Podcasts about med palm

Episode 126I spoke with Vivek Natarajan about:* Improving access to medical knowledge with AI* How an LLM for medicine should behave* Aspects of training Med-PaLM and AMIE* How to facilitate appropriate amounts of trust in users of medical AI systemsVivek Natarajan is a Research Scientist at Google Health AI advancing biomedical AI to help scale world class healthcare to everyone. Vivek is particularly interested in building large language models and multimodal foundation models for biomedical applications and leads the Google Brain moonshot behind Med-PaLM, Google's flagship medical large language model. Med-PaLM has been featured in The Scientific American, The Economist, STAT News, CNBC, Forbes, New Scientist among others.I spend a lot of time on this podcast—if you like my work, you can support me on Patreon :)Reach me at editor@thegradient.pub for feedback, ideas, guest suggestions. Subscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Intro* (00:35) The concept of an “AI doctor”* (06:54) Accessibility to medical expertise* (10:31) Enabling doctors to do better/different work* (14:35) Med-PaLM* (15:30) Instruction tuning, desirable traits in LLMs for medicine* (23:41) Axes for evaluation of medical QA systems* (30:03) Medical LLMs and scientific consensus* (35:32) Demographic data and patient interventions* (40:14) Data contamination in Med-PaLM* (42:45) Grounded claims about capabilities* (45:48) Building trust* (50:54) Genetic Discovery enabled by a LLM* (51:33) Novel hypotheses in genetic discovery* (57:10) Levels of abstraction for hypotheses* (1:01:10) Directions for continued progress* (1:03:05) Conversational Diagnostic AI* (1:03:30) Objective Structures Clinical Examination as an evaluative framework* (1:09:08) Relative importance of different types of data* (1:13:52) Self-play — conversational dispositions and handling patients* (1:16:41) Chain of reasoning and information retention* (1:20:00) Performance in different areas of medical expertise* (1:22:35) Towards accurate differential diagnosis* (1:31:40) Feedback mechanisms and expertise, disagreement among clinicians* (1:35:26) Studying trust, user interfaces* (1:38:08) Self-trust in using medical AI models* (1:41:39) UI for medical AI systems* (1:43:50) Model reasoning in complex scenarios* (1:46:33) Prompting* (1:48:41) Future outlooks* (1:54:53) OutroLinks:* Vivek's Twitter and homepage* Papers* Towards Expert-Level Medical Question Answering with LLMs (2023)* LLMs encode clinical knowledge (2023)* Towards Generalist Biomedical AI (2024)* AMIE* Genetic Discovery enabled by a LLM (2023) Get full access to The Gradient at thegradientpub.substack.com/subscribe

Welcome to BrainX AI in Medicine Podcast series, where we bring in leaders within fields of AI and Medicine to discuss their cutting-edge research, explore boundaries of current knowledge, and provide insightful commentary on how to effectively lead the AI revolution in Medicine. Today's guest is Vivek Natarajan, who is a Research Scientist at Google leading research at the intersection of large language models (LLMs) and biomedicine. In particular, Vivek is the lead researcher behind Med-PaLM and Med-PaLM 2, which were the first AI systems to obtain passing and expert level scores on US Medical License exam questions respectively. Med-PaLM was recently published in Nature and has been featured in The Scientific American, Wall Street Journal, The Economist, among many others. More recently, Vivek also led the development of Med-PaLM M, the first demonstration of a generalist biomedical AI system and AMIE, a research AI system, which surpassed Primary Care Physicians on multiple axes pertaining to diagnostic dialogue in an randomized study conducted in the style of a virtual Objective Structured Clinical Examination (OSCE). Over the years, Vivek's research has been published in well-regarded journals and conferences like Nature, Nature Medicine, Nature Biomedical Engineering, JMLR, CVPR, and NeurIPS. It also forms the basis for several regulated medical device products under clinical trials at Google, including the NHS AI award winning breast cancer detection system Mammo Reader and the skin condition classification system DermAssist.

In this episode, we delve into the incredible advancements in brain cancer treatment support facilitated by Google's Med-PaLM 2 AI chatbot, discussing how hospitals are integrating this technology and its impact on patient care. Invest in AI Box: https://Republic.com/ai-box Get on the AI Box Waitlist: https://AIBox.ai/ AI Facebook Community

Witness the transformative potential of AI in healthcare, specifically in revolutionizing brain cancer treatments. Join hospitals as they explore the capabilities of Google's Med-PaLM 2 AI Chatbot, paving the way for more effective and personalized patient care. Get on the AI Box Waitlist: https://AIBox.ai/Join our ChatGPT Community: https://www.facebook.com/groups/739308654562189/Follow me on Twitter: https://twitter.com/jaeden_ai

Explore the pivotal role of AI in cancer care, specifically in advancing brain cancer treatments, as hospitals delve into the trials of Google's Med-PaLM 2 AI Chatbot. Dive into the promising intersection of artificial intelligence and healthcare innovation. Get on the AI Box Waitlist: https://AIBox.ai/Join our ChatGPT Community: https://www.facebook.com/groups/739308654562189/Follow me on Twitter: https://twitter.com/jaeden_ai

Explore the realm of AI-driven innovation in cancer care, specifically in the enhancement of brain cancer treatments. Follow hospitals as they embark on trials with Google's Med-PaLM 2 AI Chatbot, uncovering the potential for a more advanced and efficient healthcare landscape. Get on the AI Box Waitlist: https://AIBox.ai/Join our ChatGPT Community: https://www.facebook.com/groups/739308654562189/Follow me on Twitter: https://twitter.com/jaeden_ai

Step into the future of cancer care with AI leading the way, especially in brain cancer treatments. Witness hospitals testing the waters with Google's Med-PaLM 2 AI Chatbot, exploring the potential for more effective and personalized healthcare solutions. Get on the AI Box Waitlist: https://AIBox.ai/Join our ChatGPT Community: https://www.facebook.com/groups/739308654562189/Follow me on Twitter: https://twitter.com/jaeden_ai

Ce mardi, sur Europe 1, Nicolas Bouzou s'intéresse aux résultats d'une étude sur le Chatbot médical de Google, Med-PaLM. 

Ce mardi, sur Europe 1, Nicolas Bouzou s'intéresse aux résultats d'une étude sur le Chatbot médical de Google, Med-PaLM. 

In this episode, we investigate the transformative impact of Google's Med-PaLM 2 chatbot on redefining protocols for brain cancer treatments in hospitals. Explore the intersection of artificial intelligence and medical advancements. 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

In this episode, I unravel the groundbreaking experiments in healthcare, focusing on hospitals utilizing Google's Med-PaLM 2 AI chatbot for brain cancer treatment trials. Tune in for a closer look at the transformative potential of artificial intelligence in the medical field. 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

In this episode, we explore the significant role of AI in healthcare, specifically focusing on how Google's Med-PaLM 2 AI chatbot is transforming brain cancer treatment approaches, detailing its utilization in hospitals and its potential implications for patients. Invest in AI Box: https://Republic.com/ai-box Get on the AI Box Waitlist: https://AIBox.ai/ AI Facebook Community

In this episode, we discuss how Google's Med-PaLM 2 AI chatbot is being tested in hospitals as a novel approach to brain cancer treatment. We explore its implications for patient care, treatment optimization, and the future of AI in oncology. Invest in AI Box: https://Republic.com/ai-box Get on the AI Box Waitlist: ⁠⁠https://AIBox.ai/⁠⁠ AI Facebook Community Learn more about AI in Video Learn more about Open AI

Exploring the application of Google's Med-PaLM 2 AI chatbot in brain cancer care, this episode delves into hospitals' trials and the potential impact on treatment methodologies. Discussing the prospects and challenges, we evaluate the role of AI in enhancing healthcare practices. Invest in AI Box: https://Republic.com/ai-box Get on the AI Box Waitlist: ⁠⁠https://AIBox.ai/⁠⁠ AI Facebook Community Learn more about LLM's Learn more about AI

Shek Azizi is a senior research scientist at Google DeepMind. Her research is focused on translational AI with tangible clinical impact. She designs foundation models for biomedical applications. She has led the moonshot project behind Med-PaLM, Med-PaLM 2 and Med-PaLM M. Large Language Models Encode Clinical Knowledge

Step into the world of cutting-edge medical technology in this episode as we explore how AI is transforming brain cancer treatments. Hospitals are at the forefront of innovation, conducting trials to test Google's groundbreaking Med-PaLM 2 AI Chatbot. Join us for a deep dive into the potential revolution in patient care, diagnosis, and treatment for brain cancer, driven by artificial intelligence. Get on the AI Box Waitlist: https://AIBox.ai/Join our ChatGPT Community: ⁠https://www.facebook.com/groups/739308654562189/⁠Follow me on Twitter: ⁠https://twitter.com/jaeden_ai⁠

Join us for an enlightening episode as we explore the critical role of AI in brain cancer treatments. Hospitals are currently in the midst of trials, testing Google's advanced Med-PaLM 2 AI Chatbot, aiming to revolutionize patient care and diagnosis. Dive into the realm of medical innovation, where AI is making a profound impact on the fight against brain cancer. Get on the AI Box Waitlist: https://AIBox.ai/Join our ChatGPT Community: ⁠https://www.facebook.com/groups/739308654562189/⁠Follow me on Twitter: ⁠https://twitter.com/jaeden_ai⁠

Hoy vamos a embarcarnos en un viaje digital fascinante. ¿Alguna vez has visitado un sitio web y un pequeño ayudante virtual apareció para asistirte? Sí, estamos hablando de chatbots, esos asistentes digitales que nos guían, nos ayudan y responden a nuestras preguntas en línea. Pero, ¿alguna vez has pensado en tener uno para tu propio negocio? ¿Y si te dijera que puedes crear uno tú mismo, incluso sin ser un experto en tecnología? Hoy, vamos a explorar el mundo de los chatbots, desentrañando sus misterios y descubriendo cómo, con las herramientas adecuadas, puedes implementar esta tecnología en tu pequeño negocio para mejorar la experiencia del cliente, optimizar tus operaciones y, por supuesto, impresionar a tus visitantes. Así que ajusta tu volumen, relájate y acompáñanos en este emocionante episodio donde la tecnología y los negocios se encuentran para crear experiencias increíbles. ¡Vamos a sumergirnos en el futuro digital juntos!" Noticias: "LLaVA: Un Sistema de IA de Código Abierto que Combina Visión y Lenguaje, Rivalizando con GPT-4" "Anthropic Desarrolla un Método para Entender Mejor las 'Neuronas' de los Modelos de IA y Controlar sus Respuestas" "Geoffrey Hinton Advierte sobre el Potencial de los Chatbots para Superar y Posiblemente Controlar la Inteligencia Humana" "Google Lanza Nuevas Funciones en Vertex AI y Combina Tecnología con Med-PaLM 2 para Asistir en la Unificación de Datos Clínicos" "Adobe Mejora Firefly, su Sistema de IA, y Añade Nuevas Funciones de Diseño Asistido por IA en Illustrator y Express" "ElevenLabs Lanza Herramienta de Traducción de Voz que Mantiene la Voz Original del Hablante en Más de 20 Idiomas" "EVEscape: Una Herramienta de IA que Predice Futuras Variantes de Virus y Podría Ayudar a Prevenir Pandemias" "Investigadores en los Países Bajos Desarrollan Sistema de IA que Analiza el ADN de Tumores Durante la Cirugía" "Meta Crea Chatbots de IA que Imitan a Celebridades con Nombres Cambiados, Generando Confusión y Preocupaciones Éticas" "Adobe Presenta 'Project Fast Fill', una Herramienta Experimental de IA para Edición de Video Rápida y Sencilla" Herramientas: TalkNotes- Convierta rápidamente sus pensamientos en notas prácticas (Enlace) Chatcare- Reduce your customer support volume by 60% (Enlace) Airparser- Extracción de datos con GPT-4 (Enlace) Business Idea Generator- Genere 6 ideas de negocio únicas en sólo 10 segundos (Enlace) Kua.ai- Mejore el éxito de su comercio electrónico con la creación de contenidos asistida por IA (Enlace) My AskAI- Crea tu propio ChatGPT a partir de tus contenidos (Enlace) Fine-Tuner.ai- Cree sus propios agentes de IA a medida sin conocimientos técnicos ni codificación (Enlace) HeyGen- Cree vídeos a partir de texto en cuestión de minutos con avatares y voces generados por IA (Enlace) AnimeGenius- Crea arte anime AI y lleva tu imaginación a la realidad (Enlace) Fliki- Creación de vídeos 10 veces más sencilla y rápida gracias a la IA (Enlace) Softr AI App Generator- Cree aplicaciones empresariales con sólo pulsar un botón (Enlace) TranslateVideo 2.0- Traducir vídeos a más de 75 idiomas y subtítulos gratuitos ilimitados (Enlace) Relay.app- Automatización del flujo de trabajo más allá de los activadores y las acciones (Enlace) Section- Personalice sus herramientas de IA con este marco descargable, diseñado para ayudarle a elegir la herramienta de IA adecuada y adaptarla a su estilo de trabajo. (Enlace)* Photoshift- Intercambia tu producto en una escena a mitad del viaje, colocaciones de productos impulsadas por IA (Enlace) LALAL AI- Vocal Remover & Instrumental AI Splitter (Enlace)

Alex and Emily are taking another stab at Google and other companies' aspirations to be part of the healthcare system - this time with the expertise of Stanford incoming assistant professor of dermatology and biomedical data science Roxana Daneshjou. A look at the gap between medical licensing examination questions and real life, and the inherently two-tiered system that might emerge if LLMs are brought into the diagnostic process.References:Google blog post describing Med-PaLMNature: Large language models encode clinical knowledgePolitico: Microsoft teaming up with Epic Systems to integrate generative AI into electronic medical records softwareMedRXiv: Beyond the hype: large language models propagate race-based medicine (Omiye, Daneshjou, et al)Fresh AI hell:Fake summaries of fake reviewshttps://bsky.app/profile/hypervisible.bsky.social/post/3k4wouet3pg2uSchool administrators asking ChatGPT which books they have to remove from school libraries, given Iowa's book banMason City Globe Gazette: “Each of these texts was reviewed using AI software to determine if it contains a depiction of a sex act. Based on this review, there are 19 texts that will be removed from our 7-12 school library collections and stored in the Administrative Center while we await further guidance or clarity.”Loquacity and Visible Emotion: ChatGPT as a Policy AdvisorWritten by authors at the Bank of ItalyAI generated school bus routes get students home at 10pmLethal AI generated mushroom-hunting booksHow would RBG respond?You can check out future livestreams at https://twitch.tv/DAIR_Institute. Follow us!Emily Twitter: https://twitter.com/EmilyMBender Mastodon: https://dair-community.social/@EmilyMBender Bluesky: https://bsky.app/profile/emilymbender.bsky.social Alex Twitter: https://twitter.com/@alexhanna Mastodon: https://dair-community.social/@alex Bluesky: https://bsky.app/profile/alexhanna.bsky.social Music by Toby Menon.Artwork by Naomi Pleasure-Park. Production by Christie Taylor.

Explore how AI is transforming brain cancer treatments with the help of Google's Med-PaLM 2 AI chatbot. Hospitals are now testing this innovative technology, revolutionizing the way medical professionals approach brain cancer care. Join us in this episode as we delve into the potential impact of AI on the future of healthcare. Get on the AI Box Waitlist: https://AIBox.ai/Join our ChatGPT Community: ⁠https://www.facebook.com/groups/739308654562189/⁠Follow me on Twitter: ⁠https://twitter.com/jaeden_ai⁠

In this episode, Nathan sits down with Vivek Natarajan and Tao Tu of Google's Med-PaLM, diving into how they used one of the world's largest medical datasets ever compiled to develop Med-PaLM M, an AI agent specialized in medical tasks. In this episode, they discuss: Med-PaLM M's “clinically superhuman” abilities and limitations, the rigorous testing and validation that went into the model, and their vision for AI to take over repetitive clerical tasks and allow doctors to focus on patients. RECOMMENDED PODCAST: The HR industry is at a crossroads. What will it take to construct the next generation of incredible businesses – and where can people leaders have the most business impact? Hosts Nolan Church and Kelli Dragovich have been through it all, the highs and the lows – IPOs, layoffs, executive turnover, board meetings, culture changes, and more. With a lineup of industry vets and experts, Nolan and Kelli break down the nitty-gritty details, trade offs, and dynamics of constructing high performing companies. Through unfiltered conversations that can only happen between seasoned practitioners, Kelli and Nolan dive deep into the kind of leadership-level strategy that often happens behind closed doors. Check out the first episode with the architect of Netflix's culture deck Patty McCord. https://link.chtbl.com/hrheretics TIMESTAMPS: (00:00) Episode Preview (00:00:56) Introducing Vivek Natarajan and Tao Tu (00:04:18) The story of Google's Medical AI research progress (00:07:11) Multi-modal Med-PaLM (00:10:32) Genomic data - how do you represent it? (00:11:13) Google's Deep Variant (00:14:44) The successes and failures behind the incredible pace of progress (00:15:02) Sponsors: Netsuite | Omneky (00:21:54) Google's research culture and assembling an interdisciplinary team (00:31:36) Google's Pathways (00:33:40) Med-PaLM M's architecture (00:37:28) Working with 3 different model sizes and what you learn (00:46:56) Data and compute required for Med-PaLM M (00:49:38) Med-PaLM M's cycle time (00:54:56) Is a bridge or adapter structure worth implementing? (01:00:09) Can we create an AI doctor? (01:02:39) Emergent capabilities like identifying tuberculosis (01:09:37) Reactions to these emergent capabilities (01:11:13) Moving towards clinical trials and real-world testing (01:13:01) Regulatory and safety considerations (01:15:03) AI safety in the healthcare domain (01:17:00) Potential to transform healthcare access worldwide LINKS: Med-PaLM: https://sites.research.google/med-palm/ Med-PaLM M paper: https://arxiv.org/abs/2307.14334 Our earlier conversation with Vivek Natarajan on Med-PaLM: https://www.youtube.com/watch?v=nPBd7i5tnEE X/TWITTER: @vivnat (Vivek) @taotu831 (Tao) @labenz (Nathan) @eriktorenberg @CogRev_Podcast SPONSORS: NetSuite | Omneky NetSuite has 25 years of providing financial software for all your business needs. More than 36,000 businesses have already upgraded to NetSuite by Oracle, gaining visibility and control over their financials, inventory, HR, eCommerce, and more. If you're looking for an ERP platform ✅ head to NetSuite: http://netsuite.com/cognitive and download your own customized KPI checklist. Omneky is an omnichannel creative generation platform that lets you launch hundreds of thousands of ad iterations that actually work customized across all platforms, with a click of a button. Omneky combines generative AI and real-time advertising data. Mention "Cog Rev" for 10% off.

Today, we're excited to get to know Vivek, AI researcher at Google and one of the lead researchers for Med-PaLM2, and Viswesh, CTO and Founder of Valar Labs! Vivek is a Research Scientist at Google Health AI advancing biomedical AI to help scale world class healthcare to everyone. Vivek is particularly interested in building large language models and multimodal foundation models for biomedical applications and leads the Google Brain moonshot behind Med-PaLM, Google's flagship medical large language model. Med-PaLM has been featured in publications such as The Scientific American and Forbes. Vivek graduated with his masters from UT Austin in Computer Science and Bachelors at National Institute of Technology in India. Viswesh is the CTO and Co-Founder of Valar Labs. Valar Labs is building clinical grade deep learning to analyze each patient's characteristics and provide clarity to oncologists during decision making. Their AI is built with oncologists at the center and provides interpretable and actionable insights. Prior to founding Valar Labs, Viswesh was a Research Assistant in Stanford's Artificial Intelligence Laboratory (SAIL) leveraging cutting edge artificial intelligence to solve healthcare problems. He was also the founder of Kanna, a patented and clinically-validated method to detect Amblyopia in children in India. Viswesh graduated with a bachelors in Computer Science at Stanford. In this episode, Vivek and Viswesh shares how they got into Healthcare AI research and how they fell into different career paths, one leading Research at Google Health AI and the other as the CTO and Co-Founder of Valar Labs. We talk about the future of LLMs in healthcare, and also how to build defensibility in AI healthcare startups.

Top 4 AI models for stock analysis/valuation?- Boosted.ai - AI stock screening, portfolio management, risk management- Danielfin - Rates stocks and ETFs with an easy-to-understand global AI Score- JENOVA - AI stock valuation model that uses fundamental analysis to calculate intrinsic value- Comparables.ai - AI designed to find comparables for market analysis quickly and intelligentlyGoogle AI will replace your Doctor soon: Google DeepMind Advances Biomedical AI with ‘Med-PaLM M'Meta is building AI friends for you. SourceAn Asian woman asked AI to improve her headshot and it turned her white... which leads to the broader issue of racial bias in AIHow China Is Using AI In Schools To Improve Education & EfficiencyWhat Machine Learning Reveals About Forming a Healthy Habit.What Else Is Happening in AI?Uber is creating a ChatGPT-like AI bot, following competitors DoorDash & Instacart. YouTube testing AI-generated video summaries.AMD plans AI chips to compete Nvidia and calls it an opportunity to sell it in China.Kickstarter needs AI projects to disclose model training methods.UC hosting AI forum with experts from Microsoft, P&G, Kroger, and TQL.AI employment opportunities are open at Coca-Cola and Amazon.This podcast is generated using the Wondercraft AI platform (https://www.wondercraft.ai/?via=etienne), a tool that makes it super easy to start your own podcast, by enabling you to use hyper-realistic AI voices as your host. Like mine! Get a 50% discount the first month with the code AIUNRAVELED50Are you eager to expand your understanding of artificial intelligence? Look no further than the essential book "AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence," by Etienne Noumen, now available at Shopify, Apple, Google, or Amazon today!

Drs. Douglas Flora and Shaalan Beg discuss the use of artificial intelligence in oncology, its potential to revolutionize cancer care, from early detection to precision medicine, and its limitations in some aspects of care. TRANSCRIPT Dr. Shaalan Beg: Hello and welcome to the ASCO Daily News Podcast. I'm Dr. Shaalan Beg, your guest host of the podcast today. I'm the vice president of oncology at Science37 and an adjunct associate professor at the UT Southwestern Medical Center in Dallas.   On today's episode, we'll be discussing the use of artificial intelligence in oncology, its potential to revolutionize cancer care from early detection to precision medicine, and we'll also go over limitations in some aspects of care. I'm joined by Dr. Douglas Flora, the executive medical director of oncology services at St. Elizabeth Healthcare in northern Kentucky, and the founding editor-in-chief of AI in Precision Oncology, the first peer-reviewed, academic medical journal dedicated specifically to advancing the applications of AI in oncology. The journal will launch early next year.   You'll find our full disclosures in the transcript of this episode and disclosures of all guests on the podcast are available at asco.org/DNpod. Doug, it's great to have you on the podcast today.  Dr. Douglas Flora: I'm glad to be here. Thanks for having me.  Dr. Shaalan Beg: First of all, Doug, congrats on the upcoming launch of the journal. There has been a lot of excitement on the role of AI in oncology and medicine, and also some concern over ethical implications of some of these applications. So, it's great to have you here to address some of these issues. Can you talk about how you got into this space and what motivated you to pursue this endeavor?  Dr. Douglas Flora: I think, Shaalan, I've embraced my inner nerd.  I think that's pretty obvious. This is right along brand for me, along with my love of tech. And so, I started reading about this maybe 5, 6, 7 years ago, and I was struck by how little I understood and how much was going on in our field, and then really accelerated when I read a book that the brilliant Eric Topol wrote in 2019. I don't know if you've seen it, but everything he writes is brilliant. This was called Deep Medicine, and it touched on how we might embrace these new technologies as they're rapidly accelerating to ultimately make our care more human. And that really resonated with me. You know, I've been in clinical practice for almost 20 years now, and the same treadmill many medical oncologists are on as we run from room to room to room and wish we had more time to spend in the depths of the caves with our patients. And this technology has maybe lit me up again in my now 50-year-old age, say, wow, wouldn't it be great if we could use this stuff to provide softer, better, smarter care?  Dr. Shaalan Beg: When I think about different applications in oncology specifically, my mind goes to precision oncology. There are many challenges in the precision oncology space from the discovery of new targets, from finding people to enroll them on clinical trials, ensuring the right person is started on the right treatment at the right time. And we've been talking a lot about and we've been reading and hearing a lot about how artificial intelligence can affect various aspects of the entire spectrum of precision medicine. And I was hoping that you can help our listeners identify which one of those efforts you find are closest to impacting the care that we deliver for our patients come Monday morning in our clinics and which have the highest clinical impact in terms of maturity.  Dr. Douglas Flora: You know, I think the things that are here today, presently, the products that exist, the industry partners that have validated their instruments, it's in 2 things. One is certainly image recognition, right? Pattern doctors like dermatologists and people that read eye grounds and radiologists are seeing increasing levels of accuracy that now are starting to eclipse even specialists in chest radiology and CT or digital pathology with pixelated images now for companies like Path AI and others are publishing peer review data that suggests that the accuracy can be higher than that of a board-certified pathologist. We're all seeing stuff in USA Today and the New York Times about passing medical boards and passing the bar. I think image recognition is actually right here right now. So that's number 1.   Number 2, I think is less sexy, but more important. And that is getting rid of all the rote mechanical mundane tasks that pollute your days as a doc. And I mean specifically time spent on keyboard, pajama time, documenting the vast amounts of material we need for payers and for medical documentation. That can be corrected in hours with the right programming. And so, I think as these large language models start to make their way into clinic, we're going to give doctors back 3, 4, 6 hours a day that they currently spend documenting their care and let them pay attention to their patients again, face to face, eye to eye.  Dr. Shaalan Beg: I love the concept of pajama time. It's sort of become normalized in many folks that the time to do your charting is when you're at home and with your family or in your bedroom in your pajamas, cleaning notes and that's not normal behavior. But it has been normalized in clinical care for many reasons, some necessary and just some not maybe so much. We hear about some of the applications that are coming into electronic medical records. It's been many years since I saw this one demo which one of the vendors had placed where the doctor talks to the patient and then asks the electronic medical record to sum up the visit in a note and then voila, you have a note and you have the orders and you have the billing all tied up. It's been at least 4 years since I've seen that. And I'm not seeing the applications in the clinic or maybe something's turning around the corner because for a lot of people, AI and machine learning was just an idea. It was pie in the sky until chat GPT dropped and everybody got to put their hands on it and see what it can produce. And that's literally scratching the surface of what's possible. So, when you think about giving the doctors their pajama time back, and you think about decision support, trial matching, documentation, which one of those applications are you most excited about as an oncologist?  Dr. Douglas Flora: I'm still in the trenches.  I just finished my Wednesday clinic notes Friday afternoon at 4:30 pm, so I think medical documentation is such a burden and it's so tedious and so unnecessary to redouble the efforts again and again to copy a note that four other doctors have already written on rounds It's silly. So, I think that's going to be one of the early salvos that Hospital systems recognize because there's a higher ROI if you can give 400 doctors back two hours a day. It's also satisfying because the notes will be better. The notes will be carefully curated. They may bring in order sets for the MRI with gadolinium that you forgot you wanted to order; the digital personal assistant will get that. It will set a reminder on your calendar to call the patient back with their test results. It will order the next set of labs, and you're going in the next room, and you're going to be watching that patient in the room. And I've talked to other colleagues about this earlier today. You'll be able to see the daughter getting hives because you're watching her or the look that fleets across the husband's face when you go a little bit too far and you go out too much information when they're not quite ready for that. And I think that's the art of oncology that we're missing when we're flying in a room, and we've got our face on the screen and a keyboard, and we're buried in our own task and we're not there to be present for our patients. So, I'm hopeful that that's going be one of the easy and early wins for oncologists.  Dr. Shaalan Beg: Fantastic. And when we think about the spectrum of cancer care for the people who we care for, a lot happens before they walk into their medical oncologist's office in terms of early identification of cancer, just the diagnosis of cancer, the challenges around tissue acquisition, imaging acquisition. You mentioned a couple of the tools around radiomics, which are being implemented right now. Again, same question: Separate fact from fiction, which ones are we going to see in 2023 or 2024 in the clinical practice that we have? We've been hearing that pathologists and radiologists are going to be out of their jobs if AI takes off, right? Of course, that is a lot of hyperbole there. But how do you view that space and how do you see it impacting the overall burden of care that people receive, and the burden of care that physicians are experiencing?  Dr. Douglas Flora: I'm an eternal optimist, almost infuriating optimist to my partners and colleagues. So, I'm going to lean into this and say, burdens are going be reduced all over the place. We're going to have personal digital navigators to help our patients from the first touch so that they're going to have honest and empathetic questions answered within an hour of diagnosis. The information that they're going have at their fingertips with Chatbot 4 or Med-PALM 2 with Google that's about to be released as a medical generative AI. These are going to give sensitive and empathetic answers that don't put our patients on the cliff, you know, that they're falling off waiting for a doctor's visit 10 days down the road. So, I think the emotional burdens will be improved with better access to better information. I think that the physicians will also have access to that, giving us reassurance that we're going down the right path in terms of really complicated patients taking very, very large datasets and saying a digital twin of this patient would have been more successful with this approach and those sorts of things. And those are probably 3 to 5 years down the road but being tested heavily right now in academic settings with good data coming.  Dr. Shaalan Beg: Robotic empathy sounds like an oxymoron.    Dr. Douglas Flora: Yeah, look at the published studies.   Dr. Shaalan Beg: We've all seen the data on how a chatbot can outperform physicians in terms of empathy. I really find that to be hard to stomach. Help me out.  Dr. Douglas Flora: Yeah, we say that, and we say that to be provocative, but no, there's no substitute for a clinician laying a hand on a patient. We talked about how you need to see that fleeting glance or the hives on the daughter's chest and that you've gone too far and shared too much too soon before that family is ready for it. I have no doubt in my mind, these tools can make us more efficient at our care, but don't get me wrong. There's no chance that these will replace us in the room, giving a hug to a patient or a scared daughter. They're going to remember every word you say; I just want it to be the right words delivered carefully and I don't want us to rush it. So ultimately, as we make our care more human, these tools might actually give us time back in the room to repair that doctor-patient relationship that's been so transactional for the last 4 or 5 or 10 years. And my hope is, we're going to go back to doing what we went into oncology to do, to care deeply about the patients in our care and let the computers handle the rote mechanical stuff; let me be the doctor again and deserve that patient's attention and give it right back in return.   Dr. Shaalan Beg: And I think we're hearing a lot of themes in terms of AI helping the existing clinical enterprise and helping make that better. And it's not your deep blue versus Kasparov, one person is going to win. It's the co-pilot. It's reducing burden. It's making the work more meaningful so that the actual time that's spent with our patients is more meaningful and hopefully can help us make deeper connections.   Let's talk about challenges. What are some of the challenges that worry you? There've been many innovations that have come and gone, and health systems and hospitals have resisted change. And we all remember saying during COVID that we're never going to go back to the old ways. And here we are in 2023 and we are back to the old ways for a lot of things. So, what are the major limitations of AI, even at its... peak success that you see, which our listeners should be aware of, and which may worry you at times.  Dr. Douglas Flora: Well, you've actually spoken to why I started this journal. I want to make sure that clinicians are guiding some of those conversations to make sure that guardrails are up so that we're ethical and we are making sure that we are policing bias. It's no secret now you've seen these things – a lot of language models, a lot of the deep learning was programmed by people that look like me and did not include things that were culturally competent. You can look at data that's been published on Amazon and facial recognition software for Facebook and Instagram and others. And they can identify me out of a crowd as a middle-aged white guy, but 60% of the time they will not recognize Oprah Winfrey or Serena Williams or Michelle Obama. I mean, iconic global icons. And with darker skin, with darker features, with different facial features than my white Caucasian, Eurocentric features, these recognition softwares are not as good. And I'm worried about that for clinical trial selection and screening for that. I'm really, really worried about building databases that don't represent the patients in our charge. So bias is a big deal and that's got to be transparent. That's got to be published how you arrived at this decision. And so that would be number 1. Number 2 is probably that we don't have as much. visibility to how decisions are made, this so-called black box in AI. And that's vexing for doctors, especially conservative oncologists that need 3 published randomized phase 3, blinded, placebo-controlled trials before we move an inch. So, there must be more transparency. And that again is in publications, it's in peer review. They say we need real scientific rigor and not to belabor this, but our industry partners are well ahead of us. We're not generally inclined to believe them until we see it because I've got 150 AI companies coming to my hospital system as vendors some of them are worthy great partners and some of them are a little bit over their skis and selling more than they can actually deliver yet. So, I'd like to give that an opportunity to see the papers. There's about 300 produced a day in AI in medicine. Let's give them a forum and we'll duke it out with letters of the editor and careful review.  Dr. Shaalan Beg: I will say Doug, it is becoming hard to separate fact from fiction. There is so much information which is coming across us in medical journals and through our email, through our professional social media accounts that I sometimes worry that people will just start tuning it all out because they can't separate the high impact discoveries from the more pie in the sky ideas. So, tell us more about how we got here and how you see this curve of enthusiasm shifting maybe in the next 6 months or 1 year.  Dr. Douglas Flora: Yeah, it's a great question. And it's rapidly accelerating, isn't it? We can't escape this. It's entering our hourly lives, much like the iPhone did before, or me having to switch from my BlackBerry to a smartphone that didn't have buttons. I felt like I was adapting. And maybe this is what people felt like when Henry Ford was out there, and all the buggy drivers were getting fired. The reality is it's here and it was here 6 months ago. And maybe we're feeling that urgency and maybe it's starting to catch on in general society because the advent of generative AI is easier to understand. These aren't complicated mathematical models with stacking diagrams and high-tech stuff that's just happening in Palo Alto. It's Siri, it's Cortana. It's my Google digital assistant notifying me that it's time to get on for my next meeting. And those things have been infiltrating our daily lives and our minds quietly for some time. About November 30th when chatbot GPT-3 came out from OpenAI and we started toying with it, you started to see the power. It can be creative, it can be funny, it can articulate your thoughts better than you can articulate them on paper immediately. English students have figured it out. People in marketing and writing legal briefs have figured it out and it's coming to medicine now. It is actually here, and this might be one instance where I think the hype is legit. and these tools will probably reshape our lives.   There have been some estimates by Accenture that 70% of jobs in medicine are going to be altered irretrievably by generative AI. And so, I think it's incumbent upon those of us that are leaders in healthcare systems to at least assemble the team that can help make sense and separate, like you said, the signal from the noise. I know we're doing that here at St. Elizabeth Healthcare. We've got a whole team being formed around this. We have 5 or 6 different products we bought. that we're using to help read mammograms and read lung nodules and read urinalyses, etc. You need a construct to do that appropriately. You need a team of people that are well read and well-studied and able to separate that fact from fiction. I think we're all going to have to work towards that in the next 6 to 12 months.  Dr. Shaalan Beg: Tell me about that construct. How did you, what is the framework that you use to evaluate opportunities as they come through the door?  Dr. Douglas Flora: It's something I think we're all struggling with. As I mentioned, we've got all of these fantastic industry partners, but you can't buy 200 products off the shelf as Epic add-ons as third-party software to solve 200 problems. So, it's interesting, you've just said this. I just shared a piece on LinkedIn that I loved. “Don't pave the cow's path.” It's a really thoughtful thing to say, “Before you build an AI solution, let's make sure we're solving the correct problem.” And the author of that piece on Substack said: Let's not use AI to figure out how to have more efficient meetings by capturing our minutes and transcribing them immediately. Let's first assess how many of these meetings are absolutely necessary. What's the real job to be done and why would you have 50% of your leadership team in meetings all day long and capture those in yet another form? Let's take a look first at the structure around the meetings and say, are these necessary in 2023 and are these productive?   So, my thought would be as we're starting this. We're going to get other smart people who are well-read, who are studying, who are listening to experts that do it six months ahead of us, and really doing a careful contemplative look at this as a team before we dive in with both feet. And there are absolutely tools that are going to be useful, but I think the idea, how do we figure this out without having 200 members of my medical staff coming to me saying, you've got to purchase all 200 of these products, and have a way to vet them scientifically with the same rigor you would for a journal before you put out that kind of outsource.  Dr. Shaalan Beg: Doug, thanks for coming on the podcast today and sharing your valuable insights with us on the ASCO Daily News Podcast. We'll be looking out for your journal, AI in Precision Oncology, early next year. Tell our listeners where they can learn more about your journal.  Dr. Douglas Flora: I really appreciate you guys having me. I love this topic, obviously, I'm excited about it. So, this journal will be ready for a launch in early October in a preview. And then our premier issue will come out in January. We're about to invite manuscripts in mid-August. I guess parties that are interested right now go to Doug Flora's LinkedIn page because that's where I'm sharing most of this and I'll put links in there that will lead you to Liebert's site and our formal page and I think we can probably put it in the transcript here for interested parties.   Dr. Shaalan Beg: Wonderful. Thank you very much and thank you to our listeners for your time today. Finally, if you have any insights on if you value the insights a little. And thank you to our listeners for your time today. Finally, if you value the insights that you hear on the podcast, please take a moment to rate, review and subscribe wherever you get your podcast.  Disclaimer:   The purpose of this podcast is to educate and to inform. This is not a substitute for professional medical care and is not intended for use in the diagnosis or treatment of individual conditions.    Guests on this podcast express their own opinions, experiences, and conclusions. Guest statements on the podcast do not express the opinions of ASCO. The mention of any product, service, organization, activity, or therapy should not be construed as an ASCO endorsement.      Find out more about today's speakers:     Dr. Shaalan Beg   @ShaalanBeg     Dr. Douglas Flora   St. Elizabeth Healthcare     Follow ASCO on social media:    @ASCO on Twitter    ASCO on Facebook    ASCO on LinkedIn      Disclosures:   Dr. Shaalan Beg:   Employment: Science 37   Consulting or Advisory Role: Ipsen, Array BioPharma, AstraZeneca/MedImmune, Cancer Commons, Legend Biotech, Foundation Medicine   Research Funding (Inst.): Bristol-Myers Squibb, AstraZeneca/MedImmune, Merck Serono, Five Prime Therapeutics, MedImmune, Genentech, Immunesensor, Tolero Pharmaceuticals   Dr. Douglas Flora:  Honoraria: Flatiron Health   

This isn't news, it's analysis! Nathan Labenz sits down for an with Zvi Mowshowitz, the writer behind Don't Worry About the Vase to talk about the major players in AI over the last few months. In this extended conversation, Nathan and Zvi debate if AI has attained the intelligence of a well-read college graduate (per OpenAI's Jan Leike), a live player analysis (who to count/ who not to count), and the role of independent red teaming organizations. If you're looking for an ERP platform, check out our sponsor, NetSuite: http://netsuite.com/cognitive Definitely also take a moment to subscribe to Zvi's blog Don't Worry About the Vase (https://thezvi.wordpress.com/) - Zvi is an information hyperprocessor who synthesizes vast amounts of new and ever-evolving information into extremely clear summaries that help educated people keep up with the latest news. Highly recommend. RECOMMENDED PODCAST: The HR industry is at a crossroads. What will it take to construct the next generation of incredible businesses – and where can people leaders have the most business impact? Hosts Nolan Church and Kelli Dragovich have been through it all, the highs and the lows – IPOs, layoffs, executive turnover, board meetings, culture changes, and more. With a lineup of industry vets and experts, Nolan and Kelli break down the nitty-gritty details, trade offs, and dynamics of constructing high performing companies. Through unfiltered conversations that can only happen between seasoned practitioners, Kelli and Nolan dive deep into the kind of leadership-level strategy that often happens behind closed doors. Check out the first episode with the architect of Netflix's culture deck Patty McCord. https://link.chtbl.com/hrheretics TIMESTAMPS: (00:00) Episode preview (03:15) Is AI as intelligent as a college grad? (07:45) Memories and context processing (15:45) Sponsor: NetSuite | Omneky (17:13) Is AI as intelligent as a college grad? cont'd (20:47) Strengths and weaknesses of AI vs human (31:05) OpenAI Superalignment (37:23) The relationship between OpenAI and Anthropic (44:31) Anthropic's security recommendations and adversarial attacks (50:50) Is OpenAI using a constitutional AI approach?  (01:01:26) Context and stochastic parrots (01:10) Is more context better? (01:15:29) Should Nathan work at Anthropic? (01:21:35) Google DeepMind's RT-2 (01:27:47) Multi-modal Med-PaLM (01:31:50) Speculating about Gato (01:35:10) Skepticism about Med-PaLM usage in radiology (01:41:37) Llama 2 - what is going on at Meta?? (01:51:14) Llama 2 vs other models (01:55:29) Who are the live players? (02:01:38) China's AI developments (02:02:41) Character AI and Inflection (02:05:26) Replit as the perfect substrate for AGI (02:10) AI girlfriends (02:18:53) AI safety: The White House (02:25:43) Bottlenecks to progress (02:35:27) Can new players influence AI policy? (02:39:00) Liabilities (02:47:54) Independent red teaming organizations (02:57:18) Mechanistic interpretability X: @labenz (Nathan) @thezvi (Zvi) @eriktorenberg (Erik) @cogrev_podcast  SPONSORS: NetSuite | Omneky -NetSuite provides financial software for all your business needs. More than thirty-six thousand companies have already upgraded to NetSuite, gaining visibility and control over their financials, inventory, HR, eCommerce, and more. If you're looking for an ERP platform: NetSuite (http://netsuite.com/cognitive) and defer payments of a FULL NetSuite implementation for six months. -Omneky is an omnichannel creative generation platform that lets you launch hundreds of thousands of ad iterations that *actually work* customized across all platforms, with a click of a button. Omneky combines generative AI and real-time advertising data. Mention "Cog Rev" for 10% off. MUSIC CREDIT: MusicLM

Implement Al is a next generation consultancy dedicated to supporting small and medium-sized businesses to understand, select and implement Al and continually optimise as the technology evolves. Implement Al is your partner to transform your business to being Al-assisted, before your competition does. In this episode Piers and Aalok discuss Fractional Chief AI Officers, Google's Medical AI Med-PaLM 2 & Specialist LLMs, Meta Intel & Amazon updates, Anthropic Claude 2 Demo & Rewind AI of the week. Want to become AI-assisted? Get in touch https://www.implementai.io/

Nell'ultima pellicola del mitico Tom Cruise il cattivo è niente meno che.... un IA. Quanto delle scelte cinematografiche utilizzate nel film potrebbero avverarsi? Che futuro ha immaginato il regista? News di questa puntata: Elon annuncia FSD entro fine anno Elon fonda xAI Google mette in test Med-Palm-2. L'AI che si finge un medico Google lancia NotebookLM. Il tool per gli studenti Stability rilascia SDXL 0.9 Per chi vuole approfondire e leggere ulteriori riflessioni sulla AI di Mission Impossible (con anche piu spoiler) -> Clicca qui

Esta es la información que encontrarás este jueves 13 de julio en Reforma.com: Aumenta uso de explosivos por el narcotráfico Falló mantenimiento de elevador de IMSS donde falleció niña de 8 años El chatbot Med-PaLM pasó el examen para ejercer como médico en Estados Unidos C.H.U.E.C.O., una sitcom México-Argentina

Dans la course à la meilleure IA entre Bard et ChatGPT, Google a pris de l'avance sur Microsoft dans le domaine de la santé. Un article du Wall Street Journal révèle comment le géant de la technologie s'est immiscé avec succès dans les services de santé aux États-Unis.En effet, des essais cliniques sont déjà en cours dans plusieurs cliniques et hôpitaux américains avec le modèle de langage Med-PaLM 2 de Google. Comme son nom l'indique, cette IA est spécialisée dans le domaine médical. Ce chatbot en est à sa deuxième version et devrait, selon Google, je cite "atteindre une précision de 86,5 % dans les questions de l'examen américain de certification médicale, ce qui représente une amélioration de 19 % par rapport aux résultats obtenus avec la première version du modèle de langage".La société a proposé Med-PaLM 2 à plusieurs services de santé américains. L'objectif de cette IA n'est pas de remplacer les médecins, mais de faciliter leur travail. En effet, elle est capable de résumer rapidement une grande quantité de documents et de présenter les données de manière plus facilement accessible aux professionnels de la santé. De plus, selon les responsables de Google, elle serait particulièrement bénéfique dans les "déserts médicaux", où il y a peu, voire pas du tout, de professionnels de santé.Est-ce à dire que l'automédication est l'avenir de la médecine ? Difficile à dire, mais l'hypothèse n'est pas à exclure, car selon les experts, Med-PaLM 2 génère je cite des "réponses précises et utiles aux questions de santé des patients". Cette évolution comporte cependant des risques, ce qui n'arrête pas les géants de la Silicon Valley, pour qui le secteur de la santé présente de grandes opportunités. De son côté, Microsoft travaille également sur une IA spécialisée dans le domaine médical, cette fois, basée sur ChatGPT. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

Dans la course à la meilleure IA entre Bard et ChatGPT, Google a pris de l'avance sur Microsoft dans le domaine de la santé. Un article du Wall Street Journal révèle comment le géant de la technologie s'est immiscé avec succès dans les services de santé aux États-Unis. En effet, des essais cliniques sont déjà en cours dans plusieurs cliniques et hôpitaux américains avec le modèle de langage Med-PaLM 2 de Google. Comme son nom l'indique, cette IA est spécialisée dans le domaine médical. Ce chatbot en est à sa deuxième version et devrait, selon Google, je cite "atteindre une précision de 86,5 % dans les questions de l'examen américain de certification médicale, ce qui représente une amélioration de 19 % par rapport aux résultats obtenus avec la première version du modèle de langage". La société a proposé Med-PaLM 2 à plusieurs services de santé américains. L'objectif de cette IA n'est pas de remplacer les médecins, mais de faciliter leur travail. En effet, elle est capable de résumer rapidement une grande quantité de documents et de présenter les données de manière plus facilement accessible aux professionnels de la santé. De plus, selon les responsables de Google, elle serait particulièrement bénéfique dans les "déserts médicaux", où il y a peu, voire pas du tout, de professionnels de santé. Est-ce à dire que l'automédication est l'avenir de la médecine ? Difficile à dire, mais l'hypothèse n'est pas à exclure, car selon les experts, Med-PaLM 2 génère je cite des "réponses précises et utiles aux questions de santé des patients". Cette évolution comporte cependant des risques, ce qui n'arrête pas les géants de la Silicon Valley, pour qui le secteur de la santé présente de grandes opportunités. De son côté, Microsoft travaille également sur une IA spécialisée dans le domaine médical, cette fois, basée sur ChatGPT. Learn more about your ad choices. Visit megaphone.fm/adchoices

You think you've got grit? Wait till you hear Andrew's journey of blazing the entrepreneurial trail as a first-gen business founder in our latest episode. Offering a fresh perspective on the challenges and victories that come with being the pioneer in your family to venture into the world of business. Speaking of firsts, we have an exciting discussion about RJ's newfound obsession - pickleball. This trending sport has more to it than meets the eye, with opportunities for financial growth and even collectors' items like the coveted Ben Johns rookie card.But what's a game without injury risks? Especially when majority of the players are over 60. As we delve into the world of pickleball, we also touch upon the importance of using quality equipment to prevent injuries. Moving from sports to tech, we explore Google's latest innovation, Med Palm. This AI tool is not just a product of technological advancement, but a potential game-changer in bringing medical expertise to remote corners of the world. And let's not forget about its ability to flag potential treatment issues, raising a new wave of data-led healthcare.We wrap up with a reflection on the lessons we've gleaned from our entrepreneurial journey: a series of 52 theses that has shaped our company's growth. From Ohio to the wider world, the advice we've received has been instrumental in our expansion. We hope our experiences inspire you just as much as they've empowered us. So tune in, as we continue this riveting conversation in our next episode. Prepare to be enlightened, entertained, and inspired.Follow Us on Social: Jan Almasy: https://www.linkedin.com/in/jan-almasy-57063b34RJ Holliday: https://www.linkedin.com/in/robert-j-holliday-jr-b470a6204/ James Warnken: https://www.linkedin.com/in/jameswarnken --LinkedIn: https://www.linkedin.com/company/51645349/Facebook: https://www.facebook.com/ApexCommunicationsNetworkWebsite: https://www.apexcommunicationsnetwork.com

0:00 China releases "homegrown" OS 1:23 Nvidia pressuring partners to skip Intel 3:01 Google's Med-PaLM-2 medical chatbot 4:43 Newegg FantasTech sale 5:25 QUICK BITS 5:31 Windows Update back for 95, 98 etc 6:15 Emulation's back on Xbox Series 6:53 Denuvo makers doing PR for DRM 7:32 NASA Valkyrie robot sent to Australia 8:07 Competitive SSD smuggling News Sources: https://lmg.gg/KcHHE

In this episode, we delve into the cutting-edge applications of AI in brain cancer treatments, exploring how advanced technologies are making strides in the medical field. We'll also examine the real-world testing of Google's Med-PaLM 2 AI Chatbot in hospitals and discuss its potential to revolutionize patient care. Get on the AI Box Waitlist: https://AIBox.ai/ Investor Contact Email: jaeden@aibox.ai Facebook Community: ⁠https://www.facebook.com/groups/739308654562189/⁠ Discord Community: https://aibox.ai/discord Download Selfpause: https://selfpause.com/Podcast ⁠Inflection AI Report⁠

The Wall Street Journal's sources say Google is testing a language model called Med-PaLM 2 with some health care companies, including the Mayo Clinic. Is the start of AI integration into healthcare? We discussed Engadget's article about NYU Media Lab's AI & Local News Initiative. And Instagram head Adam Mosseri confirmed that Meta's Threads app surpassed 100 million accounts since launching on July 5th. Is this the start of a new social media platform comparable to Twitter?Starring Tom Merritt, Rich Stroffolino, Justin Robert Young, Roger Chang, Joe.Link to the Show Notes. Become a member at https://plus.acast.com/s/dtns. Hosted on Acast. See acast.com/privacy for more information.

Today NLW reads the companion piece to yesterday's episode -- 5 Ways AI Could Improve the World https://www.theguardian.com/technology/2023/jul/06/ai-artificial-intelligence-world-diseases-climate-scenarios-experts Before that on the Brief: Comedian and actress Sarah Silverman has sued Meta and OpenAI for infringing against her copyright by training their models on her book; TSMC rises after beating earnings expectations; corporates are spending big on AI; and Google is testing Med-PaLM 2 in hospitals. Today's Sponsor: Supermanage - AI for 1-on-1's - https://supermanage.ai/breakdown ABOUT THE AI BREAKDOWN The AI Breakdown helps you understand the most important news and discussions in AI.  Subscribe to The AI Breakdown newsletter: https://theaibreakdown.beehiiv.com/subscribe Subscribe to The AI Breakdown on YouTube: https://www.youtube.com/@TheAIBreakdown Join the community: bit.ly/aibreakdown Learn more: http://breakdown.network/

The Wall Street Journal's sources say Google is testing a language model called Med-PaLM 2 with some health care companies, including the Mayo Clinic. Is the start of AI integration into healthcare? We discussed Engadget's article about NYU Media Lab's AI & Local News Initiative. And Instagram head Adam Mosseri confirmed that Meta's Threads app surpassed 100 million accounts since launching on July 5th. Is this the start of a new social media platform comparable to Twitter? Starring Tom Merritt, Rich Stroffolino, Justin Robert Young, Roger Chang, Joe To read the show notes in a separate page click here! Support the show on Patreon by becoming a supporter!

Google tests Med-PaLM 2 with health providers, the EU approves new data transfer deal with US, Sega partners with Line Next for Web3 game development. MP3 Please SUBSCRIBE HERE. You can get an ad-free feed of Daily Tech Headlines for $3 a month here. A special thanks to all our supporters–without you, none of thisContinue reading "Instagram Threads Passes 100M Signups in Five Days – DTH"

Thanks to the over 1m people that have checked out the Rise of the AI Engineer. It's a long July 4 weekend in the US, and we're celebrating with a podcast feed swap!We've been big fans of Nathan Labenz and Erik Torenberg's work at the Cognitive Revolution podcast for a while, which started around the same time as we did and has done an incredible job of hosting discussions with top researchers and thinkers in the field, with a wide range of topics across computer vision (a special focus thanks to Nathan's work at Waymark), GPT-4 (with exceptional insight due to Nathan's time on the GPT-4 “red team”), healthcare/medicine/biotech (Harvard Medical School, Med-PaLM, Tanishq Abraham, Neal Khosla), investing and tech strategy (Sarah Guo, Elad Gil, Emad Mostaque, Sam Lessin), safety and policy, curators and influencers and exceptional AI founders (Josh Browder, Eugenia Kuyda, Flo Crivello, Suhail Doshi, Jungwon Byun, Raza Habib, Mahmoud Felfel, Andrew Feldman, Matt Welsh, Anton Troynikov, Aravind Srinivas). If Latent Space is for AI Engineers, then Cognitive Revolution covers the much broader field of AI in tech, business and society at large, with a longer runtime to go deep on research papers like TinyStories. We hope you love this episode as much as we do, and check out CogRev wherever fine podcasts are sold!Subscribe to the Cognitive Revolution on:* Website* Apple Podcasts* Spotify* YoutubeGood Data is All You NeedThe work of Ronen and Yuanzhi echoes a broader theme emerging in the midgame of 2023: * Falcon-40B (trained on 1T tokens) outperformed LLaMA-65B (trained on 1.4T tokens), primarily due to the RefinedWeb Dataset that runs CommonCrawl through extensive preprocessing and cleaning in their MacroData Refinement pipeline. * UC Berkeley LMSYS's Vicuna-13B is near GPT-3.5/Bard quality at a tenth of their size, thanks to fine-tuning from 70k user-highlighted ChatGPT conversations (indicating some amount of quality). * Replit's finetuned 2.7B model outperforms the 12B OpenAI Codex model based on HumanEval, thanks to high quality data from Replit usersThe path to smaller models leans on better data (and tokenization!), whether from cleaning, from user feedback, or from synthetic data generation, i.e. finetuning high quality on outputs from larger models. TinyStories and Phi-1 are the strongest new entries in that line of work, and we hope you'll pick through the show notes to read up further.Show Notes* TinyStories (Apr 2023)* Paper: TinyStories: How Small Can Language Models Be and Still Speak Coherent English?* Internal presentation with Sebastien Bubeck at MSR* Twitter thread from Ronen Eldan* Will future LLMs be based almost entirely on synthetic training data? In a new paper, we introduce TinyStories, a dataset of short stories generated by GPT-3.5&4. We use it to train tiny LMs (< 10M params) that produce fluent stories and exhibit reasoning.* Phi-1 (Jun 2023)* Paper: Textbooks are all you need (HN discussion)* Twitter announcement from Sebastien Bubeck:* phi-1 achieves 51% on HumanEval w. only 1.3B parameters & 7B tokens training dataset and 8 A100s x 4 days = 800 A100-hours. Any other >50% HumanEval model is >1000x bigger (e.g., WizardCoder from last week is 10x in model size and 100x in dataset size). Get full access to Latent Space at www.latent.space/subscribe

Dans cet épisode, Antonio, Emmanuel et Guillaume reviennent sur les nouveautés et annonces faites à Google I/O 2023 : de nouveaux téléphones Pixel qui se plient ou pas, et surtout de l'intelligence artificielle du sol au plafond ! Que ce soit dans Android, dans Google Workspace, dans Google Cloud, une tonne de produits passe en mode survitaminé à l'IA. Guillaume, Antonio et Emmanuel discutent aussi de l'impact qu'ils voient sur l'AI, et de comment les Large Language Models sont raffinés et pourquoi on les fait halluciner, de subtilités du langage des signes. Enregistré le 23 mai 2023 Téléchargement de l'épisode LesCastCodeurs-Episode-296.mp3 Google I/O 2023 Site web : https://io.google/2023/ Keynote principale : https://io.google/2023/program/396cd2d5-9fe1-4725-a3dc-c01bb2e2f38a/ Keynote développeur : https://io.google/2023/program/9fe491dd-cadc-4e03-b084-f75e695993ea/ Vidéo résumée en 10 minutes de toutes les annonces : https://www.youtube.com/watch?v=QpBTM0GO6xI&list=TLGGCy91ScdjTPYxNjA1MjAyMw Vidéo de toutes les sessions techniques : https://io.google/2023/program/?q=technical-session Google I/O s'est tenu il y a 10 jours en Californie, dans l'amphithéâtre de Shoreline, près du campus de Google. Seulement 2000 personnes sur place, un chat et un jeu en ligne pour assister à distance. Jeu en ligne I/O Flip créé avec Flutter, Dart, Firebase, et Cloud Run, et tous les assets graphiques générés par Generative AI https://blog.google/technology/ai/google-card-game-io-flip-ai/ Des Pixels plein les yeux ! Des détails sur le design des nouveaux appareils : https://blog.google/products/pixel/google-pixel-fold-tablet-7a-design/ Pixel Fold Article : https://blog.google/products/pixel/google-pixel-fold/ Premier téléphone foldable de Google (après Samsung et Oppo) Un écran sur le dessus, et un grand écran pliable à l'intérieur Pratique pour la traduction où peut voir une discussion traduire en deux langues d'un côté sur un écran et dans l'autre langue sur l'autre Utilisation créative de la pliure : mode “laptop”, pour les selfies, pour poser l'appareil pour des photos de nuit Par contre… pas disponible en France, et tout de même presque 1900€ ! Pixel Tablet Article : https://blog.google/products/pixel/google-pixel-tablet/ Une belle tablette de 11 pouces, avec un dock de recharge avec enceinte intégrée Processeur Tensor G2, Chromecast intégré C'est un peu comme le Google Nest Hub Max mais avec un écran détachable Une coque pratique avec un trépied intégré et qui n'empêche pas de recharger la tablette sur le dock En mode dock, c'est comme l'écran du Google Home App, et dès qu'on la décroche, on est en mode multi-utilisateur, chacun avec son profil Pixel 7a Article : https://blog.google/products/pixel/pixel-7a-io-2023/ Écran de 6 pouces Triple appareil photo (grand angle, principal, et photo avant pour les selfies) 509 euros Magic Eraser pour effacer les trucs qu'on veut pas dans la photo, Magic Unblur pour rendre une photo floue plus nette, Real Tone pour rendre les peaux foncées plus naturelles Android Article quoi de neuf dans Android : https://blog.google/products/android/android-updates-io-2023/ Dans Messages, Magic Compose dans les conversations, l'IA nous aide à concevoir nos messages, dans différents styles (plus pro, plus fun, dans le style de Shakespeare) Android 14 devrait arriver un peu plus tard dans l'année, avec plus de possibilités de customisation (fond d'écran généré par Gen AI, fond d'écran Emojis, couleurs associées, fond d'écran 3D issus de ses photos) https://blog.google/products/android/new-android-features-generative-ai/ StudioBot : un chatbot intégré à Android Studio pour aider au développement d'applis Android https://io.google/2023/program/d94e89c5-1efa-4ab2-a13a-d61c5eb4e49c/ 800 millions d'utilisateurs sont passés à RCS pour le messaging Adaptation de 50 applications Android pour s'adapter aux foldables https://blog.google/products/android/android-app-redesign-tablet-foldable/ Wear OS 4 va rajouter le backup restore quand on change de montre et autres nouveautés https://blog.google/products/wear-os/wear-os-update-google-io-2023/ 800 chaînes TV gratuites dans Google TV sur Android et dans la voiture Android Auto va être disponible de 200 millions de voitures https://blog.google/products/android/android-auto-new-features-google-io-2023/ Waze disponible globalement sur le playstore dans toutes les voitures avec Android Auto Google Maps Article : https://blog.google/products/maps/google-maps-updates-io-2023/ Maps propose 20 milliards de km de direction tous les jours Immersive View for Routes 15 villes : Amsterdam, Berlin, Dublin, Florence, Las Vegas, London, Los Angeles, Miami, New York, Paris, San Francisco, San Jose, Seattle, Tokyo et Venice Possibilité pour les développeurs de s'intégrer et rajouter des augmentations 3D, des marqueurs Google Photos Article Magic Editor : https://blog.google/products/photos/google-photos-magic-editor-pixel-io-2023/ Magic Editor survitaminé à l'IA pour améliorer les photos, en déplaçant des gens, en rajoutant des parties coupées, ou bien rendre le ciel plus beau Possible que ce soit limité aux téléphones Pixel au début Projets expérimentaux Project Starline (écran avec caméra 3D qui donne un rendu 3D de son interlocuteur comme s'il était en face de soi) a été amélioré pour prendre moins de place https://blog.google/technology/research/project-starline-prototype/ Universal Translator : une nouvelle expérimentation pour faire du doublage et traduction automatique avec synchronisation des mouvements des lèvres Project Tailwind, une sorte de notebook dans lequel on peut rajouter tous ses documents à partir de drive, et poser des questions sur leur contenu, proposer des résumés, de faire du brainstorming sur ces thèmes https://thoughtful.sandbox.google.com/about MusicLM : un large language model pour générer de la musique à partir d'un texte de prompt (waitlist pour s'inscrire) https://blog.google/technology/ai/musiclm-google-ai-test-kitchen/ Project Gameface : utilisation des expressions du visage pour commander une souris et un ordinateur, pour les personnes qui ont perdu leur mobilité https://blog.google/technology/ai/google-project-gameface/ VisualBlocks : pour expérimenter dans une interface drag'n drop avec le développement de modèles pour Tensorflow lite et js https://visualblocks.withgoogle.com/ MakerStudio : pour les bidouilleurs et développeurs https://makersuite.google.com/ https://developers.googleblog.com/2023/05/palm-api-and-makersuite-moving-into-public-preview.html Search Labs Article : https://blog.google/products/search/generative-ai-search/ Expérimentations pour rajouter l'IA générative dans Google Search Faire des recherches avec des requêtes avec des phrases plus complexes, en intégrant des réponses comme Bard, avec des liens, des suggestions d'autres recherches associées Mais aussi proposer des publicités mieux ciblées On peut s'inscrire à Search Labs pour tester cette nouvelle expérience, mais au début juste en Anglais et juste pour les US Des intégrations avec Google Shopping pour proposer et filtrer des produits qui correspondent à la requête Recherche à l'aide d'image, avec Google Lens : 12 milliards de recherches visuelles par mois Palm et Bard Annonce du modèle LLM Palm 2 utilisé dans Bard et dans Google Cloud https://blog.google/technology/ai/google-palm-2-ai-large-language-model/ PaLM 2 est en cours d'intégration dans 25 produits de Google Supportera 100 langues différentes (pour l'instant seulement l'anglais, japonais et coréen), avec déjà les 40 langues les plus parlées d'ici la fin de l'année Maintenant disponible dans 180 pays… sauf l'Europe !!! Capacité de raisonnement accrue Peut coder dans une vingtaine de langages de programmation différents dont Groovy Différentes tailles de modèles : Gecko, Otter, Bison et Unicorn, mais le nombre de paramètres n'est pas communiquée, comme pour GPT-4 d'OpenAI Utilisable pour des requêtes et pour du chat Des modèles dérivées fine-tunés Med-PaLM 2 sur du savoir médical, sur l'analyse visuelle des radios et Sec-PaLM, entrainé sur des cas d'utilisation sur le thème de la cybersécurité, pour aider à déceler des scripts malicieux, des vecteurs d'attaque Sundar Pichai a aussi annoncé que Google travaillait déjà sur la prochaine évolution de ses LLM avec un modèle appelé Gemini. Peu de détails à part qu'il sera multimodal (en particulier recherche combinée image et texte par ex.) Partenariat et intégration de Adobe Firefly dans Bard pour générer des images https://blog.adobe.com/en/publish/2023/05/10/adobe-firefly-adobe-express-google-bard Duet AI pour Google Workspace Article : https://workspace.google.com/blog/product-announcements/duet-ai Dans Gmails et Docs, propose d'aider à la rédaction de vos emails et documents une extension de “smart compose” qui va permettre de générer des emails entiers, d'améliorer le style, de corriger la grammaire, éviter les répétitions de texte Dans Docs, des nouveaux “smart chips” pour rajouter des variables, des templates Dans Slides, rajouter des images générées par IA Des prompts dans Sheets pour générer un draft de table Dans Google Meet, possibilité de créer une image de fond customisée avec Generative AI Ces améliorations font parties de Workspace Labs auquel on peut s'inscrire dans la liste d'attente https://workspace.google.com/labs-sign-up/ Google Cloud Intégration de Generative AI partout https://cloud.google.com/blog/products/ai-machine-learning/google-cloud-launches-new-ai-models-opens-generative-ai-studio Nouvelles VM A3 avec les GPUs H100 de Nvidia, idéal pour l'entrainement de modèles de machine learning, avec 26 exaFlops de performance https://cloud.google.com/blog/products/compute/introducing-a3-supercomputers-with-nvidia-h100-gpus Trois nouveaux modèles LLM dans Vertex AI : Imagen (private preview) pour générer des images, Codey pour la génération de code, et Chirp pour la génération de la parole supportant 100 langues différentes avec 2 milliards de paramètres vocaux Model Garden : avec les modèles de machine learning y compris externes et open sources Ajout des embeddings pour le texte et l'image RLHF, Reinforcement Learning from Human Feedback bientôt intégrer pour étendre Vertex AI tuning et prompt design avec une boucle de feedback humaine Generative AI Studio pour tester ses prompts zero-shot, one-shot, multi-shots Duet AI pour Google Cloud https://cloud.google.com/blog/products/application-modernization/introducing-duet-ai-for-google-cloud Assistance de code dans VSCode et bientôt les IDEs JetBrains grâce au plugin Cloud Code, et dans Cloud Workstations. Intégration dans les IDEs d'un chat pour comme un compagnon pour discuter d'architecture, trouver les commandes à lancer pour son projet Le modèle de code de Codey fonctionne sur une vingtaine de languages de programmation, mais un modèle fine-tuné a été entrainé sur toute la doc de Google Cloud, donc pourra aider en particulier sur l'utilisation des APIs de Google Cloud, ou l'utilisation de la ligne de commande gcloud Duet AI est aussi dans App Sheet, la plateforme low/no-code, et permettra de chatter avec un chatbot pour générer une application App Sheet Quoi de neuf dans Firebase https://firebase.blog/posts/2023/05/whats-new-at-google-io Web Article : https://developers.googleblog.com/2023/05/io23-developer-keynote-recap.html Flutter 3 et Dart 3.10 https://io.google/2023/program/7a253260-3941-470b-8a4d-4253af000119/ WebAssembly https://io.google/2023/program/1d176349-7cf8-4b51-b816-a90fc9d7d479/ WebGPU https://io.google/2023/program/0da196f5-5169-43ff-91db-8762e2c424a2/ Baseline https://io.google/2023/program/528a223c-a3d6-46c5-84e4-88af2cf62670/ https://web.dev/baseline/ Nous contacter Pour réagir à cet épisode, venez discuter sur le groupe Google https://groups.google.com/group/lescastcodeurs Contactez-nous via twitter https://twitter.com/lescastcodeurs Faire un crowdcast ou une crowdquestion Soutenez Les Cast Codeurs sur Patreon https://www.patreon.com/LesCastCodeurs Tous les épisodes et toutes les infos sur https://lescastcodeurs.com/

Large language models are already changing the business of search. But now they're about to change the practice of medicine. Harry's guests, Vivek Natarajan and Shek Azizi, are both researchers on the Health AI team at Google, where they're pushing the boundaries of what large language models can achieve in specialized domains like  health. This spring their team announced it would start rolling out a new large language model called Med-PaLM 2 that's designed to answer medical questions with high accuracy. (The model got an 85 percent score on the U.S. Medical License Exam, the test all doctors have to take before they're allowed to practice.)  It's been clear for a while that consulting with an AI would eventually become an indispensable part of every medical journey—whether you're a patient searching for information about your symptoms, or a doctor looking for an expert second opinion. And now that such a future is almost here, the work Vivek and Shek are doing at Google feels both exciting and a little bit scary.For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:1. Open the Podcasts app on your iPhone, iPad, or Mac. 2. Navigate to The Harry Glorikian Show podcast. You can find it by searching for it or selecting it from your library. Just note that you'll have to go to the series page which shows all the episodes, not just the page for a single episode.3. Scroll down to find the subhead titled "Ratings & Reviews."4. Under one of the highlighted reviews, select "Write a Review."5. Next, select a star rating at the top — you have the option of choosing between one and five stars. 6. Using the text box at the top, write a title for your review. Then, in the lower text box, write your review. Your review can be up to 300 words long.7. Once you've finished, select "Send" or "Save" in the top-right corner. 8. If you've never left a podcast review before, enter a nickname. Your nickname will be displayed next to any reviews you leave from here on out. 9. After selecting a nickname, tap OK. Your review may not be immediately visible.That's it! Thanks so much.

Apple is limiting the internal use of some AI-powered tools, while Hippocratic AI has raised $50 million to develop healthcare chatbots. We also discuss a new approach to teaching AI called Guidance and a new large language model called Med-PaLM 2 that improves upon previous work in medical question answering. Contact:  sergi@earkind.com Timestamps: 566:54 Introduction 1905:32 Apple reportedly limits internal use of AI-powered tools like ChatGPT and GitHub Copilot 3376:42 Hippocratic AI Raises $50 Million To Power The Healthcare Bot Workforce 5500:37 Guidance: an alternative to prompting or chaining 6601:09 Fake sponsor 8624:46 A Generalist Dynamics Model for Control 9843:34 GPT detectors are biased against non-native English writers 11499:34 Towards Expert-Level Medical Question Answering with Large Language Models 13531:16 Outro

Nathan sits down with Vivek Natarajan, research scientist at Google Health. Vivek leads the Google Brain moonshot behind Med-PaLM, Google's flagship medical large language model, featured in The Economist, The Scientific American, CNBC, and Forbes. In this episode, they discuss the foundational models that Vivek and team built before Med-PaLM, the techniques used to develop Med-PaLM which will be of interest to anyone developing AI systems for high-stakes use cases, and the capabilities for Med-PaLM to equalize access to medical knowledge and care. This episode is part of a series centered on talking to the people at the cutting edge of building AI-driven solutions in medicine. RECOMMENDED PODCAST: The HR industry is at a crossroads. What will it take to construct the next generation of incredible businesses – and where can people leaders have the most business impact? Hosts Nolan Church and Kelli Dragovich have been through it all, the highs and the lows – IPOs, layoffs, executive turnover, board meetings, culture changes, and more. With a lineup of industry vets and experts, Nolan and Kelli break down the nitty-gritty details, trade offs, and dynamics of constructing high performing companies. Through unfiltered conversations that can only happen between seasoned practitioners, Kelli and Nolan dive deep into the kind of leadership-level strategy that often happens behind closed doors. Check out the first episode with the architect of Netflix's culture deck Patty McCord. https://link.chtbl.com/hrheretics LINKS: https://sites.research.google/med-palm/ FEEDBACK / COLLABORATE WITH NATHAN: Email: TCR@turpentine.co TIMESTAMPS: (00:00) Episode preview (03:43) The story of how Med-PaLM came to be (09:41) Building Med-PaLM's infrastructure (13:10) The US medical licensing exam as a measure of AI progress (15:23) Sponsor: Omneky (18:17) Practicality of benchmarking in real-world usage (21:39) Overcoming the shortfalls of Flan-PaLM with Med-PaLM (25:08) Choosing to use soft prompting over few shot prompting (30:36) The process of training Flan-PaLM (37:31) A curriculum approach to soft-prompting (38:43) Layperson vs expert interactions with LLMs (43:54) How did the Google team facilitate user exploration of the model's capabilities? (46:58) Shift in techniques from Med-PaLM to Med-PaLM2 (50:21) Using different prompting strategies with Med-PaLM2 (57:33) Is Med-PaLM 2 preferred over clinicians? (01:02:28) Will there be a multimodal version of Med-PaLM? (01:04:52) Breakthroughs required for AI to further advance human potential (01:10:23) The Med-PaLM business plan (01:12:08) Is there a vision for a consumer product? (01:15:46) The pros and cons of pre-training a model (01:19:45) Vivek's favorite AI products (01:21:01) Would Vivek get a Neuralink implant? (01:23:08) AI hopes and fears TWITTER: @CogRev_Podcast @vivnat (Vivek) @labenz (Nathan) @eriktorenberg (Erik) Thank you Omneky for sponsoring The Cognitive Revolution. Omneky is an omnichannel creative generation platform that lets you launch hundreds of thousands of ad iterations that actually work, customized across all platforms, with a click of a button. Omneky combines generative AI and real-time advertising data. Mention "Cog Rev" for 10% off. Music Credit: MusicLM More show notes and reading material released in our Substack: https://cognitiverevolution.substack.com/

I react to highlights from the recent Google I/O keynote presentation, which features impressive competitive technologies to OpenAI's GPT-4 and wildly popular ChatGPT. Google showcases its many new AI powered products within its existing suite of products and services, including Help Me Write in Gmail and Workspace apps, Magic Editor in Google Photos, PaLM 2 (competitor to GPT-4), Med-PaLM for the healthcare sector, next generation language model Gemini, Bard (competitor to ChatGPT), Google Search AI integration (competitor to Microsofts Bing Chat AI), and more.  Timestamps: 00:00 intro 00:38 AI Help Me Write in Gmail 01:52 AI Magic Editor in Google Photos 03:23 Google PaLM 2 vs OpenAI GPT-4 04:34 Med-PaLM 2 for healthcare 06:02 Google Brain + Google Deepmind consolidated 06:26 Google Gemini - next foundation model 07:30 Responsible AI  08:23 Google Bard vs OpenAI's ChatGPT 13:32 AI features in Google Workspace apps 17:26 Generative AI in Google Search  23:15 Google's AI infrastructure vs Microsoft 24:02 Wrapping up Related episodes: Microsoft to Challenge Google Search Using OpenAI's ChatGPT https://youtu.be/Vh42ZgADo5k Is OpenAI ChatGPT Disrupting Google? Founders Come Back! https://youtu.be/7dLT_CDITmg Microsoft Bing Now Using OpenAI's New GPT4 To Challenge Google https://youtu.be/WNr9KE3nobE OpenAI ChatGPT ALREADY at $29B Valuation! https://youtu.be/PPSDBc7lx9g The main investment brokerage I use to buy and sell international stocks is Interactive Brokers (referral link): https://ibkr.com/referral/john5664 Using a referral link below helps support the pod, thanks! I use GuruFocus for historical, financial and valuation data, screeners, charts and comparison tools, to help me make smarter long-term investing decisions (refferal link): https://www.gurufocus.com/?r=2c95d5930bb2537b2e0265075fb66581 Disclaimer: I am not a financial adviser. This content is for education and entertainment purposes only. Do your own analysis and/or seek professional financial advice before making any investment decision. --- Send in a voice message: https://podcasters.spotify.com/pod/show/theartofvalue/message

I react to highlights from the recent Google I/O keynote presentation, which features impressive competitive technologies to OpenAI's GPT-4 and wildly popular ChatGPT. Google showcases its many new AI powered products within its existing suite of products and services, including Help Me Write in Gmail and Workspace apps, Magic Editor in Google Photos, PaLM 2 (competitor to GPT-4), Med-PaLM for the healthcare sector, next generation language model Gemini, Bard (competitor to ChatGPT), Google Search AI integration (competitor to Microsofts Bing Chat AI), and more.  Timestamps: 00:00 intro 00:38 AI Help Me Write in Gmail 01:52 AI Magic Editor in Google Photos 03:23 Google PaLM 2 vs OpenAI GPT-4 04:34 Med-PaLM 2 for healthcare 06:02 Google Brain + Google Deepmind consolidated 06:26 Google Gemini - next foundation model 07:30 Responsible AI  08:23 Google Bard vs OpenAI's ChatGPT 13:32 AI features in Google Workspace apps 17:26 Generative AI in Google Search  23:15 Google's AI infrastructure vs Microsoft 24:02 Wrapping up Related episodes: Microsoft to Challenge Google Search Using OpenAI's ChatGPT https://youtu.be/Vh42ZgADo5k Is OpenAI ChatGPT Disrupting Google? Founders Come Back! https://youtu.be/7dLT_CDITmg Microsoft Bing Now Using OpenAI's New GPT4 To Challenge Google https://youtu.be/WNr9KE3nobE OpenAI ChatGPT ALREADY at $29B Valuation! https://youtu.be/PPSDBc7lx9g The main investment brokerage I use to buy and sell international stocks is Interactive Brokers (referral link): https://ibkr.com/referral/john5664 Using a referral link below helps support the pod, thanks! I use GuruFocus for historical, financial and valuation data, screeners, charts and comparison tools, to help me make smarter long-term investing decisions (refferal link): https://www.gurufocus.com/?r=2c95d5930bb2537b2e0265075fb66581 Disclaimer: I am not a financial adviser. This content is for education and entertainment purposes only. Do your own analysis and/or seek professional financial advice before making any investment decision. --- Send in a voice message: https://podcasters.spotify.com/pod/show/theartofvalue/message

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: Some Intuitions Around Short AI Timelines Based on Recent Progress, published by Aaron Scher on April 11, 2023 on LessWrong. tldr: I give some informal evidence and intuitions that point toward AGI coming soon. These include thinking about how crazy the last year has been, beliefs from those in major AI labs, and progress on MMLU. Intro This post is intended to be a low-effort reference I can point people to when I say I think there is some evidence for short AI timelines. I might describe the various bits of evidence and intuitions presented here as “intuitions around short AI timelines based on recent progress” (though perhaps there are better terms). They are not a thorough model like Ajeya's; insofar as somebody is using multiple models when putting together a timelines estimate, I think it would be unreasonable to place less than 20% or greater than 95% weight on extrapolation from current systems and recent progress. In the spirit of being informal, you can use whatever definition of AGI you like. I mostly use AGI to refer to something like “an AI system which can do pretty much all the human cognitive tasks as well or better than humans (~99% of tasks people in 2023 do).” Some evidence I (Aaron) started following AI and AI existential safety around the beginning of 2022; it's been a little over a year. Some of that time was my understanding catching up with advances from the past couple years, but there have also been major advances. Some major advances since I've been paying attention: Chinchilla paper popularized the scaling laws that were already known to some, there was some DALL-E and related stuff which was cool, CICERO happened which I didn't follow but indicates we're probably going to train the AIs to do all the dangerous stuff (see also Auto-GPT and Chaos-GPT, or GPT-4 getting plugins within 2 weeks of release, as more recent updates in this saga of indignity), ChatGPT shows how much more usable models are with RLHF (popularizing methods that have been known for a while), Med-PaLM gets a passing score on the US medical licensing exam (also tons of other PaLM and Flan-PaLM results I haven't followed but which seem impressive). LLaMA and Alpaca take powerful capabilities from compute-efficient (and over) training and hand them to the public. GPT-4 blows the competition out of the water on many benchmarks. I probably missed a couple big things (including projects which honorably didn't publicly push SOTA, 1, 2); the list is probably a bit out of order; I've also included things from 2023; but man, that sure is a year of progress. I don't expect there are all that many more years with this much progress before we hit AGI — maybe 3-12 years. More importantly, I think this ~15 month period, especially November 2022-now, has generated a large amount of hype and investment in AI research and products. We seem to be on a path such that — in every future year before we die — there is more talent+effort+money working on improving AI capabilities than there was in 2022. I hold some hope that major warning shots and/or regulation would change this picture, in fact I think it's pretty likely we'll see warning shots beyond those we have already seen, but I am not too optimistic about what the response will be. As crazy as 2022 was, we should be pretty prepared for a world that gets significantly crazier. I find it hard to imagine that we could have all that many more years that look like 2022 AI progress-wise, especially with significantly increased interest in AI. A large amount of the public thinks AGI is near. I believe these people are mostly just thinking about how good current systems (GPT-4) are and informally extrapolating [image description: A Twitter poll from Lex Fridman where he asks “When will superintelligent general AI (AGI) arrive?” There are 270,00...

Link to original articleWelcome 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: Some Intuitions Around Short AI Timelines Based on Recent Progress, published by Aaron Scher on April 11, 2023 on LessWrong. tldr: I give some informal evidence and intuitions that point toward AGI coming soon. These include thinking about how crazy the last year has been, beliefs from those in major AI labs, and progress on MMLU. Intro This post is intended to be a low-effort reference I can point people to when I say I think there is some evidence for short AI timelines. I might describe the various bits of evidence and intuitions presented here as “intuitions around short AI timelines based on recent progress” (though perhaps there are better terms). They are not a thorough model like Ajeya's; insofar as somebody is using multiple models when putting together a timelines estimate, I think it would be unreasonable to place less than 20% or greater than 95% weight on extrapolation from current systems and recent progress. In the spirit of being informal, you can use whatever definition of AGI you like. I mostly use AGI to refer to something like “an AI system which can do pretty much all the human cognitive tasks as well or better than humans (~99% of tasks people in 2023 do).” Some evidence I (Aaron) started following AI and AI existential safety around the beginning of 2022; it's been a little over a year. Some of that time was my understanding catching up with advances from the past couple years, but there have also been major advances. Some major advances since I've been paying attention: Chinchilla paper popularized the scaling laws that were already known to some, there was some DALL-E and related stuff which was cool, CICERO happened which I didn't follow but indicates we're probably going to train the AIs to do all the dangerous stuff (see also Auto-GPT and Chaos-GPT, or GPT-4 getting plugins within 2 weeks of release, as more recent updates in this saga of indignity), ChatGPT shows how much more usable models are with RLHF (popularizing methods that have been known for a while), Med-PaLM gets a passing score on the US medical licensing exam (also tons of other PaLM and Flan-PaLM results I haven't followed but which seem impressive). LLaMA and Alpaca take powerful capabilities from compute-efficient (and over) training and hand them to the public. GPT-4 blows the competition out of the water on many benchmarks. I probably missed a couple big things (including projects which honorably didn't publicly push SOTA, 1, 2); the list is probably a bit out of order; I've also included things from 2023; but man, that sure is a year of progress. I don't expect there are all that many more years with this much progress before we hit AGI — maybe 3-12 years. More importantly, I think this ~15 month period, especially November 2022-now, has generated a large amount of hype and investment in AI research and products. We seem to be on a path such that — in every future year before we die — there is more talent+effort+money working on improving AI capabilities than there was in 2022. I hold some hope that major warning shots and/or regulation would change this picture, in fact I think it's pretty likely we'll see warning shots beyond those we have already seen, but I am not too optimistic about what the response will be. As crazy as 2022 was, we should be pretty prepared for a world that gets significantly crazier. I find it hard to imagine that we could have all that many more years that look like 2022 AI progress-wise, especially with significantly increased interest in AI. A large amount of the public thinks AGI is near. I believe these people are mostly just thinking about how good current systems (GPT-4) are and informally extrapolating [image description: A Twitter poll from Lex Fridman where he asks “When will superintelligent general AI (AGI) arrive?” There are 270,00...

De tre första tävlingarna i Ski Classics är avgjorda och vi går nu på några veckors juluppehåll. Tillsammans med Stefan Palm summerar vi inledningen av vintern där Stefan ger sina tankar på den inledande tävlingarna under säsongen. Å den som känner Anton och Stefan förstår givetvis att det blir lite cykel i podden.