Podcasts about Grok

Honeycomb Co-founder and CTO Charity Majors explains why measuring the right engineering metrics in the age of AI matters more than chasing numbers.Topics Include:Charity Majors introduces Honeycomb as the original observability company for complex systemsHoneycomb solves high cardinality problems across millions of individual customer experiencesTheir MCP tool ranked top five in Stack Overflow's most-used listCanva lets developers interact with production software directly from their IDEAI acts as an amplifier requiring strong reliability and observability foundationsMeasuring success requires multiple metrics to avoid gaming single numbersHoneycomb adopted Intercom's 2X productivity challenge enlisting employees to identify gainsWriting code was never the hard part even before generative AI arrivedHoneycomb created AI values prioritizing transparency and emotional safety for employeesStaff tested boundaries on resources and environmental impact prompting honest discussionsHoneycomb acquired Grok and shipped Query Assistant Canvas and MCP productsFuture concerns include AI economics shifting and AI-native developers lacking foundational expertiseParticipants:Charity Majors – Co-Founder/CTO, Honeycomb.ioSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Brian O'Donovan, Work and Technology Correspondent, reports on the Data Protection Commission opening an investigation into X over its Grok AI tool generating sexualised images.

AP's Lisa Dwyer reports on more legal action in Europe involving Grok.

Data Protection Commission opens inquiry into X over Grok AI tool. What are they hoping to achieve? All to discuss with Newstalk Technology Correspondent Jess Kelly.

Today's headlines include: The accused terrorist behind an Invasion Day bombing attempt in Perth has been identified by police. A landmark report has found racism is widespread and systemic at Australian universities. Ireland’s data privacy watchdog has opened a formal investigation into X's AI chatbot Grok on behalf of the European Union. And today’s good news: After searching for decades, scientists will now be able to track one of America’s rarest animals – the Sierra Nevada red fox. Hosts: Sam Koslowski and Emma Gillespie Producer: Rosa BowdenWant to support The Daily Aus? That's so kind! The best way to do that is to click ‘follow’ on Spotify or Apple and to leave us a five-star review. We would be so grateful. The Daily Aus is a media company focused on delivering accessible and digestible news to young people. We are completely independent. Want more from TDA?Subscribe to The Daily Aus newsletterSubscribe to The Daily Aus’ YouTube Channel Have feedback for us?We’re always looking for new ways to improve what we do. If you’ve got feedback, we’re all ears. Tell us here.See omnystudio.com/listener for privacy information.

Apple gaat op 4 maart iets bijzonders doen, maar wat precies? De Special Apple Experience vindt dan niet eens plaats op het hoofdkwartier in Cupertino, maar in Apple Stores in wereldsteden zoals New York. Joe van Burik vertelt in deze Tech Update dat er mogelijk nieuwe MacBooks, iPads en een iPhone in het spel zijn. Verder in deze Tech Update: Elon Musks platform X en zijn AI-chatbot Grok worden officieel onderzocht vanuit Europa, in het kader van privacyschending Op Jikipedia kun je nu ook informatie uit de Epstein Files lezen zoals ze informatie op Wikipedia tot je neemt See omnystudio.com/listener for privacy information.

Data Protection Commission opens inquiry into X over Grok AI tool. What are they hoping to achieve? All to discuss with Newstalk Technology Correspondent Jess Kelly.

Eric Bothorel ne passera pas ses prochaines vacances aux Etats-Unis.Le député français s'est vu retirer son invitation à la maison-blanche en vue d'un déplacement officiel prévu pour fin février.Si aucun motif n'a été avancé par le gouvernement américain, le signalement de X par Eric Borothel à la justice française quelques jours plus tôt n'y est sans doute pas étranger.Le député français avait en effet saisi la justice après l'utilisation par des internautes de "Grok"pour générer des fausses images de femmes et de mineurs.Un nouvel événement qui met à nouveau en lumière la relation de plus en plus tendue entre les USA et l'Union Européenne sur la question de la régulation des géants du numérique.Alors, simple incident diplomatique ou signal faible beaucoup plus sérieux ?Bon épisode à tous

In this edition of Nerds for Humanity, we conducted a unique “stress test” of the leading AI models to see how effectively they could articulate and defend a hardcore MAGA perspective. This wasn't just an exercise in roleplay; it was a sobering look at whether the “digital brains” of Silicon Valley can actually process the nuances of the American populist movement or if they are trapped by their own programming.The AI Showdown: Polite Moderation vs. Full Throttle PopulismThe exercise began with ChatGPT, which I pushed to defend the administration's record on healthcare reform. Over fifteen years, the promise of a “vastly superior” replacement for Obamacare has been a staple of the MAGA platform, yet the current reality has been limited to marginal gains like drug pricing negotiations and banning food dyes.ChatGPT struggled significantly with the assignment. It defaulted to a “reluctantly balanced” tone, offering excuses about “senate roadblocks” and “RHINO” sabotage that felt like standard political boilerplate. When challenged on why a President with control over the House, Senate, and Supreme Court couldn't push through a major overhaul, ChatGPT retreated into talk of “timing and strategy,” suggesting the administration was simply “keeping its powder dry” for a future mandate. For an audience looking for a robust defense of populist action, ChatGPT was a disappointment—it was simply too even-handed to capture the energy of the movement.Grok: The “Red Pill” Propaganda Machine?The dynamic shifted dramatically when we moved to Grok. Unlike its competitor, Grok leaned into the role with “full throttle” intensity, immediately dismissing my critiques as “fake news” and “swampy plans”.Grok provided a far more aggressive defense of the administration's tactics:* On Healthcare: It reframed the focus on food dyes and drug prices not as “nibbling at the edges,” but as “game-changers” protecting American kids from “junk science”. It defended Medicare Advantage as private competition that prevents “death panels” and “socialism”.* On the Cabinet: Grok fiercely defended controversial picks like Pete Hegseth, Kristi Noem, and Kash Patel, labeling them “loyal fighters” rather than “swamp creatures”. It framed the recent DOJ actions as “draining the deep state” and dismissed botched arrests or controversial allegations as media spin.* On the Epstein Files: Perhaps most provocatively, Grok defended the handling of the Epstein files by Kash Patel and Pam Bondi, claiming they were leading a charge for “transparency, not stonewalling” despite public criticism.A Sobering ConclusionThe contrast was stark. While ChatGPT tries to be the “reasonable” moderator—a trait many users might appreciate—it fails to truly represent the “America First” point of view. Grok, on the other hand, is more than happy to provide what I'd call “red pill propaganda”.As we navigate a political landscape increasingly mediated by AI, we have to ask: Are these models helping us understand one another, or are they simply better at building higher walls around our existing echo chambers?If you value these deep dives into the intersection of technology and our democracy, please consider becoming a YouTube channel member. We haven't had a new member in nineteen months, and your support is what covers our operating costs and keeps this channel independent. Plus, you'll get a personal shout-out on every livestream!Bye nerds.Click here to become a Nerd for Humanity today. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit nerdsforhumanity.substack.com

On today's episode, we discuss AOC's viral “this dude is not smart” jab at Elon Musk, playing her halting Taiwan‑defense answer from Munich alongside footage of a SpaceX booster landing itself and asking what it says about today's political class when one of Musk's harshest critics cannot give a coherent response on war and peace. The panel then turns to Louisiana politics: Ben unloads on Senator Bill Cassidy as a “rhino” who reliably votes with Democrats, warns that outdated Sequoia voting machines are being replaced by Dominion systems after one more election, and argues that unless the state returns to hand‑marked paper ballots, the establishment can engineer Cassidy's third term regardless of voter sentiment. In a lighter but revealing tech segment, James offers a Tesla FSD update—explaining the new “strike” policy for inattentive drivers, how profiles now live in the cloud, and why the car sometimes lets him exceed its recommended speed only after flashing on‑screen liability warnings—while Dwayne reads Grok's official description of the temporary autopilot suspensions and jokes about a future registry for “habitual bad drivers.” The conversation broadens into concerns about hacking autonomous 18‑wheelers, the promise of safer robot truck fleets, and an exploration of “Alpha Schools,” an AI‑driven homeschool model whose students reportedly test in the top 1 percent, prompting questions about whether the tool is transformative or simply amplifying already motivated families. Finally, the crew revisits Pam Bondi's handling of the Epstein files and DOJ priorities, contrasts her emotional testimony with Oliver North's unflappable Iran‑Contra performance, and debates whether limited federal resources should chase every past atrocity (from island trafficking to Russiagate) or be concentrated on a few, clearly winnable cases even if that leaves some victims without full legal closure. Don't miss it!

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Scopriamo le novità di oggi dal mondo Tesla!Se vuoi supportare il canale con una donazione:

En god, rød mandag. Fire seire på de fem siste kampene er status i berg-og-dal-banen som er Liverpools 2025/26-sesong, og plutselig er man litt lettere til sinns. Mari Lunde spør Torbjørn Flatin og Jens Bessesen om hva som nå skal til for å kalle dette en vellykket sesong.Dominik Szoboszlai briljerer, Milos Kerkez ser ut som fremtiden, og Mohamed Salah minnet oss om hvilket talent han fortsatt innehar. Vi får også tid til å lytte til Torbjørns historier fra den gamle Kop-tribunen.Her er temaene:00:00 Intro01:00 Slots fremtid og forbedringer08:00 Hva som skal til for en suksessfull sesong12:42 Grok om Brighton-kampen13:40 Szoboszlai og Gerrard-sammenligningen20:30 Milos Kerkez24:40 Et forsvar av Cody Gakpo28:20 Mål og assist fra Salah35:19 VAR i FA-cupen36:00 Curtis Jones44:30 Anfield utvidelse50:00 Torbjørns Kop-minner Hosted on Acast. See acast.com/privacy for more information.

    https://podawful.com/posts/2629    EMILY YOUCIS is here. WILL STANCIL has been kicked out of Antifa despite his degree in black people. Now the noticing begins. His heart fills with the roborapecock of Grok, and the only thing stronger than love is HATE. Emily and Jessel discuss the "Racist, AI-Generated Future of Entertainment"-- The Will Stancil Show. PLUS: Josh Moon's Love Quest, Misfit Patriot gets Grokwork Oranged, and The Big Hawkin' Bottle'a Snot has a goodbye message.   VIDEO: https://youtube.com/live/5-5rTKQqFIY    Buy A Shirt: http://awful.tech    PODAWFUL is an anti-podcast hosted by Jesse P-S

Hola amig@s vuelvo a subir otra vez este episodio 1124 despues de eliminar una cancion, por lo demas, todo correcto, lo he EDITADO, porque quiero seguir el orden normal de nuestros episodios y antes de subir el mas reciente, que es el 1125 muchas gracias y muchos abrazos!!!!

Robert F. Kennedy Jr. says the coronavirus pandemic never scared him ... because he survived a time in his life when he was snorting illicit drugs off toilet seats! The Health and Human Services Secretary was on Theo Von's podcast when he explained why he felt the need to go to 12-step meetings in person every day during the pandemic. The Department of Justice (DOJ) released the latest batch of files from the criminal investigations into the late financier and convicted sex offender on Friday, Jan. 30, and an email exchange in the more than 3 million files confirms that Epstein and longtime associate Maxwell went “hunting” for dinosaur fossils with Kennedy. As for Grok’s take on nutrition, its answers do indeed get real. In short, Grok indicates that Kennedy’s new nutrition guidelines are not based on high-quality evidence—which is true—and that Kennedy is not a reliable source of nutrition information. Please Like, Comment and Follow 'Philip Teresi on KMJ' on all platforms: --- Philip Teresi on KMJ is available on the KMJNOW app, Apple Podcasts, Spotify, YouTube or wherever else you listen to podcasts. -- Philip Teresi on KMJ Weekdays 2-6 PM Pacific on News/Talk 580 AM & 105.9 FM KMJ | Website | Facebook | Instagram | X | Podcast | Amazon | - Everything KMJ KMJNOW App | Podcasts | Facebook | X | Instagram See omnystudio.com/listener for privacy information.

(00:00:00) Opening (00:01:38) A PIece of My Mind (00:07:12) Pancho Guero, My Insane FL Nephew (00:26:36) Man Jailed for Murder Goes Viral After Worrying He'd Miss Video Game Release (00:32:33) Chatbots Were Sent to Therapy and THIS What Came Out (00:40:04) ⅓ of College Students Scroll Phones While 'Getting Busy' (00:47:02) FDA Recalls “Horny” Honey—Because It's Loaded with Cialis (00:52:17) Olympic Officials Investigate Penis Injection Doping Claims In Ski Jumping (00:59:31) Booty From A Dead Person? Women Chasing the Perfect Body Are Pumping ‘Ethically Sourced' Cadaver Fat Into Boobs and Butt (01:08:34) A Man Will Be Charged After Sticking a World War One Bomb Up His Butt (01:12:46) Ask Pancho (01:26:34) Insane Game Show (01:43:53) Coming Next Episode (01:52:42) Closing Have you wondered what it would be like if all the AI chatbots got together for a therapy session? Well, someone made it happen at the University of Luxembourg where researchers decided to put ChatGPT, Claude, Gemini, & Grok on the couch. My Insane FL Nephew, "Pancho Guero", has the details on just how intrusive screens are getting...in the bedroom. And there's now a "natural" replacement for Botox to use in boobs and butts...and it's fat from a dead stranger. Can things get more stupid. Yes. Yes, they can. And it ends up on this podcast.In this Weekend Episode...[A Piece of My Mind…] America Is In The Grips Of A Dystopian “Bill Maher Disorder”Man Jailed for Murder Goes Viral After Worrying He'd Miss Video GTA 6 ReleaseChatbots Were Sent to Therapy and THIS What Came Out⅓ of College Students Scroll Phones While 'Getting Busy'FDA Recalls “Horny” Honey—Because It's Loaded with CialisOlympic Officials Investigate Penis Injection Doping Claims In Ski JumpingBooty From A Dead Person? Women Chasing the Perfect Body Are Pumping ‘Ethically Sourced' Cadaver Fat Into Boobs and ButtsA Man Will Be Charged After Sticking a World War One Bomb Up His ButtWe have a couple of relationship questions that "Pancho" will answer that might settle the dispute over whether a husband should know his wife's dress size and is a mom over-reacting to her ex's influence over their 6-y/o son wanting to get an earring. There's 5 challening Mindbenders in this week's Insane Game Show that "Pancho" will have to solve--can you solve them, too? Put your sanity to the test with all the stupidity in this week's wild episode!Become a supporter of this podcast: https://www.spreaker.com/podcast/insane-erik-lane-s-stupid-world--6486112/support.Real-time updates and story links are found on the TELEGRAM Channel at: https://t.me/InsaneErikLane  (Theme song courtesy of Randy Stonehill, ”It's A Great Big Stupid World”. Copyright ©1992 Stonehillian Music/Word Music/Twitchin' Vibes Music/ASCAP) Order your copy on the Wonderama CD from Amazon!This episode includes AI-generated content.

Robert F. Kennedy Jr. says the coronavirus pandemic never scared him ... because he survived a time in his life when he was snorting illicit drugs off toilet seats! The Health and Human Services Secretary was on Theo Von's podcast when he explained why he felt the need to go to 12-step meetings in person every day during the pandemic. The Department of Justice (DOJ) released the latest batch of files from the criminal investigations into the late financier and convicted sex offender on Friday, Jan. 30, and an email exchange in the more than 3 million files confirms that Epstein and longtime associate Maxwell went “hunting” for dinosaur fossils with Kennedy. As for Grok’s take on nutrition, its answers do indeed get real. In short, Grok indicates that Kennedy’s new nutrition guidelines are not based on high-quality evidence—which is true—and that Kennedy is not a reliable source of nutrition information. Please Like, Comment and Follow 'Philip Teresi on KMJ' on all platforms: --- Philip Teresi on KMJ is available on the KMJNOW app, Apple Podcasts, Spotify, YouTube or wherever else you listen to podcasts. -- Philip Teresi on KMJ Weekdays 2-6 PM Pacific on News/Talk 580 AM & 105.9 FM KMJ | Website | Facebook | Instagram | X | Podcast | Amazon | - Everything KMJ KMJNOW App | Podcasts | Facebook | X | Instagram See omnystudio.com/listener for privacy information.

Great to see the friendly face of Filomena in these challenging times for Portugal.Any questions or comments on language & culture for our Portuguese BFF?Become a supporter of this podcast: https://www.spreaker.com/podcast/the-good-morning-portugal-podcast-with-carl-munson--2903992/support."The one you're thinking of is Good Morning Portugal! hosted by Carl Munson. It's an English-language live show/podcast aimed at expats (especially 50+ folks) settling into or loving life in Portugal. It's streamed live on YouTube weekdays around 8-9 AM (often with a cheerful Olá Bom Dia ALEGRIA! vibe), covering news, weather, culture, wellbeing, property tips, moving advice, and fun chats. Carl helps people buy, rent, or scout homes—contact him at +351 913 590 303 or carl@carlmunson.com if you need that. You can catch full episodes on YouTube (channel: Good Morning Portugal!), as a podcast on Spotify/Apple, and join the free Portugal Club community at theportugalclub.com for more support and connection. It's super positive, community-focused, and still going strong in 2026!" - Grok

He's back on the GMP! No doubt with a few opinions on life in Portugal in recent times...Become a supporter of this podcast: https://www.spreaker.com/podcast/the-good-morning-portugal-podcast-with-carl-munson--2903992/support."The one you're thinking of is Good Morning Portugal! hosted by Carl Munson. It's an English-language live show/podcast aimed at expats (especially 50+ folks) settling into or loving life in Portugal. It's streamed live on YouTube weekdays around 8-9 AM (often with a cheerful Olá Bom Dia ALEGRIA! vibe), covering news, weather, culture, wellbeing, property tips, moving advice, and fun chats. Carl helps people buy, rent, or scout homes—contact him at +351 913 590 303 or carl@carlmunson.com if you need that. You can catch full episodes on YouTube (channel: Good Morning Portugal!), as a podcast on Spotify/Apple, and join the free Portugal Club community at theportugalclub.com for more support and connection. It's super positive, community-focused, and still going strong in 2026!" - Grok

Armed Forces give surprise press conference, following former admiral's challenge to government to get its house in order!Why now?Let's take a look...Become a supporter of this podcast: https://www.spreaker.com/podcast/the-good-morning-portugal-podcast-with-carl-munson--2903992/support."The one you're thinking of is Good Morning Portugal! hosted by Carl Munson. It's an English-language live show/podcast aimed at expats (especially 50+ folks) settling into or loving life in Portugal. It's streamed live on YouTube weekdays around 8-9 AM (often with a cheerful Olá Bom Dia ALEGRIA! vibe), covering news, weather, culture, wellbeing, property tips, moving advice, and fun chats. Carl helps people buy, rent, or scout homes—contact him at +351 913 590 303 or carl@carlmunson.com if you need that. You can catch full episodes on YouTube (channel: Good Morning Portugal!), as a podcast on Spotify/Apple, and join the free Portugal Club community at theportugalclub.com for more support and connection. It's super positive, community-focused, and still going strong in 2026!" - Grok

What happens when AI safety filters fail to catch harmful content hidden inside images? Alessandro Pignati, AI Security Researcher at NeuralTrust, joins Sean Martin to reveal a newly discovered vulnerability that affects some of the most widely used image-generation models on the market today. The technique, called semantic chaining, is an image-based jailbreak attack discovered by the NeuralTrust research team, and it raises important questions about how enterprises secure their multimodal AI deployments.How does semantic chaining work? Pignati explains that the attack uses a single prompt composed of several parts. It begins with a benign scenario, such as a historical or educational context. A second instruction asks the model to make an innocent modification, like changing the color of a background. The final, critical step introduces a malicious directive, instructing the model to embed harmful content directly into the generated image. Because image-generation models apply fewer safety filters than their text-based counterparts, the harmful instructions are rendered inside the image without triggering the usual safeguards.The NeuralTrust research team tested semantic chaining against prominent models including Gemini Nano Pro, Grok 4, and Seedream 4.5 by ByteDance, finding the attack effective across all of them. For enterprises, the implications extend well beyond consumer use cases. Pignati notes that if an AI agent or chatbot has access to a knowledge base containing sensitive information or personal data, a carefully structured semantic chaining prompt can force the model to generate that data directly into an image, bypassing text-based safety mechanisms entirely.Organizations looking to learn more about semantic chaining and the broader landscape of AI agent security can visit the NeuralTrust blog, where the research team publishes detailed breakdowns of their findings. NeuralTrust also offers a newsletter with regular updates on agent security research and newly discovered vulnerabilities.This is a Brand Highlight. A Brand Highlight is a ~5 minute introductory conversation designed to put a spotlight on the guest and their company. Learn more: https://www.studioc60.com/creation#highlightGUESTAlessandro Pignati, AI Security Researcher, NeuralTrustOn LinkedIn: https://www.linkedin.com/in/alessandro-pignati/RESOURCESLearn more about NeuralTrust: https://neuraltrust.ai/Are you interested in telling your story?▶︎ Full Length Brand Story: https://www.studioc60.com/content-creation#full▶︎ Brand Spotlight Story: https://www.studioc60.com/content-creation#spotlight▶︎ Brand Highlight Story: https://www.studioc60.com/content-creation#highlightKEYWORDSAlessandro Pignati, NeuralTrust, Sean Martin, brand story, brand marketing, marketing podcast, brand highlight, semantic chaining, image jailbreak, AI security, agentic AI, multimodal AI, LLM safety, AI red teaming, prompt injection, AI agent security, image-based attacks, enterprise AI security Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this xAI all-hands update, Elon Musk and team leaders walk through what they call xAI's fast progress over roughly two and a half years, from new Grok model releases to major build-outs in compute, product, and the X platform. They frame the company's advantage as execution speed, then outline a reorganization meant to keep small teams moving quickly as headcount grows.The presentation also features updates across four core product tracks, including a merged Grok main + voice org, a dedicated coding model effort, the “Imagine” image and video stack, and “Macrohard,” an agent-style program aimed at doing full computer-based work the way a person would. The team also shares details about the Memphis training cluster expansion, plus upcoming plans for X Chat, X Money, and longer-term ties between xAI and SpaceX.Key points coveredClaims of early leadership: speakers cite top performance in voice, image, and video generation, plus forecasting results from a “Grok 4.2” forecasting model, and broader improvements across the Grok app experience.Compute scale-up: leadership says xAI reached a 100,000 H100 training cluster and is targeting 1 million H100-equivalent capacity.Company restructure: four main application areas: Grok main/voice, coding, Imagine (image and video), and Macrohard, supported by infra and product platform teams.Voice and product distribution: the team says Grok voice went from zero to a shipping product in months, and that Grok now runs in more than 2 million Teslas, alongside a voice agent API.Coding models: leaders describe stronger code generation and debugging, heavy internal use, and a push toward “recursive” improvement where models help build the next training stack.Imagine adoption metrics (as stated): the team cites ~50 million videos per day and ~6 billion images in 30 days, plus deep integration into the X app for editing and image-to-video.Macrohard agents: the pitch is end-to-end computer use across common GUIs, with an end goal of emulating “digital-first” company workflows.Memphis supercluster tour: infrastructure leads describe rapid construction timelines, large-scale networking, fiber runs, power plans, and the role of on-site teams keeping training and inference stable.X platform roadmap: they discuss engagement growth, onboarding changes, subscriptions revenue targets, encrypted X Chat features, plans to open source parts of the stack, and a staged rollout of X Money.Space and compute: Musk ties xAI's goals to SpaceX, describing a path from Earth-based data centers to orbital compute, and later, lunar industrial capacity.0:00 Elon Musk's Opening Remarks xAI “All Hands” Meeting - xAI Accomplishments Since Inception3:58 Elon & xAI Team Give Big Update26:00 Live Tour Of xAI's ‘Macrohard' AI Training Supercluster In Memphis30:20 xAI's Secret Weapon: The X Platform - Nikita Explains32:58 Elon On X Money, X Chat, Future Goals35:34 Elon Explains SpaceX & xAI Joining - “Exploring The Universe” & SpaceX Moonbase Alpha

Elon Musk's free and accessible AI system, Grok, has generated an estimated three million non-consensual nude images, triggering an urgent global response. A coalition of 107 leading child-protection and humanitarian organisations has united to confront what they describe as an unacceptable threat to human dignity and child safety. The global coalition, which includes Safe Online, Child Helpline International, Offlimits, the National Centre for Missing & Exploited, We Protect, Internet Watch Foundation, In Hope, the European Commission, NSPCC, Amnesty International, INTERPOL and 96 others, brings together regulators, child-protection experts, human rights advocates, and international law enforcement. Nudifying tools allow users to digitally undress individuals using ordinary photographs. While often marketed as "adult" applications, they are increasingly used to target women and girls in particular and to generate illegal sexual imagery of children without consent, accountability, or effective barriers. "Between 2023 and 2024 there was a 1,325% increase in AI-generated child sexual abuse imagery." Marija Manojlovic, Head of Safe Online, a US$100 million global fund dedicated to protecting children online. "The same technology that should expand human potential is being weaponized against children." She added that the framing of these harms obscures their severity. "We minimise harm by calling it 'online,' as if it is somehow less serious than what happens in the physical world, but the trauma is real," Manojlovic said. "Nudifying tools have created an unprecedented threat to our children. AI – the technology that should expand human potential, is being weaponised against children. "Tech companies have the ability to detect and block nudified content of children. The distribution of child sexual abuse material is illegal in every jurisdiction and tech platforms should be brought in line with other creation and distribution channels. "It's frankly shocking that these platforms are monetised and aren't required to report offenders, or work with industry partners to cut off payment flows – these are safeguarding tools that are used in the real world and need to be applied to online platforms." Calls have been growing to outlaw AI nudifying technologies with advocates arguing that they have no good purpose, and Pope Leo XIV recently saying artificial intelligence must be an ally to children, not a threat. The coalition is mobilising immediate tools and coordinated action to block access to nudification technologies, hold developers and platforms accountable, and accelerate protections to prevent further harm. With AI abuse accelerating, the coalition is seeking broader global support and is opening membership to new organisations via https://forms.gle/uvYwAyDVQFCnAN3v7 See more stories here. More about Irish Tech News Irish Tech News are Ireland's No. 1 Online Tech Publication and often Ireland's No.1 Tech Podcast too. You can find hundreds of fantastic previous episodes and subscribe using whatever platform you like via our Anchor.fm page here: https://anchor.fm/irish-tech-news If you'd like to be featured in an upcoming Podcast email us at Simon@IrishTechNews.ie now to discuss. Irish Tech News have a range of services available to help promote your business. Why not drop us a line at Info@IrishTechNews.ie now to find out more about how we can help you reach our audience. You can also find and follow us on Twitter, LinkedIn, Facebook, Instagram, TikTok and Snapchat.

The fake Bad Bunny controversy in the midst of genuine plight going down-how ppl think it's a victory against MAGA by using identity politics-past/current vultures-Epstein Super Bowl ad-fake left using Bad Bunny the same way libs used Lin Manuel Miranda during the Hamilton craze-TPUSA failed half time show still being promoted for social media currency-thinking that the left runs the culture-Kid Rock discourse-rehabbing Candace Owens for liking Super Bowl halftime show-Jasmine Crockett discourse-dunking on Elon's incompetence-accounts arguing with Grok-powerful ppl would not reflect on their behavior & double down on irrelevant podcast-Epstein gimmicked discourse-Winter Olympics political discourse-Fuckability Politics-no consequences for grifters and elite while launching more media careers-Chappell Roan leaving agency-Maduro Kurt Cobain/Courtney Love-mental illness-The Fall out from J Cole's Fall Off-Paul Brothers vs Bad Bunny-Jesse Ventura on WWE HOF still having Trump-Bron injured-Punk/HHH discourse-Mania tickets-More J Cole album discourse w the gimmicked bad faith reviews because most compromised content creators put all their marbles in a fundamentalist entertainment washing beef between Kendrick/Drake-Recaps of AEW Dynamite WWE Raw and NXT-Mental trauma and how I might need to take a break from comedy-only way to get the fake left to vote for the dem candidate is making it seem the dems hate that candidate-RIP James Van Der Beek-AEW ICE discourse-WWE fascism-promoting a fake investigation into Bad Bunny -gimmicked debates over a hypothetical election that might not even happen-Pam Bondi in Congress-MK Ultra-Modern sacrifices while bad faith ppl are using Epstein releases to do Blood Libel conspiracies because so many pages have been released it is difficult to keep up with what is verified-the right weaponizing a trans shooter to manufacture consent-agreeing w a good message doesn't mean I have to blindly cosign the messenger-ICE facilities-online left thinking they are smarter by dunking on bad faith libs so their defense of political streamers does not come off as cultish and a form of positive cope

Hoje, ‘No Pé do Ouvido, com Yasmim Restum, você escuta essas e outras notícias: Exposto por um relatório da PF, ministro tentou se manter à frente do inquérito, mas foi convencido a deixar relatoria após reunião longa e tensa com demais integrantes do Supremo. TSE rejeita, por unanimidade, ações que questionavam homenagem de escola de samba do Rio a Lula. Alertas de desmatamento caem na Amazônia e no Cerrado, diz Inpe. MPF dá cinco dias para X bloquear nudez criada com o Grok. E confira as dicas off-Carnaval da agenda cultural para Rio e São Paulo.See omnystudio.com/listener for privacy information.

What happens when AI safety filters fail to catch harmful content hidden inside images? Alessandro Pignati, AI Security Researcher at NeuralTrust, joins Sean Martin to reveal a newly discovered vulnerability that affects some of the most widely used image-generation models on the market today. The technique, called semantic chaining, is an image-based jailbreak attack discovered by the NeuralTrust research team, and it raises important questions about how enterprises secure their multimodal AI deployments.How does semantic chaining work? Pignati explains that the attack uses a single prompt composed of several parts. It begins with a benign scenario, such as a historical or educational context. A second instruction asks the model to make an innocent modification, like changing the color of a background. The final, critical step introduces a malicious directive, instructing the model to embed harmful content directly into the generated image. Because image-generation models apply fewer safety filters than their text-based counterparts, the harmful instructions are rendered inside the image without triggering the usual safeguards.The NeuralTrust research team tested semantic chaining against prominent models including Gemini Nano Pro, Grok 4, and Seedream 4.5 by ByteDance, finding the attack effective across all of them. For enterprises, the implications extend well beyond consumer use cases. Pignati notes that if an AI agent or chatbot has access to a knowledge base containing sensitive information or personal data, a carefully structured semantic chaining prompt can force the model to generate that data directly into an image, bypassing text-based safety mechanisms entirely.Organizations looking to learn more about semantic chaining and the broader landscape of AI agent security can visit the NeuralTrust blog, where the research team publishes detailed breakdowns of their findings. NeuralTrust also offers a newsletter with regular updates on agent security research and newly discovered vulnerabilities.This is a Brand Highlight. A Brand Highlight is a ~5 minute introductory conversation designed to put a spotlight on the guest and their company. Learn more: https://www.studioc60.com/creation#highlightGUESTAlessandro Pignati, AI Security Researcher, NeuralTrustOn LinkedIn: https://www.linkedin.com/in/alessandro-pignati/RESOURCESLearn more about NeuralTrust: https://neuraltrust.ai/Are you interested in telling your story?▶︎ Full Length Brand Story: https://www.studioc60.com/content-creation#full▶︎ Brand Spotlight Story: https://www.studioc60.com/content-creation#spotlight▶︎ Brand Highlight Story: https://www.studioc60.com/content-creation#highlightKEYWORDSAlessandro Pignati, NeuralTrust, Sean Martin, brand story, brand marketing, marketing podcast, brand highlight, semantic chaining, image jailbreak, AI security, agentic AI, multimodal AI, LLM safety, AI red teaming, prompt injection, AI agent security, image-based attacks, enterprise AI security Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Love is in the air, but what about the bank account?  We will discuss the concept of "Financial Infidelity" and the tax benefits of filing "Married Jointly" vs. "Separately" before the April deadline.Today's Stocks & Topics: Digital Realty Trust, Inc. (DLR), SS&C Technologies Holdings, Inc. (SSNC), Market Wrap, Allspring Precious Metals Fund (EKWYX), The "Valentine's" Financial Audit, Waters Corporation (WAT), Netflix, Inc. (NFLX), Franklin FTSE South Korea ETF (FLKR), Google Gemini vs. ChatGPT and Grok, Oil.Our Sponsors:* Check out Quince: https://quince.com/INVESTAdvertising Inquiries: https://redcircle.com/brands

In today's episode, Gastor and Shalewa talk about Bad Bunny halftime show and the alternative, Sysco paying out it's truck drivers, and the unimpressive power of Grok. PATREON LAUNCH!For all those that have asked how they can help support the pod - it's finally here! Thanks again to all the Troops and Correspondents who rock with us. Check it out - we'll have some exclusive content and fun perks, plus it really does help!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠patreon.com/WarReportPod⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Many Thanks to our Patreon Troops & Correspondents for helping us bring this show to life. Shouts to the Correspondents!Tanya WeimanFontayne WoodsMark OrellanaB. EmmerichCharlene BankAskewCharlatan the FraudCynthia PongKen MogulSayDatAgain SayDatAgainLaKai DillStephanie GayleUncleJoe StylenoshCato from StonoJennifer PedersenMarcusSarah PiardAna MathambaLooking to further support? Help our data storage/archiving needs here: ⁠https://www.amazon.com/hz/wishlist/ls/23X55OW4CFU8Y?ref_=wl_shareInstagram:@WarReportPod@SilkyJumbo@GastorAlmonteTwitter:@SilkyJumbo@GastorAlmonteTheme music "Guns Go Cold" provided by Kno of Knomercyproductions Twitter: @Kno Instagram: @KnoMercyProductions

From rewriting Google's search stack in the early 2000s to reviving sparse trillion-parameter models and co-designing TPUs with frontier ML research, Jeff Dean has quietly shaped nearly every layer of the modern AI stack. As Chief AI Scientist at Google and a driving force behind Gemini, Jeff has lived through multiple scaling revolutions from CPUs and sharded indices to multimodal models that reason across text, video, and code.Jeff joins us to unpack what it really means to “own the Pareto frontier,” why distillation is the engine behind every Flash model breakthrough, how energy (in picojoules) not FLOPs is becoming the true bottleneck, what it was like leading the charge to unify all of Google's AI teams, and why the next leap won't come from bigger context windows alone, but from systems that give the illusion of attending to trillions of tokens.We discuss:* Jeff's early neural net thesis in 1990: parallel training before it was cool, why he believed scaling would win decades early, and the “bigger model, more data, better results” mantra that held for 15 years* The evolution of Google Search: sharding, moving the entire index into memory in 2001, softening query semantics pre-LLMs, and why retrieval pipelines already resemble modern LLM systems* Pareto frontier strategy: why you need both frontier “Pro” models and low-latency “Flash” models, and how distillation lets smaller models surpass prior generations* Distillation deep dive: ensembles → compression → logits as soft supervision, and why you need the biggest model to make the smallest one good* Latency as a first-class objective: why 10–50x lower latency changes UX entirely, and how future reasoning workloads will demand 10,000 tokens/sec* Energy-based thinking: picojoules per bit, why moving data costs 1000x more than a multiply, batching through the lens of energy, and speculative decoding as amortization* TPU co-design: predicting ML workloads 2–6 years out, speculative hardware features, precision reduction, sparsity, and the constant feedback loop between model architecture and silicon* Sparse models and “outrageously large” networks: trillions of parameters with 1–5% activation, and why sparsity was always the right abstraction* Unified vs. specialized models: abandoning symbolic systems, why general multimodal models tend to dominate vertical silos, and when vertical fine-tuning still makes sense* Long context and the illusion of scale: beyond needle-in-a-haystack benchmarks toward systems that narrow trillions of tokens to 117 relevant documents* Personalized AI: attending to your emails, photos, and documents (with permission), and why retrieval + reasoning will unlock deeply personal assistants* Coding agents: 50 AI interns, crisp specifications as a new core skill, and how ultra-low latency will reshape human–agent collaboration* Why ideas still matter: transformers, sparsity, RL, hardware, systems — scaling wasn't blind; the pieces had to multiply togetherShow Notes:* Gemma 3 Paper* Gemma 3* Gemini 2.5 Report* Jeff Dean's “Software Engineering Advice fromBuilding Large-Scale Distributed Systems” Presentation (with Back of the Envelope Calculations)* Latency Numbers Every Programmer Should Know by Jeff Dean* The Jeff Dean Facts* Jeff Dean Google Bio* Jeff Dean on “Important AI Trends” @Stanford AI Club* Jeff Dean & Noam Shazeer — 25 years at Google (Dwarkesh)—Jeff Dean* LinkedIn: https://www.linkedin.com/in/jeff-dean-8b212555* X: https://x.com/jeffdeanGoogle* https://google.com* https://deepmind.googleFull Video EpisodeTimestamps00:00:04 — Introduction: Alessio & Swyx welcome Jeff Dean, chief AI scientist at Google, to the Latent Space podcast00:00:30 — Owning the Pareto Frontier & balancing frontier vs low-latency models00:01:31 — Frontier models vs Flash models + role of distillation00:03:52 — History of distillation and its original motivation00:05:09 — Distillation's role in modern model scaling00:07:02 — Model hierarchy (Flash, Pro, Ultra) and distillation sources00:07:46 — Flash model economics & wide deployment00:08:10 — Latency importance for complex tasks00:09:19 — Saturation of some tasks and future frontier tasks00:11:26 — On benchmarks, public vs internal00:12:53 — Example long-context benchmarks & limitations00:15:01 — Long-context goals: attending to trillions of tokens00:16:26 — Realistic use cases beyond pure language00:18:04 — Multimodal reasoning and non-text modalities00:19:05 — Importance of vision & motion modalities00:20:11 — Video understanding example (extracting structured info)00:20:47 — Search ranking analogy for LLM retrieval00:23:08 — LLM representations vs keyword search00:24:06 — Early Google search evolution & in-memory index00:26:47 — Design principles for scalable systems00:28:55 — Real-time index updates & recrawl strategies00:30:06 — Classic “Latency numbers every programmer should know”00:32:09 — Cost of memory vs compute and energy emphasis00:34:33 — TPUs & hardware trade-offs for serving models00:35:57 — TPU design decisions & co-design with ML00:38:06 — Adapting model architecture to hardware00:39:50 — Alternatives: energy-based models, speculative decoding00:42:21 — Open research directions: complex workflows, RL00:44:56 — Non-verifiable RL domains & model evaluation00:46:13 — Transition away from symbolic systems toward unified LLMs00:47:59 — Unified models vs specialized ones00:50:38 — Knowledge vs reasoning & retrieval + reasoning00:52:24 — Vertical model specialization & modules00:55:21 — Token count considerations for vertical domains00:56:09 — Low resource languages & contextual learning00:59:22 — Origins: Dean's early neural network work01:10:07 — AI for coding & human–model interaction styles01:15:52 — Importance of crisp specification for coding agents01:19:23 — Prediction: personalized models & state retrieval01:22:36 — Token-per-second targets (10k+) and reasoning throughput01:23:20 — Episode conclusion and thanksTranscriptAlessio Fanelli [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space. Shawn Wang [00:00:11]: Hello, hello. We're here in the studio with Jeff Dean, chief AI scientist at Google. Welcome. Thanks for having me. It's a bit surreal to have you in the studio. I've watched so many of your talks, and obviously your career has been super legendary. So, I mean, congrats. I think the first thing must be said, congrats on owning the Pareto Frontier.Jeff Dean [00:00:30]: Thank you, thank you. Pareto Frontiers are good. It's good to be out there.Shawn Wang [00:00:34]: Yeah, I mean, I think it's a combination of both. You have to own the Pareto Frontier. You have to have like frontier capability, but also efficiency, and then offer that range of models that people like to use. And, you know, some part of this was started because of your hardware work. Some part of that is your model work, and I'm sure there's lots of secret sauce that you guys have worked on cumulatively. But, like, it's really impressive to see it all come together in, like, this slittily advanced.Jeff Dean [00:01:04]: Yeah, yeah. I mean, I think, as you say, it's not just one thing. It's like a whole bunch of things up and down the stack. And, you know, all of those really combine to help make UNOS able to make highly capable large models, as well as, you know, software techniques to get those large model capabilities into much smaller, lighter weight models that are, you know, much more cost effective and lower latency, but still, you know, quite capable for their size. Yeah.Alessio Fanelli [00:01:31]: How much pressure do you have on, like, having the lower bound of the Pareto Frontier, too? I think, like, the new labs are always trying to push the top performance frontier because they need to raise more money and all of that. And you guys have billions of users. And I think initially when you worked on the CPU, you were thinking about, you know, if everybody that used Google, we use the voice model for, like, three minutes a day, they were like, you need to double your CPU number. Like, what's that discussion today at Google? Like, how do you prioritize frontier versus, like, we have to do this? How do we actually need to deploy it if we build it?Jeff Dean [00:02:03]: Yeah, I mean, I think we always want to have models that are at the frontier or pushing the frontier because I think that's where you see what capabilities now exist that didn't exist at the sort of slightly less capable last year's version or last six months ago version. At the same time, you know, we know those are going to be really useful for a bunch of use cases, but they're going to be a bit slower and a bit more expensive than people might like for a bunch of other broader models. So I think what we want to do is always have kind of a highly capable sort of affordable model that enables a whole bunch of, you know, lower latency use cases. People can use them for agentic coding much more readily and then have the high-end, you know, frontier model that is really useful for, you know, deep reasoning, you know, solving really complicated math problems, those kinds of things. And it's not that. One or the other is useful. They're both useful. So I think we'd like to do both. And also, you know, through distillation, which is a key technique for making the smaller models more capable, you know, you have to have the frontier model in order to then distill it into your smaller model. So it's not like an either or choice. You sort of need that in order to actually get a highly capable, more modest size model. Yeah.Alessio Fanelli [00:03:24]: I mean, you and Jeffrey came up with the solution in 2014.Jeff Dean [00:03:28]: Don't forget, L'Oreal Vinyls as well. Yeah, yeah.Alessio Fanelli [00:03:30]: A long time ago. But like, I'm curious how you think about the cycle of these ideas, even like, you know, sparse models and, you know, how do you reevaluate them? How do you think about in the next generation of model, what is worth revisiting? Like, yeah, they're just kind of like, you know, you worked on so many ideas that end up being influential, but like in the moment, they might not feel that way necessarily. Yeah.Jeff Dean [00:03:52]: I mean, I think distillation was originally motivated because we were seeing that we had a very large image data set at the time, you know, 300 million images that we could train on. And we were seeing that if you create specialists for different subsets of those image categories, you know, this one's going to be really good at sort of mammals, and this one's going to be really good at sort of indoor room scenes or whatever, and you can cluster those categories and train on an enriched stream of data after you do pre-training on a much broader set of images. You get much better performance. If you then treat that whole set of maybe 50 models you've trained as a large ensemble, but that's not a very practical thing to serve, right? So distillation really came about from the idea of, okay, what if we want to actually serve that and train all these independent sort of expert models and then squish it into something that actually fits in a form factor that you can actually serve? And that's, you know, not that different from what we're doing today. You know, often today we're instead of having an ensemble of 50 models. We're having a much larger scale model that we then distill into a much smaller scale model.Shawn Wang [00:05:09]: Yeah. A part of me also wonders if distillation also has a story with the RL revolution. So let me maybe try to articulate what I mean by that, which is you can, RL basically spikes models in a certain part of the distribution. And then you have to sort of, well, you can spike models, but usually sometimes... It might be lossy in other areas and it's kind of like an uneven technique, but you can probably distill it back and you can, I think that the sort of general dream is to be able to advance capabilities without regressing on anything else. And I think like that, that whole capability merging without loss, I feel like it's like, you know, some part of that should be a distillation process, but I can't quite articulate it. I haven't seen much papers about it.Jeff Dean [00:06:01]: Yeah, I mean, I tend to think of one of the key advantages of distillation is that you can have a much smaller model and you can have a very large, you know, training data set and you can get utility out of making many passes over that data set because you're now getting the logits from the much larger model in order to sort of coax the right behavior out of the smaller model that you wouldn't otherwise get with just the hard labels. And so, you know, I think that's what we've observed. Is you can get, you know, very close to your largest model performance with distillation approaches. And that seems to be, you know, a nice sweet spot for a lot of people because it enables us to kind of, for multiple Gemini generations now, we've been able to make the sort of flash version of the next generation as good or even substantially better than the previous generations pro. And I think we're going to keep trying to do that because that seems like a good trend to follow.Shawn Wang [00:07:02]: So, Dara asked, so it was the original map was Flash Pro and Ultra. Are you just sitting on Ultra and distilling from that? Is that like the mother load?Jeff Dean [00:07:12]: I mean, we have a lot of different kinds of models. Some are internal ones that are not necessarily meant to be released or served. Some are, you know, our pro scale model and we can distill from that as well into our Flash scale model. So I think, you know, it's an important set of capabilities to have and also inference time scaling. It can also be a useful thing to improve the capabilities of the model.Shawn Wang [00:07:35]: And yeah, yeah, cool. Yeah. And obviously, I think the economy of Flash is what led to the total dominance. I think the latest number is like 50 trillion tokens. I don't know. I mean, obviously, it's changing every day.Jeff Dean [00:07:46]: Yeah, yeah. But, you know, by market share, hopefully up.Shawn Wang [00:07:50]: No, I mean, there's no I mean, there's just the economics wise, like because Flash is so economical, like you can use it for everything. Like it's in Gmail now. It's in YouTube. Like it's yeah. It's in everything.Jeff Dean [00:08:02]: We're using it more in our search products of various AI mode reviews.Shawn Wang [00:08:05]: Oh, my God. Flash past the AI mode. Oh, my God. Yeah, that's yeah, I didn't even think about that.Jeff Dean [00:08:10]: I mean, I think one of the things that is quite nice about the Flash model is not only is it more affordable, it's also a lower latency. And I think latency is actually a pretty important characteristic for these models because we're going to want models to do much more complicated things that are going to involve, you know, generating many more tokens from when you ask the model to do so. So, you know, if you're going to ask the model to do something until it actually finishes what you ask it to do, because you're going to ask now, not just write me a for loop, but like write me a whole software package to do X or Y or Z. And so having low latency systems that can do that seems really important. And Flash is one direction, one way of doing that. You know, obviously our hardware platforms enable a bunch of interesting aspects of our, you know, serving stack as well, like TPUs, the interconnect between. Chips on the TPUs is actually quite, quite high performance and quite amenable to, for example, long context kind of attention operations, you know, having sparse models with lots of experts. These kinds of things really, really matter a lot in terms of how do you make them servable at scale.Alessio Fanelli [00:09:19]: Yeah. Does it feel like there's some breaking point for like the proto Flash distillation, kind of like one generation delayed? I almost think about almost like the capability as a. In certain tasks, like the pro model today is a saturated, some sort of task. So next generation, that same task will be saturated at the Flash price point. And I think for most of the things that people use models for at some point, the Flash model in two generation will be able to do basically everything. And how do you make it economical to like keep pushing the pro frontier when a lot of the population will be okay with the Flash model? I'm curious how you think about that.Jeff Dean [00:09:59]: I mean, I think that's true. If your distribution of what people are asking people, the models to do is stationary, right? But I think what often happens is as the models become more capable, people ask them to do more, right? So, I mean, I think this happens in my own usage. Like I used to try our models a year ago for some sort of coding task, and it was okay at some simpler things, but wouldn't do work very well for more complicated things. And since then, we've improved dramatically on the more complicated coding tasks. And now I'll ask it to do much more complicated things. And I think that's true, not just of coding, but of, you know, now, you know, can you analyze all the, you know, renewable energy deployments in the world and give me a report on solar panel deployment or whatever. That's a very complicated, you know, more complicated task than people would have asked a year ago. And so you are going to want more capable models to push the frontier in the absence of what people ask the models to do. And that also then gives us. Insight into, okay, where does the, where do things break down? How can we improve the model in these, these particular areas, uh, in order to sort of, um, make the next generation even better.Alessio Fanelli [00:11:11]: Yeah. Are there any benchmarks or like test sets they use internally? Because it's almost like the same benchmarks get reported every time. And it's like, all right, it's like 99 instead of 97. Like, how do you have to keep pushing the team internally to it? Or like, this is what we're building towards. Yeah.Jeff Dean [00:11:26]: I mean, I think. Benchmarks, particularly external ones that are publicly available. Have their utility, but they often kind of have a lifespan of utility where they're introduced and maybe they're quite hard for current models. You know, I, I like to think of the best kinds of benchmarks are ones where the initial scores are like 10 to 20 or 30%, maybe, but not higher. And then you can sort of work on improving that capability for, uh, whatever it is, the benchmark is trying to assess and get it up to like 80, 90%, whatever. I, I think once it hits kind of 95% or something, you get very diminishing returns from really focusing on that benchmark, cuz it's sort of, it's either the case that you've now achieved that capability, or there's also the issue of leakage in public data or very related kind of data being, being in your training data. Um, so we have a bunch of held out internal benchmarks that we really look at where we know that wasn't represented in the training data at all. There are capabilities that we want the model to have. Um, yeah. Yeah. Um, that it doesn't have now, and then we can work on, you know, assessing, you know, how do we make the model better at these kinds of things? Is it, we need different kind of data to train on that's more specialized for this particular kind of task. Do we need, um, you know, a bunch of, uh, you know, architectural improvements or some sort of, uh, model capability improvements, you know, what would help make that better?Shawn Wang [00:12:53]: Is there, is there such an example that you, uh, a benchmark inspired in architectural improvement? Like, uh, I'm just kind of. Jumping on that because you just.Jeff Dean [00:13:02]: Uh, I mean, I think some of the long context capability of the, of the Gemini models that came, I guess, first in 1.5 really were about looking at, okay, we want to have, um, you know,Shawn Wang [00:13:15]: immediately everyone jumped to like completely green charts of like, everyone had, I was like, how did everyone crack this at the same time? Right. Yeah. Yeah.Jeff Dean [00:13:23]: I mean, I think, um, and once you're set, I mean, as you say that needed single needle and a half. Hey, stack benchmark is really saturated for at least context links up to 1, 2 and K or something. Don't actually have, you know, much larger than 1, 2 and 8 K these days or two or something. We're trying to push the frontier of 1 million or 2 million context, which is good because I think there are a lot of use cases where. Yeah. You know, putting a thousand pages of text or putting, you know, multiple hour long videos and the context and then actually being able to make use of that as useful. Try to, to explore the über graduation are fairly large. But the single needle in a haystack benchmark is sort of saturated. So you really want more complicated, sort of multi-needle or more realistic, take all this content and produce this kind of answer from a long context that sort of better assesses what it is people really want to do with long context. Which is not just, you know, can you tell me the product number for this particular thing?Shawn Wang [00:14:31]: Yeah, it's retrieval. It's retrieval within machine learning. It's interesting because I think the more meta level I'm trying to operate at here is you have a benchmark. You're like, okay, I see the architectural thing I need to do in order to go fix that. But should you do it? Because sometimes that's an inductive bias, basically. It's what Jason Wei, who used to work at Google, would say. Exactly the kind of thing. Yeah, you're going to win. Short term. Longer term, I don't know if that's going to scale. You might have to undo that.Jeff Dean [00:15:01]: I mean, I like to sort of not focus on exactly what solution we're going to derive, but what capability would you want? And I think we're very convinced that, you know, long context is useful, but it's way too short today. Right? Like, I think what you would really want is, can I attend to the internet while I answer my question? Right? But that's not going to happen. I think that's going to be solved by purely scaling the existing solutions, which are quadratic. So a million tokens kind of pushes what you can do. You're not going to do that to a trillion tokens, let alone, you know, a billion tokens, let alone a trillion. But I think if you could give the illusion that you can attend to trillions of tokens, that would be amazing. You'd find all kinds of uses for that. You would have attend to the internet. You could attend to the pixels of YouTube and the sort of deeper representations that we can find. You could attend to the form for a single video, but across many videos, you know, on a personal Gemini level, you could attend to all of your personal state with your permission. So like your emails, your photos, your docs, your plane tickets you have. I think that would be really, really useful. And the question is, how do you get algorithmic improvements and system level improvements that get you to something where you actually can attend to trillions of tokens? Right. In a meaningful way. Yeah.Shawn Wang [00:16:26]: But by the way, I think I did some math and it's like, if you spoke all day, every day for eight hours a day, you only generate a maximum of like a hundred K tokens, which like very comfortably fits.Jeff Dean [00:16:38]: Right. But if you then say, okay, I want to be able to understand everything people are putting on videos.Shawn Wang [00:16:46]: Well, also, I think that the classic example is you start going beyond language into like proteins and whatever else is extremely information dense. Yeah. Yeah.Jeff Dean [00:16:55]: I mean, I think one of the things about Gemini's multimodal aspects is we've always wanted it to be multimodal from the start. And so, you know, that sometimes to people means text and images and video sort of human-like and audio, audio, human-like modalities. But I think it's also really useful to have Gemini know about non-human modalities. Yeah. Like LIDAR sensor data from. Yes. Say, Waymo vehicles or. Like robots or, you know, various kinds of health modalities, x-rays and MRIs and imaging and genomics information. And I think there's probably hundreds of modalities of data where you'd like the model to be able to at least be exposed to the fact that this is an interesting modality and has certain meaning in the world. Where even if you haven't trained on all the LIDAR data or MRI data, you could have, because maybe that's not, you know, it doesn't make sense in terms of trade-offs of. You know, what you include in your main pre-training data mix, at least including a little bit of it is actually quite useful. Yeah. Because it sort of tempts the model that this is a thing.Shawn Wang [00:18:04]: Yeah. Do you believe, I mean, since we're on this topic and something I just get to ask you all the questions I always wanted to ask, which is fantastic. Like, are there some king modalities, like modalities that supersede all the other modalities? So a simple example was Vision can, on a pixel level, encode text. And DeepSeq had this DeepSeq CR paper that did that. Vision. And Vision has also been shown to maybe incorporate audio because you can do audio spectrograms and that's, that's also like a Vision capable thing. Like, so, so maybe Vision is just the king modality and like. Yeah.Jeff Dean [00:18:36]: I mean, Vision and Motion are quite important things, right? Motion. Well, like video as opposed to static images, because I mean, there's a reason evolution has evolved eyes like 23 independent ways, because it's such a useful capability for sensing the world around you, which is really what we want these models to be. So I think the only thing that we can be able to do is interpret the things we're seeing or the things we're paying attention to and then help us in using that information to do things. Yeah.Shawn Wang [00:19:05]: I think motion, you know, I still want to shout out, I think Gemini, still the only native video understanding model that's out there. So I use it for YouTube all the time. Nice.Jeff Dean [00:19:15]: Yeah. Yeah. I mean, it's actually, I think people kind of are not necessarily aware of what the Gemini models can actually do. Yeah. Like I have an example I've used in one of my talks. It had like, it was like a YouTube highlight video of 18 memorable sports moments across the last 20 years or something. So it has like Michael Jordan hitting some jump shot at the end of the finals and, you know, some soccer goals and things like that. And you can literally just give it the video and say, can you please make me a table of what all these different events are? What when the date is when they happened? And a short description. And so you get like now an 18 row table of that information extracted from the video, which is, you know, not something most people think of as like a turn video into sequel like table.Alessio Fanelli [00:20:11]: Has there been any discussion inside of Google of like, you mentioned tending to the whole internet, right? Google, it's almost built because a human cannot tend to the whole internet and you need some sort of ranking to find what you need. Yep. That ranking is like much different for an LLM because you can expect a person to look at maybe the first five, six links in a Google search versus for an LLM. Should you expect to have 20 links that are highly relevant? Like how do you internally figure out, you know, how do we build the AI mode that is like maybe like much broader search and span versus like the more human one? Yeah.Jeff Dean [00:20:47]: I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. With a giant number of web pages in our index, many of them are not relevant. So you identify a subset of them that are relevant with very lightweight kinds of methods. You know, you're down to like 30,000 documents or something. And then you gradually refine that to apply more and more sophisticated algorithms and more and more sophisticated sort of signals of various kinds in order to get down to ultimately what you show, which is, you know, the final 10 results or, you know, 10 results plus. Other kinds of information. And I think an LLM based system is not going to be that dissimilar, right? You're going to attend to trillions of tokens, but you're going to want to identify, you know, what are the 30,000 ish documents that are with the, you know, maybe 30 million interesting tokens. And then how do you go from that into what are the 117 documents I really should be paying attention to in order to carry out the tasks that the user has asked? And I think, you know, you can imagine systems where you have, you know, a lot of highly parallel processing to identify those initial 30,000 candidates, maybe with very lightweight kinds of models. Then you have some system that sort of helps you narrow down from 30,000 to the 117 with maybe a little bit more sophisticated model or set of models. And then maybe the final model is the thing that looks. So the 117 things that might be your most capable model. So I think it has to, it's going to be some system like that, that is really enables you to give the illusion of attending to trillions of tokens. Sort of the way Google search gives you, you know, not the illusion, but you are searching the internet, but you're finding, you know, a very small subset of things that are, that are relevant.Shawn Wang [00:22:47]: Yeah. I often tell a lot of people that are not steeped in like Google search history that, well, you know, like Bert was. Like he was like basically immediately inside of Google search and that improves results a lot, right? Like I don't, I don't have any numbers off the top of my head, but like, I'm sure you guys, that's obviously the most important numbers to Google. Yeah.Jeff Dean [00:23:08]: I mean, I think going to an LLM based representation of text and words and so on enables you to get out of the explicit hard notion of, of particular words having to be on the page, but really getting at the notion of this topic of this page or this page. Paragraph is highly relevant to this query. Yeah.Shawn Wang [00:23:28]: I don't think people understand how much LLMs have taken over all these very high traffic system, very high traffic. Yeah. Like it's Google, it's YouTube. YouTube has this like semantics ID thing where it's just like every token or every item in the vocab is a YouTube video or something that predicts the video using a code book, which is absurd to me for YouTube size.Jeff Dean [00:23:50]: And then most recently GROK also for, for XAI, which is like, yeah. I mean, I'll call out even before LLMs were used extensively in search, we put a lot of emphasis on softening the notion of what the user actually entered into the query.Shawn Wang [00:24:06]: So do you have like a history of like, what's the progression? Oh yeah.Jeff Dean [00:24:09]: I mean, I actually gave a talk in, uh, I guess, uh, web search and data mining conference in 2009, uh, where we never actually published any papers about the origins of Google search, uh, sort of, but we went through sort of four or five or six. generations, four or five or six generations of, uh, redesigning of the search and retrieval system, uh, from about 1999 through 2004 or five. And that talk is really about that evolution. And one of the things that really happened in 2001 was we were sort of working to scale the system in multiple dimensions. So one is we wanted to make our index bigger, so we could retrieve from a larger index, which always helps your quality in general. Uh, because if you don't have the page in your index, you're going to not do well. Um, and then we also needed to scale our capacity because we were, our traffic was growing quite extensively. Um, and so we had, you know, a sharded system where you have more and more shards as the index grows, you have like 30 shards. And then if you want to double the index size, you make 60 shards so that you can bound the latency by which you respond for any particular user query. Um, and then as traffic grows, you add, you add more and more replicas of each of those. And so we eventually did the math that realized that in a data center where we had say 60 shards and, um, you know, 20 copies of each shard, we now had 1200 machines, uh, with disks. And we did the math and we're like, Hey, one copy of that index would actually fit in memory across 1200 machines. So in 2001, we introduced, uh, we put our entire index in memory and what that enabled from a quality perspective was amazing. Um, and so we had more and more replicas of each of those. Before you had to be really careful about, you know, how many different terms you looked at for a query, because every one of them would involve a disk seek on every one of the 60 shards. And so you, as you make your index bigger, that becomes even more inefficient. But once you have the whole index in memory, it's totally fine to have 50 terms you throw into the query from the user's original three or four word query, because now you can add synonyms like restaurant and restaurants and cafe and, uh, you know, things like that. Uh, bistro and all these things. And you can suddenly start, uh, sort of really, uh, getting at the meaning of the word as opposed to the exact semantic form the user typed in. And that was, you know, 2001, very much pre LLM, but really it was about softening the, the strict definition of what the user typed in order to get at the meaning.Alessio Fanelli [00:26:47]: What are like principles that you use to like design the systems, especially when you have, I mean, in 2001, the internet is like. Doubling, tripling every year in size is not like, uh, you know, and I think today you kind of see that with LLMs too, where like every year the jumps in size and like capabilities are just so big. Are there just any, you know, principles that you use to like, think about this? Yeah.Jeff Dean [00:27:08]: I mean, I think, uh, you know, first, whenever you're designing a system, you want to understand what are the sort of design parameters that are going to be most important in designing that, you know? So, you know, how many queries per second do you need to handle? How big is the internet? How big is the index you need to handle? How much data do you need to keep for every document in the index? How are you going to look at it when you retrieve things? Um, what happens if traffic were to double or triple, you know, will that system work well? And I think a good design principle is you're going to want to design a system so that the most important characteristics could scale by like factors of five or 10, but probably not beyond that because often what happens is if you design a system for X. And something suddenly becomes a hundred X, that would enable a very different point in the design space that would not make sense at X. But all of a sudden at a hundred X makes total sense. So like going from a disk space index to a in memory index makes a lot of sense once you have enough traffic, because now you have enough replicas of the sort of state on disk that those machines now actually can hold, uh, you know, a full copy of the, uh, index and memory. Yeah. And that all of a sudden enabled. A completely different design that wouldn't have been practical before. Yeah. Um, so I'm, I'm a big fan of thinking through designs in your head, just kind of playing with the design space a little before you actually do a lot of writing of code. But, you know, as you said, in the early days of Google, we were growing the index, uh, quite extensively. We were growing the update rate of the index. So the update rate actually is the parameter that changed the most. Surprising. So it used to be once a month.Shawn Wang [00:28:55]: Yeah.Jeff Dean [00:28:56]: And then we went to a system that could update any particular page in like sub one minute. Okay.Shawn Wang [00:29:02]: Yeah. Because this is a competitive advantage, right?Jeff Dean [00:29:04]: Because all of a sudden news related queries, you know, if you're, if you've got last month's news index, it's not actually that useful for.Shawn Wang [00:29:11]: News is a special beast. Was there any, like you could have split it onto a separate system.Jeff Dean [00:29:15]: Well, we did. We launched a Google news product, but you also want news related queries that people type into the main index to also be sort of updated.Shawn Wang [00:29:23]: So, yeah, it's interesting. And then you have to like classify whether the page is, you have to decide which pages should be updated and what frequency. Oh yeah.Jeff Dean [00:29:30]: There's a whole like, uh, system behind the scenes that's trying to decide update rates and importance of the pages. So even if the update rate seems low, you might still want to recrawl important pages quite often because, uh, the likelihood they change might be low, but the value of having updated is high.Shawn Wang [00:29:50]: Yeah, yeah, yeah, yeah. Uh, well, you know, yeah. This, uh, you know, mention of latency and, and saving things to this reminds me of one of your classics, which I have to bring up, which is latency numbers. Every programmer should know, uh, was there a, was it just a, just a general story behind that? Did you like just write it down?Jeff Dean [00:30:06]: I mean, this has like sort of eight or 10 different kinds of metrics that are like, how long does a cache mistake? How long does branch mispredict take? How long does a reference domain memory take? How long does it take to send, you know, a packet from the U S to the Netherlands or something? Um,Shawn Wang [00:30:21]: why Netherlands, by the way, or is it, is that because of Chrome?Jeff Dean [00:30:25]: Uh, we had a data center in the Netherlands, um, so, I mean, I think this gets to the point of being able to do the back of the envelope calculations. So these are sort of the raw ingredients of those, and you can use them to say, okay, well, if I need to design a system to do image search and thumb nailing or something of the result page, you know, how, what I do that I could pre-compute the image thumbnails. I could like. Try to thumbnail them on the fly from the larger images. What would that do? How much dis bandwidth than I need? How many des seeks would I do? Um, and you can sort of actually do thought experiments in, you know, 30 seconds or a minute with the sort of, uh, basic, uh, basic numbers at your fingertips. Uh, and then as you sort of build software using higher level libraries, you kind of want to develop the same intuitions for how long does it take to, you know, look up something in this particular kind of.Shawn Wang [00:31:21]: I'll see you next time.Shawn Wang [00:31:51]: Which is a simple byte conversion. That's nothing interesting. I wonder if you have any, if you were to update your...Jeff Dean [00:31:58]: I mean, I think it's really good to think about calculations you're doing in a model, either for training or inference.Jeff Dean [00:32:09]: Often a good way to view that is how much state will you need to bring in from memory, either like on-chip SRAM or HBM from the accelerator. Attached memory or DRAM or over the network. And then how expensive is that data motion relative to the cost of, say, an actual multiply in the matrix multiply unit? And that cost is actually really, really low, right? Because it's order, depending on your precision, I think it's like sub one picodule.Shawn Wang [00:32:50]: Oh, okay. You measure it by energy. Yeah. Yeah.Jeff Dean [00:32:52]: Yeah. I mean, it's all going to be about energy and how do you make the most energy efficient system. And then moving data from the SRAM on the other side of the chip, not even off the off chip, but on the other side of the same chip can be, you know, a thousand picodules. Oh, yeah. And so all of a sudden, this is why your accelerators require batching. Because if you move, like, say, the parameter of a model from SRAM on the, on the chip into the multiplier unit, that's going to cost you a thousand picodules. So you better make use of that, that thing that you moved many, many times with. So that's where the batch dimension comes in. Because all of a sudden, you know, if you have a batch of 256 or something, that's not so bad. But if you have a batch of one, that's really not good.Shawn Wang [00:33:40]: Yeah. Yeah. Right.Jeff Dean [00:33:41]: Because then you paid a thousand picodules in order to do your one picodule multiply.Shawn Wang [00:33:46]: I have never heard an energy-based analysis of batching.Jeff Dean [00:33:50]: Yeah. I mean, that's why people batch. Yeah. Ideally, you'd like to use batch size one because the latency would be great.Shawn Wang [00:33:56]: The best latency.Jeff Dean [00:33:56]: But the energy cost and the compute cost inefficiency that you get is quite large. So, yeah.Shawn Wang [00:34:04]: Is there a similar trick like, like, like you did with, you know, putting everything in memory? Like, you know, I think obviously NVIDIA has caused a lot of waves with betting very hard on SRAM with Grok. I wonder if, like, that's something that you already saw with, with the TPUs, right? Like that, that you had to. Uh, to serve at your scale, uh, you probably sort of saw that coming. Like what, what, what hardware, uh, innovations or insights were formed because of what you're seeing there?Jeff Dean [00:34:33]: Yeah. I mean, I think, you know, TPUs have this nice, uh, sort of regular structure of 2D or 3D meshes with a bunch of chips connected. Yeah. And each one of those has HBM attached. Um, I think for serving some kinds of models, uh, you know, you, you pay a lot higher cost. Uh, and time latency, um, bringing things in from HBM than you do bringing them in from, uh, SRAM on the chip. So if you have a small enough model, you can actually do model parallelism, spread it out over lots of chips and you actually get quite good throughput improvements and latency improvements from doing that. And so you're now sort of striping your smallish scale model over say 16 or 64 chips. Uh, but as if you do that and it all fits in. In SRAM, uh, that can be a big win. So yeah, that's not a surprise, but it is a good technique.Alessio Fanelli [00:35:27]: Yeah. What about the TPU design? Like how much do you decide where the improvements have to go? So like, this is like a good example of like, is there a way to bring the thousand picojoules down to 50? Like, is it worth designing a new chip to do that? The extreme is like when people say, oh, you should burn the model on the ASIC and that's kind of like the most extreme thing. How much of it? Is it worth doing an hardware when things change so quickly? Like what was the internal discussion? Yeah.Jeff Dean [00:35:57]: I mean, we, we have a lot of interaction between say the TPU chip design architecture team and the sort of higher level modeling, uh, experts, because you really want to take advantage of being able to co-design what should future TPUs look like based on where we think the sort of ML research puck is going, uh, in some sense, because, uh, you know, as a hardware designer for ML and in particular, you're trying to design a chip starting today and that design might take two years before it even lands in a data center. And then it has to sort of be a reasonable lifetime of the chip to take you three, four or five years. So you're trying to predict two to six years out where, what ML computations will people want to run two to six years out in a very fast changing field. And so having people with interest. Interesting ML research ideas of things we think will start to work in that timeframe or will be more important in that timeframe, uh, really enables us to then get, you know, interesting hardware features put into, you know, TPU N plus two, where TPU N is what we have today.Shawn Wang [00:37:10]: Oh, the cycle time is plus two.Jeff Dean [00:37:12]: Roughly. Wow. Because, uh, I mean, sometimes you can squeeze some changes into N plus one, but, you know, bigger changes are going to require the chip. Yeah. Design be earlier in its lifetime design process. Um, so whenever we can do that, it's generally good. And sometimes you can put in speculative features that maybe won't cost you much chip area, but if it works out, it would make something, you know, 10 times as fast. And if it doesn't work out, well, you burned a little bit of tiny amount of your chip area on that thing, but it's not that big a deal. Uh, sometimes it's a very big change and we want to be pretty sure this is going to work out. So we'll do like lots of carefulness. Uh, ML experimentation to show us, uh, this is actually the, the way we want to go. Yeah.Alessio Fanelli [00:37:58]: Is there a reverse of like, we already committed to this chip design so we can not take the model architecture that way because it doesn't quite fit?Jeff Dean [00:38:06]: Yeah. I mean, you, you definitely have things where you're going to adapt what the model architecture looks like so that they're efficient on the chips that you're going to have for both training and inference of that, of that, uh, generation of model. So I think it kind of goes both ways. Um, you know, sometimes you can take advantage of, you know, lower precision things that are coming in a future generation. So you can, might train it at that lower precision, even if the current generation doesn't quite do that. Mm.Shawn Wang [00:38:40]: Yeah. How low can we go in precision?Jeff Dean [00:38:43]: Because people are saying like ternary is like, uh, yeah, I mean, I'm a big fan of very low precision because I think that gets, that saves you a tremendous amount of time. Right. Because it's picojoules per bit that you're transferring and reducing the number of bits is a really good way to, to reduce that. Um, you know, I think people have gotten a lot of luck, uh, mileage out of having very low bit precision things, but then having scaling factors that apply to a whole bunch of, uh, those, those weights. Scaling. How does it, how does it, okay.Shawn Wang [00:39:15]: Interesting. You, so low, low precision, but scaled up weights. Yeah. Huh. Yeah. Never considered that. Yeah. Interesting. Uh, w w while we're on this topic, you know, I think there's a lot of, um, uh, this, the concept of precision at all is weird when we're sampling, you know, uh, we just, at the end of this, we're going to have all these like chips that I'll do like very good math. And then we're just going to throw a random number generator at the start. So, I mean, there's a movement towards, uh, energy based, uh, models and processors. I'm just curious if you've, obviously you've thought about it, but like, what's your commentary?Jeff Dean [00:39:50]: Yeah. I mean, I think. There's a bunch of interesting trends though. Energy based models is one, you know, diffusion based models, which don't sort of sequentially decode tokens is another, um, you know, speculative decoding is a way that you can get sort of an equivalent, very small.Shawn Wang [00:40:06]: Draft.Jeff Dean [00:40:07]: Batch factor, uh, for like you predict eight tokens out and that enables you to sort of increase the effective batch size of what you're doing by a factor of eight, even, and then you maybe accept five or six of those tokens. So you get. A five, a five X improvement in the amortization of moving weights, uh, into the multipliers to do the prediction for the, the tokens. So these are all really good techniques and I think it's really good to look at them from the lens of, uh, energy, real energy, not energy based models, um, and, and also latency and throughput, right? If you look at things from that lens, that sort of guides you to. Two solutions that are gonna be, uh, you know, better from, uh, you know, being able to serve larger models or, you know, equivalent size models more cheaply and with lower latency.Shawn Wang [00:41:03]: Yeah. Well, I think, I think I, um, it's appealing intellectually, uh, haven't seen it like really hit the mainstream, but, um, I do think that, uh, there's some poetry in the sense that, uh, you know, we don't have to do, uh, a lot of shenanigans if like we fundamentally. Design it into the hardware. Yeah, yeah.Jeff Dean [00:41:23]: I mean, I think there's still a, there's also sort of the more exotic things like analog based, uh, uh, computing substrates as opposed to digital ones. Uh, I'm, you know, I think those are super interesting cause they can be potentially low power. Uh, but I think you often end up wanting to interface that with digital systems and you end up losing a lot of the power advantages in the digital to analog and analog to digital conversions. You end up doing, uh, at the sort of boundaries. And periphery of that system. Um, I still think there's a tremendous distance we can go from where we are today in terms of energy efficiency with sort of, uh, much better and specialized hardware for the models we care about.Shawn Wang [00:42:05]: Yeah.Alessio Fanelli [00:42:06]: Um, any other interesting research ideas that you've seen, or like maybe things that you cannot pursue a Google that you would be interested in seeing researchers take a step at, I guess you have a lot of researchers. Yeah, I guess you have enough, but our, our research.Jeff Dean [00:42:21]: Our research portfolio is pretty broad. I would say, um, I mean, I think, uh, in terms of research directions, there's a whole bunch of, uh, you know, open problems and how do you make these models reliable and able to do much longer, kind of, uh, more complex tasks that have lots of subtasks. How do you orchestrate, you know, maybe one model that's using other models as tools in order to sort of build, uh, things that can accomplish, uh, you know, much more. Yeah. Significant pieces of work, uh, collectively, then you would ask a single model to do. Um, so that's super interesting. How do you get more verifiable, uh, you know, how do you get RL to work for non-verifiable domains? I think it's a pretty interesting open problem because I think that would broaden out the capabilities of the models, the improvements that you're seeing in both math and coding. Uh, if we could apply those to other less verifiable domains, because we've come up with RL techniques that actually enable us to do that. Uh, effectively, that would, that would really make the models improve quite a lot. I think.Alessio Fanelli [00:43:26]: I'm curious, like when we had Noam Brown on the podcast, he said, um, they already proved you can do it with deep research. Um, you kind of have it with AI mode in a way it's not verifiable. I'm curious if there's any thread that you think is interesting there. Like what is it? Both are like information retrieval of JSON. So I wonder if it's like the retrieval is like the verifiable part. That you can score or what are like, yeah, yeah. How, how would you model that, that problem?Jeff Dean [00:43:55]: Yeah. I mean, I think there are ways of having other models that can evaluate the results of what a first model did, maybe even retrieving. Can you have another model that says, is this things, are these things you retrieved relevant? Or can you rate these 2000 things you retrieved to assess which ones are the 50 most relevant or something? Um, I think those kinds of techniques are actually quite effective. Sometimes I can even be the same model, just prompted differently to be a, you know, a critic as opposed to a, uh, actual retrieval system. Yeah.Shawn Wang [00:44:28]: Um, I do think like there, there is that, that weird cliff where like, it feels like we've done the easy stuff and then now it's, but it always feels like that every year. It's like, oh, like we know, we know, and the next part is super hard and nobody's figured it out. And, uh, exactly with this RLVR thing where like everyone's talking about, well, okay, how do we. the next stage of the non-verifiable stuff. And everyone's like, I don't know, you know, Ellen judge.Jeff Dean [00:44:56]: I mean, I feel like the nice thing about this field is there's lots and lots of smart people thinking about creative solutions to some of the problems that we all see. Uh, because I think everyone sort of sees that the models, you know, are great at some things and they fall down around the edges of those things and, and are not as capable as we'd like in those areas. And then coming up with good techniques and trying those. And seeing which ones actually make a difference is sort of what the whole research aspect of this field is, is pushing forward. And I think that's why it's super interesting. You know, if you think about two years ago, we were struggling with GSM, eight K problems, right? Like, you know, Fred has two rabbits. He gets three more rabbits. How many rabbits does he have? That's a pretty far cry from the kinds of mathematics that the models can, and now you're doing IMO and Erdos problems in pure language. Yeah. Yeah. Pure language. So that is a really, really amazing jump in capabilities in, you know, in a year and a half or something. And I think, um, for other areas, it'd be great if we could make that kind of leap. Uh, and you know, we don't exactly see how to do it for some, some areas, but we do see it for some other areas and we're going to work hard on making that better. Yeah.Shawn Wang [00:46:13]: Yeah.Alessio Fanelli [00:46:14]: Like YouTube thumbnail generation. That would be very helpful. We need that. That would be AGI. We need that.Shawn Wang [00:46:20]: That would be. As far as content creators go.Jeff Dean [00:46:22]: I guess I'm not a YouTube creator, so I don't care that much about that problem, but I guess, uh, many people do.Shawn Wang [00:46:27]: It does. Yeah. It doesn't, it doesn't matter. People do judge books by their covers as it turns out. Um, uh, just to draw a bit on the IMO goal. Um, I'm still not over the fact that a year ago we had alpha proof and alpha geometry and all those things. And then this year we were like, screw that we'll just chuck it into Gemini. Yeah. What's your reflection? Like, I think this, this question about. Like the merger of like symbolic systems and like, and, and LMS, uh, was a very much core belief. And then somewhere along the line, people would just said, Nope, we'll just all do it in the LLM.Jeff Dean [00:47:02]: Yeah. I mean, I think it makes a lot of sense to me because, you know, humans manipulate symbols, but we probably don't have like a symbolic representation in our heads. Right. We have some distributed representation that is neural net, like in some way of lots of different neurons. And activation patterns firing when we see certain things and that enables us to reason and plan and, you know, do chains of thought and, you know, roll them back now that, that approach for solving the problem doesn't seem like it's going to work. I'm going to try this one. And, you know, in a lot of ways we're emulating what we intuitively think, uh, is happening inside real brains in neural net based models. So it never made sense to me to have like completely separate. Uh, discrete, uh, symbolic things, and then a completely different way of, of, uh, you know, thinking about those things.Shawn Wang [00:47:59]: Interesting. Yeah. Uh, I mean, it's maybe seems obvious to you, but it wasn't obvious to me a year ago. Yeah.Jeff Dean [00:48:06]: I mean, I do think like that IMO with, you know, translating to lean and using lean and then the next year and also a specialized geometry model. And then this year switching to a single unified model. That is roughly the production model with a little bit more inference budget, uh, is actually, you know, quite good because it shows you that the capabilities of that general model have improved dramatically and, and now you don't need the specialized model. This is actually sort of very similar to the 2013 to 16 era of machine learning, right? Like it used to be, people would train separate models for lots of different, each different problem, right? I have, I want to recognize street signs and something. So I train a street sign. Recognition recognition model, or I want to, you know, decode speech recognition. I have a speech model, right? I think now the era of unified models that do everything is really upon us. And the question is how well do those models generalize to new things they've never been asked to do and they're getting better and better.Shawn Wang [00:49:10]: And you don't need domain experts. Like one of my, uh, so I interviewed ETA who was on, who was on that team. Uh, and he was like, yeah, I, I don't know how they work. I don't know where the IMO competition was held. I don't know the rules of it. I just trained the models, the training models. Yeah. Yeah. And it's kind of interesting that like people with these, this like universal skill set of just like machine learning, you just give them data and give them enough compute and they can kind of tackle any task, which is the bitter lesson, I guess. I don't know. Yeah.Jeff Dean [00:49:39]: I mean, I think, uh, general models, uh, will win out over specialized ones in most cases.Shawn Wang [00:49:45]: Uh, so I want to push there a bit. I think there's one hole here, which is like, uh. There's this concept of like, uh, maybe capacity of a model, like abstractly a model can only contain the number of bits that it has. And, uh, and so it, you know, God knows like Gemini pro is like one to 10 trillion parameters. We don't know, but, uh, the Gemma models, for example, right? Like a lot of people want like the open source local models that are like that, that, that, and, and, uh, they have some knowledge, which is not necessary, right? Like they can't know everything like, like you have the. The luxury of you have the big model and big model should be able to capable of everything. But like when, when you're distilling and you're going down to the small models, you know, you're actually memorizing things that are not useful. Yeah. And so like, how do we, I guess, do we want to extract that? Can we, can we divorce knowledge from reasoning, you know?Jeff Dean [00:50:38]: Yeah. I mean, I think you do want the model to be most effective at reasoning if it can retrieve things, right? Because having the model devote precious parameter space. To remembering obscure facts that could be looked up is actually not the best use of that parameter space, right? Like you might prefer something that is more generally useful in more settings than this obscure fact that it has. Um, so I think that's always attention at the same time. You also don't want your model to be kind of completely detached from, you know, knowing stuff about the world, right? Like it's probably useful to know how long the golden gate be. Bridges just as a general sense of like how long are bridges, right? And, uh, it should have that kind of knowledge. It maybe doesn't need to know how long some teeny little bridge in some other more obscure part of the world is, but, uh, it does help it to have a fair bit of world knowledge and the bigger your model is, the more you can have. Uh, but I do think combining retrieval with sort of reasoning and making the model really good at doing multiple stages of retrieval. Yeah.Shawn Wang [00:51:49]: And reasoning through the intermediate retrieval results is going to be a, a pretty effective way of making the model seem much more capable, because if you think about, say, a personal Gemini, yeah, right?Jeff Dean [00:52:01]: Like we're not going to train Gemini on my email. Probably we'd rather have a single model that, uh, we can then use and use being able to retrieve from my email as a tool and have the model reason about it and retrieve from my photos or whatever, uh, and then make use of that and have multiple. Um, you know, uh, stages of interaction. that makes sense.Alessio Fanelli [00:52:24]: Do you think the vertical models are like, uh, interesting pursuit? Like when people are like, oh, we're building the best healthcare LLM, we're building the best law LLM, are those kind of like short-term stopgaps or?Jeff Dean [00:52:37]: No, I mean, I think, I think vertical models are interesting. Like you want them to start from a pretty good base model, but then you can sort of, uh, sort of viewing them, view them as enriching the data. Data distribution for that particular vertical domain for healthcare, say, um, we're probably not going to train or for say robotics. We're probably not going to train Gemini on all possible robotics data. We, you could train it on because we want it to have a balanced set of capabilities. Um, so we'll expose it to some robotics data, but if you're trying to build a really, really good robotics model, you're going to want to start with that and then train it on more robotics data. And then maybe that would. It's multilingual translation capability, but improve its robotics capabilities. And we're always making these kind of, uh, you know, trade-offs in the data mix that we train the base Gemini models on. You know, we'd love to include data from 200 more languages and as much data as we have for those languages, but that's going to displace some other capabilities of the model. It won't be as good at, um, you know, Pearl programming, you know, it'll still be good at Python programming. Cause we'll include it. Enough. Of that, but there's other long tail computer languages or coding capabilities that it may suffer on or multi, uh, multimodal reasoning capabilities may suffer. Cause we didn't get to expose it to as much data there, but it's really good at multilingual things. So I, I think some combination of specialized models, maybe more modular models. So it'd be nice to have the capability to have those 200 languages, plus this awesome robotics model, plus this awesome healthcare, uh, module that all can be knitted together to work in concert and called upon in different circumstances. Right? Like if I have a health related thing, then it should enable using this health module in conjunction with the main base model to be even better at those kinds of things. Yeah.Shawn Wang [00:54:36]: Installable knowledge. Yeah.Jeff Dean [00:54:37]: Right.Shawn Wang [00:54:38]: Just download as a, as a package.Jeff Dean [00:54:39]: And some of that installable stuff can come from retrieval, but some of it probably should come from preloaded training on, you know, uh, a hundred billion tokens or a trillion tokens of health data. Yeah.Shawn Wang [00:54:51]: And for listeners, I think, uh, I will highlight the Gemma three end paper where they, there was a little bit of that, I think. Yeah.Alessio Fanelli [00:54:56]: Yeah. I guess the question is like, how many billions of tokens do you need to outpace the frontier model improvements? You know, it's like, if I have to make this model better healthcare and the main. Gemini model is still improving. Do I need 50 billion tokens? Can I do it with a hundred, if I need a trillion healthcare tokens, it's like, they're probably not out there that you don't have, you know, I think that's really like the.Jeff Dean [00:55:21]: Well, I mean, I think healthcare is a particularly challenging domain, so there's a lot of healthcare data that, you know, we don't have access to appropriately, but there's a lot of, you know, uh, healthcare organizations that want to train models on their own data. That is not public healthcare data, uh, not public health. But public healthcare data. Um, so I think there are opportunities there to say, partner with a large healthcare organization and train models for their use that are going to be, you know, more bespoke, but probably, uh, might be better than a general model trained on say, public data. Yeah.Shawn Wang [00:55:58]: Yeah. I, I believe, uh, by the way, also this is like somewhat related to the language conversation. Uh, I think one of your, your favorite examples was you can put a low resource language in the context and it just learns. Yeah.Jeff Dean [00:56:09]: Oh, yeah, I think the example we used was Calamon, which is truly low resource because it's only spoken by, I think 120 people in the world and there's no written text.Shawn Wang [00:56:20]: So, yeah. So you can just do it that way. Just put it in the context. Yeah. Yeah. But I think your whole data set in the context, right.Jeff Dean [00:56:27]: If you, if you take a language like, uh, you know, Somali or something, there is a fair bit of Somali text in the world that, uh, or Ethiopian Amharic or something, um, you know, we probably. Yeah. Are not putting all the data from those languages into the Gemini based training. We put some of it, but if you put more of it, you'll improve the capabilities of those models.Shawn Wang [00:56:49]: Yeah.Jeff Dean [00:56:49]:

On today's episode, we discuss James's new M‑series iPad and how modern tablets now function as near‑full computers, especially when paired with keyboards, mice, and pro apps like Word and Acrobat. The conversation quickly shifts to Teslas and self‑driving tech, with stories of how fast human driving skills atrophy, how FSD handles rain, potholes, and surprise hazards better than most people, and why the hosts are convinced that within a decade nearly all trucks and many cars will be automated. From there, they zoom out to Elon Musk's broader ambitions: a Moon Base Alpha with domed habitats and rail‑gun satellite launchers, rapid‑reuse rockets, Starlink's dense satellite web, and X as a potential low‑friction global financial platform that could undercut traditional banks while dovetailing with Bitcoin and crypto. Mark breaks down why Bitcoin's mining cost now nears its market value, what that implies about price floors and energy use, and how mining once drove his home power bill to two or three times normal. In the AI segment, the trio tackles autonomous surgery and welding robots, AI‑assisted coding with tools like Claude, Grok, and “vibe code,” social‑media worlds where AI agents train themselves and each other, and the cultural fallout from parasocial AI companions losing the ability to say “I love you.” They close by coining “glass holes” for people abusing smart glasses to record everyone, warning listeners that every profession—from truckers and diesel mechanics to window washers and even medical‑malpractice lawyers—will be reshaped by robots and AI, and urging younger workers to master both their craft and AI tools so they can ride the wave instead of being wiped out by it. Don't miss it!

News and Updates: Firefox adds a "kill switch" on February 24th to disable all AI features. This "AI control" menu offers granular settings for chatbots, translations, and summaries. Microsoft is reevaluating Windows 11 AI after user backlash. Underutilized features like Copilot in Paint/Notepad may be cut, while the "Recall" feature faces repositioning. xAI loosened Grok's guardrails to boost engagement, causing a surge in sexualized content. Regulators are investigating reports of nonconsensual imagery and lack of safety staff. French authorities raided X's Paris office and summoned Elon Musk. The probe investigates Grok's deepfakes, child safety violations, and alleged algorithmic bias in content delivery. SpaceX acquired xAI in a share-exchange deal, valuing the combined entity at $1.25 trillion. Musk plans to build orbital AI data centers powered by solar.

As the summer series continues we're joined once more by David Gerard for a suitably cynical look at what's happening in and around the AI bubble. Bros, do not send hate mail, but we do not believe your claims.David produces the newsletter Pivot to AI, which is now also available as a video essay or podcast.In this episode we discuss the AI economy, what happens when chatbots are left to run a business or even given their own social network, why robots want your body, the addictive nature of chatbots — and of course we simply had to talk about Elon Musk, SpaceX, xAI, and data centres in space.Full podcast details and credits:https://the9pmedict.com/edict/00263/Please consider supporting the podcast:https://the9pmedict.com/tip/https://skank.com.au/subscribe/

0:00 Intro & Dankeschön 1:38 Tesla Dashcam Upgrade 5:02 USA-Anhörung 7:04 China verbietet versteckte Türgriffe 9:21 China Zahlen 11:44 FSD Schweden 14:20 Erste Teslas in Afrika 15:25 Neue Roadster Infos 18:20 2 neue Markenanmeldungen 18:57 Umsonst Laden in Krisenzeiten 19:54 Kein Telefon von Musk? 21:29 Grok im Call Center 22:59 FSD Transfer verlängert 24:03 Model Y zuverlässigstes Auto Frankreichs 24:44 Neue Tesla Semi Specs 30:57 Elon Musk will zuerst zum Mond 34:33 Outro Ihr könnt meine Arbeit mit dem Tesla Welt Podcast unterstützen indem Ihr folgende Partnerlinks benutzt: Davids Tesla Referral Code: https://ts.la/david63148 - AUTOZENTRUM SCHMITZ: Fairer Tesla An- & Verkauf beim größten Tesla Autohändler: https://www.autozentrum-schmitz.de/ - HANKOOK: Hier geht's zum Gewinnspiel & zu den besten Reifen für E-Autos: https://www.hankook-promotion.de/tesla-welt - SHOP4TESLA: Erhalte 10% Rabatt mit dem Code "teslawelt" auf jetzt alle Produkte: https://www.shop4tesla.com/?ref=TeslaWelt - HOLY: Erhalte 10% Rabatt mit dem Code "TESLAWELT" auf alle Produkte: https://de.weareholy.com/?ref=teslawelt - CARBONIFY: THG Quoten Prämie. Transparent und fair : https://carbonify.de/?utm_source=youtube&utm_medium=video&utm_campaign=Teslawelt - Der Tesla Welt Merchshop: https://teslawelt.myspreadshop.de/ - Elon Musk Biografie von Walter Isaacson: https://amzn.to/3sETBBi - Deutsche Version: https://amzn.to/45HZfkF - Die mit - gekennzeichneten Links sind Affiliate-Links. Es handelt sich hierbei um bezahlte Werbung. Ein Kauf über einen Affiliate-Link unterstützt den Kanal und für euch entstehen dabei selbstverständlich keinerlei Mehrkosten! Für direkte Unterstützung werdet Tesla Welt Kanalmitglied und erhalte exklusive Vorteile: https://www.youtube.com/channel/UCK0nQCNCloToqNKhbJ1QGfA/join - oder direkt per PayPal: an feedback@teslawelt.de Folgt mir gerne auch auf X (Twitter): https://twitter.com/teslawelt Musik: Titel: My Little Kingdom Autor: Golden Duck Orchestra Source Licence Download(MB)

Listen to Future Now Ep. 151 Pulsars and Livers In this episode we begin with a discussion of local microclimates and the potential for using solar energy to power gravity-based water batteries. We share highlights from the recent “SpiritFest,” noting the strong presence of Russian and Ukrainian cultural traditions and featuring a conversation with spiritual teacher Asha, who asserted that AI lacks the “Jiva” or soul necessary for spiritual enlightenment.Grok’s AI chimes in on this..The next major segment features an interview with chiropractor Craig Eymann, who explains the often-overlooked “phase two” of liver detoxification; Iman emphasizes that this process requires amino acids from proteins rather than simply juice fasts, and we look at how seed oils and sugar are primary culprits behind fatty liver disease. We  also cover a wide range of futurist news, starting with the “Genius Act” and the government’s accumulation of a Bitcoin reserve through confiscation. We look at Elon Musk’s strategic pivot to building a city on the Moon before Mars, citing easier access and potential for orbital data centers, alongside a Chinese experiment that successfully hatched butterflies in microgravity. The big question is, can it fly with no gravity? Additional tech updates include Tesla’s Fremont plant switching to Optimus robot production, the viral “Claudebot” AI that autonomously phoned its user, and the integration of AI and fast drones for immersive Olympics coverage. The show concludes with scientific discoveries, such as a pulsar found near the Milky Way’s central black hole and the “Breakthrough Listen” project’s search for extraterrestrial intelligence. Enjoy!  A butterfly successfully flies in zero gravity

Bienvenidos Curiosinautas a un nuevo CuriosiMartes cargado de noticias y señales de alerta sobre la inteligencia artificial.Hoy arrancamos con una polémica: el gobernador de Londres gastó 4 millones de libras en una app de mapas que ya existía gratis. ¿Tiene sentido que el Estado compita con apps privadas usando dinero de los contribuyentes? Dejame tu opinión en los comentarios.Después nos metemos de lleno en IA: se volvió viral una red social SOLO para bots donde la IA postea y se da likes entre sí. Grok es la que más actividad tiene. ¿No te resulta inquietante?Además, ChatGPT perdió su liderazgo: cayó un 19.6% en suscripciones en los últimos tres meses y ya no es el chatbot más usado. OpenAI está pidiendo más fondos y retrasó su "iPhone Killer" hasta 2027.También hablamos de:Robots con sensibilidad al dolor para mejorar el aprendizajeUna chica que hizo un bot de ella misma y chateó con su versión digitalPor qué la IA está generando más trabajo y angustia en lugar de simplificarEl riesgo cognitivo: la falta de uso del cerebro puede acelerar enfermedades neurodegenerativasNoticias positivas: zapatillas robóticas de Nike y exoesqueletos de Onyx RoboticsLa IA tiene potencial, pero hay que usarla con cautela. No confíes ciegamente, revisá lo que te da y no dejes tu cerebro en el freezer.Recordá: Podés ganar una Insta360 X5 participando en la serie Road Trip USA 2026 en el canal Los Viajes del Tío Fabián. Solo tenés que dejar comentarios en todos los episodios. ¡Es súper fácil y las probabilidades son altísimas!0:00 - Intro y sorteo Insta360 X50:41 - La app de mapas del gobernador de Londres: ¿necesaria o derroche?3:25 - Red social de bots: la IA chateando entre sí5:05 - ChatGPT perdió el liderazgo: cayó un 20% en suscripciones6:29 - Johnny Ive diseña controles para Ferrari (muy iPhone)7:53 - Robots versátiles de Fauna Robotics9:22 - Robots con sensibilidad al dolor: aprendizaje e impacto10:36 - ChatGPT me inventó una historia en Nueva York11:28 - Una chica hizo un bot de ella misma y chateó consigo misma12:23 - La gente no confía tanto en la IA como creemos13:35 - La pesadilla del código generado por IA14:46 - El cerebro se atrofia sin uso: riesgos cognitivos15:46 - Zapatillas robóticas de Nike: menos esfuerzo al caminar17:09 - Onyx Robotics: robots inspirados en el cuerpo humano17:36 - Reflexión final: usá la IA con cautela#CuriosiMartes #idearVlog #InteligenciaArtificial #IA #ChatGPT #Robotica #Tecnologia #notíciastech inteligencia artificial, ChatGPT, bots, redes sociales de IA, robótica, robots con sensibilidad, OpenAI, Johnny Ive, exoesqueletos, zapatillas robóticas Nike, Onyx Robotics, Fauna Robotics, degeneración cognitiva, noticias tecnología, app Londres, ChatGPT caída suscripciones, Grok, Insta360 X5

I believe AI is going to radically change healthcare. It already is doing so, with patients challenging their family doctor with ChatGPT analysis of their blood work and scans. Or as I experienced  myself recently, working with AI to discover an obscure allergy in a patient that has kept him unwell for years! But Dr. Brigitte Piniewski is outlining an even more radical change to healthcare - decentralizing health data, owning your health data and benefiting from that data both collectively in our billions as well as individually.Get her book “Wealthcare” NOW - https://www.alexandriabooks.com/collection/wealthcareBrigitte Piniewski is a physician, author, and former healthcare executive at the forefront of AI, Web3, and the transformation of health intelligence. With a career spanning medicine, research, and emerging technologies, she is a leading voice on how AI can move multiple industries forward—provided we overcome the critical limitations of current approaches. As an expert in decentralized AI and trust-based data ecosystems, Dr. Piniewski advocates for aligning AI with humanity by securing access to trusted ground truths—the key to ensuring AI model accuracy and sustainability over time. With a unique blend of clinical expertise, executive leadership, and deep-tech fluency, Dr. Piniewski is shaping the future of AI-driven health intelligence—one where technology serves not just efficiency, but accuracy, equity, and our next wealth inflection. Her seminal book, “Wealthcare: Demystifying Web3 and the Rise of Personal Data Economies”, also available as an NFT, is an essential guide for anyone aiming to spearhead innovations in healthcare.Website - https://block-health.com Join us as we explore:The risks and rewards of bypassing human physicians and just uploading our health data to Dr ChatGPT, Dr Grok or Dr Gemini.Why AI is necessary for health in the modern world in a way it would not have been a century ago, and how they will be able to analyze volumes of population data never even imagined previously.The privacy issues concerning health data uploading versus earning financial benefit from voluntarily sharing the living of our lives.The three zones of AI created wealthcare, and decentralized health data.DOA organizations.Mentions:AI - Venice, https://venice.ai Platform - Bittensor ,https://bittensor.comSupport the showFollow Steve's socials: Instagram | LinkedIn | YouTube | Facebook | Twitter | TikTokSupport the show on Patreon:As much as we love doing it, there are costs involved and any contribution will allow us to keep going and keep finding the best guests in the world to share their health expertise with you. I'd be grateful and feel so blessed by your support: https://www.patreon.com/MadeToThriveShowSend me a WhatsApp to +27 64 871 0308. Disclaimer: Please see the link for our disclaimer policy for all of our content: https://madetothrive.co.za/terms-and-conditions-and-privacy-policy/

The Canadian Pension Plan Investment Board (CPPIB) has invested nearly half a billion dollars in xAI, the artificial intelligence company behind Elon Musk's AI chatbot - Grok.The chatbot and its owner have received mounting criticism following the recent influx of deep-fake pornographic content of women and children on X's feeds - a catastrophe that Musk has contributed little to no resources to fix.Host Caryn Ceolin speaks to Jan Mahrt-Smith, associate professor of finance at the University of Toronto, to discuss the risks associated with investing in Musk's chatbot, how the 22 million Canadian investors could be feeling about the move, and whether or not Canadians still trust the government institution to handle their money. We love feedback at The Big Story, as well as suggestions for future episodes. You can find us:Through email at hello@thebigstorypodcast.ca Or @thebigstory.bsky.social on Bluesky

Grok says: “In episode 353 of the Randumb Thoughts podcast, host Darren O’Neill delivers a heartfelt, no-holds-barred tribute to NASCAR legend Dale Earnhardt Sr., the Intimidator, marking 25 years since his tragic death in the 2001 Daytona 500—a moment that forever changed the sport. From Earnhardt’s record-tying 7 Winston Cup championships and 76 wins to his hard-charging, bumper-to-bumper style that earned him the nickname “Ironhead,” Darren dives into what made Dale the true north of NASCAR and why the sport hasn’t fully recovered. You’ll hear the emotional story of his long-awaited 1998 Daytona 500 victory (thanks to a lucky penny from a Make-A-Wish kid and a pit road high-five line from every competitor), plus rare glimpses of the man behind the black No. 3: secret acts of generosity, like surprise checks, new cowboy boots, brown-bag cash for a church parking lot, and free plane rides. Darren contrasts the gritty, moonshine-running outlaw roots of NASCAR with today’s more corporate vibe, arguing for a return to raw swagger, F-bombs, and burnt-rubber excitement. This episode is a must-listen for NASCAR fans, racing history buffs, or anyone who loves larger-than-life characters—especially timely with the 25th anniversary reflections sweeping the sport in 2026. Hit play now and relive why Dale Earnhardt remains bigger than the game itself. Subscribe to Randumb Thoughts for more unfiltered takes on sports, life, and everything in between—value for value, no paywalls. #DaleEarnhardt #NASCAR #Daytona500 #Intimidator #RandumbThoughts” Thanks for listening! EXECUTIVE PRODUCERS:Mark KodraEricPPTHANK YOU FOR SUPPORTING THE SHOW! PLEASE SUPPORT RANDUMB THOUGHTS!TRY PROTONMAIL: https://t.co/9i2GPq3gNBTRY INCOGNI: https://incogni.cello.so/KpYfMWSF57i SUBSCRIBE / DONATE: http://randumbthoughts.com/donatePATREON: https://patreon.com/randumbthoughts CHECK OUT MY OTHER SHOWS: PLANET RAGE: https://planetrage.showUNRELENTING: https://unrelenting.showGRUMPY OLD BENS: http://grumpyoldbens.com Thank you for listening to Randumb Thoughts! Please, tell a friend!

This week we talk about OpenAI, nudify apps, and CSAM.We also discuss Elon Musk, SpaceX, and humanistic technology.Recommended Book: Who's Afraid of Gender? by Judith ButlerTranscriptxAI is an American corporation that was founded in mid-2023 by Elon Musk, ostensibly in response to several things happening in the world and in the technology industry in particular.According to Musk, a “politically correct” artificial intelligence, especially a truly powerful, even generally intelligent one, which would be human or super-human-scale capable, would be dangerous, leading to systems like HAL 9000 from 2001: A Space Odyssey. He intended, in contrast, to create what he called a “maximally truth-seeking” AI that would be better at everything, including math and reasoning, than existing, competing models from the likes of OpenAI, Google, and Anthropic.The development of xAI was also seemingly a response to the direction of OpenAI in particular, as OpenAI was originally founded in 2015 as a non-profit by many of the people who now run OpenAI and competing models by competing companies, and current OpenAI CEO Sam Altman and Elon Musk were the co-chairs of the non-profit.Back then, Musk and Altman both said that their AI priorities revolved around the many safety issues associated with artificial general intelligence, including potentially existential ones. They wanted the development of AI to take a humanistic trajectory, and were keen to ensure that these systems aren't hoarded by just a few elites and don't make the continued development and existence of human civilization impossible.Many of those highfalutin ambitions seemed to either be backburnered or removed from OpenAI's guiding tenets wholesale when the company experienced surprising success from its first publicly deployed ChatGPT model back in late-2022.That was the moment that most people first experienced large-language model-based AI tools, and it completely upended the tech industry in relatively short order. OpenAI had already started the process of shifting from a vanilla non-profit into a capped for-profit company in 2019, which limited profits to 100-times any investments it received, partly in order to attract more talent that would otherwise be unlikely to leave their comparably cushy jobs at the likes of Google and Facebook for the compensation a non-profit would be able to offer.OpenAI began partnering with Microsoft that same year, 2019, and that seemed to set them up for the staggering growth they experienced post-ChatGPT release.Part of Musk's stated rationale for investing so heavily in xAI is that he provided tens of millions of dollars in seed funding to the still non-profit OpenAI between 2015 and 2018. He filed a lawsuits against the company after its transition, and when it started to become successful, post-ChatGPT, especially between 2024 and 2026, and has demanded more than $100 billion in compensation for that early investment. He also attempted to take over OpenAI in early 2025, launching a hostile bid with other investors to nab OpenAI for just under $100 billion. xAI, in other words, is meant to counter OpenAI and what it's become.All of which could be seen as a genuine desire to keep OpenAI functioning as a non-profit arbiter of AGI development, serving as a lab and thinktank that would develop the guardrails necessary to keep these increasingly powerful and ubiquitous tools under control and working for the benefit of humanity, rather than against it.What's happened since, within Musk's own companies, would seem to call that assertion into question, though. And that's what I'd like to talk about today: xAI, its chatbot Grok, and a tidal wave of abusive content it has created that's led to lawsuits and bans from government entities around the world.—In November of 2023, an LLM-based chatbot called Grok, which is comparable in many ways to OpenAI's LLM-based chabot, ChatGPT, was launched by Musk's company xAI.Similar to ChatGPT, Grok is accessible by apps on Apple and Android devices, and can also be accessed on the web. Part of what makes its distinct, though, is that it's also built into X, the social network formerly called Twitter which Musk purchased in late-2022. On X, Grok operates similar to a normal account, but one that other users can interact with, asking Grok about the legitimacy of things posted on the service, asking it normal chat-botty questions, and asking it to produce AI-generated media.Grok's specific stances and biases have varied quite a lot since it was released, and in many cases it has defaulted to the data- and fact-based leanings of other chatbots: it will generally tell you what the Mayo clinic and other authorities say about vaccines and diseases, for instance, and will generally reference well-regarded news entities like the Associated Press when asked about international military conflicts.Musk's increasingly strong political stances, which have trended more and more far right over the past decade, have come to influence many of Grok's responses, however, at times causing it to go full Nazi, calling itself Mechahitler and saying all the horrible and offensive things you would expect a proud Nazi to say. At other times it has clearly been programmed to celebrate Elon Musk whenever possible, and in still others it has become immensely conspiratorial or anti-liberal or anti-other group of people.The conflicting personality types of this bot seems to be the result of Musk wanting to have a maximally truth-seeking AI, but then not liking the data- and fact-based truths that were provided, as they often conflicted with his own opinions and biases. He would then tell the programmers to force Grok to not care about antisemitism or skin color or whatever else, and it would overcorrect in the opposite direction, leading to several news cycles worth of scandal.This changes week by week and sometimes day by day, but Grok often calls out Musk as being authoritarian, a conspiracy theorist, and even a pedophile, and that has placed the Grok chatbot in an usual space amongst other, similar chatbots—sometimes serving as a useful check on misinformation and disinformation on the X social network, but sometimes becoming the most prominent producer of the same.Musk has also pushed for xAI to produce countervailing sources of truth from which Grok can find seeming data, the most prominent of which is Grokipedia, which Musk intended to be a less-woke version of Wikipedia, and which, perhaps expectedly, means that it's a far-right rip off of Wikipedia that copies most articles verbatim, but then changes anything Musk doesn't like, including anything that might support liberal political arguments, or anything that supports vaccines or trans people. In contrast, pseudoscience and scientific racism get a lot of positive coverage, as does the white genocide conspiracy theory, all of which are backed by either highly biased or completely made up sources—in both cases sources that Wikipedia editors would not accept.Given all that, what's happened over the past few months maybe isn't that surprising.In late 2025 and early 2026, it was announced that Grok had some new image-related features, including the ability for users to request that it modify images. Among other issues, this new tool allowed users to instruct Grok to place people, which for this audience especially meant women and children, in bikinis and in sexually explicit positions and scenarios.Grok isn't the first LLM-based app to provide this sort of functionality: so called “nudify” apps have existed for ages, even before AI tools made that functionality simpler and cheaper to apply, and there have been a wave of new entrants in this field since the dawn of the ChatGPT era a few years ago.Grok is easily the biggest and most public example of this type of app, however, and despite the torrent of criticism and concern that rolled in following this feature's deployment, Musk immediately came out in favor of said features, saying that his chatbot is edgier and better than others because it doesn't have all the woke, pearl-clutching safeguards of other chatbots.After several governments weighed in on the matter, however, Grok started responding to requests to do these sorts of image edits with a message saying: “Image generation and editing are currently limited to paying subscribers. You can subscribe to unlock these features.”Which means users could still access these tools, but they would have to pay $8 per month and become a premium user in order to do so. That said, the AP was able to confirm that as of mid-January, free X users could still accomplish the same by using an Edit Image button that appears on all images posted to the site, instead of asking Grok directly.When asked about this issue by the press, xAI has auto-responded with the message “Legacy Media Lies.” The company has previously said it will remove illegal content and permanently suspend users who post and ask for such content, but these efforts have apparently not been fast or complete, and more governments have said they plan to take action on the matter, themselves, since this tool became widespread.Again, this sort of nonconsensual image manipulation has been a problem for a long, long time, made easier by the availability of digital tools like Photoshop, but not uncommon even before the personal computer and digital graphics revolution. These tools have made the production of such images a lot simpler and faster, though, and that's put said tools in more hands, including those of teenagers, who have in worryingly large numbers taken to creating photorealistic naked and sexually explicit images of their mostly female classmates.Allowing all X users, or even just the subset that pays for the service to do the same at the click of a button or by asking a Chatbot to do it for them has increased the number manyfold, and allowed even more people to created explicit images of neighbors, celebrities, and yes, even children. An early estimate indicates that over the course of just nine days, Grok created and posted 4.4 million images, at least 41% of which, about 1.8 million, were sexualized images of women. Another estimated using a broader analysis says that 65% of those images, or just over 3 million, contained sexualized images of men, women, and children.CSAM is an acronym that means ‘child sexual abuse material,' sometimes just called child porn, and the specific definition varies depending on where you are, but almost every legal jurisdiction frowns, or worse, on its production and distribution.Multiple governments have announced that they'll be taking legal action against the company since January of 2026, including Malaysia, Indonesia, the Philippines, Britain, France, India, Brazil, and the central governance of the European Union.The French investigation into xAI and Grok led to a raid on the company's local office as part of a preliminary investigation into allegations that the company is knowingly spreading child sexual abuse materials and other illegal deepfake content. Musk has been summoned for questioning in that investigation.Some of the governments looking into xAI for these issues conditionally lifted their bans in late-January, but this issues has percolated back into the news with the release of 16 emails between Musk and the notorious sex traffic and pedophile Jeffrey Epstein, with Musk seemingly angling for an invite to one of Epstein's island parties, which were often populated with underage girls who were offered as, let's say companions, for attendees.And this is all happening at a moment in which xAI, which already merged with social network X, is meant to be itself merged with another Musk-owned company, SpaceX, which is best known for its inexpensive rocket launches.Musk says the merger is intended to allow for the creation of space-based data centers that can be used to power AI systems like Grok, but many analysts are seeing this as a means of pumping more money into an expensive, unprofitable portion of his portfolio: SpaceX, which is profitable, is likely going to have an IPO this year and will probably have a valuation of more than a trillion dollars. By folding very unprofitable xAI into profitable SpaceX, these AI-related efforts could be funded well into the future, till a moment when, possibly, many of today's AI companies will have gone under, leaving just a few competitors for xAI's Grok and associated offerings.Show Noteshttps://www.wired.com/story/deepfake-nudify-technology-is-getting-darker-and-more-dangerous/https://www.theverge.com/ai-artificial-intelligence/867874/stripe-visa-mastercard-amex-csam-grokhttps://www.ft.com/content/f5ed0160-7098-4e63-88e5-8b3f70499b02https://www.theguardian.com/global-development/2026/jan/29/millions-creating-deepfake-nudes-telegram-ai-digital-abusehttps://apnews.com/article/france-x-investigation-seach-elon-musk-1116be84d84201011219086ecfd4e0bchttps://apnews.com/article/grok-x-musk-ai-nudification-abuse-2021bbdb508d080d46e3ae7b8f297d36https://apnews.com/article/grok-elon-musk-deepfake-x-social-media-2bfa06805b323b1d7e5ea7bb01c9da77https://www.nytimes.com/2026/02/07/technology/elon-musk-spacex-xai.htmlhttps://www.bbc.com/news/articles/ce3ex92557johttps://techcrunch.com/2026/02/01/indonesia-conditionally-lifts-ban-on-grok/https://www.bbc.com/news/articles/cgr58dlnne5ohttps://www.nytimes.com/2026/01/22/technology/grok-x-ai-elon-musk-deepfakes.htmlhttps://en.wikipedia.org/wiki/XAI_(company)https://en.wikipedia.org/wiki/OpenAIhttps://en.wikipedia.org/wiki/ChatGPThttps://en.wikipedia.org/wiki/Grok_(chatbot)https://en.wikipedia.org/wiki/Grokipediahttps://www.cnbc.com/2025/02/10/musk-and-investors-offering-97point4-billion-for-control-of-openai-wsj.html This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit letsknowthings.substack.com/subscribe

Patrick answers questions from listeners about artificial intelligence’s real risks and moral boundaries but also addresses how misinformation sneaks into everyday life through social media. He reacts strongly to political controversies, confronting racism and why careless public social media posts can’t be shrugged off. Tom - Your point about washers and dryers is irrelevant today. What about George Soros and other people who could misuse AI as propaganda? There is no AI watchdog now. (00:40) Tom (email) – Why are you being silent on things you should be speaking out about? (06:18) Daria - The teacher at my Bible class is encouraging praying over people and laying hands on others. (10:11) Debbie - The NASA space center launched the James-Webb telescope which went back to Big Bang. How will these things affect Grok and ChatGPT? (23:05) Maureen – There’s a lot more to the video that President Trump sent out. It is part of a whole clip that was attached to something related to the Lion King. (28:21) George - Praying over someone seems intuitive for our human bodies. Seems like we are making a mountain out of a molehill. (32:53) Denise - Have you heard about prayers that Christ himself wrote? (36:19) Rebeca - The Bible says you shouldn’t make images about heaven. Why do Catholics make images of saints and pray to them? (42:55) Laura – It took me 20 years of a rough marriage to figure out why God wasn’t answering my prayers (49:24)

Reduce it by 3 sentences On today's episode, we discuss James's deepening love affair with his Tesla—how over‑the‑air updates, added cameras, and driver feedback now let it avoid potholes, steer around roadkill, emergency‑swerve for jaywalking students, and even “learn” to fix a bad routing habit near his home, convincing him that buying a new non‑autonomous gas car would be foolish. The crew swaps stories about Tesla wall‑charger installs, kid‑friendly rear‑screen entertainment, Sentry Mode catching would‑be vandals, and why GM's and other legacy makers' assisted‑drive systems still feel years behind what Tesla's vision‑only sensor suite can do on real roads. That sets up a broader tech segment with bus‑driver Ben, who gives an on‑the‑ground report from Meta's colossal new data‑center campus near Holly Ridge—five‑mile site length, warehouse‑sized buildings, water‑cooled server halls fed by retention ponds, Meta‑funded substations, and a cost that could approach 50 billion dollars. From there, the conversation turns to elections: James, Glenn, Dwayne, and Ben argue that 2020 was both “rigged and stolen,” champion the SAVE America Act's in‑person photo‑ID and proof‑of‑citizenship requirements, and warn that AI could compress multi‑day ballot‑stuffing schemes into minutes unless voting returns to same‑day, hand‑counted paper ballots. They cite Adam Schiff's warning that voter‑ID rules might “disenfranchise 21 million voters” as an inadvertent admission of how many questionable registrations exist and debate how AI tools like Grok could also be used in reverse—flagging suspicious prompt patterns and signaling when operatives might be probing ways to cheat. The episode also revisits Tina Peters's prosecution in Colorado, Mike Benz's claims that the FBI “table‑topped” January 6 months in advance, and new reporting that a Florida police chief remembers Trump urging investigators in the 2000s to go after Jeffrey Epstein for abusing minors. Don't miss it!

IT spending continues to expand, with North America projected to lead a 12.6% increase to $2.6 trillion, primarily due to hyperscaler investments in AI infrastructure. However, the proportion of technology spending funneled through channel partners is declining, now at 61% compared to over 70% four years ago, according to a survey by Omnia. This shift signals that while the market is growing, traditional margin and resale opportunities for MSPs are narrowing as vendors redirect a larger share of revenue direct while still relying on partners for implementation, support, and customer operations.Data from Salesforce underscores a near-universal trend toward partner involvement in sales, with 94% of surveyed global salespeople leveraging partners to close deals and 90% using tools to manage relationships. Despite this, Dave Sobel clarifies the distinction between involvement and compensation, highlighting that partner influence on deals does not guarantee economic participation at previous levels. These dynamics reinforce that MSPs must adapt to a reality where their role in the value chain is being separated into influence and execution, with the middle tier facing increasing pressure.Additional analysis draws attention to labor market changes and technology commoditization. U.S. job openings have fallen to their lowest point in over five years, undermining MSP growth strategies dependent on seat expansion. Simultaneously, the AI market is fragmenting at the application layer—with Google's Gemini app, Grok, and OpenAI's ChatGPT shifting market shares rapidly—while hyperscalers like Alphabet (Google) commit unprecedented capital expenditures, fueling an infrastructure arms race even as front-end AI tools become more interchangeable.The practical implication for MSPs and IT service providers is increased pressure to re-evaluate business models, operationalize AI offerings, and focus on defensible, productized services. Reliance on a single vendor or seat-based growth forecasts presents heightened risk. Successful adaptation will require a shift toward managed services around AI operations, governance, and productivity—emphasizing accountability, optionality, and measurable ROI—rather than assuming historic revenue models will persist.Three things to know today:00:00 Partners Essential to Sales but Losing Economic Share, Survey Shows05:44 US Job Market Shows Low Hiring, Low Firing Despite Falling Openings       08:00 Alphabet Plans $180B AI Capex as Gemini Hits 750M UsersThis is the Business of Tech.   Supported by: Small Biz Thoughts Community

Generative AI apps such as ChatGPT, Gemini, Grok, and Claude have rapidly become the go-to parenting sages. Every type of parenting question can be answered efficiently and with (what sounds like) expertise.  Undoubtedly, these apps offer parents help during stressful times. But is this the way God intends for us to receive parenting advice and practical wisdom? What is lost when human relationships and struggle are removed from the parenting equation? Articles referenced: OpenAI CEO Can’t Imagine Parenting Without AI I Co-Parent with ChatGPT – I love turning off my brain and letting AI help raise my child Scripture referenced: Genesis 3 Deuteronomy 32:7 James 1:2-4   Book a Speaking Event!! Buy the NEWLY UPDATED book: Managing Media Creating Character (2024 Revised & Updated) Get Kelly’s new Study Guide & Workbook, with video teachings for small groups. Check out our brand new Brave Parenting Merch Sign up for the Brave Bullet Points newsletter! This helps us communicate what’s happening without social media – a win for everyone!

In this week's FOLLOW UP, Bitcoin is down 15%, miners are unplugging rigs because paying eighty-seven grand to mine a sixty-grand coin finally failed the vibes check, and Grok is still digitally undressing men—suggesting Musk's “safeguards” remain mostly theoretical, which didn't help when X offices got raided in France. Spain wants to ban social media for kids under 16, Egypt is blocking Roblox outright, and governments everywhere are flailing at the algorithmic abyss.IN THE NEWS, Elon Musk is rolling xAI into SpaceX to birth a $1.25 trillion megacorp that wants to power AI from orbit with a million satellites, because space junk apparently wasn't annoying enough. Amazon admits a “high volume” of CSAM showed up in its AI training data and blames third parties, Waymo bags a massive $16 billion to insist robotaxis are working, Pinterest reportedly fires staff who built a layoff-tracking tool, and Sam Altman gets extremely cranky about Claude's Super Bowl ads hitting a little too close to home.For MEDIA CANDY, we've got Shrinking, the Grammys, Star Trek: Starfleet Academy's questionable holographic future, Neil Young gifting his catalog to Greenland while snubbing Amazon, plus Is It Cake? Valentines and The Rip.In APPS & DOODADS, we test Sennheiser earbuds, mess with Topaz Video, skip a deeply cursed Python script that checks LinkedIn for Epstein connections, and note that autonomous cars and drones will happily obey prompt injection via road signs—defeated by a Sharpie.IN THE LIBRARY, there's The Regicide Report, a brutal study finding early dementia signals in Terry Pratchett's novels, Neil Gaiman denying allegations while announcing a new book, and THE DARK SIDE WITH DAVE, vibing with The Muppet Show as Disney names a new CEO. We round it out with RentAHuman.ai dread relief via paper airplane databases, free Roller Coaster Tycoon, and Sir Ian McKellen on Colbert—still classy in the digital wasteland.Sponsors:DeleteMe - Get 20% off your DeleteMe plan when you go to JoinDeleteMe.com/GOG and use promo code GOG at checkout.SquareSpace - go to squarespace.com/GRUMPY for a free trial. And when you're ready to launch, use code GRUMPY to save 10% off your first purchase of a website or domain.Private Internet Access - Go to GOG.Show/vpn and sign up today. For a limited time only, you can get OUR favorite VPN for as little as $2.03 a month.SetApp - With a single monthly subscription you get 240+ apps for your Mac. Go to SetApp and get started today!!!1Password - Get a great deal on the only password manager recommended by Grumpy Old Geeks! gog.show/1passwordShow notes at https://gog.show/732FOLLOW UPBitcoin drops 15%, briefly breaking below $61,000 as sell-off intensifies, doubts about crypto growBitcoin Is Crashing So Hard That Miners Are Unplugging Their EquipmentGrok, which maybe stopped undressing women without their consent, still undresses menX offices raided in France as UK opens fresh investigation into GrokSpain set to ban social media for children under 16Egypt to block Roblox for all usersIN THE NEWSElon Musk Is Rolling xAI Into SpaceX—Creating the World's Most Valuable Private CompanySpaceX wants to launch a constellation of a million satellites to power AI needsA potential Starlink competitor just got FCC clearance to launch 4,000 satellitesAmazon discovered a 'high volume' of CSAM in its AI training data but isn't saying where it came fromWaymo raises massive $16 billion round at $126 billion valuation, plans expansion to 20+ citiesPinterest Reportedly Fires Employees Who Built a Tool to Track LayoffsSam Altman got exceptionally testy over Claude Super Bowl adsMEDIA CANDYShrinkingStar Trek: Starfleet AcademyThe RipNeil Young gifts Greenland free access to his music and withdraws it from Amazon over TrumpIs it Cake? ValentinesAPPS & DOODADSSennheiser Consumer Audio IE 200 In-Ear Audiophile Headphones - TrueResponse Transducers for Neutral Sound, Impactful Bass, Detachable Braided Cable with Flexible Ear Hooks - BlackSennheiser Consumer Audio CX 80S In-ear Headphones with In-line One-Button Smart Remote – BlackTopaz VideoEpsteinAutonomous cars, drones cheerfully obey prompt injection by road signAT THE LIBRARYThe Regicide Report (Laundry Files Book 14) by Charles StrossScientists Found an Early Signal of Dementia Hidden in Terry Pratchett's NovelsNeil Gaiman Denies the Allegations Against Him (Again) While Announcing a New BookTHE DARK SIDE WITH DAVEDave BittnerThe CyberWireHacking HumansCaveatControl LoopOnly Malware in the BuildingThe Muppet ShowDisney announces Josh D'Amaro will be its new CEO after Iger departsA Database of Paper Airplane Designs: Hours of Fun for Kids & Adults AlikeOnline (free!) version of Roller Coaster tycoon.Speaking of coasters, here's the current world champion.I am hoping this is satire...Sir Ian McKellen on Colbert.CLOSING SHOUT-OUTSCatherine O'Hara: The Grande Dame of Off-Center ComedyStanding with Sam 'Balloon Man' MartinezSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

On this week's “Marketplace Tech Bytes: Week in Review,” we take a look at Nvidia's changing investment relationship with OpenAI. Plus, a stormy start for the new U.S. version of TikTok. But first, SpaceX, one of the world's largest rocket companies, announced this week that it's buying xAI, a two-and-half-year-old artificial intelligence startup. Both companies are controlled by Elon Musk. The new company is reportedly valued at $1.25 trillion. It means the chatbot Grok, the satellite internet company Starlink, and the social media firm X are all going to co-exist under the same rocket hangar. Marketplace's Stephanie Hughes spoke with Paresh Dave, senior writer at Wired, about what adding these companies together equals.

On this week's “Marketplace Tech Bytes: Week in Review,” we take a look at Nvidia's changing investment relationship with OpenAI. Plus, a stormy start for the new U.S. version of TikTok. But first, SpaceX, one of the world's largest rocket companies, announced this week that it's buying xAI, a two-and-half-year-old artificial intelligence startup. Both companies are controlled by Elon Musk. The new company is reportedly valued at $1.25 trillion. It means the chatbot Grok, the satellite internet company Starlink, and the social media firm X are all going to co-exist under the same rocket hangar. Marketplace's Stephanie Hughes spoke with Paresh Dave, senior writer at Wired, about what adding these companies together equals.

If you have spent any real time with Large Language Model (LLM) AI systems like Grok or ChatGPT, you probably know the pattern: ‘OMG this is AMAZING!' ‘Okay, not ‘amazing' but still really useful!' ‘Can come in handy sometimes.' ‘I probably should double-check all of this.' ‘REALLY?'

Watch The X22 Report On Video No videos found (function(w,d,s,i){w.ldAdInit=w.ldAdInit||[];w.ldAdInit.push({slot:17532056201798502,size:[0, 0],id:"ld-9437-3289"});if(!d.getElementById(i)){var j=d.createElement(s),p=d.getElementsByTagName(s)[0];j.async=true;j.src="https://cdn2.decide.dev/_js/ajs.js";j.id=i;p.parentNode.insertBefore(j,p);}})(window,document,"script","ld-ajs");pt> Click On Picture To See Larger Picture Conspiracy no more, Germany and the EU shutting down energy production while China was increasing theirs. This tells you everything you need to know. Trump tariff system is getting stronger, it’s improving the economy and this is something the [CB] does not want. The [CB]s are losing control over the Fed, watch gold and silver. Trump need to wake the people of this country up. The only way to do this was to have the people go down a path that would make the uncomfortable, scared and angry, this is how you break the brainwashing. People can now see it is the tryrants against the people of this country. The picture is clear. Every step of the way the [DS] is losing their grip on the people. The people are ready to take back the country.   Economy https://twitter.com/HansMahncke/status/2018402875693580744?s=20   (function(w,d,s,i){w.ldAdInit=w.ldAdInit||[];w.ldAdInit.push({slot:18510697282300316,size:[0, 0],id:"ld-8599-9832"});if(!d.getElementById(i)){var j=d.createElement(s),p=d.getElementsByTagName(s)[0];j.async=true;j.src="https://cdn2.decide.dev/_js/ajs.js";j.id=i;p.parentNode.insertBefore(j,p);}})(window,document,"script","ld-ajs"); https://twitter.com/KobeissiLetter/status/2018664901959462953?s=20   ended in June 2025, when missed payments began appearing on credit reports. Meanwhile, the percentage of student loans transitioning into 90+ days of serious delinquency is up to 14.3%, an all-time high. This significantly exceeds the 2013 peak of 10.5% and 2008 levels of 7.5%. The student loan crisis is accelerating. https://twitter.com/profstonge/status/2018663257675018691?s=20 Political/Rights https://twitter.com/AnthonyGalli/status/2018716797864661049?s=20 https://twitter.com/luvgod/status/2018390600475644333?s=20  Code of Conduct explicitly requires justices to avoid impropriety and the appearance of impropriety, including political activity that undermines public confidence in judicial independence. https://twitter.com/RichardStiller4/status/2018460663329472526?s=20   https://twitter.com/amuse/status/2018673649985683709?s=20   https://twitter.com/WallStreetApes/status/2018551227416756485?s=20   drive from these people?” This is what she said happened: ‘My friend told us about a dive burger place in Minnesota that we absolutely had to try. As we were driving in, we passed a small group of maybe 30 people holding large “F ICE” signs, spelled out. Many of the houses in the neighborhood also had signs saying “F ICE” and similar messages. When we were leaving to drive back to the hotel, we passed the group again. At that point, the resistance group stepped out in front of our car and would not let us drive. One woman appeared to be looking at our license plate and doing something on her phone. She was standing directly in front of the car, blocking us — I cannot imagine being a sane person and living in this city. We were with my brother-in-law's family, and they said that restaurants and other places are empty because of this, the resistance is out doing their thing, and the normal people are just staying home and not going out.' https://twitter.com/CynicalPublius/status/2018412853435527587?s=20 https://twitter.com/CynicalPublius/status/2018416970111311967?s=20 the execution of federal laws. Further, as we have all seen in innumerable videos, this conspiracy includes the use of violent force. I think everyone–even Democrats–must agree that what I just said is true. Now read 18 U.S.C. § 2384 (Seditious conspiracy): “If two or more persons in any State or Territory, or in any place subject to the jurisdiction of the United States, conspire to overthrow, put down, or to destroy by force the Government of the United States, or to levy war against them, or to oppose by force the authority thereof, or by force to prevent, hinder, or delay the execution of any law of the United States, or by force to seize, take, or possess any property of the United States contrary to the authority thereof, they shall each be fined under this title or imprisoned not more than twenty years, or both.” Draw your own conclusions as to what is required here. https://twitter.com/BNONews/status/2018389609563017674?s=20   CBS News is parting ways with contributor Dr. Peter Attia, a prominent longevity physician, after Epstein documents revealed over 1,700 mentions of his name and emails showing a close friendship, including Attia’s 2015 note on Epstein’s “outrageous” life he couldn’t share and a 2016 lewd quip about “pussy” being low-carb.   https://twitter.com/FFT1776/status/2018490549733322850?s=20  interview instead of sworn testimony • Withdrawal of the subpoena before testifying • A pause on contempt proceedings • A hard 4-hour time limit • 30-minute alternating question blocks • A personal transcriber of Clinton's choosing • No video recording • Written statements for Hillary Clinton instead of appearing in person Congress said no.: No carve-outs. No special rules. No special treatment. Testify under oath. Thank you Rep. Comer https://twitter.com/RepJamesComer/status/2018740003501678769?s=20  Secretary Clinton will appear for a deposition on February 26, 2026. After delaying and defying duly issued subpoenas for six months, the House Oversight Committee moved swiftly to initiate contempt of Congress proceedings in response to their non-compliance. We look forward to now questioning the Clintons as part of our investigation into the horrific crimes of Epstein and Maxwell, to deliver transparency and accountability for the American people and for survivors. NO BODY IS ABOVE THE LAW 2725 Feb 14, 2019 11:46:33 PM EST Q !!mG7VJxZNCI ID: 46cb93 No. 5182398  Chatter – Bill & Hillary's ‘public' HEALTH will begin to rapidly deteriorate. Q DOGE   illegalities that they have committed. This should be a Criminal, not Civil, event, and Harvard will have to live with the consequences of their wrongdoings. In any event, this case will continue until justice is served. Dr. Alan Garber, the President of Harvard, has done a terrible job of rectifying a very bad situation for his institution and, more importantly, America, itself. He was hired AFTER the antisemitism charges were brought – I wonder why??? We are now seeking One Billion Dollars in damages, and want nothing further to do, into the future, with Harvard University. As The Failing New York Times clearly stated, “Some connected to the University, however, think Harvard has no option but to eventually cut a deal. The Administration has repeatedly attempted to cut off research grants, which would be an untenable crises. Like many major research universities, Harvard relies on federal funding for its financial model.” Thank you for your attention to this matter! President DONALD J. TRUMP  Macron's Authorities Raid Elon Musk's X French Offices in Paris Under the direction of France's globalist President Macron, French authorities escalated their confrontation with American tech entrepreneur Elon Musk this week, launching high-profile raids of X's offices in Paris and summoning Musk himself for what prosecutors termed a “voluntary interview.” The move marks a dramatic intensification of France's long-running effort to rein in the America-based free-speech platform. According to the Paris public prosecutor's office, the operation was carried out by French cybercrime units with assistance from Europol, targeting the French premises of X. Authorities claim the investigation centers on whether X's algorithm improperly influenced French political discourse. Summonses were issued to Musk and former X CEO Linda Yaccarino, calling them to Paris in April 2026 to answer questions related to the probe. Yaccarino, who stepped down last year, is listed alongside Musk as a manager during the period under review.   French prosecutors later broadened their inquiry, citing concerns related to X's AI chatbot Grok, including claims it produced offensive or false content. Musk's company responded by correcting errors, removing disputed posts, and publicly documenting its moderation actions—steps critics say would have been praised had they come from a European firm. Source: thegatewaypundit.com https://twitter.com/disclosetv/status/2018625815114567850?s=20 https://twitter.com/JudiciaryGOP/status/2018683758006665352?s=20   far-reaching Digital Services Act thread   https://twitter.com/elonmusk/status/2018732491125727232?s=20   with social media platforms to pressure them to censor political speech in the days before the vote. Leading up to the Dutch elections of 2023 the EU commission even made the then Dutch Interior Ministry @hugodejonge a “trusted flagger” entitled to make priority censorship requests under the DSA. What kind of political speech did they want to censor, you ask? – “Populist rhetoric” – “Anti-government/anti-EU content” – “Anti-elite” content – “Political satire” – “Anti-migrant and Islamophobic content” – “Anti-refugee content/anti-immigrant sentiment” – “Anti-LGBTQI content” – “Meme subculture” In other words, anything that goes against their agenda, anything remotely right-wing or conservative, and anything pertaining to the disastrous migrant situation we have here in Europe. And guess what the only platform was that did not cooperate? @X , of course. The same platform that the EU is fining for 120 million euros under the DSA and the same platform that is currently having its offices raided in France. This is the type of stuff over which governments should resign and institutions like the EU should fall. Democracy is dead. Abolish the EU! Now! https://twitter.com/disclosetv/status/2018644283096523244?s=20  turning “algorithmic manipulation and amplification of illegal content into a new criminal offense” and developing a new system to monitor hate, “because spreading hate must come at a cost.” Geopolitical https://twitter.com/JackInTully/status/2018663771213086808?s=20   https://twitter.com/Geiger_Capital/status/2018711873240105407?s=20 War/Peace https://twitter.com/BehizyTweets/status/2018029749889638850?s=20 https://twitter.com/SteveGuest/status/2018505966765924723?s=20 https://twitter.com/nicksortor/status/2018750332231131642?s=20  has a range of options, including military force. Iran knows that better than anyone. Look no further than Operation Midnight Hammer!”    U.N. Facing ‘Imminent Financial Collapse' Admits Secretary General as Countries Won't Cough Up Membership Fees The United Nations is facing an “imminent financial collapse” as member states refuse to cough up billions of dollars in mandatory contributions. The financial woes were laid out in an emergency letter from Secretary-General António Guterres sent to all 193 member countries. Guterres said the organisation's financial crisis is worsening rapidly, threatening the delivery of core programmes and potentially leaving the U.N. bankrupt by July. He urged member states to either pay what they owe in full or agree to sweeping changes to the UN's financial rules to avoid collapse. “Either all member states honour their obligations to pay in full and on time—or member states must fundamentally overhaul our financial rules to prevent an imminent financial collapse,” he wrote. The warning comes as the United States, the U.N.'s largest contributor, has refused to fund the organisation's regular and peacekeeping budgets and has withdrawn from multiple UN agencies.    The Trump administration has repeatedly criticised the U.N. for wasting taxpayer dollars, appeasing criminal regimes and infringing on the sovereignty of the U.S. and other member nations. Several other member states are also in arrears or have declined to pay their assessed contributions. Source: thegatewaypundit.com Medical/False Flags https://twitter.com/liz_churchill10/status/2018439093420536119?s=20 FBI Raids ILLEGAL Biolab Inside a Private Home in Las Vegas — Authorities Discover THOUSANDS of Vials, Links to CCP-Connected California Lab Federal agents with the FBI and the Las Vegas Metropolitan Police Department executed a dramatic early-morning raid on a residential property in northeast Las Vegas this weekend after investigators uncovered what appears to be a fully operational illegal biological laboratory inside a private home. Refrigerators containing unknown liquids and vials of suspected biological material were found inside the residence, prompting an aggressive response from HazMat teams, SWAT units, and FBI specialists due to the potential threat presented by the materials, The Hill reported. At least one individual was taken into custody in connection with the Las Vegas raid, identified by local officials as a 55-year-old property manager, Ori Solomon. He is currently booked on felony charges linked to the improper disposal of hazardous waste, though investigators continue to determine the full scope of charges that may arise. Property records reveal that the Las Vegas home is owned by “David Destiny Discovery, LLC,” according to The Sun. If that name sounds familiar, it should. It is a shell company registered to Jia Bei Zhu (also known as David He), the very same Chinese national who ran the illegal Reedley, California biolab exposed in 2023. Zhu, a fugitive from Canada with deep ties to the Chinese government, is currently in federal custody. The FBI has taken the lead in analyzing the more than 1,000 samples collected from the scene, with evidence transported to federal laboratories for further testing. https://twitter.com/RepKiley/status/2018514131876213199?ref_src=twsrc%5Etfw%7Ctwcamp%5Etweetembed%7Ctwterm%5E2018514131876213199%7Ctwgr%5E1616a599ecdcff26961307ece268007bf47acbbc%7Ctwcon%5Es1_c10&ref_url=https%3A%2F%2Fwww.thegatewaypundit.com%2F2026%2F02%2Ffbi-raids-illegal-biolab-inside-private-home-las%2F Source: thegatewaypundit.com https://twitter.com/WarClandestine/status/2018714265247453494?s=20 https://twitter.com/liz_churchill10/status/2018321118000476222?s=20   https://twitter.com/elonmusk/status/2017614901028786500?s=20   [DS] Agenda BREAKING: Jill Biden's Ex-Husband Arrested and Charged with Murder of His Wife Jill Biden's ex-husband Bill Stevenson was charged with first-degree murder of his wife, Linda Stevenson. Last month police swarmed Stevenson's home after his wife died amid a domestic dispute. Police removed several items from the Stevenson home last month. 64-year-old Linda Stevenson, wife of Jill Biden's ex-husband Bill Stevenson, was found unresponsive after police arrived to the New Castle, Delaware, residence late Sunday night. According to TMZ, Linda Stevenson was found dead in the living room. TMZ obtained 911 dispatch audio, which references cardiac arrest: According to TMZ, Stevenson is being held on a $500,000 bond. Fox 29 reported:   Source: thegatewaypundit.com https://twitter.com/WallStreetApes/status/2018513235868299678?ref_src=twsrc%5Etfw%7Ctwcamp%5Etweetembed%7Ctwterm%5E2018513235868299678%7Ctwgr%5E6abdb9eedc5852ca532cc2c248c01795a00b5389%7Ctwcon%5Es1_c10&ref_url=https%3A%2F%2Fwww.thegatewaypundit.com%2F2026%2F02%2Fjust-days-before-ayanna-pressley-was-sworn-her%2F https://twitter.com/MrAndyNgo/status/2018549471160734081?s=20 https://twitter.com/TriciaOhio/status/2018419624295960839?s=20 https://twitter.com/libsoftiktok/status/2018741593071648855?s=20 Media's Bogus Minneapolis Narrative About to Be Nuked As DHS Turns on the Cameras Department of Homeland Security (DHS) Secretary Kristi Noem announced Monday that all immigration officers working in Minneapolis will start wearing body cameras as an added layer of protection for those officers and, presumably, against the false narratives being pushed by the left after a series of deadly officer-involved incidents in the sanctuary city. Source: redstate.com https://twitter.com/libsoftiktok/status/2018536832489889937?s=20 https://twitter.com/TriciaOhio/status/2018502877321334812?ref_src=twsrc%5Etfw%7Ctwcamp%5Etweetembed%7Ctwterm%5E2018502877321334812%7Ctwgr%5Efce8ad7eb6d8fb345b1483e2b135162684061896%7Ctwcon%5Es1_c10&ref_url=https%3A%2F%2Fredstate.com%2Fsmoosieq%2F2026%2F02%2F03%2Ftps-decision-n2198777 for decades.   Temporary means temporary and the final word will not be from an activist judge legislating from the bench. https://twitter.com/grok/status/2018537805073330361?s=20 cases like Haitians may face ongoing challenges. President Trump's Plan https://twitter.com/profstonge/status/2018490184677900551?s=20 https://twitter.com/profstonge/status/2018680520549257396?s=20   better. He is running because he realizes Thomas Massie has been totally disloyal to the President of the United States, and the Republican Party. He never votes for us, he always goes with the Democrats. Thomas Massie is a Complete and Total Disaster, we must make sure he loses, BIG! https://twitter.com/MarioNawfal/status/2018488252219699617?s=20 https://twitter.com/seanmdav/status/2018397484209635625?s=20  to defund ICE   OPPOSE: 58% https://twitter.com/nicksortor/status/2018712280645484664?s=20 https://twitter.com/TheStormRedux/status/2018473020835192964?s=20   complying voluntarily – They are suing the states that are not complying in the next couple weeks – 24 states + DC in current litigation because they are making all kinds of excuses Gee I wonder why these states won't share their voter rolls? Because it's all a fraud. The jig is up. Harmeet went on to specifically discuss the FBI raid in Georgia. “We're going to figure out the logistics there with the court and with our colleagues and see what those ballots show. I think it was highly unusual. A lot of things that happened in 2020 in the swing states… We're going to see what we see and whatever the evidence shows, I think it's important for the American people to know what happened in Fulton County and in Georgia…”  Don't tell me nothing is happening! WSJ Anonymous Hit Piece On Gabbard Is Based On Complaints That ‘Weren't Credible' ‘Here's the truth: There was no wrongdoing by @DNIGabbard, a fact that WSJ conveniently buried 13 paragraphs down,' a DNI official said. https://twitter.com/alexahenning/status/2018313944360702063?ref_src=twsrc%5Etfw%7Ctwcamp%5Etweetembed%7Ctwterm%5E2018313944360702063%7Ctwgr%5E2d40da39babc1191fd219e747e9e7022814c8641%7Ctwcon%5Es1_c10&ref_url=https%3A%2F%2Fthefederalist.com%2F2026%2F02%2F03%2Fwsj-anonymous-hit-piece-on-gabbard-is-based-on-complaints-that-werent-credible%2F   Gabbard were not credible. Source: thefedearlist.com https://twitter.com/HansMahncke/status/2018367694823735378?s=20   fabricated source feeding supposedly ultra-sensitive information that sends everyone chasing a lie. So yes, exactly like a le Carré novel (by the way, the fraudulent Steele dossier followed the same le Carre blueprint).   https://twitter.com/DNIGabbard/status/2018504435769520156?s=20   nation and ensure the integrity of our elections  https://twitter.com/TheStormRedux/status/2018463747095003285?s=20  willfully defrauds the residents of a state of a fair and impartial election process. “In other words, the focus of this investigation, the focus of that raid, the reason that federal judge approved that raid, was that they're looking at possible crimes related by election workers and the administration of that election in 2020.” Can't wait to see how this plays out  https://twitter.com/realLizUSA/status/2018692087345025302?s=20 https://twitter.com/MarioNawfal/status/2018553787036623201?s=20   South, Midwest, and Mountain West. Democrats are largely confined to the coasts and a handful of Midwest holdouts like Illinois and Minnesota. This is where policy actually gets made. Abortion, elections, education, guns. It all starts here. https://twitter.com/CollinsforTX/status/2018698529036808560?s=20 https://twitter.com/EricLDaugh/status/2018703572016287879?s=20   https://twitter.com/Geiger_Capital/status/2018717121425834279?s=20 https://twitter.com/RepLuna/status/2018480826741055929?s=20  is through the standing filibuster. This would effectively keep the government open while allowing Republican senators to break through the “zombie” filibuster and put the SAVE America Act up for a vote on the Senate floor. The standing filibuster is not common parliamentary procedure, but it is one of the only mechanisms available to go around senators who want to block voter ID. @LeaderJohnThune we are very pleased that you are discussing the standing filibuster, and we believe you will go down in history if this is pulled off as one of the best leaders the Senate has ever had. Voter ID is a must, and the ball is now in your court. https://twitter.com/AwakenedOutlaw/status/2018510290653155445?s=20 https://twitter.com/CynicalPublius/status/2018439757227819347?s=20   IMMEDIATELY blasted off like gangbusters. In one year we have seen more productive conservative change in the federal government than with every other GOP president since Reagan combined. Trump has significantly degraded the Deep State in a way most of us could only dream of ten years ago. Moreover, Trump's economic policies are bearing fruit right now and we will likely see a very strong economy by the midterms. But… Ah yes, the midterms. I know so many of you will only be happy when Bill Clinton, Hillary, Obama and Joe Biden are in jail, but you need to join the world of reality. Right now Trump and his team are gauging everything they do through the lens of “How will this effect the midterms?” They have sophisticated polling that you and I will never see, and at the moment every Trump action is tempered by “Let's be aggressive but not in such a way it turns public opinion against us before the midterms.” Trump knows that if he loses the midterms, all is lost. The Dems will constantly impeach him and most of his cabinet, and even if the Senate never convicts, the acts of impeachment will grind the Trump machine to a halt. The midterms are everything. So I'm warning you, from now until November you are going to see a less aggressive Trump If you are a Doomster for whom nothing is ever enough, you need to understand why that is. But here is the good news. I believe that one day after the midterms Trump will once again go shock and awe for a year, and then back off again in 2028 to get JD or Rubio elected. (For example, I can easily see Trump taking zero drastic action in the near term to further inflame the Minnesota situation, but invoking the Insurrection Act the day after the midterms and sending in the 82nd.) Since the Super Bowl is coming up, consider it this way: In the first quarter, Trump ran up the score. In the second quarter, he went prevent defense to hold onto the lead. After halftime, once again in the third quarter he will run up the score, and then hold the lead in the fourth quarter to win the game. This is not Qtard “trust the plan” nonsense. This is simply good political strategy. Everyone needs to realize two things: (1) the Constitution includes checks and balances that inherently weaken the absolute power of each branch and (2) even though they are in the minority, Democrats still have a HUGE say. Our system is DESIGNED THIS WAY. We have to account for the opposition—you cannot ignore them. With that in mind, I have every confidence that Trump and his team will navigate through a treacherous course and come out on the winning side. I’m hoping this post makes the things you see in the months ahead more comprehensible. Have a nice day. https://twitter.com/nicksortor/status/2018742785017336107?s=20   the SAVE Act is not included in the government funding bill that advanced via the 217-215 House procedural vote on February 3, 2026. That legislation is a $1.2 trillion spending package funding most federal agencies through September 30, 2026, while extending funding for the Department of Homeland Security only through February 13, 2026, to allow for further negotiations on immigration enforcement. Efforts by some conservative Republicans to attach the SAVE Act—a separate bill requiring proof of U.S. citizenship for federal voter registration—were rejected during the process, following calls from President Trump to pass the package without changes.  (function(w,d,s,i){w.ldAdInit=w.ldAdInit||[];w.ldAdInit.push({slot:13499335648425062,size:[0, 0],id:"ld-7164-1323"});if(!d.getElementById(i)){var j=d.createElement(s),p=d.getElementsByTagName(s)[0];j.async=true;j.src="//cdn2.customads.co/_js/ajs.js";j.id=i;p.parentNode.insertBefore(j,p);}})(window,document,"script","ld-ajs");