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Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Get early access to Alex's next live-cohort courses!Intro to Bayes Course (first 2 lessons free)Advanced Regression Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Takeaways:BART as a core tool: Gabriel explains how Bayesian Additive Regression Trees provide robust uncertainty quantification and serve as a reliable baseline model in many domains.Rust for performance: His Rust re-implementation of BART dramatically improves speed and scalability, making it feasible for larger datasets and real-world IoT applications.Strengths and trade-offs: BART avoids overfitting and handles missing data gracefully, though it is slower than other tree-based approaches.Big data meets Bayes: Gabriel shares strategies for applying Bayesian methods with big data, including when variational inference helps balance scale with rigor.Optimization and decision-making: He highlights how BART models can be embedded into optimization frameworks, opening doors for sequential decision-making.Open source matters: Gabriel emphasizes the importance of communities like PyMC and Bambi, encouraging newcomers to start with small contributions.Chapters:05:10 – From economics to IoT and Bayesian statistics18:55 – Introduction to BART (Bayesian Additive Regression Trees)24:40 – Re-implementing BART in Rust for speed and scalability32:05 – Comparing BART with Gaussian Processes and other tree methods39:50 – Strengths and limitations of BART47:15 – Handling missing data and different likelihoods54:30 – Variational inference and big data challenges01:01:10 – Embedding BART into optimization and decision-making frameworks01:08:45 – Open source, PyMC, and community support01:15:20 – Advice for newcomers01:20:55 – Future of BART, Rust, and probabilistic programmingThank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian...
Before Siri had sass and Alexa started judging your music taste, the original virtual assistant was quietly revolutionizing the '90s—powered by many patents and a whole lot of foresight. Now, as AI goes from buzzword to boss, we ask, will it transform your job, your home… or just steal your knowledge? This week, Dave, Esmee and Rob speak with Kevin Surace, Futurist, Inventor & "Father" of the Virtual Assistant, about exploring the evolution of AI, what the future might hold, and how disruptive innovation can shake up your organization in ways you might not expect. TLDR: 00:40 – Introduction of Kevin Surace 05:12 – Rob gets confused by Google Maps reviews and selfies 08:15 – Deep dive into the evolution of AI with Kevin 52:00 – How intelligent agents can help manage digital noise and support mental well-being 1:07:30 – Wrapping up the book the Joy Success Cycle and heading to a concert GuestKevin Surace: https://www.linkedin.com/in/ksurace/ HostsDave Chapman: https://www.linkedin.com/in/chapmandr/Rob Kernahan: https://www.linkedin.com/in/rob-kernahan/Esmee van de Giessen: https://www.linkedin.com/in/esmeevandegiessen/ ProductionMarcel van der Burg: https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman: https://www.linkedin.com/in/chapmandr/ SoundBen Corbett: https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett: https://www.linkedin.com/in/louis-corbett-087250264/ 'Cloud Realities' is an original podcast from Capgemini
Cette semaine je lance un nouveau format premium pour le RDV Tech ! Chaque trimestre je vous proposerai un épisode qui fera un état des lieux des grandes tendances de l'industrie et de ce que je vois dans ma veille, à l'horizon de quelques mois ou de l'année plutôt que celui du quotidien ou de la semaine. L'idée est de fournir une vision complémentaire de l'actu, qui vous permettra de mieux appréhender les évolutions réelles de l'industrie, ainsi que la manière dont elles influencent nos sociétés. A trop avoir la tête dans le guidon, on peut rater un bus qui déboule, pourrait-on dire. :)Au programme :Les grandes tendances :Autonomie stratégique (disponible pour tous)Contrôle de l'âge sur Internet (réservé aux patreotes)Montée en puissance de l'IA (réservé aux patreotes)Tendances émergeantes (réservé aux patreotes)Infos :Animé par Patrick Beja (Bluesky, Instagram, Twitter, TikTok).Produit par Patrick Beja (LinkedIn) et Fanny Cohen Moreau (LinkedIn).Musique libre de droit par Daniel BejaLe Rendez-vous Tech épisode 634 – État des Lieux – sept 2025---Liens :
Longtime friend of the show Rajiv Shah returns to unpack lessons from a year of building retrieval-augmented generation (RAG) pipelines and reasoning models integrations. We dive into why so many AI pilots stumble, why evaluation and error analysis remain essential data science skills, and why not every enterprise challenge calls for a large language model.Featuring:Rajiv Shah – LinkedIn Daniel Whitenack – Website, GitHub, XUpcoming Events: Join us at the Midwest AI Summit on November 13 in Indianapolis to hear world-class speakers share how they've scaled AI solutions. Don't miss the AI Engineering Lounge, where you can sit down with experts for hands-on guidance. Reserve your spot today!Register for upcoming webinars here!
Christianity is sometimes branded as anti-intellectual, and its followers labelled unteachable. But in an increasingly divided age, the church - with its rich history of learning - might be able to help the world recover what it means to have a teachable spirit … and to know who to learn from.(00:00) - - How we learn (04:17) - - Teachability (10:44) - - Humility in learning (16:00) - - Is faith a block to thinking critically? (20:58) - - Combating fear with knowledge (28:36) - - Learning from strangers (33:10) - - Learning from the dead (46:26) - - Learning from our enemies (56:33) - - Five Minute Jesus (01:03:56) - - Why learn from Christians? CREDITS Undeceptions is hosted by John Dickson, produced by Kaley Payne and directed by Mark Hadley. Alasdair Belling is a writer-researcher.Siobhan McGuiness is our online librarian. Lyndie Leviston remains John's wonderful assistant. Santino Dimarco is Chief Finance and Operations Consultant. Editing by Richard Hamwi.Our voice actor for this episode was Suzanne Ellis.Special thanks to our series sponsor Zondervan for making this Undeception possible. Undeceptions is the flagship podcast of Undeceptions.com - letting the truth out.
We're back! In this Season 5 premiere, the team reunites after their summer break to kick off an exciting new chapter. Join us as we catch up, share bold predictions for the year ahead, and explore big questions, like whether 2026 will be the year of the autonomous organization. Expect candid reflections, lively discussion, and a sneak peek at what's coming up this season. We are very keen this season to establish a feedback loop with listeners, so will be doing shows exploring listener questions and challenges - something we are really looking forward to. Please get in touch with us, via LinkedIn, Substack or cloudrealities@capgemini.com, if you have questions or challenges for us, we'd love to hear from you!TLDR: 00:20 – We're back! 00:35 – Catching up on what we did during the summer break 10:48 – Planning ahead until Christmas: Microsoft Ignite, AWS re:Invent, an AI mini-series and cool guests 20:27 – Tech talk: iPhone 17, deep democracy training, and the human impact of innovation 32:10 – Will autonomous organizations powered by agents emerge within 12–18 months? 40:45 – Reflections inspired by Jaws, climbing adventures, and Bruce Springsteen HostsDave Chapman: https://www.linkedin.com/in/chapmandr/Rob Kernahan: https://www.linkedin.com/in/rob-kernahan/Esmee van de Giessen: https://www.linkedin.com/in/esmeevandegiessen/ ProductionMarcel van der Burg: https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman: https://www.linkedin.com/in/chapmandr/ SoundBen Corbett: https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett: https://www.linkedin.com/in/louis-corbett-087250264/ 'Cloud Realities' is an original podcast from Capgemini
In part 2 of this episode, MacKenzie sits down with Stacy Hock, Vice Chair of the Texas Higher Education Coordinating Board, to discuss the current landscape of K-12 education reform. Drawing on her experience as an education leader and mom of four with kids in public, private, and Alpha schools, Stacy shares her perspective on tailoring education to each child's unique needs and path. She also digs into the red tape and regulations that often hold schools back, and how reform could open doors for more innovation in learning.
In this episode, Daniel and Chris are joined by Chris Aquino, software engineer at Thunderbird to hear the story of how they developed a privacy-preserving AI executive assistant. They discuss various design decisions including remote (but confidential) inference, local encryption, and model selection. Chris A. does an amazing job describing the journey from "let the big LLM do everything" to splitting apart the workflow to be handled by multiple models. Featuring:Chris Aquino – LinkedInChris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks: ThunderbirdThunderbird ProSponsors:Shopify – The commerce platform trusted by millions. From idea to checkout, Shopify gives you everything you need to launch and scale your business—no matter your level of experience. Build beautiful storefronts, market with built-in AI tools, and tap into the platform powering 10% of all U.S. eCommerce.Start your one-dollar trial at shopify.com/practicalaiUpcoming Events: Join us at the Midwest AI Summit on November 13 in Indianapolis to hear world-class speakers share how they've scaled AI solutions. Don't miss the AI Engineering Lounge, where you can sit down with experts for hands-on guidance. Reserve your spot today!Register for upcoming webinars here!
Au programme :iPhone 17, Air et Pro: le plein de bonnes surprisesMeta présente ses premières lunettes IA avec écranYouTube veut aider tout le monde à devenir créateurLe reste de l'actualité: visas H-1B, TikTok US…Infos :Animé par Patrick Beja (Bluesky, Instagram, Twitter, TikTok).Co-animé par Guillaume Vendé (Bluesky).Produit par Patrick Beja (LinkedIn) et Fanny Cohen Moreau (LinkedIn).Musique libre de droit par Daniel BejaLe Rendez-vous Tech épisode 633 – Tout est du contenu, tout le monde est créateur – iPhone 17, Meta Ray Ban Display, YouTube Summit, TikTok US, visa H-1B---Liens :
A merciless serial killer terrorised women in the United Kingdom with a spate of brutal attacks, leaving police and forensic experts stumped. Now, almost 50 years later, forensics has learned, and built more advanced technologies that could've helped catch the killer sooner, and potentially saved lives. In this Rewind episode of Crime Insiders Forensics, former host Kathryn Fox sits down with Dr Marie Morelato to understand how forensics has evolved from this case, and to unpack the current strategies being employed to catch criminals and save lives. This episode contains references to sexual violence against women and children. If you or someone you know needs help, dial 1800 RESPECT (1800 737 732). See omnystudio.com/listener for privacy information.
Professor Andrew Wilson from NYU explains why many common-sense ideas in artificial intelligence might be wrong. For decades, the rule of thumb in machine learning has been to fear complexity. The thinking goes: if your model has too many parameters (is "too complex") for the amount of data you have, it will "overfit" by essentially memorizing the data instead of learning the underlying patterns. This leads to poor performance on new, unseen data. This is known as the classic "bias-variance trade-off" i.e. a balancing act between a model that's too simple and one that's too complex.**SPONSOR MESSAGES**—Tufa AI Labs is an AI research lab based in Zurich. **They are hiring ML research engineers!** This is a once in a lifetime opportunity to work with one of the best labs in EuropeContact Benjamin Crouzier - https://tufalabs.ai/ —Take the Prolific human data survey - https://www.prolific.com/humandatasurvey?utm_source=mlst and be the first to see the results and benchmark their practices against the wider community!—cyber•Fund https://cyber.fund/?utm_source=mlst is a founder-led investment firm accelerating the cybernetic economyOct SF conference - https://dagihouse.com/?utm_source=mlst - Joscha Bach keynoting(!) + OAI, Anthropic, NVDA,++Hiring a SF VC Principal: https://talent.cyber.fund/companies/cyber-fund-2/jobs/57674170-ai-investment-principal#content?utm_source=mlstSubmit investment deck: https://cyber.fund/contact?utm_source=mlst— Description Continued:Professor Wilson challenges this fundamental belief (fearing complexity). He makes a few surprising points:**Bigger Can Be Better**: massive models don't just get more flexible; they also develop a stronger "simplicity bias". So, if your model is overfitting, the solution might paradoxically be to make it even bigger.**The "Bias-Variance Trade-off" is a Misnomer**: Wilson claims you don't actually have to trade one for the other. You can have a model that is incredibly expressive and flexible while also being strongly biased toward simple solutions. He points to the "double descent" phenomenon, where performance first gets worse as models get more complex, but then surprisingly starts getting better again.**Honest Beliefs and Bayesian Thinking**: His core philosophy is that we should build models that honestly represent our beliefs about the world. We believe the world is complex, so our models should be expressive. But we also believe in Occam's razor—that the simplest explanation is often the best. He champions Bayesian methods, which naturally balance these two ideas through a process called marginalization, which he describes as an automatic Occam's razor.TOC:[00:00:00] Introduction and Thesis[00:04:19] Challenging Conventional Wisdom[00:11:17] The Philosophy of a Scientist-Engineer[00:16:47] Expressiveness, Overfitting, and Bias[00:28:15] Understanding, Compression, and Kolmogorov Complexity[01:05:06] The Surprising Power of Generalization[01:13:21] The Elegance of Bayesian Inference[01:33:02] The Geometry of Learning[01:46:28] Practical Advice and The Future of AIProf. Andrew Gordon Wilson:https://x.com/andrewgwilshttps://cims.nyu.edu/~andrewgw/https://scholar.google.com/citations?user=twWX2LIAAAAJ&hl=en https://www.youtube.com/watch?v=Aja0kZeWRy4 https://www.youtube.com/watch?v=HEp4TOrkwV4 TRANSCRIPT:https://app.rescript.info/public/share/H4Io1Y7Rr54MM05FuZgAv4yphoukCfkqokyzSYJwCK8Hosts:Dr. Tim Scarfe / Dr. Keith Duggar (MIT Ph.D)REFS:Deep Learning is Not So Mysterious or Different [Andrew Gordon Wilson]https://arxiv.org/abs/2503.02113Bayesian Deep Learning and a Probabilistic Perspective of Generalization [Andrew Gordon Wilson, Pavel Izmailov]https://arxiv.org/abs/2002.08791Compute-Optimal LLMs Provably Generalize Better With Scale [Marc Finzi, Sanyam Kapoor, Diego Granziol, Anming Gu, Christopher De Sa, J. Zico Kolter, Andrew Gordon Wilson]https://arxiv.org/abs/2504.15208
Dave, Esmee, and Rob are strapping in for another season of bold, brain-bending conversations—and they're bringing the flux capacitor with them from Back to the Future.Season 5 beams in global leaders and innovators who challenge how we think about technology, business, and humanity. From AI disruption to digital sovereignty, from leadership to culture—this season's guests are ready to shake things up.Our first full episode drops on September 25, but before we hit 88 miles per hour, here's a quick trailer to set the timeline straight, or at least bend it a little.HostsDave Chapman: https://www.linkedin.com/in/chapmandr/Rob Kernahan: https://www.linkedin.com/in/rob-kernahan/Esmee van de Giessen: https://www.linkedin.com/in/esmeevandegiessen/ProductionMarcel van der Burg: https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman: https://www.linkedin.com/in/chapmandr/SoundBen Corbett: https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett: https://www.linkedin.com/in/louis-corbett-087250264/'Cloud Realities' is an original podcast from Capgemini
Fala galera, nesse epsiódio bem especial do podcast eu falo com o Carlos Eduardo, VP senior architect no Citi e vencedor da competição do Kaggle do curso!!Foi um episódio bem legal onde o Cadu explicou a solução vencedora dele para a competição do Kaggle que fizemos no meu curso de Deep Learning. A solução ficou bem complexa e legal, tenho certeza que voces vão gostar!Aqui está o link para a página de vendas para saber mais sobre mim e sobre o curso: https://www.cursovidacomia.com.br/Aqui está o link direto para se inscrever no curso: https://pay.hotmart.com/W98240617ULink do grupo do wpp:https://chat.whatsapp.com/GNLhf8aCurbHQc9ayX5oCPFInstagram do podcast: https://www.instagram.com/podcast.lifewithaiMeu Linkedin: https://www.linkedin.com/in/filipe-lauar/Linkedin do Cadu: https://www.linkedin.com/in/carlos-eduardo-gabriel-santos/Solução do Cadu: https://github.com/filipelauar/projects/blob/main/desafio_kaggle_curso_solucao_cadu.ipynbMinha solução do kaggle do desafio na França: https://github.com/filipelauar/projects/blob/main/cancer_detection_kaggle_challange_notebook.ipynb
Au programme :La meilleure annonce d'Apple est une camera carréeUne IA devient ministre en Albanie (ce n'est pas une blague)En Suisse, le gouvernement propose de tuer la vie privéeLe reste de l'actualitéInfos :Animé par Patrick Beja (Bluesky, Instagram, Twitter, TikTok).Co-animé par Marion Doumeingts (Instagram, Bluesky, Twitter).Co-animé par Jeff Clavier (Instagram, Twitter).Produit par Patrick Beja (LinkedIn) et Fanny Cohen Moreau (LinkedIn).Musique libre de droit par Daniel BejaLe Rendez-vous Tech épisode 632 – Apple annonce un lineup plus solide qu'éblouissant---Liens :
Nick Tammaro and PTF lead off with stakes analysis from Woodbine and Churchill Downs. Where does Notable Speech fit in the BC Mile picture? Is Bentornato a good bet in the Sprint? Where will be see Deep Learning and Teddy's Rocket next? They answer these questions and many more.Next up, Goffs USA agent Jacob West joins PTF and they talk about the general positive health of the industry from a breeding and sales side and look ahead to the Goffs Orby sale and the myriad opportunities afforded to the buyers who shop there, We also get an update on some of the impressive two-year-olds and BC Classic contenders (Fierceness, Mindframe), Jacob is associated with through his role on the Repole bloodstock team.
Nick Tammaro and PTF lead off with stakes analysis from Woodbine and Churchill Downs. Where does Notable Speech fit in the BC Mile picture. Is Bentornato a good bet in the Sprint? Where will be see Deep Learning and Teddy's Rocket next? They answer these questions and many more.Next up, Goffs USA agent Jacob West joins PTF and they talk about the general positive health of the industry from a breeding and sales side and look ahead to the Goffs Orby sale and the myriad opportunities afforded to the buyers who shop there, We also get an update on some of the impressive two-year-olds and BC Classic contenders (Fierceness, Mindframe), Jacob is associated with through his role on the Repole bloodstock team.
In this Fully Connected episode, we dig into the recent MIT report revealing that 95% of AI pilots fail before reaching production and explore what it actually takes to succeed with AI solutions. We dive into the importance of AI model integration, asking the right questions when adopting new technologies, and why simply accessing a powerful model isn't enough. We explore the latest AI trends, from GPT-5 to open source models, and their impact on jobs, machine learning, and enterprise strategy. Featuring:Chris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks: The GenAI Divide: State of AI in Business 2025MIT Report: 95% of generative AI pilots at companies are failingSponsors:Miro – The innovation workspace for the age of AI. Built for modern teams, Miro helps you turn unstructured ideas into structured outcomes—fast. Diagramming, product design, and AI-powered collaboration, all in one shared space. Start building at miro.comShopify – The commerce platform trusted by millions. From idea to checkout, Shopify gives you everything you need to launch and scale your business—no matter your level of experience. Build beautiful storefronts, market with built-in AI tools, and tap into the platform powering 10% of all U.S. eCommerce.Start your one-dollar trial at shopify.com/practicalaiUpcoming Events: Join us at the Midwest AI Summit on November 13 in Indianapolis to hear world-class speakers share how they've scaled AI solutions. Don't miss the AI Engineering Lounge, where you can sit down with experts for hands-on guidance. Reserve your spot today!Register for upcoming webinars here!
Au programme :Procès pour monopole: Google évite le pireAnthropic va payer $1,5 Mrds aux auteurs qu'ils ont piratéDes mini prédictions pour la conf AppleLe reste de l'actualitéEt merci à Freelance Informatique, le sponsor de cet épisode ! Retrouvez toutes les infos sur http://freelance-informatique.fr/.Infos :Animé par Patrick Beja (Bluesky, Instagram, Twitter, TikTok).Co-animé par Cédric Ingrand (Twitter et Bluesky).Produit par Patrick Beja (LinkedIn) et Fanny Cohen Moreau (LinkedIn).Musique libre de droit par Daniel BejaLe Rendez-vous Tech épisode 631 – Google échappe au pire dans son procès avec les US – Google vs US, Anthropic paie les auteurs, Musk pilote Gork, Euro numérique---Liens :
Today I'm joined by Jamie Gerlach, an educator and leader who believes in the power of deep learning to build human agency and sees access to rigorous play as a basic right for all. For over a decade, he has designed and led professional learning in embodied literacy, 4C learning and school transformation, working with teachers and leaders across Australia and internationally. Jamie has also lectured at the University of Sydney and spoken at national conferences. In this conversation, we explore his philosophy, passion and practical insights for reimagining education.
How do you decide whether to buy a ready-made AI solution or build one from the ground up? The choice is more than just a technical decision; it's about aligning AI with your business goals. In this episode, Lois Houston and Nikita Abraham are joined by Principal Instructor Yunus Mohammed to examine the critical factors influencing the buy vs. build debate. They explore real-world examples where businesses must weigh speed, customization, and long-term strategy. From a startup using a SaaS chatbot to a bank developing a custom fraud detection model, Yunus provides practical insights on when to choose one approach over the other. AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/ Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. --------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:26 Nikita: Welcome to the Oracle University Podcast! I'm Nikita Abraham, Team Lead: Editorial Services with Oracle University, and with me is Lois Houston, Director of Innovation Programs. Lois: Hi there! Last week, we spoke about the key stages in a typical AI workflow and how data quality, feedback loops, and business goals influence AI success. 00:50 Nikita: In today's episode, we're going to explore whether you should buy or build AI apps. Joining us again is Principal Instructor Yunus Mohammed. Hi Yunus, let's jump right in. Why does the decision of buy versus build matter? Yunus: So when we talk about buy versus build matters, we need to consider the strategic business decisions over here. They are related to the strategic decisions which the business makes, and it is evaluated in the decision lens. So the center of the decision lens is the business objective, which identifies what are we trying to solve. Then evaluate our constraints based on that particular business objective like the cost, the time, and the talent. And finally, we can decide whether we need to buy or build. But remember, there is no single correct answer. What's right for one business may not be working for the other one. 01:54 Lois: OK, can you give us examples of both approaches? Yunus: The first example where we have got a startup using a SaaS AI chatbot. Now, being a startup, they have to choose a ready-made solution, which is an AI chatbot. Now, the question is, why did they do this? Because speed and simplicity mattered more than deep customization that is required for the chatbot. So, their main aim was to have it ready in short period of time and make it more simpler. And this actually lead them to get to the market fast with low upfront cost and minimal technical complexities. But in some situations, it might be different. Like, your bank, which needs to build a fraud model. It cannot be outsourced or got from the shelf. So, they build a custom model in-house. With this custom model, they actually have a tighter control, and it is tuned to their standards. And it is created by their experts. So these two generic examples, the chatbot and the fraud model example, helps you in identifying whether I should go for a SaaS product with simple choice of selecting an existing LLM endpoint and not making any changes. Or should I go with model depending on my business and organization requirement and fine tuning that model later to define a better implementation of the scenarios or conditions that I want to do which are specific to my organization. So here you decide with the reference whether I want it to be done faster, or whether I want to be more customized to my organization. So buy it, when it is generic, or build when it is strategic. The SaaS, which is basically software as a service, refers to ready to use cloud-based applications that you access via internet. You can log into the platform and use the built-in AI, there's no setup requirement for those. Real-world examples can be Oracle Fusion apps with AI features enabled. So in-house integration means embedding AI with my own requirements into your own systems, often using custom APIs, data pipelines, and hosting it. It gives you more flexibility but requires a lot of resources and expertise. So real-world example for this scenario can be a logistics heavy company, which is integrating a customer support model into their CX. 04:41 Lois: But what are the pros and cons of each approach? Yunus: So, SaaS and Fusion Applications, basically, they offer fast deployment with little to no coding required, making them ideal for business looking to get started quickly and faster. And they typically come with lower upfront costs and are maintained by vendor, which means updates, security, support are handled externally. However, there are limited customizations and are best suited for common, repeatable use cases. Like, it can be a standard chatbot, or it can be reporting tools, or off the shelf analytics that you want to use. But the in-house or custom integration, you have more control, but it takes longer to build and requires a higher initial investment. The in-house or custom integration approach allows full customization of the features and the workflows, enabling you to design and tailor the AI system to your specific needs. 05:47 Nikita: If you're weighing the choice between buying or building, what are the critical business considerations you'd need to take into account? Yunus: So let's take one of the key business consideration which is time to market. If your goal is to launch fast, maybe you're a startup trying to gain traction quickly, then a prebuilt plug and play AI solution, for example, a chatbot or any other standard analytical tool, might be your best bet. But if you have time and you are aiming for precision, a custom model could be worth the wait. Prebuilt SaaS tools usually have lower upfront costs and a subscription model. It works with putting subscriptions. Custom solutions, on the other hand, may require a bigger investment upfront. In development, you require high talent and infrastructures, but could offer cost savings in the long run. So, ask yourself a question here. Is this AI helping us stand out in the market? If the answer is yes, you may want to build something which is your proprietary. For example, an organization would use a generic recommendation engine. It's a part of their secret sauce. Some use cases require flexibility, like you want to tailor the rules to match your specific risk criteria. So, under that scenarios, you will go for customizing. So, you will go with off the shelf solutions may not give you deep enough requirements that you want to evaluate. So, you get those and you try to customize those. You can go for customization of your AI features. The other important key business consideration is the talent and expertise that your organization have. So, the question that you need to ask in the organization is, do you have an internal team who is well versed in developing AI solutions for you? Or do you have access to one of the teams which can help you build your own proprietary products? If not, you'll go with SaaS. If you do have, then building could unlock greater control over your AI features and AI models. The next core component is your security and data privacy. If you're handling sensitive information, like for example, the health care or finance data, you might not want to send your data to the third-party tools. So in-house models offer better control over data security and compliance. When we leverage a model, it could be a prebuilt or custom model. 08:50 Oracle University is proud to announce three brand new courses that will help your teams unlock the power of Redwood—the next generation design system. Redwood enhances the user experience, boosts efficiency, and ensures consistency across Oracle Fusion Cloud Applications. Whether you're a functional lead, configuration consultant, administrator, developer, or IT support analyst, these courses will introduce you to the Redwood philosophy and its business impact. They'll also teach you how to use Visual Builder Studio to personalize and extend your Fusion environment. Get started today by visiting mylearn.oracle.com. 09:31 Nikita: Welcome back! So, getting back to what you were saying before the break, what are pre-built and custom models? Yunus: A prebuilt model is an AI solution that has already been trained by someone else, typically a tech provider. It can be used to perform a specific task like recognizing images, translating text, or detecting sentiments. You can think of it like buying a preassembled appliance. You plug it in, configure a few settings, and it's ready to use. You don't need to know how the internal parts work. You benefit from the speed, ease, and reliability of this particular model, which is a prebuilt model. But you can't easily change how it works under the hood. Whereas, a custom model is an AI solution that your organization designs and trains and tunes specifically for their business problems using their own data. You can think of it like designing your own suit. It takes more time and effort to create. It is built to your exact measurements and needs. And you have full control over how it performs and evolves. 10:53 Lois: So, when would you choose a pre-built versus a custom model? Yunus: Depending on speed, simplicity, control, and customization, you can decide on using a prebuilt or to create a custom model. Prebuilt models are like plug and play solutions. Think of tools like Google Translate for languages. OpenAI APIs for summarizing sentiment analysis or chatbots, they are quick to deploy, require low technical effort, great for getting started fast, but they also have limits. Customization is very minimal, and you may not be able to fine tune it to your specific tone or business logic. These work well when the problem is common and nonstrategic, like, scanning documents or auto tagging images. The custom-build model, on the other hand, is a model that is built from the ground up. Using your own data and objectives, they take longer, and they require technical expertise. But they offer precise control, full alignment with your business needs. And these are ideal when you are dealing with sensitive data, competitive workflows, highly specific customer interactions. For example, a bank may build a custom model which can be used for fraud detection, which can be tuned to their exact transaction standards and the patterns of their transactions. 12:37 Nikita: What if someone wants the best of both worlds? Yunus: We've also got a hybrid approach. In hybrid approach, we actually talk about the adaptation of AI with a strategy which is termed as hybrid strategy. Many companies today don't start by building AI from scratch. Instead, they begin with prebuilt models, like using an API, which can be already performing tasks like summarizing, translating, or answering questions using generic knowledge. This set will help you in getting up and running quickly with a small level results. As your business matures, you can start to layer in your custom data. Think internal policies, frequently asked questions, or customer interactions. And then you can fine tune the model to behave the way your business needs it to behave. Now, your AI starts producing business-ready output, smarter, more relevant, and aligned with your tone, brand, or compliance needs. 13:45 Lois: Ok…let's think of AI deployment in the hybrid approach as following a pyramid or ladder like structure. Can you take us through the different levels? Yunus: So, on the top, quick start, minimal setup, great for business automation, which can be used as a pilot use case. So, if I'm taking off the shelf APIs or platforms, they can be giving me a faster, less set of requirements, and they are basically acting like a pilot use. Later, you can add your own data or logic so you can add your data. You can fine tune or change your business logic. And this is where fine tuning and prompt engineering helps tailor the AI to your workflows and your language. And then at the end, which is at the bottom, you build your own model. It is reserved for core capabilities or competitive advantages where total control and differentiation matters in building that particular model. You don't need to go all in from one day. So, start with what is available, like, use an off shelf, API, or platform, customize as you grow. Build only when it gives you a true edge. This is what we call the best of both worlds, build and buy. 15:05 Lois: Thank you so much, Yunus, for joining us again. To learn more about the topics covered today, visit mylearn.oracle.com and search for the AI for You course. Nikita: Join us next week for another episode of the Oracle University Podcast where we discuss the Oracle AI stack and Oracle AI services. Until then, this is Nikita Abraham… Lois: And Lois Houston, signing off! 15:29 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.
The Yellow Block is back, you lucky people. Hosted by Dan, Rob and Nick. On the talkSPORT fan network Sponsored by QCS (Quince Contracting Services – Your end-to-end solution for Facilities Management, Compliance, Project Management and Installation). Hosted on Acast. See acast.com/privacy for more information.
This is the story of a dream, perhaps one of humanity's oldest and most audacious: the dream of a thinking machine. It's a tale that begins not with silicon and code, but with myths of bronze giants and legends of clay golems. We'll journey from the smoke-filled parlors of Victorian England, where the first computers were imagined, to a pivotal summer conference in 1956 where a handful of brilliant, tweed-clad optimists officially christened a new field: Artificial Intelligence. But this is no simple tale of progress. It's a story of dizzying highs and crushing lows, of a dream that was promised, then deferred, left to freeze in the long "AI Winter." We'll uncover how it survived in obscurity, fueled by niche expert systems and a quiet, stubborn belief in its potential. Then, we'll witness its spectacular rebirth, a renaissance powered by two unlikely forces: the explosion of the internet and the graphical demands of video games. This is the story of Deep Learning, of machines that could finally see, and of the revolution that followed. We'll arrive in our present moment, a strange new world where we converse daily with Large Language Models—our new, slightly unhinged, and endlessly fascinating artificial companions. This isn't just a history of technology; it's the biography of an idea, and a look at how it's finally, complicatedly, come of age. To unlock full access to all our episodes, consider becoming a premium subscriber on Apple Podcasts or Patreon. And don't forget to visit englishpluspodcast.com for even more content, including articles, in-depth studies, and our brand-new audio series and courses now available in our Patreon Shop!
Au programme :Korben a quitté les réseaux sociaux, il nous explique pourquoiPourquoi l'explosion de l'IA menace la bourse américaineL'UE ne veut pas céder face pas à Trump et aux GAFAMLe reste de l'actualitéInfos :Animé par Patrick Beja (Bluesky, Instagram, Twitter, TikTok).Co-animé par Jérôme Keinborg (Bluesky).Co-animé par Korben (site)Produit par Patrick Beja (LinkedIn) et Fanny Cohen Moreau (LinkedIn).Musique libre de droit par Daniel BejaLe Rendez-vous Tech épisode 630 – L'IA menace la bourse, mais c'est pas (si) grave ?---Liens :
Join Lois Houston and Nikita Abraham as they chat with Yunus Mohammed, a Principal Instructor at Oracle University, about the key stages of AI model development. From gathering and preparing data to selecting, training, and deploying models, learn how each phase impacts AI's real-world effectiveness. The discussion also highlights why monitoring AI performance and addressing evolving challenges are critical for long-term success. AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/252500 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. -------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Lois: Welcome to the Oracle University Podcast! I'm Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hey everyone! In our last episode, we spoke about generative AI and gen AI agents. Today, we're going to look at the key stages in a typical AI workflow. We'll also discuss how data quality, feedback loops, and business goals influence AI success. With us today is Yunus Mohammed, a Principal Instructor at Oracle University. 01:00 Lois: Hi Yunus! We're excited to have you here! Can you walk us through the various steps in developing and deploying an AI model? Yunus: The first point is the collect data. We gather relevant data, either historical or real time. Like customer transactions, support tickets, survey feedbacks, or sensor logs. A travel company, for example, can collect past booking data to predict future demand. So, data is the most crucial and the important component for building your AI models. But it's not just the data. You need to prepare the data. In the prepared data process, we clean, organize, and label the data. AI can't learn from messy spreadsheets. We try to make the data more understandable and organized, like removing duplicates, filling missing values in the data with some default values or formatting dates. All these comes under organization of the data and give a label to the data, so that the data becomes more supervised. After preparing the data, I go for selecting the model to train. So now, we pick what type of model fits your goals. It can be a traditional ML model or a deep learning network model, or it can be a generative model. The model is chosen based on the business problems and the data we have. So, we train the model using the prepared data, so it can learn the patterns of the data. Then after the model is trained, I need to evaluate the model. You check how well the model performs. Is it accurate? Is it fair? The metrics of the evaluation will vary based on the goal that you're trying to reach. If your model misclassifies emails as spam and it is doing it very much often, then it is not ready. So I need to train it further. So I need to train it to a level when it identifies the official mail as official mail and spam mail as spam mail accurately. After evaluating and making sure your model is perfectly fitting, you go for the next step, which is called the deploy model. Once we are happy, we put it into the real world, like into a CRM, or a web application, or an API. So, I can configure that with an API, which is application programming interface, or I add it to a CRM, Customer Relationship Management, or I add it to a web application that I've got. Like for example, a chatbot becomes available on your company's website, and the chatbot might be using a generative AI model. Once I have deployed the model and it is working fine, I need to keep track of this model, how it is working, and need to monitor and improve whenever needed. So I go for a stage, which is called as monitor and improve. So AI isn't set in and forget it. So over time, there are lot of changes that is happening to the data. So we monitor performance and retrain when needed. An e-commerce recommendation model needs updates as there might be trends which are shifting. So the end user finally sees the results after all the processes. A better product, or a smarter service, or a faster decision-making model, if we do this right. That is, if we process the flow perfectly, they may not even realize AI is behind it to give them the accurate results. 04:59 Nikita: Got it. So, everything in AI begins with data. But what are the different types of data used in AI development? Yunus: We work with three main types of data: structured, unstructured, and semi-structured. Structured data is like a clean set of tables in Excel or databases, which consists of rows and columns with clear and consistent data information. Unstructured is messy data, like your email or customer calls that records videos or social media posts, so they all comes under unstructured data. Semi-structured data is things like logs on XML files or JSON files. Not quite neat but not entirely messy either. So they are, they are termed semi-structured. So structured, unstructured, and then you've got the semi-structured. 05:58 Nikita: Ok… and how do the data needs vary for different AI approaches? Yunus: Machine learning often needs labeled data. Like a bank might feed past transactions labeled as fraud or not fraud to train a fraud detection model. But machine learning also includes unsupervised learning, like clustering customer spending behavior. Here, no labels are needed. In deep learning, it needs a lot of data, usually unstructured, like thousands of loan documents, call recordings, or scan checks. These are fed into the models and the neural networks to detect and complex patterns. Data science focus on insights rather than the predictions. So a data scientist at the bank might use customer relationship management exports and customer demographies to analyze which age group prefers credit cards over the loans. Then we have got generative AI that thrives on diverse, unstructured internet scalable data. Like it is getting data from books, code, images, chat logs. So these models, like ChatGPT, are trained to generate responses or mimic the styles and synthesize content. So generative AI can power a banking virtual assistant trained on chat logs and frequently asked questions to answer customer queries 24/7. 07:35 Lois: What are the challenges when dealing with data? Yunus: Data isn't just about having enough. We must also think about quality. Is it accurate and relevant? Volume. Do we have enough for the model to learn from? And is my data consisting of any kind of unfairly defined structures, like rejecting more loan applications from a certain zip code, which actually gives you a bias of data? And also the privacy. Are we handling personal data responsibly or not? Especially data which is critical or which is regulated, like the banking sector or health data of the patients. Before building anything smart, we must start smart. 08:23 Lois: So, we've established that collecting the right data is non-negotiable for success. Then comes preparing it, right? Yunus: This is arguably the most important part of any AI or data science project. Clean data leads to reliable predictions. Imagine you have a column for age, and someone accidentally entered an age of like 999. That's likely a data entry error. Or maybe a few rows have missing ages. So we either fix, remove, or impute such issues. This step ensures our model isn't misled by incorrect values. Dates are often stored in different formats. For instance, a date, can be stored as the month and the day values, or it can be stored in some places as day first and month next. We want to bring everything into a consistent, usable format. This process is called as transformation. The machine learning models can get confused if one feature, like example the income ranges from 10,000 to 100,000, and another, like the number of kids, range from 0 to 5. So we normalize or scale values to bring them to a similar range, say 0 or 1. So we actually put it as yes or no options. So models don't understand words like small, medium, or large. We convert them into numbers using encoding. One simple way is assigning 1, 2, and 3 respectively. And then you have got removing stop words like the punctuations, et cetera, and break the sentence into smaller meaningful units called as tokens. This is actually used for generative AI tasks. In deep learning, especially for Gen AI, image or audio inputs must be of uniform size and format. 10:31 Lois: And does each AI system have a different way of preparing data? Yunus: For machine learning ML, focus is on cleaning, encoding, and scaling. Deep learning needs resizing and normalization for text and images. Data science, about reshaping, aggregating, and getting it ready for insights. The generative AI needs special preparation like chunking, tokenizing large documents, or compressing images. 11:06 Oracle University's Race to Certification 2025 is your ticket to free training and certification in today's hottest tech. Whether you're starting with Artificial Intelligence, Oracle Cloud Infrastructure, Multicloud, or Oracle Data Platform, this challenge covers it all! Learn more about your chance to win prizes and see your name on the Leaderboard by visiting education.oracle.com/race-to-certification-2025. That's education.oracle.com/race-to-certification-2025. 11:50 Nikita: Welcome back! Yunus, how does a user choose the right model to solve their business problem? Yunus: Just like a business uses different dashboards for marketing versus finance, in AI, we use different model types, depending on what we are trying to solve. Like classification is choosing a category. Real-world example can be whether the email is a spam or not. Use in fraud detection, medical diagnosis, et cetera. So what you do is you classify that particular data and then accurately access that classification of data. Regression, which is used for predicting a number, like, what will be the price of a house next month? Or it can be a useful in common forecasting sales demands or on the cost. Clustering, things without labels. So real-world examples can be segmenting customers based on behavior for targeted marketing. It helps discovering hidden patterns in large data sets. Generation, that is creating new content. So AI writing product description or generating images can be a real-world example for this. And it can be used in a concept of generative AI models like ChatGPT or Dall-E, which operates on the generative AI principles. 13:16 Nikita: And how do you train a model? Yunus: We feed it with data in small chunks or batches and then compare its guesses to the correct values, adjusting its thinking like weights to improve next time, and the cycle repeats until the model gets good at making predictions. So if you're building a fraud detection system, ML may be enough. If you want to analyze medical images, you will need deep learning. If you're building a chatbot, go for a generative model like the LLM. And for all of these use cases, you need to select and train the applicable models as and when appropriate. 14:04 Lois: OK, now that the model's been trained, what else needs to happen before it can be deployed? Yunus: Evaluate the model, assess a model's accuracy, reliability, and real-world usefulness before it's put to work. That is, how often is the model right? Does it consistently perform well? Is it practical in the real world to use this model or not? Because if I have bad predictions, doesn't just look bad, it can lead to costly business mistakes. Think of recommending the wrong product to a customer or misidentifying a financial risk. So what we do here is we start with splitting the data into two parts. So we train the data by training data. And this is like teaching the model. And then we have got the testing data. This is actually used for checking how well the model has learned. So once trained, the model makes predictions. We compare the predictions to the actual answers, just like checking your answer after a quiz. We try to go in for tailored evaluation based on AI types. Like machine learning, we care about accuracy in prediction. Deep learning is about fitting complex data like voice or images, where the model repeatedly sees examples and tunes itself to reduce errors. Data science, we look for patterns and insights, such as which features will matter. In generative AI, we judge by output quality. Is it coherent, useful, and is it natural? The model improves with the accuracy and the number of epochs the training has been done on. 15:59 Nikita: So, after all that, we finally come to deploying the model… Yunus: Deploying a model means we are integrating it into our actual business system. So it can start making decisions, automating tasks, or supporting customer experiences in real time. Think of it like this. Training is teaching the model. Evaluating is testing it. And deployment is giving it a job. The model needs a home either in the cloud or inside your company's own servers. Think of it like putting the AI in place where it can be reached by other tools. Exposed via API or embedded in an app, or you can say application, this is how the AI becomes usable. Then, we have got the concept of receives live data and returns predictions. So receives live data and returns prediction is when the model listens to real-time inputs like a user typing, or user trying to search or click or making a transaction, and then instantly, your AI responds with a recommendation, decisions, or results. Deploying the model isn't the end of the story. It is just the beginning of the AI's real-world journey. Models may work well on day one, but things change. Customer behavior might shift. New products get introduced in the market. Economic conditions might evolve, like the era of COVID, where the demand shifted and the economical conditions actually changed. 17:48 Lois: Then it's about monitoring and improving the model to keep things reliable over time. Yunus: The monitor and improve loop is a continuous process that ensures an AI model remains accurate, fair, and effective after deployment. The live predictions, the model is running in real time, making decisions or recommendations. The monitor performance are those predictions still accurate and helpful. Is latency acceptable? This is where we track metrics, user feedbacks, and operational impact. Then, we go for detect issues, like accuracy is declining, are responses feeling biased, are customers dropping off due to long response times? And the next step will be to reframe or update the model. So we add fresh data, tweak the logic, or even use better architectures to deploy the uploaded model, and the new version replaces the old one and the cycle continues again. 18:58 Lois: And are there challenges during this step? Yunus: The common issues, which are related to monitor and improve consist of model drift, bias, and latency of failures. In model drift, the model becomes less accurate as the environment changes. Or bias, the model may favor or penalize certain groups unfairly. Latency or failures, if the model is too slow or fails unpredictably, it disrupts the user experience. Let's take the loan approvals. In loan approvals, if we notice an unusually high rejection rate due to model bias, we might retrain the model with more diverse or balanced data. For a chatbot, we watch for customer satisfaction, which might arise due to model failure and fine-tune the responses for the model. So in forecasting demand, if the predictions no longer match real trends, say post-pandemic, due to the model drift, we update the model with fresh data. 20:11 Nikita: Thanks for that, Yunus. Any final thoughts before we let you go? Yunus: No matter how advanced your model is, its effectiveness depends on the quality of the data you feed it. That means, the data needs to be clean, structured, and relevant. It should map itself to the problem you're solving. If the foundation is weak, the results will be also. So data preparation is not just a technical step, it is a business critical stage. Once deployed, AI systems must be monitored continuously, and you need to watch for drops in performance for any bias being generated or outdated logic, and improve the model with new data or refinements. That's what makes AI reliable, ethical, and sustainable in the long run. 21:09 Nikita: Yunus, thank you for this really insightful session. If you're interested in learning more about the topics we discussed today, go to mylearn.oracle.com and search for the AI for You course. Lois: That's right. You'll find skill checks to help you assess your understanding of these concepts. In our next episode, we'll discuss the idea of buy versus build in the context of AI. Until then, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 21:39 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.
Daniel and Chris sit with Citadel AI's Rick Kobayashi and Kenny Song and unpack AI safety and security challenges in the generative AI era. They compare Japan's approach to AI adoption with the US's, and explore the implications of real-world failures in AI systems, along with strategies for AI monitoring and evaluation.Featuring:Rick Kobayashi – LinkedInKenny Song – LinkedInChris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks:Citadel AIRegister for upcoming webinars here!
Au programme :La légitimité de Luc Julia remise en question dans le cadre d'un debunkDécès de Jean Permanove en live sur KickGPT-5 et GPT-OSSRevue des innovations en matière d'intelligence artificielleNouveaux appareils Google Pixel et le reste du matosToujours plus d'enjeux de sécuritéInfos :Animé par Guillaume Vendé (Bluesky, Mastodon, Threads, Instagram, TikTok, YouTube, techcafe.fr)Co-animé par Mat (profduweb.com, Apple Différemment, Threads, Instagram, YouTube)Produit par Patrick Beja (LinkedIn) et Fanny Cohen Moreau (LinkedIn).Musique libre de droit par Daniel BejaLe Rendez-vous Tech épisode 629 – Drames et dramas d'août---Liens :
We are told by people working in the field, researchers and those who publish academic papers on the topic that artificial intelligence or deep learning or LLMs or Machine Learning or Recurrent Neural Networks - call them what you like - employ some form of inductive reasoning. But do they? What is inductive reasoning? What is deductive or adductive for that matter? Is "new physics" or other new science being discovered by the most recent and best chatbots or other "artificially intelligent" computer systems? My response to all that is contained herein. For images see: https://youtu.be/9Dimv7mOls4 For more information: https://www.bretthall.org/blog/induction
In this episode, We talked with Pastor, a medical doctor who built a career in machine learning while studying medicine. Pastor shares how he balanced both fields, leveraged live courses and public sharing to grow his skills, and found opportunities through freelancing and mentoring.TIMECODES00:00 Pastor's background and early programming journey06:05 Learning new tools and skills on the job while studying medicine11:44 Balancing medical studies with data science work and motivation13:48 Applying medical knowledge to data science and vice versa18:44 Starting freelance work on Upwork and overcoming language challenges24:03 Joining the machine learning engineering course and benefits of live cohorts27:41 Engaging with the course community and sharing progress publicly35:16 Using LinkedIn and social media for career growth and interview opportunities41:03 Building reputation, structuring learning, and leveraging course projects50:53 Volunteering and mentoring with DeepLearning.AI and Stanford Coding Place57:00 Managing time and staying productive while studying medicine and machine learningConnect with PastorTwitter - https://x.com/PastorSotoB1Linkedin - / pastorsoto Github - https://github.com/sotoblancoWebsite - https://substack.com/@pastorsotoConnect with DataTalks.Club:Join the community - https://datatalks.club/slack.htmlSubscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/...Check other upcoming events - https://lu.ma/dtc-eventsGitHub: https://github.com/DataTalksClubLinkedIn - / datatalks-club Twitter - / datatalksclub Website - https://datatalks.club/
No Priors: Artificial Intelligence | Machine Learning | Technology | Startups
Andrew Ng has always been at the bleeding edge of fast-evolving AI technologies, founding companies and projects like Google Brain, AI Fund, and DeepLearning.AI. So he knows better than anyone that founders who operate the same way in 2025 as they did in 2022 are doing it wrong. Sarah Guo and Elad Gil sit down with Andrew Ng, the godfather of the AI revolution, to discuss the rise of agentic AI, and how the technology has changed everything from what makes a successful founder to the value of small teams. They talk about where future capability growth may come from, the potential for models to bootstrap themselves, and why Andrew doesn't like the term “vibe coding.” Also, Andrew makes the case for why everybody in an organization—not just the engineers—should learn to code. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @AndrewYNg Chapters: 00:00 – Andrew Ng Introduction 00:32 – The Next Frontier for Capability Growth 01:29 – Andrew's Definition of Agentic AI 02:44 – Obstacles to Building True Agents 06:09 – The Bleeding Edge of Agentic AI 08:12 – Will Models Bootstrap Themselves? 09:05 – Vibe Coding vs. AI Assisted Coding 09:56 – Is Vibe Coding Changing the Nature of Startups? 11:35 – Speeding Up Project Management 12:55 – The Evolution of the Successful Founder Profile 19:23 – Finding Great Product People 21:14 – Building for One User Profile vs. Many 22:47 – Requisites for Leaders and Teams in the AI Age 28:21 – The Value of Keeping Teams Small 32:13 – The Next Industry Transformations 34:04 – Future of Automation in Investing Firms and Incubators 37:39 – Technical People as First Time Founders 41:08– Broad Impact of AI Over the Next 5 Years 41:49 – Conclusion
Dan and Chris break down Winning the Race: America's AI Action Plan, issued by the White House in July 2025. Structured as three "pillars" — Accelerate AI Innovation, Build American AI Infrastructure, and Lead in International AI Diplomacy and Security — our dynamic duo unpack the plan's policy goals and its associated suggestions — while also exploring the mixed reactions it's sparked across political lines. They connect the plan to international AI diplomacy and national security interests, discuss its implications for practitioners, and consider how political realities could shape its success in the years ahead. Featuring:Chris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks:Press Release: White House Unveils America's AI Action PlanPaper: America's AI Action PlanSponsors:Shopify – The commerce platform trusted by millions. From idea to checkout, Shopify gives you everything you need to launch and scale your business—no matter your level of experience. Build beautiful storefronts, market with built-in AI tools, and tap into the platform powering 10% of all U.S. eCommerce. Start your one-dollar trial at shopify.com/practicalaiRegister for upcoming webinars here!
Au programme :Comme le mois dernier je vous propose un épisode "crossover" pour découvrir mon autre podcast, le RDV Jeux.Cette fois-ci c'est l'épisode 393, où on décortique l'annonce de la Switch 2 et la possible scission de Ubisoft avec son partenaire Tencent, en plus des jeux du moment bien sûr.Plus d'infos : https://frenchspin.fr/2025/04/switch-2-nintendo-veut-votre-argent-combien-rdv-jeux/Infos :Animé par Patrick Beja (Bluesky, Instagram, Twitter, TikTok)Produit par Patrick Beja (LinkedIn) et Fanny Cohen Moreau (LinkedIn).Musique libre de droit par Daniel Beja---Liens :
In this episode, Lois Houston and Nikita Abraham continue their discussion on AI fundamentals, diving into Data Science with Principal AI/ML Instructor Himanshu Raj. They explore key concepts like data collection, cleaning, and analysis, and talk about how quality data drives impactful insights. AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/252500 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ---------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Lois: Hello and welcome to the Oracle University Podcast. I'm Lois Houston, Director of Innovation Programs with Oracle University, and with me today is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hi everyone! Last week, we began our exploration of core AI concepts, specifically machine learning and deep learning. I'd really encourage you to go back and listen to the episode if you missed it. 00:52 Lois: Yeah, today we're continuing that discussion, focusing on data science, with our Principal AI/ML Instructor Himanshu Raj. Nikita: Hi Himanshu! Thanks for joining us again. So, let's get cracking! What is data science? 01:06 Himanshu: It's about collecting, organizing, analyzing, and interpreting data to uncover valuable insights that help us make better business decisions. Think of data science as the engine that transforms raw information into strategic action. You can think of a data scientist as a detective. They gather clues, which is our data. Connect the dots between those clues and ultimately solve mysteries, meaning they find hidden patterns that can drive value. 01:33 Nikita: Ok, and how does this happen exactly? Himanshu: Just like a detective relies on both instincts and evidence, data science blends domain expertise and analytical techniques. First, we collect raw data. Then we prepare and clean it because messy data leads to messy conclusions. Next, we analyze to find meaningful patterns in that data. And finally, we turn those patterns into actionable insights that businesses can trust. 02:00 Lois: So what you're saying is, data science is not just about technology; it's about turning information into intelligence that organizations can act on. Can you walk us through the typical steps a data scientist follows in a real-world project? Himanshu: So it all begins with business understanding. Identifying the real problem we are trying to solve. It's not about collecting data blindly. It's about asking the right business questions first. And once we know the problem, we move to data collection, which is gathering the relevant data from available sources, whether internal or external. Next one is data cleaning. Probably the least glamorous but one of the most important steps. And this is where we fix missing values, remove errors, and ensure that the data is usable. Then we perform data analysis or what we call exploratory data analysis. Here we look for patterns, prints, and initial signals hidden inside the data. After that comes the modeling and evaluation, where we apply machine learning or deep learning techniques to predict, classify, or forecast outcomes. Machine learning, deep learning are like specialized equipment in a data science detective's toolkit. Powerful but not the whole investigation. We also check how good the models are in terms of accuracy, relevance, and business usefulness. Finally, if the model meets expectations, we move to deployment and monitoring, putting the model into real world use and continuously watching how it performs over time. 03:34 Nikita: So, it's a linear process? Himanshu: It's not linear. That's because in real world data science projects, the process does not stop after deployment. Once the model is live, business needs may evolve, new data may become available, or unexpected patterns may emerge. And that's why we come back to business understanding again, defining the questions, the strategy, and sometimes even the goals based on what we have learned. In a way, a good data science project behaves like living in a system which grows, adapts, and improves over time. Continuous improvement keeps it aligned with business value. Now, think of it like adjusting your GPS while driving. The route you plan initially might change as new traffic data comes in. Similarly, in data science, new information constantly help refine our course. The quality of our data determines the quality of our results. If the data we feed into our models is messy, inaccurate, or incomplete, the outputs, no matter how sophisticated the technology, will be also unreliable. And this concept is often called garbage in, garbage out. Bad input leads to bad output. Now, think of it like cooking. Even the world's best Michelin star chef can't create a masterpiece with spoiled or poor-quality ingredients. In the same way, even the most advanced AI models can't perform well if the data they are trained on is flawed. 05:05 Lois: Yeah, that's why high-quality data is not just nice to have, it's absolutely essential. But Himanshu, what makes data good? Himanshu: Good data has a few essential qualities. The first one is complete. Make sure we aren't missing any critical field. For example, every customer record must have a phone number and an email. It should be accurate. The data should reflect reality. If a customer's address has changed, it must be updated, not outdated. Third, it should be consistent. Similar data must follow the same format. Imagine if the dates are written differently, like 2024/04/28 versus April 28, 2024. We must standardize them. Fourth one. Good data should be relevant. We collect only the data that actually helps solve our business question, not unnecessary noise. And last one, it should be timely. So data should be up to date. Using last year's purchase data for a real time recommendation engine wouldn't be helpful. 06:13 Nikita: Ok, so ideally, we should use good data. But that's a bit difficult in reality, right? Because what comes to us is often pretty messy. So, how do we convert bad data into good data? I'm sure there are processes we use to do this. Himanshu: First one is cleaning. So this is about correcting simple mistakes, like fixing typos in city names or standardizing dates. The second one is imputation. So if some values are missing, we fill them intelligently, for instance, using the average income for a missing salary field. Third one is filtering. In this, we remove irrelevant or noisy records, like discarding fake email signups from marketing data. The fourth one is enriching. We can even enhance our data by adding trusted external sources, like appending credit scores from a verified bureau. And the last one is transformation. Here, we finally reshape data formats to be consistent, for example, converting all units to the same currency. So even messy data can become usable, but it takes deliberate effort, structured process, and attention to quality at every step. 07:26 Oracle University's Race to Certification 2025 is your ticket to free training and certification in today's hottest technology. Whether you're starting with Artificial Intelligence, Oracle Cloud Infrastructure, Multicloud, or Oracle Data Platform, this challenge covers it all! Learn more about your chance to win prizes and see your name on the Leaderboard by visiting education.oracle.com/race-to-certification-2025. That's education.oracle.com/race-to-certification-2025. 08:10 Nikita: Welcome back! Himanshu, we spoke about how to clean data. Now, once we get high-quality data, how do we analyze it? Himanshu: In data science, there are four primary types of analysis we typically apply depending on the business goal we are trying to achieve. The first one is descriptive analysis. It helps summarize and report what has happened. So often using averages, totals, or percentages. For example, retailers use descriptive analysis to understand things like what was the average customer spend last quarter? How did store foot traffic trend across months? The second one is diagnostic analysis. Diagnostic analysis digs deeper into why something happened. For example, hospitals use this type of analysis to find out, for example, why a certain department has higher patient readmission rates. Was it due to staffing, post-treatment care, or patient demographics? The third one is predictive analysis. Predictive analysis looks forward, trying to forecast future outcomes based on historical patterns. For example, energy companies predict future electricity demand, so they can better manage resources and avoid shortages. And the last one is prescriptive analysis. So it does not just predict. It recommends specific actions to take. So logistics and supply chain companies use prescriptive analytics to suggest the most efficient delivery routes or warehouse stocking strategies based on traffic patterns, order volume, and delivery deadlines. 09:42 Lois: So really, we're using data science to solve everyday problems. Can you walk us through some practical examples of how it's being applied? Himanshu: The first one is predictive maintenance. It is done in manufacturing a lot. A factory collects real time sensor data from machines. Data scientists first clean and organize this massive data stream, explore patterns of past failures, and design predictive models. The goal is not just to predict breakdowns but to optimize maintenance schedules, reducing downtime and saving millions. The second one is a recommendation system. It's prevalent in retail and entertainment industries. Companies like Netflix or Amazon gather massive user interaction data such as views, purchases, likes. Data scientists structure and analyze this behavioral data to find meaningful patterns of preferences and build models that suggest relevant content, eventually driving more engagement and loyalty. The third one is fraud detection. It's applied in finance and banking sector. Banks store vast amounts of transaction record records. Data scientists clean and prepare this data, understand typical spending behaviors, and then use statistical techniques and machine learning to spot unusual patterns, catching fraud faster than manual checks could ever achieve. The last one is customer segmentation, which is often applied in marketing. Businesses collect demographics and behavioral data about their customers. Instead of treating all the customers same, data scientists use clustering techniques to find natural groupings, and this insight helps businesses tailor their marketing efforts, offers, and communication for each of those individual groups, making them far more effective. Across all these examples, notice that data science isn't just building a model. Again, it's understanding the business need, reviewing the data, analyzing it thoughtfully, and building the right solution while helping the business act smarter. 11:44 Lois: Thank you, Himanshu, for joining us on this episode of the Oracle University Podcast. We can't wait to have you back next week for part 3 of this conversation on core AI concepts, where we'll talk about generative AI and gen AI agents. Nikita: And if you want to learn more about data science, visit mylearn.oracle.com and search for the AI for You course. Until next time, this is Nikita Abraham… Lois: And Lois Houston signing off! 12:13 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.
Today's clip is from episode 138 of the podcast, with Mélodie Monod, François-Xavier Briol and Yingzhen Li.During this live show at Imperial College London, Alex and his guests delve into the complexities and advancements in Bayesian deep learning, focusing on uncertainty quantification, the integration of machine learning tools, and the challenges faced in simulation-based inference.The speakers discuss their current projects, the evolution of Bayesian models, and the need for better computational tools in the field.Get the full discussion here.Attend Alex's tutorial at PyData Berlin: A Beginner's Guide to State Space Modeling Intro to Bayes Course (first 2 lessons free)Advanced Regression Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)TranscriptThis is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.
Allegra Guinan of Lumiera helps leaders turn uncertainty about AI into confident, strategic leadership. In this conversation, she brings some actionable insights for navigating the hype and complexity of AI. The discussion covers challenges with implementing responsible AI practices, the growing importance of user experience and product thinking, and how leaders can focus on real-world business problems over abstract experimentation.Featuring:Allegra Guinan – LinkedInChris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks:LumieraSponsors:Shopify – The commerce platform trusted by millions. From idea to checkout, Shopify gives you everything you need to launch and scale your business—no matter your level of experience. Build beautiful storefronts, market with built-in AI tools, and tap into the platform powering 10% of all U.S. eCommerce. Start your one-dollar trial at shopify.com/practicalaiRegister for upcoming webinars here!
Au programme :Alexandre nous parle de la manière dont la tech et l'IA a fait évoluer son métier de radiologue depuis presque 20 ans.Infos :Animé par Patrick Beja (Bluesky, Instagram, Twitter, TikTok)Co-animé par AlexandreProduit par Patrick Beja (LinkedIn) et Fanny Cohen Moreau (LinkedIn).Musique libre de droit par Daniel BejaLe Rendez-vous Tech épisode 628 – Spécial : Tech et IA dans l'imagerie médicale---Liens :
Dan sits down with guests Mark Daniel Ward and Katie Sanders from The Data Mine at Purdue University to explore how higher education is evolving to meet the demands of the AI-driven workforce. They share how their program blends interdisciplinary learning, corporate partnerships, and real-world data science projects to better prepare students across 160+ majors. From AI chatbots to agricultural forecasting, they discuss the power of living-learning communities, how the data mine model is spreading to other institutions and what it reveals about the future of education, workforce development, and applied AI training.Featuring:Mark Daniel Ward – LinkedInKatie Sanders – LinkedInDaniel Whitenack – Website, GitHub, XLinks:The Data MineSponsors:Shopify – The commerce platform trusted by millions. From idea to checkout, Shopify gives you everything you need to launch and scale your business—no matter your level of experience. Build beautiful storefronts, market with built-in AI tools, and tap into the platform powering 10% of all U.S. eCommerce. Start your one-dollar trial at shopify.com/practicalaiRegister for upcoming webinars here!
On this episode, host Paul W. Grimm speaks with Professor Maura R. Grossman about the fundamentals of artificial intelligence and its growing influence on the legal system. They explore what AI is (and isn't), how machine learning and natural language processing work, and the differences between traditional automation and modern generative AI. In layman's terms, they discuss other key concepts, such as supervised and unsupervised learning, reinforcement training, and deepfakes, and other advances that have accelerated AI's development. Finally, they address a few potential risks of generative AI, including hallucinations, bias, and misuse in court, which sets the stage for a deeper conversation about legal implications on the next episode, "To Trust or Not to Trust: AI in Legal Practice." ABOUT THE HOSTJudge Paul W. Grimm (ret.) is the David F. Levi Professor of the Practice of Law and Director of the Bolch Judicial Institute at Duke Law School. From December 2012 until his retirement in December 2022, he served as a district judge of the United States District Court for the District of Maryland, with chambers in Greenbelt, Maryland. Click here to read his full bio.
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Intro to Bayes Course (first 2 lessons free)Advanced Regression Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Takeaways:Bayesian deep learning is a growing field with many challenges.Current research focuses on applying Bayesian methods to neural networks.Diffusion methods are emerging as a new approach for uncertainty quantification.The integration of machine learning tools into Bayesian models is a key area of research.The complexity of Bayesian neural networks poses significant computational challenges.Future research will focus on improving methods for uncertainty quantification. Generalized Bayesian inference offers a more robust approach to uncertainty.Uncertainty quantification is crucial in fields like medicine and epidemiology.Detecting out-of-distribution examples is essential for model reliability.Exploration-exploitation trade-off is vital in reinforcement learning.Marginal likelihood can be misleading for model selection.The integration of Bayesian methods in LLMs presents unique challenges.Chapters:00:00 Introduction to Bayesian Deep Learning03:12 Panelist Introductions and Backgrounds10:37 Current Research and Challenges in Bayesian Deep Learning18:04 Contrasting Approaches: Bayesian vs. Machine Learning26:09 Tools and Techniques for Bayesian Deep Learning31:18 Innovative Methods in Uncertainty Quantification36:23 Generalized Bayesian Inference and Its Implications41:38 Robust Bayesian Inference and Gaussian Processes44:24 Software Development in Bayesian Statistics46:51 Understanding Uncertainty in Language Models50:03 Hallucinations in Language Models53:48 Bayesian Neural Networks vs Traditional Neural Networks58:00 Challenges with Likelihood Assumptions01:01:22 Practical Applications of Uncertainty Quantification01:04:33 Meta Decision-Making with Uncertainty01:06:50 Exploring Bayesian Priors in Neural Networks01:09:17 Model Complexity and Data Signal01:12:10 Marginal Likelihood and Model Selection01:15:03 Implementing Bayesian Methods in LLMs01:19:21 Out-of-Distribution Detection in LLMsThank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer,...
Многие знают, что когда модели обучаются, где-то под капотом перемножаются матрицы и тензоры, и все это связано с дифференцированием. Мы с Денисом Степановым взялись за нелегкую задачу – разобраться, что же именно там происходит! Также ждем вас, ваши лайки, репосты и комменты в мессенджерах и соцсетях! Telegram-чат: https://t.me/podlodka Telegram-канал: https://t.me/podlodkanews Страница в Facebook: www.facebook.com/podlodkacast/ Twitter-аккаунт: https://twitter.com/PodcastPodlodka Ведущие в выпуске: Женя Кателла, Аня Симонова Полезные ссылки: Dive into Deep Learning Aston Zhang, Zachary C. Lipton, Mu Li, Alexander J. Smola (online book with code and formulas) https://d2l.ai/ https://www.amazon.com/s/ref=dp_byline_sr_book_2?ie=UTF8&field-author=Zachary+C.+Lipton&text=Zachary+C.+Lipton&sort=relevancerank&search-alias=books Micrograd by Andrej Karpathy https://github.com/karpathy/micrograd Andrej Karpathy builds GPT from scratch https://www.youtube.com/watch?v=kCc8FmEb1nY Scott Aaronson on LLM Watermarking https://www.youtube.com/watch?v=YzuVet3YkkA Annotated history of Modern AI and Deep Learning by Jurgen Schmidhuber https://people.idsia.ch/~juergen/deep-learning-history.html Probabilistic Machine Learning: An Introduction Kevin Patrick Murphy https://probml.github.io/pml-book/book1.html Probabilistic Machine Learning: Advanced Topics Kevin Patrick Murphy https://probml.github.io/pml-book/book2.html Pattern Recognition and Machine Learning Christopher Bishop https://www.microsoft.com/en-us/research/wp-content/uploads/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf Deep Learning: Foundations and Concepts Christopher Bishop, Hugh Bishop https://www.bishopbook.com/ Deep Learning Ian Goodfellow, Yoshua Bengio, Aaron Courville https://www.deeplearningbook.org/ Глубокое обучение: Погружение в мир нейронных сетей С. Николенко, А. Кадурин, Е. Архангельская https://www.k0d.cc/storage/books/AI,%20Neural%20Networks/%D0%93%D0%BB%D1%83%D0%B1%D0%BE%D0%BA%D0%BE%D0%B5%20%D0%BE%D0%B1%D1%83%D1%87%D0%B5%D0%BD%D0%B8%D0%B5%20(%D0%9D%D0%B8%D0%BA%D0%BE%D0%BB%D0%B5%D0%BD%D0%BA%D0%BE).pdf Gonzo-обзоры ML статей Григорий Сапунов, Алексей Тихонов https://t.me/gonzo_ML Machine Learning Street Talk podcast https://www.youtube.com/c/machinelearningstreettalk Feedforward NNs, Autograd, Backprop (Datalore report, Denis Stepanov) https://datalore.jetbrains.com/report/static/Ht_isxs4iB2.BNIqv-C3WUp/pEpNv2eMVU9tEkPsaboR9y Softmax Regression, Adversarial Attacks (Datalore report, Denis Stepanov) https://datalore.jetbrains.com/report/static/Ht_isxs4iB2.BNIqv-C3WUp/cIvd6zX1B5I3kULNiVCEyy Dual Numbers, PINN (Datalore report, Denis Stepanov) https://datalore.jetbrains.com/report/static/Ht_isxs4iB2.BNIqv-C3WUp/3oa1BNrPGpQ8uc82tCaz5d
Au programme :Dans cet épisode hors-série je vous propose une plongée dans les archives du RDV Tech on va remonter en octobre 2011 au moment de la mort de Steve Jobs. On en parlait dans le RDV Tech 71 avec au micro Guillaume Main, Olivier Frigara et Cédric Ingrand, une longue discussion de 45 minutes très intéressante, vous verrez que la qualité de certains invités n'est pas optimale mais c'est largement écoutable. Infos :Animé par Patrick Beja (Bluesky, Instagram, Twitter, TikTok)Produit par Patrick Beja (LinkedIn) et Fanny Cohen Moreau (LinkedIn).Musique libre de droit par Daniel Beja---Liens :
We unpack how AI is reshaping hiring decisions, shifting job roles, and creating new expectations for professionals — from engineers to marketers. They explore the rise of AI-assisted teams, the growing compensation bubble, why continuous learning is now table stakes, and how some service providers are quietly riding the AI wave.Featuring:Chris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XSponsors:Outshift by Cisco: AGNTCY is an open source collective building the Internet of Agents. It's a collaboration layer where AI agents can communicate, discover each other, and work across frameworks. For developers, this means standardized agent discovery tools, seamless protocols for inter-agent communication, and modular components to compose and scale multi-agent workflows."Register for upcoming webinars here!
Au programme :OpenAI, Perplexity et même Proton : ils profitent de l'été pour continuer à lancer toujours plus de solutions par intelligence artificielleVibe coding et no code : des possibilités toujours plus grandes de faire du développer sans saisir de ligne de code… pour toujours plus de risques ?Réflexion de l'été : les smartphones sont-ils de parasites ?Infos :Animé par Guillaume Vendé (Bluesky, Mastodon, Threads, Instagram, TikTok, YouTube, techcafe.fr)Co-animé par Benoît Curdy (X, Niptech)Co-animé par Baptiste Freydt (X, Niptech)Produit par Patrick Beja (LinkedIn) et Fanny Cohen Moreau (LinkedIn).Musique libre de droit par Daniel BejaLe Rendez-vous Tech épisode 627 – Les agents IA ne prennent pas de vacances---Liens :
Unser heutiger Gast wurde in Dresden geboren, studierte Computerlinguistik in Leipzig und Saarbrücken und promovierte später an der Stanford University – betreut von keinem Geringeren als Andrew Ng und Chris Manning. Seine Dissertation wurde als beste Informatik-Promotion ausgezeichnet. Nach Stationen bei Microsoft und Siemens gründete er sein erstes Unternehmen: MetaMind, ein Deep-Learning-Startup, das 2016 von Salesforce übernommen wurde. Dort war er anschließend Chief Scientist, leitete große Forschungsteams und trieb die KI-Strategie des Konzerns maßgeblich voran. Heute ist er Gründer und CEO von you.com, einer KI-basierten Suchmaschine, die als datenschutzfreundliche, transparente und anpassbare Alternative zu klassischen Anbietern auftritt, mit einem starken Fokus auf Nutzendenkontrolle und verantwortungsvoller KI. Zudem investiert er über seinen Fonds AI+X in KI-Startups weltweit. Seine wissenschaftlichen Arbeiten zählen zu den meistzitierten im Bereich NLP und Deep Learning, über 170.000 Mal, und viele seiner Ideen haben die Entwicklung heutiger Sprachmodelle mitgeprägt. Ein herzliches Dankeschön an Adrian Locher, CEO und Gründer von Merantix, für die Vermittlung dieses Gesprächs. Seit über acht Jahren beschäftigen wir uns in diesem Podcast mit der Frage, wie Arbeit den Menschen stärkt, statt ihn zu schwächen. In 500 Gesprächen mit über 600 Menschen haben wir darüber gesprochen, was sich für sie geändert hat, und was sich noch ändern muss. Wie können wir verhindern, dass KI-Systeme nur effizienter, aber nicht gerechter werden und worauf kommt es bei der Gestaltung wirklich an? Welche Rolle spielt Transparenz, wenn es um Vertrauen in KI geht, besonders in sensiblen Anwendungen wie Suche, Bildung oder Arbeit? Und was braucht es, um KI so zu entwickeln, dass sie unsere Fähigkeiten erweitert, statt sie zu ersetzen? Fest steht: Für die Lösung unserer aktuellen Herausforderungen brauchen wir neue Impulse. Daher suchen wir weiter nach Methoden, Vorbildern, Erfahrungen, Tools und Ideen, die uns dem Kern von New Work näherbringen. Darüber hinaus beschäftigt uns von Anfang an die Frage, ob wirklich alle Menschen das finden und leben können, was sie im Innersten wirklich, wirklich wollen. Ihr seid bei On the Way to New Work – heute mit Richard Socher. [Hier](https://linktr.ee/onthewaytonewwork) findet ihr alle Links zum Podcast und unseren aktuellen Werbepartnern
In this episode, Chris sits down with Igor Nikitin, CEO and co-founder of Nice Technologies, to explore how AI and modern engineering practices are transforming the actuarial field and setting the stage for the future of actuarial modeling. We discuss the introduction of programming into insurance pricing workflows, and how their Python-based calc engine, AI copilots, and DevOps-inspired workflows are enabling actuaries to collaborate more effectively across teams while accelerating innovation. Featuring:Igor Nikitin – LinkedInChris Benson – Website, LinkedIn, Bluesky, GitHub, XLinks:Nice TechnologiesSponsors:Shopify – The commerce platform trusted by millions. From idea to checkout, Shopify gives you everything you need to launch and scale your business—no matter your level of experience. Build beautiful storefronts, market with built-in AI tools, and tap into the platform powering 10% of all U.S. eCommerce. Start your one-dollar trial at shopify.com/practicalai
Dave, Esmee, and Rob take a moment to look back on the wild ride that was Season 4—revisiting the themes that sparked the biggest conversations and the guests who left a lasting impression. They also reveal what's on their summer to-do lists and drop a few juicy hints about what's coming in Season 5. Get ready—it's going to be even bigger and bolder.Thank you to all our listeners and guests for joining us in Season 4 - have a great summer and we will see you in September!TLDR:00:40 Season 4 by the numbers – and a fun mix-up with round figures03:20 Reflecting on standout topics and memorable guests03:42 Scaling AI: Hyperscaler narratives, tech momentum, and the adoption gap13:18 Ethics in the AI era – how organizations can and must stay grounded18:12 The human factor: Why “human-in-the-loop” matters more than ever27:29 Sovereignty in tech – geopolitics, shifting narratives, and the rise of Sovereign AI37:16 A deep dive into Telco – highlights from our dedicated mini-series53:48 2025 tech trends with Gene Kim55:33 Listener Q&A: Daniel Delicate on Cynefin vs. IT operating models1:01:44 Andrea Kis on keeping humanity in fast-paced tech1:06:09 Ezhil Suresh on how we prep and record our podcast with top-tier guests1:11:38 John Eaton-Griffin on how guests have shaped our thinking1:17:19 A word from our co-host1:19:57 Looking ahead to Season 5: AAA episodes, new industry mini-series, and Hyperscaler events1:22:09 Meet our new AI companions: Substack and the Cloud Realities chatbot1:23:40 What's next for us this summerHostsDave Chapman: https://www.linkedin.com/in/chapmandr/Esmee van de Giessen: https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan: https://www.linkedin.com/in/rob-kernahan/ProductionMarcel van der Burg: https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman: https://www.linkedin.com/in/chapmandr/SoundBen Corbett: https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett: https://www.linkedin.com/in/louis-corbett-087250264/'Cloud Realities' is an original podcast from Capgemini
Au programme :Cette semaine je vous propose un épisode "crossover" pour découvrir mon autre podcast, le RDV Jeux.C'est l'épisode 370, où on traite de la stratégie de Microsoft / Xbox, de l'équation impossible entre gros jeux gamers et jeux mobiles, et de nos jeux du moment. Enjoy!Plus d'infos : https://frenchspin.fr/2024/10/microsoft-na-quune-solution-rdv-jeux/Infos :Animé par Patrick Beja (Bluesky, Instagram, Twitter, TikTok)Produit par Patrick Beja (LinkedIn) et Fanny Cohen Moreau (LinkedIn).Musique libre de droit par Daniel Beja---Liens :
The rise of structure software fueled globalization by streamlining operations across borders. Now, Cloud and AI are accelerating this momentum, enabling faster innovation, smarter decision-making, and scalable growth. By modernizing ERP with intelligent technologies, organizations can stay agile, competitive, and ready for the next wave of global transformation.This week, Dave, Esmee and Rob talk to Timo Elliott, Innovation Evangelist at SAP, to explore how SAP is driving globalization—and how organizations can accelerate innovation through the power of Cloud and AI. TLDR00:55 Introduction of Timo Elliott02:40 Rob shares his confusion about misleading online ads08:06 In-depth conversation with Timo46:32 Rethinking control in enterprise systems1:00:00 Brunch at a Paris café or joining an event?GuestTimo Elliott: https://www.linkedin.com/in/timoelliott/HostsDave Chapman: https://www.linkedin.com/in/chapmandr/Esmee van de Giessen: https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan: https://www.linkedin.com/in/rob-kernahan/ProductionMarcel van der Burg: https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman: https://www.linkedin.com/in/chapmandr/SoundBen Corbett: https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett: https://www.linkedin.com/in/louis-corbett-087250264/'Cloud Realities' is an original podcast from Capgemini
In this episode of Practical AI, Chris and Daniel explore the fascinating world of agentic AI for drone and robotic swarms, which is Chris's passion and professional focus. They unpack how autonomous vehicles (UxV), drones (UaV), and other autonomous multi-agent systems can collaborate without centralized control while exhibiting complex emergent behavior with agency and self-governance to accomplish a mission or shared goals. Chris and Dan delve into the role of AI real-time inference and edge computing to enable complex agentic multi-model autonomy, especially in challenging environments like disaster zones and remote industrial operations.Featuring:Chris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks:ROS - Robotic Operating SystemGazeboHugging Face Agents CourseSwarm Robotics | WikipediaChris's definition of Swarming:Swarming occurs when numerous independent fully-autonomous multi-agentic platforms exhibit highly-coordinated locomotive and emergent behaviors with agency and self-governance in any domain (air, ground, sea, undersea, space), functioning as a single independent logical distributed decentralized decisioning entity for purposes of C3 (command, control, communications) with human operators on-the-loop, to implement actions that achieve strategic, tactical, or operational effects in the furtherance of a mission.© 2025 Chris BensonSponsors:Outshift by Cisco: AGNTCY is an open source collective building the Internet of Agents. It's a collaboration layer where AI agents can communicate, discover each other, and work across frameworks. For developers, this means standardized agent discovery tools, seamless protocols for inter-agent communication, and modular components to compose and scale multi-agent workflows.
Au programme :Le parcours de Jeff, de la France et l'informatique aux États-Unis et l'investissementInfos :Animé par Patrick Beja (Bluesky, Instagram, Twitter, TikTok)Co-animé par Jeff ClavierProduit par Patrick Beja (LinkedIn) et Fanny Cohen Moreau (LinkedIn).Musique libre de droit par Daniel BejaLe Rendez-vous Tech épisode626 - Spécial : Portrait de Jeff Clavier---Liens :
Are we on the brink of advertising becoming too smart for its own good, or is Deep Learning finally getting us closer to what customers actually want? Agility requires us to constantly evaluate how technology like AI reshapes the relationships between brands and consumers—sometimes for better, sometimes for far more complex. The advertising landscape is shifting under our feet, with new rules, new tech, and frankly, a lot of new guesswork.Today we're going to talk about how Deep Learning and AI are impacting advertising effectiveness, personalization, and the future of advertising—with or without cookies. To help me discuss this topic, I'd like to welcome Jaysen Gillespie, VP, Global Head of Analytics and Product Marketing at RTB House. About Jaysen Gillespie Jaysen is a Southern California analytics pro with 15+ years in tech leadership. Currently holding the position of VP, Global Head of Product Marketing and Analytics at RTB House, he turns data into insights that drive relevant decisions. He is an experienced speaker and content creator, simplifying complex ideas and making them easily consumable and applicable. For Jaysen, analytics isn't just interesting—it's essential. Resources RTB House: https://www.rtbhouse.com https://www.rtbhouse.com The Agile Brand podcast is brought to you by TEKsystems. Learn more here: https://www.teksystems.com/versionnextnow Catch the future of e-commerce at eTail Boston, August 11-14, 2025. Register now: https://bit.ly/etailboston and use code PARTNER20 for 20% off for retailers and brandsDon't Miss MAICON 2025, October 14-16 in Cleveland - the event bringing together the brights minds and leading voices in AI. Use Code AGILE150 for $150 off registration. Go here to register: https://bit.ly/agile150" Connect with Greg on LinkedIn: https://www.linkedin.com/in/gregkihlstromDon't miss a thing: get the latest episodes, sign up for our newsletter and more: https://www.theagilebrand.showCheck out The Agile Brand Guide website with articles, insights, and Martechipedia, the wiki for marketing technology: https://www.agilebrandguide.com The Agile Brand is produced by Missing Link—a Latina-owned strategy-driven, creatively fueled production co-op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. https://www.missinglink.company
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