Podcasts about Natural language

Amanda Cupido doesn't speak Spanish or French. But using AI, she and her team helped a global nonprofit make their internal podcast more accessible to as many employees as possible. Amanda is an audio producer and the founder of a production company called Lead Podcasting. One of her clients is a global nonprofit with over 35,000 employees—and not all of them speak English. So she made them a pitch: what if they added AI into the mix? They would make the podcast in English, and then use generative AI voice tools to translate it into Spanish and French—with a lot of human oversight, of course. Driven by a desire to use these tools for good, the goal was never to replace people, but to reach more people, and it worked.On this episode, Amanda shows what it's like—and what it sounds like—to make a podcast with AI that's still human at its core.You can learn more about Lead Podcasting at leadpodcasting.com~ ~ ~Working Smarter is brought to you by Dropbox Dash—the AI universal search and knowledge management tool from Dropbox. Learn more at workingsmarter.ai/dashYou can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube Music, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.aiThis show would not be possible without the talented team at Cosmic Standard: producer Dominic Girard, sound engineer Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to our illustrators Justin Tran and Fanny Luor, marketing consultant Meggan Ellingboe, and editorial support from Catie Keck. Our theme song was composed by Doug Stuart. Working Smarter is hosted by Matthew Braga. Thanks for listening!

In this episode, David and Dwight continue their “AI-Off,” a deep dive into using AI development tools to automate everyday tasks and create custom applications for HR. They explore the concept of "vibe coding" with platforms like Replit and Base44, discussing how HR professionals can leverage natural language to build powerful tools without extensive coding knowledge. They cover the iterative development process, the importance of subject matter expertise, and the practical realities of cost and security when deploying AI solutions. [0:00] Introduction Welcome, David and Dwight! Today's Topic: The AI-Off Part Two - Building AI Applications with Natural Language [8:19] The Pros and Cons of AI Development Tools How platforms like Replit and Base 44 handle the entire application stack, from the user experience to the backend data.  The power of these tools to quickly build dashboards, scorecards, and data aggregation tools that once took significant time and effort.  [17:38] How to build AI Applications with Natural Language Why subject matter expertise is crucial for guiding the AI to build a useful and accurate application.  The importance of providing context to the AI, much like a product manager creates user stories for a development team.  [24:09] The Reality of Building with AI: Costs, Security, and Deployment Understanding the costs associated with using these platforms and weighing them against the time and effort saved.  The need for human oversight and professional review, especially for security and compliance, before deploying an application externally.  [30:24] Closing Thanks for listening! Quick Quote “If you want [AI] to help you use your job better, [using tools like Replit or Base44] is potentially a really great way of being able to utilize your skills and your knowledge in this area.”

We're joined by Leif Weatherby, associate professor at NYU, founding director of the Digital Theory Lab, and author of the new Language Machines: Cultural AI and the End of Remainder Humanism, to think with us about AI, structure, and what happens when computation meets language on their own shared turf. Language Machines is easily the best book about AI written this year and is just a killer antidote to so much dreary doomer consensus, it really feels like one of the first truly constructive pieces of writing we've seen out of academia on this subject. This episode follows really well after two others — our talk with Catherine Malabou earlier this summer and the episode with M. Beatrice Fazi about a year ago (both faves). It feels like theory is opening back up again into simultaneously speculative and structural returns, powered in no small part by the challenges posed to conventional theories of language (from Derrida to Chomsky) by Large Language Models. This episode absolutely rips, literally required listening. Structuralism is so back (and we're here for it). Some important references among many from the episode:Roman Jakobson, “Linguistics and Poetics.”N. Katherine Hayles, Unthought: The Power of the Cognitive Nonconscious .Beatrice Fazi, Contingent Computation: Abstraction, Experience, and Indeterminacy in Computational Aesthetics.Steven Pinker, The Language Instinct (1994).e.g. Noam Chomsky, Ian Roberts & Jeffrey Watumull, “The False Promise of ChatGPT,” NYT (link) Anthropic, “Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet” (featuring the Golden Gate Bridge example - link)LAION-5B dataset paper and post-hoc analyses noting strong Shopify/e-commerce presence in training scrapes.Weatherby in the NYT

Very few people get paid to visit shipwrecks—but for Stephanie Gandulla, it's all part of the job. Stephanie is a scuba diver, maritime archeologist, and resource protection coordinator for the Thunder Bay National Marine Sanctuary. The agency safeguards Lake Huron's historic shipwrecks, many of which have yet to be discovered. That's where Katie Skinner comes in. She's an assistant professor at the University of Michigan and the director of the school's Field Robotics Group. Skinner and her team have been developing autonomous underwater vehicles that can find new shipwreck sites, all on their own. For humans, a search is costly, time-consuming, manual work. But for AI? Skinner thinks it could help us find answers in a snap. On this episode, Stephanie and Katie talk about using AI to find shipwrecks in a literal lake of data, so that they can spend less time searching and more time exploring—as only humans can do.You can learn more about some of the people and projects featured in this episode, including… The Thunder Bay National Marine Sanctuary at thunderbay.noaa.govKatie Skinner and the University of Michigan's Field Robotics Group at fieldrobotics.engin.umich.eduPrevious efforts to autonomously map Thunder Bay's historical shipwrecks at theverge.com/2020/3/5/21157791/drone-autonomous-boat-ben-shipwreck-alley-unh-noaa-great-lakes-thunder-bay~ ~ ~Working Smarter is brought to you by Dropbox Dash—the AI universal search and knowledge management tool from Dropbox. Learn more at workingsmarter.ai/dashYou can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube Music, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.aiThis show would not be possible without the talented team at Cosmic Standard: producer Dominic Girard, sound engineer Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to our illustrators Justin Tran and Fanny Luor, marketing consultant Meggan Ellingboe, and editorial support from Catie Keck. Our theme song was composed by Doug Stuart. Working Smarter is hosted by Matthew Braga. Thanks for listening!

Loxo just unveiled Natural Language Search, which is already changing the way recruiters source top talent. In this Tactical Tuesday episode, Sam and Vivien dive into just how much of an impact NLS can have on how we search — especially when it comes to the distinction between hard and soft requirements.It all comes back to adaptability — and this conversation, like every Tactical Tuesday episode before it, will be another tool in your kit as you seek to adapt to the ever-changing world of modern talent acquisition.Chapters:00:00 - The future of AI sourcing: Loxo's Natural Language Search02:39 - Escaping the trap of the unicorn candidate search05:50 - Moving beyond boolean for your core requirements12:40 - How to find top talent despite inconsistent resumesExplore all our episodes and catch the full video experience at loxo.co/podcastBecoming a Hiring Machine is brought to you by Loxo. To discover more about us, just visit loxo.co

In this episode, Sam chats with Loxo's CTO, Ilia Cheishvili, about something hyper-relevant: Natural Language Search. In case you missed it, Loxo just launched NLS — and it's already saving recruiters so much time and mental energy. Throughout the conversation, they explore how NLS allows recruiters to source candidates more intuitively, moving away from traditional Boolean searches that can exclude potential talent. The real kicker? It's becoming increasingly important for recruiters to acknowledge the importance of flexibility in candidate requirements — which leads to the need to cast a wider net in their initial sourcing efforts. As someone who has built the product from the ground up, Ilia shares insights on how NLS can surface the most relevant candidates and improve hiring outcomes — while also addressing common misconceptions about candidate filtering.Long story short: If you've been curious about all the buzz around NLS but need a bit of convincing before giving it a try yourself, you've come to the right place!  Explore all our episodes and catch the full video experience at loxo.co/podcastBecoming a Hiring Machine is brought to you by Loxo. To discover more about us, just visit loxo.co

Loxo just unveiled Natural Language Search, which is already changing the way recruiters source top talent. In this Tactical Tuesday episode, Sam and Vivien dive into just how much of an impact NLS can have on how we search — especially when it comes to the assumptions we make about "the right fit" and the distinction between hard and soft requirements.It all comes back to adaptability — and this conversation, like every Tactical Tuesday episode before it, will be another tool in your kit as you seek to adapt to the ever-changing world of modern talent acquisition. Chapters:00:00 - The AI search revolution that will redefine your sourcing strategy.02:59 - How to decode what a candidate truly wants.11:15 - Breaking your assumptions to find hidden talent.Explore all our episodes and catch the full video experience at loxo.co/podcastBecoming a Hiring Machine is brought to you by Loxo. To discover more about us, just visit loxo.co

Ian Ramirez has spent his career finding innovative ways to make mouth-watering meals for clients—and one of his latest ingredients is artificial intelligence. As a chef, culinary consultant, and co-founder of Mad Honey Culinary Studio and Goods, he's the guy that brands hire to get their product on restaurant menus, and make it look and taste good—whether it's a sauce, syrup, spread, or spice. Ian uses AI to tackle the repetitive, time-consuming parts of menu planning for commercial kitchens, and help clients visualize new concepts before anything gets sliced or diced. It's a tool that augments his creativity, he says, and makes prep less of a grind. On this episode, Ian talks about how AI is helping him and his team spend more time doing what they love: cooking and getting creative in the kitchen.Learn more about Mad Honey Culinary Studio and Goods at madhoneyculinary.comLearn more about Dropbox Dash—the AI universal search and knowledge management tool from Dropbox—at workingsmarter.ai/dash~ ~ ~Working Smarter is brought to you by Dropbox Dash—the AI universal search and knowledge management tool from Dropbox. Learn more at workingsmarter.ai/dashYou can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube Music, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.aiThis show would not be possible without the talented team at Cosmic Standard: producer Dominic Girard, sound engineer Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to our illustrators Justin Tran and Fanny Luor, marketing consultant Meggan Ellingboe, and editorial support from Catie Keck. Our theme song was composed by Doug Stuart. Working Smarter is hosted by Matthew Braga. Thanks for listening!

Working Smarter is back for season two! Starting September 3, we're going beyond the hype and headlines to bring you stories about real people using AI to do more of what they love about their jobs. From the F1 track to the kitchen—and even the bottom of a lake—learn how new tools are helping creatives, makers, visionaries, and their teams think big, move faster, and focus on the work that matters most.~ ~ ~Working Smarter is brought to you by Dropbox Dash—the AI universal search and knowledge management tool from Dropbox. Learn more at workingsmarter.ai/dashYou can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube Music, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.aiThis show would not be possible without the talented team at Cosmic Standard: producer Dominic Girard, sound engineer Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to our illustrators Justin Tran and Fanny Luor, marketing consultant Meggan Ellingboe, and editorial support from Catie Keck. Our theme song was composed by Doug Stuart. Working Smarter is hosted by Matthew Braga. Thanks for listening!

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.

// GUEST //X: https://x.com/Croesus_BTCSubstack: https://www.onceinaspecies.com/ // SPONSORS //The Farm at Okefenokee: https://okefarm.com/iCoin: https://icointechnology.com/breedloveHeart and Soil Supplements (use discount code BREEDLOVE): https://heartandsoil.co/In Wolf's Clothing: https://wolfnyc.com/Blockware Solutions: https://mining.blockwaresolutions.com/breedloveOn Ramp: https://onrampbitcoin.com/?grsf=breedloveMindlab Pro: https://www.mindlabpro.com/breedloveCoinbits: https://coinbits.app/breedlove // PRODUCTS I ENDORSE //Protect your mobile phone from SIM swap attacks: https://www.efani.com/breedloveLineage Provisions (use discount code BREEDLOVE): https://lineageprovisions.com/?ref=breedlove_22Colorado Craft Beef (use discount code BREEDLOVE): https://coloradocraftbeef.com/Salt of the Earth Electrolytes: http://drinksote.com/breedloveJawzrsize (code RobertBreedlove for 20% off): https://jawzrsize.com // SUBSCRIBE TO THE CLIPS CHANNEL //https://www.youtube.com/@robertbreedloveclips2996/videos // OUTLINE //0:00 - WiM Episode Trailer1:31 - Shelling Out: Once in a Species7:50 - Economics: The Youngest Science11:29 - Humanity's Relationship with Art15:25 - Money and Dunbar's Number 24:19 - The Farm at Okefenokee25:46 - iCoin Bitcoin Wallet27:16 - Money: The Keystone to Civilization34:03 - Money and Moral Structure48:14 - Scarcity as a Starting Point55:49 - Heart and Soil Supplements56:49 - Helping Lightning Startups with In Wolf's Clothing57:42 - Beauty vs Scarcity1:05:23 - The Development of Natural Language and Tools1:13:34 - Defining Scarcity1:19:55 - Mine Bitcoin with Blockware Solutions1:21:21 - Onramp Bitcoin Custody1:23:17 - Has Bitcoin Perfected Scarcity?1:32:51 - The Evolution of Money1:42:06 - Mind Lab Pro Supplements1:43:16 - Buy Bitcoin with Coinbits1:44:44 - The Self-Fulfilling Prophecy of Bitcoin1:50:16 - The Bright Orange Future2:00:17 - Bitcoin Defunds War2:04:41 - The Timeline of Money2:07:28 - Where to Find Jesse Meyers // PODCAST //Podcast Website: https://whatismoneypodcast.com/Apple Podcast: https://podcasts.apple.com/us/podcast/the-what-is-money-show/id1541404400Spotify: https://open.spotify.com/show/25LPvm8EewBGyfQQ1abIsERSS Feed: https://feeds.simplecast.com/MLdpYXYI // SUPPORT THIS CHANNEL //Bitcoin: 3D1gfxKZKMtfWaD1bkwiR6JsDzu6e9bZQ7Sats via Strike: https://strike.me/breedlove22Dollars via Paypal: https://www.paypal.com/paypalme/RBreedloveDollars via Venmo: https://account.venmo.com/u/Robert-Breedlove-2 // SOCIAL //Breedlove X: https://x.com/Breedlove22WiM? X: https://x.com/WhatisMoneyShowLinkedin: https://www.linkedin.com/in/breedlove22/Instagram: https://www.instagram.com/breedlove_22/TikTok: https://www.tiktok.com/@breedlove22Substack: https://breedlove22.substack.com/All My Current Work: https://linktr.ee/robertbreedlove

**(Note: Spotify listeners can also watch the screen sharing video accompanying the audio. Other podcast platforms offer the audio-only version.)**In this episode of MongoDB Podcast Live, host Shane McAllister is joined by Sachin Hejip from Dataworkz. Sachin will showcase “Dataworkz Agent Builder” which is built with MongoDB Atlas Vector Search, and demonstrate how it can use Natural Language to create Agents and in turn, automate and simplify the creation of Agentic RAG applications. Sachin will demo the MongoDB Leafy Portal Chatbot Agent, which combines operational data with unstructured data for personalised customer experience and support, built using Dataworkz and MongoDB.Struggling with millions of unstructured documents, legacy records, or scattered data formats? Discover how AI, Large Language Models (LLMs), and MongoDB are revolutionizing data management in this episode of the MongoDB Podcast.Join host Shane McAllister and the team as they delve into tackling complex data challenges using cutting-edge technology. Learn how MongoDB Atlas Vector Search enables powerful semantic search and Retrieval Augmented Generation (RAG) applications, transforming chaotic information into valuable insights. Explore integrations with popular frameworks like Langchain and Llama Index.Find out how to efficiently process and make sense of your unstructured data, potentially saving significant costs and unlocking new possibilities.Ready to dive deeper?#MongoDB #AI #LLM #LargeLanguageModels #VectorSearch #AtlasVectorSearch #UnstructuredData #Podcast #DataManagement #Dataworkz #Observability #Developer #BigData #RAG

Guests: Ken Lempit, James Ollerenshaw, and Rob CurtisAI isn't just a bolt-on anymore—it's rewriting the rules of SaaS from the ground up.In this episode, Standup Hiro co-founder Rob Curtis and tech strategist James Ollerenshaw join host and GTM expert Ken Lempit to unpack how Agentic AI is forcing SaaS leaders to rethink everything: product design, go-to-market, customer trust, and even internal culture.From natural language interfaces to the rise of AI “co-workers,” they break down why SaaS companies that adapt will dominate—and why bolt-on AI features won't be enough to survive.

In this conversation, Mayur Mistry interviews Stevan Lukic, founder of Civils AI, discussing his career journey, the inception of Civils AI, and the challenges and innovations in the construction industry through AI. They explore the features of Civils AI, the importance of domain-specific workflows, and the debate between open source and closed source models in construction technology. The conversation concludes with insights on the future of AI in the industry.   Takeaways Stevan Lukic emphasizes the importance of making AI accessible in construction. His career highlights include working on tunneling projects and transitioning to AI startups. Civil's AI aims to automate repetitive processes in construction using natural language. The platform allows users to automate tasks without needing programming skills. Stevan discusses the challenges of extracting information from complex drawings. He highlights the need for domain-specific workflows in AI applications. The conversation touches on the difficulties of measuring accuracy in AI systems. Stevan shares insights on the importance of structuring information effectively. The discussion includes the pros and cons of open source versus closed source models. Stevan expresses excitement about the future of AI in the construction industry.   Chapters 00:00 Introduction to Stevan Lukic and Civil's AI 02:59 Career Highlights and Journey to AI 03:30 The Birth of Civils AI 04:54 Understanding Civils AI and Its Benefits 06:48 Demonstration of Civils AI Platform 12:19 Challenges in Drawing Recognition 19:00 Startup Challenges and Successes 20:56 Customer Acquisition Strategies 23:16 Open Source vs Closed Source Solutions 27:24 AI Agents and Future Opportunities 29:26 How to Reach Out and Collaborate 30:21 Closing Thoughts and Future Outlook  

// GUEST //Website: https://www.mattridley.co.uk/X: https://x.com/mattwridleySubstack: https://rationaloptimistsociety.substack.com/ // SPONSORS //The Farm at Okefenokee: https://okefarm.com/iCoin: https://icointechnology.com/breedloveHeart and Soil Supplements (use discount code BREEDLOVE): https://heartandsoil.co/In Wolf's Clothing: https://wolfnyc.com/Blockware Solutions: https://mining.blockwaresolutions.com/breedloveOn Ramp: https://onrampbitcoin.com/?grsf=breedloveMindlab Pro: https://www.mindlabpro.com/breedloveCoinbits: https://coinbits.app/breedlove // PRODUCTS I ENDORSE //Protect your mobile phone from SIM swap attacks: https://www.efani.com/breedloveNoble Protein (discount code BREEDLOVE for 15% off): https://nobleorigins.com/Lineage Provisions (use discount code BREEDLOVE): https://lineageprovisions.com/?ref=breedlove_22Colorado Craft Beef (use discount code BREEDLOVE): https://coloradocraftbeef.com/ // SUBSCRIBE TO THE CLIPS CHANNEL //https://www.youtube.com/@robertbreedloveclips2996/videos // OUTLINE //0:00 - WiM Episode Trailer1:22 - The Rational Optimist, 5:34 - No One Person Can Make a Computer Mouse11:50 - The Role of Money in the Global Hivemind19:51 - Money as a Language24:42 - The Farm at Okefenokee26:08 - iCoin Technology27:38 - Ideas Having Sex35:42 - Nature vs Nurture36:37 - Evolution of Everything: Adam Smith and Charles Darwin 40:58 - Natural Language as Software47:11 - Heart and Soil Supplements48:11 - Helping Lightning Startups with In Wolf's Clothing49:02 - Evolution as Biological Innovation57:51 - Energy and Entropy1:09:29 - Mine Bitcoin with Blockware Solutions1:10:54 - OnRamp Bitcoin Custody1:12:51 - The Evolution of Language1:19:33 - Birds, Sex, and Beauty1:24:29 - Costly Signaling Theory1:31:04 - Mind Lab Pro Supplements1:32:14 - Buy Bitcoin with Coinbits1:33:41 - Objective vs Subjective vs Transjective 1:40:04 - If the Product is Free, You are the Product1:48:03 - Lab Leak or Intentional?1:51:13 - Closing Thoughts and Where to Find Matt Ridley // PODCAST //Podcast Website: https://whatismoneypodcast.com/Apple Podcast: https://podcasts.apple.com/us/podcast/the-what-is-money-show/id1541404400Spotify: https://open.spotify.com/show/25LPvm8EewBGyfQQ1abIsERSS Feed: https://feeds.simplecast.com/MLdpYXYI // SUPPORT THIS CHANNEL //Bitcoin: 3D1gfxKZKMtfWaD1bkwiR6JsDzu6e9bZQ7Sats via Strike: https://strike.me/breedlove22Dollars via Paypal: https://www.paypal.com/paypalme/RBreedloveDollars via Venmo: https://account.venmo.com/u/Robert-Breedlove-2 // SOCIAL //Breedlove X: https://x.com/Breedlove22WiM? X: https://x.com/WhatisMoneyShowLinkedin: https://www.linkedin.com/in/breedlove22/Instagram: https://www.instagram.com/breedlove_22/TikTok: https://www.tiktok.com/@breedlove22Substack: https://breedlove22.substack.com/All My Current Work: https://linktr.ee/robertbreedlove

How did no one notice these AI Agents?

We've arrived at the big one, the breakthrough book of 1985 – White Noise. In Episodes 21 and 22, DDSWTNP extend our White Noise “residency” and turn in-depth attention to DeLillo's most popular piece of fiction in another double episode.  Episode 21: White Noise (1) takes an expansive view of the novel's narrative and goes into depth on (among many other subjects) the iconic opening chapter's commentary on America and Americana, the meaning of Mylex suits, Jack's relationships with Heinrich and Orest Mercator, and what it means to be a rat, a snake, a fascist, and a scholar of Hitler in this book's universe. Episode 22: White Noise (2) interprets passages mainly from the book's second half, including scenes featuring the dark humor of Vernon Dickey and of SIMUVAC, the meaning of DeLillo's desired title “Panasonic,” Jack's shooting of Willie Mink (and what it owes to Nabokov), a riveting fire and a fascinating trash compactor cube, and the Dostoevskyan interrogation of belief by Sister Hermann Marie. Every minute features original ideas on the enduring meanings of White Noise in so many political, social, technological, and moral dimensions – what it teaches us about the roots and implications of our many epistemological crises, how it does all this in writing that somehow manages to be self-conscious, philosophical, hilarious, and warm all at once. Texts and artifacts discussed and mentioned in these episodes: Ernest Becker, The Denial of Death (Free Press, 1973).  Adam Begley, “Don DeLillo: The Art of Fiction CXXXV,” The Paris Review 128 (1993): 274-306.            (DeLillo: “And White Noise develops a trite adultery plot that enmeshes the hero, justifying his fears about the death energies contained in plots. When I think of highly plotted novels I think of detective fiction or mystery fiction, the kind of work that always produces a few dead bodies. But these bodies are basically plot points, not worked-out characters. The book's plot either moves inexorably toward a dead body or flows directly from it, and the more artificial the situation the better. Readers can play off their fears by encountering the death experience in a superficial way.”) Buddha, Ādittapariyāya Sutta (“Fire Sermon Discourse”). https://en.wikipedia.org/wiki/%C4%80dittapariy%C4%81ya_Sutta Don DeLillo, White Noise: Text and Criticism, Mark Osteen, ed. (Penguin, 1998). ---. “The Sightings.” Weekend Magazine (August 4, 1979), 26-30. Mary Douglas, Purity and Danger: An Analysis of Concepts of Pollution and Taboo (Routledge, 1966). Fyodor Dostoevsky, The Brothers Karamazov (1880). Franz Kafka, “A Hunger Artist” (1922). Édouard Manet's Olympia (1863). https://en.wikipedia.org/wiki/Olympia_(Manet) Vladimir Nabokov, Lolita (1955). Mark Osteen, “‘The Natural Language of the Culture': Exploring Commodities through White Noise.” Approaches to Teaching DeLillo's White Noise, eds. Tim Engles and John N. Duvall (MLA, 2006), pp. 192-203. Ronald Reagan, “Farewell Address to the Nation,” January 11, 1989. https://www.youtube.com/watch?v=FjECSv8KFN4            (“I've spoken of the ‘shining city' all my political life . . .”) Mark L. Sample, “Unseen and Unremarked On: Don DeLillo and the Failure of the Digital Humanities.” https://dhdebates.gc.cuny.edu/read/untitled-88c11800-9446-469b-a3be-3fdb36bfbd1e/section/be12b589-a9ca-4897-9475-f8c0b03ca648(See this article for DeLillo's list of alternate titles, including “Panasonic” and “Matshushita” (Panasonic's parent corporation).)

We've arrived at the big one, the breakthrough book of 1985 – White Noise. In Episodes 21 and 22, DDSWTNP extend our White Noise “residency” and turn in-depth attention to DeLillo's most popular piece of fiction in another double episode.  Episode 21: White Noise (1) takes an expansive view of the novel's narrative and goes into depth on (among many other subjects) the iconic opening chapter's commentary on America and Americana, the meaning of Mylex suits, Jack's relationships with Heinrich and Orest Mercator, and what it means to be a rat, a snake, a fascist, and a scholar of Hitler in this book's universe. Episode 22: White Noise (2) interprets passages mainly from the book's second half, including scenes featuring the dark humor of Vernon Dickey and of SIMUVAC, the meaning of DeLillo's desired title “Panasonic,” Jack's shooting of Willie Mink (and what it owes to Nabokov), a riveting fire and a fascinating trash compactor cube, and the Dostoevskyan interrogation of belief by Sister Hermann Marie. Every minute features original ideas on the enduring meanings of White Noise in so many political, social, technological, and moral dimensions – what it teaches us about the roots and implications of our many epistemological crises, how it does all this in writing that somehow manages to be self-conscious, philosophical, hilarious, and warm all at once. Texts and artifacts discussed and mentioned in these episodes: Ernest Becker, The Denial of Death (Free Press, 1973).  Adam Begley, “Don DeLillo: The Art of Fiction CXXXV,” The Paris Review 128 (1993): 274-306.            (DeLillo: “And White Noise develops a trite adultery plot that enmeshes the hero, justifying his fears about the death energies contained in plots. When I think of highly plotted novels I think of detective fiction or mystery fiction, the kind of work that always produces a few dead bodies. But these bodies are basically plot points, not worked-out characters. The book's plot either moves inexorably toward a dead body or flows directly from it, and the more artificial the situation the better. Readers can play off their fears by encountering the death experience in a superficial way.”) Buddha, Ādittapariyāya Sutta (“Fire Sermon Discourse”). https://en.wikipedia.org/wiki/%C4%80dittapariy%C4%81ya_Sutta Don DeLillo, White Noise: Text and Criticism, Mark Osteen, ed. (Penguin, 1998). ---. “The Sightings.” Weekend Magazine (August 4, 1979), 26-30. Mary Douglas, Purity and Danger: An Analysis of Concepts of Pollution and Taboo (Routledge, 1966). Fyodor Dostoevsky, The Brothers Karamazov (1880). Franz Kafka, “A Hunger Artist” (1922). Édouard Manet's Olympia (1863). https://en.wikipedia.org/wiki/Olympia_(Manet) Vladimir Nabokov, Lolita (1955). Mark Osteen, “‘The Natural Language of the Culture': Exploring Commodities through White Noise.” Approaches to Teaching DeLillo's White Noise, eds. Tim Engles and John N. Duvall (MLA, 2006), pp. 192-203. Ronald Reagan, “Farewell Address to the Nation,” January 11, 1989. https://www.youtube.com/watch?v=FjECSv8KFN4            (“I've spoken of the ‘shining city' all my political life . . .”) Mark L. Sample, “Unseen and Unremarked On: Don DeLillo and the Failure of the Digital Humanities.” https://dhdebates.gc.cuny.edu/read/untitled-88c11800-9446-469b-a3be-3fdb36bfbd1e/section/be12b589-a9ca-4897-9475-f8c0b03ca648(See this article for DeLillo's list of alternate titles, including “Panasonic” and “Matshushita” (Panasonic's parent corporation).)

Jonathan Godwin, founder and CEO of Orbital Materials, alongside researcher Tim Duignan, discuss the transformative potential of AI in material science on the Cognitive Revolution podcast. They explore foundational concepts, the integration of computational simulations, and the development of new materials for various applications such as data centers and combating climate change. They also delve into the latest advancements, including a groundbreaking study on the potassium ion channel, and speculate on the future of AI in scientific discovery and material synthesis. Check out some of Tim's work: Google Colab to run you own simulation: https://colab.research.google.com/github/timduignan/orb-models/blob/main/examples/OrbMDTut.ipynb GitHub repository "Orb force fields": https://github.com/orbital-materials/orb-models Preprint "A potassium ion channel simulated with a universal neural network potential": https://arxiv.org/abs/2411.18931 Help shape our show by taking our quick listener survey at https://bit.ly/TurpentinePulse SPONSORS: Oracle Cloud Infrastructure (OCI): Oracle's next-generation cloud platform delivers blazing-fast AI and ML performance with 50% less for compute and 80% less for outbound networking compared to other cloud providers. OCI powers industry leaders like Vodafone and Thomson Reuters with secure infrastructure and application development capabilities. New U.S. customers can get their cloud bill cut in half by switching to OCI before March 31, 2024 at https://oracle.com/cognitive NetSuite: Over 41,000 businesses trust NetSuite by Oracle, the #1 cloud ERP, to future-proof their operations. With a unified platform for accounting, financial management, inventory, and HR, NetSuite provides real-time insights and forecasting to help you make quick, informed decisions. Whether you're earning millions or hundreds of millions, NetSuite empowers you to tackle challenges and seize opportunities. Download the free CFO's guide to AI and machine learning at https://netsuite.com/cognitive Shopify: Dreaming of starting your own business? Shopify makes it easier than ever. With customizable templates, shoppable social media posts, and their new AI sidekick, Shopify Magic, you can focus on creating great products while delegating the rest. Manage everything from shipping to payments in one place. Start your journey with a $1/month trial at https://shopify.com/cognitive and turn your 2025 dreams into reality. Vanta: Vanta simplifies security and compliance for businesses of all sizes. Automate compliance across 35+ frameworks like SOC 2 and ISO 27001, streamline security workflows, and complete questionnaires up to 5x faster. Trusted by over 9,000 companies, Vanta helps you manage risk and prove security in real time. Get $1,000 off at https://vanta.com/revolution CHAPTERS: (00:00) Teaser (01:05) About the Episode (05:10) Welcome to Orbital (06:15) Semiconductors (07:44) Material Science Today (09:22) Experimental Cycle (12:06) Orbital's Founding (14:51) AI in Materials (Part 1) (21:05) Sponsors: OCI | NetSuite (23:45) AI in Materials (Part 2) (35:00) Sponsors: Shopify | Vanta (38:15) Generative Models (38:16) Diffusion Models (50:50) Orbital Applications (58:19) Perfect Sponge (59:43) AI Simulations (01:01:27) Natural Language (01:02:35) Compute Needs (01:05:05) Human Electrical Nature (01:06:11) Potassium Channels (01:15:51) Scaling Simulations (01:23:56) Roadmap: Carbon Removal (01:30:37) AI & Job Satisfaction (01:36:14) LLMs & Potentials (01:37:19) AGI & Discovery (01:39:58) Outro

Summary In this episode, Liam interviews Philip Alm, co-founder and CEO of Norditech and Viss.AI. They discuss Philip's entrepreneurial journey, starting from his early days programming Minecraft servers to founding Norditech, a company focused on making AI accessible for everyone. The conversation covers the challenges of starting a business, finding product-market fit, and the vision behind Norditech's automation solutions. Philip shares insights on the technical challenges of AI, the importance of user experience, and the future of AI in the business landscape. He also emphasizes the significance of team culture and the role of mentorship in growth. Chapters 00:00 The Journey of an Entrepreneur 05:55 The Birth of Norditech AI 11:36 Finding Product-Market Fit 17:08 Technical Challenges and Solutions 22:54 The Future of AI Automation 29:41 Navigating the API Jungle 34:37 The Power of Natural Language in Automation 40:21 Building a Strong Company Culture 51:52 Predictions for the Future of AI 57:09 Advice for Aspiring Entrepreneurs Connect with Philip – LinkedIn

Everyone is talking about the efficacy of Gestalt Language Processing and Natural Language Acquisition, so we thought we better have a look into what recent research is telling us. In this insightful episode of The Talking DLD Podcast, Professor Bronwyn Hemsley and Dr Caroline Bowen AM discuss their recent systematic review of interventions based on Gestalt Language Processing and Natural Language Acquisition (GLP/NLA): Clinical implications of absence of evidence and cautions for clinicians and parents. Resources Bryant, L., Bowen, C., Grove, R. Dixon, G., Beals, K., Shane, H., & Hemsley, B. (2024). Systematic review of interventions based on Gestalt Language Processing and Natural Language Acquisition (GLP/NLA): Clinical implications of absence of evidence and cautions for clinicians and parents. Current Developmental Disorders Reports, 12, 1–14 (2024). https://doi.org/10.1007/s40474-024-00312-z The National Clearinghouse on Autism Evidence and Practice The Autism CRE Australia Supporting Autistic Children Guideline The Communication Hub  

  In this episode, I had the absolute joy of chatting with Marge Blanc about Gestalt Language Processing (GLP) and Natural Language Acquisition (NLA). Marge has spent over 50 years supporting children's language development, and her openness and curiosity have shaped so much of what we know today. We discuss how paying close attention to children's unique communication and interactions can inform how we support them.   We cover these areas and more:   Key terms - Marge defines GLP, ALP, and NLA History and shifts in our understanding of GLP and NLA The state of research and the various forms that our 'evidence' can and should take Research gaps and future directions Responding to critics of NLA and GLP Simple and deeply important strategies to support Autistic children - for parents and professionals   This episode is an insightful overview of 'where we've been and where we're going'... and my deep overt admiration for Marge's work! Whether you're a speech therapist, educator, or parent, you'll come away inspired to support Neurodivergent children in meaningful, individualised ways.   Marge and I would both love to hear your feedback and thoughts, so feel free to email me hi@playlearnchat.com or get in touch with us on Instagram - Marge is @blancmarge and I'm @play.learn.chat   Links and resources discussed: The Communication Development Center: https://communicationdevelopmentcenter.com/ The Speech Den Gestalt Language Processors Conference: https://register.glpconference.co.uk/gestalt-language-processors-2024-conference Barry Prizant's articles about Echolalia: https://barryprizant.com/resources/downloads/echolalia-articles/ Marge Blanc's article 'Finding the Words to tell the "whole" story - Natural Language Acquisition on the Autism Spectrum(2005) https://communicationdevelopmentcenter.com/wp-content/uploads/2022/08/Finding-the-words-to-tell-the-whole-story.pdf Uniquely Human Podcast Episode 114 - Marge Blanc and Alex Zachos speaking with Barry Prizant - https://uniquelyhuman.com/2024/09/13/gestalt-language-processing-and-natural-language-acquisition-marge-blanc-alex-zachos/ Marge's training through AGOSCI - What's Good About Echolalia? It's Language Development! https://www.agosci.org.au/event-4909602 Facebook Group: Gestalt language processor - natural language acquisition https://www.facebook.com/groups/997863334533291   Podcast Link: https://pod.link/1625478932 Website: www.playlearnchat.com Instagram: https://www.instagram.com/play.learn.chat Facebook: https://www.facebook.com/play.learn.chat

AI is spamming up job applications. 404 Media's Jason Koebler writes about how he used a free tool, AI Hawk, to apply for 17 jobs in an hour while working a restaurant shift — only stopping when he'd reached 2,843; The Internet Archive, the nonprofit organization that digitizes and archives materials like web pages, came under attack Wednesday; The startup, which already monitors the climate impact of digital advertising, is expanding to include artificial intelligence; Opera browser has a new AI-powered feature that lets you take action on tabs through natural language queries. Things you can do include grouping, pinning, bookmarking and closing tabs with these commands. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Augmenting Statistical Models with Natural Language Parameters, published by jsteinhardt on September 22, 2024 on LessWrong. This is a guest post by my student Ruiqi Zhong, who has some very exciting work defining new families of statistical models that can take natural language explanations as parameters. The motivation is that existing statistical models are bad at explaining structured data. To address this problem, we agument these models with natural language parameters, which can represent interpretable abstract features and be learned automatically. Imagine the following scenario: It is the year 3024. We are historians trying to understand what happened between 2016 and 2024, by looking at how Twitter topics changed across that time period. We are given a dataset of user-posted images sorted by time, $x_1$, $x_2$ ... $x_T$, and our goal is to find trends in this dataset to help interpret what happened. If we successfully achieve our goal, we would discover, for instance, (1) a recurring spike of images depicting athletes every four years for the Olympics, and (2) a large increase in images containing medical concepts during and after the COVID-19 pandemic. How do we usually discover temporal trends from a dataset? One common approach is to fit a time series model to predict how the features evolve and then interpret the learned model. However, it is unclear what features to use: pixels and neural image embeddings are high-dimensional and uninterpretable, undermining the goal of extracting explainable trends. We address this problem by augmenting statistical models with interpretable natural language parameters. The figure below depicts a graphical model representation for the case of time series data. We explain the trends in the observed data [ $x_1$ ... $x_T$] by learning two sets of latent parameters: natural language parameters $phi$ (the learned features) and real-valued parameters $w$ (the time-varying trends). $phi$: the natural language descriptions of $K$ different topics, e.g. "depicts athletes competing". $phi$ is an element of $Sigma$, the universe of all natural language predicates. $w_t$: the frequency of each of the K topics at the time $t$. If our model successfully recovers the underlying trends, then we can visualize $w$ and $phi$ below and see that: 1) more pictures contain medical concepts (red) starting from 2020, and 2) there are recurring (blue) spikes of athletes competing. In the rest of this post, we will explain in detail how to specify and learn models with natural language parameters and showcase the model on several real-world applications. We will cover: A warm-up example of a statistical model with natural language explanations A modeling language for specifying natural language parameters Applications of our framework, which can be used to specify models for time series, clustering, and applications. We will go over: A machine learning application that uses our time series model to monitor trends in LLM usage A business application that uses our clustering model to taxonomize product reviews A cognitive science application that uses our classification model to explain what images are more memorable for humans Thanks to Louise Verkin for helping to typeset the post in Ghost format. Warm-up Example: Logistic Regression with Natural Language Parameters Instead of understanding topic shifts across the entire time window of 2016-2024, let's first study a much simpler question: what images are more likely to appear after 2020? The usual way to approach this problem is to, 1. brainstorm some features, 2. extract the real-valued features from each image, and 3. run a logistic regression model on these features to predict the target $Y$ =1 if the image appears after 2020, $Y$ =0 otherwise. More concretely: Step 1: Propose different...

Gestalt language Processing and Natural Language Acquisition: A Discussion with Marge Blanc, CCC-SLP, and Alex Zachos, CCC-SLP Gestalt language processing and natural language acquisition are topics that have received a great deal of attention in recent years, grounded in research that goes back to the 1970s. These concepts have helped to explain and describe a process, often observed in autistic children and those with other neurodevelopmental conditions, in children move from the early use of echolalia to self-generated conversational use of language. Marge and Alex join Barry in a discussion of the history and current status in understanding children who demonstrate these patterns of language acquisition, in order to assist professionals and parents in supporting such children.

Dive into the future of virtual production with Tim Moore, CEO of Vū. Discover the trends he's seen in smaller stages, AI adoption (and looming crash), and the democratization of filmmaking in this insightful interview.

On our final episode this season of Working Smarter we talk to Sophia Wang, an assistant professor of ophthalmology at Stanford University. Wang leads the school's ophthalmic informatics and artificial intelligence group, which uses the latest machine learning techniques to analyze electronic health records. In practice, that means looking at disparate sources of data—from doctors' notes and eye exam data to diagnostic imagery and billing codes—and finding the sorts of patterns that can be difficult for humans to spot.Hear Wang talk about using AI to extract useful information from a sea of unstructured data, and how to make better decisions with the data you already have—which, in Wang's case, means improving outcomes for glaucoma patients and providing a better quality of care.Show notes:Learn more about Sophia and her researchVisit Stanford University's Ophthalmic Informatics and Artificial Intelligence Group~ ~ ~Working Smarter is a new podcast from Dropbox about how AI is changing the way we work and get stuff done.You can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube Music, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.aiThis show would not be possible without the talented team at Cosmic Standard, namely: our producers Samiah Adams and Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to Benjy Baptiste for production assistance, our marketing and PR consultant Meggan Ellingboe, and our illustrators, Fanny Luor and Justin Tran. Our theme song was created by Doug Stuart. Working Smarter is hosted by Matthew Braga.

In this episode, I welcome Jason Gilman, a Principal Software Engineer at Element 84, to explore the exciting world of natural language geocoding. Key Topics Discussed: Introduction to Natural Language Geocoding: Jason explains the concept of natural language geocoding and its significance in converting textual descriptions of locations into precise geographical data. This involves using large language models to interpret a user's natural language input, such as "the coast of Florida south of Miami," and transform it into an accurate polygon that represents that specific area on a map. This process automates and simplifies how users interact with geospatial data, making it more accessible and user-friendly. The Evolution of AI and ML in Geospatial Work: Over the last six months, Jason has shifted focus to AI and machine learning, leveraging large language models to enhance geospatial data processing. Challenges and Solutions: Jason discusses the challenges of interpreting natural language descriptions and the solutions they've implemented, such as using JSON schemas and OpenStreetMap data. Applications and Use Cases: From finding specific datasets to processing geographical queries, the applications of natural language geocoding are vast. Jason shares some real-world examples and potential future uses. Future of Geospatial AIML: Jason touches on the broader implications of geospatial AI and ML, including the potential for natural language geoprocessing and its impact on scientific research and everyday applications. Interesting Insights: The use of large language models can simplify complex geospatial queries, making advanced geospatial analysis accessible to non-experts. Integration of AI and machine learning with traditional geospatial tools opens new avenues for research and application, from environmental monitoring to urban planning. Quotes: "Natural language geocoding is about turning a user's textual description of a place on Earth into a precise polygon." "The combination of vision models and large language models allows us to automate complex tasks that previously required manual effort." Additional Resources: Element 84 Website State of the Map US Conference Talk on YouTube Blog Posts on Natural Language Geocoding Connect with Jason: Visit Element 84's website for more information and contact details. Google "Element 84 Natural Language Geocoding" for additional resources and talks.

For our seventh episode of Working Smarter we're talking to Drew Houston, the co-founder and CEO of Dropbox. If you've been online long enough, it's likely Dropbox was your introduction to the cloud. The goal is still more or less the same—give you one organized place for all your stuff—but it's no longer just about storing and syncing files. A hundred files on your desktop is now a hundred tabs in your browser, and Houston believes AI is what will finally bring calm to the chaos that's been created by the tools of modern work.For Houston, AI's potential is so great that its arrival feels like a civilization shift. It's also not just a professional preoccupation; AI is a personal interest too. A few years ago he decided to teach himself machine learning in his spare time—and some of the AI tools Houston now uses to run Dropbox are ones he built himself. Hear Houston discuss why it's gotten so hard to find the information you need to do your job, the types of tasks we'll increasingly offload to our silicon brains, and what Dropbox is doing to help make modern work more meaningful and fulfilling.Show notes:To learn more about Dropbox Dash and try Dash for free, visit dropbox.com/dashThe two books Houston mentions are “High Output Management” by Andy Grove and “The Effective Executive” by Peter Drucker~ ~ ~Working Smarter is a new podcast from Dropbox about how AI is changing the way we work and get stuff done.You can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube Music, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.aiThis show would not be possible without the talented team at Cosmic Standard, namely: our producers Samiah Adams and Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to Benjy Baptiste for production assistance, our marketing and PR consultant Meggan Ellingboe, and our illustrators, Fanny Luor and Justin Tran. Our theme song was created by Doug Stuart. Working Smarter is hosted by Matthew Braga.

On this episode of the Getting Smart Podcast, Tom Vander Ark talks to Conrad Wolfram, CEO of Wolfram Research and author of The Math Fix, to discuss the evolving role of computational thinking in education. They explore how the surge in computational power and AI can transform math education by moving away from manual calculations and focusing on real-world problem-solving. Conrad Wolfram shares insights on the necessity of integrating computational tools into the curriculum, emphasizing that modern education should prepare students for complex problem-solving using AI and natural language interfaces. They also discuss the challenges and opportunities in updating math education to reflect these advancements, aiming to equip students with skills relevant to today's tech-driven world. Outline The Evolution of Human-Computer Interaction The Role of Natural Language in AI Revolutionizing Math Education Future of Computational Thinking in Education Links Watch the Full Conversation Conrad Wolfram Website Conrad Wolfram Bio Language Matters, and What Matters Has Changed by Conrad Wolfram Conrad Wolfram on Computational Thinking The Math(s) Fix Review by Rachelle Dene Poth South Fayette Computational Thinking Digital Promise - Computational Thinking US Math Wars by Conrad Wolfram Subscribe to Our Newsletter!

Leverage Azure Cosmos DB for generative AI workloads for automatic scalability, low latency, and global distribution to handle massive data volumes and real-time processing. With support for versatile data models and built-in vector indexing, it efficiently retrieves natural language queries, making it ideal for grounding large language models. Seamlessly integrate with Azure OpenAI Studio for API-level access to GPT models and access a comprehensive gallery of open-source tools and frameworks in Azure AI Studio to enhance your AI applications. ► QUICK LINKS: 00:00 - Azure Cosmos DB for generative AI workloads 00:18 - Versatile Data Models 00:39 - Scalability and performance 01:19 - Global distribution 01:31 - Vector indexing and search 02:07 - Grounding LLMs 02:30 - Wrap up   ► Unfamiliar with Microsoft Mechanics?  As Microsoft's official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft. • Subscribe to our YouTube: https://www.youtube.com/c/MicrosoftMechanicsSeries • Talk with other IT Pros, join us on the Microsoft Tech Community: https://techcommunity.microsoft.com/t5/microsoft-mechanics-blog/bg-p/MicrosoftMechanicsBlog • Watch or listen from anywhere, subscribe to our podcast: https://microsoftmechanics.libsyn.com/podcast   ► Keep getting this insider knowledge, join us on social: • Follow us on Twitter: https://twitter.com/MSFTMechanics  • Share knowledge on LinkedIn: https://www.linkedin.com/company/microsoft-mechanics/ • Enjoy us on Instagram: https://www.instagram.com/msftmechanics/ • Loosen up with us on TikTok: https://www.tiktok.com/@msftmechanics

Get a unified solution for secure access management, identity verification, and Zero Trust security for cloud and on-premises resources. The new Microsoft Entra suite integrates five capabilities: Private Access, Internet Access, ID Protection, ID Governance, and Face Check as part of Verified ID Premium, included with Microsoft Entra Suite. With these capabilities, you can streamline user onboarding, enhance security with automated workflows, and protect against threats using Conditional Access policies. See how to reduce security gaps, block lateral attacks, and replace legacy VPNs, ensuring efficient and secure access to necessary resources. Jarred Boone, Identity Security Senior Product Manager, shares how to experience advanced security and management with Microsoft Entra Suite.   ► QUICK LINKS: 00:00 - Unified solution with Microsoft Entra Suite 00:38 - Microsoft Entra Private Access 01:39 - Microsoft Entra Internet Access 02:42 - Microsoft Entra ID Protection 03:31 - Microsoft Entra ID Governance 04:18 - Face Check in Verified ID Premium, included with Microsoft Entra Suite 04:52 - How core capabilities work with onboarding process 06:08 - Protect access to resources 07:22 - Control access to internet endpoints 08:05 - Establish policies to dynamically adjust 08:45 - Wrap up   ► Link References Try it out at https://entra.microsoft.com  Watch our related deep dives at https://aka.ms/EntraSuitePlaylist  Check out https://aka.ms/EntraSuiteDocs  ► Unfamiliar with Microsoft Mechanics?  As Microsoft's official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft. • Subscribe to our YouTube: https://www.youtube.com/c/MicrosoftMechanicsSeries • Talk with other IT Pros, join us on the Microsoft Tech Community: https://techcommunity.microsoft.com/t5/microsoft-mechanics-blog/bg-p/MicrosoftMechanicsBlog • Watch or listen from anywhere, subscribe to our podcast: https://microsoftmechanics.libsyn.com/podcast   ► Keep getting this insider knowledge, join us on social: • Follow us on Twitter: https://twitter.com/MSFTMechanics  • Share knowledge on LinkedIn: https://www.linkedin.com/company/microsoft-mechanics/ • Enjoy us on Instagram: https://www.instagram.com/msftmechanics/ • Loosen up with us on TikTok: https://www.tiktok.com/@msftmechanics

For our sixth episode of Working Smarter we're talking to Pascal Weinberger, the co-founder and CEO of Bardeen, an AI-powered automation platform. Weinberger wants to help people effortlessly automate repetitive tasks in the apps they already use for work—no code required. His hope is that by removing some of the friction that makes it hard for people to do their jobs, they can use the time they save on more rewarding, impactful work.Hear Weinberger talk about what people are already automating, how automation can benefit teams as much as individuals, and why he doesn't want to build tools that replace us.Show notes:To learn more about Bardeen, visit bardeen.ai~ ~ ~Working Smarter is a new podcast from Dropbox about how AI is changing the way we work and get stuff done.You can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube Music, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.aiThis show would not be possible without the talented team at Cosmic Standard, namely: our producers Samiah Adams and Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to Benjy Baptiste for production assistance, our marketing and PR consultant Meggan Ellingboe, and our illustrators, Fanny Luor and Justin Tran. Our theme song was created by Doug Stuart. Working Smarter is hosted by Matthew Braga.Thanks for listening!

Bring your MySQL workloads to run on Azure. Azure Database for MySQL - Flexible Server offers a powerful, fully managed solution for MySQL workloads, providing unique platform-level optimizations that significantly enhance connection scaling and cost performance. Ensure high efficiency and reduced latency for mission-critical applications for up to twice the performance of other offerings. The integration with Azure OpenAI Service and Azure AI Search further extends its capabilities, enabling intelligent vector-based search and generative AI responses for more accurate and relevant user queries. Join Parikshit Savjani, Azure Database for MySQL Principal Group PM, shares how Azure Database for MySQL - Flexible Server transforms traditional MySQL applications into high-performance, intelligent systems by combining the scalability and security of Azure with advanced AI-driven insights.   ► QUICK LINKS: 00:00 - Run MySQL on Azure 00:56 - Run your LAMP stack on Azure 01:24 - Accelerated Logs capability 03:02 - Vector-based search and generative AI 04:02 - Leverage Azure AI Search & Azure OpenAI services 04:29 - Establish database connection 05:52 - Build vector index 07:04 - Create an Indexer 07:32 - Semantic Search 07:54 - Test it in the Azure AI playground 08:49 - Wrap up   ► Link References  Check out https://aka.ms/mysql-resources   Join the community for Azure Database for MySQL at https://aka.ms/mysql-contributors   ► Unfamiliar with Microsoft Mechanics?  As Microsoft's official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft. • Subscribe to our YouTube: https://www.youtube.com/c/MicrosoftMechanicsSeries • Talk with other IT Pros, join us on the Microsoft Tech Community: https://techcommunity.microsoft.com/t5/microsoft-mechanics-blog/bg-p/MicrosoftMechanicsBlog • Watch or listen from anywhere, subscribe to our podcast: https://microsoftmechanics.libsyn.com/podcast   ► Keep getting this insider knowledge, join us on social: • Follow us on Twitter: https://twitter.com/MSFTMechanics  • Share knowledge on LinkedIn: https://www.linkedin.com/company/microsoft-mechanics/ • Enjoy us on Instagram: https://www.instagram.com/msftmechanics/ • Loosen up with us on TikTok: https://www.tiktok.com/@msftmechanics

Check out new AI integrations for your Azure SQL databases. With Retrieval Augmented Generation, you can bridge structured data with generative AI, enhancing natural language queries across applications. With advanced vector-based semantic search, discover precise insights tailored to your data, while Copilot in Azure streamlines troubleshooting and T-SQL query authoring. Optimize workflows, personalize responses, and unlock new levels of efficiency in SQL-driven AI applications. Accelerate performance troubleshooting and complex query authoring tasks with Copilot in Azure. Quickly diagnose database issues and receive expert recommendations for optimization, ensuring optimal performance and reliability. Seamlessly traverse hierarchies within tables and generate intricate queries with ease, saving time and resources. Bob Ward, Azure Principal Architect, shows how to unleash the full potential of your SQL data, driving innovation and intelligence across your applications.   ► QUICK LINKS: 00:00 - AI and Azure SQL 01:40 - Using T-SQL for search 02:30 - Using Azure AI Search 03:17 - Vector embeddings and skillsets 04:08 - Connect your SQL data to an AI app 05:44 - Test it in Azure OpenAI Studio playground 07:22 - Combine native JSON data type in SQL 08:30 - Hybrid search 09:56 - Copilot in Azure: Performance troubleshooting 11:11 - Copilot in Azure: Query authoring 12:24 - Permissions 12:40 - Wrap up   ► Link References For building AI apps, check out https://aka.ms/sqlai  Try out new copilot experiences at https://aka.ms/sqlcopilot   ► Unfamiliar with Microsoft Mechanics?  As Microsoft's official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft. • Subscribe to our YouTube: https://www.youtube.com/c/MicrosoftMechanicsSeries • Talk with other IT Pros, join us on the Microsoft Tech Community: https://techcommunity.microsoft.com/t5/microsoft-mechanics-blog/bg-p/MicrosoftMechanicsBlog • Watch or listen from anywhere, subscribe to our podcast: https://microsoftmechanics.libsyn.com/podcast   ► Keep getting this insider knowledge, join us on social: • Follow us on Twitter: https://twitter.com/MSFTMechanics  • Share knowledge on LinkedIn: https://www.linkedin.com/company/microsoft-mechanics/ • Enjoy us on Instagram: https://www.instagram.com/msftmechanics/ • Loosen up with us on TikTok: https://www.tiktok.com/@msftmechanics  

Improve search capabilities for your PostgreSQL-backed applications using vector search and embeddings generated in under 10 milliseconds without sending data outside your PostgreSQL instance. Integrate real-time translation, sentiment analysis, and advanced AI functionalities securely within your database environment with Azure Local AI and Azure AI Service. Combine the Azure Local AI extension with the Azure AI extension to maximize the potential of AI-driven features in your applications, such as semantic search and real-time data translation, all while maintaining data security and efficiency. Joshua Johnson, Principal Technical PM for Azure Database for PostgreSQL, demonstrates how you can reduce latency and ensure predictable performance by running locally deployed models, making it ideal for highly transactional applications.   ► QUICK LINKS: 00:00 - Improve search for PostgreSQL 01:21 - Increased speed 02:47 - Plain text descriptive query 03:20 - Improve search results 04:57 - Semantic search with vector embeddings 06:10 - Test it out 06:41 - Azure local AI extension with Azure AI Service 07:39 - Wrap up   ► Link References Check out our previous episode on Azure AI extension at https://aka.ms/PGAIMechanics  Get started with Azure Database for PostgreSQL - Flexible Server at https://aka.ms/postgresql  To stay current with all the updates, check out our blog at https://aka.ms/azurepostgresblog   ► Unfamiliar with Microsoft Mechanics?  As Microsoft's official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft. • Subscribe to our YouTube: https://www.youtube.com/c/MicrosoftMechanicsSeries • Talk with other IT Pros, join us on the Microsoft Tech Community: https://techcommunity.microsoft.com/t5/microsoft-mechanics-blog/bg-p/MicrosoftMechanicsBlog • Watch or listen from anywhere, subscribe to our podcast: https://microsoftmechanics.libsyn.com/podcast   ► Keep getting this insider knowledge, join us on social: • Follow us on Twitter: https://twitter.com/MSFTMechanics  • Share knowledge on LinkedIn: https://www.linkedin.com/company/microsoft-mechanics/ • Enjoy us on Instagram: https://www.instagram.com/msftmechanics/ • Loosen up with us on TikTok: https://www.tiktok.com/@msftmechanics

Topics covered in this episode: Dataherald Python's many command-line utilities Distroless Python functools.cache, cachetools, and cachebox Extras Joke Watch on YouTube About the show Sponsored by ScoutAPM: pythonbytes.fm/scout Connect with the hosts Michael: @mkennedy@fosstodon.org Brian: @brianokken@fosstodon.org Show: @pythonbytes@fosstodon.org Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesdays at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: Dataherald Interact with your SQL database, Natural Language to SQL using LLMs. Allows you to set up an API from your database that can answer questions in plain English Uses include Allow business users to get insights from the data warehouse without going through a data analyst Enable Q+A from your production DBs inside your SaaS application Create a ChatGPT plug-in from your proprietary data Brian #2: Python's many command-line utilities Trey Hunner Too many to list, but here's some fun ones json.tool - nicely format json data calendar - print the calendar current by default, but you can pass in year and month gzip, ftplib, tarfile, and other unixy things handy on Windows cProfile & pstats Michael #3: Distroless Python via Patrick Smyth What is distroless anyway? These are container images without package managers or shells included. Debugging these images presents some wrinkles (can't just exec into a shell inside the image), but they're a lot more secure. Chainguard, creates low/no CVE distroless images based on our FOSS distroless OS, Wolfi. Some Python use-cases: docker run -it cgr.dev/chainguard/python:latest # The entrypoint is a Python REPL, since no b/a/sh is included docker run -it cgr.dev/chainguard/python:latest-dev # This is their dev version and has pip, bash, apk, etc. Brian #4: functools.cache, cachetools, and cachebox functools cache and lru_cache - built in cachetools - “This module provides various memoizing collections and decorators, including variants of the Python Standard Library's @lru_cache function decorator.” cachebox - “The fastest caching Python library written in Rust” Extras Brian: Python 3.12.4 is out VSCode has some pytest improvements Michael: Time for a bartender alternative, I've switched to Ice. Rocket.chat as an alternative to Slack Joke: CSS Cartoons

The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

One of the most common mistakes business owners make is thinking that there's some single magic key word or phrase that they must dominate. But that just isn't how search works anymore. Why? Because today, people are more likely to use natural language as they type or dictate to Siri. So instead of typing a short phrase like furnace repair, they're more likely to ask “Who can I call to replace my furnace?” or “Why is my furnace making a funny sound?”   Today if a prospective customer goes  to Google to look for a business like yours, they are using phrases and questions.  And your website must contain those words. They tell Google and other search engines exactly what your website or page is about. That information is then stored in the search engine database so it can be presented to prospective customers when they're looking for a product or service.   As you think about your keywords, think about natural language and how people are more likely to talk when they're looking for a business like yours.   If you've enjoyed this conversation sign up for a weekly newsletter get links to episodes you might've missed and other resources for your business :  https://morethanafewwords.com/avoid-fomo/

We have a perplexing couple of messages this week that appear to disable useful functionality in Teams and Microsoft 365. But that is countered by giving Copilot Insights and introducing a way to share meeting notes, files and other artifacts automatically for recurring Teams meetings. You want to know more. Come on. That teaser was cleverly crafted to create intrigue. Click the link. Darrell and Daniel cover:   - Updates to natural language-based search in Microsoft 365   - The Microsoft Copilot Dashboard in Viva Insights will become available with Copilot for Microsoft 365   - Ask to join a shared channel with a channel link in Teams   - Auto-create Microsoft Loop workspaces to share Teams meeting content   - Microsoft Viva Connections now available on the web   - Manage Do Not Disturb presence status when screen sharing in Teams meetings   Join Daniel Glenn and Darrell as a Service Webster as they cover the latest messages in the Microsoft 365 Message Center.   Follow us!   Twitter - Facebook - LinkedIn  Check out Daniel and Darrell's own YouTube channels at:  Daniel - https://youtube.com/DanielGlenn  Darrell - https://youtube.com/modernworkmentor 

After an illustrious career collaborating with universities and research centres, Enric Trillas remains set on working towards a new experimental science, managing the concepts and tools of computer science, and actually interacting with other disciplines on the way. Trillas sheds light on his recently translated The Genesis of Logic to explore the theoretical promise and real-world applications of fuzzy logic. Explore Fuzzy Logic and the FMsquare Foundation: researchoutreach.org/fmsquare-foundation-forging-fuzzy-future Read more on The Genesis of Logic and the Fuzzy Management Methods series

Build low-latency recommendation engines with Azure Cosmos DB and Azure OpenAI Service. Elevate user experience with vector-based semantic search, going beyond traditional keyword limitations to deliver personalized recommendations in real-time. With pre-trained models stored in Azure Cosmos DB, tailor product predictions based on user interactions and preferences. Explore the power of augmented vector search for optimized results prioritized by relevance. Kirill Gavrylyuk, Azure Cosmos DB General Manager, shows how to build recommendation systems with limitless scalability, leveraging pre-computed vectors and collaborative filtering for next-level, real-time insights.   ► QUICK LINKS: 00:00 - Build a low latency recommendation engine 00:59 - Keyword search 01:46 - Vector-based semantic search 02:39 - Vector search built-in to Cosmos DB 03:56 - Model training 05:18 - Code for product predictions 06:02 - Test code for product prediction 06:39 - Augmented vector search 08:23 - Test code for augmented vector search 09:16 - Wrap up   ► Link References Walk through an example at https://aka.ms/CosmosDBvectorSample  Try out Cosmos DB for MongoDB for free at https://aka.ms/TryC4M   ► Unfamiliar with Microsoft Mechanics?  As Microsoft's official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft. • Subscribe to our YouTube: https://www.youtube.com/c/MicrosoftMechanicsSeries • Talk with other IT Pros, join us on the Microsoft Tech Community: https://techcommunity.microsoft.com/t5/microsoft-mechanics-blog/bg-p/MicrosoftMechanicsBlog • Watch or listen from anywhere, subscribe to our podcast: https://microsoftmechanics.libsyn.com/podcast   ► Keep getting this insider knowledge, join us on social: • Follow us on Twitter: https://twitter.com/MSFTMechanics  • Share knowledge on LinkedIn: https://www.linkedin.com/company/microsoft-mechanics/ • Enjoy us on Instagram: https://www.instagram.com/msftmechanics/ • Loosen up with us on TikTok: https://www.tiktok.com/@msftmechanics  

What if Natural Language interface was just part of every applications features. Crazy thought. Can agent power AI outperform the latest models? Today we introduce the concept.

Aldo talks to Pelonomi Moiloa, CEO and co-founder of Lelapa AI and discusses the importance of building language models for African languages and the challenges faced in using models built in other places.  Pelonomi emphasizes the need to make AI accessible for everyone and explains how Lelapa AI is working towards that goal. Pelonomi shares her background and journey, as well as the challenges in growing the company. She also discusses the recognition and achievements of  Lelapa AI as well as the future plans for the company.  Takeaways Building language models for African languages is important for providing access to products, services, and information for people in Africa. Using models built in other places often fails to capture the context and nuances of African languages, highlighting the need for locally developed models. Lelapa AI aims to make AI accessible to everyone.  The company is expanding to more African languages and domains, such as education, agriculture, and healthcare. You can find out more about Pelonomi and the work of Lelapa AI here: https://lelapa.ai/about/the-team/ This Episode is made in partnership with: Cold Case Inc (use the code MESSY and get an exclusive 15% discount): https://bit.ly/3HN75PD Riverside (get a 20% discount by signing up via this link): https://bit.ly/3HCU4IC Preworn: Get a 25% discount using the code MESSY25: https://bit.ly/49bEXlD The Code Zone: https://bit.ly/3UlspmU

GPTScript is a new scripting language to automate your interactions with LLMs, Adam Wiggins conducts a retrospective on Muse, Nikita Prokopov surveyed a bunch of popular websites to see how much JS they loaded on their pages, Pages CMS is a no-hassle CMS for GitHub pages & Jim Nielsen writes about the subversive hyperlink.

GPTScript is a new scripting language to automate your interactions with LLMs, Adam Wiggins conducts a retrospective on Muse, Nikita Prokopov surveyed a bunch of popular websites to see how much JS they loaded on their pages, Pages CMS is a no-hassle CMS for GitHub pages & Jim Nielsen writes about the subversive hyperlink.

In this episode, we explore Databricks' strategic acquisition of the Einblick team and its implications for the future of natural language processing notebooks. We delve into how this move positions Databricks in the competitive landscape of data analytics and AI development. Invest in AI Box: ⁠https://Republic.com/ai-box⁠ Get on the AI Box Waitlist: ⁠https://AIBox.ai/⁠ ⁠AI Facebook Community Learn About ChatGPT Learn About AI at Tesla

Bard is our creative collaborator. It's a place where you can come in and have a conversation with the large language model which really helps you to boost your productivity and bring your ideas to life. –Yuri Pinsky, 02:16 Step behind the curtain and into the world of Google's Bard with its Director of Product Management, Yury Pinsky, in an exclusive conversation with SEJ Editor-in-Chief Amanda Zantal-Wiener.  Hear about the origins and journey to Bard's unveiling, and discover how the team behind it envisions a collaborative future with AI. SEO pros and seasoned digital marketers alike will get an up-close look at the nuances of generative AI and a glimpse at what predictions for what's next.  So prep your popcorn -- grab your notetaking method of choice -- and tune in to learn how to incorporate Google's current and forward-looking AI initiatives into your own business innovation.   [07:11] - The origin of Bard and its market niche. [13:23] - Impact of generative AI and Bard on SEO and content creation. [17:37] - Using Bard for audience evaluation in content creation. [23:54] - Distinctive features of Bard compared to other AI models. [28:33] - Most interesting prompts seen in Bard. [33:29] - Future vision for Bard and generative AI.   I'm inspired by this idea that technology can work together with us, and we can bring Bard in as a creative partner in your editorial work or when we're trying to write a document for work or something in our personal lives. –Yuri Pinsky, 09:46 It's a very vibrant, fast-paced, fast moving industry right now. I think some of the unique things we have with Bard are things like the ability to plug into Google tools. –Yuri Pinsky, 23:54 In the sciences and the medical field, there could be lots of interesting breakthroughs in drug discovery in climate applications. How can they use the power of these foundational models to really benefit all of us in some way? –Yuri Pinsky, 25:44 Your ideas still have to be your own in order for AI to work with you best and work for you best. –Yuri Pinsky, 28:33 It is not the end of search. Bard is an experiment. It's complementary to search. It's this conversational collaborator. –Yuri Pinsky, 32:47   Connect with Yury Pinsky: Yury is a Product Manager for Bard, leading areas including Extensions, Factuality, and multi-modality. Yury is passionate about cutting edge technology and finding ways to bring it to users around the world. Prior to serving in his current role, Yury led product teams around Natural Language and Speech Recognition for the Google Assistant, spent time building wearables in Google [X], and helped build out Google Search on mobile devices. Outside of work, Yury enjoys spending time with his family, planning his next vacation, and the daily logistics of kids' extracurricular activities. Connect on LinkedIn: https://www.linkedin.com/in/ypinsky    Connect with Amanda Zantal-Wiener: Follow her on Twitter: https://twitter.com/Amanda_ZW Connect with her on LinkedIn: https://www.linkedin.com/in/amandazantalwiener/ 

In this episode, host Jon Krohn explores with his guest Ajay Jain, Co-Founder of Genmo.ai, how creative general intelligence could take the video industry by storm. They also discuss the models that got Genmo to this point, the applications of NeRF, and how understanding human psychology is so essential to developing models that output high-fidelity video. This episode is brought to you by the Zerve data science dev environment (https://zerve.ai), by Grafbase (https://grafbase.com), the unified data layer, and by Modelbit (https://modelbit.com), for deploying models in seconds. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information. In this episode you will learn: • About Genmo.ai and the term “creative general intelligence” [03:47] • Why Ajay started Genmo.ai [09:26] • The increased performance of multimodal models [21:12] • All about Denoising Diffusion Probabilistic Models (DDPMs) [31:03] • The application of Neural Radiance Fields (NeRF) [55:26] • Predicting pedestrian behavior at Uber [1:01:50] • How to save money in the process of training models [1:12:42] Additional materials: www.superdatascience.com/711

Nvidia demoed its ACE for Games tech to let gamers talk to NPCs with natural language, JioCinema sets a concurrent streaming record, and Mark Gurman gets an early hands on with Meta's Quest 3 headset.Get the show notes here. Become a member at https://plus.acast.com/s/dtns. Hosted on Acast. See acast.com/privacy for more information.