Podcasts about Kaggle

Internet platform for data science competitions

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Best podcasts about Kaggle

Latest podcast episodes about Kaggle

The MAD Podcast with Matt Turck
Jeremy Howard on Building 5,000 AI Products with 14 People (Answer AI Deep-Dive)

The MAD Podcast with Matt Turck

Play Episode Listen Later May 15, 2025 55:02


What happens when you try to build the “General Electric of AI” with just 14 people? In this episode, Jeremy Howard reveals the radical inside story of Answer AI — a new kind of AI R&D lab that's not chasing AGI, but instead aims to ship thousands of real-world products, all while staying tiny, open, and mission-driven.Jeremy shares how open-source models like DeepSeek and Qwen are quietly outpacing closed-source giants, why the best new AI is coming out of China. You'll hear the surprising truth about the so-called “DeepSeek moment,” why efficiency and cost are the real battlegrounds in AI, and how Answer AI's “dialogue engineering” approach is already changing lives—sometimes literally.We go deep on the tools and systems powering Answer AI's insane product velocity, including Solve It (the platform that's helped users land jobs and launch startups), Shell Sage (AI in your terminal), and Fast HTML (a new way to build web apps in pure Python). Jeremy also opens up about his unconventional path from philosophy major and computer game enthusiast to world-class AI scientist, and why he believes the future belongs to small, nimble teams who build for societal benefit, not just profit.Fast.aiWebsite - https://www.fast.aiX/Twitter - https://twitter.com/fastdotaiAnswer.aiWebsite - https://www.answer.ai/X/Twitter - https://x.com/answerdotaiJeremy HowardLinkedIn - https://linkedin.com/in/howardjeremyX/Twitter - https://x.com/jeremyphowardFIRSTMARKWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCapMatt Turck (Managing Director)LinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturck(00:00) Intro (01:39) Highlights and takeaways from ICLR Singapore (02:39) Current state of open-source AI (03:45) Thoughts on Microsoft Phi and open source moves (05:41) Responding to OpenAI's open source announcements (06:29) The real impact of the Deepseek ‘moment' (09:02) Progress and promise in test-time compute (10:53) Where we really stand on AGI and ASI (15:05) Jeremy's journey from philosophy to AI (20:07) Becoming a Kaggle champion and starting Fast.ai (23:04) Answer.ai mission and unique vision (28:15) Answer.ai's business model and early monetization (29:33) How a small team at Answer.ai ships so fast (30:25) Why Devin AI agent isn't that great (33:10) The future of autonomous agents in AI development (34:43) Dialogue Engineering and Solve It (43:54) How Answer.ai decides which projects to build (49:47) Future of Answer.ai: staying small while scaling impact

DataTalks.Club
Build a Strong Career in Data - Lavanya Gupta

DataTalks.Club

Play Episode Listen Later May 9, 2025 51:59


In this podcast episode, we talked with Lavanya Gupta about Building a Strong Career in Data.About the Speaker: Lavanya is a Carnegie Mellon University (CMU) alumni of the Language Technologies Institute (LTI). She works as a Sr. AI/ML Applied Associate at JPMorgan Chase in their specialized Machine Learning Center of Excellence (MLCOE) vertical. Her latest research on long-context evaluation of LLMs was published in EMNLP 2024. In addition to having a strong industrial research background of 5+ years, she is also an enthusiastic technical speaker. She has delivered talks at events such as Women in Data Science (WiDS) 2021, PyData, Illuminate AI 2021, TensorFlow User Group (TFUG), and MindHack! Summit. She also serves as a reviewer at top-tier NLP conferences (NeurIPS 2024, ICLR 2025, NAACL 2025). Additionally, through her collaborations with various prestigious organizations, like Anita BOrg and Women in Coding and Data Science (WiCDS), she is committed to mentoring aspiring machine learning enthusiasts.In this episode, we talk about Lavanya Gupta's journey from software engineer to AI researcher. She shares how hackathons sparked her passion for machine learning, her transition into NLP, and her current work benchmarking large language models in finance. Tune in for practical insights on building a strong data career and navigating the evolving AI landscape.

This Week in Google (MP3)
IM 816: Flappy Jeff - Harper Reed, Cluely, Bluesky Bluecheck

This Week in Google (MP3)

Play Episode Listen Later Apr 24, 2025 171:19


Interview with Harper Reed AI Horseless Carriages Columbia student suspended over interview cheating tool raises $5.3M to 'cheat on everything' Cluely, the AI cheating tool's new (insane) ad: After 5 years of jaw clicking (TMJ), ChatGPT cured it in 60 seconds — no BS : r/ChatGPT China cracks down on 'autonomous' car claims after fatal accident Famed AI Researcher Launches Controversial Startup to Replace All Human Workers Everywhere Welcome to slop world: how the hostile internet is driving us crazy Kaggle and the Wikimedia Foundation are partnering on open data. Draft executive order outlines plan to integrate AI into K-12 schools A New Form of Verification on Bluesky Survey: Americans Averaged Over $700 in TikTok Shop Purchases in the Last Year The Washington Post partners with OpenAI on search content Films made with AI can win Oscars, Academy says The Welikia project 18 years ago today, Tay Zonday uploaded Chocolate Rain Brilliant Senate testimony in Oregon Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Harper Reed Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: bigid.com/im storyblok.com/twittv-25 outsystems.com/twit

All TWiT.tv Shows (MP3)
Intelligent Machines 816: Flappy Jeff

All TWiT.tv Shows (MP3)

Play Episode Listen Later Apr 24, 2025 171:19


Interview with Harper Reed AI Horseless Carriages Columbia student suspended over interview cheating tool raises $5.3M to 'cheat on everything' Cluely, the AI cheating tool's new (insane) ad: After 5 years of jaw clicking (TMJ), ChatGPT cured it in 60 seconds — no BS : r/ChatGPT China cracks down on 'autonomous' car claims after fatal accident Famed AI Researcher Launches Controversial Startup to Replace All Human Workers Everywhere Welcome to slop world: how the hostile internet is driving us crazy Kaggle and the Wikimedia Foundation are partnering on open data. Draft executive order outlines plan to integrate AI into K-12 schools A New Form of Verification on Bluesky Survey: Americans Averaged Over $700 in TikTok Shop Purchases in the Last Year The Washington Post partners with OpenAI on search content Films made with AI can win Oscars, Academy says The Welikia project 18 years ago today, Tay Zonday uploaded Chocolate Rain Brilliant Senate testimony in Oregon Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Harper Reed Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: bigid.com/im storyblok.com/twittv-25 outsystems.com/twit

Radio Leo (Audio)
Intelligent Machines 816: Flappy Jeff

Radio Leo (Audio)

Play Episode Listen Later Apr 24, 2025 171:19


Interview with Harper Reed AI Horseless Carriages Columbia student suspended over interview cheating tool raises $5.3M to 'cheat on everything' Cluely, the AI cheating tool's new (insane) ad: After 5 years of jaw clicking (TMJ), ChatGPT cured it in 60 seconds — no BS : r/ChatGPT China cracks down on 'autonomous' car claims after fatal accident Famed AI Researcher Launches Controversial Startup to Replace All Human Workers Everywhere Welcome to slop world: how the hostile internet is driving us crazy Kaggle and the Wikimedia Foundation are partnering on open data. Draft executive order outlines plan to integrate AI into K-12 schools A New Form of Verification on Bluesky Survey: Americans Averaged Over $700 in TikTok Shop Purchases in the Last Year The Washington Post partners with OpenAI on search content Films made with AI can win Oscars, Academy says The Welikia project 18 years ago today, Tay Zonday uploaded Chocolate Rain Brilliant Senate testimony in Oregon Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Harper Reed Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: bigid.com/im storyblok.com/twittv-25 outsystems.com/twit

This Week in Google (Video HI)
IM 816: Flappy Jeff - Harper Reed, Cluely, Bluesky Bluecheck

This Week in Google (Video HI)

Play Episode Listen Later Apr 24, 2025 171:19


Interview with Harper Reed AI Horseless Carriages Columbia student suspended over interview cheating tool raises $5.3M to 'cheat on everything' Cluely, the AI cheating tool's new (insane) ad: After 5 years of jaw clicking (TMJ), ChatGPT cured it in 60 seconds — no BS : r/ChatGPT China cracks down on 'autonomous' car claims after fatal accident Famed AI Researcher Launches Controversial Startup to Replace All Human Workers Everywhere Welcome to slop world: how the hostile internet is driving us crazy Kaggle and the Wikimedia Foundation are partnering on open data. Draft executive order outlines plan to integrate AI into K-12 schools A New Form of Verification on Bluesky Survey: Americans Averaged Over $700 in TikTok Shop Purchases in the Last Year The Washington Post partners with OpenAI on search content Films made with AI can win Oscars, Academy says The Welikia project 18 years ago today, Tay Zonday uploaded Chocolate Rain Brilliant Senate testimony in Oregon Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Harper Reed Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: bigid.com/im storyblok.com/twittv-25 outsystems.com/twit

All TWiT.tv Shows (Video LO)
Intelligent Machines 816: Flappy Jeff

All TWiT.tv Shows (Video LO)

Play Episode Listen Later Apr 24, 2025 171:19


Interview with Harper Reed AI Horseless Carriages Columbia student suspended over interview cheating tool raises $5.3M to 'cheat on everything' Cluely, the AI cheating tool's new (insane) ad: After 5 years of jaw clicking (TMJ), ChatGPT cured it in 60 seconds — no BS : r/ChatGPT China cracks down on 'autonomous' car claims after fatal accident Famed AI Researcher Launches Controversial Startup to Replace All Human Workers Everywhere Welcome to slop world: how the hostile internet is driving us crazy Kaggle and the Wikimedia Foundation are partnering on open data. Draft executive order outlines plan to integrate AI into K-12 schools A New Form of Verification on Bluesky Survey: Americans Averaged Over $700 in TikTok Shop Purchases in the Last Year The Washington Post partners with OpenAI on search content Films made with AI can win Oscars, Academy says The Welikia project 18 years ago today, Tay Zonday uploaded Chocolate Rain Brilliant Senate testimony in Oregon Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Harper Reed Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: bigid.com/im storyblok.com/twittv-25 outsystems.com/twit

Radio Leo (Video HD)
Intelligent Machines 816: Flappy Jeff

Radio Leo (Video HD)

Play Episode Listen Later Apr 24, 2025 171:19


Interview with Harper Reed AI Horseless Carriages Columbia student suspended over interview cheating tool raises $5.3M to 'cheat on everything' Cluely, the AI cheating tool's new (insane) ad: After 5 years of jaw clicking (TMJ), ChatGPT cured it in 60 seconds — no BS : r/ChatGPT China cracks down on 'autonomous' car claims after fatal accident Famed AI Researcher Launches Controversial Startup to Replace All Human Workers Everywhere Welcome to slop world: how the hostile internet is driving us crazy Kaggle and the Wikimedia Foundation are partnering on open data. Draft executive order outlines plan to integrate AI into K-12 schools A New Form of Verification on Bluesky Survey: Americans Averaged Over $700 in TikTok Shop Purchases in the Last Year The Washington Post partners with OpenAI on search content Films made with AI can win Oscars, Academy says The Welikia project 18 years ago today, Tay Zonday uploaded Chocolate Rain Brilliant Senate testimony in Oregon Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Harper Reed Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: bigid.com/im storyblok.com/twittv-25 outsystems.com/twit

Business of Tech
Ransomware Hits SMBs Hard, Google OAuth Exploited, Gladinet's Security Flaw, and AI Scraping Issues

Business of Tech

Play Episode Listen Later Apr 22, 2025 15:27


Ransomware attacks targeting small and medium-sized businesses (SMBs) have reached alarming levels, with a recent UK government survey revealing that 1% of organizations reported such incidents, affecting approximately 19,000 entities. This marks a significant increase from the previous year, highlighting a troubling trend where nation-state actors are increasingly focusing on SMBs due to their often inadequate cybersecurity measures. The survey also indicates a decline in board-level cybersecurity responsibility, with only 27% of businesses having a cyber specialist on their board, down from 38% four years ago. As the frequency of ransomware incidents decreases, the cost per incident is rising, emphasizing the need for resilience-focused security measures.In addition to ransomware, a vulnerability in Google's OAuth system has been exploited by phishers to create sophisticated attacks that mimic legitimate emails from Google. This DKIM replay phishing attack allows hackers to bypass security checks, making it difficult for users to detect scams. A notable case involved a developer receiving a fraudulent email that appeared to be a legitimate security alert. This incident underscores the importance of updating security awareness training, as traditional methods may not adequately prepare users for such advanced phishing techniques.Another significant security concern arose from a flaw in Gladinet's Centristack file-sharing platform, which allows remote code execution due to a deserialization issue linked to hard-coded cryptographic keys. This vulnerability has already been exploited in multiple cases, raising alarms within the cybersecurity community. Gladinet has advised customers to upgrade or change their keys to mitigate potential threats. Additionally, Microsoft acknowledged a flaw in its Intune device management tool that inadvertently allowed unauthorized Windows 11 upgrades, prompting organizations to revert affected devices.On a different note, Wikipedia has partnered with Kaggle to create a machine-readable dataset of its content for training AI models, addressing the challenges posed by content scraping. This initiative aims to manage the rising costs associated with non-human traffic while protecting contributors' rights under Creative Commons licensing. Meanwhile, concerns have emerged regarding the impact of AI on human intelligence, with studies indicating that reliance on AI tools may inhibit critical thinking skills, particularly among younger users. As organizations navigate the complexities of AI integration, the need for resilient systems that can adapt to these changes becomes increasingly critical. Four things to know today 00:00 Ransomware Evolves: Targeting Improves, Board Accountability Wanes, and SMBs Face Growing Geopolitical Risk03:32 Secure by Default? Not This Week — Google, Microsoft, and Gladinet Say Otherwise07:32 Wikipedia Feeds the AI Beast—But Wants to on Its Own Terms10:04 AI Overload: How Education, Cognitive Skills, and Enterprise Strategy Are Buckling Under Pressure  Supported by:  https://cometbackup.com/?utm_source=mspradio&utm_medium=podcast&utm_campaign=sponsorship https://getflexpoint.com/msp-radio/ All our Sponsors: https://businessof.tech/sponsors/ Do you want the show on your podcast app or the written versions of the stories? Subscribe to the Business of Tech: https://www.businessof.tech/subscribe/Looking for a link from the stories? The entire script of the show, with links to articles, are posted in each story on https://www.businessof.tech/ Support the show on Patreon: https://patreon.com/mspradio/ Want to be a guest on Business of Tech: Daily 10-Minute IT Services Insights? Send Dave Sobel a message on PodMatch, here: https://www.podmatch.com/hostdetailpreview/businessoftech Want our stuff? Cool Merch? Wear “Why Do We Care?” - Visit https://mspradio.myspreadshop.com Follow us on:LinkedIn: https://www.linkedin.com/company/28908079/YouTube: https://youtube.com/mspradio/Facebook: https://www.facebook.com/mspradionews/Instagram: https://www.instagram.com/mspradio/TikTok: https://www.tiktok.com/@businessoftechBluesky: https://bsky.app/profile/businessof.tech

INSiDER - Dentro la Tecnologia
AI WEEK: a che punto siamo con l'intelligenza artificiale?

INSiDER - Dentro la Tecnologia

Play Episode Listen Later Apr 19, 2025 36:14 Transcription Available


Negli ultimi anni l'intelligenza artificiale ha fatto passi da gigante, entrando in settori che fino a poco tempo fa sembravano lontani anni luce: dalla sanità alla logistica, dai servizi alla clientela alla creatività. Non si tratta più solo di automazione o di algoritmi invisibili, ma di tecnologie sempre più accessibili, che stanno cambiando il modo in cui lavoriamo, comunichiamo, prendiamo decisioni. Approfittando dell'occasione dell'AI WEEK 2025, l'evento che a maggio metterà al centro proprio questi temi è il momento giusto per fermarsi e provare a fare il punto: dove siamo arrivati davvero con l'IA? E quali sfide ci aspettano nei prossimi mesi? Per parlarne abbiamo invitato Pasquale Viscanti, co-founder di Intelligenza Artificiale Spiegata Semplice e organizzatore dell'AI WEEK.Nella sezione delle notizie parliamo di Wikipedia che ha pubblicato dei dataset specifici ottimizzati per l'addestramento dei modelli di intelligenza artificiale e del primo test di guida autonoma in Italia su una strada trafficata, realizzato sulla Tangenziale di Napoli.--Indice--00:00 - Introduzione01:22 - Wikipedia pubblica dei dataset specifici per l'IA (TheVerge.com, Luca Martinelli)02:42 - Il primo test di guida autonoma su strada trafficata (DMove.it, Matteo Gallo)03:55 - AI WEEK: a che punto siamo con l'intelligenza artificiale? (Pasquale Viscanti, Davide Fasoli, Luca Martinelli)35:22 - Conclusione--Testo--Leggi la trascrizione: https://www.dentrolatecnologia.it/S7E16#testo--Contatti--• www.dentrolatecnologia.it• Instagram (@dentrolatecnologia)• Telegram (@dentrolatecnologia)• YouTube (@dentrolatecnologia)• redazione@dentrolatecnologia.it--Brani--• Ecstasy by Rabbit Theft• No Pressure by Tim Beeren & xChenda

The WAB Podcast
Student STEM Innovation: From Code to Cardiovascular Care

The WAB Podcast

Play Episode Listen Later Apr 18, 2025 12:38


In this episode of the WAB Podcast, we take a look at STEM learning at WAB. Grade 9 student Zane and Grade 10 students Anna, David, and Xander take us behind the scenes of their robotics, game development, and biomedical engineering projects. What they share isn't just about technology, it's about creativity, collaboration, and learning with real-world impact.  Building, Breaking, Rebuilding: Robotics in Action  Xander and David have been working together to design a competition robot using the VEX V5 system. David focuses on 3D modeling with Onshape, while Xander brings the mechanical build to life.  "Dex V5 is an educational platform where students design, build, and program the robots to compete in yearly challenges," explains Xander. "Every new season, a new game is released, which means teams must create brand-new robots to match updated growth in the field and objectives." When asked about challenges, David shares, "Testing parts and different designs of robots takes a lot of time, especially when you physically have to swap parts in and out. To solve this problem, we started using 3D modeling code Onshape, which has helped us reduce a lot of errors during the actual building process." Using AI for Health Innovation  Anna's project is a low-cost cardiovascular risk detection device that uses AI to assess and rank heart disease risk factors. “The device uses an AI algorithm to analyze your risk factors that we'll be extracting using the same device,” she explains. “It analyzes this risk factor and also ranks them in importance.”  The hardest part, she says, was sourcing reliable training data. “Algorithms need an unbiased and balanced dataset. Patient data is hard to get due to patient privacy, but at last we found two, one from Kaggle and another from UCI.”  Anna believes that accessible AI is opening doors. “A lot of the algorithms are open source, so you can get them from the internet, and you don't have to code them on your own. With the help of AI language models, for example, ChatGPT, you're able to code these things on your own.”  Code, Graphics, and Game Design  Zane is creating a top-down RPG using GameMaker Studio. “It's free and it's really easy to learn and use,” he shares. “It's comfortable coding software that can be picked up pretty easily.”  His biggest early challenge? "Getting used to the coding software. It has a lot of functions that are convenient once you learn how to use them, but don't make much sense initially." Working in a two-person team, Zane focuses on programming while his friend Nick handles the graphics. “In the last month or so, we've kind of switched around, which is a good experience for me, and I think he's having a good time working on his programming skills.”  Reflecting on the process, Zane says, “Developing using code, I learned that mistakes are going to happen, and if you expect them, then it's easier. Expecting mistakes, one of the best things I've learned.”  Learning That Feels Real  All four students shared how these projects have pushed them, technically and personally. They've built new skills, worked through challenges, and found confidence in solving problems that matter to them.  They also spoke about how this learning is shaping their futures. For some, it sparked a clear career interest, robotics, game design, or biomedical engineering. For others, it's the mindset that's sticking with them: creativity, resilience, and the power of working with others.  “I think more people can do this than they realize,” Anna says. “With open-source tools, AI models, and even things like ChatGPT, you don't need to be an expert to start creating something meaningful.” These stories reflect the kind of purposeful, real-world learning happening all across WAB. Students are exploring big ideas, applying what they know in new ways, and connecting their passions to real-world challenges.  STEM at WAB isn't about following a textbook, it's about curiosity, collaboration, and creating with purpose.  Listen to the full conversation in Episode 5 of the WAB Podcast.

Daily Tech Headlines
The Trump Administration Considers Barring DeepSeek In The U.S. – DTH

Daily Tech Headlines

Play Episode Listen Later Apr 17, 2025


The Trump administration considers barring Americans from using DeepSeek, TikTok is introducing a new crowd-sourced fact-checking feature called “Footnotes”, and Wikimedia partners with Kaggle to release a beta dataset optimized for AI model training. MP3 Please SUBSCRIBE HERE for free or get DTNS Live ad-free. A special thanks to all our supporters–without you, none ofContinue reading "The Trump Administration Considers Barring DeepSeek In The U.S. – DTH"

regonn&curry.fm
290 Kaggle実験管理術 とか

regonn&curry.fm

Play Episode Listen Later Apr 1, 2025 33:32


⁠話した内容Blog⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠今回は、ChatGPT関連(実験管理術)、Dify v1.0.0、今週のKaggle・分析コンペ、雑談・来週話したいこと(YouTube配信)について話しました。#regonn_curry_fm  へのお便りはこちら https://forms.gle/BZsrPSa4znoQNfww8

The top AI news from the past week, every ThursdAI

LET'S GO! Happy second birthday to ThursdAI, your favorite weekly AI news show! Can you believe it's been two whole years since we jumped into that random Twitter Space to rant about GPT-4? From humble beginnings as a late-night Twitter chat to a full-blown podcast, Newsletter and YouTube show with hundreds of thousands of downloads, it's been an absolutely wild ride! That's right, two whole years of me, Alex Volkov, your friendly AI Evangelist, along with my amazing co-hosts, trying to keep you up-to-date on the breakneck speed of the AI worldAnd what better way to celebrate than with a week PACKED with insane AI news? Buckle up, folks, because this week Google went OPEN SOURCE crazy, Gemini got even cooler, OpenAI created a whole new Agents SDK and the open-source community continues to blow our minds. We've got it all - from game-changing model releases to mind-bending demos.This week I'm also on the Weights & Biases company retreat, so TL;DR first and then the newsletter, but honestly, I'll start embedding the live show here in the substack from now on, because we're getting so good at it, I barely have to edit lately and there's a LOT to show you guys! TL;DR and Show Notes & Links* Hosts & Guests* Alex Volkov - AI Eveangelist & Weights & Biases (@altryne)* Co Hosts - @WolframRvnwlf @ldjconfirmed @nisten * Sandra Kublik - DevRel at Cohere (@itsSandraKublik)* Open Source LLMs * Google open sources Gemma 3 - 1B - 27B - 128K context (Blog, AI Studio, HF)* EuroBERT - multilingual encoder models (210M to 2.1B params)* Reka Flash 3 (reasoning) 21B parameters is open sourced (Blog, HF)* Cohere Command A 111B model - 256K context (Blog)* Nous Research Deep Hermes 24B / 3B Hybrid Reasoners (X, HF)* AllenAI OLMo 2 32B - fully open source GPT4 level model (X, Blog, Try It)* Big CO LLMs + APIs* Gemini Flash generates images natively (X, AI Studio)* Google deep research is now free in Gemini app and powered by Gemini Thinking (Try It no cost)* OpenAI released new responses API, Web Search, File search and Computer USE tools (X, Blog)* This weeks Buzz * The whole company is at an offsite at oceanside, CA* W&B internal MCP hackathon and had cool projects - launching an MCP server soon!* Vision & Video* Remade AI - 8 LORA video effects for WANX (HF)* AI Art & Diffusion & 3D* ByteDance Seedream 2.0 - A Native Chinese-English Bilingual Image Generation Foundation Model by ByteDance (Blog, Paper)* Tools* Everyone's talking about Manus - (manus.im)* Google AI studio now supports youtube understanding via link droppingOpen Source LLMs: Gemma 3, EuroBERT, Reka Flash 3, and Cohere Command-A Unleashed!This week was absolutely HUGE for open source, folks. Google dropped a BOMBSHELL with Gemma 3! As Wolfram pointed out, this is a "very technical achievement," and it's not just one model, but a whole family ranging from 1 billion to 27 billion parameters. And get this – the 27B model can run on a SINGLE GPU! Sundar Pichai himself claimed you'd need "at least 10X compute to get similar performance from other models." Insane!Gemma 3 isn't just about size; it's packed with features. We're talking multimodal capabilities (text, images, and video!), support for over 140 languages, and a massive 128k context window. As Nisten pointed out, "it might actually end up being the best at multimodal in that regard" for local models. Plus, it's fine-tuned for safety and comes with ShieldGemma 2 for content moderation. You can grab Gemma 3 on Google AI Studio, Hugging Face, Ollama, Kaggle – everywhere! Huge shoutout to Omar Sanseviero and the Google team for this incredible release and for supporting the open-source community from day one! Colin aka Bartowski, was right, "The best thing about Gemma is the fact that Google specifically helped the open source communities to get day one support." This is how you do open source right!Next up, we have EuroBERT, a new family of multilingual encoder models. Wolfram, our European representative, was particularly excited about this one: "In European languages, you have different characters than in other languages. And, um, yeah, encoding everything properly is, uh, difficult." Ranging from 210 million to 2.1 billion parameters, EuroBERT is designed to push the boundaries of NLP in European and global languages. With training on a massive 5 trillion-token dataset across 15 languages and support for 8K context tokens, EuroBERT is a workhorse for RAG and other NLP tasks. Plus, how cool is their mascot?Reka Flash 3 - a 21B reasoner with apache 2 trained with RLOOAnd the open source train keeps rolling! Reka AI dropped Reka Flash 3, a 21 billion parameter reasoning model with an Apache 2.0 license! Nisten was blown away by the benchmarks: "This might be one of the best like 20B size models that there is right now. And it's Apache 2.0. Uh, I, I think this is a much bigger deal than most people realize." Reka Flash 3 is compact, efficient, and excels at chat, coding, instruction following, and function calling. They even used a new reinforcement learning technique called REINFORCE Leave One-Out (RLOO). Go give it a whirl on Hugging Face or their chat interface – chat.reka.ai!Last but definitely not least in the open-source realm, we had a special guest, Sandra (@itsSandraKublik) from Cohere, join us to announce Command-A! This beast of a model clocks in at 111 BILLION parameters with a massive 256K context window. Sandra emphasized its efficiency, "It requires only two GPUs. Typically the models of this size require 32 GPUs. So it's a huge, huge difference." Command-A is designed for enterprises, focusing on agentic tasks, tool use, and multilingual performance. It's optimized for private deployments and boasts enterprise-grade security. Congrats to Sandra and the Cohere team on this massive release!Big CO LLMs + APIs: Gemini Flash Gets Visual, Deep Research Goes Free, and OpenAI Builds for AgentsThe big companies weren't sleeping either! Google continued their awesome week by unleashing native image generation in Gemini Flash Experimental! This is seriously f*****g cool, folks! Sorry for my French, but it's true. You can now directly interact with images, tell Gemini what to do, and it just does it. We even showed it live on the stream, turning ourselves into cat-confetti-birthday-hat-wearing masterpieces! Wolfram was right, "It's also a sign what we will see in, like, Photoshop, for example. Where you, you expect to just talk to it and have it do everything that a graphic designer would be doing." The future of creative tools is HERE.And guess what else Google did? They made Deep Research FREE in the Gemini app and powered by Gemini Thinking! Nisten jumped in to test it live, and we were all impressed. "This is the nicest interface so far that I've seen," he said. Deep Research now digs through HUNDREDS of websites (Nisten's test hit 156!) to give you comprehensive answers, and the interface is slick and user-friendly. Plus, you can export to Google Docs! Intelligence too cheap to meter? Google is definitely pushing that boundary.Last second additions - Allen Institute for AI released OLMo 2 32B - their biggest open model yetJust as I'm writing this, friend of the pod, Nathan from Allen Institute for AI announced the release of a FULLY OPEN OLMo 2, which includes weights, code, dataset, everything and apparently it beats the latest GPT 3.5, GPT 4o mini, and leading open weight models like Qwen and Mistral. Evals look legit, but nore than that, this is an Apache 2 model with everything in place to advance open AI and open science! Check out Nathans tweet for more info, and congrats to Allen team for this awesome release! OpenAI new responses API and Agent ASK with Web, File and CUA toolsOf course, OpenAI wasn't going to let Google have all the fun. They dropped a new SDK for agents called the Responses API. This is a whole new way to build with OpenAI, designed specifically for the agentic era we're entering. They also released three new tools: Web Search, Computer Use Tool, and File Search Tool. The Web Search tool is self-explanatory – finally, built-in web search from OpenAI!The Computer Use Tool, while currently limited in availability, opens up exciting possibilities for agent automation, letting agents interact with computer interfaces. And the File Search Tool gives you a built-in RAG system, simplifying knowledge retrieval from your own files. As always, OpenAI is adapting to the agentic world and giving developers more power.Finally in the big company space, Nous Research released PORTAL, their new Inference API service. Now you can access their awesome models, like Hermes 3 Llama 70B and DeepHermes 3 8B, directly via API. It's great to see more open-source labs offering API access, making these powerful models even more accessible.This Week's Buzz at Weights & Biases: Offsite Hackathon and MCP Mania!This week's "This Week's Buzz" segment comes to you live from Oceanside, California! The whole Weights & Biases team is here for our company offsite. Despite the not-so-sunny California weather (thanks, storm!), it's been an incredible week of meeting colleagues, strategizing, and HACKING!And speaking of hacking, we had an MCP hackathon! After last week's MCP-pilling episode, we were all hyped about Model Context Protocol, and the team didn't disappoint. In just three hours, the innovation was flowing! We saw agents built for WordPress, MCP support integrated into Weave playground, and even MCP servers for Weights & Biases itself! Get ready, folks, because an MCP server for Weights & Biases is COMING SOON! You'll be able to talk to your W&B data like never before. Huge shoutout to the W&B team for their incredible talent and for embracing the agentic future! And in case you missed it, Weights & Biases is now part of the CoreWeave family! Exciting times ahead!Vision & Video: LoRA Video Effects and OpenSora 2.0Moving into vision and video, Remade AI released 8 LoRA video effects for 1X! Remember 1X from Alibaba? Now you can add crazy effects like "squish," "inflate," "deflate," and even "cakeify" to your videos using LoRAs. It's open source and super cool to see video effects becoming trainable and customizable.And in the realm of open-source video generation, OpenSora 2.0 dropped! This 11 billion parameter model claims state-of-the-art video generation trained for just $200,000! They're even claiming performance close to Sora itself on some benchmarks. Nisten checked out the demos, and while we're all a bit jaded now with the rapid pace of video AI, it's still mind-blowing how far we've come. Open source video is getting seriously impressive, seriously fast.AI Art & Diffusion & 3D: ByteDance's Bilingual Seedream 2.0ByteDance, the folks behind TikTok, released Seedream 2.0, a native Chinese-English bilingual image generation foundation model. This model, from ByteDream, excels at text rendering, cultural nuance, and human preference alignment. Seedream 2.0 boasts "powerful general capability," "native bilingual comprehension ability," and "excellent text rendering." It's designed to understand both Chinese and English prompts natively, generating high-quality, culturally relevant images. The examples look stunning, especially its ability to render Chinese text beautifully.Tools: Manus AI Agent, Google AI Studio YouTube Links, and Cursor EmbeddingsFinally, in the tools section, everyone's buzzing about Manus, a new AI research agent. We gave it a try live on the show, asking it to do some research. The UI is slick, and it seems to be using Claude 3.7 behind the scenes. Manus creates a to-do list, browses the web in a real Chrome browser, and even generates files. It's like Operator on steroids. We'll be keeping an eye on Manus and will report back on its performance in future episodes.And Google AI Studio keeps getting better! Now you can drop YouTube links into Google AI Studio, and it will natively understand the video! This is HUGE for video analysis and content understanding. Imagine using this for support, content summarization, and so much more.PHEW! What a week to celebrate two years of ThursdAI! From open source explosions to Gemini's visual prowess and OpenAI's agentic advancements, the AI world is moving faster than ever. As Wolfram aptly put it, "The acceleration, you can feel it." And Nisten reminded us of the incredible journey, "I remember I had early access to GPT-4 32K, and, uh, then... the person for the contract that had given me access, they cut it off because on the one weekend, I didn't realize how expensive it was. So I had to use $180 worth of tokens just trying it out." Now, we have models that are more powerful and more accessible than ever before. Thank you to Wolfram, Nisten, and LDJ for co-hosting and bringing their insights every week. And most importantly, THANK YOU to our amazing community for tuning in, listening, and supporting ThursdAI for two incredible years! We couldn't do it without you. Here's to another year of staying up-to-date so YOU don't have to! Don't forget to subscribe to the podcast, YouTube channel, and newsletter to stay in the loop. And share ThursdAI with a friend – it's the best birthday gift you can give us! Until next week, keep building and keep exploring the amazing world of AI! LET'S GO! This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

DataTalks.Club
Competitive Machine Leaning And Teaching – Alexander Guschin

DataTalks.Club

Play Episode Listen Later Feb 14, 2025 53:27


In this podcast episode, we talked with Alexander Guschin about launching a career off Kaggle.About the Speaker: Alexander Guschin is a Machine Learning Engineer with 10+ years of experience, a Kaggle Grandmaster ranked 5th globally, and a teacher to 100K+ students. He leads DS and SE teams and contributes to open-source ML tools.00:00 Starting with Machine Learning: Challenges and Early Steps 13:05 Community and Learning Through Kaggle Sessions 17:10 Broadening Skills Through Kaggle Participation 18:54 Early Competitions and Lessons Learned 21:10 Transitioning to Simpler Solutions Over Time 23:51 Benefits of Kaggle for Starting a Career in Machine Learning 29:08 Teamwork vs. Solo Participation in Competitions 31:14 Schoolchildren in AI Competitions42:33 Transition to Industry and MLOps50:13 Encouraging teamwork in student projects50:48 Designing competitive machine learning tasks52:22 Leaderboard types for tracking performance53:44 Managing small-scale university classes54:17 Experience with Coursera and online teaching59:40 Convincing managers about Kaggle's value61:38 Secrets of Kaggle competition success63:11 Generative AI's impact on competitive ML65:13 Evolution of automated ML solutions66:22 Reflecting on competitive data science experience

Teaching in Higher Ed
A Big Picture Look at AI Detection Tools

Teaching in Higher Ed

Play Episode Listen Later Jan 30, 2025 48:34


Christopher Ostro shares a big picture look at AI detection tools on episode 555 of the Teaching in Higher Ed podcast. Quotes from the episode I think there are tons of students I interact with who are really just curious and trying to use these tools to dig deeper. -Christopher Ostro I want them getting practice on these things that are going to be part of their future careers and lives. I love that my classroom is a stage for that. -Christopher Ostro I think AI detection has a place, but its place is limited. I don't think it should ever be the sole reason a student is getting honor coded. -Christopher Ostro I love to tell my students if all you're doing with these tools is taking the output and submitting as your own work, you don't have a job. -Christopher Ostro Resources Video: AI Detection: A Literature Review with Christopher Ostro Slides: AI Detection: A Literature Review University of Colorado Boulder Learning Design Group Video: Student Use of AI: A Panel Dialogue GPTZero, TurnItIn AI Detector, Writer.AI Can linguists distinguish between ChatGPT/AI and human writing?: A study of research ethics and academic publishing, by J. Elliott Casal & Matt Kessler A real-world test of artificial intelligence infiltration of a university examinations system: A “Turing Test” case study, by Peter Scarfe, Kelly Watcham, and Alasdair Clarke Simple techniques to bypass GenAI text detectors: implications for inclusive education, by Mike Perkins et al Can AI-Generated Text be Reliably Detected? by Vinu Sankar Sadasivan et al Testing of detection tools for AI-generated text, by Debora Weber-Wulff et al GPT detectors are biased against non-native English writers, by Weixin Liang et al Detecting ChatGPT-generated essays in a large-scale writing assessment: Is there a bias against non-native English speakers? by Yang Jiang et al Kaggle competition 2023 - 2024 h/t to Janae Cohn who shared the article on LinkedIn and posted some additional reflective questions we might ask, as we refuse GenAI in writing studies Refusing GenAI in Writing Studies: A Quickstart Guide, by Jennifer Sano-Franchini, West Virginia University; Megan McIntyre, University of Arkansas;Maggie Fernandes, University of Arkansas Maha Bali's writing on AI (and other topics) A Man on the Inside Daytripper (DC Comics)

Radio Wnet
WEJDŹ DO ŚWIATA AI! Dołącz do arcybractwa Kaggle! 30 odcinek audycji Limes inferior

Radio Wnet

Play Episode Listen Later Jan 29, 2025 30:42


Kaggle to platforma oferująca środowisko i udział w społeczności uczącej się zastosowania technologii uczenia maszynowego i sztucznej inteligencji do rozwiązywania rzeczywistych problemów.Sławne konkursy ogłaszane na Kaggle przez czołowe firmy przyciągały tuzów AI czy noblistów, ale wielu osobom pozwoliły też wejść w świat AI przy wsparciu najlepszych ekspertów - startując niekiedy od zera. Wśród ok. 22 mln kont Kaggle na świecie jest około 2,5 tys. mistrzów i ok. 350 arcymistrzów Kaggle.Jednym z arcymistrzów Kaggle jest dzisiejszy gość Limes Inferior: Paweł Godula, założyciel firmy Jobs-in-data.com, który od lat wprowadza na Kaggle młodych adeptów data science - uczniów liceów i studentów, pokazując im jak korzystać z tej platformy do rozwoju umiejętności, zwycięstw w konkursach i budowy własnej kariery w świecie uczenia maszynowego, sztucznej inteligencji.W Polsce działa pod jego opieką Zdalne Koło Naukowe AI, które gromadzi i przygotowuje młodzież ze szkół średnich m.in. do udziału w konkursach Kaggle.- jaka jest bariera wejścia w świat AI/ML/data science?- czym jest i jak działa Kaggle?- czego uczy Kaggle?- w jaki sposób działa Zdalne Koło AI?- co dalej z unikalnym potencjałem Polski w naukach ścisłych?

ExplAInable
בין תחרות למציאות: הצצה לתחרויות Kaggle עם דן עופר

ExplAInable

Play Episode Listen Later Jan 20, 2025 39:46


בפרק זה, אירחנו את דן עופר, מדען נתונים בכיר ב-Medtronic ודוקטורנט באוניברסיטה העברית, נדבר על ניסיונו בתחרויות Kaggle ובתחום מדעי הנתונים. נדון גם בתרומתה של Kaggle לפיתוח יכולות מעשיות, באתגרים שבין התחרויות לבין המציאות המקצועית, ובתובנות שנרכשו מתחרות ProteinBERT לאנליזת חלבונים באמצעות מודלים מבוססי שפה. גיטהאב של דן לינקדאין של דן  

Software Engineering Daily
AI Developer Tools at Google with Paige Bailey

Software Engineering Daily

Play Episode Listen Later Jan 9, 2025 37:28


Over the years, Google has released a variety of ML, data science, and AI developer tools and platforms. Prominent examples include Colab, Kaggle, AI Studio, and the Gemini API. Paige Bailey is the Uber Technical Lead of the Developer Relations team at Google ML Developer Tools, working on Gemini APIs, Gemma, AI Studio, Kaggle, Colab The post AI Developer Tools at Google with Paige Bailey appeared first on Software Engineering Daily.

Podcast – Software Engineering Daily
AI Developer Tools at Google with Paige Bailey

Podcast – Software Engineering Daily

Play Episode Listen Later Jan 9, 2025 37:28


Over the years, Google has released a variety of ML, data science, and AI developer tools and platforms. Prominent examples include Colab, Kaggle, AI Studio, and the Gemini API. Paige Bailey is the Uber Technical Lead of the Developer Relations team at Google ML Developer Tools, working on Gemini APIs, Gemma, AI Studio, Kaggle, Colab The post AI Developer Tools at Google with Paige Bailey appeared first on Software Engineering Daily.

The Lunar Society
Adam Brown – How Future Civilizations Could Change The Laws of Physics

The Lunar Society

Play Episode Listen Later Dec 26, 2024 163:37


Adam Brown is a founder and lead of BlueShift with is cracking maths and reasoning at Google DeepMind and a theoretical physicist at Stanford.We discuss: destroying the light cone with vacuum decay, holographic principle, mining black holes, & what it would take to train LLMs that can make Einstein level conceptual breakthroughs.Stupefying, entertaining, & terrifying.Enjoy!Watch on YouTube, read the transcript, listen on Apple Podcasts, Spotify, or your favorite platform.Sponsors- Deepmind, Meta, Anthropic, and OpenAI, partner with Scale for high quality data to fuel post-training Publicly available data is running out - to keep developing smarter and smarter models, labs will need to rely on Scale's data foundry, which combines subject matter experts with AI models to generate fresh data and break through the data wall. Learn more at scale.ai/dwarkesh.- Jane Street is looking to hire their next generation of leaders. Their deep learning team is looking for ML researchers, FPGA programmers, and CUDA programmers. Summer internships are open for just a few more weeks. If you want to stand out, take a crack at their new Kaggle competition. To learn more, go janestreet.com/dwarkersh.- This episode is brought to you by Stripe, financial infrastructure for the internet. Millions of companies from Anthropic to Amazon use Stripe to accept payments, automate financial processes and grow their revenue.Timestamps(00:00:00) - Changing the laws of physics(00:26:05) - Why is our universe the way it is(00:37:30) - Making Einstein level AGI(01:00:31) - Physics stagnation and particle colliders(01:11:10) - Hitchhiking(01:29:00) - Nagasaki(01:36:19) - Adam's career(01:43:25) - Mining black holes(01:59:42) - The holographic principle(02:23:25) - Philosophy of infinities(02:31:42) - Engineering constraints for future civilizations Get full access to Dwarkesh Podcast at www.dwarkeshpatel.com/subscribe

The Superposition Guy's Podcast
Jannes Stubbeman, CEO, Aqora

The Superposition Guy's Podcast

Play Episode Listen Later Nov 25, 2024 21:28


Yuval Boger interviews Jannes Stubbeman, co-founder and CEO of Aqora, a platform aimed at creating a “Kaggle for Quantum” by providing an operating system for both in-person and online hackathons in the quantum computing space. Jannes and Yuval discuss the origins of Aqora, how it supports hardware vendors in organizing impactful hackathons, the intricacies of benchmarking quantum use cases, the challenges and logistics of hosting competitions, and much more.

The Lunar Society
Gwern Branwen - How an Anonymous Researcher Predicted AI's Trajectory

The Lunar Society

Play Episode Listen Later Nov 13, 2024 96:43


Gwern is a pseudonymous researcher and writer. He was one of the first people to see LLM scaling coming. If you've read his blog, you know he's one of the most interesting polymathic thinkers alive.In order to protect Gwern's anonymity, I proposed interviewing him in person, and having my friend Chris Painter voice over his words after. This amused him enough that he agreed.After the episode, I convinced Gwern to create a donation page where people can help sustain what he's up to. Please go here to contribute.Read the full transcript here.Sponsors:* Jane Street is looking to hire their next generation of leaders. Their deep learning team is looking for ML researchers, FPGA programmers, and CUDA programmers. Summer internships are open - if you want to stand out, take a crack at their new Kaggle competition. To learn more, go here: https://jane-st.co/dwarkesh* Turing provides complete post-training services for leading AI labs like OpenAI, Anthropic, Meta, and Gemini. They specialize in model evaluation, SFT, RLHF, and DPO to enhance models' reasoning, coding, and multimodal capabilities. Learn more at turing.com/dwarkesh.* This episode is brought to you by Stripe, financial infrastructure for the internet. Millions of companies from Anthropic to Amazon use Stripe to accept payments, automate financial processes and grow their revenue.If you're interested in advertising on the podcast, check out this page.Timestamps00:00:00 - Anonymity00:01:09 - Automating Steve Jobs00:04:38 - Isaac Newton's theory of progress00:06:36 - Grand theory of intelligence00:10:39 - Seeing scaling early00:21:04 - AGI Timelines00:22:54 - What to do in remaining 3 years until AGI00:26:29 - Influencing the shoggoth with writing00:30:50 - Human vs artificial intelligence00:33:52 - Rabbit holes00:38:48 - Hearing impairment00:43:00 - Wikipedia editing00:47:43 - Gwern.net00:50:20 - Counterfactual careers00:54:30 - Borges & literature01:01:32 - Gwern's intelligence and process01:11:03 - A day in the life of Gwern01:19:16 - Gwern's finances01:25:05 - The diversity of AI minds01:27:24 - GLP drugs and obesity01:31:08 - Drug experimentation01:33:40 - Parasocial relationships01:35:23 - Open rabbit holes Get full access to Dwarkesh Podcast at www.dwarkeshpatel.com/subscribe

People of AI
How AI is revolutionizing sign language recognition with Sam Sepah and Thad Starner

People of AI

Play Episode Listen Later Oct 24, 2024 76:03


Meet today's guests Sam Sepah and Thad Starner. Sam Sepah, an AI/ML Research Manager at Google, drives innovation in accessibility technology for users with deafness and other disabilities. Thad Starner is a Georgia Tech Professor, wearable computing pioneer, and staff research scientist at Google working on sign language recognition. Explore the transformative impact of AI on sign language recognition and accessibility from the Kaggle competition to Pop Sign AI, and much more! Resources: Kaggle → https://goo.gle/3YCcjau Kaggle Competitions → https://goo.gle/3NDqzcz PopSign Game → https://goo.gle/3UinxhJ Precious PopSign Kaggle Competition → https://goo.gle/3Ui5s3q  #PeopleofAI  

Ken's Nearest Neighbors
How She Went From Analytics Executive to Solopreneur (Serena Huang PhD) - KNN Ep. 195

Ken's Nearest Neighbors

Play Episode Listen Later Oct 21, 2024 74:45


Today, I had the pleasure of interviewing Dr. Serena Huang. She is one of the leaders in the field of people analytics. In this episode, we dive deep into what people analytics are and how companies are evaluating their workforce with data. We also touch on her transition from executive to solopreneur and speaker. Serena's Links: Her LinkedIn: https://www.linkedin.com/in/serenahhuangphd/Her Thought leadership program - apply here https://www.datawithserena.com/tla Her First Book book, The Inclusion Equation - Leveraging Data & AI for Organizational Diversity and Wellbeing, will be published early next year. Resources cited in podcast:Research: Women Ask for Raises as Often as Men, but Are Less Likely to Get Themhttps://hbr.org/2016/09/diverse-teams-feel-less-comfortable-and-thats-why-they-perform-betterPodcast Sponsors, Affiliates, and Partners:- Pathrise - http://pathrise.com/KenJee | Career mentorship for job applicants (Free till you land a job)- Taro - http://jointaro.com/r/kenj308 (20% discount) | Career mentorship if you already have a job - 365 Data Science (57% discount) - https://365datascience.pxf.io/P0jbBY | Learn data science today- Interview Query (10% discount) - https://www.interviewquery.com/?ref=kenjee |  Interview prep questionsListen to Ken's Nearest Neighbors on all the main podcast platforms! On Apple Podcasts: https://podcasts.apple.com/us/podcast/kens-nearest-neighbors/id1538368692 (Please rate if you enjoy it!)On Spotify: https://open.spotify.com/show/7fJsuxiZl4TS1hqPUmDFblOn Google: https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5idXp6c3Byb3V0LmNvbS8xNDMwMDQxLnJzcw?sa=X&ved=0CAMQ4aUDahcKEwjQ2bGBhfbsAhUAAAAAHQAAAAAQAQMORE DATA SCIENCE CONTENT HERE:

Ken's Nearest Neighbors
How Can Data Teams Get Out of Their Own Way (Alex Gold) - KNN Ep. 196

Ken's Nearest Neighbors

Play Episode Listen Later Oct 21, 2024 59:00


Today I had the pleasure of interviewing Alex Gold. Alex is the Director of Solutions Engineering + Support at posit and author of DevOps for Data Science. In this episode we talk about Alex's experience working in data for presidential campaigns, his experience in solutions engineering, and the role that DevOps plays in the data domain. Book: https://www.routledge.com/DevOps-for-Data-Science/Gold/p/book/9781003213345(You can use Code AFLY04 for a 20% discount)Alex's Links:LinkedIn - https://www.linkedin.com/in/alexkgold/Twitter - https://x.com/alexkgoldPodcast Sponsors, Affiliates, and Partners:- Pathrise - http://pathrise.com/KenJee | Career mentorship for job applicants (Free till you land a job)- Taro - http://jointaro.com/r/kenj308 (20% discount) | Career mentorship if you already have a job - 365 Data Science (57% discount) - https://365datascience.pxf.io/P0jbBY | Learn data science today- Interview Query (10% discount) - https://www.interviewquery.com/?ref=kenjee |  Interview prep questionsListen to Ken's Nearest Neighbors on all the main podcast platforms! On Apple Podcasts: https://podcasts.apple.com/us/podcast/kens-nearest-neighbors/id1538368692 (Please rate if you enjoy it!)On Spotify: https://open.spotify.com/show/7fJsuxiZl4TS1hqPUmDFblOn Google: https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5idXp6c3Byb3V0LmNvbS8xNDMwMDQxLnJzcw?sa=X&ved=0CAMQ4aUDahcKEwjQ2bGBhfbsAhUAAAAAHQAAAAAQAQMORE DATA SCIENCE CONTENT HERE:

The Lunar Society
Dylan Patel & Jon (Asianometry) – How the Semiconductor Industry Actually Works

The Lunar Society

Play Episode Listen Later Oct 2, 2024 129:57


A bonanza on the semiconductor industry and hardware scaling to AGI by the end of the decade.Dylan Patel runs Semianalysis, the leading publication and research firm on AI hardware. Jon Y runs Asianometry, the world's best YouTube channel on semiconductors and business history.* What Xi would do if he became scaling pilled* $ 1T+ in datacenter buildout by end of decadeWatch on YouTube. Listen on Apple Podcasts, Spotify, or any other podcast platform. Read the full transcript here. Follow me on Twitter for updates on future episodes.Sponsors:* Jane Street is looking to hire their next generation of leaders. Their deep learning team is looking for FPGA programmers, CUDA programmers, and ML researchers. To learn more about their full time roles, internship, tech podcast, and upcoming Kaggle competition, go here.* This episode is brought to you by Stripe, financial infrastructure for the internet. Millions of companies from Anthropic to Amazon use Stripe to accept payments, automate financial processes and grow their revenue.If you're interested in advertising on the podcast, check out this page.Timestamps00:00:00 – Xi's path to AGI00:04:20 – Liang Mong Song00:08:25 – How semiconductors get better00:11:16 – China can centralize compute00:18:50 – Export controls & sanctions00:32:51 – Huawei's intense culture00:38:51 – Why the semiconductor industry is so stratified00:40:58 – N2 should not exist00:45:53 – Taiwan invasion hypothetical00:49:21 – Mind-boggling complexity of semiconductors00:59:13 – Chip architecture design01:04:36 – Architectures lead to different AI models? China vs. US01:10:12 – Being head of compute at an AI lab01:16:24 – Scaling costs and power demand01:37:05 – Are we financing an AI bubble?01:50:20 – Starting Asianometry and SemiAnalysis02:06:10 – Opportunities in the semiconductor stack Get full access to Dwarkesh Podcast at www.dwarkeshpatel.com/subscribe

Data Science at Home
Kaggle Kommando's Data Disco: Laughing our Way Through AI Trends (Ep. 265) [RB]

Data Science at Home

Play Episode Listen Later Oct 1, 2024 42:46


In this episode, join me and the Kaggle Grand Master, Konrad Banachewicz, for a hilarious journey into the zany world of data science trends. From algorithm acrobatics to AI, creativity, Hollywood movies, and music, we just can't get enough. It's the typical episode with a dose of nerdy comedy you didn't know you needed. Buckle up, it's a data disco, and we're breaking down the binary!   Sponsors Intrepid AI is an AI assisted all-in-one platform for robotics teams. Build robotics applications in minutes, not months. Learn what the new year holds for ransomware as a service, Active Directory, artificial intelligence and more when you download the 2024 Arctic Wolf Labs Predictions Report today at arcticwolf.com/datascience  

My First Million
Building A 100+ Year Legacy + Peter Thiel's Fellowship + Bomb Hiring Questions

My First Million

Play Episode Listen Later Aug 28, 2024 56:44


Episode 623: Sam Parr ( https://x.com/theSamParr ) and Shaan Puri ( https://x.com/ShaanVP ) talk about the underrated philanthropy style of Peter Thiel, Elon Musk, and Alfred Nobel. MFM Scholarship: Apply for $10K to start The Onion For College - https://www.shaanpuri.com/collegepaper — Show Notes:  (0:00) Alfred Nobel's $266M Legacy (6:06) Competitions vs. Trusts (15:28) Idea: XPRIZE but for companies (19:15) Shaan's Prize: Win $10K to start a college paper (23:07) How Barstool Sports started (26:47) Peter Thiel's contrarian philanthropy (32:30) Elon Musks sexy indifference (37:22) Interview questions designed to repel and attract (44:25) Be a harsh grader of people (47:49) Great people are great in the first 2 weeks — Links: • Get our business idea database here https://clickhubspot.com/mfm • Nobel Peace Prize - https://www.nobelpeaceprize.org/ • XPRIZE - https://www.xprize.org/ • Darpa - https://www.darpa.mil/ • Vesuvius Challenge - https://scrollprize.org/ • Kaggle - https://www.kaggle.com/competitions/ • Thiel Foundation - https://www.thielfoundation.org/ • Musk Foundation - https://www.muskfoundation.org/ — Check Out Shaan's Stuff: Need to hire? You should use the same service Shaan uses to hire developers, designers, & Virtual Assistants → it's called Shepherd (tell ‘em Shaan sent you): https://bit.ly/SupportShepherd — Check Out Sam's Stuff: • Hampton - https://www.joinhampton.com/ • Ideation Bootcamp - https://www.ideationbootcamp.co/ • Copy That - https://copythat.com • Hampton Wealth Survey - https://joinhampton.com/wealth • Sam's List - http://samslist.co/ My First Million is a HubSpot Original Podcast // Brought to you by The HubSpot Podcast Network // Production by Arie Desormeaux // Editing by Ezra Bakker Trupiano

Machine Learning Podcast
#061 ML Александр Алерон Миленькин. Надо ли строить бизнес вокруг ML (Про LLM, RAG-системы, насмотренность и виртуальных помощников)

Machine Learning Podcast

Play Episode Listen Later Aug 26, 2024 55:05


Общаемся с Александром (Алероном) Миленькиным - ML лидером в Dodo Brands, IT-предпринимателем, Kaggle-экспертом, преподавателем. Обсуждаем то, как можно использовать современные ИИ-технологии, чтобы иметь конкурентное преимущество. Почему лучше строить ML вокруг бизнеса, а не бизнес вокруг ML. Нужны ли в современных реалиях свои большие ML-модели или достаточно пользоваться сторонними сервисами с внешним API. Что такое и как устроены RAG-системы. Кто такие агенты и как заставить их работать на себя. Можно ли подкупить языковые модели, чтобы они выдавали полезную для тебя информацию. Почему надо качать насмотренность и как это может помочь находить лучшие бизнес-идеи. Почему даже только знание о том, что существует ChatGPT может быть тем самым конкурентным преимуществом. Долго ли ждать нашествия тьюторов в виртуальной реальности. Когда уже, наконец, языковые модели заменят программистов. Обо всем этом в выпуске!Ссылки выпуска:Телеграм-канал Александра Data Feeling (https://t.me/datafeeling)Телеграм-бот на базе AI для изучения английского Speakadora AI (https://t.me/Speakadora_bot)Курс Александра "Введение в соревновательный Data Science" (https://stepik.org/a/108888)Буду благодарен за обратную связь!Мой телеграм для связи (https://t.me/kmsint)Я сделал бесплатный курс по созданию телеграм-ботов на Python и aiogram на Степике (https://stepik.org/120924). Присоединяйтесь, если хотите научиться разрабатывать телеграм-ботов!Также в соавторстве с крутыми разработчиками я пишу курс по продвинутой разработке телеграм-ботов с элементами микросервисной архитектуры (https://stepik.org/a/153850?utm_source=mlpodcast&utm_campaign=ep_61).Выразить благодарность можно добрым словом и/или донатом (https://www.tinkoff.ru/rm/kryzhanovskiy.mikhail11/NkwE718878/)

The AI Fundamentalists
Preparing AI for the unexpected: Lessons from recent IT incidents

The AI Fundamentalists

Play Episode Listen Later Aug 20, 2024 34:13 Transcription Available


Can your AI models survive a big disaster? While a recent major IT incident with CrowdStrike wasn't AI related, the magnitude and reaction reminded us that no system no matter how proven is immune to failure. AI modeling systems are no different. Neglecting the best practices of building models can lead to unrecoverable failures. Discover how the three-tiered framework of robustness, resiliency, and anti-fragility can guide your approach to creating AI infrastructures that not only perform reliably under stress but also fail gracefully when the unexpected happens.Show NotesTechnology, incidents, and why basics matter (00:00:03)While the recent Crowdstrike incident wasn't caused by AI, it's impact was a wakeup call for people and processes that support critical systemsAs AI is increasingly being used at both experimental and production levels, we can expect AI incidents are a matter of if, not when. What can you do to prepare?The "7P's": Are you capable of handling the unexpected? (00:09:05)The 7Ps is an adage, dating back to WWII, that aligns with our "do things the hard way" approach to AI governance and modeling systems.Let's consider the levels of building a performant system: Robustness, Resiliency, and AntifragilityModel robustness (00:10:03)Robustness is a very important but often overlooked component of building modeling systems. We suspect that part of the problem is due to: The Kaggle-driven upbringing of data scientistsAssumed generalizability of modeling systems, when models are optimized to perform well on their training data but do not generalize enough to perform well on unseen data.Model resilience (00:16:10)Resiliency is the ability to absorb adverse stimuli without destruction and return to its pre-event state.In practice, robustness and resiliency, testing, and planning are often easy components to leave out. This is where risks and threats are exposed.See also, Episode 8. Model validation: Robustness and resilienceModels and antifragility (00:25:04)Unlike resiliency, which is the ability to absorb damaging inputs without breaking, antifragility is the ability of a system to improve from challenging stimuli. (i.e. the human body)A key question we need to ask ourselves if we are not actively building our AI systems to be antifragile, why are we using AI systems at all?What did you think? Let us know.Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics: LinkedIn - Episode summaries, shares of cited articles, and more. YouTube - Was it something that we said? Good. Share your favorite quotes. Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.

Latent Space: The AI Engineer Podcast — CodeGen, Agents, Computer Vision, Data Science, AI UX and all things Software 3.0
AI Magic: Shipping 1000s of successful products with no managers and a team of 12 — Jeremy Howard of Answer.ai

Latent Space: The AI Engineer Podcast — CodeGen, Agents, Computer Vision, Data Science, AI UX and all things Software 3.0

Play Episode Listen Later Aug 16, 2024 58:56


Disclaimer: We recorded this episode ~1.5 months ago, timing for the FastHTML release. It then got bottlenecked by Llama3.1, Winds of AI Winter, and SAM2 episodes, so we're a little late. Since then FastHTML was released, swyx is building an app in it for AINews, and Anthropic has also released their prompt caching API. Remember when Dylan Patel of SemiAnalysis coined the GPU Rich vs GPU Poor war? (if not, see our pod with him). The idea was that if you're GPU poor you shouldn't waste your time trying to solve GPU rich problems (i.e. pre-training large models) and are better off working on fine-tuning, optimized inference, etc. Jeremy Howard (see our “End of Finetuning” episode to catchup on his background) and Eric Ries founded Answer.AI to do exactly that: “Practical AI R&D”, which is very in-line with the GPU poor needs. For example, one of their first releases was a system based on FSDP + QLoRA that let anyone train a 70B model on two NVIDIA 4090s. Since then, they have come out with a long list of super useful projects (in no particular order, and non-exhaustive):* FSDP QDoRA: this is just as memory efficient and scalable as FSDP/QLoRA, and critically is also as accurate for continued pre-training as full weight training.* Cold Compress: a KV cache compression toolkit that lets you scale sequence length without impacting speed.* colbert-small: state of the art retriever at only 33M params* JaColBERTv2.5: a new state-of-the-art retrievers on all Japanese benchmarks.* gpu.cpp: portable GPU compute for C++ with WebGPU.* Claudette: a better Anthropic API SDK. They also recently released FastHTML, a new way to create modern interactive web apps. Jeremy recently released a 1 hour “Getting started” tutorial on YouTube; while this isn't AI related per se, but it's close to home for any AI Engineer who are looking to iterate quickly on new products: In this episode we broke down 1) how they recruit 2) how they organize what to research 3) and how the community comes together. At the end, Jeremy gave us a sneak peek at something new that he's working on that he calls dialogue engineering: So I've created a new approach. It's not called prompt engineering. I'm creating a system for doing dialogue engineering. It's currently called AI magic. I'm doing most of my work in this system and it's making me much more productive than I was before I used it.He explains it a bit more ~44:53 in the pod, but we'll just have to wait for the public release to figure out exactly what he means.Timestamps* [00:00:00] Intro by Suno AI* [00:03:02] Continuous Pre-Training is Here* [00:06:07] Schedule-Free Optimizers and Learning Rate Schedules* [00:07:08] Governance and Structural Issues within OpenAI and Other AI Labs* [00:13:01] How Answer.ai works* [00:23:40] How to Recruit Productive Researchers* [00:27:45] Building a new BERT* [00:31:57] FSDP, QLoRA, and QDoRA: Innovations in Fine-Tuning Large Models* [00:36:36] Research and Development on Model Inference Optimization* [00:39:49] FastHTML for Web Application Development* [00:46:53] AI Magic & Dialogue Engineering* [00:52:19] AI wishlist & predictionsShow Notes* Jeremy Howard* Previously on Latent Space: The End of Finetuning, NeurIPS Startups* Answer.ai* Fast.ai* FastHTML* answerai-colbert-small-v1* gpu.cpp* Eric Ries* Aaron DeFazio* Yi Tai* Less Wright* Benjamin Warner* Benjamin Clavié* Jono Whitaker* Austin Huang* Eric Gilliam* Tim Dettmers* Colin Raffel* Sebastian Raschka* Carson Gross* Simon Willison* Sepp Hochreiter* Llama3.1 episode* Snowflake Arctic* Ranger Optimizer* Gemma.cpp* HTMX* UL2* BERT* DeBERTa* Efficient finetuning of Llama 3 with FSDP QDoRA* xLSTMTranscriptAlessio [00:00:00]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO-in-Residence at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol AI.Swyx [00:00:14]: And today we're back with Jeremy Howard, I think your third appearance on Latent Space. Welcome.Jeremy [00:00:19]: Wait, third? Second?Swyx [00:00:21]: Well, I grabbed you at NeurIPS.Jeremy [00:00:23]: I see.Swyx [00:00:24]: Very fun, standing outside street episode.Jeremy [00:00:27]: I never heard that, by the way. You've got to send me a link. I've got to hear what it sounded like.Swyx [00:00:30]: Yeah. Yeah, it's a NeurIPS podcast.Alessio [00:00:32]: I think the two episodes are six hours, so there's plenty to listen, we'll make sure to send it over.Swyx [00:00:37]: Yeah, we're trying this thing where at the major ML conferences, we, you know, do a little audio tour of, give people a sense of what it's like. But the last time you were on, you declared the end of fine tuning. I hope that I sort of editorialized the title a little bit, and I know you were slightly uncomfortable with it, but you just own it anyway. I think you're very good at the hot takes. And we were just discussing in our pre-show that it's really happening, that the continued pre-training is really happening.Jeremy [00:01:02]: Yeah, absolutely. I think people are starting to understand that treating the three ULM FIT steps of like pre-training, you know, and then the kind of like what people now call instruction tuning, and then, I don't know if we've got a general term for this, DPO, RLHFE step, you know, or the task training, they're not actually as separate as we originally suggested they were in our paper, and when you treat it more as a continuum, and that you make sure that you have, you know, more of kind of the original data set incorporated into the later stages, and that, you know, we've also seen with LLAMA3, this idea that those later stages can be done for a lot longer. These are all of the things I was kind of trying to describe there. It wasn't the end of fine tuning, but more that we should treat it as a continuum, and we should have much higher expectations of how much you can do with an already trained model. You can really add a lot of behavior to it, you can change its behavior, you can do a lot. So a lot of our research has been around trying to figure out how to modify the model by a larger amount rather than starting from random weights, because I get very offended at the idea of starting from random weights.Swyx [00:02:14]: Yeah, I saw that in ICLR in Vienna, there was an outstanding paper about starting transformers from data-driven piers. I don't know if you saw that one, they called it sort of never trained from scratch, and I think it was kind of rebelling against like the sort of random initialization.Jeremy [00:02:28]: Yeah, I've, you know, that's been our kind of continuous message since we started Fast AI, is if you're training for random weights, you better have a really good reason, you know, because it seems so unlikely to me that nobody has ever trained on data that has any similarity whatsoever to the general class of data you're working with, and that's the only situation in which I think starting from random weights makes sense.Swyx [00:02:51]: The other trends since our last pod that I would point people to is I'm seeing a rise in multi-phase pre-training. So Snowflake released a large model called Snowflake Arctic, where they detailed three phases of training where they had like a different mixture of like, there was like 75% web in the first instance, and then they reduced the percentage of the web text by 10% each time and increased the amount of code in each phase. And I feel like multi-phase is being called out in papers more. I feel like it's always been a thing, like changing data mix is not something new, but calling it a distinct phase is new, and I wonder if there's something that you're seeingJeremy [00:03:32]: on your end. Well, so they're getting there, right? So the point at which they're doing proper continued pre-training is the point at which that becomes a continuum rather than a phase. So the only difference with what I was describing last time is to say like, oh, there's a function or whatever, which is happening every batch. It's not a huge difference. You know, I always used to get offended when people had learning rates that like jumped. And so one of the things I started doing early on in Fast.ai was to say to people like, no, you should actually have your learning rate schedule should be a function, not a list of numbers. So now I'm trying to give the same idea about training mix.Swyx [00:04:07]: There's been pretty public work from Meta on schedule-free optimizers. I don't know if you've been following Aaron DeFazio and what he's doing, just because you mentioned learning rate schedules, you know, what if you didn't have a schedule?Jeremy [00:04:18]: I don't care very much, honestly. I don't think that schedule-free optimizer is that exciting. It's fine. We've had non-scheduled optimizers for ages, like Less Wright, who's now at Meta, who was part of the Fast.ai community there, created something called the Ranger optimizer. I actually like having more hyperparameters. You know, as soon as you say schedule-free, then like, well, now I don't get to choose. And there isn't really a mathematically correct way of, like, I actually try to schedule more parameters rather than less. So like, I like scheduling my epsilon in my atom, for example. I schedule all the things. But then the other thing we always did with the Fast.ai library was make it so you don't have to set any schedules. So Fast.ai always supported, like, you didn't even have to pass a learning rate. Like, it would always just try to have good defaults and do the right thing. But to me, I like to have more parameters I can play with if I want to, but you don't have to.Alessio [00:05:08]: And then the more less technical side, I guess, of your issue, I guess, with the market was some of the large research labs taking all this innovation kind of behind closed doors and whether or not that's good, which it isn't. And now we could maybe make it more available to people. And then a month after we released the episode, there was the whole Sam Altman drama and like all the OpenAI governance issues. And maybe people started to think more, okay, what happens if some of these kind of labs, you know, start to break from within, so to speak? And the alignment of the humans is probably going to fall before the alignment of the models. So I'm curious, like, if you have any new thoughts and maybe we can also tie in some of the way that we've been building Answer as like a public benefit corp and some of those aspects.Jeremy [00:05:51]: Sure. So, yeah, I mean, it was kind of uncomfortable because two days before Altman got fired, I did a small public video interview in which I said, I'm quite sure that OpenAI's current governance structure can't continue and that it was definitely going to fall apart. And then it fell apart two days later and a bunch of people were like, what did you know, Jeremy?Alessio [00:06:13]: What did Jeremy see?Jeremy [00:06:15]: I didn't see anything. It's just obviously true. Yeah. So my friend Eric Ries and I spoke a lot before that about, you know, Eric's, I think probably most people would agree, the top expert in the world on startup and AI governance. And you know, we could both clearly see that this didn't make sense to have like a so-called non-profit where then there are people working at a company, a commercial company that's owned by or controlled nominally by the non-profit, where the people in the company are being given the equivalent of stock options, like everybody there was working there with expecting to make money largely from their equity. So the idea that then a board could exercise control by saying like, oh, we're worried about safety issues and so we're going to do something that decreases the profit of the company, when every stakeholder in the company, their remuneration pretty much is tied to their profit, it obviously couldn't work. So I mean, that was a huge oversight there by someone. I guess part of the problem is that the kind of people who work at non-profits and in this case the board, you know, who are kind of academics and, you know, people who are kind of true believers. I think it's hard for them to realize that 99.999% of the world is driven very heavily by money, especially huge amounts of money. So yeah, Eric and I had been talking for a long time before that about what could be done differently, because also companies are sociopathic by design and so the alignment problem as it relates to companies has not been solved. Like, companies become huge, they devour their founders, they devour their communities and they do things where even the CEOs, you know, often of big companies tell me like, I wish our company didn't do that thing. You know, I know that if I didn't do it, then I would just get fired and the board would put in somebody else and the board knows if they don't do it, then their shareholders can sue them because they're not maximizing profitability or whatever. So what Eric's spent a lot of time doing is trying to think about how do we make companies less sociopathic, you know, how to, or more, you know, maybe a better way to think of it is like, how do we make it so that the founders of companies can ensure that their companies continue to actually do the things they want them to do? You know, when we started a company, hey, we very explicitly decided we got to start a company, not a academic lab, not a nonprofit, you know, we created a Delaware Seacorp, you know, the most company kind of company. But when we did so, we told everybody, you know, including our first investors, which was you Alessio. They sound great. We are going to run this company on the basis of maximizing long-term value. And in fact, so when we did our second round, which was an angel round, we had everybody invest through a long-term SPV, which we set up where everybody had to agree to vote in line with long-term value principles. So like never enough just to say to people, okay, we're trying to create long-term value here for society as well as for ourselves and everybody's like, oh, yeah, yeah, I totally agree with that. But when it comes to like, okay, well, here's a specific decision we have to make, which will not maximize short-term value, people suddenly change their mind. So you know, it has to be written into the legal documents of everybody so that no question that that's the way the company has to be managed. So then you mentioned the PBC aspect, Public Benefit Corporation, which I never quite understood previously. And turns out it's incredibly simple, like it took, you know, like one paragraph added to our corporate documents to become a PBC. It was cheap, it was easy, but it's got this huge benefit, which is if you're not a public benefit corporation, then somebody can come along and offer to buy you with a stated description of like turning your company into the thing you most hate, right? And if they offer you more than the market value of your company and you don't accept it, then you are not necessarily meeting the kind of your fiduciary responsibilities. So the way like Eric always described it to me is like, if Philip Morris came along and said that you've got great technology for marketing cigarettes to children, so we're going to pivot your company to do that entirely, and we're going to pay you 50% more than the market value, you're going to have to say yes. If you have a PBC, then you are more than welcome to say no, if that offer is not in line with your stated public benefit. So our stated public benefit is to maximize the benefit to society through using AI. So given that more children smoking doesn't do that, then we can say like, no, we're not selling to you.Alessio [00:11:01]: I was looking back at some of our emails. You sent me an email on November 13th about talking and then on the 14th, I sent you an email working together to free AI was the subject line. And then that was kind of the start of the C round. And then two days later, someone got fired. So you know, you were having these thoughts even before we had like a public example of like why some of the current structures didn't work. So yeah, you were very ahead of the curve, so to speak. You know, people can read your awesome introduction blog and answer and the idea of having a R&D lab versus our lab and then a D lab somewhere else. I think to me, the most interesting thing has been hiring and some of the awesome people that you've been bringing on that maybe don't fit the central casting of Silicon Valley, so to speak. Like sometimes I got it like playing baseball cards, you know, people are like, oh, what teams was this person on, where did they work versus focusing on ability. So I would love for you to give a shout out to some of the awesome folks that you have on the team.Jeremy [00:11:58]: So, you know, there's like a graphic going around describing like the people at XAI, you know, Elon Musk thing. And like they are all connected to like multiple of Stanford, Meta, DeepMind, OpenAI, Berkeley, Oxford. Look, these are all great institutions and they have good people. And I'm definitely not at all against that, but damn, there's so many other people. And one of the things I found really interesting is almost any time I see something which I think like this is really high quality work and it's something I don't think would have been built if that person hadn't built the thing right now, I nearly always reach out to them and ask to chat. And I tend to dig in to find out like, okay, you know, why did you do that thing? Everybody else has done this other thing, your thing's much better, but it's not what other people are working on. And like 80% of the time, I find out the person has a really unusual background. So like often they'll have like, either they like came from poverty and didn't get an opportunity to go to a good school or had dyslexia and, you know, got kicked out of school in year 11, or they had a health issue that meant they couldn't go to university or something happened in their past and they ended up out of the mainstream. And then they kind of succeeded anyway. Those are the people that throughout my career, I've tended to kind of accidentally hire more of, but it's not exactly accidentally. It's like when I see somebody who's done, two people who have done extremely well, one of them did extremely well in exactly the normal way from the background entirely pointing in that direction and they achieved all the hurdles to get there. And like, okay, that's quite impressive, you know, but another person who did just as well, despite lots of constraints and doing things in really unusual ways and came up with different approaches. That's normally the person I'm likely to find useful to work with because they're often like risk-takers, they're often creative, they're often extremely tenacious, they're often very open-minded. So that's the kind of folks I tend to find myself hiring. So now at Answer.ai, it's a group of people that are strong enough that nearly every one of them has independently come to me in the past few weeks and told me that they have imposter syndrome and they're not convinced that they're good enough to be here. And I kind of heard it at the point where I was like, okay, I don't think it's possible that all of you are so far behind your peers that you shouldn't get to be here. But I think part of the problem is as an R&D lab, the great developers look at the great researchers and they're like, wow, these big-brained, crazy research people with all their math and s**t, they're too cool for me, oh my God. And then the researchers look at the developers and they're like, oh, they're killing it, making all this stuff with all these people using it and talking on Twitter about how great it is. I think they're both a bit intimidated by each other, you know. And so I have to kind of remind them like, okay, there are lots of things in this world where you suck compared to lots of other people in this company, but also vice versa, you know, for all things. And the reason you came here is because you wanted to learn about those other things from those other people and have an opportunity to like bring them all together into a single unit. You know, it's not reasonable to expect you're going to be better at everything than everybody else. I guess the other part of it is for nearly all of the people in the company, to be honest, they have nearly always been better than everybody else at nearly everything they're doing nearly everywhere they've been. So it's kind of weird to be in this situation now where it's like, gee, I can clearly see that I suck at this thing that I'm meant to be able to do compared to these other people where I'm like the worst in the company at this thing for some things. So I think that's a healthy place to be, you know, as long as you keep reminding each other about that's actually why we're here. And like, it's all a bit of an experiment, like we don't have any managers. We don't have any hierarchy from that point of view. So for example, I'm not a manager, which means I don't get to tell people what to do or how to do it or when to do it. Yeah, it's been a bit of an experiment to see how that would work out. And it's been great. So for instance, Ben Clavier, who you might have come across, he's the author of Ragatouille, he's the author of Rerankers, super strong information retrieval guy. And a few weeks ago, you know, this additional channel appeared on Discord, on our private Discord called Bert24. And these people started appearing, as in our collab sections, we have a collab section for like collaborating with outsiders. And these people started appearing, there are all these names that I recognize, like Bert24, and they're all talking about like the next generation of Bert. And I start following along, it's like, okay, Ben decided that I think, quite rightly, we need a new Bert. Because everybody, like so many people are still using Bert, and it's still the best at so many things, but it actually doesn't take advantage of lots of best practices. And so he just went out and found basically everybody who's created better Berts in the last four or five years, brought them all together, suddenly there's this huge collaboration going on. So yeah, I didn't tell him to do that. He didn't ask my permission to do that. And then, like, Benjamin Warner dived in, and he's like, oh, I created a whole transformers from scratch implementation designed to be maximally hackable. He originally did it largely as a teaching exercise to show other people, but he was like, I could, you know, use that to create a really hackable BERT implementation. In fact, he didn't say that. He said, I just did do that, you know, and I created a repo, and then everybody's like starts using it. They're like, oh my god, this is amazing. I can now implement all these other BERT things. And it's not just answer AI guys there, you know, there's lots of folks, you know, who have like contributed new data set mixes and blah, blah, blah. So, I mean, I can help in the same way that other people can help. So like, then Ben Clavier reached out to me at one point and said, can you help me, like, what have you learned over time about how to manage intimidatingly capable and large groups of people who you're nominally meant to be leading? And so, you know, I like to try to help, but I don't direct. Another great example was Kerem, who, after our FSTP QLORA work, decided quite correctly that it didn't really make sense to use LoRa in today's world. You want to use the normalized version, which is called Dora. Like two or three weeks after we did FSTP QLORA, he just popped up and said, okay, I've just converted the whole thing to Dora, and I've also created these VLLM extensions, and I've got all these benchmarks, and, you know, now I've got training of quantized models with adapters that are as fast as LoRa, and as actually better than, weirdly, fine tuning. Just like, okay, that's great, you know. And yeah, so the things we've done to try to help make these things happen as well is we don't have any required meetings, you know, but we do have a meeting for each pair of major time zones that everybody's invited to, and, you know, people see their colleagues doing stuff that looks really cool and say, like, oh, how can I help, you know, or how can I learn or whatever. So another example is Austin, who, you know, amazing background. He ran AI at Fidelity, he ran AI at Pfizer, he ran browsing and retrieval for Google's DeepMind stuff, created Jemma.cpp, and he's been working on a new system to make it easier to do web GPU programming, because, again, he quite correctly identified, yeah, so I said to him, like, okay, I want to learn about that. Not an area that I have much expertise in, so, you know, he's going to show me what he's working on and teach me a bit about it, and hopefully I can help contribute. I think one of the key things that's happened in all of these is everybody understands what Eric Gilliam, who wrote the second blog post in our series, the R&D historian, describes as a large yard with narrow fences. Everybody has total flexibility to do what they want. We all understand kind of roughly why we're here, you know, we agree with the premises around, like, everything's too expensive, everything's too complicated, people are building too many vanity foundation models rather than taking better advantage of fine-tuning, like, there's this kind of general, like, sense of we're all on the same wavelength about, you know, all the ways in which current research is fucked up, and, you know, all the ways in which we're worried about centralization. We all care a lot about not just research for the point of citations, but research that actually wouldn't have happened otherwise, and actually is going to lead to real-world outcomes. And so, yeah, with this kind of, like, shared vision, people understand, like, you know, so when I say, like, oh, well, you know, tell me, Ben, about BERT 24, what's that about? And he's like, you know, like, oh, well, you know, you can see from an accessibility point of view, or you can see from a kind of a actual practical impact point of view, there's far too much focus on decoder-only models, and, you know, like, BERT's used in all of these different places and industry, and so I can see, like, in terms of our basic principles, what we're trying to achieve, this seems like something important. And so I think that's, like, a really helpful that we have that kind of shared perspective, you know?Alessio [00:21:14]: Yeah. And before we maybe talk about some of the specific research, when you're, like, reaching out to people, interviewing them, what are some of the traits, like, how do these things come out, you know, usually? Is it working on side projects that you, you know, you're already familiar with? Is there anything, like, in the interview process that, like, helps you screen for people that are less pragmatic and more research-driven versus some of these folks that are just gonna do it, you know? They're not waiting for, like, the perfect process.Jeremy [00:21:40]: Everybody who comes through the recruiting is interviewed by everybody in the company. You know, our goal is 12 people, so it's not an unreasonable amount. So the other thing to say is everybody so far who's come into the recruiting pipeline, everybody bar one, has been hired. So which is to say our original curation has been good. And that's actually pretty easy, because nearly everybody who's come in through the recruiting pipeline are people I know pretty well. So Jono Whitaker and I, you know, he worked on the stable diffusion course we did. He's outrageously creative and talented, and he's super, like, enthusiastic tinkerer, just likes making things. Benjamin was one of the strongest parts of the fast.ai community, which is now the alumni. It's, like, hundreds of thousands of people. And you know, again, like, they're not people who a normal interview process would pick up, right? So Benjamin doesn't have any qualifications in math or computer science. Jono was living in Zimbabwe, you know, he was working on, like, helping some African startups, you know, but not FAANG kind of credentials. But yeah, I mean, when you actually see people doing real work and they stand out above, you know, we've got lots of Stanford graduates and open AI people and whatever in our alumni community as well. You know, when you stand out above all of those people anyway, obviously you've got something going for you. You know, Austin, him and I worked together on the masks study we did in the proceeding at the National Academy of Science. You know, we had worked together, and again, that was a group of, like, basically the 18 or 19 top experts in the world on public health and epidemiology and research design and so forth. And Austin, you know, one of the strongest people in that collaboration. So yeah, you know, like, I've been lucky enough to have had opportunities to work with some people who are great and, you know, I'm a very open-minded person, so I kind of am always happy to try working with pretty much anybody and some people stand out. You know, there have been some exceptions, people I haven't previously known, like Ben Clavier, actually, I didn't know before. But you know, with him, you just read his code, and I'm like, oh, that's really well-written code. And like, it's not written exactly the same way as everybody else's code, and it's not written to do exactly the same thing as everybody else's code. So yeah, and then when I chatted to him, it's just like, I don't know, I felt like we'd known each other for years, like we just were on the same wavelength, but I could pretty much tell that was going to happen just by reading his code. I think you express a lot in the code you choose to write and how you choose to write it, I guess. You know, or another example, a guy named Vic, who was previously the CEO of DataQuest, and like, in that case, you know, he's created a really successful startup. He won the first, basically, Kaggle NLP competition, which was automatic essay grading. He's got the current state-of-the-art OCR system, Surya. Again, he's just a guy who obviously just builds stuff, you know, he doesn't ask for permission, he doesn't need any, like, external resources. Actually, Karim's another great example of this, I mean, I already knew Karim very well because he was my best ever master's student, but it wasn't a surprise to me then when he then went off to create the world's state-of-the-art language model in Turkish on his own, in his spare time, with no budget, from scratch. This is not fine-tuning or whatever, he, like, went back to Common Crawl and did everything. Yeah, it's kind of, I don't know what I'd describe that process as, but it's not at all based on credentials.Swyx [00:25:17]: Assemble based on talent, yeah. We wanted to dive in a little bit more on, you know, turning from the people side of things into the technical bets that you're making. Just a little bit more on Bert. I was actually, we just did an interview with Yi Tay from Reka, I don't know if you're familiar with his work, but also another encoder-decoder bet, and one of his arguments was actually people kind of over-index on the decoder-only GPT-3 type paradigm. I wonder if you have thoughts there that is maybe non-consensus as well. Yeah, no, absolutely.Jeremy [00:25:45]: So I think it's a great example. So one of the people we're collaborating with a little bit with BERT24 is Colin Raffle, who is the guy behind, yeah, most of that stuff, you know, between that and UL2, there's a lot of really interesting work. And so one of the things I've been encouraging the BERT group to do, Colin has as well, is to consider using a T5 pre-trained encoder backbone as a thing you fine-tune, which I think would be really cool. You know, Colin was also saying actually just use encoder-decoder as your Bert, you know, why don't you like use that as a baseline, which I also think is a good idea. Yeah, look.Swyx [00:26:25]: What technical arguments are people under-weighting?Jeremy [00:26:27]: I mean, Colin would be able to describe this much better than I can, but I'll give my slightly non-expert attempt. Look, I mean, think about like diffusion models, right? Like in stable diffusion, like we use things like UNet. You have this kind of downward path and then in the upward path you have the cross connections, which it's not a tension, but it's like a similar idea, right? You're inputting the original encoding path into your decoding path. It's critical to make it work, right? Because otherwise in the decoding part, the model has to do so much kind of from scratch. So like if you're doing translation, like that's a classic kind of encoder-decoder example. If it's decoder only, you never get the opportunity to find the right, you know, feature engineering, the right feature encoding for the original sentence. And it kind of means then on every token that you generate, you have to recreate the whole thing, you know? So if you have an encoder, it's basically saying like, okay, this is your opportunity model to create a really useful feature representation for your input information. So I think there's really strong arguments for encoder-decoder models anywhere that there is this kind of like context or source thing. And then why encoder only? Well, because so much of the time what we actually care about is a classification, you know? It's like an output. It's like generating an arbitrary length sequence of tokens. So anytime you're not generating an arbitrary length sequence of tokens, decoder models don't seem to make much sense. Now the interesting thing is, you see on like Kaggle competitions, that decoder models still are at least competitive with things like Deberta v3. They have to be way bigger to be competitive with things like Deberta v3. And the only reason they are competitive is because people have put a lot more time and money and effort into training the decoder only ones, you know? There isn't a recent Deberta. There isn't a recent Bert. Yeah, it's a whole part of the world that people have slept on a little bit. And this is just what happens. This is how trends happen rather than like, to me, everybody should be like, oh, let's look at the thing that has shown signs of being useful in the past, but nobody really followed up with properly. That's the more interesting path, you know, where people tend to be like, oh, I need to get citations. So what's everybody else doing? Can I make it 0.1% better, you know, or 0.1% faster? That's what everybody tends to do. Yeah. So I think it's like, Itay's work commercially now is interesting because here's like a whole, here's a whole model that's been trained in a different way. So there's probably a whole lot of tasks it's probably better at than GPT and Gemini and Claude. So that should be a good commercial opportunity for them if they can figure out what those tasks are.Swyx [00:29:07]: Well, if rumors are to be believed, and he didn't comment on this, but, you know, Snowflake may figure out the commercialization for them. So we'll see.Jeremy [00:29:14]: Good.Alessio [00:29:16]: Let's talk about FSDP, Qlora, Qdora, and all of that awesome stuff. One of the things we talked about last time, some of these models are meant to run on systems that nobody can really own, no single person. And then you were like, well, what if you could fine tune a 70B model on like a 4090? And I was like, no, that sounds great, Jeremy, but like, can we actually do it? And then obviously you all figured it out. Can you maybe tell us some of the worst stories behind that, like the idea behind FSDP, which is kind of taking sharded data, parallel computation, and then Qlora, which is do not touch all the weights, just go quantize some of the model, and then within the quantized model only do certain layers instead of doing everything.Jeremy [00:29:57]: Well, do the adapters. Yeah.Alessio [00:29:59]: Yeah. Yeah. Do the adapters. Yeah. I will leave the floor to you. I think before you published it, nobody thought this was like a short term thing that we're just going to have. And now it's like, oh, obviously you can do it, but it's not that easy.Jeremy [00:30:12]: Yeah. I mean, to be honest, it was extremely unpleasant work to do. It's like not at all enjoyable. I kind of did version 0.1 of it myself before we had launched the company, or at least the kind of like the pieces. They're all pieces that are difficult to work with, right? So for the quantization, you know, I chatted to Tim Detmers quite a bit and, you know, he very much encouraged me by saying like, yeah, it's possible. He actually thought it'd be easy. It probably would be easy for him, but I'm not Tim Detmers. And, you know, so he wrote bits and bytes, which is his quantization library. You know, he wrote that for a paper. He didn't write that to be production like code. It's now like everybody's using it, at least the CUDA bits. So like, it's not particularly well structured. There's lots of code paths that never get used. There's multiple versions of the same thing. You have to try to figure it out. So trying to get my head around that was hard. And you know, because the interesting bits are all written in CUDA, it's hard to like to step through it and see what's happening. And then, you know, FSTP is this very complicated library and PyTorch, which not particularly well documented. So the only really, really way to understand it properly is again, just read the code and step through the code. And then like bits and bytes doesn't really work in practice unless it's used with PEF, the HuggingFace library and PEF doesn't really work in practice unless you use it with other things. And there's a lot of coupling in the HuggingFace ecosystem where like none of it works separately. You have to use it all together, which I don't love. So yeah, trying to just get a minimal example that I can play with was really hard. And so I ended up having to rewrite a lot of it myself to kind of create this like minimal script. One thing that helped a lot was Medec had this LlamaRecipes repo that came out just a little bit before I started working on that. And like they had a kind of role model example of like, here's how to train FSTP, LoRa, didn't work with QLoRa on Llama. A lot of the stuff I discovered, the interesting stuff would be put together by Les Wright, who's, he was actually the guy in the Fast.ai community I mentioned who created the Ranger Optimizer. So he's doing a lot of great stuff at Meta now. So yeah, I kind of, that helped get some minimum stuff going and then it was great once Benjamin and Jono joined full time. And so we basically hacked at that together and then Kerim joined like a month later or something. And it was like, gee, it was just a lot of like fiddly detailed engineering on like barely documented bits of obscure internals. So my focus was to see if it kind of could work and I kind of got a bit of a proof of concept working and then the rest of the guys actually did all the work to make it work properly. And, you know, every time we thought we had something, you know, we needed to have good benchmarks, right? So we'd like, it's very easy to convince yourself you've done the work when you haven't, you know, so then we'd actually try lots of things and be like, oh, and these like really important cases, the memory use is higher, you know, or it's actually slower. And we'd go in and we just find like all these things that were nothing to do with our library that just didn't work properly. And nobody had noticed they hadn't worked properly because nobody had really benchmarked it properly. So we ended up, you know, trying to fix a whole lot of different things. And even as we did so, new regressions were appearing in like transformers and stuff that Benjamin then had to go away and figure out like, oh, how come flash attention doesn't work in this version of transformers anymore with this set of models and like, oh, it turns out they accidentally changed this thing, so it doesn't work. You know, there's just, there's not a lot of really good performance type evals going on in the open source ecosystem. So there's an extraordinary amount of like things where people say like, oh, we built this thing and it has this result. And when you actually check it, so yeah, there's a shitload of war stories from getting that thing to work. And it did require a particularly like tenacious group of people and a group of people who don't mind doing a whole lot of kind of like really janitorial work, to be honest, to get the details right, to check them. Yeah.Alessio [00:34:09]: We had a trade out on the podcast and we talked about how a lot of it is like systems work to make some of these things work. It's not just like beautiful, pure math that you do on a blackboard. It's like, how do you get into the nitty gritty?Jeremy [00:34:22]: I mean, flash attention is a great example of that. Like it's, it basically is just like, oh, let's just take the attention and just do the tiled version of it, which sounds simple enough, you know, but then implementing that is challenging at lots of levels.Alessio [00:34:36]: Yeah. What about inference? You know, obviously you've done all this amazing work on fine tuning. Do you have any research you've been doing on the inference side, how to make local inference really fast on these models too?Jeremy [00:34:47]: We're doing quite a bit on that at the moment. We haven't released too much there yet. But one of the things I've been trying to do is also just to help other people. And one of the nice things that's happened is that a couple of folks at Meta, including Mark Seraphim, have done a nice job of creating this CUDA mode community of people working on like CUDA kernels or learning about that. And I tried to help get that going well as well and did some lessons to help people get into it. So there's a lot going on in both inference and fine tuning performance. And a lot of it's actually happening kind of related to that. So PyTorch team have created this Torch AO project on quantization. And so there's a big overlap now between kind of the FastAI and AnswerAI and CUDA mode communities of people working on stuff for both inference and fine tuning. But we're getting close now. You know, our goal is that nobody should be merging models, nobody should be downloading merged models, everybody should be using basically quantized plus adapters for almost everything and just downloading the adapters. And that should be much faster. So that's kind of the place we're trying to get to. It's difficult, you know, because like Karim's been doing a lot of work with VLM, for example. These inference engines are pretty complex bits of code. They have a whole lot of custom kernel stuff going on as well, as do the quantization libraries. So we've been working on, we're also quite a bit of collaborating with the folks who do HQQ, which is a really great quantization library and works super well. So yeah, there's a lot of other people outside AnswerAI that we're working with a lot who are really helping on all this performance optimization stuff, open source.Swyx [00:36:27]: Just to follow up on merging models, I picked up there that you said nobody should be merging models. That's interesting because obviously a lot of people are experimenting with this and finding interesting results. I would say in defense of merging models, you can do it without data. That's probably the only thing that's going for it.Jeremy [00:36:45]: To explain, it's not that you shouldn't merge models. You shouldn't be distributing a merged model. You should distribute a merged adapter 99% of the time. And actually often one of the best things happening in the model merging world is actually that often merging adapters works better anyway. The point is, Sean, that once you've got your new model, if you distribute it as an adapter that sits on top of a quantized model that somebody's already downloaded, then it's a much smaller download for them. And also the inference should be much faster because you're not having to transfer FB16 weights from HPM memory at all or ever load them off disk. You know, all the main weights are quantized and the only floating point weights are in the adapters. So that should make both inference and fine tuning faster. Okay, perfect.Swyx [00:37:33]: We're moving on a little bit to the rest of the fast universe. I would have thought that, you know, once you started Answer.ai, that the sort of fast universe would be kind of on hold. And then today you just dropped Fastlight and it looks like, you know, there's more activity going on in sort of Fastland.Jeremy [00:37:49]: Yeah. So Fastland and Answerland are not really distinct things. Answerland is kind of like the Fastland grown up and funded. They both have the same mission, which is to maximize the societal benefit of AI broadly. We want to create thousands of commercially successful products at Answer.ai. And we want to do that with like 12 people. So that means we need a pretty efficient stack, you know, like quite a few orders of magnitude more efficient, not just for creation, but for deployment and maintenance than anything that currently exists. People often forget about the D part of our R&D firm. So we've got to be extremely good at creating, deploying and maintaining applications, not just models. Much to my horror, the story around creating web applications is much worse now than it was 10 or 15 years ago in terms of, if I say to a data scientist, here's how to create and deploy a web application, you know, either you have to learn JavaScript or TypeScript and about all the complex libraries like React and stuff, and all the complex like details around security and web protocol stuff around how you then talk to a backend and then all the details about creating the backend. You know, if that's your job and, you know, you have specialists who work in just one of those areas, it is possible for that to all work. But compared to like, oh, write a PHP script and put it in the home directory that you get when you sign up to this shell provider, which is what it was like in the nineties, you know, here are those 25 lines of code and you're done and now you can pass that URL around to all your friends, or put this, you know, .pl file inside the CGI bin directory that you got when you signed up to this web host. So yeah, the thing I've been mainly working on the last few weeks is fixing all that. And I think I fixed it. I don't know if this is an announcement, but I tell you guys, so yeah, there's this thing called fastHTML, which basically lets you create a complete web application in a single Python file. Unlike excellent projects like Streamlit and Gradio, you're not working on top of a highly abstracted thing. That's got nothing to do with web foundations. You're working with web foundations directly, but you're able to do it by using pure Python. There's no template, there's no ginger, there's no separate like CSS and JavaScript files. It looks and behaves like a modern SPA web application. And you can create components for like daisy UI, or bootstrap, or shoelace, or whatever fancy JavaScript and or CSS tailwind etc library you like, but you can write it all in Python. You can pip install somebody else's set of components and use them entirely from Python. You can develop and prototype it all in a Jupyter notebook if you want to. It all displays correctly, so you can like interactively do that. And then you mentioned Fastlight, so specifically now if you're using SQLite in particular, it's like ridiculously easy to have that persistence, and all of your handlers will be passed database ready objects automatically, that you can just call dot delete dot update dot insert on. Yeah, you get session, you get security, you get all that. So again, like with most everything I do, it's very little code. It's mainly tying together really cool stuff that other people have written. You don't have to use it, but a lot of the best stuff comes from its incorporation of HTMX, which to me is basically the thing that changes your browser to make it work the way it always should have. So it just does four small things, but those four small things are the things that are basically unnecessary constraints that HTML should never have had, so it removes the constraints. It sits on top of Starlet, which is a very nice kind of lower level platform for building these kind of web applications. The actual interface matches as closely as possible to FastAPI, which is a really nice system for creating the kind of classic JavaScript type applications. And Sebastian, who wrote FastAPI, has been kind enough to help me think through some of these design decisions, and so forth. I mean, everybody involved has been super helpful. Actually, I chatted to Carson, who created HTMX, you know, so about it. Some of the folks involved in Django, like everybody in the community I've spoken to definitely realizes there's a big gap to be filled around, like, highly scalable, web foundation-based, pure Python framework with a minimum of fuss. So yeah, I'm getting a lot of support and trying to make sure that FastHTML works well for people.Swyx [00:42:38]: I would say, when I heard about this, I texted Alexio. I think this is going to be pretty huge. People consider Streamlit and Gradio to be the state of the art, but I think there's so much to improve, and having what you call web foundations and web fundamentals at the core of it, I think, would be really helpful.Jeremy [00:42:54]: I mean, it's based on 25 years of thinking and work for me. So like, FastML was built on a system much like this one, but that was of hell. And so I spent, you know, 10 years working on that. We had millions of people using that every day, really pushing it hard. And I really always enjoyed working in that. Yeah. So, you know, and obviously lots of other people have done like great stuff, and particularly HTMX. So I've been thinking about like, yeah, how do I pull together the best of the web framework I created for FastML with HTMX? There's also things like PicoCSS, which is the CSS system, which by default, FastHTML comes with. Although, as I say, you can pip install anything you want to, but it makes it like super easy to, you know, so we try to make it so that just out of the box, you don't have any choices to make. Yeah. You can make choices, but for most people, you just, you know, it's like the PHP in your home directory thing. You just start typing and just by default, you'll get something which looks and feels, you know, pretty okay. And if you want to then write a version of Gradio or Streamlit on top of that, you totally can. And then the nice thing is if you then write it in kind of the Gradio equivalent, which will be, you know, I imagine we'll create some kind of pip installable thing for that. Once you've outgrown, or if you outgrow that, it's not like, okay, throw that all away and start again. And this like whole separate language that it's like this kind of smooth, gentle path that you can take step-by-step because it's all just standard web foundations all the way, you know.Swyx [00:44:29]: Just to wrap up the sort of open source work that you're doing, you're aiming to create thousands of projects with a very, very small team. I haven't heard you mention once AI agents or AI developer tooling or AI code maintenance. I know you're very productive, but you know, what is the role of AI in your own work?Jeremy [00:44:47]: So I'm making something. I'm not sure how much I want to say just yet.Swyx [00:44:52]: Give us a nibble.Jeremy [00:44:53]: All right. I'll give you the key thing. So I've created a new approach. It's not called prompt engineering. It's called dialogue engineering. But I'm creating a system for doing dialogue engineering. It's currently called AI magic. I'm doing most of my work in this system and it's making me much more productive than I was before I used it. So I always just build stuff for myself and hope that it'll be useful for somebody else. Think about chat GPT with code interpreter, right? The basic UX is the same as a 1970s teletype, right? So if you wrote APL on a teletype in the 1970s, you typed onto a thing, your words appeared at the bottom of a sheet of paper and you'd like hit enter and it would scroll up. And then the answer from APL would be printed out, scroll up, and then you would type the next thing. And like, which is also the way, for example, a shell works like bash or ZSH or whatever. It's not terrible, you know, like we all get a lot done in these like very, very basic teletype style REPL environments, but I've never felt like it's optimal and everybody else has just copied chat GPT. So it's also the way BART and Gemini work. It's also the way the Claude web app works. And then you add code interpreter. And the most you can do is to like plead with chat GPT to write the kind of code I want. It's pretty good for very, very, very beginner users who like can't code at all, like by default now the code's even hidden away, so you never even have to see it ever happened. But for somebody who's like wanting to learn to code or who already knows a bit of code or whatever, it's, it seems really not ideal. So okay, that's one end of the spectrum. The other end of the spectrum, which is where Sean's work comes in, is, oh, you want to do more than chat GPT? No worries. Here is Visual Studio Code. I run it. There's an empty screen with a flashing cursor. Okay, start coding, you know, and it's like, okay, you can use systems like Sean's or like cursor or whatever to be like, okay, Apple K in cursors, like a creative form that blah, blah, blah. But in the end, it's like a convenience over the top of this incredibly complicated system that full-time sophisticated software engineers have designed over the past few decades in a totally different environment as a way to build software, you know. And so we're trying to like shoehorn in AI into that. And it's not easy to do. And I think there are like much better ways of thinking about the craft of software development in a language model world to be much more interactive, you know. So the thing that I'm building is neither of those things. It's something between the two. And it's built around this idea of crafting a dialogue, you know, where the outcome of the dialogue is the artifacts that you want, whether it be a piece of analysis or whether it be a Python library or whether it be a technical blog post or whatever. So as part of building that, I've created something called Claudette, which is a library for Claude. I've created something called Cosette, which is a library for OpenAI. They're libraries which are designed to make those APIs much more usable, much easier to use, much more concise. And then I've written AI magic on top of those. And that's been an interesting exercise because I did Claudette first, and I was looking at what Simon Willison did with his fantastic LLM library. And his library is designed around like, let's make something that supports all the LLM inference engines and commercial providers. I thought, okay, what if I did something different, which is like make something that's as Claude friendly as possible and forget everything else. So that's what Claudette was. So for example, one of the really nice things in Claude is prefill. So by telling the assistant that this is what your response started with, there's a lot of powerful things you can take advantage of. So yeah, I created Claudette to be as Claude friendly as possible. And then after I did that, and then particularly with GPT 4.0 coming out, I kind of thought, okay, now let's create something that's as OpenAI friendly as possible. And then I tried to look to see, well, where are the similarities and where are the differences? And now can I make them compatible in places where it makes sense for them to be compatible without losing out on the things that make each one special for what they are. So yeah, those are some of the things I've been working on in that space. And I'm thinking we might launch AI magic via a course called how to solve it with code. The name is based on the classic Polya book, if you know how to solve it, which is, you know, one of the classic math books of all time, where we're basically going to try to show people how to solve challenging problems that they didn't think they could solve without doing a full computer science course, by taking advantage of a bit of AI and a bit of like practical skills, as particularly for this like whole generation of people who are learning to code with and because of ChatGPT. Like I love it, I know a lot of people who didn't really know how to code, but they've created things because they use ChatGPT, but they don't really know how to maintain them or fix them or add things to them that ChatGPT can't do, because they don't really know how to code. And so this course will be designed to show you how you can like either become a developer who can like supercharge their capabilities by using language models, or become a language model first developer who can supercharge their capabilities by understanding a bit about process and fundamentals.Alessio [00:50:19]: Nice. That's a great spoiler. You know, I guess the fourth time you're going to be on learning space, we're going to talk about AI magic. Jeremy, before we wrap, this was just a great run through everything. What are the things that when you next come on the podcast in nine, 12 months, we're going to be like, man, Jeremy was like really ahead of it. Like, is there anything that you see in the space that maybe people are not talking enough? You know, what's the next company that's going to fall, like have drama internally, anything in your mind?Jeremy [00:50:47]: You know, hopefully we'll be talking a lot about fast HTML and hopefully the international community that at that point has come up around that. And also about AI magic and about dialogue engineering. Hopefully dialogue engineering catches on because I think it's the right way to think about a lot of this stuff. What else? Just trying to think about all on the research side. Yeah. I think, you know, I mean, we've talked about a lot of it. Like I think encoder decoder architectures, encoder only architectures, hopefully we'll be talking about like the whole re-interest in BERT that BERT 24 stimulated.Swyx [00:51:17]: There's a safe space model that came out today that might be interesting for this general discussion. One thing that stood out to me with Cartesia's blog posts was that they were talking about real time ingestion, billions and trillions of tokens, and keeping that context, obviously in the state space that they have.Jeremy [00:51:34]: Yeah.Swyx [00:51:35]: I'm wondering what your thoughts are because you've been entirely transformers the whole time.Jeremy [00:51:38]: Yeah. No. So obviously my background is RNNs and LSTMs. Of course. And I'm still a believer in the idea that state is something you can update, you know? So obviously Sepp Hochreiter came up, came out with xLSTM recently. Oh my God. Okay. Another whole thing we haven't talked about, just somewhat related. I've been going crazy for like a long time about like, why can I not pay anybody to save my KV cash? I just ingested the Great Gatsby or the documentation for Starlet or whatever, you know, I'm sending it as my prompt context. Why are you redoing it every time? So Gemini is about to finally come out with KV caching, and this is something that Austin actually in Gemma.cpp had had on his roadmap for years, well not years, months, long time. The idea that the KV cache is like a thing that, it's a third thing, right? So there's RAG, you know, there's in-context learning, you know, and prompt engineering, and there's KV cache creation. I think it creates like a whole new class almost of applications or as techniques where, you know, for me, for example, I very often work with really new libraries or I've created my own library that I'm now writing with rather than on. So I want all the docs in my new library to be there all the time. So I want to upload them once, and then we have a whole discussion about building this application using FastHTML. Well nobody's got FastHTML in their language model yet, I don't want to send all the FastHTML docs across every time. So one of the things I'm looking at doing in AI Magic actually is taking advantage of some of these ideas so that you can have the documentation of the libraries you're working on be kind of always available. Something over the next 12 months people will be spending time thinking about is how to like, where to use RAG, where to use fine-tuning, where to use KV cache storage, you know. And how to use state, because in state models and XLSTM, again, state is something you update. So how do we combine the best of all of these worlds?Alessio [00:53:46]: And Jeremy, I know before you talked about how some of the autoregressive models are not maybe a great fit for agents. Any other thoughts on like JEPA, diffusion for text, any interesting thing that you've seen pop up?Jeremy [00:53:58]: In the same way that we probably ought to have state that you can update, i.e. XLSTM and state models, in the same way that a lot of things probably should have an encoder, JEPA and diffusion both seem like the right conceptual mapping for a lot of things we probably want to do. So the idea of like, there should be a piece of the generative pipeline, which is like thinking about the answer and coming up with a sketch of what the answer looks like before you start outputting tokens. That's where it kind of feels like diffusion ought to fit, you know. And diffusion is, because it's not autoregressive, it's like, let's try to like gradually de-blur the picture of how to solve this. So this is also where dialogue engineering fits in, by the way. So with dialogue engineering, one of the reasons it's working so well for me is I use it to kind of like craft the thought process before I generate the code, you know. So yeah, there's a lot of different pieces here and I don't know how they'll all kind of exactly fit together. I don't know if JEPA is going to actually end up working in the text world. I don't know if diffusion will end up working in the text world, but they seem to be like trying to solve a class of problem which is currently unsolved.Alessio [00:55:13]: Awesome, Jeremy. This was great, as usual. Thanks again for coming back on the pod and thank you all for listening. Yeah, that was fantastic. Get full access to Latent Space at www.latent.space/subscribe

Data Hackers
Anthropic lança o Claude no Brasil, com foco em uso corporativo; Vazamento de dados atinge 39 milhões de registros de brasileiros - DaTa Hackers News #42

Data Hackers

Play Episode Listen Later Aug 14, 2024 13:18


Está no ar, o Data Hackers News !! Os assuntos mais quentes da semana, com as principais notícias da área de Dados, IA e Tecnologia, que você também encontra na nossa Newsletter semanal, agora no Podcast do Data Hackers !! Aperte o play e ouça agora, o Data Hackers News dessa semana ! Para saber tudo sobre o que está acontecendo na área de dados, se inscreva na Newsletter semanal: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.datahackers.news/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Conheça nossos comentaristas do Data Hackers News: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Monique Femme⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Paulo Vasconcellos ⁠Matérias/assuntos comentados: Anthropic lança Claude no Brasil com foco em uso corporativo; Vazamento de dados atinge 39 milhões de registros de brasileiros. Baixe o relatório completo do State of Data Brazil e os highlights da pesquisa : ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://stateofdata.datahackers.com.br/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠Dados Liberados do State of Data Brazil 2023 no Kaggle; Demais canais do Data Hackers: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Site⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Linkedin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Tik Tok⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠You Tube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Já aproveita, para nos seguir no Spotify, Apple Podcasts, ou no seu player de podcasts favoritos !

Hipsters Ponto Tech
DALL·E 3 gratuito, desenvolvendo IA na NVIDIA, mais ML no Kaggle – Hipsters: Fora de Controle #69

Hipsters Ponto Tech

Play Episode Listen Later Aug 9, 2024 60:42


O Hipsters: Fora de Controle é o podcast da Alura com notícias sobre Inteligência Artificial aplicada e todo esse novo mundo no qual estamos começando a engatinhar, e que você vai poder explorar conosco! Nesse episódio conversamos com Gilberto "Giba" Titericz, Cientista de Dados na NVIDIA Rapids, sobre como é trabalhar na empresa mais importante do cenário atual de IA. Junto disso, também exploramos seu histórico e desempenho no Kaggle, além do uso da plataforma para o aprendizado de ML. Antes, é claro, repercutimos as principais notícias da semana, incluindo os teasers de Sam Altman para a liberação do projeto de codinome Strawberry, e as ambições do Hugging Face de se tornar ainda mais central para o desenvolvimento de grandes modelos. Vem ver quem participou desse papo: Marcus Mendes, host fora de controle Fabrício Carraro, host fora de controle, Program Manager da Alura, autor de IA e host do podcast Dev Sem Fronteiras Gilberto "Giba" Titericz, Cientista de Dados na NVIDIA Rapids

Data Hackers
Lançamento do Amazonia IA, primeiro modelo de IA feito por brasileiros; Governo brasileiro, anuncia o plano nacional da IA; Data Hackers no Hacktown 2024 - Data Hackers News #41

Data Hackers

Play Episode Listen Later Aug 7, 2024 21:43


Está no ar, o Data Hackers News !! Os assuntos mais quentes da semana, com as principais notícias da área de Dados, IA e Tecnologia, que você também encontra na nossa Newsletter semanal, agora no Podcast do Data Hackers !! Aperte o play e ouça agora, o Data Hackers News dessa semana ! Para saber tudo sobre o que está acontecendo na área de dados, se inscreva na Newsletter semanal: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.datahackers.news/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Conheça nossos comentaristas do Data Hackers News: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Monique Femme⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Paulo Vasconcellos ⁠Matérias/assuntos comentados: Lançamento do Amazonia IA, primeiro modelo de IA por brasileiros; Governo brasileiro anuncia plano nacional da IA; Data Hackers no Hacktown 2024 Baixe o relatório completo do State of Data Brazil e os highlights da pesquisa : ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://stateofdata.datahackers.com.br/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠Dados Liberados do State of Data Brazil 2023 no Kaggle; Demais canais do Data Hackers: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Site⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Linkedin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Tik Tok⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠You Tube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Já aproveita, para nos seguir no Spotify, Apple Podcasts, ou no seu player de podcasts favoritos !

Hipsters Ponto Tech
Anthropic Claude no Brasil, Google Gemma 2, vencendo no Kaggle – Hipsters: Fora de Controle #68

Hipsters Ponto Tech

Play Episode Listen Later Aug 2, 2024 77:32


O Hipsters: Fora de Controle é o podcast da Alura com notícias sobre Inteligência Artificial aplicada e todo esse novo mundo no qual estamos começando a engatinhar, e que você vai poder explorar conosco! Nesse episódio conversamos com Mario Filho sobre autodidatismo para machine learning e inteligência artificial, além de seu desempenho mundialmente reconhecido no Kaggle. Antes disso, repercutimos as principais notícias da semana, incluindo a chegada do Claude no Brasil, e os novos modelos anunciados pela Meta e pelo Google. Vem ver quem participou desse papo: Marcus Mendes, host fora de controle Fabrício Carraro, host fora de controle, Program Manager da Alura, autor de IA e host do podcast Dev Sem Fronteiras Mário Filho, Especialista em Machine Learning

Data Hackers
Pesquisa Stack Overflow mostra que AI não substituirá desenvolvedores; ⁠OpenAI anuncia seu sistema de busca para competir com Google; “X" usará seus dados para treinar IA - Data Hackers News #40

Data Hackers

Play Episode Listen Later Jul 31, 2024 6:57


Está no ar, o Data Hackers News !! Os assuntos mais quentes da semana, com as principais notícias da área de Dados, IA e Tecnologia, que você também encontra na nossa Newsletter semanal, agora no Podcast do Data Hackers !! Aperte o play e ouça agora, o Data Hackers News dessa semana ! Para saber tudo sobre o que está acontecendo na área de dados, se inscreva na Newsletter semanal: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.datahackers.news/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Conheça nossos comentaristas do Data Hackers News: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Monique Femme⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠**Matérias/assuntos comentados: Pesquisa da Stack Overflow mostra que desenvolvedores não estão com medo de perder seus empregos para IA; ⁠ OpenAI anuncia seu próprio sistema de busca para competir com Google; ⁠ Twitter (X) usará seus dados para treinar IA Podcast mencionado: Podcast Data Hackers #85 - Você deveria continuar aprendendo programação ? Baixe o relatório completo do State of Data Brazil e os highlights da pesquisa : ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://stateofdata.datahackers.com.br/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠Dados Liberados do State of Data Brazil 2023 no Kaggle; Demais canais do Data Hackers: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Site⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Linkedin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Tik Tok⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠You Tube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Já aproveita, para nos seguir no Spotify, Apple Podcasts, ou no seu player de podcasts favoritos !

Data Hackers
OpenAI anuncia novo modelo de baixo custo: o GPT-4o mini; ⁠Meta suspende recursos de inteligência artificial generativa no Brasil; Alexa dando prejuízo de bilhões a Amazon - Data Hackers News #39

Data Hackers

Play Episode Listen Later Jul 24, 2024 17:46


Está no ar, o Data Hackers News !! Os assuntos mais quentes da semana, com as principais notícias da área de Dados, IA e Tecnologia, que você também encontra na nossa Newsletter semanal, agora no Podcast do Data Hackers !! Aperte o play e ouça agora, o Data Hackers News dessa semana ! Para saber tudo sobre o que está acontecendo na área de dados, se inscreva na Newsletter semanal: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.datahackers.news/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Conheça nossos comentaristas do Data Hackers News: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Monique Femme⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠; ⁠Paulo Vasconcellos⁠. ⁠**Matérias/assuntos comentados:**⁠ ⁠⁠OpenAI anuncia novo modelo de baixo custo: o GPT-4o mini; ⁠Meta suspende recursos de inteligência artificial generativa no Brasil; Alexa dando prejuízo de bilhões a Amazon. Baixe o relatório completo do State of Data Brazil e os highlights da pesquisa : ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://stateofdata.datahackers.com.br/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠Dados Liberados do State of Data Brazil 2023 no Kaggle; Demais canais do Data Hackers: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Site⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Linkedin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Tik Tok⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠You Tube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Já aproveita, para nos seguir no Spotify, Apple Podcasts, ou no seu player de podcasts favoritos !

Consensus in Conversation
Rich Sorkin of Jupiter Intelligence on Climate Risk, Tech Readiness, and Anticipating the Future

Consensus in Conversation

Play Episode Listen Later Jul 18, 2024 51:39


With climate change causing unprecedented extreme weather, fluctuating temperatures, and rising sea levels, only one thing is certain: the future isn't going to look like the past. And yet, all of our risk analysis models are based on past data from historical patterns that no longer hold true. How can businesses make intelligent decisions on building for that future while protecting themselves from tomorrow's risks? That's the question that motivated Rich Sorkin to found Jupiter Intelligence, a climate risk analytics firm with a mission to help organizations build resilience and mitigate the effects of the changing climate. As co-founder, CEO, and chairman of Jupiter, Rich has combined his wealth of entrepreneurial experience and expertise in emerging technology to successfully scale the company since its launch in 2018, leveraging cutting-edge science, real-time data, and advanced modeling to achieve next-level climate risk insights and analytics. Rich earned an economics degree from Yale before attending Stanford's Graduate Business School in 1986. Whether researching under a Nobel Laureate, cutting his teeth at Apple, or leading game-changing technology to market at firms like Creative Labs, Zip2, and Kaggle, Rich is ever at the forefront of innovation and entrepreneurship.Hear Rich share why a change in perspective led him to launch Jupiter Intelligence, what the current assumptions about climate risk are missing, and how the startup is leveraging generative AI and real science to enhance risk analysis for the future.Episode Highlights00:00 Rich Sorkin on the impact of climate change 00:49 Conor Gaughan introduces Rich and Jupiter Intelligence06:30 Early interests and work for a future Nobel Prize winner at Yale10:55 Emerging tech, mapping trends, and career in Silicon Valley 17:41 Entrepreneurial lessons, Zeus, and the origin of Jupiter Intel22:50 What Jupiter solves, climate infrastructure, and risk management31:08 Misconceptions, driving change with data, and generative AI40:46 Aligning profit with purpose, innovations to watch, and leaving a legacy49:24 Where to learn more and conclusion50:06 End credits If you liked this episode, listen next to Max Evans of ClimateAi on Dorm Room Entrepreneurship and AI-Based Climate SolutionsMore on Jupiter and Rich Sorkin:jupiterintel.comlinkedin.com/company/jupiterintellinkedin.com/in/richsorkin Connect with Conor Gaughan on linkedin.com/in/ckgone and threads.net/@ckgoneHave questions, or a great idea for a potential guest? Email us at CiC@consensus-digital.com If you enjoyed this episode, please rate and review the show on Apple Podcasts and Spotify – it really makes a difference! Consensus in Conversation is a podcast by Consensus Digital Media produced in association with Reasonable Volume. Hosted on Acast. See acast.com/privacy for more information.

My First Million
Business Tricks We've Learned From Gamblers, Pickup Artists, & Feynman

My First Million

Play Episode Listen Later Jun 28, 2024 62:13


Episode 602: Sam Parr ( https://twitter.com/theSamParr ) and Shaan Puri ( https://twitter.com/ShaanVP ) talk about how to 10x your odds of success.  — Show Notes: (0:00) Ed Thorp and the mathematics of gambling (7:51) World's first hedge fund (12:12) Sam and Shaan revisit “The Game” (16:06) The Feynman technique for learning (21:14) Shaan's one criteria for action (24:58) Why you don't need privilege to start (29:06) Playing "the game" of business (33:37) Switching to better games (36:15) How the Roger Bannister Effect crushes limiting beliefs (40:13) Step 0: Believe it can be done — Links: • [Steal This] Get our proven writing frameworks that have made us millions https://clickhubspot.com/copy • Ed Thorp biography - https://tinyurl.com/yjcp4645 • Beat The Dealer - https://tinyurl.com/yc426fc5 • Ed Thorp website - https://www.edwardothorp.com/ • The Game - https://tinyurl.com/3kh3xfhn • Jack Smith's episode - https://tinyurl.com/yc4zdnem • Kaggle - https://www.kaggle.com/ • Bringing Down The House - https://tinyurl.com/4txm492n • Grab HubSpot's free AI-Powered Customer Platform and watch your business grow https://clickhubspot.com/fmf — Check Out Sam's Stuff: • Hampton - https://www.joinhampton.com/ • Ideation Bootcamp - https://www.ideationbootcamp.co/ • Copy That - https://copythat.com • Hampton Wealth Survey - https://joinhampton.com/wealth • Sam's List - http://samslist.co/ — Check Out Shaan's Stuff: Need to hire? You should use the same service Shaan uses to hire developers, designers, & Virtual Assistants → it's called Shepherd (tell ‘em Shaan sent you): https://bit.ly/SupportShepherd My First Million is a HubSpot Original Podcast // Brought to you by The HubSpot Podcast Network // Production by Arie Desormeaux // Editing by Ezra Bakker Trupiano

Ken's Nearest Neighbors
4X Kaggle GM Shares His Surprising Secret to Success in Competitions (Chris Deotte) - KNN Ep. 192

Ken's Nearest Neighbors

Play Episode Listen Later Jun 11, 2024 49:50


Today I had the pleasure of interviewing Chris Deotte. He is a member of NVIDIA's KGMON Team (Kaggle Grandmasters of NVIDIA), and one of the greatest kaggle grandmasters of all time. He has been previously ranked first in Discussions, Notebooks, and Datasets. Currently he is ranked 11th in competitions and has been ranked as high as 4th. Chris also has an immense love for sports and board games. He has worn all sorts of hats throughout his life and he believes that all of them have helped him to be successful in kaggle and in his career. In this episode we learn how Chris got into kaggle competitions, how he hacked the meta for a popular board game, and how he has able to have so much success in the kaggle domain. Chris on Kaggle: https://www.kaggle.com/cdeotte Podcast Sponsors, Affiliates, and Partners:- Taro - http://jointaro.com/r/kenj308 (20% discount) | Career mentorship if you already have a job - 365 Data Science (57% discount) - https://365datascience.pxf.io/P0jbBY | Learn data science today- Interview Query (10% discount) - https://www.interviewquery.com/?ref=kenjee |  Interview prep questions

The Cloud Pod
257: Who Let the LLamas Out? *Bleat Bleat*

The Cloud Pod

Play Episode Listen Later May 1, 2024 61:47


Welcome to episode 257 of the Cloud Pod podcast – where the forecast is always cloudy! This week your hosts Justin, Matthew, Ryan, and Jonathan are in the barnyard bringing you the latest news, which this week is really just Meta's release of Llama 3. Seriously. That's every announcement this week. Don’t say we didn't warn you.  Titles we almost went with this week: Meta Llama says no Drama No Meta Prob-llama Keep Calm and Llama on  Redis did not embrace the Llama MK The bedrock of good AI is built on Llamas The CloudPod announces support for Llama3 since everyone else was doing it Llama3, better know as Llama Llama Llama The Cloud Pod now known as the LLMPod Cloud Pod is considering changing its name to LlamaPod Unlike WinAMP nothing whips the llamas ass A big thanks to this week's sponsor: Check out Sonrai Securities‘ new Cloud Permission Firewall. Just for our listeners, enjoy a 14 day trial at www.sonrai.co/cloudpod Follow Up  01:27 Valkey is Rapidly Overtaking Redis  Valkey has continued to rack up support from AWS, Ericsson, Google, Oracle and Verizon initially, to now being joined by Alibaba, Aiven, Heroku and Percona backing Valkey as well.   Numerous blog posts have come out touting Valkey adoption. I'm not sure this whole thing is working out as well as Redis CEO Rowan Trollope had hoped.  AI Is Going Great – Or How AI Makes All It's Money  03:26 Introducing Meta Llama 3: The most capable openly available LLM to date  Meta has launched Llama 3, the next generation of their state-of-the-art open source large language model.  Llama 3 will be available on AWS, Databricks, GCP, Hugging Face, Kaggle, IBM WatsonX, Microsoft Azure, Nvidia NIM, and Snowflake with support from hardware platforms offered by AMD, AWS, Dell, Intel, Nvidia and Qualcomm Includes new trust and safety tools such as Llama Guard 2, Code Shield and Cybersec eval 2 They plan to introduce new capabilities, including longer context windows, additional model sizes and enhanced performance. The first two models from Meta Lama3 are the 8B and 70B parameter variants that can support a broad range of use cases.  Meta shared some benchmarks against Gemma 7B and Mistral 7B vs the Lama 3 8B models and showed improvements across all major benchmarks.  Including Math with Gemma 7b doing 12.2 vs 30 with Llama 3 It had highly comparable performance with the 70B model against Gemini Pro 1.5 and Claude 3 Sonnet scoring within a few points of most of the other scores.  Jonathan recommends using LM Studio to get start playing around with LLMS, which you can find at https://lmstudio.ai/ 04:42 Jonathan – “Isn’t it funny how you go from an 8 billion parameter model to a 70 billion parameter model but nothing in between? Like you would have thought there would be some kind of like, some middle ground maybe? But, uh, but… No. But, um,

Ken's Nearest Neighbors
The Youngest Ever 3x Kaggle GM Explains Why She Switched to Product (Ruchi Bhatia) - KNN Ep. 188

Ken's Nearest Neighbors

Play Episode Listen Later Apr 21, 2024 32:42


Today I had the pleasure of interviewing Ruchi Bhatia. Ruchi is the youngest ever 3x Kaggle Grandmaster. She recently started work as a product marketing manager at HP after being a Z by HP global data science ambassador. In this episode we talk about why Ruchi shifted to product, what she has learned from transitioning into the workforce at HP, and what she thinks of all of the new changes going on in the AI space. Podcast Sponsors, Affiliates, and Partners:- Pathrise - http://pathrise.com/KenJee | Career mentorship for job applicants (Free till you land a job)- Taro - http://jointaro.com/r/kenj308 (20% discount) | Career mentorship if you already have a job - 365 Data Science (57% discount) - https://365datascience.pxf.io/P0jbBY | Learn data science today- Interview Query (10% discount) - https://www.interviewquery.com/?ref=kenjee |  Interview prep questionsRuchi's Links: Linkedin - https://www.linkedin.com/in/ruchi798/Instagram - https://www.instagram.com/techbyruchi/

People of AI
François Chollet - Creating Keras 3

People of AI

Play Episode Listen Later Apr 18, 2024 65:32


Meet François Chollet, creator of Keras, software engineer, and AI researcher at Google. Join François and hosts Ashley Oldacre and Gus Martins as they discuss how Keras 3 was created, integrating Keras 3 with Gemma and Kaggle, artificial general intelligence (AGI), and much more! Resources: François Chollet research → https://goo.gle/443V3vG Deep Learning With Python, Second Edition → https://goo.gle/3UnpdH1  Intelligence: On Intelligence: How a New Understanding of the Brain Will Lead to the Creation of Truly Intelligent Machines → https://goo.gle/3xDE33s Researcher Pierre-Yves Oudeyer → https://goo.gle/3W8a39V Monty Hall Challenge → https://goo.gle/3VYXAW5  Machine Learning: Keras 3 → https://goo.gle/3JqRgis Gemma on Keras → https://goo.gle/49Q0pfy The ARC challenge on Kaggle → https://goo.gle/3xQsDcr 

The Nonlinear Library
LW - A D&D.Sci Dodecalogue by abstractapplic

The Nonlinear Library

Play Episode Listen Later Apr 12, 2024 6:03


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: A D&D.Sci Dodecalogue, published by abstractapplic on April 12, 2024 on LessWrong. Below is some advice on making D&D.Sci scenarios. I'm mostly yelling it in my own ear, and you shouldn't take any of it as gospel; but if you want some guidance on how to run your first game, you may find it helpful. 1. The scoring function should be fair, transparent, and monotonic D&D.Sci players should frequently be confused, but about how to best reach their goals, not the goals themselves. By the end of the challenge, it should be obvious who won[1]. 2. The scoring function should be platform-agnostic, and futureproof Where possible, someone looking through old D&D.Sci games should be able to play them, and easily confirm their performance after-the-fact. As far as I know, the best way to facilitate this for most challenges is with a HTML/JS web interactive, hosted on github. 3. The challenge should resist pure ML It should not be possible to reach an optimal answer just training a predictive model and looking at the output: if players wanted a "who can apply XGBoost/Tensorflow/whatever the best?" competition, they would be on Kaggle. The counterspell for this is making sure there's a nontrivial amount of task left in the task after players have good guesses for all the relevant response variables, and/or creating datasets specifically intended to flummox conventional use of conventional ML[2]. 4. The challenge should resist simple subsetting It should not be possible to reach an optimal answer by filtering for rows exactly like the situation the protagonist is (or could be) in: this is just too easy. The counterspell for this is making sure at least a few of the columns are continuous, and take a wide enough variety of values that a player who attempts a like-for-like analysis has to - at the very least - think carefully about what to treat as "basically the same". 5. The challenge should resist good luck It should not be plausible[3] to reach an optimal answer through sheer good luck: hours spent poring over spreadsheets should not give the same results as a good diceroll. The counterspell for this is giving players enough choices that the odds of them getting all of them right by chance approach zero. ("Pick the best option from this six-entry list" is a bad goal; "Pick the best three options from this twenty-entry list" is much better.) 6. Data should be abundant It is very, very hard to make a good "work around the fact that you're short on data" challenge. Not having enough information to be sure whether your hypotheses are right is a situation which players are likely to find awkward, irritating, and uncomfortably familiar: if you're uncertain about whether you should give players more rows, you almost certainly should. A five- or six-digit number of rows is reasonable for a dataset with 5-20 columns. (It is possible, but difficult, to be overly generous. A dataset with >1m rows cannot easily be fully loaded into current-gen Excel; a dataset too large to be hosted on github will be awkward to analyze with a home computer. But any dataset which doesn't approach either of those limitations will probably not be too big.) 7. Data should be preternaturally (but not perfectly) clean Data in the real world is messy and unreliable. Most real-life data work is accounting for impurities, setting up pipelines, making judgement calls, refitting existing models on slightly new datasets, and noticing when your supplier decides to randomly redefine a column. D&D.Sci shouldn't be more of this: instead, it should focus on the inferential and strategic problems people can face even when datasets are uncannily well-behaved. (It is good when players get a chance to practice splitting columns, joining dataframes, and handling unknowns: however, these subtasks should not make up the meat of a ch...

People of AI
Upskill your career in AI

People of AI

Play Episode Listen Later Apr 11, 2024 52:01


Meet Tina Huang, a YouTuber and the Founder of Lonely Octopus,  a program that teaches students AI skills and then matches them with real companies to work on developing AI solutions. Ashley Oldacre and Tina Huang discuss how to incorporate AI into your career, unfair advantages and identity capital, if AI will steal our jobs, and much more on this podcast People of AI episode.  Resources: Tina Huang's YouTube Channel → https://goo.gle/3VW2O50 Which jobs will survive AI → https://goo.gle/3xz0BlT Lunch and Learn → https://goo.gle/4cPjO2L Lonely Octopus → https://goo.gle/3TXuvrc  The Defining Decade & Identity Capital → https://goo.gle/3TXukfE The Unfair Advantage → https://goo.gle/3vUMNkY The Future of Jobs Report 2023 → https://goo.gle/3vVBSHJ AI Exposure and Complementarity → https://goo.gle/43QTGR7 Building Agents → https://goo.gle/43QTVeZ   Gemma: Introducing new state-of-the-art open models → https://goo.gle/3UYQksT Fine-Tuning Gemma Models in Hugging Face → https://goo.gle/3TiAU1m Gemma models in Kaggle → https://goo.gle/48ypQ4p Shining Brighter Together: Google's Gemma Optimized to Run on NVIDIA GPUs → https://goo.gle/3Tf1wjA Google AI Studios → https://goo.gle/3P3eCxM   Gemini Era links: Announcement → https://goo.gle/3uYRhGM Gemini API → https://goo.gle/3T1kmJS

People of AI
Tris Warkentin - Introducing Gemma, Google's family of open models

People of AI

Play Episode Listen Later Apr 4, 2024 58:02


Meet Tris Warkentin, Product Management lead for Google DeepMind's next-generation AI research, working to achieve Artificial General Intelligence (AGI). Learn about the Gemini ecosystem and Google's newest family of open models, Gemma! Discover what it can do and why this is a monumental step for the developer community. Unpack the power and exciting new capabilities this will unleash in the hands of developers and how this has been a long tradition in Google history. Dive into what it means to release a generative model like this into the public, addressing potential concerns and the Responsible AI measures in place to mitigate unethical use. Learn about AGI, Tris's start in the tech industry as a technical writing intern, and more on this People of AI episode hosted by Ashley and Gus! Resources: Gemma links: Gemma Developer Day 2024 → https://goo.gle/4as2nnu Gemma: Introducing new state-of-the-art open models → https://goo.gle/3UYQksT Fine-Tuning Gemma Models in Hugging Face → https://goo.gle/3TiAU1m Gemma models in Kaggle → https://goo.gle/48ypQ4p Shining Brighter Together: Google's Gemma Optimized to Run on NVIDIA GPUs → https://goo.gle/3Tf1wjA Google AI Studios → https://goo.gle/3P3eCxM  Gemini Era links:  Announcement → https://goo.gle/3uYRhGM Gemini API → https://goo.gle/3T1kmJS  Responsible AI: Conversation with Tulsee Doshi → https://goo.gle/4a75OzA Responsible Generative AI Toolkit → https://goo.gle/4czolWO  Levels of AGI: Operationalizing Progress on the Path to AGI → https://goo.gle/3TJMKR4  Carl McBride Notebook → https://goo.gle/3vEgfM0 

People of AI
Kathleen Kenealy - Creating, building, and releasing Gemma, Google's open model family

People of AI

Play Episode Listen Later Mar 28, 2024 54:49


Meet Kathleen Kenealy, a Software Engineer at Google DeepMind, working on the next generation of large language models (LLMs) and open models. In the episode we talk about Gemma, Google's newest family of lightweight, state-of-the-art open models built from the same research and technology used to create the Gemini models.  Join us as we unpack the history and the importance of open technology at Google and the advantages and risks of sharing cutting edge technology with the world. We learn how that open technology started Kathleen on her own journey into AI. Kathleen shares her experience growing her team from being the only team member at first working on Gemma, growing up loving STEM, her graduate school experience working with AI Chatbots, and more on this episode of People of AI hosted by Ashley Oldacre! Resources: Gemma links: Gemma: Introducing new state-of-the-art open models → https://goo.gle/3UYQksT Fine-Tuning Gemma Models in Hugging Face → https://goo.gle/3TiAU1m Gemma models in Kaggle → https://goo.gle/48ypQ4p Shining Brighter Together: Google's Gemma Optimized to Run on NVIDIA GPUs → https://goo.gle/3Tf1wjA Google AI Studios → https://goo.gle/3P3eCxM  Gemini Era links: Announcement → https://goo.gle/3uYRhGM Gemini API → https://goo.gle/3T1kmJS  TensorFlow → https://goo.gle/3BwLZSN  Responsible AI: Conversation with Tulsee Doshi → https://goo.gle/4a75OzA Responsible Generative AI Toolkit → https://goo.gle/4czolWO 

People of AI
Jeanine Banks - Leveraging the power of the developer community

People of AI

Play Episode Listen Later Mar 21, 2024 56:31


Meet Jeanine Banks, VP and GM of the Developer X and Developer Relations Business at Google. In this role, she empowers millions of developers to build AI enabled businesses and applications for billions of users worldwide. Hear about the latest Google AI ecosystem of tools from Gemini and Gemma to Projet IDX. Learn about her journey into the tech industry, solving real-world problems with AI, and how she is growing, connecting and supporting developer communities through multiple channels and events.  Resources: Gemma links: Gemma: Introducing new state-of-the-art open models → https://goo.gle/3UYQksT Fine-Tuning Gemma Models in Hugging Face → https://goo.gle/3TiAU1m Gemma models in Kaggle → https://goo.gle/48ypQ4p Shining Brighter Together: Google's Gemma Optimized to Run on NVIDIA GPUs → https://goo.gle/3Tf1wjA  Google AI Studios → https://goo.gle/3P3eCxM  Gemini Era links: Announcement → https://goo.gle/3uYRhGM Gemini API → https://goo.gle/3T1kmJS  Project IDX → https://goo.gle/3Pq6HLx Introducing Project IDX → https://goo.gle/49Aib6W   

Data Science at Home
Kaggle Kommando's Data Disco: Laughing our Way Through AI Trends (Ep. 252)

Data Science at Home

Play Episode Listen Later Mar 7, 2024 42:46


In this episode, join me and the Kaggle Grand Master, Konrad Banachewicz, for a hilarious journey into the zany world of data science trends. From algorithm acrobatics to AI, creativity, Hollywood movies, and music, we just can't get enough. It's the typical episode with a dose of nerdy comedy you didn't know you needed. Buckle up, it's a data disco, and we're breaking down the binary!   Sponsors Intrepid AI is an AI assisted all-in-one platform for robotics teams. Build robotics applications in minutes, not months. Learn what the new year holds for ransomware as a service, Active Directory, artificial intelligence and more when you download the 2024 Arctic Wolf Labs Predictions Report today at arcticwolf.com/datascience