Vanishing Gradients

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 (https://youtube.com/live/9CFs06UDbGI?feature=share) Jason's Upcoming course: AI Consultant Accelerator: From Expert to High-Demand Business (https://maven.com/indie-consulting/ai-consultant-accelerator?utm_campaign=9532cc&utm_medium=partner&utm_source=instructor) Hugo's upcoming course: Building LLM Applications for Data Scientists and Software Engineers (https://maven.com/s/course/d56067f338) Jason's website (https://jxnl.co/) Jason's indie consulting newsletter (https://indieconsulting.podia.com/) Your AI Product Needs Evals by Hamel Husain (https://hamel.dev/blog/posts/evals/) What We’ve Learned From A Year of Building with LLMs (https://applied-llms.org/) Dear Future AI Consultant by Jason (https://jxnl.co/writing/#dear-future-ai-consultant) Alex Hormozi's books (https://www.acquisition.com/books) The Burnout Society by Byung-Chul Han (https://www.sup.org/books/theory-and-philosophy/burnout-society) Jason on Twitter (https://x.com/jxnlco) Vanishing Gradients on Twitter (https://twitter.com/vanishingdata) Hugo on Twitter (https://twitter.com/hugobowne) Vanishing Gradients' lu.ma calendar (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk) Vanishing Gradients on YouTube (https://www.youtube.com/@vanishinggradients)

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 (https://youtube.com/live/FreXovgG-9A?feature=share) The Prompt Report: A Systematic Survey of Prompting Techniques (https://arxiv.org/abs/2406.06608) Learn Prompting: Your Guide to Communicating with AI (https://learnprompting.org/) Vanishing Gradients on Twitter (https://twitter.com/vanishingdata) Hugo on Twitter (https://twitter.com/hugobowne) Vanishing Gradients' lu.ma calendar (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk) Vanishing Gradients on YouTube (https://www.youtube.com/@vanishinggradients)

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 (https://youtube.com/live/FreXovgG-9A?feature=share) The Prompt Report: A Systematic Survey of Prompting Techniques (https://arxiv.org/abs/2406.06608) Learn Prompting: Your Guide to Communicating with AI (https://learnprompting.org/) Vanishing Gradients on Twitter (https://twitter.com/vanishingdata) Hugo on Twitter (https://twitter.com/hugobowne) Vanishing Gradients' lu.ma calendar (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk) Vanishing Gradients on YouTube (https://www.youtube.com/@vanishinggradients)

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 (https://youtube.com/live/VJDg3ZbkNOE?feature=share) NASA's Open Science 101 course <-- do it to learn and also to get NASA Swag! (https://openscience101.org/) Science Cast (https://sciencecast.org/) NASA and IBM Openly Release Geospatial AI Foundation Model for NASA Earth Observation Data (https://www.earthdata.nasa.gov/news/impact-ibm-hls-foundation-model) Jake VanderPlas' daily conundrum tweet from 2013 (https://x.com/jakevdp/status/408678764705378304) Replit, "an AI-powered software development & deployment platform for building, sharing, and shipping software fast." (https://replit.com/)