Vanishing Gradients

Jason Liu, an independent consultant in recommendation systems, discusses building AI apps, playbook for ML, and avoiding pitfalls. They focus on building terrible AI systems to learn how to prevent failures. The podcast explores consulting in various industries, future tooling, and creating robust AI systems.

Sebastian Raschka discusses developing and training large language models (LLMs) from scratch, covering topics like prompt engineering, fine-tuning, and RAG systems. They explore the skills, resources, and hardware needed, the lifecycle of LLMs, live coding to create a spam classifier, and the importance of hands-on experience. They also touch on using PyTorch Lightning and fabric for managing large models, and reveal insights on techniques in natural language processing models and evaluating LLMs for classification problems.

Omoju Miller, a machine learning expert and CEO of Fimio, shares her vision for transparent and reproducible ML workflows. She discusses the necessity of open tools and data in combating the monopolization of tech by closed-source APIs. Topics include the evolution of developer tools, the importance of data provenance, and the potential of a collaborative open compute ecosystem. Omoju also emphasizes user accessibility in machine learning and envisions a future where everyone can build production-ready applications with ease.

Hugo and Johno discuss the evolution of tooling and accessibility in AI over the past decade, highlighting advancements in using big models from Hugging Face and hi-res satellite data. They delve into the Generative AI mindset, democratizing deep learning with fast.ai, and the importance of UX in generative AI applications. The discussion also covers the skill set needed to be an LLM and AI guru, as well as efforts at answer.ai to democratize LLMs and foundation models.

Allen Downey discusses statistical paradoxes and fallacies in using data, including the base rate fallacy and algorithmic fairness. They dive into examples like COVID vaccination data and explore the challenges of interpreting statistical information correctly. The conversation also covers topics such as epidemiological paradoxes, Gaussian distributions, and the importance of understanding biases in data interpretation for media consumption.

Jeremy Howard (Fast.ai), Shreya Shankar (UC Berkeley), and Hamel Husain (Parlance Labs) join Hugo Bowne-Anderson to talk about how LLMs and OpenAI are changing the worlds of data science, machine learning, and machine learning engineering. Jeremy Howard is co-founder of fast.ai, an ex-Chief Scientist at Kaggle, and creator of the ULMFiT approach on which all modern language models are based. Shreya Shankar is at UC Berkeley, ex Google brain, Facebook, and Viaduct. Hamel Husain has his own generative AI and LLM consultancy Parlance Labs and was previously at Outerbounds, Github, and Airbnb. They talk about How LLMs shift the nature of the work we do in DS and ML, How they change the tools we use, The ways in which they could displace the role of traditional ML (e.g. will we stop using xgboost any time soon?), How to navigate all the new tools and techniques, The trade-offs between open and closed models, Reactions to the recent Open Developer Day and the increasing existential crisis for ML. LINKS The panel on YouTube Hugo and Jeremy's upcoming livestream on what the hell happened recently at OpenAI, among many other things Vanishing Gradients on YouTube Vanishing Gradients on twitter

Guest Hamel Husain, a machine learning engineer, discusses the business value of large language models (LLMs) and generative AI. They cover common misconceptions, necessary skills, and techniques for working with LLMs. The podcast explores the challenges of working with ML software and chat GPT, the importance of data cleaning and analysis, and deploying LLMs in production with guardrails. They also discuss an AI-powered real estate CRM and optimizing marketing strategies through data analysis.

Chris Wiggins, Chief data scientist for the New York Times, and Matthew Jones, professor of history at Princeton University, discuss their book on the history of data and its impact on society. They explore topics such as the use of data for decision making, the development of statistical techniques, the influence of Francis Galton on eugenics, and the rise of data, compute, and algorithms in various fields.

Hugo chats with Katharine Jarmul, a Principal Data Scientist at Thoughtworks Germany, specializing in privacy and ethics in data workflows. They dive into the vital distinctions between data privacy and security, demystifying common misconceptions. Katharine highlights the impact of GDPR and CCPA, and explores advanced concepts like federated learning and differential privacy. They also tackle real-world issues like privacy attacks and the ethical responsibilities of data scientists, making a compelling case for prioritizing privacy in data practices.

Eric Ma, a leader in the research team at Moderna Therapeutics, discusses the tools and techniques used for drug discovery, the importance of machine learning and Bayesian inference, and the cultural questions surrounding hiring and management in research data science in biotech. They also explore the tech stack used in their work, the skills and hiring considerations in biotech, the importance of data testing and standardizing Excel spreadsheets, and the current state and challenges of Bayesian inference.