Is consumer sentiment really near an all time low? And what does that mean? We get to use (at the very end) our definitional follow-up, which reveals an asto...

🧠 Get my official NotebookLM notebook – ask questions and get AI coaching on Building a Second Brain: https://bit.ly/42MCM6K🚀 Join the next cohort of Secon...

Stephen Colbert looks at the ways artificial intelligence companies are seeking to boost revenue as investors begin to worry that the A.I. stock bubble could...

[Recorded March 25, 2025] Chatbots based on large language models (LLMs), like ChatGPT, answer sophisticated questions, pass professional exams, analyze texts, generate everything from poems to computer programs, and more. But is there genuine understanding behind what LLMs can do? Do they really understand our world? Or, are they a triumph of mathematics and masses of data and calculations simulating true understanding? Join CHM, in partnership with IEEE Spectrum, for a fundamental debate on the nature of today’s AI: Do LLMs demonstrate genuine understanding, the “sparks” of true intelligence, or are they “stochastic parrots,” lacking understanding and meaning? FEATURED PARTICIPANTS Speaker Emily M. Bender Professor of Linguistics, University of Washington Emily M. Bender is a professor of linguistics and director of the Computational Linguistics Laboratory at the University of Washington, where she also serves as faculty director of the CLMS program, and adjunct professor at the School of Computer Science and Engineering and the Information School. Known for her critical perspectives on AI language models, notably coauthoring the paper "On the Dangers of Stochastic Parrots," Bender is also the author of the forthcoming book, The AI Con: How to Fight Big Tech's Hype and Create the Future We Want. Speaker Sébastien Bubeck Member of Technical Staff, OpenAI Sébastien Bubeck is a member of the technical staff at OpenAI. Previously, he served as VP, AI and distinguished scientist at Microsoft, where he spent a decade at Microsoft Research. Prior to that, he was an assistant professor at Princeton University. Bubeck's 2023 paper, "Sparks of Artificial General Intelligence: Early experiments with GPT-4," drove widespread discussion and debate about the evolution of AI both in the scientific community and mainstream media like the New York Times and Wired. Bubeck has been recognized with best paper awards at a number of conferences, and he is the author of the book Convex Optimization: Algorithms and Complexity. Moderator Eliza Strickland Senior Editor, IEEE Spectrum Eliza Strickland is a senior editor at IEEE Spectrum, where she covers artificial intelligence, biomedical technology, and other advanced technologies. In addition to her writing and editing work, she also hosts podcasts, creates radio segments, and moderates talks at events such as SXSW. Prior to joining IEEE Spectrum in 2011, she oversaw a daily science blog for Discover magazine and wrote for outlets including Wired, The New York Times, Sierra, and Foreign Policy. Strickland received her master’s degree in journalism from Columbia University. Catalog Number: 300000014 Acquisition Number: 2025.0036

Let's explore ChatGPT & how to get started with it. Get the latest AI news: https://app.beehiiv.com/Learn how to use ChatGPT with Francesco and the basics yo...

This is a 1 hour general-audience introduction to Large Language Models: the core technical component behind systems like ChatGPT, Claude, and Bard. What the...

🔍 In this video, we unravel the layers of AI, Machine Learning, Deep Learning, and their applications in tools like #ChatGPT and Google #BardWe first go through how AI is a broad field of study that encompasses #MachineLearning as a sub-field.We then break down Machine Learning into supervised and unsupervised models, using real-world examples to illustrate their functions and differences.We move deeper into Deep Learning: Learn about artificial neural networks and the power of semi-supervised learning in applications like fraud detection in banking.Then we delve into Generative AI, differentiating it from discriminative models and demonstrating its capabilities in creating new, innovative outputs.Finally we walk through Large Language Models (LLMs) and uncover the significance of LLMs in AI, their pre-training processes, and their customization for specific industry applications*THE NO BS PRODUCTIVITY NEWSLETTER* 📧https://www.jeffsu.org/productivity-ping/*TIMESTAMPS*00:00 Google’s AI Course in 10 Minutes00:38 What is Artificial Intelligence?01:27 What is Machine Learning?03:28 What is Deep Learning?05:15 What is Generative AI?07:05 What are Large Language Models?*RESOURCES I MENTION IN THE VIDEO*Google’s full course: https://www.cloudskillsboost.google/course_templates/536*MY FAVORITE GEAR*🎬 My YouTube Gear - https://www.jeffsu.org/yt-gear/🎒 Everyday Carry - https://www.jeffsu.org/my-edc/*MY TOP 3 FAVORITE SOFTWARE*❎ CleanShot X - https://geni.us/cleanshotx✍️ Skillshare - https://geni.us/skillshare-jeff📖 Readwise - https://readwise.io/jeffsu/*BE MY FRIEND:*📧 Subscribe to my Productivity newsletter - https://www.jeffsu.org/productivity-ping/📸 Instagram - https://instagram.com/j.sushie🤝 LinkedIn - https://www.linkedin.com/in/jsu05/*👨🏻‍💻 WHO AM I:*I'm Jeff, a tech professional trying to figure life out. What I do end up figuring out, I share!_PS: Some of the links in this description are affiliate links I get a kickback from and my opinions are my own and may not reflect that of my employer_ 😇