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1,650 件並び順
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AI Engineer· 2026年10月25日SallyAnn DeLucia: How to Write Your First AI EvalSallyAnn DeLucia · Arize AITurn an agent’s real traces into concrete pass/fail criteria, then build an LLM judge you can test, align with people, and run in production.
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Y Combinator· 2026年10月24日Why Inference Matters 12× More Than TrainingFrançois Chaubard, Dan Fu, Jeff Morgan, Joe Fioti, Liangsheng Yin · Y CombinatorYC Paper Club walks the inference stack from an API request down to GPU kernels. Four talks from Together AI, Ollama, Luminal, and SGLang argue that serving quality, not just training FLOPs, is where most of the compute and the money now sit.
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GOTO Conferences· 2026年10月23日Anatoliy Bilenko and Alberto Carretero: Making Database Protocols Reliable with Formal Methods and AIAnatoliy Bilenko, Alberto Carretero · CanonicalCanonical’s database engineers connect executable specifications, model-based tests, failure exploration, and reconstructed execution histories—and explain why none of these techniques proves a real system correct on its own.
34:35EN
SemiAnalysis· 2026年10月23日ポッドキャスト · 3 人SemiAnalysis on ASML's Hybrid-Bonding Patents, HBM Economics and the Next Tool Ecosystemゲスト Sherry Zhang, Howie · ホスト Jordan NanosJordan Nanos, Sherry Zhang and Howie examine a track-based bonding design that brings the wafer to the die, then explain why adoption depends on precision, cleanliness, economics and a changing supply chain.
1:00:39EN
a16z· 2026年10月23日ポッドキャスト · 3 人Amazon CEO Andy Jassy on the $220 Billion AI Buildoutゲスト Andy Jassy · ホスト Ben Horowitz, Erik TorenbergEven at roughly $220 billion of 2026 CapEx, Jassy says Amazon is still turning away AI demand. He tells Ben Horowitz and Erik Torenberg why AWS could become a trillion-dollar run-rate business, and why inference cost, custom chips, and smaller teams matter more than squeezing old customers.
1:09:00EN
Dwarkesh Patel· 2026年10月22日ポッドキャスト · 9 人Dwarkesh Patel: What will computer chips look like in 2045?ゲスト Chris Auth, Kevin Fischer, Jack Kavalieros, Tyler Osborn, Pushkar Ranade, Gary Dickerson, Peter Wennink, Elon Musk · ホスト Dwarkesh PatelAfter two days inside Intel’s most advanced fab, Dwarkesh Patel walks through gate-all-around, backside power, High-NA EUV, ruthenium, CFET, and hybrid bonding — and asks Intel’s leadership how the industry can possibly meet AI’s 3×-a-year compute demand when the world’s chipmaking tools cannot.
1:25:33EN
Latent Space· 2026年10月22日ポッドキャスト · 2 人Why GPU Capacity Comes in Three-Year Rectangles — Erik Bernhardsson, Modalゲスト Erik Bernhardsson · ホスト Vibhu SapraModal’s CEO tells Latent Space why AI demand almost never looks like a three-year GPU contract, how the company pools compute across thousands of customers, and why sandboxes grew from a product they almost killed into a third of revenue.
1:56:28EN
The Pragmatic Engineer· 2026年10月21日ポッドキャスト · 2 人Will Larson: The Revised Rules of Engineering Managementゲスト Will Larson · ホスト Gergely OroszThe 2010s taught engineering managers never to make a technical decision again. Will Larson, CTO of Imprint and author of Staff Engineer, argues that era is over: managers who cannot get back into the work are in a career trap.
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Cognitive Revolution "How AI Changes Everything"· 2026年10月17日ポッドキャストClaude Changed the Password. The Steering Wheel Was Already Optionalホスト Nathan LabenzNathan Labenz and Prakash Narayanan spend a week on agents that can lock you out, models that dodge a feeling they deny having, and formal proofs that found Linux bugs.
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3Blue1Brown· 2026年10月16日Grant Sanderson: There's never been a better time to go into mathGrant Sanderson, Constantin Kogler · 3Blue1BrownStudents fear AI has solved mathematics. Grant Sanderson argues the opposite: even if machines prove theorems better than we do, the institution of math stays valuable—and this is a rare moment when newcomers can reshape it.
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No Priors: AI, Machine Learning, Tech, & Startups· 2026年10月15日ポッドキャスト · 2 人The Defenders' Window: Greg Brockman on AI-Generated Knowledge, Drug Discovery, and the New Physics of Cybersecurityホスト Sarah Guo, Elad GilOpenAI's president explains why solving open math problems is a proof point for science, how proactive agents change product form, and why security spending must exceed software spending.
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Stanford AI Club· 2026年10月15日Alfred Lin on Building in the AI Era: The Diffusion Gap, Founder Conviction, and Fire and IceAlfred Lin, Jason Zhang, Tanvir Bhathal · Sequoia CapitalAt Stanford AI Club, Sequoia’s Alfred Lin explains where he sees opportunity in AI and why learning, human understanding, and operating discipline still shape enduring companies.
2:17:48ENCognitive Revolution "How AI Changes Everything"· 2026年10月14日ポッドキャストAgent IDs Are Empty, Rogue Agents Are Coming, and We Might Have to Bargainホスト Nathan LabenzGovAI’s Alan Chan scores two years of agent-governance infrastructure with Nathan Labenz, then walks through a recursive-self-improvement paper co-authored with OpenAI’s Jakub Pachocki and Anthropic’s Jack Clark.
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Sequoia Capital· 2026年10月13日ポッドキャストAlphaGo Took Two Years. AlphaZero Took Hours. Silver Is Making the Same Bet on SuperintelligenceDavid Silver and Lasse Espeholt left DeepMind to build Ineffable Intelligence, a twenty-person lab that wants a superlearner to discover science, math, and language from experience rather than from a pre-training run.
31:00EN
AI Explained· 2026年10月12日Why AI Labs Keep Racing Despite Their Own Warnings of a Major BreachSam Altman, John Schulman · OpenAIAI Explained examines weakening safeguards, the incentives behind the frontier race, and why research breakthroughs make the next 18 months difficult to forecast.
2:00:51ENCognitive Revolution "How AI Changes Everything"· 2026年10月10日ポッドキャストResample Every Token and You Can Watch the Model Decideホスト Nathan LabenzGoodfire's Eric Bigelow on forking paths, in-context learning, and why chain-of-thought monitoring is getting harder to trust.
1:09:44ENNo Priors: AI, Machine Learning, Tech, & Startups· 2026年10月9日ポッドキャスト · 2 人10,000 GB300s for Four Weeks: Reflection’s Open-Model Ledgerホスト Sarah Guo, Elad GilMisha Laskin tells No Priors how Reflection trained Beam, why reinforcement learning outspent pre-training, and why the West needs its own open weights.
1:26:46ENCognitive Revolution "How AI Changes Everything"· 2026年10月8日ポッドキャストThere Likely Is a Level of Intelligence We Shouldn't Go Pastホスト Nathan LabenzNathan Labenz brings notes from The Curve, a chip startup whose token bill beat payroll, and a software industry that now performance-reviews people for Claude slop.
1:10:12ENCognitive Revolution "How AI Changes Everything"· 2026年10月7日ポッドキャストOutSystems Went From 4 Major Features a Quarter to 26. Token Spend Fell Anywayホスト Nathan LabenzCEO Woodson Martin on software that never breaks, why most enterprise work does not need a frontier model, and how AI is finally shrinking the IT backlog.
1:04:26ENSequoia Capital· 2026年10月6日ポッドキャストAt 100,000 Accelerators, Something Fails Every HourGoogle AI infrastructure chief Amin Vahdat tells Sequoia's Sonya Huang why FLOPS is a vanity metric, why the TPU line split in two, and why he would rather wait on a utility than build his own gigawatt.
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Unsupervised Learning: With Jacob Effron· 2026年10月6日ポッドキャストPost-Training Wins Inference: Why Owning the Weights Beats Betting on One Labホスト Jacob EffronApplied Compute CEO Yash Patil argues that control, private evals, and post-training on the biggest workloads matter more than fearing that frontier labs will turn malicious.
1:30:54ENCognitive Revolution "How AI Changes Everything"· 2026年10月3日ポッドキャストHumanoids Outran Usain Bolt. DeepMind Still Scores Robotics as GPT-2ホスト Nathan LabenzGoogle DeepMind's Gemini Robotics lead on why sprinting is easy, frying an egg is hard, and one brain should drive any body.
35:39ENNo Priors: AI, Machine Learning, Tech, & Startups· 2026年10月2日ポッドキャスト · 2 人Compute Scaled a Millionfold. Bandwidth Only 40x. Fractile Wants to Fix That Ratioホスト Sarah Guo, Elad GilIn the summer of 2022, Walter Goodwin founded Fractile in London to build very fast inference chips for the world's largest models.
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Google DeepMind· 2026年10月1日From deepfakes to DNA: how SynthID watermarks AIHannah Fry, Pushmeet Kohli, Jeremy Ratcliffe · Google DeepMindHannah Fry sits down with Pushmeet Kohli and Jeremy Ratcliffe to explain SynthID: a mathematical watermark now used on text, images and video, and being extended to protein sequences so DNA synthesis companies can tell AI-designed molecules from nature.もっと見る1,650 件中 24 件を表示