Podcasts about image recognition

One of the most significant VoiceOver improvements in iOS 27 is the arrival of much richer, AI-powered image descriptions. In this episode, David Nason takes the feature for a hands-on test drive, showing how VoiceOver can provide far more detailed information about images than the simpler descriptions users may be familiar with. Rather than having to share an image with another app, these descriptions are integrated directly into VoiceOver and can be accessed using the rotor or a gesture of your choice.David demonstrates the different ways you can interact with visual content. Image Description provides a detailed account of an image, while Ask About Image lets you pose a specific question about what is shown. Explore Image combines the two approaches, first providing a description and then letting you type or dictate follow-up questions. These capabilities aren't limited to individual pictures: VoiceOver can describe an entire screen, answer questions about it, and even describe an individual item under VoiceOver focus. That last option could prove especially useful when encountering an unlabeled button or another inaccessible interface element.The episode then turns to customization. David walks through adding Image Recognition to the VoiceOver rotor, choosing which recognition options appear there, and rearranging them to suit your preferences. He also demonstrates assigning Intelligent Image Description directly to a VoiceOver gesture for almost instant access. Alternatively, you can assign a gesture to Show Recognition Options, which presents a menu and lets you choose the type of description or exploration you want each time.There's also a particularly useful option in Photos. David demonstrates generating an intelligent description of a photograph and saving it as that photo's VoiceOver label, meaning the description will subsequently be announced while browsing your photo library. He also discusses some areas where he would like to see the feature improve, including the ability to edit saved descriptions and receive more complete information about people and certain types of images. Despite these limitations, he comes away impressed by how quickly and conveniently the feature makes visual information accessible from within VoiceOver itself.If you're a blind, DeafBlind, or low-vision iPhone user who regularly encounters photographs, app artwork, unlabeled controls, or other visual information, this episode offers a practical look at what these new VoiceOver capabilities can do. It's also worth a listen if you already use third-party AI image-description tools, as David demonstrates why having this functionality integrated directly into VoiceOver could make exploring visual content considerably quicker and easier.TranscriptDisclaimer: This transcript was generated by AI Note Taker – VoicePen, an AI-powered transcription app. It is not edited or formatted, and it may not accurately capture the speakers' names, voices, or content.David: Hallo there and welcome to the AppleVis Podcast. My name is David Nason. This podcast is to demo one of the new and I think most important features, certainly for those of us using voiceover, in iOS 27. That feature is the new AI-powered image descriptions. Now you may be thinking we already had image descriptions in voiceover, but this…

In this episode, Levi Gobin demonstrates the new Live Recognition capabilities in iOS 27.TranscriptDisclaimer: This transcript was generated using Google Gemini, an artificial intelligence (AI) tool. It was lightly edited and formatted, and it may not accurately capture the speakers' names, voices, or content.Announcer: You're listening to another AppleVis podcast. Levi Gobin: Hello, everyone, and in this podcast, I'm going to be demonstrating the new features for Live Recognition and Magnifier in iOS 27. So the first big new feature is the fact that you can now ask Magnifier a question, and it will use Apple Intelligence to describe the scene. And you can access this from either the rotor or a VoiceOver gesture, or from the Magnifier app itself. I will be discussing all of these today. So I'm going to go ahead and unlock my phone. VoiceOver: 12:34, 98% charged. Levi's iPhone 17 Pro Max. Levi Gobin: Okay, it's reading my battery widget. And I'm going to now navigate to the the Live Recognition rotor and swipe down to Ask. VoiceOver: Characters. Actions, activate. Image Recognition, item. Text Navigation. L, Live Recognition, Ask, off. Levi Gobin: And I'm already on it. Um and now I'm going to double tap, and it will bring up the Ask flow like this. VoiceOver: Close. Question, text field, is editing, Ask, insertion point at start. Levi Gobin: Okay, and so now it puts me in a text field where I can type, but first I'm going to explore the interface and show you what else is here. VoiceOver: Close Transcript, button. Levi Gobin: So, a button to close the transcript, which will take you back to the Live Recognition area. VoiceOver: Ask, heading. Close Live Recognition, button. Question, text field, is editing, Ask, insertion point at start. Ask, dimmed, button. Hi, button. Levi Gobin: And uh that's all you have. So I can then type in a question. VoiceOver: Speech off. Speech on. Levi Gobin: Oops. Um, like VoiceOver: w h Do you see on the dot 15 Translated, box. Levi Gobin: on the box. I didn't type the a for some reason in the word what, but it will be able to understand it. And now I'm going to hit the Ask button. VoiceOver: Question, text Close Live Question, Ask, button. Levi Gobin: If you're using braille screen input, you can also swipe up with three fingers. Um but I have this box right here, and I'm going to hit the Ask button on it. VoiceOver: Just a moment. This is a box for an Instamic microphone recorder, which is labeled as a wearable, wireless, and waterproof device featuring 32-bit float recording. Levi Gobin: And say I wanted to um ask it VoiceOver: Space. Does it say how long you can dot 1 2 3 Record record Record on the box. Translated, box. Levi Gobin: And then I'm going to VoiceOver: Question, text Ask, button. Levi Gobin: hit the Ask button. VoiceOver: Just a moment. No, the visible text does not state the recording duration. Levi Gobin: Now I'm going to flip the box over. I'm not sure if it does say the actual duration on it, but that's why we have AI. I flipped it over, and I will say, VoiceOver: D How about Translated, now. Levi Gobin: And I will then hit the Ask button again. VoiceOver: F O P w h t Do you see on Question, text Ask, button. How Just a moment. No, the text on this side of the box does not state the recording duration. Levi Gobin: Okay, so the recording duration is not on the box. Um, so you can ask it directly from there, and you can also get to this flow by assigning a…

The CPG Guys are joined in this episode by David Gottlieb, Chief Revenue officer and Jeff Wrona, VP Product, Image Recognition for FORM, the makers of the award-winning market execution software GoSpotCheck and FORM OpX, and Trax, the industry-recognized global pioneer of Image Recognition, delivering AI-powered shelf-level insights that help brands and retailers improve execution, availability, and growth in the physical store, have merged. Follow David on LinkedIn at: https://www.linkedin.com/in/dmgottlieb/ Follow Jeff on LinkedIn at: https://www.linkedin.com/in/gospotcheckjw/Follow FORM online at: https://www.form.com/ This episode is sponsored by FORM.They answer these questions:When you combine Trax's global reach with FORM's innovative model training and deployment capabilities, what fundamentally changes for CPG brands on the ground?How does proactively onboarding the most popular SKUs in each region shift Image Recognition from just reactive reporting to a proactive competitive advantage?What does 'agentic AI' realistically look like inside a CPG organization over the next three to five years? Is it hype, or are we looking at an operational revolution? If you were building the modern CPG tech stack from scratch today, what happens when IR data is integrated directly into sales, supply chain, and marketing systems?Could shelf-level data become the fastest leading indicator of these generational behavior changes—even faster than syndicated data?In this margin-compressed world, does flawless in-store execution become the single biggest lever brands still control?How does integrating FORM's AI-powered image recognition directly with FORM's mobile task management fundamentally close that gap between identifying a shelf issue and executing a fix right there in the aisle?What unique execution challenges do traditional CPGs face when competing with the speed and emotional connection of these newer brands?How does leveraging AI and granular, SKU-level shelf intelligence help brands manage their physical presence with the same precision and responsiveness as their digital storefronts?If two brands have equal product quality and trade support, does the one with superior IR-driven visibility win every time?CPG Guys Website: http://CPGguys.comFMCG Guys Website: http://FMCGguys.comSheCOMMERCE Website: https://shecommercepodcast.com/Rhea Raj's Website: http://rhearaj.comLara Raj in Katseye: https://www.katseye.world/DISCLAIMER: The content in this podcast episode is provided for general informational purposes only. By listening to our episode, you understand that no information contained in this episode should be construed as advice from CPGGUYS, LLC or the individual author, hosts, or guests, nor is it intended to be a substitute for research on any subject matter. Reference to any specific product or entity does not constitute an endorsement or recommendation by CPGGUYS, LLC. The views expressed by guests are their own and their appearance on the program does not imply an endorsement of them or any entity they represent.CPGGUYS LLC expressly disclaims any and all liability or responsibility for any direct, indirect, incidental, special, consequential or other damages arising out of any individual's use of, reference to, or inability to use this podcast or the information we presented in this podcast.

The Rare Lens: AI-based image recognition for rare disease diagnoses There are around 6000 rare diseases, which affect over 300 million people worldwide. Rare diseases are often difficult to diagnose, and people affected by a rare disease wait an average of five years or more for a correct diagnosis, which can be a heavy burden on patients and also hinders rare disease research and drug development. Artificial Intelligence (AI) can help address these diagnostic challenges. One promising approach is Next-Generation Phenotyping (NGP), which uses AI to detect disease-specific phenotypic patterns – such as distinct facial features – commonly associated with rare diseases, to support diagnosis and guide genetic testing. In the newest episode of the BioRevolution podcast, Andreas Horchler and Louise von Stechow spoke to two researchers from the University of Bonn working on next-generation phenotyping approaches: Dr. Adele Ruder, MSL for GestaltMatcher, and Dr. Behnam Javanmardi, Group Leader: AI for Rare Diseases and Head of Bone2Gene. We explored the potential of AI-driven diagnosis for rare diseases and what this could mean for people affected by rare diseases, their physicians, and researchers and drug developers in the rare disease space. Find Adele and GestaltMatcher here: https://www.linkedin.com/in/adeleruder/ https://www.gestaltmatcher.org/ Find Behnam and Bone2Gene here: https://www.linkedin.com/in/behnam-javanmardi/ https://bone2gene.org For further reading explore the following references: 1. https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(24)00056-1/fulltext https://www.nature.com/articles/s41431-024-01604-z 2. https://pubmed.ncbi.nlm.nih.gov/19627523/ 3. https://www.researchsquare.com/article/rs-2110140/v1 4. https://elifesciences.org/articles/02020 5. https://www.face2gene.com/ 6. https://facematch.org.au/home 7. https://cliniface.org/ 8. https://www.gestaltmatcher.org/ 9. https://bone2gene.org/ 10. https://www.nature.com/articles/s41591-018-0279-0 11. https://www.nature.com/articles/s10038-019-0619-z 12. https://www.nature.com/articles/s41588-021-01010-x 13. https://ieeexplore.ieee.org/document/10030218 14. https://www.nature.com/articles/s41431-025-01787-z https://doi.org/10.1007/s00112-024-02118-0 15. https://pubmed.ncbi.nlm.nih.gov/36779427/ 16. https://www.nature.com/articles/s41588-023-01469-w 17. https://www.medrxiv.org/content/10.1101/2023.06.06.23290887v4 18. https://www.nature.com/articles/s41599-024-02894-w 19. https://www.nature.com/articles/d41586-022-03050-7 20. https://www.nature.com/articles/s41746-024-01232-3 21. https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202414507 22. https://www.medrxiv.org/content/10.1101/2023.06.06.23290887v4.full.pdf Disclaimer: Louise von Stechow & Andreas Horchler and their guests express their personal opinions, which are founded on research on the respective topics, but do not claim to give medical, investment or even life advice in the podcast. Learn more about the future of biotech in our podcasts and keynotes. Contact us here: scientific communication: https://science-tales.com/ Podcasts: https://www.podcon.de/ Keynotes: https://www.zukunftsinstitut.de/louise-von-stechow

4:45 – two (maybe three) rules for AI prompts5:15 – Rule 0 – mindset 5:45 – Rule 1 - be clear and specific8:05 – don't be discouraged8:25 – Rule 2 - have a conversation10:00 – keep going, don't settle10:50 – the Magic School conundrum14:00 – Khanmigo – one for teachers and one for students15:15 – Khanmigo will not provide answers – it's a tutor16:15 – Microsoft Copilot16:35 – Coach.microsoft (reading support)17:45 – Perplexity (powered by Claude and by ChatGPT)19:15 – to increase the quality of student work, give them an audience20:35 – students have stories to tell and they just don't know how21:00 – music, curiosity, passion, engagement, poetry, content areas22:00 – ChatGPT is the Coca-Cola of AI22:30 – there are a lot of AI chatbot options available, and a number are free23:45 – image, audio, video “categories” of AI24:30 – exponential vs. additive potential of AI growth27:05 – machine learning, language comprehension, image recognition28:00 – Neuralink – a brain interface chip – drive a computer with your mind alone28:45 – Blindsight – resolution improving and possibly humans with infared vision30:30 – the connection between and mutual dependence across: Power the energy sector, AI data and power consumption, national security, and climate concerns32:25 – data sets (prior knowledge), compute power (processing time or general intelligence + effort), algorithms for training (teaching, formative assessment)34:40 – how AI entered the most recent presidential election conversation35:30 – military, environmental, academic, geopolitical, and economic growth concerns are inextricably connected with AI39:45 – Donald Dowdy, high school band director40:40 – Bruce Little, Art Education Practicum instructor, Georgia Southern University42:30 – honor, discipline, respect, the craft of teaching43:25 – You can't replace relationships with AI BlindsightChatGPTClaudeCoach (Microsoft - reading support)Khanmigo (main page)Khanmigo for parentsKhanmigo for teachersMagic SchoolMicrosoft CopilotNeuralinkPerplexity Background image on cover is by Albert Stoynov, on Unsplash. This image replaces the standard cover art by Simon Berger (details in the footer). Music for Lead. Learn. Change. is Sweet Adrenaline by Delicate BeatsPodcast cover art is a view from Brunnkogel (mountaintop) over the mountains of the Salzkammergut in Austria, courtesy of photographer Simon Berger, published on www.unsplash.com.Professional Association of Georgia EducatorsDavid's LinkedIn pageLead. Learn. Change. the bookInstagram - lead.learn.change

Crazy Wisdom Key Takeaways  FarmBot is a robotic farmer for your garden, designed to take care of your garden by performing functions such as planting seeds, watering, weeding, and monitoringSimply being open source is not enough. For a project to be genuinely useful, it must also have extensive, clear documentation and use open, affordable file formatsToday, the vast majority of food that people eat is grown very far away and in ways that is not great for the food or environment We have very little control over the food production system, which is vital to our existence  Let us get back to the smaller scale, more diverse polycrop system of food production; many follow-on benefits will result Building a resilient alternative to industrial food systems (which often rely on single-crop farming) reduces single points of failure along vulnerable supply chains The more that we can distribute the food system and bring it closer to the end-eater, the more robust our overall food system becomes Read the full notes @ podcastnotes.orgOn this episode of Crazy Wisdom, Stewart Alsop speaks with Rory Aronson, CEO of FarmBot, about how his open-source hardware project is transforming home gardening into a more automated and accessible practice. Rory explains how FarmBot works—essentially as a CNC machine for your garden—covering its evolution from Arduino-based electronics to custom boards, the challenges of integrating hardware and software, and the role of closed-loop feedback systems to prevent errors. They explore solarpunk visions of distributed food systems, discuss the importance of “useful source” documentation in open-source hardware, and imagine a future where growing food is as easy as running a dishwasher. For more on Rory and FarmBot, check out farm.bot and the open-source resources at docs.farm.bot.Check out this GPT we trained on the conversationTimestamps00:00 Rory explains FarmBot as a CNC machine for gardens, using Arduino and Raspberry Pi, automating planting, watering, and weeding.05:00 Discussion on the hardware stack evolution, open-source electronics roots, and moving to custom boards for better integration.10:00 Stewart shares his Raspberry Pi experiments, Rory breaks down the software layers from cloud apps to firmware, emphasizing complexity.15:00 Conversation shifts to closed-loop feedback with rotary encoders, avoiding 3D printer-style “spaghetti” errors in outdoor environments.20:00 Rory explores open-source challenges, highlighting “useful source” documentation and hardware accessibility for modifications.25:00 Solarpunk vision emerges: distributed food systems, automation enabling home-grown fresh food without expert knowledge.30:00 Raised bed setup, energy efficiency, and FarmBot as a home appliance concept for urban and suburban gardens.35:00 Small-scale versus industrial farming, niche commercial uses like seedling automation, and user creativity with custom tools.40:00 AI potential with vision systems, LLMs for garden planning, and enhancing FarmBot intelligence for real-time adaptation.45:00 Sensors, soil monitoring, image analysis for plant health, and empowering users to integrate FarmBot into smart homes.50:00 Rory describes community innovations, auxiliary hardware, and open documentation supporting experimentation.55:00 Final reflections on solarpunk futures, automation as empowerment, and how to access FarmBot's resources online.Key InsightsRory Aronson shares how FarmBot began as a DIY project built on Arduino and Raspberry Pi, leveraging the open-source 3D printing ecosystem to prototype quickly. Over time, they transitioned to custom circuit boards to meet the specific demands of automating gardening tasks like seed planting, watering, and weeding, highlighting the tradeoffs between speed to market and long-term hardware optimization.The conversation unpacks the complexity of FarmBot's “stack,” which integrates cloud-based software, a web app, a message broker, a Raspberry Pi running a custom OS, and firmware on both Arduino and auxiliary chips for real-time feedback. This layered approach is crucial for precision in an unpredictable outdoor environment where mechanical errors could damage growing plants.Aronson emphasizes that being open source isn't enough; to be genuinely useful, projects must provide extensive, accessible documentation and export files in open, affordable formats. Without this, open source risks being a hollow promise for most users, especially in hardware where barriers to modification are higher.They explore the solarpunk potential of FarmBot, imagining a future where growing food at home is as effortless as using a washing machine. By turning gardening into an automated process, FarmBot enables people to produce fresh vegetables without needing expertise, offering resilience against industrial food systems reliant on monoculture and long supply chains.Aronson points out that while FarmBot isn't designed for industrial agriculture, its modularity allows it to support niche commercial use cases, like automating seedling production in cleanroom environments. This adaptability reflects the broader vision of empowering both individuals and small operations with accessible automation tools.The episode highlights user creativity enabled by FarmBot's open hardware, including custom tools like side-mounted mirrors for alternative camera angles and pneumatic grippers for harvesting. These community-driven innovations showcase the platform's flexibility and the value of encouraging experimentation.Finally, Aronson sees great potential for integrating AI, particularly vision systems and multimodal LLMs, to make FarmBot smarter—detecting pests, diagnosing plant health, and even planning gardens tailored to user goals like nutrient needs or event timelines, moving closer to a truly intelligent gardening companion.

Crazy Wisdom Key Takeaways  FarmBot is a robotic farmer for your garden, designed to take care of your garden by performing functions such as planting seeds, watering, weeding, and monitoringSimply being open source is not enough. For a project to be genuinely useful, it must also have extensive, clear documentation and use open, affordable file formatsToday, the vast majority of food that people eat is grown very far away and in ways that is not great for the food or environment We have very little control over the food production system, which is vital to our existence  Let us get back to the smaller scale, more diverse polycrop system of food production; many follow-on benefits will result Building a resilient alternative to industrial food systems (which often rely on single-crop farming) reduces single points of failure along vulnerable supply chains The more that we can distribute the food system and bring it closer to the end-eater, the more robust our overall food system becomes Read the full notes @ podcastnotes.orgOn this episode of Crazy Wisdom, Stewart Alsop speaks with Rory Aronson, CEO of FarmBot, about how his open-source hardware project is transforming home gardening into a more automated and accessible practice. Rory explains how FarmBot works—essentially as a CNC machine for your garden—covering its evolution from Arduino-based electronics to custom boards, the challenges of integrating hardware and software, and the role of closed-loop feedback systems to prevent errors. They explore solarpunk visions of distributed food systems, discuss the importance of “useful source” documentation in open-source hardware, and imagine a future where growing food is as easy as running a dishwasher. For more on Rory and FarmBot, check out farm.bot and the open-source resources at docs.farm.bot.Check out this GPT we trained on the conversationTimestamps00:00 Rory explains FarmBot as a CNC machine for gardens, using Arduino and Raspberry Pi, automating planting, watering, and weeding.05:00 Discussion on the hardware stack evolution, open-source electronics roots, and moving to custom boards for better integration.10:00 Stewart shares his Raspberry Pi experiments, Rory breaks down the software layers from cloud apps to firmware, emphasizing complexity.15:00 Conversation shifts to closed-loop feedback with rotary encoders, avoiding 3D printer-style “spaghetti” errors in outdoor environments.20:00 Rory explores open-source challenges, highlighting “useful source” documentation and hardware accessibility for modifications.25:00 Solarpunk vision emerges: distributed food systems, automation enabling home-grown fresh food without expert knowledge.30:00 Raised bed setup, energy efficiency, and FarmBot as a home appliance concept for urban and suburban gardens.35:00 Small-scale versus industrial farming, niche commercial uses like seedling automation, and user creativity with custom tools.40:00 AI potential with vision systems, LLMs for garden planning, and enhancing FarmBot intelligence for real-time adaptation.45:00 Sensors, soil monitoring, image analysis for plant health, and empowering users to integrate FarmBot into smart homes.50:00 Rory describes community innovations, auxiliary hardware, and open documentation supporting experimentation.55:00 Final reflections on solarpunk futures, automation as empowerment, and how to access FarmBot's resources online.Key InsightsRory Aronson shares how FarmBot began as a DIY project built on Arduino and Raspberry Pi, leveraging the open-source 3D printing ecosystem to prototype quickly. Over time, they transitioned to custom circuit boards to meet the specific demands of automating gardening tasks like seed planting, watering, and weeding, highlighting the tradeoffs between speed to market and long-term hardware optimization.The conversation unpacks the complexity of FarmBot's “stack,” which integrates cloud-based software, a web app, a message broker, a Raspberry Pi running a custom OS, and firmware on both Arduino and auxiliary chips for real-time feedback. This layered approach is crucial for precision in an unpredictable outdoor environment where mechanical errors could damage growing plants.Aronson emphasizes that being open source isn't enough; to be genuinely useful, projects must provide extensive, accessible documentation and export files in open, affordable formats. Without this, open source risks being a hollow promise for most users, especially in hardware where barriers to modification are higher.They explore the solarpunk potential of FarmBot, imagining a future where growing food at home is as effortless as using a washing machine. By turning gardening into an automated process, FarmBot enables people to produce fresh vegetables without needing expertise, offering resilience against industrial food systems reliant on monoculture and long supply chains.Aronson points out that while FarmBot isn't designed for industrial agriculture, its modularity allows it to support niche commercial use cases, like automating seedling production in cleanroom environments. This adaptability reflects the broader vision of empowering both individuals and small operations with accessible automation tools.The episode highlights user creativity enabled by FarmBot's open hardware, including custom tools like side-mounted mirrors for alternative camera angles and pneumatic grippers for harvesting. These community-driven innovations showcase the platform's flexibility and the value of encouraging experimentation.Finally, Aronson sees great potential for integrating AI, particularly vision systems and multimodal LLMs, to make FarmBot smarter—detecting pests, diagnosing plant health, and even planning gardens tailored to user goals like nutrient needs or event timelines, moving closer to a truly intelligent gardening companion.

On this episode of Crazy Wisdom, Stewart Alsop speaks with Rory Aronson, CEO of FarmBot, about how his open-source hardware project is transforming home gardening into a more automated and accessible practice. Rory explains how FarmBot works—essentially as a CNC machine for your garden—covering its evolution from Arduino-based electronics to custom boards, the challenges of integrating hardware and software, and the role of closed-loop feedback systems to prevent errors. They explore solarpunk visions of distributed food systems, discuss the importance of “useful source” documentation in open-source hardware, and imagine a future where growing food is as easy as running a dishwasher. For more on Rory and FarmBot, check out farm.bot and the open-source resources at docs.farm.bot.Check out this GPT we trained on the conversationTimestamps00:00 Rory explains FarmBot as a CNC machine for gardens, using Arduino and Raspberry Pi, automating planting, watering, and weeding.05:00 Discussion on the hardware stack evolution, open-source electronics roots, and moving to custom boards for better integration.10:00 Stewart shares his Raspberry Pi experiments, Rory breaks down the software layers from cloud apps to firmware, emphasizing complexity.15:00 Conversation shifts to closed-loop feedback with rotary encoders, avoiding 3D printer-style “spaghetti” errors in outdoor environments.20:00 Rory explores open-source challenges, highlighting “useful source” documentation and hardware accessibility for modifications.25:00 Solarpunk vision emerges: distributed food systems, automation enabling home-grown fresh food without expert knowledge.30:00 Raised bed setup, energy efficiency, and FarmBot as a home appliance concept for urban and suburban gardens.35:00 Small-scale versus industrial farming, niche commercial uses like seedling automation, and user creativity with custom tools.40:00 AI potential with vision systems, LLMs for garden planning, and enhancing FarmBot intelligence for real-time adaptation.45:00 Sensors, soil monitoring, image analysis for plant health, and empowering users to integrate FarmBot into smart homes.50:00 Rory describes community innovations, auxiliary hardware, and open documentation supporting experimentation.55:00 Final reflections on solarpunk futures, automation as empowerment, and how to access FarmBot's resources online.Key InsightsRory Aronson shares how FarmBot began as a DIY project built on Arduino and Raspberry Pi, leveraging the open-source 3D printing ecosystem to prototype quickly. Over time, they transitioned to custom circuit boards to meet the specific demands of automating gardening tasks like seed planting, watering, and weeding, highlighting the tradeoffs between speed to market and long-term hardware optimization.The conversation unpacks the complexity of FarmBot's “stack,” which integrates cloud-based software, a web app, a message broker, a Raspberry Pi running a custom OS, and firmware on both Arduino and auxiliary chips for real-time feedback. This layered approach is crucial for precision in an unpredictable outdoor environment where mechanical errors could damage growing plants.Aronson emphasizes that being open source isn't enough; to be genuinely useful, projects must provide extensive, accessible documentation and export files in open, affordable formats. Without this, open source risks being a hollow promise for most users, especially in hardware where barriers to modification are higher.They explore the solarpunk potential of FarmBot, imagining a future where growing food at home is as effortless as using a washing machine. By turning gardening into an automated process, FarmBot enables people to produce fresh vegetables without needing expertise, offering resilience against industrial food systems reliant on monoculture and long supply chains.Aronson points out that while FarmBot isn't designed for industrial agriculture, its modularity allows it to support niche commercial use cases, like automating seedling production in cleanroom environments. This adaptability reflects the broader vision of empowering both individuals and small operations with accessible automation tools.The episode highlights user creativity enabled by FarmBot's open hardware, including custom tools like side-mounted mirrors for alternative camera angles and pneumatic grippers for harvesting. These community-driven innovations showcase the platform's flexibility and the value of encouraging experimentation.Finally, Aronson sees great potential for integrating AI, particularly vision systems and multimodal LLMs, to make FarmBot smarter—detecting pests, diagnosing plant health, and even planning gardens tailored to user goals like nutrient needs or event timelines, moving closer to a truly intelligent gardening companion.

Dr. Neil Parikh, Chief Innovation Officer at Connecticut GI and Chair of the GI Alliance Innovation Committee, brings a refreshingly grounded perspective to the future of gastroenterology. For him, innovation isn't research—it's real-world feasibility. It's pilot programs born from pain points like access, cost, and patient satisfaction. And it's about using the scale and clinical volume of independent practices to drive meaningful change—shaping not just care delivery, but potentially the guidelines themselves.In this candid conversation, Dr. Parikh maps out where GI is headed: AI-powered triage and documentation, actionable microbiome data, non-invasive diagnostics, and the quiet revolution of food and alternative therapies. He also confronts uncomfortable truths—how procedural pressures limit innovation, why most ideas fail, and how listening to patients might be the most radical act of all.*02:24 - Innovation Is Feasibility, Not Just ResearchDr. Parikh distinguishes innovation from academic research—it's about pilots and practical implementation at scale within large independent practices.04:11 - Pain Points Drive InnovationThe three main challenges—access, cost, and patient satisfaction—are core drivers of innovation in gastroenterology today.05:31 - AI in Every Step of the GI WorkflowFrom pre-visit triage to in-room documentation and post-visit care coordination, AI is envisioned as a co-pilot in delivering efficient, real-time GI care.08:56 - Image Recognition in Endoscopy Is Just the BeginningAI's role in computer-aided detection (CADe) is evolving beyond polyps—towards liver, pancreas, and upper GI diagnostics.09:34 - The Microbiome Will Become ActionableThe future lies not just in mapping gut flora but in personalizing interventions (diet, lifestyle) based on microbiome data—and gastroenterologists must lead this shift.12:16 - The Future of GI Will Require Non-Invasive DiagnosticsLimited capacity for colonoscopies and rising costs necessitate non-invasive tools to triage and expand screening for cancers and chronic conditions.21:49 - Innovation Needs Leadership That Sees Beyond RVUsFor roles like his to exist, organizational leaders must value long-term transformation over short-term productivity.28:28 - Podcasts as an Innovation EngineDr. Parikh's Gut Doctor podcast began as an internal education tool but evolved into a national platform.31:18 - Dr. Parikh Integrates Food, Lifestyle, and Alternative Therapies into GI CareFrom yogurt to apple cider vinegar to adult coloring books, Dr. Parikh integrates low-risk, patient-driven solutions into mainstream GI care—with humility and openness.42:07 - The Gut-Brain Axis Is Central to GI CareStress, sleep, and mental health directly affect GI function. Behavioral tools like CBT, mindfulness, and even “phone detox” are practical levers for healing.47:06 - Physicians Still Want to See Patients—The System Doesn't Let ThemContrary to perception, most GIs value patient interactions. However, systemic incentives push them toward procedures. Changing reimbursement models can rebalance the equation.52:24 - Younger Physicians Need to Be Honest About Their GoalsTrainees often say what's expected to secure jobs. Neil advises them to take time, be honest with themselves and their future employers to build meaningful careers.*#digitalhealth #gastroenterology #thescopeforwardshow #nextservices #gi #future #ai #theshift

Video - https://youtu.be/xjRjKBLt8dYPutting Gemini Live to work analyzing an old souvenir.I was amazed at the amount of detail it "sees" and how it passes that information on to the user.It isn't human but it's getting closer. Why not put it to work and let it discover some memories from your own past.

In this episode we are looking at an area which impacts every business in the world. Unstructured data - that is, how we can start to squeeze insight from the piles of text, audio, video, and every other type of data that doesn't fit into a neat table.Carefully analysed, it can contain valuable insight, to be compared against other more traditional metrics such as sales figures, or economic results.Joining us to discuss is Gokul Sathiacama, VP of data storage for AI at Hewlett Packard Enterprise.This is Technology Now, a weekly show from Hewlett Packard Enterprise. Every week we look at a story that's been making headlines, take a look at the technology behind it, and explain why it matters to organizations and what we can learn from it. About this week's guest, Gokul Sathiacama: https://www.linkedin.com/in/gokuls/Sources cited in this week's episode:Statistics on global data generation: https://www.statista.com/statistics/871513/worldwide-data-created/Statistics on global IOT devices: https://paxtechnica.org/?page_id=738#:~:text=%E2%80%9COur%20IoT%20world%20is%20growing,billion%20by%202020.%E2%80%9D%20Intel.&text=Gartner.&text=Cisco.,-2011&text=%E2%80%9CGlobal%20M2M%20connections%20will%20increase,at%20the%20end%20of%202022.Global Web Index stats on smart devices: https://www.globalwebindex.net/

In this episode we are looking at an area which impacts every business in the world. Unstructured data - that is, how we can start to squeeze insight from the piles of text, audio, video, and every other type of data that doesn't fit into a neat table.Carefully analysed, it can contain valuable insight, to be compared against other more traditional metrics such as sales figures, or economic results.Joining us to discuss is Gokul Sathiacama, VP of data storage for AI at Hewlett Packard Enterprise.This is Technology Now, a weekly show from Hewlett Packard Enterprise. Every week we look at a story that's been making headlines, take a look at the technology behind it, and explain why it matters to organizations and what we can learn from it. About this week's guest, Gokul Sathiacama: https://www.linkedin.com/in/gokuls/Sources cited in this week's episode:Statistics on global data generation: https://www.statista.com/statistics/871513/worldwide-data-created/Statistics on global IOT devices: https://paxtechnica.org/?page_id=738#:~:text=%E2%80%9COur%20IoT%20world%20is%20growing,billion%20by%202020.%E2%80%9D%20Intel.&text=Gartner.&text=Cisco.,-2011&text=%E2%80%9CGlobal%20M2M%20connections%20will%20increase,at%20the%20end%20of%202022.Global Web Index stats on smart devices: https://www.globalwebindex.net/

In this episode we are looking at an area which impacts every business in the world. Unstructured data - that is, how we can start to squeeze insight from the piles of text, audio, video, and every other type of data that doesn't fit into a neat table.Carefully analysed, it can contain valuable insight, to be compared against other more traditional metrics such as sales figures, or economic results.Joining us to discuss is Gokul Sathiacama, VP of data storage for AI at Hewlett Packard Enterprise.This is Technology Now, a weekly show from Hewlett Packard Enterprise. Every week we look at a story that's been making headlines, take a look at the technology behind it, and explain why it matters to organizations and what we can learn from it. About this week's guest, Gokul Sathiacama: https://www.linkedin.com/in/gokuls/Sources cited in this week's episode:Statistics on global data generation: https://www.statista.com/statistics/871513/worldwide-data-created/Statistics on global IOT devices: https://paxtechnica.org/?page_id=738#:~:text=%E2%80%9COur%20IoT%20world%20is%20growing,billion%20by%202020.%E2%80%9D%20Intel.&text=Gartner.&text=Cisco.,-2011&text=%E2%80%9CGlobal%20M2M%20connections%20will%20increase,at%20the%20end%20of%202022.Global Web Index stats on smart devices: https://www.globalwebindex.net/

4th AHA 2024: AI image-recognition for heart images

Co-hosts Mark Thompson and Steve Little explore the potential benefits of Meta's open-source approach to AI. Next, they discuss MyHeritage's plans to retire an AI feature. Then, they review the AI image generation features recently added to Adobe Illustrator. In this week's Tip of the Week, they share valuable insights on crafting effective, hallucination-resistant, AI prompts using the “role, task, and format” prompting method.The RapidFire segment covers Apple's AI delays, Google's impressive math achievements, Reddit's web crawling restrictions, OpenAI's venture into AI-based search, and Meta's groundbreaking image recognition advancements.This episode offers a perfect blend of practical applications and future possibilities, making it essential listening for genealogists navigating the AI revolution. Whether you're a tech enthusiast or a family history buff, this show provides the knowledge you need to stay ahead in the rapidly changing world of AI.Timestamps:## In the News03:18 Meta's Impact on Corporate Genealogy: Discussion of Meta.ai's release and its implications07:54 MyHeritage's AI Feature Removal: Retirement of AI Record Finder tool10:11 Adobe's AI Integration: New AI features in Adobe Illustrator ## Tip of the Week19:32 Building Better Prompts: Role, Task, and Format: Explanation and examples of this prompting technique ## RapidFire Topics27:55 Apple's AI Delays: Postponement of Apple Intelligence Tools33:18 Google's AI Math Achievement: AI Performance in Math Olympics36:55 Reddit's Web Crawling Restrictions: Implications for AI training data41:22 OpenAI's SearchGPT Development: Potential impact on search engines and competitors45:30 Meta's SAM 2 Release: Advancements in image and video recognition Resource Links:Adobe Acrobat: https://acrobat.adobe.com/Adobe Illustrator: https://www.adobe.com/products/illustrator.htmlAdobe Lightroom: https://www.adobe.com/products/photoshop-lightroom.htmlAdobe Photoshop: https://www.adobe.com/products/photoshop.htmlAirtable: https://www.airtable.com/Apple Intelligence: https://www.apple.com/apple-intelligence/Canva: https://www.canva.com/ChatGPT: https://chat.openai.com/Claude (Anthropic): https://claude.ai/FamilySearch: https://www.familysearch.org/Gemini (Google): https://deepmind.google/technologies/gemini/Google AI: https://ai.google/Google Docs: https://docs.google.com/Meta AI: https://ai.meta.com/Microsoft Copilot: https://copilot.microsoft.com/MyHeritage: https://www.myheritage.com/OpenAI: https://openai.com/Perplexity: https://www.perplexity.ai/Reddit: https://www.reddit.com/SearchGPT: https://openai.com/index/searchgpt-prototype/Segment Anything Model (SAM) by Meta: https://ai.meta.com/sam2/Tags: AI in Genealogy, Meta AI, MyHeritage, Adobe Illustrator, AI Prompts, Open Source AI, Apple Intelligence, Google AI, Reddit, OpenAI, SearchGPT, SAM 2, Image Recognition, Family History, Genealogy Tools, AI Ethics, Transcription, Optical Character Recognition (OCR), Handwritten Text Recognition (HTR), Perplexity, Large Language Models, AI-Enabled Search, Artificial Intelligence, Genealogical Research, AI Record Finder, Prompting Techniques, AI Integration, Web Crawling, Data Privacy, Machine Learning, Computer Vision, AI in Adobe Products, AI Math Capabilities, Digital Genealogy, AI-Powered Tools, Genealogy Software, AI Advancements, Family Tree Research, AI for Historians, Future of Genealogy

Pratik Desai is the founder of Kissan, an AI startup that provides a multilingual voice-based co-pilot app for farmers in India, primarily used for market access queries, crop health issues, and exploring new farming techniques. 

Our guest is Peter Vasilyev, Business Development Director Europe at Ailet, an Image Recognition Solution. He joins the show to speak about how this technology can empower FMCG sales forces, as well as Retailers and Distributors to really get a deep understanding of brand and category performance through quality data at scale. Tune into this conversation to learn about: How Image Recognition Technology can obtain store data with 95% accuracy Different KPIs that the technology supplies to FMCG companies Types of data that come from analyzing shelfs at scale How to implement the solution within a company What sets Ailet apart from other solutions in the market More: Follow us on Instagram: https://www.instagram.com/fmcgguys/  Follow us on LinkedIn: https://www.linkedin.com/company/fmcgguys/ Audio Mixing by Rodrigo Chávez Voice Acting by Jason Martorell Parsekian

SPREAKER, PODCAST, PODCASTING, AI, ARTIFICIALINTELLIGENCE, DIGITALMARKETING, marketing, FutureMarketing, AIFuture, FutureofAISpreaker Top Podcast of the Year in Artificial Intelligence Digital Marketing - AI DigitalMarketingis Digital Marketing Legend Leaks, Srinidhi Ranganathan - the human AI. THE BEST IN CREATIVE FICTION AND NON-FICTION!Become a supporter of this podcast: https://www.spreaker.com/podcast/digital-marketing-legend-leaks--4375666/support.

On today's episode of Virtual Sentiments, host Kristen Collins interviews John Kaufhold on the history of image recognition and deep learning. With over 30 years of experience in the artificial intelligence and machine learning world, John shares his history starting from his early days in speech recognition in the 90s. He covers the ImageNet Big Bang in 2012, the dramatic improvement of image recognition error rates and hardware power, neural networks, the development of chatbots, terminology, and discusses challenges such as data privacy, bias reproduction, existential risk, transparency in data sets, and more!Dr. John Kaufhold is an expert with over 30 years of experience in artificial intelligence and deep learning. He is the founder of Deep Learning Analytics, a machine learning company, and serves on the Advisory Board of the DC Data Community.References and related works to this episode: "Munk Debate on Artificial Intelligence | Bengio & Tegmark vs. Mitchell & LeCun" and Data Science DC's "How Attention in 2017 got us Chat GPT."Read more work from Kristen Collins.If you like the show, please subscribe, leave a 5-star review, and tell others about the show! We're available on Apple Podcasts, Spotify, Amazon Music, and wherever you get your podcasts.Follow the Hayek Program on Twitter: @HayekProgramLearn more about Academic & Student ProgramsFollow the Mercatus Center on Twitter: @mercatus

In this episode of Double Tap, Steven Scott and Shaun Preece dive into the world of accessible technology with their usual banter.The guys discuss the news from Google that the Pixel 8 smartphone won't be able to run their latest Gemini AI, while Wear OS 4 gets a new text to speech voice. Plus there are rumours about Apple's new upcoming accessibility features coming out in iOS 18 and Mac OS 15.Our main story today focuses on JAWS, the screenreader made by Vispero. The company's Vice President of Software and Product Development joins Steven and Shaun to discuss the new AI image recognition feature available now via their Early Adopters Program, and Michael Babcock joins in to share his first thoughts providing us with a short demo of the new feature in action.Keep in touch by emailing us feedback@doubletaponair.com or call 1-877-03-4567 and leave us a voicemail. You can also find us across social media.

Here are the topics covered in this episode, and the time in the file for each. Welcome to 269 0:00 Three extra episodes coming up 1:35 The founders of Envision, Karthik Mahadevan and Karthik Kannan, discuss Envision today and in the near future 4:26 iOS 17.4 is a significant release 1:07:57 JAWS introduces AI image recognition 1:22:23 Comments on the Deane Blazie interview 1:37:46 More memories of David Holladay 1:42:03 The Bonnie bulletin 1:44:28 Closing and contact info 1:59:59

Latent Space is heating up! Our paper club ran into >99 person Discord limits, oops. We are also introducing 2 new online meetups: LLM Paper Club Asia for Asia timezone (led by Ivan), and AI in Action: hands-on application of AI (led by KBall). To be notified of all upcoming Latent Space events, subscribe to our new Luma calendar (sign up for individual events, or hit the RSS icon to sync all events to calendar).In the halcyon open research days of 2022 BC (Before-ChatGPT), DeepMind was the first to create a SOTA multimodal model by taking a pre-existing LLM (Chinchilla 80B - now dead?) and pre-existing vision encoder (CLIP) and training a “glue” adapter layer, inspiring a generation of stunningly cheap and effective multimodal models including LLaVA (one of the Best Papers of NeurIPS 2023), BakLLaVA and FireLLaVA. However (for reasons we discuss in today's conversation), DeepMind's Flamingo model was never open sourced. Based on the excellent paper, LAION stepped up to create OpenFlamingo, but it never scaled beyond 9B. Simultaneously, the M4 (audio + video + image + text multimodality) research team at HuggingFace announced an independent effort to reproduce Flamingo up to the full 80B scale:The effort started in March, and was released in August 2023.We happened to visit Paris last year, and visited HuggingFace HQ to learn all about HuggingFace's research efforts, and cover all the ground knowledge LLM people need to become (what Chip Huyen has termed) “LMM” people. In other words:What is IDEFICS?IDEFICS is an Open Access Visual Language Model, available in 9B and 80B model sizes. As an attempt to re-create an open-access version of Flamingo, it seems to track very well on a range of multimodal benchmarks (which we discuss in the pod):You can see the reasoning abilities of the models to take a combination of interleaved images + text in a way that allows users to either describe images, ask questions about the images, or extend/combine the images into different artworks (e.g. poetry).

今年 OnBoard! 最后一期压轴上新！今年要谈论人工智能，怎么能错过这么一个重要的话题：机器人与AI的结合，或者说，Embodied intelligence, 具身智能。大模型的思路是否能带来机器人的ChatGPT时刻？机器人要具备泛化能力，有哪些进展又有哪些瓶颈？通过机器人让人工智能具备与环境感知和交互的能力，会为通用人工智能AGI带来哪些新的想象空间？ Hello World, who is OnBoard!? 今年下半年以来，尤其在国内，已经有不下十几家具身智能创业公司涌现。这一轮热潮中，从学术到工业落地，如何分别噪音与真实？以前将AI应用于机器人的尝试，比起这次的技术突破，又有哪些相同与不同？这次的嘉宾阵容，真是太适合回答这些问题了： 我们邀请了 Google DeepMind 的研究员Fei Xia，Deepmind 跟具身智能相关的最重磅的几个研究，从SayCan, PaLM-E，到 RT2，他都是核心参与者。 还有来自国内头部机器人创业公司高仙机器人的深度学习总监 Jiaxin, 带来产业界的视角。 以及 UCSD 的研究员 Fanbo Xiang，他参与的 Maniskill，SAPIEN 等与模拟环境相关的研究，都在学术前沿。 我们对AI泛化能力在机器人领域的落地进行了深入的讨论，也有不同观点的碰撞，精彩纷呈。其实这一期的录制已经过去了几个月，阴差阳错成了今年的压轴，也算是对于OnBoard 全年的一个圆满句号，又是整个OnBoard 旅程小小的逗号。新的一年，不论世界如何起落，我们都选择相信未来有希望，珍惜每一次对话，赞美每一个在未知中选择的勇士。Enjoy! 嘉宾介绍 Fei Xia, Google Deepmind 机器人团队资深研究员，PhD @Stanford University；PaLM-E, PaLM-SayCan, RT-2 作者 Jiaxin Li, 高仙机器人深度学习总监，ex字节跳动研究员，PhD @National University of Singapore Fanbo Xiang, PhD @UC San Diego；ManiSkill, SAPIEN 作者 OnBoard! 主持：Monica：美元VC投资人，前 AWS 硅谷团队+ AI 创业公司打工人，公众号M小姐研习录 (ID: MissMStudy) 主理人 | 即刻：莫妮卡同学 我们都聊了什么 02:47 几位嘉宾的自我介绍，主要的研究领域 05:34 大家最近看到的与具身智能相关的有意思的研究和行业进展 14:23 自动驾驶领域的生成模型，如何保证符合物理规律？ 18:34 如何定义具身智能？什么是测试机器人AGI 的“咖啡测试” ？ 27:59 梳理 Google Deepmind 机器人领域核心研究脉络：大模型对具身智能带来怎样的影响？ 40:29 Fanbo 在做的 low level 控制相关的研究，如何与大模型相结合？ 45:39 具身智能的实现目前有哪些主要技术路径？我们什么时候可以达到共识？ 50:40 从产业落地的角度，如何看待大模型对机器人领域的影响？有哪些现实的挑战？ 67:37 什么时候需要机器人具备通用能力？我们需要端到端的具身智能吗？ 72:47 对 Scaling law 的争议：在机器人领域能复现吗？如何平衡长期通用性研究和短期商业落地的需要？ 90:41 在具身智能系统的设计中，如何考虑加入人机互动的因素？ 96:29 硬件的发展会如何影响具身智能的发展？ 101:18 未来3-5年，大家最期望看到具身智能领域实现怎样的突破？有怎样值得期待的未来？ 重要论文和词汇 PaLM-E: An Embodied Multimodal Language Model SayCan: Do As I Can, Not As I Say: Grounding Language in Robotic Affordances RT-1: Robotics Transformer for Real-World Control at Scale RT-2: Vision-Language-Action Models ManiSkill: Learning-from-Demonstrations Benchmark for Generalizable Manipulation Skills ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills SAPIEN: A SimulAted Part-based Interactive ENvironment NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models, by Feifei Li VLM: Task-agnostic Video-Language Model Pre-training for Video Understanding Scaling laws for neural language models, by OpenAI Vision Transformer (ViT) - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale ALOHA: A Low-cost Open-source Hardware System for Bimanual Teleoperation, from Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware The Bitter Lesson, by Rich Sutton MIT PDDL (Planning Domain Definition Language) sim2real: simulation to reality 我们提到的公司 Wayve.ai: reimagining self-driving with embodied AI 有鹿智能 LoCoBot: An Open Source Low Cost Robot 宇树科技 欢迎关注M小姐的微信公众号，了解更多中美软件、AI与创业投资的干货内容！ M小姐研习录 (ID: MissMStudy) 大家的点赞、评论、转发是对我们最好的鼓励！如果你能在小宇宙上点个赞，Apple Podcasts 上给个五星好评，就能让更多的朋友看到我们努力制作的内容，打赏请我们喝杯咖啡，就给你比心！

  טרנספורמרים הם ללא ספק המודל המועדף בעיבוד שפה - אבל האם גם בתמונות? אז... מסתבר שהתמונה לא כל כך ברורה בעיבוד תמונה. בפרק זה נדבר על היתרונות הגדולים של טרנספורמרים בתמונות - ועל הסיטואציות שבהן CNN מנצחות אותם.   קישורים Attention is All You Need An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale End-to-End Object Detection with Transformers Vision-Transformer-papers https://medium.com/@monocosmo77/best-research-papers-on-vision-transformers-784e48a3593a https://paperswithcode.com/methods/category/vision-transformer

Build and deploy copilot style apps that leverage the power of both GPT-4 Turbo with Vision and Azure AI Vision and Search in Microsoft's Azure AI Studio. Enable direct lookups from image inputs over your organizational data to ground generative AI responses. This marks a significant improvement in the accuracy of natural language processing and image recognition tasks to enable new generative AI scenarios. Video inputs are also uniquely supported when you combine GPT-4 Turbo with Vision and Azure AI Vision. Seth Juarez, Principal Program Manager for Azure AI, shares how it's easy to build and orchestrate powerful copilot style apps.   ► QUICK LINKS: 00:00 - GPT-4 Turbo with Vision + Azure AI Vision 00:42 - Baseline capabilities of GPT-4 Turbo with Vision 02:43 - Direct lookups of image and video data 04:53 - See the two combined: Demo 05:52 - How to build it 07:17 - See the code behind your app 08:07 - Wrap up   ► Link References Start using Azure AI Studio today at https://ai.azure.com Watch a detailed overview at https://aka.ms/AzureAIStudioMechanics Check out our QuickStart guides at https://aka.ms/LearnAIStudio     ► 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

An airhacks.fm conversation with Jose Paumard (@JosePaumard) about: TI-57 was stateless Oric 1, BigDecimal use cases, the travelling salesman algorithm, the Cray, working with Sun SPARC machines, CM5 and NeXTcube, the conference in generate code, star recognition, working at research Lab in Paris, enjoying emacs, emacs vs. vim, writing documentation in LatEx working on SunOS then Solaris, HPUX and CDE, 512 MB RAM of the price of a flat in Paris, processing large images and recognising building in real time, wavelet and cosine transforms, starting as professor in 1994 , JDBC war leased in 1997 with Java 1.1., working as devrel at Oracle three years again, running AI models, project Panama is the bridge, Java innovation, pattern matching in Java, String Templates, Java 21 LTS, youbube.com/java Jose Paumard on twitter: @JosePaumard

This is the AI News Briefing of October 09, 2023.(00:39): Rise of LLaVa(01:13): Flot.AI Unveiled(01:36): Epik's Viral Yearbook(02:11): OpenAI's AI Chips(02:28): Walmart's AI Shopping(02:37): Meta's Sticker FiascoRise of LLaVa: https://llava-vl.github.io/Flot.AI Unveiled: https://flot.ai/?via=AdeptoEpik's Viral Yearbook: https://petapixel.com/2023/10/05/epik-apps-ai-90s-yearbook-photo-trend-is-taking-over-the-internet/OpenAI's AI Chips: https://www.reuters.com/technology/chatgpt-owner-openai-is-exploring-making-its-own-ai-chips-sources-2023-10-06/Walmart's AI Shopping: https://techcrunch.com/2023/10/04/walmart-experiments-with-new-generative-ai-tools-that-can-help-you-plan-a-party-or-decorate-a-space/Meta's Sticker Fiasco: https://gizmodo.com/meta-ai-stickers-generate-controversial-cartoon-images-1850898685Follow our newsletter at www.adepto.ai for a deeper dive into these fascinating developments and for the latest AI news and insights.The AI News Briefing has been produced by Adepto in cooperation with Wondercraft AI.Music: Inspire by Kevin MacLeod (incompetech.com), Licensed under Creative Commons: By Attribution 3.0 http://creativecommons.org/licenses/by/3.0/

Guest: William Wu, CEO at Artisse [@artisseai]On Linkedin | https://www.linkedin.com/in/william-wu/____________________________Host: Marco Ciappelli, Co-Founder at ITSPmagazine [@ITSPmagazine] and Host of Redefining Society PodcastOn ITSPmagazine | https://www.itspmagazine.com/itspmagazine-podcast-radio-hosts/marco-ciappelli_____________________________This Episode's SponsorsBlackCloak

This podcast is a commentary and does not contain any copyrighted material of the reference source. We strongly recommend accessing/buying the reference source at the same time. ■Reference Source https://www.ted.com/talks/matt_mills_image_recognition_that_triggers_augmented_reality ■Post on this topic (You can get FREE learning materials!) https://englist.me/67-academic-words-reference-from-matt-mills-image-recognition-that-triggers-augmented-reality-ted-talk/ ■Youtube Video https://youtu.be/pgdh9VAViDo (All Words) https://youtu.be/ZHYOy3qntSM (Advanced Words) https://youtu.be/re-PNrv-5Dg (Quick Look) ■Top Page for Further Materials https://englist.me/ ■SNS (Please follow!)

AI is another major technological innovation. AI needs data, or more precisely, big organized data. Most data processing is about making it useful for automatic systems such as machine learning, deep learning, and other AI systems. But one big problem with AI systems is that they lack context. An AI system is a pattern recognition machine devoid of any understanding of how the world works.This lecture discusses how AI systems are used in business and their limitations.A lecture by Raghavendra Rau recorded on 22 May 2023 at Barnard's Inn Hall, London.The transcript and downloadable versions of the lecture are available from the Gresham College website: https://www.gresham.ac.uk/watch-now/ai-businessGresham College has offered free public lectures for over 400 years, thanks to the generosity of our supporters. There are currently over 2,500 lectures free to access. We believe that everyone should have the opportunity to learn from some of the greatest minds. To support Gresham's mission, please consider making a donation: https://gresham.ac.uk/support/Website:  https://gresham.ac.ukTwitter:  https://twitter.com/greshamcollegeFacebook: https://facebook.com/greshamcollegeInstagram: https://instagram.com/greshamcollegeSupport the show

YT version: https://youtu.be/P1j3VoKBxbc (references in pinned comment) Support us! https://www.patreon.com/mlst MLST Discord: https://discord.gg/aNPkGUQtc5 Dan McQuillan, a visionary in digital culture and social innovation, emphasizes the importance of understanding technology's complex relationship with society. As an academic at Goldsmiths, University of London, he fosters interdisciplinary collaboration and champions data-driven equity and ethical technology. Dan's career includes roles at Amnesty International and Social Innovation Camp, showcasing technology's potential to empower and bring about positive change. In this conversation, we discuss the challenges and opportunities at the intersection of technology and society, exploring the profound impact of our digital world. Interviewer: Dr. Tim Scarfe [00:00:00] Dan's background and journey to academia [00:03:30] Dan's background and journey to academia [00:04:10] Writing the book "Resisting AI" [00:08:30] Necropolitics and its relation to AI [00:10:06] AI as a new form of colonization [00:12:57] LLMs as a new form of neo-techno-imperialism [00:15:47] Technology for good and AGI's skewed worldview [00:17:49] Transhumanism, eugenics, and intelligence [00:20:45] Valuing differences (disability) and challenging societal norms [00:26:08] Re-ontologizing and the philosophy of information [00:28:19] New materialism and the impact of technology on society [00:30:32] Intelligence, meaning, and materiality [00:31:43] The constraints of physical laws and the importance of science [00:32:44] Exploring possibilities to reduce suffering and increase well-being [00:33:29] The division between meaning and material in our experiences [00:35:36] Machine learning, data science, and neoplatonic approach to understanding reality [00:37:56] Different understandings of cognition, thought, and consciousness [00:39:15] Enactivism and its variants in cognitive science [00:40:58] Jordan Peterson [00:44:47] Relationism, relativism, and finding the correct relational framework [00:47:42] Recognizing privilege and its impact on social interactions [00:49:10] Intersectionality / Feminist thinking and the concept of care in social structures [00:51:46] Intersectionality and its role in understanding social inequalities [00:54:26] The entanglement of history, technology, and politics [00:57:39] ChatGPT article - we come to bury ChatGPT [00:59:41] Statistical pattern learning and convincing patterns in AI [01:01:27] Anthropomorphization and understanding in AI [01:03:26] AI in education and critical thinking [01:06:09] European Union policies and trustable AI [01:07:52] AI reliability and the halo effect [01:09:26] AI as a tool enmeshed in society [01:13:49] Luddites [01:15:16] AI is a scam [01:15:31] AI and Social Relations [01:16:49] Invisible Labor in AI and Machine Learning [01:21:09] Exploititative AI / alignment [01:23:50] Science fiction AI / moral frameworks [01:27:22] Discussing Stochastic Parrots and Nihilism [01:30:36] Human Intelligence vs. Language Models [01:32:22] Image Recognition and Emulation vs. Experience [01:34:32] Thought Experiments and Philosophy in AI Ethics (mimicry) [01:41:23] Abstraction, reduction, and grounding in reality [01:43:13] Process philosophy and the possibility of change [01:49:55] Mental health, AI, and epistemic injustice [01:50:30] Hermeneutic injustice and gendered techniques [01:53:57] AI and politics [01:59:24] Epistemic injustice and testimonial injustice [02:11:46] Fascism and AI discussion [02:13:24] Violence in various systems [02:16:52] Recognizing systemic violence [02:22:35] Fascism in Today's Society [02:33:33] Pace and Scale of Technological Change [02:37:38] Alternative approaches to AI and society [02:44:09] Self-Organization at Successive Scales / cybernetics

In the past year we have seen AI go from powering prediction algorithms in the background to now having seemingly intelligent conversations, answering questions, or even creating art. AI is going to transform our lives in big ways. Shailesh Chitnis and Bharath Reddy talk about where India stands in AI innovation and how can we get better at it. Reading: Rebooting AI in India — The Takshashila Institution Check out Takshashila's courses: https://school.takshashila.org.in/ Do follow IVM Podcasts on social media. We are @IVMPodcasts on Facebook, Twitter, & Instagram. https://twitter.com/IVMPodcasts https://www.instagram.com/ivmpodcasts/?hl=en https://www.facebook.com/ivmpodcasts/ You can check out our website at https://shows.ivmpodcasts.com/featured Follow the show across platforms: Spotify, Google Podcasts, Apple Podcasts, JioSaavn, Gaana, Amazon Music Do share the word with your folks!See omnystudio.com/listener for privacy information.

Today's guest is Susan Conover, cofounder and CEO of Piction Health, which brings Image Recognition AI technology to the practice of dermatology. This Boston-based company has started to launch their product throughout New England. Susan talks to Max about how the technology could be used to help patience more effectively, and the challenges of bringing change to healthcare and being an entrepreneur. https://www.localmaxradio.com/269

I speak again with my friend, Maroof Farooq, an AI engineer at Nvidia. [Note: Maroof's views are his and not that of his employer.]  We discuss facial recognition, how it can be used for surveillance, and techniques for defeating or fooling it, using props like t-shirts, hats, glasses, and believe or not, makeup! Folks, use this information for only good and not to run from the law, unless the law is the Empire, and you are the Rebel Alliance. Please enjoy this episode. We laugh. We cry. We iterate.Check out what THE MACHINES and one human say about the Super Prompt podcast:“I'm afraid I can't do that.” — HAL9000“These are not the droids you are looking for." — Obi-Wan“Like tears in rain.” — Roy Batty“Hasta la vista baby.” — T1000"I'm sorry, but I do not have information after my last knowledge update in January 2022." — GPT3

NASA is going to try again with the Artemis I launch, part of the program to return humans to the Moon's surface. Microsoft faces potential resistance to its acquisition of Activision Blizzard among UK regulators. And Dungeons & Dragons sends a D20 to space. Kind of.See omnystudio.com/listener for privacy information.

AI Eye Podcast - GBT's ( $GTCH) CEO Discusses Apollo and AI in Image Recognition

AI Eye Podcast - GBT's ( $GTCH) CEO Discusses Apollo and AI in Image Recognition

Forest fires will become more common in the future. With the help of artificial intelligence, early detection of potential flash points will help emergency responders around the world to prevent fire disasters.

Thanks to rapid analysis of satellite images and infrared data, it should be possible to issue warnings of looming environmental disasters more quickly in the future, allowing first responders to help well in advance.

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.08.24.505107v1?rss=1 Authors: Bauza, M., Krstulovic, M., Krupic, J. Abstract: Spatial working memory and image recognition tests are commonly used to facilitate the diagnosis of hippocampal-related neurological disorders such as Alzheimers disease due to their relatively high specificity and sensitivity to damage to the medial temporal lobes compared to standard commonly used clinical tests. Pathological changes in Alzheimers disease start years before the formal diagnosis is made, partially due to testing too late. To address this challenge, we developed a novel digital platform, hAge (healthy Age), which integrates double spatial alternation, image recognition and visuospatial tasks for frequent remote unsupervised assessment of spatial and non-spatial working memory. 191 healthy adults (67% females, 18-81 years old) participated in the study. In line with findings using standard laboratory tests, we showed that performance on the spatial alternation task negatively correlated with inter-trial periods and performance levels on image recognition and visuospatial tasks can be controlled by varying image similarity. Importantly, we demonstrated that frequent engagement with the double spatial alternation task leads to a strong practice effect, previously identified as a potential measure of cognitive decline in MCI patients. Finally, we discuss how lifestyle and motivation confounds may present a serious challenge for cognitive assessment in real-world uncontrolled environments. Copy rights belong to original authors. Visit the link for more info Podcast created by PaperPlayer

 Watch out !!! Il arrive !!! le fameux Web 3.0 ou Web3… après les versions 1.0 et 2.0, le web devient décentralisé porté par la technologie blockchain (elle-même très bien expliqué dans un épisode précédent d'Inno I know), et dans lequel l'internaute va pouvoir bien sûr lire, écrire et produire du contenu, mais aussi reprendre la main sur ses données et expérimenter la tokenisation de sa vie.   En effet, avec les cryptomonnaies, des simples actions sociales sur le web, tels que des likes, etc. peuvent être source de rémunération…      Mais concrètement qu'est-ce que le métavers ? Et lesNFTs à quoi servent-elles vraiment et comment fonctionnent-elles ?   Quels sont les pays les plus avancés ? Aurons-nous tous deux vies distinctes, une réelle et une virtuelle ? L'une prendra t-elle le pas sur l'autre ?     Bref un grand chamboulement virtuel s'opère en ce moment avec ce Web3, la montée du Metavers, et des NFTs. Ce grand changement se traduit lui-même de façon très réelle, puisque nous voyons de nouveaux types de jobs émerger, tel que celui de notre invité Valentin Auvinet, Chief Metaverse Officer chez Decathlon.      Plongez-vous dans le sujet avec nos deux experts, Nicolas Diacono et Guillaume Rio qui passent au crible toutes ces nouvelles tendances sous l'éclairage pragmatique de Valentin Auvinet. Nos trois compères précisent également les impacts sur le monde de la distribution et sur le consommateur final dont la vie risque bien d'être assez bousculée dans les années à venir !  

When we think of artificial intelligence, we may link it to digital channels, but winning in physical retail is still the key to winning in the world of Consumer Goods. In this episode with the Co-Founder and CEO of Iniflect, Anand Prabhu Subramanian we'll talk about how Image Recognition and AI technology can empower sales leaders. With him, we explore how AI can help Consumer Goods Companies boost sales in-store. In this conversation, we cover: Post-Pandemic Changes in Retail Levers that contribute to a successful sale at a store Challenges faced in boosting sales performance in every store How FMCG Brands addressing inefficiencies in the store today The role AI technology is playing in complementing retail operations and retail businesses How FMCG stores will evolve in the future and what FMCG leaders need to do differently Key retail intelligence metrics How Sales Leaders can use insights to activate them in proactive business decisions

Ridhima Ahuja Kahn is the VP of Business Development at Dapper Labs. Her focus is helping build meaningful partnerships with the world's top IPs, creators, and social media platforms as they look to build blockchain-based experiences.Prior to Dapper Labs, she was a Partner at Andreessen Horowitz (a16z) where she focused on sports, social, media & entertainment, collectibles (both in digital & physical), hospitality/travel and food.She has also spent time on the investment teams at the Hewlett Foundation & Grovenor Capital Management.- Tell me about how you shifted into this role at Dapper Labs and was the inspiration behind TopShot birthed at Dapper or was your sports background the impetus to this idea?- What does fandom look like in metaverse ? What does TopShot and Cryptokitties experiences look like there? And do you think experiences are the magic of an NFT and your utility? What do experiences look like in the Metaverse?- Flow blockchain technology is unique to Dapper, reducing the friction of Web 2 native users and Web 3 adventurers, do you think this shift in creating your own blockchain has been part of the the secret sauce for Dapper Labs?- Digital Fashion will likely see a ton of growth due to the concept of wearables and shopping in the Metaverse: Luxury brands will soar to the top fast bc of virtue signaling and the marketing machine they are built on. Can Dapper help creators or smaller brands with NFTs for this use case?- Because we are also an education platform on Culture Factor, can you define DAO  and is Dapper getting involved in the DAO space?- And what would a brainstorming session at Dapper look like in terms of iterating on best use cases, verticals or simply coming up with experiences?Ridhima Ahuja Kahn of Dapper LabsHolly Shannon's WebsiteZero To Podcast on AmazonHolly Shannon, LinkedinHolly Shannon, InstagramHolly Shannon, Clubhousehttps://youtu.be/PKCND4FqGLc#dapper, #metaverse, #blockchain, #creators, #digital, #flow, #create, #labs, #web3, #community, #physical, fashion, #technology, #experience, #nft, #brands 

Vladislav Ginzburg is the Chief Executive Officer at Blockparty.Ginzburg leads Blockparty and the mission to build a blockchain-agnostic platform for collectible NFTs at the intersection of art, music and culture. Blockparty launched their MVP In August 2020 with a number of mainstream oriented drops, including first digital artworks by 3lau Slime Sunday, Adventure Club, Dave Krugman and others.Earlier, Ginzburg was Chief Business Development officer at Blockparty Tickets where he introduced blockchain as an NFT powered ticketing system to music festivals and professional sports teams, including a partnership with the Sacramento Kings of the NBA.Before entering the Blockchain and entertainment spaces, Ginzburg managed a fine art fund where he transacted more than $150 million in blue chip artworks. Ginzburg studied at Miami University in Oxford, Ohio as well as The New School in New York.Let's dig into your art background first, I believe it lays the groundwork for your interest in NFT related art and event?In the art world,  Vladislav Ginzburg has managed several high-value growth funds in the fine art industry where he has executed transactions for iconic canvas works by Pablo Picasso, Andy Warhol, Jean-Michele Basquiat, Salvador Dali, Pierre Auguste Renoir, and hundreds of others for clients., including placing works into museum exhibitions globallyWhat is your relationship with Warner Music and how will it compliment Blockparty goals for the artist?Opensea is the amazon for NFTs; it's the most common and most costly to mint because there's so many people on it. Rarible and a few others are less expensive. By having their own storefront on Blockparty (part Website, Etsy and Shopify?, Is this a solution to the two ends of the spectrum of Opensea and Rarible?Wants to dive into the live event aspect because of Lively partnership. Does Blockparty expect to be more of an event platform in the end? Or are they primarily a storefront/tools provider? What's the endgame?Ginzburg is the co-founder for Moonwalk. No code: there is code that sits behind the NFT. Moonwalk, being a no code platform, allows people who don't have developers in their back pocket to mint NFTs and play in the same arena as people who do have their own developers. Is Moonwalk going to eventually live on Blockparty?Are Ginzburg's platforms working toward democratizing crypto/NFTs for any and all?We are speaking on NFT.NYC in June!!Data to market yourself, your wallet, online habits, or curated social media look?Creators pushing for Blockparty to push further with them, innovating togetherEasy vs. creating smart contracts that work for the artists on the platformDali and Warhol experimented with digital art, NFT artists that were painters, sculptors and photographers using AR and image recognitionToken, receive it into your wallet, send it out of your wallet or stake it.look forward alpha: music NFTs will thrive with UGC (user generated content) selling viral content from fans taking and creating NFTs and sending to the viral TikTok artist.Vladislav Ginsburg on Twitter Holly Shannon's WebsiteZero To Podcast on AmazonHolly Shannon, LinkedinHolly Shannon, InstagramHolly Shannon, Clubhousehttps://youtu.be/PKCND4FqGLc#utility #creator #warner #brands #art #creativetechnologist #music #musician #spinnin'records #integrity #intention #purpose #impact #lockeddiscord #accesskeys #rightsownership #Fortnight #wearables #legacy #warnermusic #indieartist #fans #nfts #nft #nftart #cryptocurrency #blockchain #metaverse #culturefactor #web3 #smartcontracts #bitcoin #nftartist #nftcollectors #eth #ethereum #youtubers #tiktok #instagram #reels #branding #bitcoin #web3 #smartcontracts #bitcoin #nftartist #nftcollectors #community #decentralizedeconomy

Vladislav Ginzburg is the Chief Executive Officer at Blockparty.Ginzburg leads Blockparty and the mission to build a blockchain-agnostic platform for collectible NFTs at the intersection of art, music and culture. Blockparty launched their MVP In August 2020 with a number of mainstream oriented drops, including first digital artworks by 3lau Slime Sunday, Adventure Club, Dave Krugman and others.Earlier, Ginzburg was Chief Business Development officer at Blockparty Tickets where he introduced blockchain as an NFT powered ticketing system to music festivals and professional sports teams, including a partnership with the Sacramento Kings of the NBA.Before entering the Blockchain and entertainment spaces, Ginzburg managed a fine art fund where he transacted more than $150 million in blue chip artworks. Ginzburg studied at Miami University in Oxford, Ohio as well as The New School in New York.Let's dig into your art background first, I believe it lays the groundwork for your interest in NFT related art and event?In the art world,  Vladislav Ginzburg has managed several high-value growth funds in the fine art industry where he has executed transactions for iconic canvas works by Pablo Picasso, Andy Warhol, Jean-Michele Basquiat, Salvador Dali, Pierre Auguste Renoir, and hundreds of others for clients., including placing works into museum exhibitions globallyWhat is your relationship with Warner Music and how will it compliment Blockparty goals for the artist?Opensea is the amazon for NFTs; it's the most common and most costly to mint because there's so many people on it. Rarible and a few others are less expensive. By having their own storefront on Blockparty (part Website, Etsy and Shopify?, Is this a solution to the two ends of the spectrum of Opensea and Rarible?Wants to dive into the live event aspect because of Lively partnership. Does Blockparty expect to be more of an event platform in the end? Or are they primarily a storefront/tools provider? What's the endgame?Ginzburg is the co-founder for Moonwalk. No code: there is code that sits behind the NFT. Moonwalk, being a no code platform, allows people who don't have developers in their back pocket to mint NFTs and play in the same arena as people who do have their own developers. Is Moonwalk going to eventually live on Blockparty?Are Ginzburg's platforms working toward democratizing crypto/NFTs for any and all?We are speaking on NFT.NYC in June!!Data to market yourself, your wallet, online habits, or curated social media look?Creators pushing for Blockparty to push further with them, innovating togetherEasy vs. creating smart contracts that work for the artists on the platformDali and Warhol experimented with digital art, NFT artists that were painters, sculptors and photographers using AR and image recognitionToken, receive it into your wallet, send it out of your wallet or stake it.look forward alpha: music NFTs will thrive with UGC (user generated content) selling viral content from fans taking and creating NFTs and sending to the viral TikTok artist.Vladislav Ginsburg on Twitter Holly Shannon's WebsiteZero To Podcast on AmazonHolly Shannon, LinkedinHolly Shannon, InstagramHolly Shannon, Clubhousehttps://youtu.be/PKCND4FqGLc#blockparty #blue-chip #authenticity #prognosticator #wallstreet #artnet #database #creatorcollector #web2.5 #web2fatigue #nocode #massadoption #digitallynative #imagerecognition #nfts #nft #nftart #cryptocurrency #blockchain #metaverse #culturefactor #web3 #smartcontracts #bitcoin #nftartist #nftcollectors #eth #ethereum #youtubers #tiktok #instagram #reels #branding #bitcoin #web3 #smartcontracts #bitcoin #nftartist #nftcollectors #community #decentralizedeconomy

In any discussion of artificial intelligence and machine learning today, artificial neural networks are bound to come up. What are artificial neural networks, how have they developed, and what are they poised to do in the future? Host Angelo Kastroulis dives into the history, compares them to biological systems that they are meant to mimic, and talks about how hard problems like this one need to be handled carefully.Angelo begins with a discussion of how biological neural networks help make our brain a powerful computer of complexity. He then talks about how artificial neural networks recruit the same structures and connections to create artificial intelligence. To understand what we mean by artificial intelligence, Angelo explains how the Turing Test works and how Turing's work forms a foundation for modern AI. He then discusses other early pioneers in this work, namely Frank Rosenblatt, who worked on models that could learn or “perceptrons.” Angelo then relates the history of how this work was criticized by Marvin Minsky and Seymour Papert and how mistakes in their own work put the potential advances of artificial neural networks back by about two decades.Using image recognition as a case study, Angelo ends the episode by talking about about various approaches' benefits and drawbacks to illustrate what we can do with artificial neural networks today.CitationsHebb, D.O. (1949). The organization of behavior: A neuropsychological theory. New York: Wiley.Minsky, M. (1954.) Theory of neural-analog reinforcement systems and its application to the brain-model problem. Doctoral dissertation. Princeton: Princeton University.Minsky, M. and Papert, S. (1969). Perceptrons: An introduction to computational geometry. Cambridge: MIT Press.Rosenblatt, F. (1957). "The perceptron: A perceiving and recognizing automaton.”Buffalo: Cornell Aeronautical Laboratory, Inc. (Accessible at https://blogs.umass.edu/brain-wars/files/2016/03/rosenblatt-1957.pdf)Rosenblatt, F. (1962). Principles of neurodynamics: Perceptrons and the theory of brain mechanisms. Washington, D.C.: Spartan Books_._Turing, A. (1950, October). "Computing machinery and intelligence," Mind, LIX: 236, pp. 433–460. https://doi.org/10.1093/mind/LIX.236.433  Further ReadingWarren McCollough and the McCollough-Pitts NeuronChurch-Turing ThesisTuring TestXOR or Exclusive orHost: Angelo KastroulisExecutive Producer: Kerri Patterson; Producer: Leslie Jennings Rowley; Communications Strategist: Albert Perrotta; Audio Engineer: Ryan ThompsonMusic: All Things Grow by Oliver Worth© 2021, Carrrera Group

Yannic Kilcher is PhD candidate at ETH Zurich researching deep learning, structured learning, and optimization for large and high-dimensional data. He produces videos on his enormously popular Youtube channel breaking down recent ML papers. Follow Yannic on Twitter: https://twitter.com/ykilcher (https://twitter.com/ykilcher) Check out Yannic's excellent Youtube channel: https://www.youtube.com/channel/UCZHmQk67mSJgfCCTn7xBfew (https://www.youtube.com/channel/UCZHmQk67mSJgfCCTn7xBfew) Listen to the ML Street Talk podcast: https://podcasts.apple.com/us/podcast/machine-learning-street-talk/id1510472996 (https://podcasts.apple.com/us/podcast/machine-learning-street-talk/id1510472996) Every Thursday I send out the most useful things I've learned, curated specifically for the busy machine learning engineer. Sign up here: http://bitly.com/mle-newsletter (http://bitly.com/mle-newsletter) Follow Charlie on Twitter: https://twitter.com/CharlieYouAI (https://twitter.com/CharlieYouAI) Subscribe to ML Engineered: https://mlengineered.com/listen (https://mlengineered.com/listen) Comments? Questions? Submit them here: http://bit.ly/mle-survey (http://bit.ly/mle-survey) Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/ (https://www.givingwhatwecan.org/) Timestamps: 02:40 Yannic Kilcher 07:05 Research for his PhD thesis and plans for the future 12:05 How he produces videos for his enormously popular Youtube channel 21:50 Yannic's research process: choosing what to read and how he reads for understanding 27:30 Why ML conference peer review is broken and what a better solution looks like 45:20 On the field's obsession with state of the art 48:30 Is deep learning is the future of AI? Is attention all you need? 56:10 Is AI overhyped right now? 01:01:00 Community Questions 01:13:30 Yannic flips the script and asks me about what I do 01:25:30 Rapid fire questions Links: https://www.youtube.com/channel/UCZHmQk67mSJgfCCTn7xBfew (Yannic's amazing Youtube Channel) https://www.notion.so/Yannic-Kilcher-e93c81f81100464399e173867815e380 (Yannic's Google Scholar) https://discord.gg/4H8xxDF (Yannic's Community Discord Channel) On the Measure of Intelligence: https://arxiv.org/abs/1911.01547 (arXiv paper) and https://www.youtube.com/watch?v=3_qGrmD6iQY (Yannic's video series) https://www.youtube.com/watch?v=Uumd2zOOz60 (How I Read a Paper: Facebook's DETR (Video Tutorial)) https://www.youtube.com/watch?v=TrdevFK_am4 (An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (Paper Explained)) https://fs.blog/2014/09/peter-thiel-zero-to-one/ (Zero to One) https://www.penguin.co.uk/books/104/1049544/the-gulag-archipelago/9781784871512.html (The Gulag Archipelago)

How are differential equations related to neural networks? What are the benefits of re-thinking neural network as a differential equation engine? In this episode we explain all this and we provide some material that is worth learning. Enjoy the show!   Residual Block     References [1] K. He, et al., “Deep Residual Learning for Image Recognition”, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770-778, 2016 [2] S. Hochreiter, et al., “Long short-term memory”, Neural Computation 9(8), pages 1735-1780, 1997. [3] Q. Liao, et al.,”Bridging the gaps between residual learning, recurrent neural networks and visual cortex”, arXiv preprint, arXiv:1604.03640, 2016. [4] Y. Lu, et al., “Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equation”, Proceedings of the 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, 2018. [5] T. Q. Chen, et al., ” Neural Ordinary Differential Equations”, Advances in Neural Information Processing Systems 31, pages 6571-6583}, 2018

Steve Hornyak / Trax Image Recognition Steve Hornyak is the CEO, Americas at Trax Image Recognition. Steve is responsible for Trax's strategy, sales, marketing, business development, and customer success (project management, professional services and support) for North and South America. Steve has over 27 years of global software and technology experience with a focus in […] The post Steve Hornyak and Terrell Tuten of Trax Image Recognition appeared first on Business RadioX ®.

with Fei-Fei Li (@drfeifei), Frank Chen (@withfries2), and Sonal Chokshi (@smc90) Who has the advantage in artificial intelligence — big companies, startups, or academia? Perhaps all three, especially as they work together when it comes to fields like this. One thing is clear though: A.I. and deep learning is where it's at. And that's why this year's newly anointed Andreessen Horowitz Distinguished Visiting Professor of Computer Science is Fei-Fei Li [who publishes under Li Fei-Fei], associate professor at Stanford University. Bridging entrepreneurs across academia and industry, we began the a16z Professor-in-Residence program just a couple years ago (most recently with Dan Boneh and beginning with Vijay Pande). Li is the Director of the Stanford Vision Lab, which focuses on connecting computer vision and human vision; is the Director of the Stanford Artificial Intelligence Lab (SAIL), which was founded in the early 1960s; and directs the new SAIL-Toyota Center for AI Research, which brings together researchers in visual computing, machine learning, robotics, human-computer interactions, intelligent systems, decision making, natural language processing, dynamic modeling, and design to develop “human-centered artificial intelligence” for intelligent vehicles. Li also co-created ImageNet, which forms the basis of the Large Scale Visual Recognition Challenge (ILSVRC) that continually demonstrates drastic advances in machine vision accuracy. So why now for A.I.? Is deep learning “it”… or what comes next? And what happens as A.I. moves from what Li calls its “in vitro phase” to its “in vivo phase”? Beyond ethical considerations — or celebrating only “geekiness” and “nerdiness” — Li argues we need to inject a stronger humanistic thinking element to design and develop algorithms and A.I. that can co-habitate with people and in social (including crowded) spaces. All this and more on this episode of the a16z Podcast.