Vanishing Gradients
a data podcast with hugo bowne-anderson
About the show
A podcast about all things data, brought to you by data scientist Hugo Bowne-Anderson.
It's time for more critical conversations about the challenges in our industry in order to build better compasses for the solution space! To this end, this podcast will consist of long-format conversations between Hugo and other people who work broadly in the data science, machine learning, and AI spaces. We'll dive deep into all the moving parts of the data world, so if you're new to the space, you'll have an opportunity to learn from the experts. And if you've been around for a while, you'll find out what's happening in many other parts of the data world.
Episodes
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Episode 37: Prompt Engineering, Security in Generative AI, and the Future of AI Research Part 2
October 8th, 2024 | Season 1 | 50 mins 36 secs
data science, genai, llms, machine learning, nlp, prompt engineering
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.
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Episode 36: Prompt Engineering, Security in Generative AI, and the Future of AI Research Part 1
September 30th, 2024 | Season 1 | 1 hr 3 mins
data science, genai, llms, machine learning, nlp, prompt engineering
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.
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Episode 35: Open Science at NASA -- Measuring Impact and the Future of AI
September 19th, 2024 | Season 1 | 58 mins 13 secs
data science, genai, llms, machine learning
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.
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Episode 34: The AI Revolution Will Not Be Monopolized
August 22nd, 2024 | Season 1 | 1 hr 42 mins
data science, genai, llms, machine learning, nlp
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.
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Episode 33: What We Learned Teaching LLMs to 1,000s of Data Scientists
August 12th, 2024 | Season 1 | 1 hr 25 mins
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 pre-cursor 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.
In this episode, we dive deep into their experience and the unique insights it gave them into the current state and future of AI education and application.
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Episode 32: Building Reliable and Robust ML/AI Pipelines
July 27th, 2024 | Season 1 | 1 hr 15 mins
data science, genai, llms, machine learning
Hugo speaks with Shreya Shankar, a researcher at UC Berkeley focusing on data management systems with a human-centered approach. Shreya's work is at the cutting edge of human-computer interaction (HCI) and AI, particularly in the realm of large language models (LLMs). Her impressive background includes being the first ML engineer at Viaduct, doing research engineering at Google Brain, and software engineering at Facebook.
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Episode 31: Rethinking Data Science, Machine Learning, and AI
July 9th, 2024 | Season 1 | 1 hr 36 mins
data science, genai, llms, machine learning
Hugo speaks with Vincent Warmerdam, a senior data professional and machine learning engineer at :probabl, the exclusive brand operator of scikit-learn. Vincent is known for challenging common assumptions and exploring innovative approaches in data science and machine learning.
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Episode 30: Lessons from a Year of Building with LLMs (Part 2)
June 26th, 2024 | Season 1 | 1 hr 15 mins
Hugo speaks about Lessons Learned from a Year of Building with LLMs with Eugene Yan from Amazon, Bryan Bischof from Hex, Charles Frye from Modal, Hamel Husain from Parlance Labs, and Shreya Shankar from UC Berkeley (Part 2).
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Episode 29: Lessons from a Year of Building with LLMs (Part 1)
June 26th, 2024 | Season 1 | 1 hr 30 mins
Hugo speaks about Lessons Learned from a Year of Building with LLMs with Eugene Yan from Amazon, Bryan Bischof from Hex, Charles Frye from Modal, Hamel Husain from Parlance Labs, and Shreya Shankar from UC Berkeley (Part 1).
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Episode 28: Beyond Supervised Learning: The Rise of In-Context Learning with LLMs
June 10th, 2024 | Season 1 | 1 hr 5 mins
data science, genai, llms, machine learning
Hugo speaks with Alan Nichol, co-founder and CTO of Rasa, where they build software to enable developers to create enterprise-grade conversational AI and chatbot systems across industries like telcos, healthcare, fintech, and government.
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Episode 27: How to Build Terrible AI Systems
May 31st, 2024 | Season 1 | 1 hr 32 mins
data science, genai, llms, machine learning
Hugo speaks with Jason Liu, an independent consultant who uses his expertise in recommendation systems to help fast-growing startups build out their RAG applications. He was previously at Meta and Stitch Fix is also the creator of Instructor, Flight, and an ML and data science educator.
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Episode 26: Developing and Training LLMs From Scratch
May 15th, 2024 | Season 1 | 1 hr 51 mins
data science, genai, machine learning
Hugo speaks with Sebastian Raschka, a machine learning & AI researcher, programmer, and author.They’ll tell you everything you need to know about LLMs, but were too afraid to ask: from covering the entire LLM lifecycle, what type of skills you need to work with them, what type of resources and hardware, prompt engineering vs fine-tuning vs RAG, how to build an LLM from scratch, and much more.
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Episode 25: Fully Reproducible ML & AI Workflows
March 18th, 2024 | Season 1 | 1 hr 20 mins
data science, genai, machine learning
Hugo speaks with Omoju Miller, a machine learning guru and founder and CEO of Fimio, where she is building 21st century dev tooling.
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Episode 24: LLM and GenAI Accessibility
February 27th, 2024 | Season 1 | 1 hr 30 mins
data science, genai, machine learning
Hugo speaks with Johno Whitaker, a Data Scientist/AI Researcher doing R&D with answer.ai, about where we’ve come from regarding tooling and accessibility for foundation models, ML, and AI, where we are, and where we’re going.
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Episode 23: Statistical and Algorithmic Thinking in the AI Age
December 21st, 2023 | Season 1 | 1 hr 20 mins
Hugo speaks with Allen Downey, curriculum designer at Brilliant, Professor Emeritus at Olin College, and author, about the key statistical and data skills we all need to navigate an increasingly data-driven and algorithmic world. The goal will be to dive deep into the statistical paradoxes and fallacies that get in the way of using data to make informed decisions.
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Episode 22: LLMs, OpenAI, and the Existential Crisis for Machine Learning Engineering
November 28th, 2023 | Season 1 | 1 hr 20 mins
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.