Podcasts about gpu

What if we told you that CES did not feature any new GPUs?  But it did feature more frames!  MSI with LIGHTNING and GPU safeguard, Phison's new controller, and that wily AMD with new Ryzen 7 9850X3D (and confirmed Ryzen 9 9950X3D2) - whee!  Remember the Reboot computer generated cartoon?  Remember D-Link Routers and Zero Days?  Remember Intel?  It's all here! That and everything old is new again with Old GPUs and CPUs coming back .. because RAM.Thanks again to our sponsor with CopilotMoney!  Get on your single pane of financial glass and bring order to your money and spending - it's even actually fun to save again.  Get the web version and use our code for 26% off at http://try.copilot.money/pcperTimestamps:0:00 Intro00:56 Patreon01:37 Food with Josh04:10 AMD announces Ryzen 7 9850X3D05:41 AMD sort of confirmed the 9950X3D207:00 NVIDIA DLSS 4.509:34 Intel was at CES12:50 MSI LIGHTNING returns14:54 MSI also launching GPU Safeguard Plus PSUs19:44 WD_Black is now Sandisk Optimus GX Pro21:54 Phison has the most efficient SSD controller26:11 ASUS ROG RGB Stripe OLED28:44 First computer-animated TV show restored33:29 Podcast sponsor - Copilot Money34:57 (In)Security Corner44:32 Gaming Quick Hits1:06:31 Picks of the Week1:24:08 Outro ★ Support this podcast on Patreon ★

A new week means new questions! Hope you have fun with these!The Oscars are presented by which professional honorary organization Headquartered in Beverly Hills, California?The French company Van Cleef & Arpels is a business mainly specializing in what?A quay is a structure primarily built on or along what kind of geographical feature?The first successful tornado warning in history occurred in 1948 at Tinker Air Field Air Force Base near what city?Till We Have Faces, The Great Divorce, and The Screwtape Letters are lesser-known novels by which author?In video and tabletop games, what does "NPC" stand for?Biellmann Spin, Lutz, and Crossover are all terms used in which sport?In a computer or video gaming system, what does the acronym GPU stand for?H2O2 is the chemical structure for what common product?In Greek myth, which monster was beheaded by the hero Perseus?Before decimalisation in the UK, how many pence made a shilling?The German state that existed from 1701 to 1918 was known as the Kingdom of what?Chad Kroeger and his Canadian chums enjoy this slightly sweet dark rye bread from Germany.In military tech, falconets, culverins, and carronades were all types of what?On The Office, what are the awards called that Michael hands out to his employees?MusicHot Swing, Fast Talkin, Bass Walker, Dances and Dames, Ambush by Kevin MacLeod (incompetech.com)Licensed under Creative Commons: By Attribution 3.0 http://creativecommons.org/licenses/by/3.0/Don't forget to follow us on social media:Patreon – patreon.com/quizbang – Please consider supporting us on Patreon. Check out our fun extras for patrons and help us keep this podcast going. We appreciate any level of support!Website – quizbangpod.com Check out our website, it will have all the links for social media that you need and while you're there, why not go to the contact us page and submit a question!Facebook – @quizbangpodcast – we post episode links and silly lego pictures to go with our trivia questions. Enjoy the silly picture and give your best guess, we will respond to your answer the next day to give everyone a chance to guess.Instagram – Quiz Quiz Bang Bang (quizquizbangbang), we post silly lego pictures to go with our trivia questions. Enjoy the silly picture and give your best guess, we will respond to your answer the next day to give everyone a chance to guess.Twitter – @quizbangpod We want to start a fun community for our fellow trivia lovers. If you hear/think of a fun or challenging trivia question, post it to our twitter feed and we will repost it so everyone can take a stab it. Come for the trivia – stay for the trivia.Ko-Fi – ko-fi.com/quizbangpod – Keep that sweet caffeine running through our body with a Ko-Fi, power us through a late night of fact checking and editing!

a16z co-founder and General Partner Marc Andreessen joins an AMA-style conversation to explain why AI is the largest technology shift he has experienced, how the cost of intelligence is collapsing, and why the market still feels early despite rapid adoption. The discussion covers how falling model costs and fast capability gains are reshaping pricing, distribution, and competition across the AI stack, why usage-based and value-based pricing are becoming standard, and how startups and incumbents are navigating big versus small models and open versus closed systems. Marc also addresses China's progress, regulatory fragmentation, lessons from Europe, and why venture portfolios are designed to back multiple, conflicting outcomes at once. Resources:Follow Marc Andreessen on X: https://twitter.com/pmarcaFollow Jen Kha on X: https://twitter.com/jkhamehl Stay Updated:If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X :https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://twitter.com/eriktorenbergPlease note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.    Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The future of AI training is shaped by one constraint: keeping GPUs fed.In this episode, Lukas Biewald talks with CoreWeave SVP Corey Sanders about why general-purpose clouds start to break down under large-scale AI workloads.According to Corey, the industry is shifting toward a "Neo Cloud" model to handle the unique demands of modern models.They dive into the hardware and software stack required to maximize GPU utilization and achieve high goodput.Corey's conclusion is clear: AI demands specialization.Connect with us here:Corey Sanders: https://www.linkedin.com/in/corey-sanders-842b72/ CoreWeave: https://www.linkedin.com/company/coreweave/ Lukas Biewald: https://www.linkedin.com/in/lbiewald/ Weights & Biases: https://www.linkedin.com/company/wandb/(00:00) Trailer(00:57) Introduction(02:51) The Evolution of AI Workloads(06:22) Core Weave's Technological Innovations(13:58) Customer Engagement and Future Prospects(28:49) Comparing Cloud Approaches(33:50) Balancing Executive Roles and Hands-On Projects(46:44) Product Development and Customer Feedback

It's been a travel-heavy hiatus—Mark's been living in Spain and Shashank's been bouncing across Asia (including a month in China)—but they're back to unpack a packed week of AI news. They start with the headline hardware story: the Groq (GROQ) deal/partnership dynamics and why ultra-fast inference is becoming the next battleground, plus how this could reshape access to cutting-edge serving across the ecosystem. From there, they pivot to NVIDIA's CES announcements and what “Vera Rubin” implies for data center upgrades, cost-per-token curves, and the messy real-world math of rolling hardware generations. Shashank then brings the future to life with on-the-ground stories from China: a Huawei “everything store” that feels like an Apple Store meets a luxury dealership, folding devices that look straight out of sci-fi, and a parade of robots—from coffee bots to delivery robots that can ride elevators and deliver to your hotel room. They also touch on companion-style consumer robots and why “cute” might be a serious product strategy. Finally, Mark announces the launch of Novacut, a long-form AI video editor built to turn hours of travel footage into a coherent vlog draft—plus export workflows for Premiere, DaVinci Resolve, and Final Cut. They close by talking about the 2026 shift from single model calls to “agentic” systems, including a fun (and slightly alarming) lesson from LLM outcome bias using poker hand reviews. Topics include: Groq inference, NVIDIA + CES, Vera Rubin GPUs, GPU depreciation math, China robotics, Huawei ecosystem, hotel delivery bots, companion robots, Novacut launch, Cursor vs agent workflows, and why agents still struggle with sparse feedback loops. Link mentioned: Novacut — https://novacut.ai

Happy New Year! NVIDIA just spent $20 billion to hollow out an AI company for its brains, while Meta and Google scramble to scoop up fresh talent before AI gets "too weird to manage." Who's winning, who's left behind, and what do these backroom deals mean for the future of artificial intelligence? Andrej Karpathy admits programmers cannot keep pace with AI advances Economic uncertainty in AI despite massive stock market influence Google, Anthropic, and Microsoft drive AI productization for business and consumers OpenAI, Claude, and Gemini battle for consumer AI dominance Journalism struggles to keep up with AI realities and misinformation tools Concerns mount over AI energy, water, and environmental impact narratives Meta buys Manus, expands AI agent ambitions with Llama model OpenAI posts high-stress "Head of Preparedness" job worth $555K+ Training breakthroughs: DeepSeek's mHC and comparisons to Action Park U.S. lawmakers push broad, controversial internet censorship bills Age verification and bans spark state laws, VPN workaround explosion U.S. drone ban labeled protectionist as industry faces tech shortages FCC security initiatives falter; Cyber Trust Mark program scrapped Waymo robotaxis stall in blackouts, raising AV urban planning issues School cellphone bans expose kids' struggle with analog clocks MetroCard era ends in NYC as tap-to-pay takes over subway access RAM, VRAM, and GPU prices soar as AI and gaming squeeze supply CES preview: Samsung QD-OLED TV, Sony AFEELA car, gadget show hype Remembering Stewart Cheifet and Computer Chronicles' legacy Host: Leo Laporte Guests: Dan Patterson and Joey de Villa Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: zscaler.com/security canary.tools/twit - use code: TWIT monarch.com with code TWIT Melissa.com/twit redis.io

Happy New Year! NVIDIA just spent $20 billion to hollow out an AI company for its brains, while Meta and Google scramble to scoop up fresh talent before AI gets "too weird to manage." Who's winning, who's left behind, and what do these backroom deals mean for the future of artificial intelligence? Andrej Karpathy admits programmers cannot keep pace with AI advances Economic uncertainty in AI despite massive stock market influence Google, Anthropic, and Microsoft drive AI productization for business and consumers OpenAI, Claude, and Gemini battle for consumer AI dominance Journalism struggles to keep up with AI realities and misinformation tools Concerns mount over AI energy, water, and environmental impact narratives Meta buys Manus, expands AI agent ambitions with Llama model OpenAI posts high-stress "Head of Preparedness" job worth $555K+ Training breakthroughs: DeepSeek's mHC and comparisons to Action Park U.S. lawmakers push broad, controversial internet censorship bills Age verification and bans spark state laws, VPN workaround explosion U.S. drone ban labeled protectionist as industry faces tech shortages FCC security initiatives falter; Cyber Trust Mark program scrapped Waymo robotaxis stall in blackouts, raising AV urban planning issues School cellphone bans expose kids' struggle with analog clocks MetroCard era ends in NYC as tap-to-pay takes over subway access RAM, VRAM, and GPU prices soar as AI and gaming squeeze supply CES preview: Samsung QD-OLED TV, Sony AFEELA car, gadget show hype Remembering Stewart Cheifet and Computer Chronicles' legacy Host: Leo Laporte Guests: Dan Patterson and Joey de Villa Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: zscaler.com/security canary.tools/twit - use code: TWIT monarch.com with code TWIT Melissa.com/twit redis.io

Happy New Year! NVIDIA just spent $20 billion to hollow out an AI company for its brains, while Meta and Google scramble to scoop up fresh talent before AI gets "too weird to manage." Who's winning, who's left behind, and what do these backroom deals mean for the future of artificial intelligence? Andrej Karpathy admits programmers cannot keep pace with AI advances Economic uncertainty in AI despite massive stock market influence Google, Anthropic, and Microsoft drive AI productization for business and consumers OpenAI, Claude, and Gemini battle for consumer AI dominance Journalism struggles to keep up with AI realities and misinformation tools Concerns mount over AI energy, water, and environmental impact narratives Meta buys Manus, expands AI agent ambitions with Llama model OpenAI posts high-stress "Head of Preparedness" job worth $555K+ Training breakthroughs: DeepSeek's mHC and comparisons to Action Park U.S. lawmakers push broad, controversial internet censorship bills Age verification and bans spark state laws, VPN workaround explosion U.S. drone ban labeled protectionist as industry faces tech shortages FCC security initiatives falter; Cyber Trust Mark program scrapped Waymo robotaxis stall in blackouts, raising AV urban planning issues School cellphone bans expose kids' struggle with analog clocks MetroCard era ends in NYC as tap-to-pay takes over subway access RAM, VRAM, and GPU prices soar as AI and gaming squeeze supply CES preview: Samsung QD-OLED TV, Sony AFEELA car, gadget show hype Remembering Stewart Cheifet and Computer Chronicles' legacy Host: Leo Laporte Guests: Dan Patterson and Joey de Villa Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: zscaler.com/security canary.tools/twit - use code: TWIT monarch.com with code TWIT Melissa.com/twit redis.io

Happy New Year! NVIDIA just spent $20 billion to hollow out an AI company for its brains, while Meta and Google scramble to scoop up fresh talent before AI gets "too weird to manage." Who's winning, who's left behind, and what do these backroom deals mean for the future of artificial intelligence? Andrej Karpathy admits programmers cannot keep pace with AI advances Economic uncertainty in AI despite massive stock market influence Google, Anthropic, and Microsoft drive AI productization for business and consumers OpenAI, Claude, and Gemini battle for consumer AI dominance Journalism struggles to keep up with AI realities and misinformation tools Concerns mount over AI energy, water, and environmental impact narratives Meta buys Manus, expands AI agent ambitions with Llama model OpenAI posts high-stress "Head of Preparedness" job worth $555K+ Training breakthroughs: DeepSeek's mHC and comparisons to Action Park U.S. lawmakers push broad, controversial internet censorship bills Age verification and bans spark state laws, VPN workaround explosion U.S. drone ban labeled protectionist as industry faces tech shortages FCC security initiatives falter; Cyber Trust Mark program scrapped Waymo robotaxis stall in blackouts, raising AV urban planning issues School cellphone bans expose kids' struggle with analog clocks MetroCard era ends in NYC as tap-to-pay takes over subway access RAM, VRAM, and GPU prices soar as AI and gaming squeeze supply CES preview: Samsung QD-OLED TV, Sony AFEELA car, gadget show hype Remembering Stewart Cheifet and Computer Chronicles' legacy Host: Leo Laporte Guests: Dan Patterson and Joey de Villa Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: zscaler.com/security canary.tools/twit - use code: TWIT monarch.com with code TWIT Melissa.com/twit redis.io

Happy New Year! NVIDIA just spent $20 billion to hollow out an AI company for its brains, while Meta and Google scramble to scoop up fresh talent before AI gets "too weird to manage." Who's winning, who's left behind, and what do these backroom deals mean for the future of artificial intelligence? Andrej Karpathy admits programmers cannot keep pace with AI advances Economic uncertainty in AI despite massive stock market influence Google, Anthropic, and Microsoft drive AI productization for business and consumers OpenAI, Claude, and Gemini battle for consumer AI dominance Journalism struggles to keep up with AI realities and misinformation tools Concerns mount over AI energy, water, and environmental impact narratives Meta buys Manus, expands AI agent ambitions with Llama model OpenAI posts high-stress "Head of Preparedness" job worth $555K+ Training breakthroughs: DeepSeek's mHC and comparisons to Action Park U.S. lawmakers push broad, controversial internet censorship bills Age verification and bans spark state laws, VPN workaround explosion U.S. drone ban labeled protectionist as industry faces tech shortages FCC security initiatives falter; Cyber Trust Mark program scrapped Waymo robotaxis stall in blackouts, raising AV urban planning issues School cellphone bans expose kids' struggle with analog clocks MetroCard era ends in NYC as tap-to-pay takes over subway access RAM, VRAM, and GPU prices soar as AI and gaming squeeze supply CES preview: Samsung QD-OLED TV, Sony AFEELA car, gadget show hype Remembering Stewart Cheifet and Computer Chronicles' legacy Host: Leo Laporte Guests: Dan Patterson and Joey de Villa Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: zscaler.com/security canary.tools/twit - use code: TWIT monarch.com with code TWIT Melissa.com/twit redis.io

How can AI weather models improve the accuracy and scale of catastrophe modelling? Matthew Grant is joined by David Wood, Managing Director at JBA Risk Management, and Jochen Papenbrock, Head of Financial Technology (EMEA) at NVIDIA, to explore how accelerated computing is unlocking new ways to simulate and manage flood risk. JBA has long been a pioneer in flood modelling, while NVIDIA's GPU technology has helped drive the recent breakthroughs in AI and generative modelling. Together, they discuss how high-resolution simulations, new ensemble methods and open-source tools are pushing the limits of what's possible in climate and catastrophe analytics. Key Talking Points: The early bet – how JBA's adoption of GPU computing over a decade ago made national-scale flood mapping possible From gaming to GenAI – how NVIDIA's evolution from graphics to AI led to the development of physics-informed weather models Ensemble power – why running 1,000+ simulations helps capture more extremes than the historic record ever could Event sets reimagined – how AI models are enabling richer, more diverse flood scenarios for Europe and beyond Real-time relevance – the potential to use AI models to simulate how a flood might unfold, as it's happening Making AI usable – how Earth-2 Studio and open-source frameworks are opening up generative models to catastrophe modellers Proving value – how NVIDIA and JBA worked together to quantify the benefits of faster, more flexible modelling approaches Looking ahead – why cross-sector collaboration will be essential to turn acceleration into real-world impact If you like what you're hearing, please leave us a review on whichever platform you use or contact Matthew Grant on LinkedIn.  Sign up to the InsTech newsletter for a fresh view on the world every Wednesday morning.

Gary Shapiro has spent decades at the center of the global consumer technology industry, leading the Consumer Technology Association (CTA) and building CES into one of the most important stages for innovation, policy, and deal-making on the planet. In this first episode of 2026, Gary joins Charlie, Rony, and Ted to preview CES, unpack the explosion of AI across every category, and deliver unusually blunt takes on tariffs, China, manufacturing, and U.S. innovation policy. He explains how CES has evolved from a TV-and-gadgets show into a global platform where boards meet, standards are set, and policymakers, chip designers, robotics firms, and health-tech startups all collide.In the News: Before Gary joins, the hosts break down Nvidia's $20 billion “not-a-deal” with Singapore's Groq, the stake in Intel, and what that combo might signal about the edge of the GPU bubble and the shift toward inference compute, x86, and U.S. industrial policy. They also dig into Netflix's acquisition of Ready Player Me and what it suggests about a Netflix metaverse and location-based entertainment strategy, plus Starlink's rapid growth and an onslaught of “AI everything” products ahead of CES.Gary walks through new features at this year's show: CES Foundry at the Fontainebleau for AI and quantum, expanded tracks on manufacturing, wearables, women's health, and accessibility, plus an AI-powered show app already fielding thousands of questions (top query: where to pick up badges). He also talks candidly about his biggest concern—that fragmented state-level AI regulation (1,200+ state bills in 2025) will crush startups while big players shrug—and why he believes federal standards via NIST are the only realistic path. The discussion ranges from AI-driven healthcare and precision agriculture to robotics, demographics, labor culture, global supply chains, and what CES might look like in 2056.5 Key Takeaways from Gary:AI is now the spine of CES. CES 2026 centers on AI as infrastructure: CES Foundry at the Fontainebleau for AI + quantum, AI training tracks for strategy, implementation, agentic AI, and AI-driven marketing, and an AI-powered app helping attendees navigate the show.Fragmented state AI laws are an existential risk for startups. Over 1,200 state AI bills in 2025—including proposals to criminalize agentic AI counseling—could create a compliance maze only large incumbents can survive, which is why Gary argues for federal standards via NIST.Wearables are becoming systems, not gadgets. Oura rings, wrist devices, body sensors, and subdermal glucose monitors are starting to be designed as interoperable families of devices, with partnerships emerging to combine data into unified health services.Robotics is breaking out of the industrial niche. CES will showcase the largest robotics presence yet, moving beyond factory arms and drones to humanoids, logistics, social companions, and applied AI systems across sectors.Tariffs, alliances, and AI will reshape manufacturing. Gary is skeptical of “Fortress USA” strategies that try to onshore everything, pointing instead to allied reshoring (Latin America, Europe, Japan, South Korea) and the long-term role of AI-powered robotics in changing labor economics and global supply chains.This episode is brought to you by Zappar, creators of Mattercraft—the leading visual development environment for building immersive 3D web experiences for mobile headsets and desktop. Mattercraft combines the power of a game engine with the flexibility of the web, and now features an AI assistant that helps you design, code, and debug in real time, right in your browser. Whether you're a developer, designer, or just getting started, start building smarter at mattercraft.io.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

From undergraduate research seminars at Princeton to winning Best Paper award at NeurIPS 2025, Kevin Wang, Ishaan Javali, Michał Bortkiewicz, Tomasz Trzcinski, Benjamin Eysenbach defied conventional wisdom by scaling reinforcement learning networks to 1,000 layers deep—unlocking performance gains that the RL community thought impossible. We caught up with the team live at NeurIPS to dig into the story behind RL1000: why deep networks have worked in language and vision but failed in RL for over a decade (spoiler: it's not just about depth, it's about the objective), how they discovered that self-supervised RL (learning representations of states, actions, and future states via contrastive learning) scales where value-based methods collapse, the critical architectural tricks that made it work (residual connections, layer normalization, and a shift from regression to classification), why scaling depth is more parameter-efficient than scaling width (linear vs. quadratic growth), how Jax and GPU-accelerated environments let them collect hundreds of millions of transitions in hours (the data abundance that unlocked scaling in the first place), the "critical depth" phenomenon where performance doesn't just improve—it multiplies once you cross 15M+ transitions and add the right architectural components, why this isn't just "make networks bigger" but a fundamental shift in RL objectives (their code doesn't have a line saying "maximize rewards"—it's pure self-supervised representation learning), how deep teacher, shallow student distillation could unlock deployment at scale (train frontier capabilities with 1000 layers, distill down to efficient inference models), the robotics implications (goal-conditioned RL without human supervision or demonstrations, scaling architecture instead of scaling manual data collection), and their thesis that RL is finally ready to scale like language and vision—not by throwing compute at value functions, but by borrowing the self-supervised, representation-learning paradigms that made the rest of deep learning work. We discuss: The self-supervised RL objective: instead of learning value functions (noisy, biased, spurious), they learn representations where states along the same trajectory are pushed together, states along different trajectories are pushed apart—turning RL into a classification problem Why naive scaling failed: doubling depth degraded performance, doubling again with residual connections and layer norm suddenly skyrocketed performance in one environment—unlocking the "critical depth" phenomenon Scaling depth vs. width: depth grows parameters linearly, width grows quadratically—depth is more parameter-efficient and sample-efficient for the same performance The Jax + GPU-accelerated environments unlock: collecting thousands of trajectories in parallel meant data wasn't the bottleneck, and crossing 15M+ transitions was when deep networks really paid off The blurring of RL and self-supervised learning: their code doesn't maximize rewards directly, it's an actor-critic goal-conditioned RL algorithm, but the learning burden shifts to classification (cross-entropy loss, representation learning) instead of TD error regression Why scaling batch size unlocks at depth: traditional RL doesn't benefit from larger batches because networks are too small to exploit the signal, but once you scale depth, batch size becomes another effective scaling dimension — RL1000 Team (Princeton) 1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities: https://openreview.net/forum?id=s0JVsx3bx1 Chapters 00:00:00 Introduction: Best Paper Award and NeurIPS Poster Experience 00:01:11 Team Introductions and Princeton Research Origins 00:03:35 The Deep Learning Anomaly: Why RL Stayed Shallow 00:04:35 Self-Supervised RL: A Different Approach to Scaling 00:05:13 The Breakthrough Moment: Residual Connections and Critical Depth 00:07:15 Architectural Choices: Borrowing from ResNets and Avoiding Vanishing Gradients 00:07:50 Clarifying the Paper: Not Just Big Networks, But Different Objectives 00:08:46 Blurring the Lines: RL Meets Self-Supervised Learning 00:09:44 From TD Errors to Classification: Why This Objective Scales 00:11:06 Architecture Details: Building on Braw and SymbaFowl 00:12:05 Robotics Applications: Goal-Conditioned RL Without Human Supervision 00:13:15 Efficiency Trade-offs: Depth vs Width and Parameter Scaling 00:15:48 JAX and GPU-Accelerated Environments: The Data Infrastructure 00:18:05 World Models and Next State Classification 00:22:37 Unlocking Batch Size Scaling Through Network Capacity 00:24:10 Compute Requirements: State-of-the-Art on a Single GPU 00:21:02 Future Directions: Distillation, VLMs, and Hierarchical Planning 00:27:15 Closing Thoughts: Challenging Conventional Wisdom in RL Scaling

Kyle Okamoto is the Chief Technology Officer at Aethir: the leading decentralized enterprise-grade cloud computing network. With over 20 years of experience in cloud and edge computing, digital media, IoT and AI, Kyle's leadership has been pivotal in scaling growth businesses and driving technological innovation at Aethir.Before joining Aethir, Kyle served as the General Manager of Aeris Communications and Ericsson's enterprise businesses, overseeing Internet of Things, Security, and Connected Vehicle portfolio companies. He was also the Chief Executive Officer of Edge Gravity, a global edge cloud platform facilitating cloud gaming, AI, and media and entertainment applications. Kyle's extensive experience also includes his tenure as Chief Network Officer of Verizon Media and his role as a founding member of Verizon Digital Media Services, which grew to a multi-billion dollar business before its acquisition by Private Equity.In addition to his work with Aethir, Kyle is an early investor and advisor to Theta Labs, holds board positions in various technology companies and non-profit organizations, and is an active angel investor and advisor in the venture capital and private equity spaces. Kyle holds a Master of Business Administration from New York University and a Bachelor of Engineering degree from Stevens Institute of Technology.In this conversation, we discuss:- AI's growth is now gated by access to compute rather than model quality - Compute is becoming a financial asset class - AI demand continues to outpace supply - GPUs - Investors are starting to treat compute like infrastructure, not software - Financial structures are becoming essential to scaling AI infrastructure - Decentralized compute offers an alternative path during the global GPU shortage- Enterprises are moving toward multi-source compute strategies - Financing compute - The financing of compute is as important as the tech side AethirX: @AethirCloudWebsite: www.aethir.comLinkedIn: AethirKyle OkamotoLinkedIn: Kyle Okamoto---------------------------------------------------------------------------------This episode is brought to you by PrimeXBT.PrimeXBT offers a robust trading system for both beginners and professional traders that demand highly reliable market data and performance. Traders of all experience levels can easily design and customize layouts and widgets to best fit their trading style. PrimeXBT is always offering innovative products and professional trading conditions to all customers.  PrimeXBT is running an exclusive promotion for listeners of the podcast. After making your first deposit, 50% of that first deposit will be credited to your account as a bonus that can be used as additional collateral to open positions. Code: CRYPTONEWS50 This promotion is available for a month after activation. Click the link below: PrimeXBT x CRYPTONEWS50FollowApple PodcastsSpotifyAmazon MusicRSS FeedSee All

Tesla's Optimus robot faced a setback when a video of it falling went viral, impacting Tesla stock. The conversation also covered Apple's use of AI in health data analysis, leadership changes, and advancements in GPU chips. Additionally, the hosts discussed Apple and Google's warnings about a hacking scheme and speculated about Tim Cook's potential successor.Conversations on technology and tech adjacent subjects since July of 2020, with two and sometime three generations of tech nerds. New shows on (mostly) TUESDAYS!

From investing through the modern data stack era (DBT, Fivetran, and the analytics explosion) to now investing at the frontier of AI infrastructure and applications at Amplify Partners, Sarah Catanzaro has spent years at the intersection of data, compute, and intelligence—watching categories emerge, merge, and occasionally disappoint. We caught up with Sarah live at NeurIPS 2025 to dig into the state of AI startups heading into 2026: why $100M+ seed rounds with no near-term roadmap are now the norm (and why that terrifies her), what the DBT-Fivetran merger really signals about the modern data stack (spoiler: it's not dead, just ready for IPO), how frontier labs are using DBT and Fivetran to manage training data and agent analytics at scale, why data catalogs failed as standalone products but might succeed as metadata services for agents, the consumerization of AI and why personalization (memory, continual learning, K-factor) is the 2026 unlock for retention and growth, why she thinks RL environments are a fad and real-world logs beat synthetic clones every time, and her thesis for the most exciting AI startups: companies that marry hard research problems (RAG, rule-following, continual learning) with killer applications that were simply impossible before. We discuss: The DBT-Fivetran merger: not the death of the modern data stack, but a path to IPO scale (targeting $600M+ combined revenue) and a signal that both companies were already winning their categories How frontier labs use data infrastructure: DBT and Fivetran for training data curation, agent analytics, and managing increasingly complex interactions—plus the rise of transactional databases (RocksDB) and efficient data loading (Vortex) for GPU-bound workloads Why data catalogs failed: built for humans when they should have been built for machines, focused on discoverability when the real opportunity was governance, and ultimately subsumed as features inside Snowflake, DBT, and Fivetran The $100M+ seed phenomenon: raising massive rounds at billion-dollar valuations with no 6-month roadmap, seven-day decision windows, and founders optimizing for signal ("we're a unicorn") over partnership or dilution discipline Why world models are overhyped but underspecified: three competing definitions, unclear generalization across use cases (video games ≠ robotics ≠ autonomous driving), and a research problem masquerading as a product category The 2026 theme: consumerization of AI via personalization—memory management, continual learning, and solving retention/churn by making products learn skills, preferences, and adapt as the world changes (not just storing facts in cursor rules) Why RL environments are a fad: labs are paying 7–8 figures for synthetic clones when real-world logs, traces, and user activity (à la Cursor) are richer, cheaper, and more generalizable Sarah's investment thesis: research-driven applications that solve hard technical problems (RAG for Harvey, rule-following for Sierra, continual learning for the next killer app) and unlock experiences that were impossible before Infrastructure bets: memory, continual learning, stateful inference, and the systems challenges of loading/unloading personalized weights at scale Why K-factor and growth fundamentals matter again: AI felt magical in 2023–2024, but as the magic fades, retention and virality are back—and most AI founders have never heard of K-factor — Sarah Catanzaro X: https://x.com/sarahcat21 Amplify Partners: https://amplifypartners.com/ Where to find Latent Space X: https://x.com/latentspacepod Substack: https://www.latent.space/ Chapters 00:00:00 Introduction: Sarah Catanzaro's Journey from Data to AI 00:01:02 The DBT-Fivetran Merger: Not the End of the Modern Data Stack 00:05:26 Data Catalogs and What Went Wrong 00:08:16 Data Infrastructure at AI Labs: Surprising Insights 00:10:13 The Crazy Funding Environment of 2024-2025 00:17:18 World Models: Hype, Confusion, and Market Potential 00:18:59 Memory Management and Continual Learning: The Next Frontier 00:23:27 Agent Environments: Just a Fad? 00:25:48 The Perfect AI Startup: Research Meets Application 00:28:02 Closing Thoughts and Where to Find Sarah

This is a special episode, highlighting a session from ELC Annual 2025! OpenAI evolved from a pure research lab into the fastest-growing product in history, scaling from 100 million to 700 million weekly users in record time. In this episode, we deconstruct the organizational design choices and cultural bets that enabled this unprecedented velocity. We explore what it means to hire "extreme generalists," how AI-native interns are redefining productivity, and the real-time trade-offs made during the world's largest product launches. Featuring Sulman Choudhry (Head of ChatGPT Engineering) and Samir Ahmed (Technical Lead), moderated by Lawrence Bruhmeller (Eng Management @ Sigma). ABOUT SULMAN CHOUDHRYSulman leads ChatGPT Engineering at OpenAI, driving the development and scaling of one of the world's most impactful AI products. He pushes the boundaries of innovation by turning cutting‑edge research into practical, accessible tools that transform how people interact with technology. Previously at Meta, Sulman founded and scaled Instagram Reels, IGTV, and Instagram Labs, and helped lead the early development of Instagram Stories.He also brought MetaAI to Instagram and Messenger, integrating generative AI into experiences used by billions. Earlier in his career, Sulman was on the founding team that built and launched UberEATS from the ground up, helping turn it into a global food delivery platform. With a track record of marrying technical vision, product strategy, and large‑scale execution, Sulman focuses on building products that meaningfully change how people live, work, and connect.ABOUT SAMIR AHMEDSamir is the Technical Lead for ChatGPT at OpenAI, where he currently leads the Personalization and Memory efforts to scale adaptive, useful, and human-centered product experiences to over 700 million users. He works broadly across the OpenAI stack—including mobile, web, services, systems, inference, and product research infrastructure.Previously, Samir spent nine years at Snap, working across Ads, AR, Content, and Growth. He led some of the company's most critical technical initiatives, including founding and scaling the machine learning platform that powered nearly all Ads, Content, and AR workloads, handling tens of billions of requests and trillions of inferences daily.ABOUT LAWRENCE BRUHMELLERLawrence Bruhmuller has over 20 years of experience in engineering management, much of it as an overall head of engineering. Previous roles include CTO/VPE roles at Great Expectations, Pave, Optimizely, and WeWork. He is currently leading the core query compiler and serving teams at Sigma Computing, the industry leading business analytics company.Lawrence is passionate about the intersection of engineering management and the growth stage of startups. He has written extensively on engineering leadership (https://lbruhmuller.medium.com/), including how to best evolve and mature engineering organizations before, during and after these growth phases. He enjoys advising and mentoring other engineering leaders in his spare time.Lawrence holds a Bachelors and Masters in Mathematics and Engineering from Harvey Mudd College. He lives in Oakland, California, with his wife and their three daughters. This episode is brought to you by Span!Span is the AI-native developer intelligence platform bringing clarity to engineering organizations with a holistic, human-centered approach to developer productivity.If you want a complete picture of your engineering impact and health, drive high performance, and make smarter business decisions…Go to Span.app to learn more! SHOW NOTES:From research lab to record-breaking product: Navigating the fastest growth in history (4:03)Unpredictable scaling: Handling growth spurts of one million users every hour (5:20)Cross-stack collaboration: How Android, systems, and GPU engineers solve crises together (7:06)The magic of trade-offs: Aligning the team on outcomes like service uptime vs. broad availability (7:57)Why throwing models "over the wall" failed and how OpenAI structures virtual teams (11:17)Lessons from OpenAI's first intern class: Why AI-native new grads are crushing expectations (13:41)Non-hierarchical culture: Using the "Member of Technical Staff" title to blur the lines of expertise (15:37)AI-native engineering: When massive code generation starts breaking traditional CI/CD systems (16:21)Asynchronous workflows: Using coding agents to reduce two-hour investigations to 15 minutes (17:35)The mindset shift: How rapid model improvements changed how leaders audit and trust code (19:00)Predicting success: "Vibes-based" decision making and iterative low-key research previews (20:43)Hiring for high variance: Why unconventional backgrounds lead to high-potential engineering hires (22:09) LINKS AND RESOURCESLink to the video for this sessionLink to all ELC Annual 2025 sessions This episode wouldn't have been possible without the help of our incredible production team:Patrick Gallagher - Producer & Co-HostJerry Li - Co-HostNoah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/Dan Overheim - Audio Engineer, Dan's also an avid 3D printer - https://www.bnd3d.com/Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In today's Tech3 from Moneycontrol, we continue our year-end wrap with a deep dive into how India's technology policy landscape unfolded in 2025. From app bans and data protection concerns to the IndiaAI Mission's GPU push and foundational model plans, we track the policy moves shaping startups and tech. The episode also looks at Bengaluru's civic flashpoints, mobility battles, infrastructure delays, and the governance questions that defined the year.

Catching yourself rereading last year's VC emails while you're back in Silicon Valley is a pretty good way to realize how wild the last 12 months have been. Colin, Chuck, Canisius, and Todd break down how Collide AI is turning fast POCs into real production workflows, why change management is the actual moat, and how a stacked forward deployed team plus community driven distribution is setting up 2026 to be the year everything scales.Click here to watch a video of this episode.Join the conversation shaping the future of energy.Collide is the community where oil & gas professionals connect, share insights, and solve real-world problems together. No noise. No fluff. Just the discussions that move our industry forward.Apply today at collide.ioClick here to view the episode transcript. 00:00 Product market fit jokes and kickoff00:28 VC email flashback and velocity01:29 Forward deployed model and AI first mindset02:18 Sam Texas and AI coding shift04:04 What AI first actually means06:18 Not just podcast bros anymore07:00 AI breaks silos across the business08:21 Doglegs example and incentives09:57 Change management is the advantage10:18 Client story and regulatory filings win12:42 Selling outcomes not hype13:36 Building the FTE team and faster delivery16:24 AI strategy as workflow ROI first18:26 Grok as a thought partner and GPU cluster20:15 Shale revolution mindset parallel22:29 Recruiting, software DNA, and stacked team26:16 Content and community as a recruiting engine29:11 Distribution flywheel in the real world30:22 Team distribution vs product debate32:32 2026 is the scaling year34:02 Community platform finally clicking36:09 Building the community platform the hard way39:20 Scaling clients, POCs, and production41:09 Why mom and pops matter41:55 Energy demand tailwinds and macro impact44:44 One word answer for next year: scale45:20 POC to production cycle time focus47:12 Scaling tech, sales, and financing49:45 Moving at AI speed story50:14 Raising capital and building serious software52:56 Collide as the operator layer vision54:02 Gratitude and community over everythinghttps://twitter.com/collide_iohttps://www.tiktok.com/@collide.iohttps://www.facebook.com/collide.iohttps://www.instagram.com/collide.iohttps://www.youtube.com/@collide_iohttps://bsky.app/profile/digitalwildcatters.bsky.socialhttps://www.linkedin.com/company/collide-digital-wildcatters

A deep dive into Neil's extraordinary journey through 3D graphics, GPU evolution, open standards, and the creation of the Metaverse Standards Forum—uniting global stakeholders to build an open, interoperable, and equitable metaverse for all.00:47- About Neil TrevettNeil Trevett has served as the elected president of the Khronos Group for more than 20 years and most recently founded and now serves as the President and Chairman of the Metaverse Standards Forum.

In this episode, Ben Bajarin, Jay Goldberg, and Austin Lyons delve into the evolving landscape of networking, particularly in the context of AI and GPU technologies. They discuss the transition from traditional networking methods to more complex AI-driven networking, the significance of scaling strategies, and the critical role of SerDes in modern data centers. The conversation also touches on the ongoing debate between copper and optical networking solutions, highlighting the challenges and innovations in the field.

שלוש ספות מהמשרדים של NextSilicon על הבמה, לוגו על המסך, תאורה, ו… קהל.הגענו לכנס Acceleration Summit 2025 ומעבר לאנשים המעניינים שפגשנו וההרצאות המרתקות ששמענו, הקלטנו גם פרק בלייב.המרואיין שלנו הפעם הוא ניצן שקד. ניצן בעל עבר עשיר בפיתוח, ויודע דבר או שניים על #HPC ועל #AI. אז ניצלנו את ההזדמנות.על מה דיברנו?- מה ההבדל בין תוכנה למחשבי על וזו ל-AI?- מה כל כך מיוחד ב#GPU?- מה היה ה-Aha moment בהקשר AI?- איך יראה עולם התוכנה והחומרה אחרי עידן הטרנספורמרים?- איפה יהיה צוואר הבקבוק הבא של עולם המיחשוב?אחרי שהאזנתם לפרק מוזמנים להצטרף לקבוצת המאזינים שלנו - שם אנחנו מאיצים יחד איתכם טרנדים טכנולוגיים >>> https://chat.whatsapp.com/KwUu8pQsxx220qS7AXv04Tמוזמנים ליצור איתנו קשר במייל podcasthardreset@gmail.comשמעתם שאנחנו מתקרבים בצעדי ענק לגמר #פודקאסט_השנה של @גיקטיים? רוצים לראות אותנו שם? כנסו לקישור, וכתבו Hard Reset בקטגוריות ״טכנולוגיה״ ו-״הייטק ופיתוח״:https://www.geektime.co.il/podcast-of-the-year-2025-pre-vote-is-now-open/האזנה נעימה.

Deze talkshow wordt mede mogelijk gemaakt door NVIDIA, Philips Hue & MSI. Alle meningen in deze video zijn onze eigen. De drie partijen hebben inhoudelijk geen inspraak op de content en zien de video net als jullie hier voor het eerst op de site.Welkom bij een monumentaal moment in 2025: de laatste aflevering van Brievenmaandag in 2025. Veel kijkers zijn al begonnen aan de kerstvakantie. En gelijk hebben ze. Bij ons op de redactie is dat ook zo. Een klein deel werkt door om alle Eindejaarsinterviews op tijd en op de juiste dag online te krijgen. Maar een aantal van de hosts geniet van een paar lekkere vrije dagen. JJ en Skate stellen dat plezier nog even uit. Zij zitten met z'n tweeën klaar om het jaar 2025 qua vragen vanuit de community uit te luiden en jullie alvast voor te bereiden of een jaar vol festiviteiten, als Gamekings in 2026 25 jaar bestaat. Nu eerst dus brieven beantwoorden. Bijvoorbeeld welke handheld iemand moet gaan halen, waarom UFC-games na deel 2 zo kut zijn geworden en of de nieuwe DLC van Diablo 4 er nog toe doet. Dit alles kun je zien en horen in de Brievenmaandag van 21 december 2025.‘Moet ik wel of niet de nieuwe DLC van Diablo 4 gaan halen?'Een vraag die de laatste maanden bij Brievenmaandag vaak binnenkwam via de mail of Discord was hoe je het probleem van een steeds maar uitdijende backlog moet tackelen. Ook deze week was er mail over in de bus. Dit keer over hoe we de backlog bijhouden en of we er ook een waardering bij zetten.Pak honderden euro's op desktops en laptops met een NVIDIA RTX 50-series GPUOp zoek naar een NVIDIA videokaart, een nieuwe PC of een laptop met een NVIDIA GPU? Zoek dan niet verder en check alle holiday deals van de videokaarten van NVIDIA  en de desk- en laptops met een RTX 50-series GPU aan boord. Je kunt bij MeGekko hier tientallen euro's korting krijgen op de videokaarten en honderden euro's korting op de hardware van onder andere ASUS, HP, MSI en Lenovo..Scoor fikse kortingen tijdens de Holiday campagne van MSIOok MSI heeft een scherpe holiday actie lopen. Bij Bol.com krijg je hier nu honderden euro's korting op diverse gaming laptops.Scoor kortingen tot 30% op Philips Hue gear tijdens de Kerst SalesTot slot is daar Philips Hue. Ook zij doen mee met de acties tijdens de Feestdagen. Bij hen heet het Kerst Sales. Andere term, zelfde mogelijkheden. Op deze pagina vind je de aanbiedingen van zo'n beetje het hele assortiment van Hue. Als je twee producten kooppt, krijg je 30% korting. En dat is altijd handig.#ad

Links James on LinkedIn (https://www.linkedin.com/in/jahuang/) Mike on LinkedIn (https://www.linkedin.com/in/dominucco/) Mike's Blog (https://dominickm.com) Show on Discord (https://discord.com/invite/k8e7gKUpEp) Alice Promo (https://go.alice.dev/data-migration-offer-hands-on) AI on Red Hat Enterprise Linux (RHEL) Trust and Stability: RHEL provides the mission-critical foundation needed for workloads where security and reliability cannot be compromised. Predictive vs. Generative: Acknowledging the hype of GenAI while maintaining support for traditional machine learning algorithms. Determinism: The challenge of bringing consistency and security to emerging AI technologies in production environments. Rama-Llama & Containerization Developer Simplicity: Rama-Llama helps developers run local LLMs easily without being "locked in" to specific engines; it supports Podman, Docker, and various inference engines like Llama.cpp and Whisper.cpp. Production Path: The tool is designed to "fade away" after helping package the model and stack into a container that can be deployed directly to Kubernetes. Behind the Firewall: Addressing the needs of industries (like aircraft maintenance) that require AI to stay strictly on-premises. Enterprise AI Infrastructure Red Hat AI: A commercial product offering tools for model customization, including pre-training, fine-tuning, and RAG (Retrieval-Augmented Generation). Inference Engines: James highlights the difference between Llama.cpp (for smaller/edge hardware) and vLLM, which has become the enterprise standard for multi-GPU data center inferencing.

Episode 93: A rumor and news episode to round out 2025. We chat a bit more about 9850X3D expectations, the current and future state of Intel CPUs following some 225F testing, Nvidia cutting GPU supply, potential new GPUs and Steve kills some hardware.CHAPTERS00:00 - Intro02:33 - More Thoughts on the 9850X3D06:13 - Where is Intel at With Their CPUs?24:42 - Nvidia Cutting GPU Supply?37:38 - AMD Launches Radeon RX 9060 XT LP43:22 - More Intel Arc B770 Rumors49:12 - Updates From Our Boring LivesSUBSCRIBE TO THE PODCASTAudio: https://shows.acast.com/the-hardware-unboxed-podcastVideo: https://www.youtube.com/channel/UCqT8Vb3jweH6_tj2SarErfwSUPPORT US DIRECTLYPatreon: https://www.patreon.com/hardwareunboxedLINKSYouTube: https://www.youtube.com/@Hardwareunboxed/Twitter: https://twitter.com/HardwareUnboxedBluesky: https://bsky.app/profile/hardwareunboxed.bsky.social Hosted on Acast. See acast.com/privacy for more information.

Dale Smothers believes the markets are correcting to the upside after "overreactions" against the A.I. CapEx story and FOMC uncertainties. That said, he believes markets won't see as stellar of gains in 2026 compared to 2025. However, Dale pounds the table on the A.I. trade as long as use cases develop. He sees Amazon (AMZN) and Apple (AAPL) rallying strong in the coming year after serving as Mag 7 laggards in 2025. He adds that Nvidia (NVDA) will continue to dominate due to its GPU business. ======== Schwab Network ========Empowering every investor and trader, every market day.Options involve risks and are not suitable for all investors. Before trading, read the Options Disclosure Document. http://bit.ly/2v9tH6DSubscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/About Schwab Network - https://schwabnetwork.com/about

As the data center industry enters the AI era in earnest, incremental upgrades are no longer enough. That was the central message of the Data Center Frontier Trends Summit 2025 session “AI Is the New Normal: Building the AI Factory for Power, Profit, and Scale,” where operators and infrastructure leaders made the case that AI is no longer a specialty workload; it is redefining the data center itself. Panelists described the AI factory as a new infrastructure archetype: purpose-built, power-intensive, liquid-cooled, and designed for constant change. Rack densities that once hovered in the low teens have now surged past 50 kilowatts and, in some cases, toward megawatt-scale configurations. Facilities designed for yesterday's assumptions simply cannot keep up. Ken Patchett of Lambda framed AI factories as inherently multi-density environments, capable of supporting everything from traditional enterprise racks to extreme GPU deployments within the same campus. These facilities are not replacements for conventional data centers, he noted, but essential additions; and they must be designed for rapid iteration as chip architectures evolve every few months. Wes Cummins of Applied Digital extended the conversation to campus scale and geography. AI demand is pushing developers toward tertiary markets where power is abundant but historically underutilized. Training and inference workloads now require hundreds of megawatts at single sites, delivered in timelines that have shrunk from years to little more than a year. Cost efficiency, ultra-low PUE, and flexible shells are becoming decisive competitive advantages. Liquid cooling emerged as a foundational requirement rather than an optimization. Patrick Pedroso of Equus Compute Solutions compared the shift to the automotive industry's move away from air-cooled engines. From rear-door heat exchangers to direct-to-chip and immersion systems, cooling strategies must now accommodate fluctuating AI workloads while enabling energy recovery—even at the edge. For Kenneth Moreano of Scott Data Center, the AI factory is as much a service model as a physical asset. By abstracting infrastructure complexity and controlling the full stack in-house, his company enables enterprise customers to move from AI experimentation to production at scale, without managing the underlying technical detail. Across the discussion, panelists agreed that the industry's traditional design and financing playbook is obsolete. AI infrastructure cannot be treated as a 25-year depreciable asset when hardware cycles move in months. Instead, data centers must be built as adaptable, elemental systems: capable of evolving as power, cooling, and compute requirements continue to shift. The session concluded with one obvious takeaway: AI is not a future state to prepare for. It is already shaping how data centers are built, where they are located, and how they generate value. The AI factory is no longer theoretical—and the industry is racing to build it fast enough.

Timestamps: 0:00 thank you for coming to this meeting 0:13 Firefox, AI, Larian, and impulsive backlash 3:22 Nvidia's rumored GPU production cuts 4:28 War Thunder! 5:11 QUICK BITS INTRO 5:20 Ford batteries for data centers 6:05 700Credit data breach 6:43 AppX high CPU usage on W11 7:29 Apple helping businesses with manufacturing 8:16 Twitter (X) sues Operation Bluebird 8:55 YouTube Playables AI games, Google '6 7' meme NEWS SOURCES: https://lmg.gg/s83nI Learn more about your ad choices. Visit megaphone.fm/adchoices

as with all demo-heavy and especially vision AI podcasts, we encourage watching along on our YouTube (and tossing us an upvote/subscribe if you like!) From SAM 1's 11-million-image data engine to SAM 2's memory-based video tracking, MSL's Segment Anything project has redefined what's possible in computer vision. Now SAM 3 takes the next leap: concept segmentation—prompting with natural language like "yellow school bus" or "tablecloth" to detect, segment, and track every instance across images and video, in real time, with human-level exhaustivity. And with the latest SAM Audio (https://x.com/aiatmeta/status/2000980784425931067?s=46), SAM can now even segment audio output! We sat down with Nikhila Ravi (SAM lead at Meta) and Pengchuan Zhang (SAM 3 researcher) alongside Joseph Nelson (CEO, Roboflow) to unpack how SAM 3 unifies interactive segmentation, open-vocabulary detection, video tracking, and more into a single model that runs in 30ms on images and scales to real-time video on multi-GPU setups. We dig into the data engine that automated exhaustive annotation from two minutes per image down to 25 seconds using AI verifiers fine-tuned on Llama, the new SACO (Segment Anything with Concepts) benchmark with 200,000+ unique concepts vs. the previous 1.2k, how SAM 3 separates recognition from localization with a presence token, why decoupling the detector and tracker was critical to preserve object identity in video, how SAM 3 Agents unlock complex visual reasoning by pairing SAM 3 with multimodal LLMs like Gemini, and the real-world impact: 106 million smart polygons created on Roboflow saving humanity an estimated 130+ years of labeling time across fields from cancer research to underwater trash cleanup to autonomous vehicle perception. We discuss: What SAM 3 is: a unified model for concept-prompted segmentation, detection, and tracking in images and video using atomic visual concepts like "purple umbrella" or "watering can" How concept prompts work: short text phrases that find all instances of a category without manual clicks, plus visual exemplars (boxes, clicks) to refine and adapt on the fly Real-time performance: 30ms per image (100 detected objects on H200), 10 objects on 2×H200 video, 28 on 4×, 64 on 8×, with parallel inference and "fast mode" tracking The SACO benchmark: 200,000+ unique concepts vs. 1.2k in prior benchmarks, designed to capture the diversity of natural language and reach human-level exhaustivity The data engine: from 2 minutes per image (all-human) to 45 seconds (model-in-loop proposals) to 25 seconds (AI verifiers for mask quality and exhaustivity checks), fine-tuned on Llama 3.2 Why exhaustivity is central: every instance must be found, verified by AI annotators, and manually corrected only when the model misses—automating the hardest part of segmentation at scale Architecture innovations: presence token to separate recognition ("is it in the image?") from localization ("where is it?"), decoupled detector and tracker to preserve identity-agnostic detection vs. identity-preserving tracking Building on Meta's ecosystem: Perception Encoder, DINO v2 detector, Llama for data annotation, and SAM 2's memory-based tracking backbone SAM 3 Agents: using SAM 3 as a visual tool for multimodal LLMs (Gemini, Llama) to solve complex visual reasoning tasks like "find the bigger character" or "what distinguishes male from female in this image" Fine-tuning with as few as 10 examples: domain adaptation for specialized use cases (Waymo vehicles, medical imaging, OCR-heavy scenes) and the outsized impact of negative examples Real-world impact at Roboflow: 106M smart polygons created, saving 130+ years of labeling time across cancer research, underwater trash cleanup, autonomous drones, industrial automation, and more — MSL FAIR team Nikhila: https://www.linkedin.com/in/nikhilaravi/ Pengchuan: https://pzzhang.github.io/pzzhang/ Joseph Nelson X: https://x.com/josephofiowa LinkedIn: https://www.linkedin.com/in/josephofiowa/ [FLIGHTCAST_CHATPERS]

Welcome to Active Reload! This week, James and Grant open the show giving their good, bad, and ugly of all the Game Awards reveal trailers!Next, Marathon got a re-reveal from Bungie with a 23-minute inside look into the development of the upcoming extraction shooter. The guys discuss if the new look, price point, and release date is enough to attract players.Finally, Nvidia is reportedly planning to cut production by 30-40% of their RTX 50 series GPU's in the first half of 2026. What should we expect from console and PC prices with the possible increase in prices. Never a better time for pre-builts?Remember to rate, follow, like, and subscribe!

GPUs dominate today's AI landscape, but Google argues they are not necessary for every workload. As AI adoption has grown, customers have increasingly demanded compute options that deliver high performance with lower cost and power consumption. Drawing on its long history of custom silicon, Google introduced Axion CPUs in 2024 to meet needs for massive scale, flexibility, and general-purpose computing alongside AI workloads. The Axion-based C4A instance is generally available, while the newer N4A virtual machines promise up to 2x price performance.In this episode, Andrei Gueletii, a technical solutions consultant for Google Cloud joined Gari Singh, a product manager for Google Kubernetes Engine (GKE), and Pranay Bakre, a principal solutions engineer at Arm for this episode, recorded at KubeCon + CloudNativeCon North America, in Atlanta. Built on Arm Neoverse V2 cores, Axion processors emphasize energy efficiency and customization, including flexible machine shapes that let users tailor memory and CPU resources. These features are particularly valuable for platform engineering teams, which must optimize centralized infrastructure for cost, FinOps goals, and price performance as they scale.Importantly, many AI tasks—such as inference for smaller models or batch-oriented jobs—do not require GPUs. CPUs can be more efficient when GPU memory is underutilized or latency demands are low. By decoupling workloads and choosing the right compute for each task, organizations can significantly reduce AI compute costs.Learn more from The New Stack about the Axion-based C4A: Beyond Speed: Why Your Next App Must Be Multi-ArchitectureArm: See a Demo About Migrating a x86-Based App to ARM64Join our community of newsletter subscribers to stay on top of the news and at the top of your game.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

This episode is sponsored by AGNTCY. Unlock agents at scale with an open Internet of Agents.  Visit https://agntcy.org/ and add your support. Why is AI so powerful in the cloud but still so limited inside everyday devices, and what would it take to run intelligent systems locally without draining battery or sacrificing privacy? In this episode of Eye on AI, host Craig Smith speaks with Steve Brightfield, Chief Marketing Officer at BrainChip, about neuromorphic computing and why brain inspired architectures may be the key to the future of edge AI. We explore how neuromorphic systems differ from traditional GPU based AI, why event driven and spiking neural networks are dramatically more power efficient, and how on device inference enables faster response times, lower costs, and stronger data privacy. Steve explains why brute force computation works in data centers but breaks down at the edge, and how edge AI is reshaping wearables, sensors, robotics, hearing aids, and autonomous systems. You will also hear real world examples of neuromorphic AI in action, from smart glasses and medical monitoring to radar, defense, and space applications. The conversation covers how developers can transition from conventional models to neuromorphic architectures, what role heterogeneous computing plays alongside CPUs and GPUs, and why the next wave of AI adoption will happen quietly inside the devices we use every day. Stay Updated: Craig Smith on X: https://x.com/craigss  Eye on A.I. on X: https://x.com/EyeOn_AI  

In this episode of the Data Center Frontier Show, DCF Editor in Chief Matt Vincent speaks with Uptime Institute research analyst Max Smolaks about the infrastructure forces reshaping AI data centers from power and racks to cooling, economics, and the question of whether the boom is sustainable. Smolaks unpacks a surprising on-ramp to today's AI buildout: former cryptocurrency mining operators that “discovered” underutilized pockets of power in nontraditional locations—and are now pivoting into AI campuses as GPU demand strains conventional markets. The conversation then turns to what OCP 2025 revealed about rack-scale AI: heavier, taller, more specialized racks; disaggregated “compute/power/network” rack groupings; and a white space that increasingly looks purpose-built for extreme density. From there, Vincent and Smolaks explore why liquid cooling is both inevitable and still resisted by many operators—along with the software, digital twins, CFD modeling, and new commissioning approaches emerging to manage the added complexity. On the power side, they discuss the industry's growing alignment around 800V DC distribution and what it signals about Nvidia's outsized influence on next-gen data center design. Finally, the conversation widens into load volatility and the economics of AI infrastructure: why “spiky” AI power profiles are driving changes in UPS systems and rack-level smoothing, and why long-term growth may hinge less on demand (which remains strong) than on whether AI profits broaden beyond a few major buyers—especially as GPU hardware depreciates far faster than the long-lived fiber built during past tech booms. A sharp, grounded look at the AI factory era—and the engineering and business realities behind the headlines.

Des géants de la tech envisagent d'installer des data centers dans l'espace pour répondre aux besoins explosifs de l'IA, en misant sur l'énergie solaire et des infrastructures orbitales inédites mondiales.Interview : Julien Villeret, directeur de l'innovation d'EDFEn partenariat avec EDFPourquoi l'idée d'installer des data centers dans l'espace séduit-elle les géants de la tech ?Un data center, ce n'est pas seulement de l'informatique et des serveurs : c'est avant tout une question d'énergie, et de beaucoup d'énergie. Même si les puces et les modèles d'IA deviennent plus sobres, les usages explosent, notamment avec l'IA générative et l'inférence. Résultat : les besoins en calcul augmentent de façon exponentielle, et donc la consommation électrique aussi. La vraie question, aujourd'hui, c'est comment fournir une énergie massive, fiable et au coût le plus bas possible à ces infrastructures. C'est là que l'espace commence à faire rêver les grands acteurs du numérique comme Google, Amazon ou Tesla.En quoi l'espace apporterait-il un avantage décisif par rapport à la Terre ?Sur Terre, raccorder un data center au réseau électrique prend des années. Il faut des autorisations, creuser des tranchées, poser des câbles à très haute tension : c'est lourd, long et peu compatible avec le rythme du numérique. Dans l'espace, l'idée est de se rapprocher du Soleil. L'énergie solaire y est quasi permanente et beaucoup plus intense qu'au sol : en orbite géostationnaire, on capte jusqu'à 20 à 50 fois plus d'énergie. Il n'y a quasiment pas de cycle jour-nuit, ce qui permet une production continue. Sur le papier, c'est une source d'énergie abondante, puissante et presque illimitée.Comment communiquer avec des data centers situés en orbite ?Les technologies existent déjà. On fait exactement comme avec des constellations de satellites type Starlink : des communications à très haut débit entre l'espace et la Terre. Certes, la latence est un peu plus élevée qu'avec des infrastructures terrestres, mais pour des services d'IA, quelques dizaines de millisecondes ne posent aucun problème. Ce n'est pas idéal pour le gaming ultra-réactif, mais pour le traitement de données ou l'IA, c'est tout à fait acceptable et déjà opérationnel.Est-ce réellement faisable aujourd'hui, ou est-ce encore de la science-fiction ?Techniquement, c'est crédible. Économiquement, c'est encore un énorme pari. Des acteurs comme la startup StarCloud, soutenue par NVIDIA, ont déjà lancé un premier satellite avec des GPU embarqués, mis en orbite par SpaceX, capable d'exécuter des modèles d'IA comme Gemma de Google. C'est encore très symbolique, mais ça fonctionne réellement.Les défis restent immenses : rayonnements cosmiques, températures extrêmes, usure accélérée des composants et surtout le refroidissement, très complexe dans le vide spatial. Sans parler du coût des lancements, encore élevé malgré les progrès. Si les promesses de lanceurs comme Starship ou New Glenn se concrétisent, avec des coûts divisés par dix, l'équation pourrait changer. Pour l'instant, on est clairement sur un moonshot, comme le projet Suncatcher développé par Google au sein de sa division X, ambitieux et audacieux… mais encore loin d'un déploiement massif.-----------♥️ Soutien : https://mondenumerique.info/don

On this episode of the Engineering Influence Podcast, host Diana O'Lare sits down with Peter Nabhan to explore how artificial intelligence—particularly GPU-driven workloads—is reshaping the future of data center development. As AI adoption accelerates, demand for power is surging, fueling the rise of massive, campus-scale data center projects across the U.S. The conversation dives into the evolving strategies of hyperscalers and co-location providers, the growing strain on the electric grid, and the increasing role of on-site power generation. Diana and Peter also unpack the critical engineering challenges around cooling, water usage, and sustainability, while spotlighting the top U.S. markets seeing the most rapid growth. Finally, they tackle the big question facing the industry: Are we heading toward an oversupply of data centers—or is this simply the next major technology cycle transforming the built environment? Read the Market Intelligence Data Brief: https://www.acec.org/resource/special-edition-data-centers-market-intelligence-brief-fall-2025/

This week in bitcoin mining news, ERCOT sees a 266 GW of interconnection requests in 2026, IREN closed a $2.3 billion convertible note offering, and GPUs are leaving ASICs in the dust. Subscribe to the Blockspace newsletter for market-making news as it hits the wire! Welcome back to The Mining Pod! Today, Ethan Vera, COO of Luxor, joins us as we dive into MicroBT's Whatsminer M70 launching into a challenging ASIC market, IREN's $2.3 billion convertible note offering, the precarious state of hashprice, Luxor's new GPU hardware sales business, the staggering 270% leap in ERCOT interconnection requests, and the controversial Cat bitcoin fork proposal aimed at filtering ordinals / inscriptions. Subscribe to the newsletter! https://newsletter.blockspacemedia.com **Notes:** - Hash price is below $40 per second - Three negative difficulty adjustments - Ercot requests leaped 270% in 2025 - 73% of requests from data centers - IREN raised $2.3B in convertible notes - M70 efficiency: 12.5 J/TH 00:00 Start 02:35 Difficulty Report by Luxor 07:26 IREN note 10:44 M70 launch 20:02 Luxor launches GPU trading 27:12 ERCOT LL requests up 270% in 2025 34:10 Cry Corner: another filter fork proposal

Episode 92: Edward Crisler from Radeon-exclusive AIB Sapphire joins the podcast to chat about the current GPU market. How will rising DRAM prices affect gaming GPUs? Can the GPU makers and AIBs absorb some of the increased cost? Also we talk about RDNA 4 and how successful it's been compared to previous generations, AMD's true market share, and of course, the Sapphire Puke box artCHAPTERS00:00 - Intro01:03 - RDNA 4 Launch at Sapphire05:11 - RDNA 4 vs Older Generations Success11:32 - The DRAM Crisis20:25 - AIBs Want More Control24:48 - Thoughts on 12VHPWR26:32 - How Are SKU Decisions Made?32:35 - Sapphire Puke35:27 - DRAM Pricing: What Can AMD and AIBs Do?44:50 - AI-Focused GPU Makers Owe Everything to Gamers50:56 - AMD's True Market Share59:05 - The Key to RDNA 4's Success1:03:13 - Outro with Ed's Favorite Sapphire GenerationSUBSCRIBE TO THE PODCASTAudio: https://shows.acast.com/the-hardware-unboxed-podcastVideo: https://www.youtube.com/channel/UCqT8Vb3jweH6_tj2SarErfwSUPPORT US DIRECTLYPatreon: https://www.patreon.com/hardwareunboxedLINKSYouTube: https://www.youtube.com/@Hardwareunboxed/Twitter: https://twitter.com/HardwareUnboxedBluesky: https://bsky.app/profile/hardwareunboxed.bsky.social Hosted on Acast. See acast.com/privacy for more information.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Intro to Bayes Course (first 2 lessons free)Advanced Regression Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Takeaways:DADVI is a new approach to variational inference that aims to improve speed and accuracy.DADVI allows for faster Bayesian inference without sacrificing model flexibility.Linear response can help recover covariance estimates from mean estimates.DADVI performs well in mixed models and hierarchical structures.Normalizing flows present an interesting avenue for enhancing variational inference.DADVI can handle large datasets effectively, improving predictive performance.Future enhancements for DADVI may include GPU support and linear response integration.Chapters:13:17 Understanding DADVI: A New Approach21:54 Mean Field Variational Inference Explained26:38 Linear Response and Covariance Estimation31:21 Deterministic vs Stochastic Optimization in DADVI35:00 Understanding DADVI and Its Optimization Landscape37:59 Theoretical Insights and Practical Applications of DADVI42:12 Comparative Performance of DADVI in Real Applications45:03 Challenges and Effectiveness of DADVI in Various Models48:51 Exploring Future Directions for Variational Inference53:04 Final Thoughts and Advice for PractitionersThank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Aubrey Clayton, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël...

Subscribe to the Blockspace newsletter for market-making news as it hits the wire! Welcome back to The Mining Pod! Today, Ethan Vera, COO of Luxor, joins us as we dive into MicroBT's Whatsminer M70 launching into a challenging ASIC market, IREN's $2.3 billion convertible note offering, the precarious state of hashprice, Luxor's new GPU hardware sales business, the staggering 270% leap in ERCOT interconnection requests, and the controversial Cat bitcoin fork proposal aimed at filtering ordinals / inscriptions. Subscribe to the newsletter! https://newsletter.blockspacemedia.com **Notes:** - Hash price is below $40 per second - Three negative difficulty adjustments - Ercot requests leaped 270% in 2025 - 73% of requests from data centers - IREN raised $2.3B in convertible notes - M70 efficiency: 12.5 J/TH 00:00 Start 02:35 Difficulty Report by Luxor 07:26 IREN note 10:44 M70 launch 20:02 Luxor launches GPU trading 27:12 ERCOT LL requests up 270% in 2025 34:10 Cry Corner: another filter fork proposal

AI demand for GPUs is exploding – and most of that capacity is locked inside underused data centers.In this episode, I talk with Mark from Aethir, a decentralized GPU cloud that aggregates idle, enterprise-grade GPUs into a global network. We discuss how Aethir feels like AWS on the front end but works like “Airbnb for data centers” behind the scenes, why compute demand outpaces supply, and how they keep latency low across 90+ countries.Mark also explains Aethir's token and revenue model, their work with EigenLayer, and why he believes solo founders now have superpowers in an AI-native world.Nothing in this episode is financial or investment advice.Key timestamps[00:00:00] Intro: Sam introduces Mark and Aethir's decentralized GPU cloud.[00:01:00] Mark's journey: From oil and gas infra and biotech to building GPU infrastructure for AI.[00:04:00] What Aethir is: AWS-style GPU cloud on the front end, “Airbnb for data centers” on the back end.[00:06:00] Enterprise-only GPUs: Why they only use data-center-grade hardware and no consumer devices.[00:07:00] Exploding demand: GPU demand 6–8x supply, with inference-heavy apps driving the next wave.[00:14:00] Global coverage: 90+ countries and routing users to nearby nodes for low latency.[00:31:00] Business model: 20% protocol fee, 80% to GPU hosts, plus token rewards and staking for large clusters.[00:39:00] Solo founder era: Why one-person AI-native companies will be extremely powerful.[00:41:00] Mark's message: Focus on projects with strong fundamentals and keep building through cycles.Connecthttp://aethir.com/https://www.linkedin.com/company/aethir-limited/https://x.com/AethirCloudhttps://www.linkedin.com/in/markrydon/https://x.com/MRRydonDisclaimerNothing mentioned in this podcast is investment advice and please do your own research. It would mean a lot if you can leave a review of this podcast on Apple Podcasts or Spotify and share this podcast with a friend.Get featuredBe a guest on the podcast or contact us – https://www.web3pod.xyz/

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Satish Bhambri is a Sr Data Scientist at Walmart Labs, working on large-scale recommendation systems and conversational AI, including RAG-powered GroceryBot agents, vector-search personalization, and transformer-based ad relevance models.Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletter// AbstractThe MLOps Community Podcast features Satish Bhambri, Senior Data Scientist with the Personalization and Ranking team at Walmart Labs and one of the emerging leaders in applied AI, in its newest episode. Satish has quietly built one of the most diverse and impactful AI portfolios in his field, spanning quantum computing, deep learning, astrophysics, computer vision, NLP, fraud detection, and enterprise-scale recommendation systems. Bhambri's nearly a decade of research across deep learning, astrophysics, quantum computing, NLP, and computer vision culminated in over 10 peer-reviewed publications released in 2025 through IEEE and Springer, and his early papers are indexed by NASA ADS and Harvard SAO, marking the start of his long-term research arc. He also holds a patent for an AI-powered smart grid optimization framework that integrates deep learning, real-time IoT sensing, and adaptive control algorithms to improve grid stability and efficiency, a demonstration of his original, high-impact contributions to intelligent infrastructure. Bhambri leads personalization and ranking initiatives at Walmart Labs, where his AI systems serve more than (5% of the world's population) 531 million users every month, roughly based on traffic data. His work with Transformers, Vision-Language Models, RAG and agentic-RAG systems, and GPU-accelerated pipelines has driven significant improvements in scale and performance, including increases in ad engagement, faster compute by and improved recommendation diversity.Satish is a Distinguished Fellow & Assessor at the Soft Computing Research Society (SCRS), a reviewer for IEEE and Springer, and has served as a judge and program evaluator for several elite platforms. He was invited to the NeurIPS Program Judge Committee, the most prestigious AI conference in the world, and to evaluate innovations for DeepInvent AI, where he reviews high-impact research and commercialization efforts. He has also judged Y Combinator Startup Hackathons, evaluating pitches for an accelerator that produced companies like Airbnb, Stripe, Coinbase, Instacart, and Reddit.Before Walmart, Satish built supply-chain intelligence systems at BlueYonder that reduced ETA errors and saved retailers millions while also bringing containers to the production pipeline. Earlier, at ASU's School of Earth & Space Exploration, he collaborated with astrophysicists on galaxy emission simulations, radio burst detection, and dark matter modeling, including work alongside Dr. Lawrence Krauss, Dr. Karen Olsen, and Dr. Adam Beardsley.On the podcast, Bhambri discusses the evolution of deep learning architectures from RNNs and CNNs to transformers and agentic RAG systems, the design of production-grade AI architectures with examples, and his long-term vision for intelligent systems that bridge research and real-world impact. and the engineering principles behind building production-grade AI at a global scale.// Related LinksPapers: https://scholar.google.com/citations?user=2cpV5GUAAAAJ&hl=enPatent: https://search.ipindia.gov.in/DesignApplicationStatus ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkm

AB sits down with Zeus Kerravala, founder of ZK Research and a leading technology industry analyst, for a great conversation on the role analysts play in the world of tech, trends and innovations related to AI infrastructure, why the network is critical to AI workloads , and more. 

fal is building the infrastructure layer for the generative media boom. In this episode, founders Gorkem Yurtseven, Burkay Gur, and Head of Engineering Batuhan Taskaya explain why video models present a completely different optimization problem than LLMs, one that is compute-bound, architecturally volatile, and changing every 30 days. They discuss how fal's tracing compiler, custom kernels, and globally distributed GPU fleet enable them to run more than 600 image and video models simultaneously, often faster than the labs that trained them. The team also shares what they're seeing from the demand side: AI-native studios, personalized education, programmatic advertising, and early engagement from Hollywood. They argue that generative video is following a trajectory similar to early CGI—initial skepticism giving way to a new medium with its own workflows, aesthetics, and economic models.Hosted by Sonya Huang, Sequoia Capital

LE sujet TECH 2025 qui m'a le plus surpris en 2025 ?Mat : Toutes les actualités sur l'iA particulièrement : NotebookLM et cette semaine NanoBanana proBaptiste: Claude Code tu as essayé Antigravity ? https://antigravity.google/ https://www.claude.com/product/claude-code Guillaume: Les Meta Rayban connectées ne sont pas un bide commercialSyde Malgré les Trump tariffs - the AI bubble continue : 30% of the S&P 500 in Mag 5": Apple, Microsoft, Amazon, Alphabet, Nvidia / $500B+ AI spend; $12B consumer revenue / stock increase +18% DAX YTD - FTSE +15% /S&P 15% / NASDAQ +20% / FR +11% / Ben: China Just Powered Up the World's First Thorium Reactor — and Reloaded It Mid-Run (La Chine a réussi à recharger un réacteur au thorium sans arrêter la production, utilisant une technologie à sels fondus qui élimine le risque de fusion du cœur et est très bonne pour les petits réacteurs). Aussi les nouvelles façon de faire de la géothermiePRÉDICTION TECH pour 2026 ?Ben: L'année des architectures de calcul "post-GPU": Exemple mais il y a en a d'autres: le "Sensory Edge" Neuromorphique : Innatera (mais aussi thermodynamique: Extropic)Mat : Le post-GPU qui sera après la bulle !Guillaume: PAF! C'est le bruit d'une bulle qui éclateSyde: AI disenchantment - hype cycle model of technology adoption - le descente from the PEAK https://www.slideteam.net/gartner-hype-cycle-model-of-technology-adoption-in-product-lifecycle.html Baptiste: Revert des politiques contre l'IA, par exemple contre les voitures autonomes ou les data centers. InspirationFilms: Mat : Running Man l'ancien comme le nouveau) on réalise la présence des régimes autoritaires et le pouvoir des médiasRunning Man (1985) https://www.imdb.com/title/tt0093894/ Running Man (2025) https://www.imdb.com/title/tt14107334/ Documentaires:BEN: Demis Hassabis et Deepmind:The Thinking Game | Full documentary | Tribeca Film Festival official selection https://www.youtube.com/watch?v=d95J8yzvjbQ&t=2s Guillaume: interview de François Jarrige sur le progrèsLa TECHNOLOGIE: PROGRÈS ou DÉSASTRE écologique? l François Jarrige https://www.youtube.com/watch?v=O0DGYoTq4r4&t=4661s Baptiste: How to Change Your Mind https://www.netflix.com/ch-en/title/80229847 Livres: SYDE :: Breath: The New Science of a Lost Art by James Nestor https://www.amazon.com/Breath-New-Science-Lost-Art/dp/0735213615 Respirer https://amzn.eu/d/gha9HEG MyoTape: Mouth Tapes https://myotape.com/ Podcasts: Mat : Moteur de recherche Moteur de recherche | Balado https://www.youtube.com/playlist?list=PLJ7ZzojPlv5D8gcq90A16Eqp0ICipabg5 Quote: “Tends la main et ouvre ton cœur, car, bien avant les médicaments et les docteurs, l'humain reste le meilleur remède pour son prochain.” du livre Déconnecter de Boucar Diouf Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

In this panel session from the 2025 Data Center Frontier Trends Summit (Aug. 26-28) in Reston, Va., JLL's Sean Farney moderates a high-energy panel on how the industry is fast-tracking AI capacity in a world of power constraints, grid delays, and record-low vacancy. Under the banner “Scaling AI: The Role of Adaptive Reuse and Power-Rich Sites in GPU Deployment,” the discussion dives into why U.S. colocation vacancy is hovering near 2%, how power has become the ultimate limiter on AI revenue, and what it really takes to stand up GPU-heavy infrastructure at speed. Schneider Electric's Lovisa Tedestedt, Aligned Data Centers' Phill Lawson-Shanks, and Sapphire Gas Solutions' Scott Johns unpack the real-world strategies they're deploying today—from adaptive reuse of industrial sites and factory-built modular systems, to behind-the-fence natural gas, microgrids, and emerging hydrogen and RNG pathways. Along the way, they explore the coming “AI inference edge,” the rebirth of the enterprise data center, and how AI is already being used to optimize data center design and operations. During this talk, you'll learn: * Why record-low vacancy and long interconnection queues are reshaping AI deployment strategy. * How adaptive reuse of legacy industrial and commercial real estate can unlock gigawatt-scale capacity and community benefits. * The growing role of liquid cooling, modular skids, and grid-to-chip efficiency in getting more power to GPUs. * How behind-the-meter gas, virtual pipelines, and microgrids are bridging multi-year grid delays. * Why many experts expect a renaissance of enterprise data centers for AI inference at the edge. Moderator: Sean Farney, VP, Data Centers, Jones Lang LaSalle (JLL) Panelists: Tony Grayson, General Manager, Northstar Lovisa Tedestedt, Strategic Account Executive – Cloud & Service Providers, Schneider Electric Phill Lawson-Shanks, Chief Innovation Officer, Aligned Data Centers Scott Johns, Chief Commercial Officer, Sapphire Gas Solutions

Jensen Huang is the founder, president, and CEO of NVIDIA, the company whose 1999 invention of the GPU helped transform gaming, computer graphics, and accelerated computing. Under his leadership, NVIDIA has grown into a full-stack computing infrastructure company reshaping AI and data-center technology across industries.www.nvidia.com www.youtube.com/nvidia Perplexity: Download the app or ask Perplexity anything at https://pplx.ai/rogan. Visible. Live in the know. Join today at https://www.visible.com/rogan Don't miss out on all the action - Download the DraftKings app today! Sign-up at https://dkng.co/rogan or with my promo code ROGAN GAMBLING PROBLEM? CALL 1-800-GAMBLER, (800) 327-5050 or visit gamblinghelplinema.org (MA). Call 877-8-HOPENY/text HOPENY (467369) (NY). Please Gamble Responsibly. 888-789-7777/visit ccpg.org (CT), or visit www.mdgamblinghelp.org (MD). 21+ and present in most states. (18+ DC/KY/NH/WY). Void in ONT/OR/NH. Eligibility restrictions apply. On behalf of Boot Hill Casino & Resort (KS). Pass-thru of per wager tax may apply in IL. 1 per new customer. Must register new account to receive reward Token. Must select Token BEFORE placing min. $5 bet to receive $200 in Bonus Bets if your bet wins. Min. -500 odds req. Token and Bonus Bets are single-use and non-withdrawable. Token expires 1/11/26. Bonus Bets expire in 7 days (168 hours). Stake removed from payout. Terms: sportsbook.draftkings.com/promos. Ends 1/4/26 at 11:59 PM ET. Sponsored by DK. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Is Black Friday really booming, or are inflated prices and AI shopping assistants just muddying the waters? This episode rips into the data, exposes retailer tactics, and debates if smarter tech is actually making us better shoppers. Black Friday data shows online sales strong, store results mixed Silicon Valley's man in the White House is benefiting himself and his friends View: Trump's AI agenda sails toward an iceberg of bipartisan populist fury 'We do fail ... a lot': Defense startup Anduril hits setbacks with weapons tech Solar's growth in US almost enough to offset rising energy use Datacenters in space are a terrible, horrible, no good idea China leapfrogs US in global market for 'open' AI models Danish authorities in rush to close security loophole in Chinese electric buses The Ford F-150 Lightning was supposed to transform the industry. Now, Ford may pull the plug Roblox is a problem — but it's a symptom of something worse Warner Music and Suno strike deal for AI music, giving artists control over their likeness Leak confirms OpenAI is preparing ads on ChatGPT for public roll out Jony Ive, Sam Altman: OpenAI plans elegantly simple device One tech tip: Modern cars are spying on you. Here's what you can do about it How a GM EV1 was sold for the first time GPU prices are coming to earth just as RAM costs shoot into the stratosphere Host: Leo Laporte Guests: Daniel Rubino, Sam Abuelsamid, and Mike Elgan Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: ventionteams.com/twit deel.com/twit zapier.com/twit Melissa.com/twit zscaler.com/security

Scott and CJ sit down live at JSNation NYC with Iwo Plaza, creator of TypeGPU, to dig into how WebGPU is unlocking a new wave of graphics and compute power on the web. They chat about shader authoring in TypeScript, the future of GPU-powered AI in the browser, and what it takes to build a killer developer-friendly graphics library. Show Notes 00:00 Welcome to Syntax! 00:32 What is TypeGPU? High-level overview and why it exists 01:20 WebGPU vs WebGL – the new era of GPU access on the web 01:47 Why shader languages are hard + making them accessible 02:24 Iwo's background in C++, OpenGL, and discovering JS 03:06 Sharing graphics work on the web vs native platforms 03:29 WebGPU frustrations that inspired TypeGPU 04:17 Making GPU–CPU data exchange easier with Zod-like schemas 05:01 Writing shaders in JavaScript + the unified type system 05:38 How the “use_gpu” directive works under the hood 06:05 Building a compiler that turns TypeScript into shader code 07:00 Type inference, primitives, structs, and TypeScript magic 08:21 Leveraging existing tooling via Unplugin + bundler integration 09:15 How TypeGPU extracts ASTs and generates TinyEST metadata 10:10 Runtime shader generation vs build-time macros 11:07 How the AST is traversed + maintaining transparency in output 11:43 Example projects like Jelly Shader and community reception 12:05 Brought to you by Sentry.io 12:30 Does TypeGPU replace 3JS? How it fits the existing ecosystem 13:20 Low-level control vs high-level abstractions 14:04 Upcoming Three.js integration – plugging TypeGPU into materials compute shaders 15:34 Making GPU development more approachable 16:26 Docs, examples, and the philosophy behind TypeGPU documentation 17:03 Building features by building examples first 18:13 Using examples as a test suite + how docs shape API design 19:00 Docs as a forcing function for intuitive APIs 20:21 GPU for AI – browser inference and future abstractions 21:11 How AI examples inform new libraries (noise, inference, etc.) 21:57 Keeping the core package small and flexible 22:44 Building “TypeGPU AI”-style extensions without bloating the core 23:07 The cost of AI examples and building everything from scratch 23:41 Standard library design and future of the ecosystem 24:04 Closing thoughts from Iwo – OSS, GPU renaissance, and encouragement 24:34 Sick Picks & Shameless Plugs Sick Picks Iwo: Perogies Shameless Plugs Iwo: Syntax Podcast Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

David Kirtley is a nuclear fusion engineer and CEO of Helion Energy, a company working on building the world's first commercial fusion power plant by 2028. Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep485-sc See below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc. Transcript: https://lexfridman.com/david-kirtley-transcript CONTACT LEX: Feedback - give feedback to Lex: https://lexfridman.com/survey AMA - submit questions, videos or call-in: https://lexfridman.com/ama Hiring - join our team: https://lexfridman.com/hiring Other - other ways to get in touch: https://lexfridman.com/contact EPISODE LINKS: David's X: https://x.com/dekirtley David's LinkedIn: https://bit.ly/4qX0KXp Helion: https://www.helionenergy.com/ Helion's YouTube: https://youtube.com/HelionEnergy SPONSORS: To support this podcast, check out our sponsors & get discounts: UPLIFT Desk: Standing desks and office ergonomics. Go to https://upliftdesk.com/lex Fin: AI agent for customer service. Go to https://fin.ai/lex Miro: Online collaborative whiteboard platform. Go to https://miro.com/ LMNT: Zero-sugar electrolyte drink mix. Go to https://drinkLMNT.com/lex BetterHelp: Online therapy and counseling. Go to https://betterhelp.com/lex Shopify: Sell stuff online. Go to https://shopify.com/lex OUTLINE: (00:00) - Introduction (03:00) - Sponsors, Comments, and Reflections (11:35) - Nuclear fission vs fusion (21:35) - Physics of E=mc^2 (26:50) - Is nuclear fusion safe? (32:11) - Chernobyl (38:38) - Geopolitics (40:33) - Extreme scenarios (47:28) - How nuclear fusion works (1:20:20) - Extreme temperatures (1:25:21) - Fusion control and simulation (1:37:15) - Electricity from fusion (2:11:20) - First fusion power plant in 2028 (2:18:13) - Energy needs of GPU clusters (2:28:38) - Kardashev scale (2:36:33) - Fermi Paradox PODCAST LINKS: - Podcast Website: https://lexfridman.com/podcast - Apple Podcasts: https://apple.co/2lwqZIr - Spotify: https://spoti.fi/2nEwCF8 - RSS: https://lexfridman.com/feed/podcast/ - Podcast Playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4 - Clips Channel: https://www.youtube.com/lexclips