Episode 36: Prompt Engineering, Security in Generative AI, and the Future of AI Research Part 1

Vanishing Gradients

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İçerik Hugo Bowne-Anderson tarafından sağlanmıştır. Bölümler, grafikler ve podcast açıklamaları dahil tüm podcast içeriği doğrudan Hugo Bowne-Anderson veya podcast platform ortağı tarafından yüklenir ve sağlanır. Birinin telif hakkıyla korunan çalışmanızı izniniz olmadan kullandığını düşünüyorsanız burada https://tr.player.fm/legal özetlenen süreci takip edebilirsiniz.

All About Change

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Jay Ruderman - How to Find Your Fight & Drive Social Change 32:17

9 gün önce32:17

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32:17

Jay is more than just the host of All About Change podcast. He is a lawyer and international activist, who has focused his life’s work on seeking social justice by advocating for the rights of people with disabilities worldwide. On the special episode of All About Change, Mijon Zulu, the managing producer of the "All About Change" podcast, is taking over hosting duties to interview Jay Ruderman about his new book, his activist journey, and why activism is even more important today. Episode Chapters (0:00) intro (02:38) How does one choose a cause to go after? (03:33) Jay’s path to activism (07:50) Practical steps a new activist can take (09:24) Confrontation vs trolling (17:36) Learning from activists operating in different sectors (19:20) Resilience in activism (22:24) Reflections on Find Your Fight and goodbye For video episodes, watch on www.youtube.com/@therudermanfamilyfoundation Stay in touch: X: @JayRuderman | @RudermanFdn LinkedIn: Jay Ruderman | Ruderman Family Foundation Instagram: All About Change Podcast | Ruderman Family Foundation To learn more about the podcast, visit https://allaboutchangepodcast.com/ Looking for more insights into the world of activism? Be sure to check out Jay’s brand new book, Find Your Fight , in which Jay teaches the next generation of activists and advocates how to step up and bring about lasting change. You can find Find Your Fight wherever you buy your books, and you can learn more about it at www.jayruderman.com .…

1 yıl önce 1:03:46

MP3•Bölüm sayfası

This is Part 1 of a special two-part episode, prompted—no pun intended—by these guys being part of a team, led by Sander, that wrote a 76-page survey analyzing prompting techniques, agents, and generative AI. The survey included contributors from OpenAI, Microsoft, the University of Maryland, Princeton, and more.

In this first part,

we’ll explore the critical role of prompt engineering,
& diving into adversarial techniques like prompt hacking and
the challenges of evaluating these techniques.
we’ll examine the impact of few-shot learning and
the groundbreaking taxonomy of prompting techniques from the Prompt Report.

Along the way,

we’ll uncover the rich history of natural language processing (NLP) and AI, showing how modern prompting techniques evolved from early rule-based systems and statistical methods.
we’ll also hear how Sander’s experimentation with GPT-3 for diplomatic tasks led him to develop Learn Prompting, and
how Dennis highlights the accessibility of AI through prompting, which allows non-technical users to interact with AI without needing to code.

Finally, we’ll explore the future of multimodal AI, where LLMs interact with images, code, and even music creation. Make sure to tune in to Part 2, where we dive deeper into security risks, prompt hacking, and more.

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#Tech #Hugo BowneAnderson Its #Data Science #Machine Learning

Episode 36: Prompt Engineering, Security in Generative AI, and the Future of AI Research Part 1

Vanishing Gradients

25 subscribers

published 1 yıl önce

Paylaş

MP3•Bölüm sayfası

In this first part,

we’ll explore the critical role of prompt engineering,
& diving into adversarial techniques like prompt hacking and
the challenges of evaluating these techniques.
we’ll examine the impact of few-shot learning and
the groundbreaking taxonomy of prompting techniques from the Prompt Report.

Along the way,

we’ll uncover the rich history of natural language processing (NLP) and AI, showing how modern prompting techniques evolved from early rule-based systems and statistical methods.
we’ll also hear how Sander’s experimentation with GPT-3 for diplomatic tasks led him to develop Learn Prompting, and
how Dennis highlights the accessibility of AI through prompting, which allows non-technical users to interact with AI without needing to code.

LINKS

47 bölüm

#Tech #Hugo BowneAnderson Its #Data Science #Machine Learning

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1
Episode 47: The Great Pacific Garbage Patch of Code Slop with Joe Reis 1:19:12

3 gün önce1:19:12

1:19:12

What if the cost of writing code dropped to zero — but the cost of understanding it skyrocketed? In this episode, Hugo sits down with Joe Reis to unpack how AI tooling is reshaping the software development lifecycle — from experimentation and prototyping to deployment, maintainability, and everything in between. Joe is the co-author of Fundamentals of Data Engineering and a longtime voice on the systems side of modern software. He’s also one of the sharpest critics of “vibe coding” — the emerging pattern of writing software by feel, with heavy reliance on LLMs and little regard for structure or quality. We dive into: • Why “vibe coding” is more than a meme — and what it says about how we build today • How AI tools expand the surface area of software creation — for better and worse • What happens to technical debt, testing, and security when generation outpaces understanding • The changing definition of “production” in a world of ephemeral, internal, or just-good-enough tools • How AI is flattening the learning curve — and threatening the talent pipeline • Joe’s view on what real craftsmanship means in an age of disposable code This conversation isn’t about doom, and it’s not about hype. It’s about mapping the real, messy terrain of what it means to build software today — and how to do it with care. LINKS Joe's Practical Data Modeling Newsletter on Substack Joe's Practical Data Modeling Server on Discord Vanishing Gradients YouTube Channel Upcoming Events on Luma 🎓 Want to go deeper? Check out my course: Building LLM Applications for Data Scientists and Software Engineers. Learn how to design, test, and deploy production-grade LLM systems — with observability, feedback loops, and structure built in. This isn’t about vibes or fragile agents. It’s about making LLMs reliable, testable, and actually useful. Includes over $2,500 in compute credits and guest lectures from experts at DeepMind, Moderna, and more. Cohort starts April 7 — Use this link for a 10% discount…

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Episode 46: Software Composition Is the New Vibe Coding 1:08:57

7 gün önce1:08:57

1:08:57

What if building software felt more like composing than coding? In this episode, Hugo and Greg explore how LLMs are reshaping the way we think about software development—from deterministic programming to a more flexible, prompt-driven, and collaborative style of building. It’s not just hype or grift—it’s a real shift in how we express intent, reason about systems, and collaborate across roles. Hugo speaks with Greg Ceccarelli—co-founder of SpecStory, former CPO at Pluralsight, and Director of Data Science at GitHub—about the rise of software composition and how it changes the way individuals and teams create with LLMs. We dive into: Why software composition is emerging as a serious alternative to traditional coding The real difference between vibe coding and production-minded prototyping How LLMs are expanding who gets to build software—and how What changes when you focus on intent, not just code What Greg is building with SpecStory to support collaborative, traceable AI-native workflows The challenges (and joys) of debugging and exploring with agentic tools like Cursor and Claude We’ve removed the visual demos from the audio—but you can catch our live-coded Chrome extension and JFK document explorer on YouTube. Links below. JFK Docs Vibe Coding Demo (YouTube) Chrome Extension Vibe Coding Demo (YouTube) Meditations on Tech (Greg’s Substack) Simon Willison on Vibe Coding Johnno Whitaker: On Vibe Coding Tim O’Reilly – The End of Programming Vanishing Gradients YouTube Channel Upcoming Events on Luma Greg Ceccarelli on LinkedIn Greg’s Hacker News Post on GOOD SpecStory: GOOD – Git Companion for AI Workflows 🎓 Want to go deeper? Check out my course: Building LLM Applications for Data Scientists and Software Engineers. Learn how to design, test, and deploy production-grade LLM systems — with observability, feedback loops, and structure built in. This isn’t about vibes or fragile agents. It’s about making LLMs reliable, testable, and actually useful. Includes over $2,500 in compute credits and guest lectures from experts at DeepMind, Moderna, and more. Cohort starts April 7 — Use this link for a 10% discount 🔍 Want to help shape the future of SpecStory? Greg and the team are looking for design partners for their new SpecStory Teams product—built for collaborative, AI-native software development. If you're working with LLMs in a team setting and want to influence the next wave of developer tools, you can apply here: 👉 specstory.com/teams…

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Episode 45: Your AI application is broken. Here’s what to do about it. 1:17:30

7 weeks önce1:17:30

1:17:30

Too many teams are building AI applications without truly understanding why their models fail. Instead of jumping straight to LLM evaluations, dashboards, or vibe checks, how do you actually fix a broken AI app? In this episode, Hugo speaks with Hamel Husain , longtime ML engineer, open-source contributor, and consultant, about why debugging generative AI systems starts with looking at your data . In this episode, we dive into: Why “look at your data” is the best debugging advice no one follows. How spreadsheet-based error analysis can uncover failure modes faster than complex dashboards. The role of synthetic data in bootstrapping evaluation. When to trust LLM judges —and when they’re misleading. Why most AI dashboards measuring truthfulness, helpfulness, and conciseness are often a waste of time. If you're building AI-powered applications, this episode will change how you approach debugging, iteration, and improving model performance in production. LINKS The podcast livestream on YouTube Hamel's blog Hamel on twitter Hugo on twitter Vanishing Gradients on twitter Vanishing Gradients on YouTube Vanishing Gradients on Twitter Vanishing Gradients on Lu.ma Building LLM Application for Data Scientists and SWEs, Hugo course on Maven (use VG25 code for 25% off) Hugo is also running a free lightning lesson next week on LLM Agents: When to Use Them (and When Not To )…

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Episode 44: The Future of AI Coding Assistants: Who’s Really in Control? 1:34:11

9 weeks önce1:34:11

1:34:11

AI coding assistants are reshaping how developers write, debug, and maintain code—but who’s really in control? In this episode, Hugo speaks with Tyler Dunn , CEO and co-founder of Continue , an open-source AI-powered code assistant that gives developers more customization and flexibility in their workflows. In this episode, we dive into: The trade-offs between proprietary vs. open-source AI coding assistants —why open-source might be the future. How structured workflows, modular AI, and customization help developers maintain control over their tools . The evolution of AI-powered coding, from autocomplete to intelligent code suggestions and beyond. Why the best developer experiences come from sensible defaults with room for deeper configuration. The future of LLM-based software engineering , where fine-tuning models on personal and team-level data could make AI coding assistants even more effective. With companies increasingly integrating AI into development workflows, this conversation explores the real impact of these tools—and the importance of keeping developers in the driver's seat. LINKS The podcast livestream on YouTube Continue's website Continue is hiring! amplified.dev: We believe in a future where developers are amplified, not automated Beyond Prompt and Pray, Building Reliable LLM-Powered Software in an Agentic World LLMOps Lessons Learned: Navigating the Wild West of Production LLMs 🚀 Building effective agents by Erik Schluntz and Barry Zhang, Anthropic Ty on LinkedIn Hugo on twitter Vanishing Gradients on twitter Vanishing Gradients on YouTube Vanishing Gradients on Twitter Vanishing Gradients on Lu.ma…

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Episode 43: Tales from 400+ LLM Deployments: Building Reliable AI Agents in Production 1:01:03

12 weeks önce1:01:03

1:01:03

Hugo speaks with Alex Strick van Linschoten, Machine Learning Engineer at ZenML and creator of a comprehensive LLMOps database documenting over 400 deployments. Alex's extensive research into real-world LLM implementations gives him unique insight into what actually works—and what doesn't—when deploying AI agents in production. In this episode, we dive into: The current state of AI agents in production, from successes to common failure modes Practical lessons learned from analyzing hundreds of real-world LLM deployments How companies like Anthropic, Klarna, and Dropbox are using patterns like ReAct, RAG, and microservices to build reliable systems The evolution of LLM capabilities, from expanding context windows to multimodal applications Why most companies still prefer structured workflows over fully autonomous agents We also explore real-world case studies of production hurdles, including cascading failures, API misfires, and hallucination challenges. Alex shares concrete strategies for integrating LLMs into your pipelines while maintaining reliability and control. Whether you're scaling agents or building LLM-powered systems, this episode offers practical insights for navigating the complex landscape of LLMOps in 2025. LINKS The podcast livestream on YouTube The LLMOps database All blog posts about the database Anthropic's Building effective agents essay Alex on LinkedIn Hugo on twitter Vanishing Gradients on twitter Vanishing Gradients on YouTube Vanishing Gradients on Twitter Vanishing Gradients on Lu.ma…

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Episode 42: Learning, Teaching, and Building in the Age of AI 1:20:03

14 weeks önce1:20:03

1:20:03

In this episode of Vanishing Gradients, the tables turn as Hugo sits down with Alex Andorra, host of Learning Bayesian Statistics. Hugo shares his journey from mathematics to AI, reflecting on how Bayesian inference shapes his approach to data science, teaching, and building AI-powered applications. They dive into the realities of deploying LLM applications, overcoming “proof-of-concept purgatory,” and why first principles and iteration are critical for success in AI. Whether you’re an educator, software engineer, or data scientist, this episode offers valuable insights into the intersection of AI, product development, and real-world deployment. LINKS The podcast on YouTube The original podcast episode Alex Andorra on LinkedIn Hugo on LinkedIn Hugo on twitter Vanishing Gradients on twitter Hugo's "Building LLM Applications for Data Scientists and Software Engineers" course…

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Episode 41: Beyond Prompt Engineering: Can AI Learn to Set Its Own Goals? 43:51

14 weeks önce43:51

43:51

Hugo Bowne-Anderson hosts a panel discussion from the MLOps World and Generative AI Summit in Austin, exploring the long-term growth of AI by distinguishing real problem-solving from trend-based solutions. If you're navigating the evolving landscape of generative AI, productionizing models, or questioning the hype, this episode dives into the tough questions shaping the field. The panel features: Ben Taylor (Jepson) – CEO and Founder at VEOX Inc., with experience in AI exploration, genetic programming, and deep learning. Joe Reis – Co-founder of Ternary Data and author of Fundamentals of Data Engineering . Juan Sequeda – Principal Scientist and Head of AI Lab at Data.World, known for his expertise in knowledge graphs and the semantic web. The discussion unpacks essential topics such as: The shift from prompt engineering to goal engineering —letting AI iterate toward well-defined objectives. Whether generative AI is having an electricity moment or more of a blockchain trajectory . The combinatorial power of AI to explore new solutions, drawing parallels to AlphaZero redefining strategy games. The POC-to-production gap and why AI projects stall. Failure modes, hallucinations, and governance risks —and how to mitigate them. The disconnect between executive optimism and employee workload. Hugo also mentions his upcoming workshop on escaping Proof-of-Concept Purgatory , which has evolved into a Maven course "Building LLM Applications for Data Scientists and Software Engineers" launching in January . Vanishing Gradient listeners can get 25% off the course (use the code VG25), with $1,000 in Modal compute credits included. A huge thanks to Dave Scharbach and the Toronto Machine Learning Society for organizing the conference and to the audience for their thoughtful questions. As we head into the new year, this conversation offers a reality check amidst the growing AI agent hype. LINKS Hugo on twitter Hugo on LinkedIn Vanishing Gradients on twitter "Building LLM Applications for Data Scientists and Software Engineers" course .…

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Episode 40: What Every LLM Developer Needs to Know About GPUs 1:43:34

15 weeks önce1:43:34

1:43:34

Hugo speaks with Charles Frye , Developer Advocate at Modal and someone who really knows GPUs inside and out. If you’re a data scientist, machine learning engineer, AI researcher, or just someone trying to make sense of hardware for LLMs and AI workflows , this episode is for you. Charles and Hugo dive into the practical side of GPUs —from running inference on large models, to fine-tuning and even training from scratch. They unpack the real pain points developers face, like figuring out: How much VRAM you actually need. Why memory—not compute—ends up being the bottleneck. How to make quick, back-of-the-envelope calculations to size up hardware for your tasks. And where things like fine-tuning, quantization, and retrieval-augmented generation (RAG) fit into the mix. One thing Hugo really appreciate is that Charles and the Modal team recently put together the GPU Glossary —a resource that breaks down GPU internals in a way that’s actually useful for developers. We reference it a few times throughout the episode, so check it out in the show notes below. 🔧 Charles also does a demo during the episode —some of it is visual, but we talk through the key points so you’ll still get value from the audio. If you’d like to see the demo in action, check out the livestream linked below. This is the "Building LLM Applications for Data Scientists and Software Engineers" course that Hugo is teaching with Stefan Krawczyk (ex-StitchFix) in January . Charles is giving a guest lecture at on hardware for LLMs, and Modal is giving all students $1K worth of compute credits (use the code VG25 for $200 off). LINKS The livestream on YouTube The GPU Glossary by the Modal team What We’ve Learned From A Year of Building with LLMs by Charles and friends Charles on twitter Hugo on twitter Vanishing Gradients on twitter…

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Episode 39: From Models to Products: Bridging Research and Practice in Generative AI at Google Labs 1:43:28

19 weeks önce1:43:28

1:43:28

Hugo speaks with Ravin Kumar,*Senior Research Data Scientist at Google Labs. Ravin’s career has taken him from building rockets at SpaceX to driving data science and technology at Sweetgreen, and now to advancing generative AI research and applications at Google Labs and DeepMind. His multidisciplinary experience gives him a rare perspective on building AI systems that combine technical rigor with practical utility. In this episode, we dive into: • Ravin’s fascinating career path, including the skills and mindsets needed to work effectively with AI and machine learning models at different stages of the pipeline. • How to build generative AI systems that are scalable, reliable, and aligned with user needs. • Real-world applications of generative AI, such as using open weight models such as Gemma to help a bakery streamline operations—an example of delivering tangible business value through AI. • The critical role of UX in AI adoption, and how Ravin approaches designing tools like Notebook LM with the user journey in mind. We also include a live demo where Ravin uses Notebook LM to analyze my website, extract insights, and even generate a podcast-style conversation about me. While some of the demo is visual, much can be appreciated through audio, and we’ve added a link to the video in the show notes for those who want to see it in action. We’ve also included the generated segment at the end of the episode for you to enjoy. LINKS The livestream on YouTube Google Labs Ravin's GenAI Handbook Breadboard: A library for prototyping generative AI applications As mentioned in the episode, Hugo is teaching a four-week course, Building LLM Applications for Data Scientists and SWEs , co-led with Stefan Krawczyk (Dagworks, ex-StitchFix). The course focuses on building scalable, production-grade generative AI systems, with hands-on sessions, $1,000+ in cloud credits, live Q&As, and guest lectures from industry experts. Listeners of Vanishing Gradients can get 25% off the course using this special link or by applying the code VG25 at checkout.…

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Episode 38: The Art of Freelance AI Consulting and Products: Data, Dollars, and Deliverables 1:23:47

22 weeks önce1:23:47

1:23:47

Hugo speaks with Jason Liu, an independent AI consultant with experience at Meta and Stitch Fix. At Stitch Fix, Jason developed impactful AI systems, like a $50 million product similarity search and the widely adopted Flight recommendation framework. Now, he helps startups and enterprises design and deploy production-level AI applications, with a focus on retrieval-augmented generation (RAG) and scalable solutions. This episode is a bit of an experiment. Instead of our usual technical deep dives, we’re focusing on the world of AI consulting and freelancing. We explore Jason’s consulting playbook, covering how he structures contracts to maximize value, strategies for moving from hourly billing to securing larger deals, and the mindset shift needed to align incentives with clients. We’ll also discuss the challenges of moving from deterministic software to probabilistic AI systems and even do a live role-playing session where Jason coaches me on client engagement and pricing pitfalls. LINKS The livestream on YouTube Jason's Upcoming course: AI Consultant Accelerator: From Expert to High-Demand Business Hugo's upcoming course: Building LLM Applications for Data Scientists and Software Engineers Jason's website Jason's indie consulting newsletter Your AI Product Needs Evals by Hamel Husain What We’ve Learned From A Year of Building with LLMs Dear Future AI Consultant by Jason Alex Hormozi's books The Burnout Society by Byung-Chul Han Jason on Twitter Vanishing Gradients on Twitter Hugo on Twitter Vanishing Gradients' lu.ma calendar Vanishing Gradients on YouTube…

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Episode 37: Prompt Engineering, Security in Generative AI, and the Future of AI Research Part 2 50:36

26 weeks önce50:36

50:36

Hugo speaks with three leading figures from the world of AI research: Sander Schulhoff, a recent University of Maryland graduate and lead contributor to the Learn Prompting initiative; Philip Resnik, professor at the University of Maryland, known for his pioneering work in computational linguistics; and Dennis Peskoff, a researcher from Princeton specializing in prompt engineering and its applications in the social sciences. This is Part 2 of a special two-part episode, prompted—no pun intended—by these guys being part of a team, led by Sander, that wrote a 76-page survey analyzing prompting techniques, agents, and generative AI. The survey included contributors from OpenAI, Microsoft, the University of Maryland, Princeton, and more. In this episode, we cover: The Prompt Report: A comprehensive survey on prompting techniques, agents, and generative AI, including advanced evaluation methods for assessing these techniques. Security Risks and Prompt Hacking: A detailed exploration of the security concerns surrounding prompt engineering, including Sander’s thoughts on its potential applications in cybersecurity and military contexts. AI’s Impact Across Fields: A discussion on how generative AI is reshaping various domains, including the social sciences and security. Multimodal AI: Updates on how large language models (LLMs) are expanding to interact with images, code, and music. Case Study - Detecting Suicide Risk: A careful examination of how prompting techniques are being used in important areas like detecting suicide risk, showcasing the critical potential of AI in addressing sensitive, real-world challenges. The episode concludes with a reflection on the evolving landscape of LLMs and multimodal AI, and what might be on the horizon. If you haven’t yet, make sure to check out Part 1 , where we discuss the history of NLP, prompt engineering techniques, and Sander’s development of the Learn Prompting initiative. LINKS The livestream on YouTube The Prompt Report: A Systematic Survey of Prompting Techniques Learn Prompting: Your Guide to Communicating with AI Vanishing Gradients on Twitter Hugo on Twitter Vanishing Gradients' lu.ma calendar Vanishing Gradients on YouTube…

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Episode 36: Prompt Engineering, Security in Generative AI, and the Future of AI Research Part 1 1:03:46

27 weeks önce1:03:46

1:03:46

Hugo speaks with three leading figures from the world of AI research: Sander Schulhoff, a recent University of Maryland graduate and lead contributor to the Learn Prompting initiative; Philip Resnik, professor at the University of Maryland, known for his pioneering work in computational linguistics; and Dennis Peskoff, a researcher from Princeton specializing in prompt engineering and its applications in the social sciences. This is Part 1 of a special two-part episode, prompted—no pun intended—by these guys being part of a team, led by Sander, that wrote a 76-page survey analyzing prompting techniques, agents, and generative AI. The survey included contributors from OpenAI, Microsoft, the University of Maryland, Princeton, and more. In this first part, we’ll explore the critical role of prompt engineering, & diving into adversarial techniques like prompt hacking and the challenges of evaluating these techniques. we’ll examine the impact of few-shot learning and the groundbreaking taxonomy of prompting techniques from the Prompt Report. Along the way, we’ll uncover the rich history of natural language processing (NLP) and AI, showing how modern prompting techniques evolved from early rule-based systems and statistical methods. we’ll also hear how Sander’s experimentation with GPT-3 for diplomatic tasks led him to develop Learn Prompting, and how Dennis highlights the accessibility of AI through prompting, which allows non-technical users to interact with AI without needing to code. Finally, we’ll explore the future of multimodal AI, where LLMs interact with images, code, and even music creation. Make sure to tune in to Part 2, where we dive deeper into security risks, prompt hacking, and more. LINKS The livestream on YouTube The Prompt Report: A Systematic Survey of Prompting Techniques Learn Prompting: Your Guide to Communicating with AI Vanishing Gradients on Twitter Hugo on Twitter Vanishing Gradients' lu.ma calendar Vanishing Gradients on YouTube…

1
Episode 35: Open Science at NASA -- Measuring Impact and the Future of AI 58:13

29 weeks önce58:13

58:13

Hugo speaks with Dr. Chelle Gentemann, Open Science Program Scientist for NASA’s Office of the Chief Science Data Officer, about NASA’s ambitious efforts to integrate AI across the research lifecycle. In this episode, we’ll dive deeper into how AI is transforming NASA’s approach to science, making data more accessible and advancing open science practices. We explore Measuring the Impact of Open Science: How NASA is developing new metrics to evaluate the effectiveness of open science, moving beyond traditional publication-based assessments. The Process of Scientific Discovery: Insights into the collaborative nature of research and how breakthroughs are achieved at NASA. ** AI Applications in NASA’s Science:** From rats in space to exploring the origins of the universe, we cover how AI is being applied across NASA’s divisions to improve data accessibility and analysis. Addressing Challenges in Open Science: The complexities of implementing open science within government agencies and research environments. Reforming Incentive Systems: How NASA is reconsidering traditional metrics like publications and citations, and starting to recognize contributions such as software development and data sharing. The Future of Open Science: How open science is shaping the future of research, fostering interdisciplinary collaboration, and increasing accessibility. This conversation offers valuable insights for researchers, data scientists, and those interested in the practical applications of AI and open science. Join us as we discuss how NASA is working to make science more collaborative, reproducible, and impactful. LINKS The livestream on YouTube NASA's Open Science 101 course <-- do it to learn and also to get NASA Swag! Science Cast NASA and IBM Openly Release Geospatial AI Foundation Model for NASA Earth Observation Data Jake VanderPlas' daily conundrum tweet from 2013 Replit, "an AI-powered software development & deployment platform for building, sharing, and shipping software fast."…

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Episode 34: The AI Revolution Will Not Be Monopolized 1:42:51

33 weeks önce1:42:51

1:42:51

Hugo speaks with Ines Montani and Matthew Honnibal, the creators of spaCy and founders of Explosion AI. Collectively, they've had a huge impact on the fields of industrial natural language processing (NLP), ML, and AI through their widely-used open-source library spaCy and their innovative annotation tool Prodigy. These tools have become essential for many data scientists and NLP practitioners in industry and academia alike. In this wide-ranging discussion, we dive into: • The evolution of applied NLP and its role in industry • The balance between large language models and smaller, specialized models • Human-in-the-loop distillation for creating faster, more data-private AI systems • The challenges and opportunities in NLP, including modularity, transparency, and privacy • The future of AI and software development • The potential impact of AI regulation on innovation and competition We also touch on their recent transition back to a smaller, more independent-minded company structure and the lessons learned from their journey in the AI startup world. Ines and Matt offer invaluable insights for data scientists, machine learning practitioners, and anyone interested in the practical applications of AI. They share their thoughts on how to approach NLP projects, the importance of data quality, and the role of open-source in advancing the field. Whether you're a seasoned NLP practitioner or just getting started with AI, this episode offers a wealth of knowledge from two of the field's most respected figures. Join us for a discussion that explores the current landscape of AI development, with insights that bridge the gap between cutting-edge research and real-world applications. LINKS The livestream on YouTube How S&P Global is making markets more transparent with NLP, spaCy and Prodigy A practical guide to human-in-the-loop distillation Laws of Tech: Commoditize Your Complement spaCy: Industrial-Strength Natural Language Processing LLMs with spaCy Explosion, building developer tools for AI, Machine Learning and Natural Language Processing Back to our roots: Company update and future plans, by Matt and Ines Matt's detailed blog post: back to our roots Ines on twitter Matt on twitter Vanishing Gradients on Twitter Hugo on Twitter Check out and subcribe to our lu.ma calendar for upcoming livestreams!…

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Episode 33: What We Learned Teaching LLMs to 1,000s of Data Scientists 1:25:10

34 weeks önce1:25:10

1:25:10

Hugo speaks with Dan Becker and Hamel Husain, two veterans in the world of data science, machine learning, and AI education. Collectively, they’ve worked at Google, DataRobot, Airbnb, Github (where Hamel built out the precursor to copilot and more) and they both currently work as independent LLM and Generative AI consultants. Dan and Hamel recently taught a course on fine-tuning large language models that evolved into a full-fledged conference, attracting over 2,000 participants. This experience gave them unique insights into the current state and future of AI education and application. In this episode, we dive into: The evolution of their course from fine-tuning to a comprehensive AI conference The unexpected challenges and insights gained from teaching LLMs to data scientists The current state of AI tooling and accessibility compared to a decade ago The role of playful experimentation in driving innovation in the field Thoughts on the economic impact and ROI of generative AI in various industries The importance of proper evaluation in machine learning projects Future predictions for AI education and application in the next five years We also touch on the challenges of using AI tools effectively, the potential for AI in physical world applications, and the need for a more nuanced understanding of AI capabilities in the workplace. During our conversation, Dan mentions an exciting project he's been working on, which we couldn't showcase live due to technical difficulties. However, I've included a link to a video demonstration in the show notes that you won't want to miss. In this demo, Dan showcases his innovative AI-powered 3D modeling tool that allows users to create 3D printable objects simply by describing them in natural language. LINKS The livestream on YouTube Educational resources from Dan and Hamel's LLM course Upwork Study Finds Employee Workloads Rising Despite Increased C-Suite Investment in Artificial Intelligence Episode 29: Lessons from a Year of Building with LLMs (Part 1) Episode 30: Lessons from a Year of Building with LLMs (Part 2) Dan's demo: Creating Physical Products with Generative AI Build Great AI, Dan's boutique consulting firm helping clients be successful with large language models Parlance Labs, Hamel's Practical consulting that improves your AI Hamel on Twitter Dan on Twitter Vanishing Gradients on Twitter Hugo on Twitter…

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The Let Them Theory: A Life-Changing Tool That Millions of People Can't Stop Talking About

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