Ethan [00:14:50]: so vocab.Swyx [00:14:51]: And so, what is, what is imp-- like a million is impossible?Ethan [00:14:54]: In generative models, the vocab is continuous. It's a continuous space. We can think about like you map an image to a vector. It's a, it's a fixed length vector. It's sixteen or forty-eight, something like that. And then you map that vector back to the image space. And the mapping is, has-- The mapping is patch-based. So you say you haveEthan [00:15:22]: a sixteen by sixteen patch and you match, you map that patch of pixels into this latent space.Swyx [00:15:29]: We've covered thisVibhu [00:15:30]: This is like the vision transformersSwyx [00:15:32]: VAEs,Ethan [00:15:33]: VAEs.Vibhu [00:15:34]: You basically compress your input, you do your generation, you're reasoning all that generation in smaller dimension, and then you project back out.Swyx [00:15:43]: VAE is a form compression, but I think the for me, the patching thing is from VIT, right?Ethan [00:15:48]: You can make those.Swyx [00:15:49]: Literally the, yeah, the paper is titled like sixteen by sixteen is all you need. something like that. and then I think also, people make a lot of comparisons with this kind of patching with convolutions.Swyx [00:16:02]: Which is you're, you're kind of re- reconstructing the old paradigm with the new.Ethan [00:16:05]: Actually, in VAEs, there are, there are both convolution networks and transformers. You can actually do both.Ethan [00:16:14]: After this VAE, so what you've got is you've got latent space tokens and you've got the language tokens. So now the training of the diffusion transformer, usually generative models use diffusion transformers. It is actually quite standard. It's, it's very similar to how you train a language transformer models. It's not that much difference. It's just the tokens, the visual tokens in, visual tokens out. The only difference is there's a denoising process. So you train the model to unmask some of the noise. So you add, you add random noise to the visual tokens, and then you train the model to remove those noise to generate the clean tokens. Any inference, the model can iteratively remove noise from a hundred percent noise.Swyx [00:17:12]: And then there's also, to speed things along on the tech tree of diffusion, there's CFG, and then there's, there's also, latent diffusion that, there's, there's someone in there. I think, somewhere along the line, obviously, like stability and all these other guys, pioneered a lot of this, architecture. I don't know if you want to get into that or just, or do the video side up to you.Bootstrapping Video from Image Models and Temporal CompressionEthan [00:17:37]: After you train such model, such image model, the reason it's a, it's a foundation for video models is that image models are cheaper to train, and they have much denser connection between language and text. So, sorry, language and images. For example, you train a billion, you train on a billion images, and there's a mapping from the text to the image. And the cost to train the same, like the, a billion, a billion text to a billion videos, that's much more expensive because videosNaturally have more tokens than images. Because the diffusion models, their understanding of, language purely come from this mapping. So if you don't have enough mapping, so if you only train on like a ten million videos or something, there-- you might not see enough language tokens in your training, so your model does not understand human intention enough. So that's why you really-- you train-- you first train this image diffusion models, and then you bootstrap the video model from there.Swyx [00:18:53]: One thing I did want to ask, because I-- actually, I think you're, you're the first per-- video model person I've ever talked to, I think. we've, we've like talked to Luma and all those folks. There's all these tricks in video compression where basically frame by frame there's not that much difference, so actually you don't have to regenerate or save the whole frame, right? but I think MP4 compression or something else like that.Swyx [00:19:16]: is it tempting to use that? Or as far as I can tell, everyone just treats it as, “No, we would just generate every frame.” Is that roughly the state-art?Ethan [00:19:27]: There are a few different approaches. Let's say first, like you want to just directly use MP4 compression and use that as the tokens for the transformers to train, right? So people actually have tried that, but the main challenge is the latent space for the MP4 tokens were not, were not very comprehensible for the models. It's, it's extremely hard to train on that. And there's aEthan [00:20:01]: So that's why they created VAEs, which creates more continuous, latent space, so the models can understand that latent space and learn from it much easier. Even within the VAEs, there are different difficulties of the latent space. So you can imagine something the simplest, the most naive VAE is like you have an image, and you just shuffle all of the images into a, into a vector. So you don't need to train any VAEs, right? But that latent space is extremely hard for models to train on top of. That's why there are some debate on like how do you compress the tokens. So you mentioned like you can compress frame by frame. Also, you can compress, the temporal dimension.Ethan [00:20:52]: The difference is if you compress the temporal dimension, you get a much higher compression rate. Because there's temporal redundancy between frames, because, this frame and the last frame, likely they are mostly similar, so there's only some small difference. for example, I think in 12.1 VAE, they have like a eight by eight by four compression rate. So the four temporal tokens are compressed into one tokens. That can save a lot of, save a lot of the context length. If you do it frame by frame, you have to do maybe like eight by eight by one. Your context length will be four times larger. That being said, the benefit of the frame-- per frame compression, we might come back to this later, is, real-timeness and interactivity. ‘Cause if you, if you strain the output of the model, frame by frame, you can-- the model can respond to any user request immediately. So if you have like a temporal four compression, four times compression, thenSwyx [00:22:06]: It might be laggyEthan [00:22:07]: there's a lag there in nature.Swyx [00:22:10]: So you're very pilled on this. let's just go ahead and bring it up ‘cause we have the visual prepared anyway. There's some frontier applications of real-time video gen. So Flipbook is one of the examples that went viral recently, right? What is Flipbook?Real-Time Generative UI: Flipbook, Neural OS, and Diffusion Front EndsEthan [00:22:23]: Flipbook is kind of like a web brow- web browser. You can see like it has the web bro- browser UI on top. The difference is all of the UIs are generated by generative image model in real time, and anything here are fake. But you can, you can explore inside this wor- this imaginary world. Say like we-- here we have engineering the Great Pyramid. Like the model generates this for us to understand how it works, and if we want to navigate around and understand further, we can click on some of the, some of the description here, and the model will generate a new page, new subpage describing the details we want to know about.Swyx [00:23:14]: So it's basically kind of we're playing a video, but it's pausing for our next interaction, and then it just plays the next thing based on our interaction.Swyx [00:23:23]: Which is kind of cool.Vibhu [00:23:25]: and you kind of decide your story. So this was, how do you make a pyramid? levering technique seemed interesting, right? It shows how do you take Okay, I want to know what is thisSwyx [00:23:35]: The demo, the demo tweet had more animation between frames.Vibhu [00:23:38]: I think it's just skipping,Swyx [00:23:39]: Oh, it's just skipping a lot of frames.Ethan [00:23:40]: they also have a video modeVibhu [00:23:42]: It takes a lot. There's a lot of peopleEthan [00:23:42]: but, a lot of people are using it.Ethan [00:23:45]: So it's not available.Vibhu [00:23:46]: There's a live video stream. We can try,Swyx [00:23:50]: So this is an example of the kind of future that you see at the extreme. We don't-- we're obviously not in it today.Swyx [00:23:56]: But in a world where inference is completely free this is better than generating code and text?Ethan [00:24:02]: So this is, this is a final state of where Viva will be at for word model, I think. Imagine internet doesn't exist, and then you type in google.com. Like what should, what should, what should a model show you?the model can imagine something, and this is what the model imagine. And these web pages, they completely do not exist. So I think as the inference costs come down, we are going to have generative UI for everything. If you think about how the coding model works, so they write code for a web page, and they render the code might be con- converted into binary, and the binary render the pixels on the screen. So we in machine learning, every time we have some breakthrough, obviously it's, it's more intuit. So why don't we have like user instruction to the pixel directly? So the generative UI will be user intention to the pixels directly. And say like even if I want email, let's say everyone have the same interface, but I want, I want it slightly different. I want the email to show to me like a TikTok, so I can swipe left and right for the emails. And or maybe you want something else. We can have completely different things. Or like I have I'm looking at, Instagram stories, and I don't like the Like button. I always may click it. And, generative UI resolved it. So it's going to be a revolutionary replacement of the interface. So in the future, we might have much more powerfulEthan [00:25:50]: LLMs and coding models running behind the scene. And in the, in the front-end, the diffusion model will actually be the front-end to show stuff to you. That's how I imagine it.Swyx [00:26:02]: Diffusion front-end, deterministic back-end.Swyx [00:26:04]: Something like that. I find that very expensive, but,Vibhu [00:26:08]: I find it interesting you called LLMs writing code on the back end deterministic, but okay.Swyx [00:26:14]: you write it onceVibhu [00:26:15]: Compare it toSwyx [00:26:16]: And then you execute.Ethan [00:26:17]: If you think about the cost, say, let's say H100 costs $1 per hour, and if you use this eight hours a day and thirty days, so, every month you're paying this two forty, you'll actually not wanna pay for that. That's even more expensive than Cloud Code Max. But if you think about the compute costs come down like two times every year, and I think the future will likely arrive like within few years.Vibhu [00:26:49]: It's everything, right? compute cost comes down, compute gets faster, model gets smarterEthan [00:26:54]: More efficientVibhu [00:26:54]: model gets smaller.Swyx [00:26:55]: I don't know why you say two times, ‘cause I think it's like 100 times. In language models, it is roughly one hundred to a thousand times every twelve to eighteen months, for the same given level of LMSys, ELO.Vibhu [00:27:08]: That's a net of everything, right? That's model performance alongside compute. So different than just compute costs come down. But, a very interesting future.Swyx [00:27:19]: So the web designers will have to shout out that accessibility is an issue, right? how do you deal with screen readers or whatever. But yes, this is higher bandwidth storytelling than anything you can possibly generate with code, right? So I think that's the rough idea.Ethan [00:27:34]: And I'd like to add a little bit that so human naturally have the maximum bandwidth when we are looking at things, look at videos, and we also have maximum output bandwidth when we are talking. So in the future, it might be something like we talk to AI models, and the AI model responds back with a generative UI. So that would be the maximum input and output bandwidth to interact with AI models before neural link happens.Vibhu [00:28:06]: And it's also very custom, right? Some people are very visual, some people are not as visual, right? They prefer the text. But the best thing about generative UI, right, it can also be text.Swyx [00:28:17]: There's another project that we wanted to highlight, which is the Neural OS. Kinda similar idea, but here you're literally operating, simulating an operating system with a video model.Swyx [00:28:27]: and you can play Doom, you can do Firefox. I find this like mildly less impressive, obviously, because it's an OS that I can run.Swyx [00:28:37]: But here everything is imagined.Vibhu [00:28:40]: I was, used to the Command+W to close the Firefox tab. It didn't crash. That's why I saidSwyx [00:28:45]: It's too immersive.Vibhu [00:28:46]: It's, it's too immersive for me.Swyx [00:28:47]: Too immersive.Vibhu [00:28:48]: I wanted to close the tab.Vibhu [00:28:49]: But yes, I can play generated diffusion.Swyx [00:28:51]: this is shockingly fast.Swyx [00:28:54]: Because I remember there was a demo about like maybe one to two years ago. Someone tried to do the first-person shooter with a image model. There was no consistency. It was very slow. But here it looks like realistically it's-- this is Doom.Vibhu [00:29:07]: I think there's two sides to that, right? There's okay, what is running a game? The heavy part of it is actually the game engine, all the lighting, all that stuff, the graphics. This is just kind of video, right? Like we've solved consistency. This is still, it looks like a few years old image generation. There's some temporal consistency, but it's, it's kind of just images stitched together as frame video. But it's a good visual representation to pi- to picture the future you wanna see, right? that's, that's what I see in these more so.Ethan [00:29:38]: This reminds me of how the video models gets better and better. So Neural OS is kinda if you just look at it feels like it's just a crappy version of the, like the Windows we could have, right? And, but the difference is, so the model, this model is overfitted on the existing operating systems. It can generate nothing different than that. But it's actually also similar to video models. So when we are training these video model, image model, we train them on internet. There's no imaginary supernatural stuff on the internet. But once we train this model, you can prompt the model to generate something supernatural that have never existed in the data set. So if you train your Neural OS or neural computer on the standard screen recordings on the entire internet. The model can imagine completely new interface to interact with the computer.Swyx [00:30:43]: This is one of those things that is magical to me. usually generalizing out of distribution is bad, but somehow we have learned some kind of internal world model that you say, this plus, but it looks like rainbows and butterflies, it'll do it and it will kind of make sense.Swyx [00:31:03]: So yeah, that's kind of cool. Yeah, I don't know if there's any comment more on there. I do, I do wanted to, I did wanted to touch a little bit more on the model architecture stuff, which I think you were getting. It's, really fascinating. We don't get a chance to talk about this enough. So one of the papers that we covered, we've covered every annual, segment anything release. and I don't know if you follow-- you're a computer vision guy, so youEthan [00:31:26]: I knowSwyx [00:31:27]: . So they did memory attention, which is kind of interesting. And I always think, anything where you can, across the temporal dimension, keep some consistency, I think it's, very fascinating, and I don't know if Basically, does that-- the CV side bleeding into video gen side, I think is underexplored, right? we talk about it for labeling, but actually you can borrow the architecture itself.Ethan [00:31:50]: There's, there's also complete different approaches, right? you brought up the term world model, so we went from video model to world model. There is diffusion, but there's also other approaches that people are doing. So maybe we get into those after as well,?Swyx [00:32:03]: He has a whole definition of world models and stuff. I feel like we threw a lot at you. Whatever you want to comment on.Why Video Models Are Expensive: Storage, I/O, and Training ScaleEthan [00:32:10]: I think one thing that we should actually comment back on is okay, so we were talking about the steps to train image gen to video model. One thing we don't see as much of is okay, you brought up the delta in training data, right? SoEthan [00:32:24]: you won't have as much a video model might not generalize, but what is the cost of training a large video model? So we know for LLMs roughly, okay, even like the poolside thing that came out today, right? It's a Gemma level model trained on roughly forty trillion tokens at this many H200s over this much time, right? You can see what is the exact cost of that. So how many GPU hours over how much H200 costs? So how do we do the back-end math of, same thing for video models, image models. How do you, how do you kind of break that down? I can share some back-envelope calculation. So surprisingly, video models is-- the cost is very-- is comparable to language models and obviously the largest scale is language model, maybe like a medium scale to language models. I said just storing the videos alone, it costs a lot. You can, you can maybe look up on AWS or something.Ethan [00:33:20]: You really, say if you have a billion videos and let's say, let's just say like each video, like five megabyte, then you need five petabyte to just store those videos. And also remember we talk about you use a VAE to compress the videos, and you also need to store, typically you need to store those continuous feature, in-- also in your storage. That's also comparable size with the videos themselves. So just storing these videos and the features is tens of petabytes alone. And,Swyx [00:33:58]: I just, I just looked up the calculation. Five petabytes on S3 Standard is one hundred K per month.Ethan [00:34:05]: AndSwyx [00:34:05]: It's comparableEthan [00:34:05]: and you needSwyx [00:34:06]: AndEthan [00:34:06]: And then like tens of petabytes, two hundred K. And even more expensive is you have the ingress and egress.Swyx [00:34:13]: Oh, yeah.Ethan [00:34:14]: Like you-- through the internet. You have to just to download those videos, I believe it's, it's more expensive on AWS than just storing those videos.Swyx [00:34:25]: Storing, yeah.Ethan [00:34:25]: And each training runs, you probably need to pull them once. If you train multiple times, it's, it's even more than that. So it's like just storing the network, those costs is just, it would be a few, a few millions per month to just storing everything, not to mention the GPU cost.Ethan [00:34:45]: AndSwyx [00:34:45]: my side tangent, the compute rental, like GPU rental is very efficient. There's one side, okay, you can be XAI and build your data center. Should we not just build our, storage compute as well? LikeEthan [00:34:57]: Of courseSwyx [00:34:57]: cloud cost compared to just,Ethan [00:34:59]: You save so muchSwyx [00:35:00]: store. Yeah, exactly.Swyx [00:35:01]: Especially with like egress and stuff. So.Ethan [00:35:04]: That's a good idea, but it also comes to-- there are some of its own challenges.Swyx [00:35:09]: Of course, of course.Ethan [00:35:10]: like people who build the GPU data centers, they might not expect this much, storage. And yeah, people build storage, typically they just build it somewhere with just CPUs.Swyx [00:35:23]: I just looked it up. Five-- AWS only charges for egress, not ingress. Tier five for five petabytes is two hundred and thirty K.Ethan [00:35:32]: Even more expensive than the storage.Swyx [00:35:34]: But storing is per month, right? You check in, then you cannot check out. so it's so cool. It's okay. So there's that side.Ethan [00:35:41]: So the TLDR, my backhand mathSwyx [00:35:42]: Data is larger than you think. Yes.Ethan [00:35:44]: my backhand math of GPU hours times GPU cost is also very much, I'm missing some storage.Swyx [00:35:49]: You're also-- you're basically like also more IO bound than normal training.Swyx [00:35:55]: Yes. ‘Cause like data loading, so caching everything, it becomes super important.Ethan [00:36:00]: So in Cosmos, we did a lot of optimizations to make it not IO bound. So, speaking of the training, actually training the model, the GPU cost, if you look up like the open source model, how big these video models are, I think like LTX has nineteen B parameters. That's a dense model. And people are also exploring, MoEs, so it might be twenty B active and, like a hun- hundreds B, total. So that's, that's even-- that's similar size as medium-sized LLM models. And if you, if you look at number of tokens-Uh, we disclose that in Cosmos. It's also like tens of trillions of tokens on the visual tokens. So putting this together, the cost of, training these video models, it's actually comparable with LLMs. Not to mention, the infra is slightly different from LLM, so it might be less efficient to train these models.Inference Speedups: Step Distillation, Consistency Models, and GANsSwyx [00:37:04]: Do you get the benefits of traditional diffusion speed-up? So for, images, there's LCM, LoRAs for, fine-tuning. There's, there's a lot of stuff that's beenEthan [00:37:15]: Flow matching.Swyx [00:37:16]: there's flow matching. There's a lot of stuff that's been done. there's some overlap that applies to diffusion on the inference side and stuff or?Ethan [00:37:23]: so the difference-- the inference side is a completely different story.Ethan [00:37:28]: I think for the training side, it might be a little bit hard to reduce that cost. And for the inference side, the biggest gain is from the distillation of these models. You can-- It's called step distillation, slightly different from knowledge distillation in LLMs. So you-- Typically, for flow matching models, you need like 100 steps or something. Like a distortion model even need even more, like 1,000 steps to generate a good image or video. A step distillation is try to learn to generate fewer step from the model itself. It's kind of like now we-- you use the full model to generate in 100 steps, and then you take a model that only generate 10 steps and let that model to learn from the perfect one.Ethan [00:38:25]: why this workSwyx [00:38:27]: Strong to weak seemingly.Ethan [00:38:28]: It is. It's kind ofSwyx [00:38:29]: DistillationEthan [00:38:29]: kind of like strong to weak. the-- from the modeling perspective, the strong model, the teacher model is trying to model the image and videos of inter-internet, and that distribution is extremely complex. But the step distilled model is just trying to learn from the teacher. The teacher is a model, and the size is fixed, as the distribution is much simpler than the whole internet. That's the intuition I have why step distillation can work. So usually these models serve in productions, they only run in a few steps. In Cosmos, I believe we have, we have like four step and eight steps. If you do some simpler task, image-image translation, it can even run in fewer step, like one step in Cosmos Transfer.Swyx [00:39:22]: I think this is the same intuition that guides a lot of the consistency model work. I sent you a link for, SCM. I don't know if you covered that. To me, that was actually one of, the most impressive papers I've ever seen from OpenAI.Swyx [00:39:34]: That this is the unifying grand concept of consistency models. I don't know if you have any comments on this.Ethan [00:39:41]: So there are, there are a few different approaches,Swyx [00:39:46]: Oh, yeah. Here it is.Swyx [00:39:47]: Two steps versus twenty or 100 steps, whatever. It's already done.Ethan [00:39:52]: So there are, there are a few different approaches, for example, consistency model, and there are also Actually, we shouldn't forget GAN. So GAN, actually, that was, that was the OG ofSwyx [00:40:05]: OGEthan [00:40:05]: step distillation ‘cause it trained just one step to begin with. So actually, a lot of, uh-- For example, there's a distribution matching distillation which use, which uses GAN, as one of the laws for distillation. It-- GAN just tells you, “Hey, generate an image,” and thenEthan [00:40:31]: it has a discriminator to tell, is this image real or not? So the model, the model just need to learn one of the distribution, not the full distribution. Because in training, the model is asked to reconstruct the ground truth image from the internet, which is extremely hard. And in-- When you're training GAN, it's a step process. It's just a, “Hey, you generate image. Does this image look as real as the image from the internet?” Which is a much simpler task. And, yeah, combining a lot of these approaches together, people typically do that, like consistency model and distribution matching and GAN, and we can get these few step models.Audio-Video Generation and Time AlignmentSwyx [00:41:21]: Then there's one step I wanted to add, which is audio and video.Ethan [00:41:26]: So, Grok Imagine zero point nine, I believe it's, it's a first audio video transmodel deployed at a large scale. SoSwyx [00:41:39]: And that was your first model?Ethan [00:41:40]: that was, Grok Imagine's first model. It's, it's audio video, joint generation. I think the hard part is, the modality alignment, ‘cause before this transmodel, we have, we have text to video alignment. We have this, correspondence between text and video. Typically, most of the VLMs, they understand images and videos. Video's very rare, and they don't understand audio mostly. And if you look at the audio generation on the LLM side, you can talk to them perfectly fine, but if you ask them to sing a song or something, it typically is not very good. Also, they don't have, they don't have music either. The hard part is thatUh, actually audio has two component. It has like a discrete component, a continuous component. The discrete component is like the language.Ethan [00:42:44]: So when we speak, it's just, someSwyx [00:42:47]: It's an ASR issue, yeah.Ethan [00:42:49]: It's, it's text token with some characteristics, I would say.Ethan [00:42:54]: But musicSwyx [00:42:56]: I think the speech guys would disagree with this.Swyx [00:42:57]: Like disfluencies and then,Vibhu [00:43:00]: There's tones you can get angry.Ethan [00:43:01]: Well, I say largely.Ethan [00:43:03]: the mu- but the music is completely different. It's, it's very continuous, and you cannot model them like discrete tokens in language models. this is like the hard part for models is, not to mention we have to align text, video, and audio together.Ethan [00:43:26]: SoVibhu [00:43:26]: How?Ethan [00:43:28]: So significant-- some significant challenges are like-- So first, like we talk about as the VLMs, they cannot understand most of them cannot understand audio.Ethan [00:43:39]: So you have to have some way to do the synthetic data generation for audio. You have to caption the model, and that involve, that involve synthetic data and human data effort a lot. And not just surprisingly, most of the LLMs are very bad at recognizing, like the beat, tone, and the details of the of music. They can, they can give some general prediction of which song is this, but it's very hard to describe the details of the music. like we mentioned in image generation, like you have to describe image as detailed as possible so that someone blind can reconstruct that. So here is like someoneVibhu [00:44:32]: DeafEthan [00:44:32]: someone deaf can reconstruct how the music sounds like without actually listening to it. Maybe you can think of it need to have the-- or they call the script.Vibhu [00:44:49]: Subtitles, yeah.Ethan [00:44:49]: You gotta have all the details of the music, and the dialogue.Vibhu [00:44:55]: So is the challenge there typically stuff like music and audio, or is it just Like is there a baseline? Okay, there's enough data where we can understand, narration, conversation, but there's nuances in audio that's where you hit all the data issues or is it just from stage zero, you just do it all right?Ethan [00:45:15]: So one important thing is like the alignment. So the model, the model has to know like the video and audio, the, uh-- it has to have a time-based alignment, like at which time step the video and the audio token correspond to each other. But we actually don't have this kind of alignment for most of the other modalities. If you think about like text and image, text and video, they are loosely aligned. So you can, you can have a description of what's going on in the video, but you don't have to exactly, You typically don't have exact description, oh, at, time step one second like what happened?Vibhu [00:46:02]: It's veryEthan [00:46:03]: At time step two second what happenedVibhu [00:46:03]: coarse. Yeah.Swyx [00:46:05]: So what was the ideal time step? You have to oblate it, and then it's like four seconds or something.Ethan [00:46:09]: So that comes down to how you design the model to, for the model to be aware of as a time, as a time modality. So the model is like a time aware. And that's something pretty unique if you think about LLMs. So if you ask LLM to complete a task, say they, uh-- you ask them and they will say, “Oh, this task will probably take twelve hours to complete,” and they come back in one hour. Say “I've already spent two days on this and I've exhausted everything.”Ethan [00:46:47]: So the LLMs them-themselves, they don't have a sense of time there.Vibhu [00:46:53]: I actually don't think that's just them not having a sense of time. I think it's somewhat based, right?Vibhu [00:46:58]: Like you tell someone, “Okay, go work on this feature. Go implement this,” there's a general understanding you would have of how long that would take without LLMs working at LLM speed, right? So you think back like two years ago, if I tell you to like build me like a new front end for latent space, have a search bar, have all this, you'll estimate that it'll take a few days, right?Vibhu [00:47:19]: So you tell an LLM, “Go build this.” It'll take me a few days. But I think it's somewhat grounded as opposed to them not having the best-- Not saying that they have a great understanding, but I think that example is like you can see where it comes from, right? You're trained on all over the text.Swyx [00:47:35]: They're, they're trying to estimate what a human would say.Vibhu [00:47:37]: because that's what the, that's what the data kind of represents. It's not themEthan [00:47:41]: It came from the corpus on the internet. People have a estimate of how much time.Vibhu [00:47:45]: And not even just in direct like training samples, right? Just your world understanding of tokens of how long stuff takes, right? Go read a book. It'll take you a while, right?Vibhu [00:47:56]: Even if you do nothing but read a book, it takes a few days. So yeah, LLM, I read it took me a few hours.Vibhu [00:48:01]: It'll take me a few hours to go through this research. But this is a tangent.Swyx [00:48:05]: Somewhat, yeah.Swyx [00:48:06]: This is a train of thought I haven't really expressed until now is, which is basically like a full world model must also be recursive, meaning that the participant in the world model must also be aware that they have a world model. which is like this whole recursive thing down the, down the line. but yes, and that the world model can be wrong and that they need to update it and blah. Yeah. We've, argued this on the, newsletter as well, that there needs to be sort of recursive or adversarial world models.World Models: Real-Time, Long-Horizon, Interactive VideoVibhu [00:48:34]: just, to ask, how do you define world model?Swyx [00:48:38]: Oh, yeah, let's go there.Ethan [00:48:40]: SoVibhu [00:48:40]: So just for context, we talked about, video generation, and then there's a-- if you say there's a distinction between world models, what's your, what's your definition? How do you see the two?Ethan [00:48:53]: So disclaimer, I'm not going to debate, what is world model. Yeah. there are many definitions, so I'll just talk about my definition. Since I came from the multi-model, multi-model domain, so mainly talking from video. So world model is like real-time interactive long horizon videos. So there are three parts. so we-- let's talk about them one by one. So the so interaction, so we just, we just look at Facebook and neural computer. So the interaction part of it, so you, world model can allow you to interact with them through keyboard, mouse, and maybe also voice. So these all is-- all is a modality. You can, you can interact with the model, and the model should respond reasonably. Second part is real time. So once you, once, say, you move your mouse, if, say, the world model generate a game, how fast can the game respond? So if you're like professional CS: GO players- -my say, oh, you have to respond- He's beginner within sub ten milliseconds or- Yeah even less. So that's not most of the- No, sixty FPS. Let's go. Oh, three hundred FPS. Oh, five hundred FPS. Wait. okay, yeah. I didn't do the math, but yeah, okay. Uh- Yeah, three hundred FPS, that's a three millisecond. So you have to respond- Oh, s**t. Okay. YeahEthan [00:50:29]: within a millisecond. Most of the video models cannot do that. Yeah. And, but if you, say, if you have a video model that is, say, like a digital human, the response time might be more generous. Maybe typically, for real-time voice interaction, it's like two hundred millisecond. So that's, that's much more generous. But even two hundred millisecond is pretty, it is pretty tricky, ‘cause remember we mentionedEthan [00:51:01]: you have this, temporal compression coming from the VAE. So if you, if you don't compress the temporal dimension, your sequence length is going to explode. So if you want to have this real-time, real-timeness in your model, you have to do is one context problem. And the third part is long horizon, ‘cause we-- if you're not going to just play with, video games just, a few seconds, most video models only a few seconds. We're going to play with minutes, hours. The model have to be able to generate long-form content.Ethan [00:51:42]: So putting these three together, it's, real-time, long horizon interactive videos. I think the final state will be, for example, like a video, a video version of Playbook, where you can, you can interact with, a neural computer. You move your mouse, and you click on the generative interface, and it will reply to you through pixels- generating in real time. But getting there, it's, it's a very long way to get there. So one of the first step, at Grok Imagine, where I led a small world model team there, was to build video extension. So, video extension- it's the first step of interactivity. Yeah. It's, it's the first step. Yeah. So it's the first step- You have it here, video editing, yeah. Yeah. Yeah. So the first step is because, this unlocks long horizon videos. Typically, for most of the video generation models, you give it a prompt or an image as an initial frame. You generate video, that's it. That's just, one time, done. And some creators would try to, use the last frame as a first frame for the second video. It can-- sometimes it works, but if you do it a few times, it says the quality would decrease. And- It doesn't have that context- Yeah over the full video, so the temporal- Yeah, exactly. Yeah, ‘cause you only gave it the last frame, of course, right? Yeah. Exactly. And- it's actually a pretty fun hack. if you've seen like- Oh, no, he's saying something better. Yeah. And for example, like Vue, I remember Vue 3 has like a second context of the last video. It is slightly better than using the last frame, but it has the same problem-- similar problem that it, the quality would decrease. if you extend a few times to, one minute, the video quality would look much worse than the first video. Second, another problem is that the model doesn't have long-range knowledge of, what's happening before. Say, if they generate some dialogue, some, two people speaking, and their voice might change, over some time, especially if the second conditioning, it does not cover the previous context. So these are the core challenges. So the Grok Imagine video extension, it has historical context of all of the previous generated videos. It can, It has, it has the context of, who is speaking and what objects have appeared and everything, having that to generate the next video. So if we naively do this, you can imagine, just, put all of the previous history video tokens into the context. The context lens will easily explode. Especially for video models, that can be like a few, a few million context, I would imagine- context lens. Yes.Yeah.Swyx [00:54:58]: Let's run with that.Ethan [00:54:59]: for example, like in Cosmos, I think just five seconds of video is like a fifty K or sixty K number of tokens. So like if you do, if you do fifty second, that's a five hundred K tokens. If you do longer than that, easily explode. This long horizon, problem was the first step we're trying to solve world model. It turns out people, yeah, people love video extension. Like a lot, a lot of the creators love using video extension to create longer form videos. This is the part I liked that you have a, you have an intermediate step toward the final goal instead of just a straight shot to the final version very much.Swyx [00:55:48]: But I can see you have a strong vision of where we want to end up.Long Context, Redundancy, and Efficient Interactive VideoVibhu [00:55:51]: Does it seem like it's an efficiency issue? okay, we're at a few million tokens context,. If you draw the parallel to language models, we had very short context, two thousand, eight thousand, then, you scale it up one million, ten million. sure, there's effective context, but at the end of the day, it's just what's it worth? sure, there's a whole training data side. In video, it might be slightly easier ‘cause we have a hundred million token video, right? Just take a movie with the full context there. Like is this efficiency from an inference standpoint that like it's expensive, but we know how to solve it? Or like why is this not the approach? So like my broader point was on your second point of world models, you say it needs to be interactive and live, right? You should be able to play a game and see the interaction live. So one thing I see with research is a lot of what you actually serve is different than what you build, right? So we talked about distillation. You train big model, you distill it, you do quantization, speculative decoding. We do all this stuff to serve it efficiently. Should we not just have a solution, like a world model that can interact well, do inference optimization, serve it, distill it secondary, so make it real time after you solve it? So like a-- another parallel is say, continual learning, right? What we need is someone to solve it and show it works inefficiently. Give it a few years, people will make it efficient. Same thing with regular attention, right? It worked. Over a few years, people have different forms of attention, and we've scaled it to be efficient at log context,? So kind of two things there, right? One is it seems like it works. You've scaled it. Can we not just scale it a lot more efficiently over time? Do we need a separate approach if this works? And same thing with interaction, right? if we can get it done, like if we can solve some way that it works, we can solve making it more efficient from an inference standpoint later.Ethan [00:57:53]: that's actually a very good point. So in videos, there's actually a lot of redundancies. So we solve a lot of the pixel redundancy from VE, but there's more redundancy in long range and long horizon videos. Say, if a character appear in the first clip and then it disappeared, it only reappear at the end of the video, you probably don't need the-- the context, like in the middle of the generation. So you only need that character, where you need. So that's why, I helped build another feature. It's a reference video.Vibhu [00:58:36]: Is it here?Swyx [00:58:36]: is it the same model release or different one?Ethan [00:58:39]: It's a different one.Ethan [00:58:41]: You probably need to search onSwyx [00:58:43]: I'll find itEthan [00:58:43]: X reference to video.Ethan [00:58:46]: So reference video allow you to like upload up to seven images as condition and generate the video. Say, if like I want-- it can, it can be characters or objects or even scenes. Say like I want, I want condition on, Sean's selfie and holding a bladeSwyx [00:59:07]: We have a dogEthan [00:59:08]: or whatever.Swyx [00:59:08]: We put the dog in the thing.Ethan [00:59:09]: you can put them there and the video models will generate the video from and copies the context over. So that can solve a lot of the problems there, like the long context problem. It doesn't need to have a very long context, but it's-- I feel like it's an intermediate solution. The modelSwyx [00:59:29]: It's cheating.Ethan [00:59:30]: the model should be able to like selectively know, where should I draw the references. So say if I want to generate a movie, I generate it autoregressive, like a ten second at a time or something. And now this character appear, I can look back to where it first appear and, bring that back. Yeah, this one, I put the references. Yeah, that's, Optimus, Einstein myself, Annie.Vibhu [01:00:02]: Oddly enough, I used Grok Search to find it, and it pulled your LinkedIn post. But yeah we found it.Ethan [01:00:08]: Interesting.Vibhu [01:00:10]: ButxAI's Underrated Work, Culture, and WatermarkingSwyx [01:00:11]: this is a problem. This is not your fault, but like XAI doesn't communicate all this work that you do very well because they just have the model release and then that's it. But actually, these details are very good.Swyx [01:00:22]: As far as I understand, everything you just described is state-art, like no one else has done it.Vibhu [01:00:30]: A lot of-- yeah, I have a lot moreSwyx [01:00:32]: And then, and then you just put this blog post with the cookies. I'm this is not enough,?Swyx [01:00:37]: but I, obviously this is like the high level numbers that people want to know. But no, okay, soVibhu [01:00:42]: And I wonder, like part of that is also some labs don't share research into what happens. And ifSwyx [01:00:50]: No, but this is literally bragging about how good they are, right?Swyx [01:00:54]: Like, why would you not say that you are capable of extending with full context? this is not a secret sauce. This is like we did the work. yeah, I don't know.Ethan [01:01:02]: different labs have slightly different communication styles.Swyx [01:01:07]: Anyway, if anyone from XAI is listening we are always happy to help you tell your story. Yeah, okay, so you did references, and I think, I think kind of the point you're, you're making is it is sort of like a kludge, right? this is-- you can do seven, but what about 100?Swyx [01:01:23]: Right? Then you need a completely different thing.Ethan [01:01:26]: So I think it's-- this is, a mechanism to, select the context from the history, and you might not put the entire history into the context. for example, there's a paper called Frame Pack, which haveEthan [01:01:41]: a heuristic that the latest history, the last one second, I put the entire history, and the history before that, I would, compress it and makes the video smaller. So they follow this pattern, this build overall pattern that the maximum sequence length is fixed. So the further you are from the current frame, you have a smaller image. So this is just a heuristic. I think it can be more automatic. The model is aware like which history part of it can be select. So this part of the research is actually being actively, worked on by a lot of people. It's also quite interesting. I feel this is actually, this part of long context is a little bit ahead of the LLM part.Ethan [01:02:31]: So for example, like in LLMs, if you-- so contexts keep growing. Let's say if you call tool and the tool call history is extremely long, that's still in context, and keep growing, keep growing. Even if you switch the topic to something else, the whole context was there. There are some agentic harnesses that help you to, say, prune the tool results and, prune Like when you, when you query a file, only show like the top 200 lines or something. Those were very heuristic-driven.Swyx [01:03:08]: For listeners, we did a write-up on the cloud code, leak where there are eight different kinds of pruning, including like you prune the tool results and all that. So you can, you can read up on that kind of thing.Ethan [01:03:17]: I think, one breakthrough in continual learning might be like a way to automatically, manage its own context.Swyx [01:03:27]: These are all heuristics, and they will be replaced by machine learning.Ethan [01:03:30]: InterestinglyVibhu [01:03:32]: TheEthan [01:03:32]: the same thing is being researched in both LLMs and video models.Vibhu [01:03:36]: The interesting thing is also like in the paper you showed, it's actually happening at the model level, right? Compared to like language models, sure, we have base attention, but we'll do our own compression, we'll do our own pruning, which is separate from model error.Vibhu [01:03:49]: Eventually, it all just boils in, hopefully.Swyx [01:03:52]: I think this is a form of like attention, but like also know sort of reasoning attention. I feel like that's different than normal attention.Swyx [01:04:03]: Does that, does that make sense?Ethan [01:04:04]: It's, it's different in the sense that attention, not to mention, set sparse attention aside,

Ein Standpunkt von Jochen Mitschka.Der Iran wurde durch eine Koalition von angreifenden Staaten schwer beschädigt. Dieser Angriffskrieg wurde ermöglicht durch Golfstaaten, welche z.B. Überflugrechte gaben, und den angreifenden Staaten Militärbasen erlaubten. Dadurch steht dem Iran völkerrechtlich Schadenersatz zu. Im Fall von Nicaragua war auch schon einmal ein solches Urteil gegen die USA gefällt worden. Und das sogar ohne einen offenen Angriffskrieg. Schauen wir uns die Details an, um dann über die Mautzahlungen zu sprechen.Der UN-Botschafter Amir-Saeid Iravani richtete Schreiben an den UN-Generalsekretär und den Sicherheitsrat und erklärte, dass die Golfstaaten verpflichtet sind, dem Iran volle Reparationen zu leisten — einschließlich Entschädigungen für alle materiellen und moralischen Schäden. Die Schreiben waren eine Reaktion auf Briefe, die sechs arabische Länder — Katar, Bahrain, VAE, Saudi-Arabien, Kuwait und Jordanien — an die UN gerichtet hatten, in denen dem Iran vorgeworfen wurde, diese Länder angegriffen zu haben.Der Vorwurf des IransDer Angriff erfolgte laut Iran jedoch am 28. Februar durch koordinierte Luftangriffe der USA und Israels, die beide Länder als Maßnahmen gegen Irans Nuklear- und Raketenprogramme bezeichneten. Die vorgeworfenen Handlungen der Staaten, gegen welche der Iran dann vorgegangen war, umfassen laut Iravani:Gewährung von Zugang zu Militärbasen, logistische und operative Unterstützung, Geheimdienstkooperation, Luftverteidigungskoordinierung sowie Nutzung des Luftraums.Dieser Schritt richtet sich gegen die strategisch wichtigen Staaten an den Knotenpunkten, die während des Konflikts seit Langem amerikanische Militärinfrastruktur beherbergt haben. Das iranische Dokument benennt ausdrücklich Katar, Bahrain, Kuwait, Saudi-Arabien, die Vereinigten Arabischen Emirate und Jordanien und wirft ihnen vor, aktiv an feindlichen Operationen gegen die Islamische Republik teilgenommen oder diese ermöglicht zu haben. Iravani argumentierte, dass diese Nationen nach geltendem Völkerrecht rechtlich verpflichtet sind, umfassende Entschädigungen für die materiellen und moralischen Schäden bereitzustellen, die der Iran erlitten hat. Dieses diplomatische Manöver erfolgte, während der Iran kürzliche Aussagen US-amerikanischer Beamter nachdrücklich ablehnte und Gegenansprüche von Ländern auf der südlichen Seite des Persischen Golfs als vollständig unbegründet abtat.Indem der Iran diesen Kampf vor den UN-Sicherheitsrat trägt, signalisiert er, dass der fragile Waffenstillstand den regionalen Monarchien keine Straffreiheit gewähren wird, die ihren Luftraum und ihre Stützpunkte den US-Streitkräften geöffnet haben.Der UN-Menschenrechtsrat verabschiedete seinerseits eine Resolution der Golfstaaten und Jordaniens, die Irans "unprovozierten und vorsätzlichen" Angriffe verurteilte und volle Reparationen für deren Opfer forderte. Ein Beispiel, wie UN-Organisationen dabei behilflich sind, Täter zu Opfern umzudefinieren....https://apolut.net/irans-mautforderungen-ersatz-fur-reparationen-von-jochen-mitschka/ Hosted on Acast. See acast.com/privacy for more information.

Vae soli!, dice el libro del Eclesiastés: ¡Pobre del que va solo! Pero nosotros nunca vamos así porque una Persona divina nos ha sido dada. Habita en nosotros el Espíritu Santo, y nos mueve con sus mociones y sus dones. Dentro de estos, pensemos en el superior, el de Sabiduría, que nos hace gustar las cosas de Dios. Podemos preguntarnos si el gozo de lo divino ha sido creciente en nuestra vida.

Ölpreis auf 40$ – während die Masse mit 100$ rechnet? Der Austritt der VAE aus der OPEC ändert das Spiel für Anleger komplett. Ich erkläre dir in diesem Podcast die analytischen Hintergründe zur Swap-Line der USA, dem Produktions-Stopp der Quoten und warum billige Energie für die KI-Revolution überlebenswichtig ist. Vereinbare jetzt dein kostenfreies Strategiegespräch: https://jensrabe.de/Q2Termin26 Livetrading jetzt auf: https://aktienkannjeder.de Trage dich hier in meinen täglichen kostenfreien Newsletter ein https://jensrabe.de/Q2NewsYT26

Coles-Rabatte laut Gericht irreführend / Kritik an neuem deutschem Heizungsgesetz / Tasmanische Senatorin Tyrrell tritt Labor bei / Opposition plant strengere Migrationsregeln / Keir Starmer parteiintern unter Druck / CENTCOM lockert Iran-Hilfsblockade teilweise / VAE dementieren Treffen mit Netanyahu / Grüne fordern schärfere Regeln für Parteispenden / Spanien plant strengere Social-Media-Regeln

Diesmal: Jugendlichen-Vergrämer, Pressefreiheit, VAE verlassen OPEC, Zweitmeinung, Klimakonferenz in Santa Marta, Sham Jaff zu Sahara-Wasser, Regenwald, Umwelt-Verbrechen. Mit einem Faktencheck von Nándor Hulverscheidt und einem Limerick von Jens Ohrenblicker.

Hey everyone, Alex here

Het zijn vreemde tijden, maar je verbaast je bijna nergens meer over. Een van de opmerkelijkste berichten deze week was het vertrek van de Verenigde Arabische Emiraten, de VAE, uit de OPEC. Toen dat nieuws naar buiten kwam, was er geen enkele reactie in de olieprijs te zien. Sterker nog, na wat verse dreigende taal en de nodige oorlogszuchtige memes uit de US, schoot de prijs van een vat naar een vers record van $ 125. Opvallend was dat de lange termijn olieprijs een heel ander beeld liet zien. Olie met levering in bijvoorbeeld 2030 bewoog helemaal niet, noppes, nada. Je ziet veel Midden-Oosten experts een beetje gissen naar de reden achter het vertrek. Al langere tijd botert het niet tussen Saoedi-Arabië, the leader of the OPEC band, en de Emiraten. Daarnaast wil de VAE al langere tijd haar productiecapaciteit uitbreiden. Als ze uit de OPEC stappen hebben ze alle vrijheid om dat te doen. OPEC-vergaderingen waren altijd al vol strijd. Alle leden willen een hogere olieprijs, maar niemand wil minder verkopen om dat te bereiken. Er zijn te veel free riders. Een van de belangrijkste is de Verenigde Staten. Die produceren steeds meer, waardoor de OPEC een steeds kleiner marktaandeel heeft. Het groepje OPEC landen moest daardoor steeds meer capaciteit inleveren om hetzelfde prijseffect te bereiken. Ook na de uitbreiding met een aantal landen naar OPEC+ in 2016 is er structureel niet veel veranderd. Voor de lange termijn maakt het nogal wat uit. De extra productie van de VAE zal de vraag – aanbodverhoudingen verder uit het lood slaan. Al voor de Iran oorlog was er al sprake van een onbalans. De OPEC leden moesten alle zeilen bijzetten om de olieprijs een beetje hoog te houden. Er was flink wat overaanbod. Tel daarbij ook nog de verwachte stijging van het aanbod uit Venezuela. Van de beloftes over voorspoed en democratie zal waarschijnlijk weinig terecht komen. Maar die extra olieproductie, daar mag je wel van uitgaan. Wie iets wil zeggen over de olieprijs moet niet alleen naar het aanbod kijken, maar ook naar de vraag. De Iran oorlog heeft ons geleerd dat spreiding van energiebronnen essentieel is. Er zal daardoor meer vraag naar alternatieven voor fossiel komen en meer uit andere regio’s. Ook als de Straat van Hormuz ooit weer eens een keer opengaat zullen eerst de leeg getrokken voorraden moeten worden aangevuld. En gezien deze olieschok ervaring zal menig land de neiging hebben om extra voorraad aan te houden. Dus op korte termijn zal de olieprijs nog wel even hoog blijven, maar op lange termijn is het een heel ander verhaal. Dat is misschien een schrale troost als je weer eens de spaarpot van de kinderen moet stuk slaan om te kunnen tanken. Over Corné van Zeijl Corné van Zeijl is analist en strateeg bij Cardano en belegt ook privé. Reageer via c.zeijl@cardano.com. Deze column kun je ook iedere donderdag lezen in het FD. See omnystudio.com/listener for privacy information.

Daily Snippet vom 29.04.2026 Warum verlassen die VAE die OPEC ausgerechnet jetzt, wo Öl bei über 100 Dollar steht? Ganz einfach: Sie können 4,8 Millionen Barrel am Tag fördern, werden aber von der OPEC auf 3,4 Millionen limitiert (30 % weniger!). Zudem wandelt sich der einstige Ally Saudi-Arabien gerade zum harten Wettbewerber. Das Timing ist perfekt, weil der Market Impact während des aktuellen Iran-Dramas minimal ist. Die ganze ungeschönte Wahrheit hinter dem Kartell-Beben gibt's heute im Blog. https://www.julianhosp.com/de/blog/daily-snippet-29-04-2026 —— Folge mir für ehrliche Finanz-Einblicke! Montag bis Freitag: Dein persönliches Finanz-Audio. Kompakt, klar und mit den wichtigsten Marktinfos für deinen Vorsprung:

VAE weg uit OPEC | Aziatische landen zoeken energie bij Rusland | Oekraïense drones raken Russische olie Iran presenteert een driefasenplan voor een staakt-het-vuren, maar koppelt dat aan tolheffing in de Straat van Hormuz en het opheffen van de blokkade, waardoor Trump het voorstel direct afwijst. De economie in Iran stort intussen in: staal- en tapijtsector liggen grotendeels stil, de internetblokkade kost miljarden en de Nationale Veiligheidsraad bereidt zich voor op mogelijke opstanden. Trump raakt door zijn Iran-politiek in een uitputtingsslag verzeild, met hogere olieprijzen, dalende approval ratings en beperkte ruimte om verder te escaleren. In de regio schuiven de Verenigde Arabische Emiraten weg van OPEC en zoeken ze nieuwe bondgenoten, mogelijk in de Europese Unie, terwijl Hezbollah met geavanceerde FPV-drones Israëlische doelen aanvalt. Tegelijk groeit in Azië de energieafhankelijkheid van Rusland, wat de geopolitieke kloof tussen Oost en West verdiept. Oekraïne opent een nieuw front met massale drone-inzet, schiet tienduizenden vijandelijke drones neer en treft Russische olie-installaties en schepen tot diep in het achterland. Kyiv bouwt een wapenindustrie met honderden bedrijven op, produceert miljoenen drones per jaar en profileert zich als toekomstige leverancier voor Europese defensies. Terwijl Rusland zelfmoordachtige infiltratiemissies uitvoert in de Donbas en zijn overwinningsparade moet inkrimpen uit angst voor drones, zoeken landen als Finland en Estland de nauwe band met Oekraïne juist als garantie voor hun eigen veiligheid. [Samenvatting geschreven door AI en gecontroleerd door mens] Over de Podcast Arend Jan Boekestijn en Rob de Wijk gaan onder leiding van Hugo Reitsma op zoek naar de nieuwe wereldorde. Wat betekenen oorlog, machtspolitiek en economische verschuivingen voor Europa en Nederland? In elke aflevering duiken zij in de geopolitieke actualiteit. In 2022 werd Boekestijn en De Wijk uitgeroepen tot winnaar in de categorie Nieuws & Politiek tijdens de Dutch Podcast Awards Reageren? Op X: @ajboekestijn en @robdewijk Bluesky: @hugoreitsma.bsky.social Mail: boekestijnendewijk@bnr.nl Over de makers: Arend Jan Boekestijn is een Nederlands historicus en voormalig politicus. Hij studeerde geschiedenis en politieke wetenschappen aan de Vrije Universiteit in Amsterdam. Boekestijn is voormalig Tweede Kamerlid (tot 2009). Sinds 1989 is hij verbonden aan de vakgroep geschiedenis van de Universiteit Utrecht en sinds 2016 lid van commissie Vrede en Veiligheid van AIV. Rob de Wijk studeerde eigentijdse geschiedenis en internationale betrekkingen, promoveerde op kernwapenstrategieën, werd hoogleraar in Leiden en richtte in 2007 het Den Haag Centrum voor Strategische Studies op. Hugo Reitsma studeerde rechten en politicologie. Hij werkte eerder als politiek verslaggever en vanuit verschillende conflictgebieden. Hij is auteur van het boek ‘Boekestijn en De Wijk voorspellen de toekomst’ (november 2023).See omnystudio.com/listener for privacy information.

Erfahre hier mehr über unseren Partner Scalable Capital - dem Broker mit einem der besten YouTube-Kanäle zu Aktien & Investments. https://www.youtube.com/@scalable.capital/videos Coca-Cola wächst mit Kleinvieh. UPS spart Milliarden, enttäuscht trotzdem. Atlas Copco liefert an Chipfabriken. Novartis leidet unter Patentabläufen. VAE verlassen OPEC. Rivian-CEO verdient 400 Mio. $. Bayer kämpft vor Gericht. Init hat Auftrag. OpenAI verfehlt interne Ziele, Gemini und Claude klauen Marktanteile. Oracle und Softbank leiden mit. Spotify verpasst 300 Mio. Abos, investiert dafür stark in KI. Corning wächst im Glasfasergeschäft, der Rest stagniert. Airbnb (WKN: A2QG35) will Hotels auf die Plattform holen und greift Booking (WKN: A2JEXP) und Expedia an. Niedrigere Gebühren sollen locken. Spannend: Booking ist mit KGV 17 deutlich günstiger als Airbnb mit KGV 30. Diesen Podcast vom 29.04.2026, 3:00 Uhr stellt dir die Podstars GmbH (Noah Leidinger) zur Verfügung. Learn more about your ad choices. Visit megaphone.fm/adchoices

De Verenigde Arabische Emiraten zorgden gisteren voor een verrassing door als een van de oprichtingslanden uit de Opec te stappen. Maar volgens macro-econoom Edin Mujagić is de olieprijs door kartelafspraken van deze organisatie in de afgelopen decennia ‘structureel te hoog geweest’. Nog even: waarom stappen de Emiraten uit de Opec? Er is al enige tijd frustratie in het land over het feit dat het als een van de weinige olieproducerende landen wel de capaciteit heeft om snel meer olie uit de grond te halen, maar daarin worden beperkt door de productiequota die binnen de Opec worden afgesproken. Nu stellen de Emiraten dat het in hun nationaal belang is om meer olie te kunnen produceren en verkopen, en dat ze zich daardoor minder willen laten begrenzen door de afspraken binnen de Opec. Maar daar is de Opec toch juist voor opgericht? Ja, maar sinds de oprichting in 1960 is de wereld veranderd, de belangen liggen nu anders. Maar de Verenigde Arabische Emiraten zijn wel een groot lid en een van de weinige landen met veel oliecapaciteit. Met het vertrek van de Emiraten daalt het marktaandeel van de Opec met zo’n 10 procent naar 40 procent. Daarmee zijn ze nog steeds een grote speler, maar wel minder dominant. Dat lijkt mij eerlijk gezegd goed nieuws. Dat heeft onder andere te maken met het feit dat de Opec een kartel is: verboden samenwerking tussen concurrenten die afspraken maken om de concurrentie te beperken. Als bijeffect gaan de prijzen vaak omhoog. Kartelvorming is economisch verstorend. Maar we hebben het hier over olie. Ligt dat anders? Een kartel is een kartel, en de Opec is daar een schoolvoorbeeld van. In Nederland, en in de Europese Unie en de Verenigde Staten, treden mededingingsautoriteiten al op bij de kleinste vermoedens over verboden prijsafspraken of kartelvorming. Maar de Opec opereert in het openbaar, er is een secretaris-generaal en een volledig ambtelijk apparaat. De lidstaten vergaderen in Wenen, in de Europese Unie nota bene. Het is dan ook eigenlijk heel vreemd dat we een organisatie die de prijs beïnvloedt van misschien wel een van de belangrijkste grondstoffen in de wereldeconomie – olie – al zo lang gedogen. Dus, jij pleit voor een verdere ontmanteling van de Opec? Nee. Ik zeg alleen dat het goed nieuws is. Als door het uittreden van de Verenigde Arabische Emiraten de macht van de Opec wordt gereduceerd, dan betekent dat simpelweg dat de olieprijzen op termijn eerder omlaag dan omhoog zouden moeten gaan. Dat impliceert ook dat de olieprijzen van de afgelopen 40, 50 jaar waarschijnlijk structureel hoger zijn geweest dan wat een volledig vrije markt zou hebben bepaald. We hebben dus met z’n allen langdurig hogere prijzen betaald. In die zin is dit goed nieuws. In het kader van de energietransitie kun je dat echter anders beoordelen. Daar zou het juist helpen als fossiele brandstoffen structureel duurder worden. In die zin kan het vertrek van de VAE uit de Opec de energietransitie juist in de wielen rijden. Kunnen de Verenigde Arabische Emiraten een lagere olieprijs beter opvangen dan andere landen? De Verenigde Arabische Emiraten hebben als een van de weinige olieproducerende landen al vroeg gezegd dat olie nu veel oplevert maar in de toekomst hoeft dat niet zo te zijn. Daarom is er sterk geïnvesteerd in de ontwikkeling van bijvoorbeeld toerisme. Dat betekent dat de overheid minder afhankelijk is geworden van olie-inkomsten voor de begroting. Daardoor kunnen zij een lagere olieprijs beter verdragen dan veel andere landen die nog sterk leunen op olie-export. Conflicten in het Midden-Oosten hebben, letterlijk en figuurlijk, schade aangericht. Het imago van Dubai als veilige haven is flink aangetast.See omnystudio.com/listener for privacy information.

In der Aufzugbranche könnte ein Mega-Deal den Markt aufmischen. Außerdem: Commerzbank-Rohstoffexpertin Thu Lan Nguyen ordnet den Austritt der Vereinigten Arabischen Emirate aus der Opec ein.

Vae and Anima delve into the abandoned mining facility.

Afrika gilt oft noch als krisenbehafteter Kontinent, doch zunehmend rückt seine Rolle als wirtschaftlicher Hoffnungsträger der Zukunft in den Fokus. Wie sehen die ökonomischen Perspektiven aktuell aus und was müssen Anleger über diesen vielfältigen Markt sonst noch wissen? Antworten darauf gibt es in dieser Podcast-Folge, wie immer von Karl Matthäus Schmidt, Vorstandsvorsitzender der Quirin Privatbank und Gründer der digitalen Geldanlage quirion. Karl beantwortet folgende Fragen: Warum glaubst Du persönlich an die Zukunft Afrikas? Warum glaubt Karl Matthäuas Schmidt an die Zukunft Afrikas? (1:28) Wo liegen derzeit die größten Probleme auf dem Kontinent? (3:11) Wie sehen die Perspektiven auf Afrikas Jobmarkt aus? (5:24) Welche sind die wichtigsten Volkswirtschaften in Afrika? (6:50) Ist Ägypten für Investoren eine Turnaround-Story oder eine Dauerbaustelle? (9:00) Wie lange kann der „schlafende Riese“ Nigeria noch schlafen, bevor das Vertrauen der Investoren schwindet? (11:08) Wie steht Südafrika aktuell unter der neuen Regierung der Nationalen Einheit da? (13:52) Südafrika vs. Nigeria: Wo liegt langfristig die bessere Balance aus Risiko, Rendite und Verlässlichkeit? (16:32) Chinesische Investitionen in Afrika: Was war gut für den Kontinent und was eher weniger? (17:51) Mit welcher Motivation investieren die Vereinigten Arabischen Emirate in Afrika? (19:18) Was verbirgt sich genau hinter Europas Abkommen für „sauberen Handel und Partnerschaft“ mit Südafrika? (20:02) Sollte man als Privatanleger in Afrika investiert sein und wenn ja, in welcher Form? (21:20) Welches Land oder welche Entwicklung in Afrika findet Karl Matthäus Schmidt persönlich besonders spannend? (23:23)  Gut zu wissen: Afrikas Wachstumschancen basieren u. A. auf natürlichen Ressourcen, der jungen Bevölkerung und einem riesigen Aufholpotenzial bei Infrastruktur, Bildung und Energie. Problemfelder liegen z. B. in fragilen staatlichen Strukturen und in der oftmals hohen Abhängigkeit von sehr wenigen Exportgütern. Nigeria, Ägypten und Südafrika gehören zu den entscheidenden Wachstumsmotoren. Ägypten fungiert als Brückenkopf zwischen Afrika, Nahost und Europa, kämpft aber z. B. noch mit mangelnden privaten Investitionen. Nigeria braucht für eine effektivere Nutzung der enormen Ölvorkommen mehr wirtschaftliche Diversifizierung. Südafrikas Comeback ist gut begründet: z. B. stabilisierte politische Führung, Staatsfinanzen und Inflation. Hohe Energiepreise und Jugendarbeitslosigkeit sind aber noch Bremsklötze. Südafrika besitzt die mit Abstand am weitesten entwickelte afrikanische Börse. China und die VAE dominieren bisher den Ausbau der Infrastruktur und Energiewirtschaft. Wirklich gut investierbar sind bislang nur wenige Märkte in Afrika. Ein breit gestreutes Portfolio nach globaler Marktkapitalisierung hat von daher bislang nur einen Afrika-Anteil von knapp 1 % - mittel-bis langfristig sollte dieser Anteil wachsen.  Folgenempfehlung: Podcast-Folge 250: „Boom der Schwellenländer-Börsen – Gründe für das Comeback und Perspektiven“   (01:28) Warum glaubt Karl Matthäuas Schmidt an die Zukunft Afrikas? (1:28) (03:11) Wo liegen derzeit die größten Probleme auf dem Kontinent? (3:11) (05:24) Wie sehen die Perspektiven auf Afrikas Jobmarkt aus? (5:24) (06:50) Welche sind die wichtigsten Volkswirtschaften in Afrika? (6:50) (09:00) Ist Ägypten für Investoren eine Turnaround-Story oder eine Dauerbaustelle? (9:00) (11:08) Wie lange kann der „schlafende Riese“ Nigeria noch schlafen, bevor das Vertrauen der Investoren schwindet? (11:08) (13:52) Wie steht Südafrika aktuell unter der neuen Regierung der Nationalen Einheit da? (13:52) (16:32) Südafrika vs. Nigeria: Wo liegt langfristig die bessere Balance aus Risiko, Rendite und Verlässlichkeit? (16:32) (17:51) Chinesische Investitionen in Afrika: Was war gut für den Kontinent und was eher weniger? (17:51) (19:18) Mit welcher Motivation investieren die Vereinigten Arabischen Emirate in Afrika? (19:18) (20:02) Was verbirgt sich genau hinter Europas Abkommen für „sauberen Handel und Partnerschaft“ mit Südafrika? (20:02) (21:20) Sollte man als Privatanleger in Afrika investiert sein und wenn ja, in welcher Form? (21:20) (23:23) Welches Land oder welche Entwicklung in Afrika findet Karl Matthäus Schmidt persönlich besonders spannend? (23:23)

Drama um Ostsee-Wal Timmy | Mehrheit für AfD? (mit Oliver Kalkofe) | Trump blockiert die Welt | Dubai Influencer: Luxus für Zensur - Christian Ehring zeigt den Irrsinn der Woche.

Drama um Ostsee-Wal Timmy | Mehrheit für AfD? (mit Oliver Kalkofe) | Trump blockiert die Welt | Dubai Influencer: Luxus für Zensur - Christian Ehring zeigt den Irrsinn der Woche.

Der Öl-Umschlagplatz auf der Insel Charg? Das angereicherte Uran tief in den iranischen Bergen? Oder nimmt Donald Trump ein anderes Ziel ins Visier? Der US-Präsident könnte Teheran mit der Eroberung drei kleiner Inseln massiv schwächen. Die Vereinigten Arabischen Emirate wären wahrscheinlich begeistert. Sie haben Fragen? Schreiben Sie eine E-Mail an podcasts@ntv.de Sie möchten "Wieder was gelernt" unterstützen? Dann bewerten Sie den Podcast gerne bei Apple Podcasts oder Spotify. Dieser Podcast wird vermarktet von Julep Media: sales@julep.de Wir verarbeiten im Zusammenhang mit dem Angebot unserer Podcasts Daten. Wenn Sie der automatischen Übermittlung der Daten widersprechen wollen, melden Sie sich hier: datenschutz@julep.de

Die Vereinigten Arabischen Emirate ermöglichen nicht nur den Krieg der USA und Israels gegen den Iran. Die repressiven Monarchien sind auch ein Knotenpunkt für Geldwäsche, Korruption und kapitalistische Ausbeutung. Artikel vom 14. März: https://jacobin.de/artikel/vae-dubai-katar-abu-dhabi-irankrieg-nahost-oel Seit 2011 veröffentlicht JACOBIN täglich Kommentare und Analysen zu Politik und Gesellschaft, seit 2020 auch in deutscher Sprache. Die besten Beiträge gibt es als Audioformat zum Nachhören. Nur dank der Unterstützung von Magazin-Abonnentinnen und Abonnenten können wir unsere Arbeit machen, mehr Menschen erreichen und kostenlose Audio-Inhalte wie diesen produzieren. Und wenn Du schon ein Abo hast und mehr tun möchtest, kannst Du gerne auch etwas regelmäßig an uns spenden via www.jacobin.de/podcast. Zu unseren anderen Kanälen: Instagram: www.instagram.com/jacobinmag_de X: www.twitter.com/jacobinmag_de YouTube: www.youtube.com/c/JacobinMagazin Webseite: www.jacobin.de

Seit dem Angriffskrieg der USA und Israel auf den Iran werden die Vereinigten Arabischen Emirate regelmäßig beschossen.Mit dem Beschuss stehen plötzlich auch die vielen deutschen Influencer im Fadenkreuz.. im Fadenkreuz der medialen Berichterstattung.Der Grund: ihre irritierende Kommunikation und fehlende Kritik an der Regierung der VAE.Warum das gerade jetzt passiert und wieso überhaupt so viele Influencer in den Emiraten leben wollen wir in dieser Ausgabe von "In kleiner Runde - Inside Medien" beleuchten.Außerdem bewerten wir die "Abschaltliste" von ARD und ZDF. Am 31.12.2026 werden die zwei öffentlich rechtlichen Anstalten gleich mehrere, wie von der Politik gefordert, Sender abschalten.Welche das sind und welche Strategie dahinter steckt erfährst du in dieser Ausgabe von "In kleiner Runde - Inside Medien".

J'ai connu Luc Royer à la grande époque des Born to Ride, des événements d'ultra-distance romancés qu'il a proposés dès 2012. Chaque thème était une découverte qui entraînait les cyclistes de fort en fort, de phare en phare, de cathédrale en cathédrale… Luc a arrêté en 2023 l'organisation de ces événements, qui ont accompagné et même devancé l'organisation d'épreuves d'ultra-distance de ce type en France. Je rencontre souvent des cyclistes qui ont eu la chance de vivre un de ces événements atypiques situés entre le cyclotourisme – fondateur de l'ultra – et les épreuves qui sont aujourd'hui très formatées. Le nom de son association, “Chilkoot la compagnie des pionniers”, résume la passion aventureuse de Luc pour le vélo, avec lequel il a découvert la liberté à 11 ans en effectuant 200 km sur son vélo d'enfant. Depuis, il roule et touche à tout, toujours à la naissance des tendances : VTT, BMX, Fixie, Aventure, Gravel… Les dizaines de vélos dans son garage sont les témoins de ce parcours itératif. Créateur d'organisations, un peu rêveur, il a montré ses capacités inventives jusqu'à imaginer une joute entre des vélos classiques et des VAE, dans une sorte de remake d'une course historique qui s'est déroulée en 1930 entre voiture et train. Luc aime ce genre de projet fou. Avec ce podcast, je vous invite à sauter dans sa roue pour découvrir un parcours de vie qui a suivi le fil rouge du vélo.Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

Die Welt wird immer verrückter. Eurer Lektor auf See und Wachtmeister Patrick Büchler ist glücklicherweise rechtzeitig vor Ausbruch des Krieges in Nahost aus Dubai zurück gekehrt. Gemeinsam mit Alina blickt er nüchtern und sachlich auf den Stand der Dinge aus Sicht der Kreuzfahrt. Denn noch immer sitzen diverse Schiffe rund um Dubai fest und können die Kriegsregion nicht verlassen. Welche Auswirkungen all dies auf die Kreuzfahrt und die laufende Saison hat erfahrt ihr aus erster Hand hier im Podcast.

Nach der Bombardierung des Irans durch die USA und Israel befinden sich die Vereinigten Arabischen Emirate mitten in der Eskalation von Angriffen und Gegenangriffen. Das sei sehr schädigend für deren Geschäftsmodell, sagt Islamwissenschaftler Sebastian Sons. Von WDR 5.

Nach dem Tod des iranischen Führers Chamenei soll ein dreiköpfiges Gremium mit dem Präsidenten das Land führen. Die Iraner haben Angriffe auf US-Stützpunkte in den Golfstaaten begonnen – in Bahrain, Katar und den VAE. Der internationale Flughafen von Dubai und das Luxushotel Burdsch Al Arab wurden durch iranische Raketenangriffe beschädigt.

von Anna Wallner. Spezialfolge „Was wichtig ist“ zum US-Angriff auf den Iran: Duygu Özkan von der "Presse" ordnet die Lage ein und blickt auch nach Wien: IAEA-Chef Rafael Grossi spricht am Montag (2.3.) in der UNO-City. Wird sich in Wien mit entscheiden, wie es weitergeht?

Editor's note: CuspAI raised a $100m Series A in September and is rumored to have reached a unicorn valuation. They have all-star advisors from Geoff Hinton to Yann Lecun and team of deep domain experts to tackle this next frontier in AI applications.In this episode, Max Welling traces the thread connecting quantum gravity, equivariant neural networks, diffusion models, and climate-focused materials discovery (yes, there is one!!!).We begin with a provocative framing: experiments as computation. Welling describes the idea of a “physics processing unit”—a world in which digital models and physical experiments work together, with nature itself acting as a kind of processor. It's a grounded but ambitious vision of AI for science: not replacing chemists, but accelerating them.Along the way, we discuss:* Why symmetry and equivariance matter in deep learning* The tradeoff between scale and inductive bias* The deep mathematical links between diffusion models and stochastic thermodynamics* Why materials—not software—may be the real bottleneck for AI and the energy transition* What it actually takes to build an AI-driven materials platformMax reflects on moving from curiosity-driven theoretical physics (including work with Gerard ‘t Hooft) toward impact-driven research in climate and energy. The result is a conversation about convergence: physics and machine learning, digital models and laboratory experiments, long-term ambition and incremental progress.Full Video EpisodeTimestamps* 00:00:00 – The Physics Processing Unit (PPU): Nature as the Ultimate Computer* Max introduces the idea of a Physics Processing Unit — using real-world experiments as computation.* 00:00:44 – From Quantum Gravity to AI for Materials* Brandon frames Max's career arc: VAE pioneer → equivariant GNNs → materials startup founder.* 00:01:34 – Curiosity vs Impact: How His Motivation Evolved* Max explains the shift from pure theoretical curiosity to climate-driven impact.* 00:02:43 – Why CaspAI Exists: Technology as Climate Strategy* Politics struggles; technology scales. Why materials innovation became the focus.* 00:03:39 – The Thread: Physics → Symmetry → Machine Learning* How gauge symmetry, group theory, and relativity informed equivariant neural networks.* 00:06:52 – AI for Science Is Exploding (Not Emerging)* The funding surge and why AI-for-Science feels like a new industrial era.* 00:07:53 – Why Now? The Two Catalysts Behind AI for Science* Protein folding, ML force fields, and the tipping point moment.* 00:10:12 – How Engineers Can Enter AI for Science* Practical pathways: curriculum, workshops, cross-disciplinary training.* 00:11:28 – Why Materials Matter More Than Software* The argument that everything—LLMs included—rests on materials innovation.* 00:13:02 – Materials as a Search Engine* The vision: automated exploration of chemical space like querying Google.* 01:14:48 – Inside CuspAI: The Platform Architecture* Generative models + multi-scale digital twin + experiment loop.* 00:21:17 – Automating Chemistry: Human-in-the-Loop First* Start manual → modular tools → agents → increasing autonomy.* 00:25:04 – Moonshots vs Incremental Wins* Balancing lighthouse materials with paid partnerships.* 00:26:22 – Why Breakthroughs Will Still Require Humans* Automation is vertical-specific and iterative.* 00:29:01 – What Is Equivariance (In Plain English)?* Symmetry in neural networks explained with the bottle example.* 00:30:01 – Why Not Just Use Data Augmentation?* The optimization trade-off between inductive bias and data scale.* 00:31:55 – Generative AI Meets Stochastic Thermodynamics* His upcoming book and the unification of diffusion models and physics.* 00:33:44 – When the Book Drops (ICLR?)TranscriptMax: I want to think of it as what I would call a physics processing unit, like a PPU, right? Which is you have digital processing units and then you have physics processing units. So it's basically nature doing computations for you. It's the fastest computer known, as possible even. It's a bit hard to program because you have to do all these experiments. Those are quite bulky, it's like a very large thing you have to do. But in a way it is a computation and that's the way I want to see it. You can do computations in a data center and then you can ask nature to do some computations. Your interface with nature is a bit more complicated. But then these things will have to seamlessly work together to get to a new material that you're interested in.[01:00:44:14 - 01:01:34:08]Brandon: Yeah, it's a pleasure to have Max Woehling as a guest today. Max has done so much over his career that I've been so excited about. If you're in the deep learning community, you probably know Max for his work on variational autocoders, which has literally stood the test of prime or officially stood the test of prime. If you are a scientist, you probably know him for his like, binary work on graph neural networks on equivariance. And if you're a material science, you probably know him about his new startup, CASPAI. Max has a long history doing lots of cool problems. You started in quantum gravity, which is I think very different than all of these other things you worked on. The first question for AI engineers and for scientists, what is the thread in how you think about problems? What is the thread in the type of things which excite you? And how do you decide what is the next big thing you want to work on?[01:01:34:08 - 01:02:41:13]Max: So it has actually evolved a lot. In my young days, let's breathe, I would just follow what I would find super interesting. I have kind of this sensor. I think many people have, but maybe not really sort of use very much, which is like, you get this feeling about getting very excited about some problem. Like it could be, what's inside of a black hole or what's at the boundary of the universe or what are quantum mechanics actually all about. And so I follow that basically throughout my career. But I have to say that as you get older, this changes a little bit in the sense that there's a new dimension coming to it and there's this impact. Going in two-dimensional quantum gravity, you pretty much guaranteed there's going to be no impact on what you do relative, maybe a few papers, but not in this world, this energy scale. As I get closer to retirement, which is fortunately still 10 years away or so, I do want to kind of make a positive impact in the world. And I got pretty worried about climate change.[01:02:43:15 - 01:03:19:11]Max: I think politics seems to have a hard time solving it, especially these days. And so I thought better work on it from the technology side. And that's why we started CaspAI. But there's also a lot of really interesting science problems in material science. And so it's kind of combining both the impact you can make with it as well as the interesting science. So it's sort of these two dimensions, like working on things which you feel there's like, well, there's something very deep going on here. And on the other hand, trying to build tools that can actually make a real impact in the world.[01:03:19:11 - 01:03:39:23]RJ: So the thread that when I look back, look at the different things that you worked out, some of them seem pretty connected, like the physics to equivariance and, yeah, and, uh, gravitational networks, maybe. And that seems to be somewhat related to Casp. Do you have a thread through there?[01:03:39:23 - 01:06:52:16]Max: Yeah. So physics is the thread. So having done, you know, spent a lot of time in theoretical physics, I think there is first very fundamental and exciting questions, like things that haven't actually been figured out in quantum gravity. So that is really the frontier. There's also a lot of mathematical tools that you can use, right? In, for instance, in particle physics, but also in general relativity, sort of symmetry space to play an enormously important role. And this goes all the way to gauge symmetries as well. And so applying these kinds of symmetries to, uh, machine learning was actually, you know, I thought of it as a very deep and interesting mathematical problem. I did this with Taco Cohen and Taco was the main driver behind this, went all the way from just simple, like rotational symmetries all the way to gauge symmetries on spheres and stuff like that. So, and, uh, Maurice Weiler, who's also here, um, when he was a PhD student, he was a very good student with me, you know, he wrote an entire book, which I can really recommend about the role of symmetries in AI and machine learning. So I find this a very deep and interesting problem. So more recently, so I've taken a sort of different path, which is the relationship between diffusion models and that field called stochastic thermodynamics. This is basically the thermodynamics, which is a theory of equilibrium. So but then formulated for out of equilibrium systems. And it turns out that the mathematics that we use for diffusion models, but even for reinforcement learning for Schrodinger bridges for MCMC sampling has the same mathematics as this theoretical, this physical theory of non-equilibrium systems. And that got me very excited. And actually, uh, when I taught a course in, um, Mauschenberg, uh, it is South Africa, close to Cape Town at the African Institute for Mathematical Sciences Ames. And I turned that into a book site. Two years later, the book was finished. I've sent it to the publisher. And this is about the deep relationship between free energy, diffusion models, basically generative AI and stochastic thermodynamics. So it's always some kind of, I don't know, I find physics very deep. I also think a lot about quantum mechanics and it's, it's, it's a completely weird theory that actually nobody really understands. And there's a very interesting story, which is maybe good to tell to connect sort of my PZ back to where I'm now. So I did my PZ with a Nobel Laureate, Gerard the toft. He says the most brilliant man I've ever met. He was never wrong about anything as long as I've seen him. And now he says quantum mechanics is wrong and he has a new theory of quantum mechanics. Nobody understands what he's saying, even though what he's writing down is not mathematically very complex, but he's trying to address this understandability, let's say of quantum mechanics head on. And I find it very courageous and I'm completely fascinated by it. So I'm also trying to think about, okay, can I actually understand quantum mechanics in a more mundane way? So that, you know, without all the weird multiverses and collapses and stuff like that. So the physics is always been the threat and I'm trying to apply the physics to the machine learning to build better algorithms.[01:06:52:16 - 01:07:05:15]Brandon: You are still very involved in understanding and understanding physics and the worlds. Yeah. And just like applications to machine learning or introducing no formalisms. That's really cool.[01:07:05:15 - 01:07:18:02]Max: Yes, I would say I'm not contributing much to physics, but I'm contributing to the interface between physics and science. And that's called AI for science or science or AI is kind of a super, it's actually a new discipline that's emerging.[01:07:18:02 - 01:07:18:19]Speaker 5: Yeah.[01:07:18:19 - 01:07:45:14]Max: And it's not just emerging, it's exploding, I would say. That's the better term because I know you go from investments into like in the hundreds of millions now in the billions. So there's now actually a startup by Jeff Bezos that is at 6.2 billion sheep round. Right. Insane. I guess it's the largest startup ever, I think. And that's in this field, AI for science. It tells you something that we are creating a new bubble here.[01:07:46:15 - 01:07:53:28]Brandon: So why do you think it is? What has changed that has motivated people to start working on AI for science type problems?[01:07:53:28 - 01:08:49:17]Max: So there's two reasons actually. One is that people have been applying sort of the new tools from AI to the sciences, which is quite natural. And there's of course, I think there's two big examples, protein folding is a big one. And the other one is machine learning forest fields or something called machine learning inter-atomic potentials. Both of them have been actually very successful. Both also had something to do with symmetries, which is a little cool. And sort of people in the AI sciences saw an opportunity to apply the tools that they had developed beyond advertised placement, right, or multimedia applications into something that could actually make a very positive impact in society like health, drug development, materials for the energy transition, carbon capture. These are all really cool, impactful applications.[01:08:50:19 - 01:09:42:14]Max: Despite that, the science and the kind of the is also very interesting. I would say the fact that these sort of these two fields are coming together and that we're now at the point that we can actually model these things effectively and move the needle on some of these sort of science sort of methodologies is also a very unique moment, I would say. People recognize that, okay, now we're at the cusp of something new, where it results whether the company is called after. We're at the cusp of something new. And of course that always creates a lot of energy. It's like, okay, there's something, it's like sort of virgin field. It's like nobody's green field. Nobody's been there. I can rush in and I can sort of start harvesting there, right? And I think that's also what's causing a lot of sort of enthusiasm in the fields.[01:09:42:14 - 01:10:12:18]RJ: If you're an AI engineer, basically if the people that listen to this podcast will be in the field, then you maybe don't have a strong science background. How does, but are excited. Most I would say most AI practitioners, BM engineers or scientists would consider themselves scientists and they have some background, a little bit of physics, a little bit of industry college, maybe even graduate school that have been working or are starting out. How does somebody who is not a scientist on a day-to-day basis, how do they get involved?[01:10:12:18 - 01:10:14:28]Max: Well, they can read my book once it's out.[01:10:16:07 - 01:11:05:24]Max: This is basically saying that there is more, we should create curricula that are on this interface. So I'm not sure there is, also we already have some universities actual courses you can take, maybe online courses you can take. These workshops where we are now are actually very good as well. And we should probably have more tutorials before the workshop starts. Actually we've, I've kind of proposed this at some point. It's like maybe first have an hour of a tutorial so that people can get new into the field. There's a lot out there. Most of it is of course inaccessible, but I would say we will create much more books and other contents that is more accessible, including this podcast I would say. So I think it will come. And these days you can watch videos and things. There's a huge amount of content you can go and see.[01:11:05:24 - 01:11:28:28]Brandon: So maybe a follow-up to that. How do people learn and get involved? But why should they get involved? I mean, we have a lot of people who are of our audience will be interested in AI engineering, but they may be looking for bigger impacts in the world. What opportunities does AI for science provide them to make an impact to change the world? That working in this the world of pure bits would not.[01:11:28:28 - 01:11:40:06]Max: So my view is that underlying almost everything is immaterial. So we are focusing a lot on LLMs now, which is kind of the software layer.[01:11:41:06 - 01:11:56:05]Max: I would say if you think very hard, underlying everything is immaterial. So underlying an LLM is a GPU, and underlying a GPU is a wafer on which we will have to deposit materials. Do we want to wait a little bit?[01:12:02:25 - 01:12:11:06]Max: Underlying everything is immaterial. So I was saying, you know, there's the LLM underlying the LLM is a GPU on which it runs. In order to make that GPU,[01:12:12:08 - 01:12:43:20]Max: you have to put materials down on a wafer and sort of shine on it with sort of EUV light in order to etch kind of the structures in. But that's now an actual material problem, because more or less we've reached the limits of scaling things down. And now we are trying to improve further by new materials. So that's a fundamental materials problem. We need to get through the energy transition fast if we don't want to kind of mess up this world. And so there is, for instance, batteries. That's a complete materials problem. There's fuel cells.[01:12:44:23 - 01:13:01:16]Max: There is solar panels. So that they can now make solar panels with new perovskite layers on top of the silicon layers that can capture, you know, theoretically up to 50% of the light, where now we're at, I don't know, maybe 22 or something. So these are huge changes all by material innovation.[01:13:02:21 - 01:13:47:15]Max: And yeah, I think wherever you go, you know, I can probably dig deep enough and then tell you, well, actually, the very foundation of what you're doing is a material problem. And so I think it's just very nice to work on this very, very foundation. And also because I think this is maybe also something that's happening now is we can start to search through this material space. This has never been the case, right? It's like scientists, the normal way of working is you read papers and then you come up with no hypothesis. You do an experiment and you learn, et cetera. So that's a very slow process. Now we can treat this as a search engine. Like we search the internet, we now search the space of all possible molecules, not just the ones that people have made or that they're in the universe, but all of them.[01:13:48:21 - 01:14:42:01]Max: And we can make this kind of fully automated. That's the hope, right? We can just type, it becomes a tool where you type what you want and something starts spinning and some experiments get going. And then, you know, outcome list of materials and then you look at it and say, maybe not. And then you refine your query a little bit. And you kind of do research with this search engine where a huge amount of computation and experimentation is happening, you know, somewhere far away in some lab or some data center or something like this. I find this a very, very promising view of how we can sort of build a much better sort of materials layer underneath almost everything. And also more sustainable materials. Our plastics are polluting the planet. If you come up with a plastic that kind of destroys itself, you know, after, I don't a few weeks, right? And actually becomes a fertilizer. These are things that are not impossible at all. These things can be done, right? And we should do it.[01:14:42:01 - 01:14:47:23]RJ: Can you tell us a little bit just generally about CUSBI and then I have a ton of questions.[01:14:47:23 - 01:14:48:15]Speaker 5: Yeah.[01:14:48:15 - 01:17:49:10]Max: So CUSBI started about 20 months ago and it was because I was worried about I'm still worried about climate change. And so I realized that in order to get, you know, to stay within two degrees, let's say, we would not only have to reduce our emissions to zero by 2050, but then, you know, another half century or even a century of removing carbon dioxide from the atmosphere, not by reducing your emissions, but actually removing it at a rate that's about half the rate that we now emit it. And that is a unsolved problem. But if we don't solve it, two degrees is not going to happen, right? It's going to be much more. And I don't think people quite understand how bad that can be, like four degrees, like very bad. So this technology needs to be developed. And so this was my and my co-founder, Chet Edwards, motivation to start this startup. And also because, you know, we saw the technology was ready, which is also very good. So if you're, you know, the time is right to do it. And yeah, so we now in the meanwhile, we've grown to about 40 people. We've kind of collected 130 million investment into the company, which is for a European company is quite a lot. I would say it's interesting that right after that, you know, other startups got even more. So that's kind of tells you how fast this is growing. But yeah, we are we are now at the we've built the platform, of course, but it's for a series of material classes and it needs to be constantly expanded to new material classes. And it can be more automated because, you know, we know putting LLMs in as the whole thing gets more and more automated. And now we're moving to sort of high throughput experimentation. So connecting the actual platform, which is computational, to the experiments so that you can get also get fast feedback from experiments. And I kind of think of experiments as something you do at the end, although that's what we've been doing so far. I want to think of it as what I would call a sort of a physics processing unit, like a PPU, right, which is you have digital processing units and then you have physics processing units. So it's basically nature doing computations for you. It's the fastest computer known as possible, even. It's a bit hard to program because you have to do all these experiments. Those are quite, quite bulky. It's like a very large thing you have to do. But in a way, it is a computation. And that's the way I want to see it. So I want to you can do computations in a data center and then you can ask nature to do some computations. Your interface with nature is a bit more complicated. But then these things will have to seamlessly work together to get to a new material that you're interested in. And that's the vision we have. We don't say super intelligence because I don't quite know what it means and I don't want to oversell it. But I do want to automate this process and give a very powerful tool in the hands of the chemists and the material scientists.[01:17:49:10 - 01:18:01:02]Brandon: That actually brings up a question I wanted to ask you. First of all, can you talk about your platform to like whatever degree, like explain kind of how it works and like what you your thought processes was in developing it?[01:18:01:02 - 01:20:47:22]Max: Yeah, I think it's been surprisingly, it's not rocket science, I would say. It's not rocket science in the sense of the design and basically the design that, you know, I wrote down at the very beginning. It's still more or less the design, although you add things like I wasn't thinking very much about multi-scale models and as the common are rated that actually multi-scale is very important. And the beginning, I wasn't thinking very much about self-driving labs. But now I think, you know, we are now at the stage we should be adding that. And so there is sort of bits and details that we're adding. But more or less, it's what you see in the slide decks here as well, which is there is a generative component that you have to train to generate candidates. And then there is a digital twin, multi-scale, multi-fidelity digital twin, which you walk through the steps of the ladder, you know, they do the cheap things first, you weed out everything that's obviously unuseful, and then you go to more and more expensive things later. And so you narrow things down to a small number. Those go into an experiment, you know, do the experiment, get feedback, etc. Now, things that also have been more recently added is sort of more agentic sort of parts. You know, we have agents that search the literature and come up with, you know, actually the chemical literature and come up with, you know, chemical suggestions for doing experiments. We have agents which sort of autonomously orchestrate all of the computations and the experiments that need to be done. You know, they're in various stages of maturity and they can be continuously improved, I would say. And so that's basically I don't think that part. There's rocket science, but, you know, the design of that thing is not like surprising. What is it's surprising hard to actually build it. Right. So that's that's the thing that is where the moat is in the data that you can get your hands on and the and actually building the platform. And I would say there's two people in particular I want to call out, which is Felix Hunker, who is actually, you know, building the scientific part of the platform and Sandra de Maria, who is building the sort of the skate that is kind of this the MLOps part of the platform. Yeah. And so and recently we also added sort of Aaron Walsh to our team, who is a very accomplished scientist from Imperial College. We're very happy about that. He's going to be a chief science officer. And we also have a partnerships team that sort of seeks out all the customers because I think this is one thing I find very important. In print, it's so complex to do to actually bring a material to the real world that you must do this, you know, in collaboration with sort of the domain experts, which are the companies typically. So we always we only start to invest in the direction if we find a good industrial partner to go on that journey with us.[01:20:47:22 - 01:20:55:12]Brandon: Makes a lot of sense. Over the evolution of the platform, did you find that you that human intervention, human,[01:20:56:18 - 01:21:17:01]Brandon: I guess you could start out with a pure, you could imagine two directions when you start up making everything purely automatic, automated, agentic, so on. And then later on, you like find that you need to have more human input and feedback different steps. Or maybe did you start out with having human feedback? You have lots of steps and then like kind of, yeah, figure out ways to remove, you know,[01:21:17:01 - 01:22:39:18]Max: that is the second one. So you build tools for you. So it's much more modular than you think. But it's like, we need these tools for this application. We need these tools. So you build all these tools, and then you go through a workflow actually in the beginning just manually. So you put them in a first this tool, then run this to them or this with sithery. So you put them in a workflow and then you figure out, oh, actually, you know, this this porous material that we are trying to make actually collapses if you shake it a bit. Okay, then you add a new tool that says test for stability. Right. Yeah. And so there's more and more tools. And then you build the agent, which could be a Bayesian optimizer, or it could be an actual other them, you know, maybe trained to be a good chemist that will then start to use all these tools in the right way in the right order. Yeah. Right. But in the beginning, it's like you as a chemist are putting the workflow together. And then you think about, okay, how am I going to automate this? Right. For one very easy question you can ask yourself is, you know, every time somebody who is not a super expert in DFT, yeah, and he wants to do a calculation has to go to somebody who knows DFT. And so could you start to automate that away, which is like, okay, make it so user friendly, so that you actually do the right DFT for the right problem and for the right length of time, and you can actually assess whether it's a good outcome, etc. So you start to automate smaller small pieces and bigger pieces, etc. And in the end, the whole thing is automated.[01:22:39:18 - 01:22:53:25]Brandon: So your philosophy is you want to provide a set of specific tools that make it so that the scientists making decisions are better informed and less so trying to create an automated process.[01:22:53:25 - 01:23:22:01]Max: I think it's this is sort of the same where you're saying because, yes, we want to automate, yeah, but we don't see something very soon where the chemists and the domain expert is out of the loop. Yeah, but it but it's a retreat, right? It's like, okay, so first, you need an expert to tell you precisely how to set the parameters of the DFT calculation. Okay, maybe we can take that out. We can maybe automate that, right? And so increasingly, more of these things are going to be removed.[01:23:22:01 - 01:23:22:19]Speaker 5: Yeah.[01:23:22:19 - 01:24:33:25]Max: In the end, the vision is it will be a search engine where you where somebody, a chemist will type things and we'll get candidates, but the chemist will still decide what is a good material and what is not a good material out of that list, right? And so the vision of a completely dark lab, where you can close the door and you just say, just, you know, find something interesting and then it will it will just figure out what's interesting and we'll figure out, you know, it's like, oh, I found this new material to blah, blah, blah, blah, right? That's not the vision I have. He's not for, you know, a long time. So for me, it's really empowering the domain experts that are sitting in the companies and in universities to be much faster in developing their materials. And I should say, it's also good to be a little humble at times, because it is very complicated, you know, to bring it to make it and to bring it into the real world. And there are people that are doing this for the entire lives. Yeah. Right. And it's like, I wonder if they scratch their head and say, well, you know, how are you going to completely automate that away, like in the next five years? I don't think that's going to happen at all.[01:24:35:01 - 01:24:39:24]Max: Yeah. So to me, it's an increasingly powerful tool in the hands of the chemists.[01:24:39:24 - 01:25:04:02]RJ: I have a question. You've talked before about getting people interested based on having, you know, sort of a big breakthrough in materials, incremental change. I'm curious what you think about the platform you have now in are sort of stepping towards and how are you chasing the big change or is this like incremental or is there they're not mutually exclusive, obviously, but what do you think about that?[01:25:04:02 - 01:26:04:27]Max: We follow a mixed strategy. So we are definitely going after a big material. Again, we do this with a partner. I'm not going to disclose precisely what it is, but we have our own kind of long term goal. You could call it lighthouse or, you know, sort of moonshot or whatever, but it is going to be a really impactful material that we want to develop as a proof point that it can be done and that it will make it into the into the real world and that AI was essential in actually making it happen. At the same time, we also are quite happy to work with companies that have more modest goals. Like I would say one is a very deep partnership where you go on a journey with a company and that's a long term commitment together. And the other one is like somebody says, I knew I need a force field. Can you help me train this force field and then maybe analyze this particular problem for me? And I'll pay you a bunch of money for that. And then maybe after that we'll see. And that's fine too. Right. But we prefer, you know, the deep partnerships where we can really change something for the good.[01:26:04:27 - 01:26:22:02]RJ: Yeah. And do you feel like from a platform standpoint you're ready for that or what are the things that and again, not asking you to disclose proprietary secret sauce, but what are the things generally speaking that need to happen from where we are to where to get those big breakthroughs?[01:26:22:02 - 01:28:40:01]Max: What I find interesting about this field is that every time you build something, it's actually immediately useful. Right. And so unlike quantum computing, which or nuclear fusion, so you work for 20, 30, 40 years and nothing, nothing, nothing, nothing. And then it has to happen. Right. And when it happens, it's huge. So it's quite different here because every time you introduce, so you go to a customer and you say, so what do you need? Right. So we work, let's say, on a problem like a water filtration. We want to remove PFAS from water. Right. So we do this with a company, Camira. So they are a deep partner for us. Right. So we on a journey together. I think that the breakthrough will happen with a lot of human in the loop because there is the chemists who have a whole lot more knowledge of their field and it's us who will help them with training, having a new message. And in that kind of interface, these interactions, something beautiful will happen and that will have to happen first before this field will really take off, I think. And so in the sense that it's not a bubble, let's put it that way. So that's people see that as actual real what's happening. So in the beginning, it will be very, you know, with a lot of humans in the loop, I would say, and I would I would hope we will have this new sort of breakthrough material before, you know, everything is completely automated because that will take a while. And also it is very vertical specific. So it's like completely automating something for problem A, you know, you can probably achieve it, but then you'll sort of have to start over again for problem B because, you know, your experimental setup looks very different in the machines that you characterize your materials look very different. Even the models in your platform will have to be retrained and fine tuned to the new class. So every time, you know, you have a lot of learnings to transfer, but also, you know, the problems are actually different. And so, yes, I would want that breakthrough material before it's completely automated, which I think is kind of a long term vision. And I would say every time you move to something new, you'll have to start retraining and humans will have to come in again and say, okay, so what does this problem look like? And now sort of, you know, point the the machine again, you know, in the new direction and then and then use it again.[01:28:40:01 - 01:28:47:17]RJ: For the non-scientists among us, me included a bit of a scientist. There's a lot of terminology. You mentioned DFT,[01:28:49:00 - 01:29:01:11]RJ: you equivariance we've talked about. Can you sort of explain in engineering terms or the level of sophistication and engineering? Well, how what is equivariance?[01:29:01:11 - 01:29:55:01]Max: So equivariance is the infusion of symmetry in neural networks. So if I build a neural network, let's say that needs to recognize this bottle, right, and then I rotate the bottle, it will then actually have to completely start again because it has no idea that the rotated bottle. Well, actually, the input that represents a rotated bottle is actually rotated bottle. It just doesn't understand that. Right. If you build equivariance in basically once you've trained it in one orientation, it will understand it in any other orientation. So that means you need a lot less data to train these models. And these are constraints on the weights of the model. So so basically you have to constrain the way such data to understand it. And you can build it in, you can hard code it in. And yeah, this the symmetry groups can be, you know, translations, rotations, but also permutations. I can graph neural network, their permutations and then physics, of course, as many more of these groups.[01:29:55:01 - 01:30:01:08]RJ: To pray devil's advocate, why not just use data augmentation by your bottle is in all the different orientations?[01:30:01:08 - 01:30:58:23]Max: As an option, it's just not exact. It's like, why would you go through the work of doing all that? Where you would really need an infinite number of augmentations to get it completely right. Where you can also hard code it in. Now, I have to say sometimes actually data augmentation works even better than hard coding the equivariance in. And this is something to do with the fact that if you constrain the optimization, the weights before the optimization starts, the optimization surface or objective becomes more complicated. And so it's harder to find good minima. So there is also a complicated interplay, I think, between the optimization process and these constraints you put in your network. And so, yeah, you'll hear kind of contradicting claims in this field. Like some people and for certain applications, it works just better than not doing it. And sometimes you hear other people, if you have a lot of data and you can do data augmentation, then actually it's easier to optimize them and it actually works better than putting the equivariance in.[01:30:58:23 - 01:31:07:16]Brandon: Do you think there's kind of a bitter lesson for mathematically founded models and strategies for doing deep learning?[01:31:07:16 - 01:31:46:06]Max: Yeah, ultimately it's a trade-off between data and inductive bias. So if your inductive bias is not perfectly correct, you have to be careful because you put a ceiling to what you can do. But if you know the symmetry is there, it's hard to imagine there isn't a way to actually leverage it. But yeah, so there is a bitter lesson. And one of the bitter lessons is you should always make sure your architecture is scale, unless you have a tiny data set, in which case it doesn't matter. But if you, you know, the same bitter lessons or lessons that you can draw in LLM space are eventually going to be true in this space as well, I think.[01:31:47:10 - 01:31:55:01]RJ: Can you talk a little bit about your upcoming book and tell the listeners, like, what's exciting about it? Yeah, I should read it.[01:31:55:01 - 01:33:42:20]Max: So this book is about, it's called Generative AI and Stochastic Thermodynamics. It basically lays bare the fact that the mathematics that goes into both generative AI, which is the technology to generate images and videos, and this field of non-equilibrium statistical mechanics, which are systems of molecules that are just moving around and relaxing to the ground state, or that you can control to have certain, you know, be in a certain state, the mathematics of these two is actually identical. And so that's fascinating. And in fact, what's interesting is that Jeff Hinton and Radford Neal already wrote down the variational free energy for machine learning a long time ago. And there's also Carl Friston's work on free energy principle and active entrance. But now we've related it to this very new field in physics, which is called stochastic thermodynamics or non-equilibrium thermodynamics, which has its own very interesting theorems, like fluctuation theorems, which we don't typically talk about, but we can learn a lot from. And I think it's just it can sort of now start to cross fertilize. When we see that these things are actually the same, we can, like we did for symmetries, we can now look at this new theory that's out there, developed by these very smart physicists, and say, okay, what can we take from here that will make our algorithms better? At the same time, we can use our models to now help the scientists do better science. And so it becomes a beautiful cross-fertilization between these two fields. The book is rather technical, I would say. And it takes all sorts of things that have been done as stochastic thermodynamics, and all sorts of models that have been done in the machine learning literature, and it basically equates them to each other. And I think hopefully that sense of unification will be revealing to people.[01:33:42:20 - 01:33:44:05]RJ: Wait, and when is it out?[01:33:44:05 - 01:33:56:09]Max: Well, it depends on the publisher now. But I hope in April, I'm going to give a keynote at ICLR. And it would be very nice if they have this book in my hand. But you know, it's hard to control these kind of timelines.[01:33:56:09 - 01:33:58:19]RJ: Yeah, I'm looking forward to it. Great.[01:33:58:19 - 01:33:59:25]Max: Thank you very much. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

Liebe Radsportfreunde, MOININGER und ein herzliches Willkommen zur 116. Folge der JAN ULLRICH ULTRAS.Neben famosen Bauernweisheiten zum Wetter, den Gesundheitszuständen vom Major und dem Stauffenberger haben wir uns mit der Algarve-Rundfahrt und der UAE-Tour beschäftigt.Die Leistungen von Del Toro und Ayuso (beide gewinnen jeweils eine Rundfahrt) versprechen viel. Sie sehen dynamisch, fit, drahtig und einfach geil aus! Lipo kommt in Portugal am Ende auf Platz 8 des GC. Nach überstandener Krankheit und vielen Trainingseinheiten eine beachtliche Leistung, weiter so!Remco kam mit 5 Siegen in die VAE, er holte das Zeitfahren im nahen Osten, konnte bei den beiden Bergankünften nicht mithalten. Wir bleiben dabei: Remco lässt POGI weiterhin ruhig schlafen!Ihr wollt uns unterstützen? Wir freuen wir uns über jede Bewertung und jede Gefolgschaft! Spotify, Amazon, Apple - egal wo! Ihr für uns, wir für Euch!Das Leben ist schön!Eure JAN ULLRICH ULTRAS.

L'absence du baccalauréat peut inquiéter, surtout lorsqu'un jeune se projette dans un métier artistique ou créatif. Pourtant, en 2026, la réussite ne passe plus uniquement par les diplômes scolaires, mais par les compétences, la pratique et la construction d'un projet cohérent. Les parcours non linéaires sont nombreux, en particulier dans les secteurs créatifs où le savoir-faire et le portfolio priment souvent sur le bac.✅ DANS CET ÉPISODE, NOUS ABORDONS :Dédramatiser l'échec scolaire : comprendre que rater le bac ne ferme pas toutes les portesLe regard des parents : inquiétudes légitimes, posture de soutien et accompagnement bienveillantPourquoi le bac n'est plus une fatalité : compétences, engagement et adaptabilité avant le diplômeSe concentrer sur un projet professionnel : partir des talents, des envies et du concretMétiers artistiques accessibles sans le bac : design, graphisme, arts visuels, spectacle, audiovisuelÉcoles et formations possibles : écoles privées, artisanat, formations sur portfolio ou auditionL'importance des formations reconnues : RNCP, alternance, débouchés réelsValoriser l'expérience dans le temps : portfolio, pratique régulière et VAE à moyen terme

Bryce and Ren kick off Monthly Portfolio Updates for 2026 sharing what they've changed over summer, how they're thinking about core vs satellite, and why both are leaning harder into systems rather than hot takes.In this episode:00:00 What to expect in our monthly portfolio updates03:08 Key portfolio changes over summer (debt recycling, leverage, structure)07:02 Ren's new core + building an all-weather income sleeve11:49 Core portfolios deep dive: ETFs, gearing & brokerage choices22:48 Satellite portfolios: active managers, Bitcoin & gold28:08 Income strategies, risk management & wrapping up

21 Then let those who are in Judea, flee to the mountains; and those who are in the midst thereof, depart out: and those who are in the countries, not enter into it.tunc qui in Judaea sunt, fugiant ad montes, et qui in medio ejus, discedant : et qui in regionibus, non intrent in eam, 22 For these are the days of vengeance, that all things may be fulfilled, that are written.quia dies ultionis hi sunt, ut impleantur omnia quae scripta sunt. 23 But woe to them that are with child, and give suck in those days; for there shall be great distress in the land, and wrath upon this people.Vae autem praegnantibus et nutrientibus in illis diebus! erit enim pressura magna super terram, et ira populo huic. 24 And they shall fall by the edge of the sword; and shall be led away captives into all nations; and Jerusalem shall be trodden down by the Gentiles; till the times of the nations be fulfilled.Et cadent in ore gladii, et captivi ducentur in omnes gentes, et Jerusalem calcabitur a gentibus, donec impleantur tempora nationum. 25 And there shall be signs in the sun, and in the moon, and in the stars; and upon the earth distress of nations, by reason of the confusion of the roaring of the sea and of the waves;Et erunt signa in sole, et luna, et stellis, et in terris pressura gentium prae confusione sonitus maris, et fluctuum : 26 Men withering away for fear, and expectation of what shall come upon the whole world. For the powers of heaven shall be moved;arescentibus hominibus prae timore, et exspectatione, quae supervenient universo orbi : nam virtutes caelorum movebuntur : 27 And then they shall see the Son of man coming in a cloud, with great power and majesty.et tunc videbunt Filium hominis venientem in nube cum potestate magna et majestate. 28 But when these things begin to come to pass, look up, and lift up your heads, because your redemption is at hand.His autem fieri incipientibus, respicite, et levate capita vestra : quoniam appropinquat redemptio vestra. 29 And he spoke to them in a similitude. See the fig tree, and all the trees:Et dixit illis similitudinem : Videte ficulneam, et omnes arbores : 30 When they now shoot forth their fruit, you know that summer is nigh;cum producunt jam ex se fructum, scitis quoniam prope est aestas. 31 So you also, when you shall see these things come to pass, know that the kingdom of God is at hand.Ita et vos cum videritis haec fieri, scitote quoniam prope est regnum Dei. 32 Amen, I say to you, this generation shall not pass away, till all things be fulfilled.Amen dico vobis, quia non praeteribit generatio haec, donec omnia fiant. 33 Heaven and earth shall pass away, but my words shall not pass away.Caelum et terra transibunt : verba autem mea non transibunt.

Öffentlich gibt sich Scheich Mansur bin Zayed als reicher Mäzen. Im Verborgenen beliefert er die RSF-Miliz mit Waffen im grossen Stil – und steuert damit den Krieg im Sudan. Heutiger Gast: Andrea Jeska, Reporterin Host: Alice Grosjean Andrea Jeskas [Text über Scheich Mansur bin Zayed](https://www.nzz.ch/international/der-scheich-der-afrikas-schlimmsten-krieg-steuert-auf-mansur-bin-zayed-al-nahyan-den-vizepraesidenten-der-vereinigten-arabischen-emirate-kann-sich-die-moerdertruppe-rsf-im-sudan-verlassen-ld.1910598) gibts zu lesen bei der NZZ. Noch kein Abo? Das [digitale Probeabo der NZZ](https://abo.nzz.ch/nzz-ch-probeabo-digital/) bietet Abhilfe.

Bundeswirtschaftsministerin Katherina Reiche wirbt auf ihrer Reise nach Katar und in die Vereinigten Arabischen Emirate um Partner für die deutsche Industrie.Kurz vor Beginn der Reise ist ein Milliarden-Deal bekannt geworden. Der staatliche Ölkonzern ADNOC aus Abu Dhabi soll den Leverkusener Kunststoffhersteller Covestro für rund 12 Milliarden Euro inklusive Schulden übernehmen.Reiche sieht das „Engagement als Auszeichnung für Covestro und als gute Kooperation für mehr Wachstum.“ Die Übernahme zeige aber auch, dass in Deutschland die Branche durch zu hohe Energiekosten, langwierige Genehmigungsverfahren und zu viel Regulierung belastet werde.Reiche wird von einer hochkarätigen Delegation begleitet: Unter anderem ist Marvel-Fusion-CEO Moritz von der Linden dabei. Im Gespräch mit Michael Bröcker beschreibt er, welche Chancen sich in den Staaten der Golf-Region bieten. Und er kritisiert die deutsche Energiepolitik der vergangenen zwanzig Jahre. Diese habe „die Blaupause geliefert, wie man es nicht macht.“Die Wirtschaftsweise Veronika Grimm sieht Katar und die VAE als mögliche Partner, aber auch als Vorbilder: „Hier sind viele Sachen möglich, die bei uns durch Regulierung eigentlich versperrt sind.“ Smart Cities mit KI, autonomes Fahren und medizinische Anwendungen entwickelten sich dort schneller als in Deutschland.UN-Generalversammlungspräsidentin Annalena Baerbock spricht mit Bernhard Pötter vom Climate.Table in Belém über die COP30. Baerbock sagt, die internationale Gemeinschaft habe im Kampf gegen den Klimawandel eine „absolute Technologierevolution“ hin zu erneuerbaren Energien in Gang gesetzt.Hier geht es zur Anmeldung für den Space.TableTable Briefings - For better informed decisions.Sie entscheiden besser, weil Sie besser informiert sind – das ist das Ziel von Table.Briefings. Wir verschaffen Ihnen mit jedem Professional Briefing, mit jeder Analyse und mit jedem Hintergrundstück einen Informationsvorsprung, am besten sogar einen Wettbewerbsvorteil. Table.Briefings bietet „Deep Journalism“, wir verbinden den Qualitätsanspruch von Leitmedien mit der Tiefenschärfe von Fachinformationen. Professional Briefings kostenlos kennenlernen: table.media/testenHier geht es zu unseren WerbepartnernImpressum: https://table.media/impressumDatenschutz: https://table.media/datenschutzerklaerungBei Interesse an Audio-Werbung in diesem Podcast melden Sie sich gerne bei Laurence Donath: laurence.donath@table.media Hosted on Acast. See acast.com/privacy for more information.

Im Sudan wütet ein brutaler Bürgerkrieg. Im Kampf gegen die sudanesische Armee hat die paramilitärische Miliz “Rapid Support Forces”, kurz RSF, die Stadt Al-Faschir eingenommen. Es wird von Vergewaltigungen, Massenmorden, und brutalen Hinrichtungen berichtet. Nach UN-Angaben steht das ganze Land am Abgrund einer humanitären Katastrophe. Nina Amin aus dem ARD-Studio Kairo ordnet in dieser 11KM-Folge ein, was gerade im Sudan passiert. Sie erklärt, was den Konflikt so kompliziert macht und ob es eine Lösung in diesem laut UN „vergessenen Krieg“ geben kann. Redaktionsschluss für diese Folge war 6. November 20 Uhr. Alle Updates und Entwicklungen zum Krieg im Sudan: https://www.tagesschau.de/thema/sudan Hier geht's zu unserer früheren 11KM-Folge „Sudan: Krieg ohne Ende - und bald ohne US-Hilfen?”: https://1.ard.de/11KM_Sudan_USHilfen Das ist unser Podcast-Tipp: Sport Inside - “Afghanistan: Manizha Talash kämpft für Freiheit”: https://1.ard.de/sportinside_Afghanistan Diese und viele weitere Folgen von 11KM findet ihr überall da, wo es Podcasts gibt, auch hier in der ARD Audiothek: https://www.ardaudiothek.de/sendung/11km-der-tagesschau-podcast/12200383/ An dieser Folge waren beteiligt: Folgenautor: Stephan Beuting Mitarbeit: Caspar von Au und Marc Hoffmann Host: Elena Kuch Produktion: Ruth-Maria Ostermann, Viktor Fölsner-Veress, Alexander Gerhardt Planung: Caspar von Au und Hardy Funk Distribution: Kerstin Ammermann Redaktionsleitung: Nicole Dienemann und Fumiko Lipp 11KM: der tagesschau-Podcast wird produziert von BR24 und NDR Info. Die redaktionelle Verantwortung für diese Episode liegt beim NDR.

Bastian wurde in Düsseldorf geboren und erhielt dort vom Vater das Hotelgen. Aufgewachsen ist er aber in der Steiermark und somit ergibt sich eine interessante Mischung, die sich Bastian Baumann nennt ;-) Von dort ging es nach seiner Hotelausbildung gleich an den Persischen Golf, denn Bastian wollte andere Kulturen inhalieren. Dies war in den eher freiheitlichen Vereinigten Arabischen Emiraten genauso möglich, wie im sehr konservativen Katar. Zwischendurch fand er sich in Wien, Frankfurt, Singapur, München und noch mal Dubai wieder, die Hotelkarriere immer weiter nach oben steigend. Seit Anfang 2025 ist Bastian am ältesten Platz Wiens General Manager des Park Hyatt. Was für eine Kombination... Da wollen wir doch mal nachhaken: - Wie unterscheidet sich das Leben in VAE und Katar und was nahm Bastian von dort mit? - Was unterscheidet die großen Hotelgesellschaften Marriott und Hyatt? - Wie lief Bastians langgehegter Wunsch, ein Hotel mitzueröffnen, im Andaz Munich Schwabinger Tor? - Was schätzt Bastian an Traditionshäusern wie dem Imperial Wien und neueren wie den Andaz Hotels in München oder Dubai? - Wie kam es zur einmaligen Chance, das Park Hyatt in seiner Heimat führen zu dürfen? - Wie schafft es Bastian, neue Mitarbeiter sein Luxushotel zu begeistern und spielen Kooperationen mit anderen Hotels eine Rolle? Fragen über Fragen auf die Bastian Baumann in seinem ersten Podcast überhaupt keine Antworten schuldig bleibt. Und dies in einer mehr als angenehmen, wertschätzenden Art... Gutes Hören!

Ohne Aktien-Zugang ist's schwer? Starte jetzt bei unserem Partner Scalable Capital. Mit eigenem KI-Chatbot, der dir alle Fragen rund ums Investieren beantwortet. Alle weiteren Infos gibt's hier: scalable.capital/oaws. KI-Deals wohin man schaut: OpenAI kauft bei AWS, AWS kauft bei Cipher, Microsoft kauft bei Iren. Microsoft verkauft in die VAE. NVIDIA profitiert. Hedgefonds feiert Aixtron. Palantir, BioNTech und Ryanair haben Zahlen und Tesla kauft wohl bei Samsung SDI. Denkt man an Autos in den USA, denkt man an Muscle-Cars oder Pickups. KIA Motors (WKN: 885677) steht dafür auf den ersten Blick nicht. Trotzdem mischen die Südkoreaner den US-Markt auf. Mit einer cleveren Strategie. Six Flags (WKN: A2QGV5) sollte nach der Fusion mit Cedar Fair DER US-Freizeitparkgigant werden. Daraus wurde aber nichts. Heute hat Six Flags hohe Schulden und viel weniger Besucher als 2019. NFL-Superstar Travis Kelce & Jana Partners wollen's ändern. Diesen Podcast vom 04.11.2025, 3:00 Uhr stellt dir die Podstars GmbH (Noah Leidinger) zur Verfügung.

De tijd dat criminelen onbevreesd en ongestoord een luxe leventje in Dubai konden leiden, is voorbij. Het aantal uitleveringen van Nederlandse criminelen uit de VAE neemt namelijk toe. En daarom wordt het criminelen heet onder de voeten, en dat heeft niks met het klimaat te maken. De afgelopen anderhalf jaar werden al 11 criminelen uitgeleverd aan Nederland. Waarom worden er nu zoveel criminelen toch uitgeleverd? En wat maakt Dubai zo aantrekkelijk voor criminelen?  Over de rise and fall van Dubai ga ik deze ochtend praten met misdaadverslaggever bij het AD, Chiel Timmermans

Isabela Maria Botea s- a specializat si a lucrat timp de aproape patru ani la cel mai important si mai mare spital privat din Italia, Istituto Clinico Humanitas, Milano, acumuland o semnificativa experienta profesionala. In prezent, colaboreaza cu cele mai prestigioase spitale si clinici din Bucuresti, dedicandu-se senologiei imagistice ( mamografii, ecografii si punctii biopsie ecoghidate si stereotaxice), dar si ecografiei generale si interventionale ( punctii de tiroida , parti moi). Este medic coordonator centrul de senologie imagistica Spital Euroclinic Regina Maria, avand colaborari si la alte clinici prestigioase din Bucuresti, precum clinica Donna, Femme Boutiqe Medical , Prof Medica , Clinica Cronos Med. Detine o importanta experienta in ecografie interventionala cu peste 4000 de biopsii efectuate. Este primul medic care a efectuat in Bucuresti (si prima in Reteaua Regina Maria Romania) punctie biopsie mamara sub ghidaj mamografic vacuum asistata VAB, si singurul medic din Bucuresti care efectueaza excizii vacuum asistate VAE

 1 At that hour the disciples came to Jesus, saying: Who thinkest thou is the greater in the kingdom of heaven?In illa hora accesserunt discipuli ad Jesum, dicentes : Quis, putas, major est in regno caelorum? 2 And Jesus calling unto him a little child, set him in the midst of them,Et advocans Jesus parvulum, statuit eum in medio eorum, 3 And said: Amen I say to you, unless you be converted, and become as little children, you shall not enter into the kingdom of heaven.et dixit : Amen dico vobis, nisi conversi fueritis, et efficiamini sicut parvuli, non intrabitis in regnum caelorum. 4 Whosoever therefore shall humble himself as this little child, he is the greater in the kingdom of heaven.Quicumque ergo humiliaverit se sicut parvulus iste, hic est major in regno caelorum. 5 And he that shall receive one such little child in my name, receiveth me.Et qui susceperit unum parvulum talem in nomine meo, me suscipit : 6 But he that shall scandalize one of these little ones that believe in me, it were better for him that a millstone should be hanged about his neck, and that he should be drowned in the depth of the sea.qui autem scandalizaverit unum de pusillis istis, qui in me credunt, expedit ei ut suspendatur mola asinaria in collo ejus, et demergatur in profundum maris. 7 Woe to the world because of scandals. For it must needs be that scandals come: but nevertheless woe to that man by whom the scandal cometh.Vae mundo a scandalis! Necesse est enim ut veniant scandala : verumtamen vae homini illi, per quem scandalum venit. 8 And if thy hand, or thy foot scandalize thee, cut it off, and cast it from thee. It is better for thee to go into life maimed or lame, than having two hands or two feet, to be cast into everlasting fire.Si autem manus tua, vel pes tuus scandalizat te, abscide eum, et projice abs te : bonum tibi est ad vitam ingredi debilem, vel claudum, quam duas manus vel duos pedes habentem mitti in ignem aeternum. 9 And if thy eye scandalize thee, pluck it out, and cast it from thee. It is better for thee having one eye to enter into life, than having two eyes to be cast into hell fire.Et si oculus tuus scandalizat te, erue eum, et projice abs te : bonum tibi est cum uno oculo in vitam intrare, quam duos oculos habentem mitti in gehennam ignis. 10 See that you despise not one of these little ones: for I say to you, that their angels in heaven always see the face of my Father who is in heaven.Videte ne contemnatis unum ex his pusillis : dico enim vobis, quia angeli eorum in caelis semper vident faciem Patris mei, qui in caelis est.God's love for us was not satisfied with giving us His Son, Jesus, for our Redeemer, and Mary for our Advocate; He has been pleased to give us also His Angels to be our guardians; "He hath given His Anges charge over thee; to keep thee in all they ways" (Ps 90,2). These holy spirits and princes of heaven are always present with us, and assist us in all our actions. And on this account, out of regard to our guardian angels, we ought carefully to refrain from every action that can displease them.Note:This can be a confusing reading. There is a good explanation of this reading at this web site...Fr Flader: Cutting off our hand?This reminds me of a time I went to confession, and talked about temptations that were on my iPhone. The priest said to me "get rid of the iPhone, or limit it's use". After confession, I thought to myself, "hey, I am plucking out my iPhone."

 1 At that hour the disciples came to Jesus, saying: Who thinkest thou is the greater in the kingdom of heaven?In illa hora accesserunt discipuli ad Jesum, dicentes : Quis, putas, major est in regno caelorum? 2 And Jesus calling unto him a little child, set him in the midst of them,Et advocans Jesus parvulum, statuit eum in medio eorum, 3 And said: Amen I say to you, unless you be converted, and become as little children, you shall not enter into the kingdom of heaven.et dixit : Amen dico vobis, nisi conversi fueritis, et efficiamini sicut parvuli, non intrabitis in regnum caelorum. 4 Whosoever therefore shall humble himself as this little child, he is the greater in the kingdom of heaven.Quicumque ergo humiliaverit se sicut parvulus iste, hic est major in regno caelorum. 5 And he that shall receive one such little child in my name, receiveth me.Et qui susceperit unum parvulum talem in nomine meo, me suscipit : 6 But he that shall scandalize one of these little ones that believe in me, it were better for him that a millstone should be hanged about his neck, and that he should be drowned in the depth of the sea.qui autem scandalizaverit unum de pusillis istis, qui in me credunt, expedit ei ut suspendatur mola asinaria in collo ejus, et demergatur in profundum maris. 7 Woe to the world because of scandals. For it must needs be that scandals come: but nevertheless woe to that man by whom the scandal cometh.Vae mundo a scandalis! Necesse est enim ut veniant scandala : verumtamen vae homini illi, per quem scandalum venit. 8 And if thy hand, or thy foot scandalize thee, cut it off, and cast it from thee. It is better for thee to go into life maimed or lame, than having two hands or two feet, to be cast into everlasting fire.Si autem manus tua, vel pes tuus scandalizat te, abscide eum, et projice abs te : bonum tibi est ad vitam ingredi debilem, vel claudum, quam duas manus vel duos pedes habentem mitti in ignem aeternum. 9 And if thy eye scandalize thee, pluck it out, and cast it from thee. It is better for thee having one eye to enter into life, than having two eyes to be cast into hell fire.Et si oculus tuus scandalizat te, erue eum, et projice abs te : bonum tibi est cum uno oculo in vitam intrare, quam duos oculos habentem mitti in gehennam ignis. 10 See that you despise not one of these little ones: for I say to you, that their angels in heaven always see the face of my Father who is in heaven.Videte ne contemnatis unum ex his pusillis : dico enim vobis, quia angeli eorum in caelis semper vident faciem Patris mei, qui in caelis est.Christ teaches humility, to beware of scandal, and to flee the occasions of sinThis Basilica was consecrated to St Michael by Boniface II.

Auto encoders are neural networks that compress data into a smaller "code," enabling dimensionality reduction, data cleaning, and lossy compression by reconstructing original inputs from this code. Advanced auto encoder types, such as denoising, sparse, and variational auto encoders, extend these concepts for applications in generative modeling, interpretability, and synthetic data generation. Links Notes and resources at ocdevel.com/mlg/36 Try a walking desk - stay healthy & sharp while you learn & code Build the future of multi-agent software with AGNTCY. Thanks to T.J. Wilder from intrep.io for recording this episode! Fundamentals of Autoencoders Autoencoders are neural networks designed to reconstruct their input data by passing data through a compressed intermediate representation called a “code.” The architecture typically follows an hourglass shape: a wide input and output separated by a narrower bottleneck layer that enforces information compression. The encoder compresses input data into the code, while the decoder reconstructs the original input from this code. Comparison with Supervised Learning Unlike traditional supervised learning, where the output differs from the input (e.g., image classification), autoencoders use the same vector for both input and output. Use Cases: Dimensionality Reduction and Representation Autoencoders perform dimensionality reduction by learning compressed forms of high-dimensional data, making it easier to visualize and process data with many features. The compressed code can be used for clustering, visualization in 2D or 3D graphs, and input into subsequent machine learning models, saving computational resources and improving scalability. Feature Learning and Embeddings Autoencoders enable feature learning by extracting abstract representations from the input data, similar in concept to learned embeddings in large language models (LLMs). While effective for many data types, autoencoder-based encodings are less suited for variable-length text compared to LLM embeddings. Data Search, Clustering, and Compression By reducing dimensionality, autoencoders facilitate vector searches, efficient clustering, and similarity retrieval. The compressed codes enable lossy compression analogous to audio codecs like MP3, with the difference that autoencoders lack domain-specific optimizations for preserving perceptually important data. Reconstruction Fidelity and Loss Types Loss functions in autoencoders are defined to compare reconstructed outputs to original inputs, often using different loss types depending on input variable types (e.g., Boolean vs. continuous). Compression via autoencoders is typically lossy, meaning some information from the input is lost during reconstruction, and the areas of information lost may not be easily controlled. Outlier Detection and Noise Reduction Since reconstruction errors tend to move data toward the mean, autoencoders can be used to reduce noise and identify data outliers. Large reconstruction errors can signal atypical or outlier samples in the dataset. Denoising Autoencoders Denoising autoencoders are trained to reconstruct clean data from noisy inputs, making them valuable for applications in image and audio de-noising as well as signal smoothing. Iterative denoising as a principle forms the basis for diffusion models, where repeated application of a denoising autoencoder can gradually turn random noise into structured output. Data Imputation Autoencoders can aid in data imputation by filling in missing values: training on complete records and reconstructing missing entries for incomplete records using learned code representations. This approach leverages the model's propensity to output ‘plausible' values learned from overall data structure. Cryptographic Analogy The separation of encoding and decoding can draw parallels to encryption and decryption, though autoencoders are not intended or suitable for secure communication due to their inherent lossiness. Advanced Architectures: Sparse and Overcomplete Autoencoders Sparse autoencoders use constraints to encourage code representations with only a few active values, increasing interpretability and explainability. Overcomplete autoencoders have a code size larger than the input, often in applications that require extraction of distinct, interpretable features from complex model states. Interpretability and Research Example Research such as Anthropic's “Towards Monosemanticity” applies sparse autoencoders to the internal activations of language models to identify interpretable features correlated with concrete linguistic or semantic concepts. These models can be used to monitor and potentially control model behaviors (e.g., detecting specific language usage or enforcing safety constraints) by manipulating feature activations. Variational Autoencoders (VAEs) VAEs extend autoencoder architecture by encoding inputs as distributions (means and standard deviations) instead of point values, enforcing a continuous, normalized code space. Decoding from sampled points within this space enables synthetic data generation, as any point near the center of the code space corresponds to plausible data according to the model. VAEs for Synthetic Data and Rare Event Amplification VAEs are powerful in domains with sparse data or rare events (e.g., healthcare), allowing generation of synthetic samples representing underrepresented cases. They can increase model performance by augmenting datasets without requiring changes to existing model pipelines. Conditional Generative Techniques Conditional autoencoders extend VAEs by allowing controlled generation based on specified conditions (e.g., generating a house with a pool), through additional decoder inputs and conditional loss terms. Practical Considerations and Limitations Training autoencoders and their variants requires computational resources, and their stochastic training can produce differing code representations across runs. Lossy reconstruction, lack of domain-specific optimizations, and limited code interpretability restrict some use cases, particularly where exact data preservation or meaningful decompositions are required.

Vae soli!, dice el libro del Eclesiastés: ¡Pobre del que va solo! Pero nosotros nunca vamos solos porque una Persona divina nos ha sido dada. Habita en nosotros el Espíritu Santo, moviéndonos con inspiraciones y sus dones. Dentro de estos, pensemos en el superior, el de Sabiduría, que nos hace gustar las cosas divinas. Podemos preguntarnos si ese gozo de lo divino ha sido creciente en nuestra vida.

CardioNerds Critical Care Cardiology Council members Dr. Gurleen Kaur and Dr. Katie Vanchiere meet with Dr. Yash Patel, Dr. Akanksha, and Dr. Mohammed El Nayir from Trinity Health Ann Arbor. They discuss a case of pulmonary air embolism, RV failure, and cardiac arrest secondary to an ocular venous air embolism. Expert insights provided by Dr. Tanmay Swadia. Audio editing by CardioNerds Academy intern, Grace Qiu. A 36-year-old man with a history of multiple ocular surgeries, including a complex retinal detachment repair, suffered a post-vitrectomy collapse at home. He was found hypoxic, tachycardic, and hypotensive, later diagnosed with a pulmonary embolism from ocular venous air embolism leading to severe right heart failure. Despite a mild embolic burden, the cardiovascular response was profound, requiring advanced hemodynamic support, including an Impella RP device (Abiomed, Inc.). Multidisciplinary management, including fluid optimization, vasopressors and mechanical support to facilitate recovery. This case underscores the need for early recognition and individualized intervention in cases of ocular venous air embolism. US Cardiology Review is now the official journal of CardioNerds! Submit your manuscript here. CardioNerds Case Reports PageCardioNerds Episode PageCardioNerds AcademyCardionerds Healy Honor Roll CardioNerds Journal ClubSubscribe to The Heartbeat Newsletter!Check out CardioNerds SWAG!Become a CardioNerds Patron! Pearls- Clear Vision, Clouded Heart: Ocular Venous Air Embolism with Pulmonary Air Embolism, RV Failure, and Cardiac Arrest Hypoxia, hypotension and tachycardia in a patient following ocular instrumentation are classic findings suggestive of pulmonary embolism from possible air embolism. The diagnosis of RV failure is based on clinical presentation, echocardiographic findings (such as McConnell's sign), and invasive hemodynamic assessment via right heart catheterization. Mechanical circulatory support can be considered as a temporary measure for patients with refractory RV failure. Central Figure: Approach to Pulmonary Embolism with Acute RV Failure Notes - Clear Vision, Clouded Heart: Ocular Venous Air Embolism with Pulmonary Air Embolism, RV Failure, and Cardiac Arrest 1. What is an Ocular Venous Air Embolism (VAE), and how can it be managed in critically ill patients? An Ocular Venous Air Embolism is defined as the entry of air into the systemic venous circulation through the ocular venous circulation, often during vitrectomy procedures. Early diagnosis is key to preventing cardiovascular collapse in cases of Ocular Venous Air Embolism (VAE).  The goal is to stop further air entry. This can be done by covering the surgical site with saline-soaked dressings and checking for air entry points. Adjusting the operating table can help, especially with a reverse Trendelenburg position for lower-body procedures. The moment VAE is suspected, discontinue nitrous oxide and switch to 100% oxygen. This helps with oxygenation, speeds up nitrogen elimination, and shrinks air bubbles. Hyperbaric Oxygen Therapy can reduce bubble size and improve oxygenation, especially in cases of cerebral air embolism, when administered within 6 hours of the incident. Though delayed hyperbaric oxygen therapy can still offer benefits, the evidence is mixed. VAE increases right heart strain, so inotropic agents like dobutamine can help boost cardiac output, while norepinephrine supports ventricular function and systemic vascular resistance, but this may also worsen pulmonary resistance.  Aspiration of air via multi-orifice or Swan-Ganz catheters has limited success, with success rates ranging from 6% to 16%. In contrast, the Bunegin-Albin catheter has shown more promise, with a 30-60% success rate. Catheterization for acute VAE-induced hemodynamic compromise is controversial, and there's insufficient evidence to support its ...