Vanishing Gradients Hugo Bowne-Anderson

Hugo speaks with Sebastian Raschka, a machine learning & AI researcher, programmer, and author. As Staff Research Engineer at Lightning AI, he focuses on the intersection of AI research, software development, and large language models (LLMs).


How do you build LLMs? How can you use them, both in prototype and production settings? What are the building blocks you need to know about?


​In this episode, we’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.


The idea here is not that you’ll need to use an LLM you’ve built from scratch, but that we’ll learn a lot about LLMs and how to use them in the process.


Near the end we also did some live coding to fine-tune GPT-2 in order to create a spam classifier!


LINKS



The livestream on YouTube
Sebastian's website
Machine Learning Q and AI: 30 Essential Questions and Answers on Machine Learning and AI by Sebastian
Build a Large Language Model (From Scratch) by Sebastian
PyTorch Lightning
Lightning Fabric
LitGPT
Sebastian's notebook for finetuning GPT-2 for spam classification!
The end of fine-tuning: Jeremy Howard on the Latent Space Podcast
Our next livestream: How to Build Terrible AI Systems with Jason Liu
Vanishing Gradients on Twitter
Hugo on Twitter

Hugo speaks with Omoju Miller, a machine learning guru and founder and CEO of Fimio, where she is building 21st century dev tooling. In the past, she was Technical Advisor to the CEO at GitHub, spent time co-leading non-profit investment in Computer Science Education for Google, and served as a volunteer advisor to the Obama administration’s White House Presidential Innovation Fellows.


We need open tools, open data, provenance, and the ability to build fully reproducible, transparent machine learning workflows. With the advent of closed-source, vendor-based APIs and compute becoming a form of gate-keeping, developer tools are at the risk of becoming commoditized and developers becoming consumers.


We’ll talk about how ideas for escaping these burgeoning walled gardens. We’ll dive into



What fully reproducible ML workflows would look like, including git for the workflow build process,
The need for loosely coupled and composable tools that embrace a UNIX-like philosophy,
What a much more scientific toolchain would look like,
What a future open sources commons for Generative AI could look like,
What an open compute ecosystem could look like,
How to create LLMs and tooling so everyone can use them to build production-ready apps,


And much more!


LINKS



The livestream on YouTube
Omoju on Twitter
Hugo on Twitter
Vanishing Gradients on Twitter
Lu.ma Calendar that includes details of Hugo's European Tour for Outerbounds
Blog post that includes details of Hugo's European Tour for Outerbounds

Hugo speaks with Johno Whitaker, a Data Scientist/AI Researcher doing R&D with answer.ai. His current focus is on generative AI, flitting between different modalities. He also likes teaching and making courses, having worked with both Hugging Face and fast.ai in these capacities.


Johno recently reminded Hugo how hard everything was 10 years ago: “Want to install TensorFlow? Good luck. Need data? Perhaps try ImageNet. But now you can use big models from Hugging Face with hi-res satellite data and do all of this in a Colab notebook. Or think ecology and vision models… or medicine and multimodal models!”


We talk about where we’ve come from regarding tooling and accessibility for foundation models, ML, and AI, where we are, and where we’re going. We’ll delve into



What the Generative AI mindset is, in terms of using atomic building blocks, and how it evolved from both the data science and ML mindsets;
How fast.ai democratized access to deep learning, what successes they had, and what was learned;
The moving parts now required to make GenAI and ML as accessible as possible;
The importance of focusing on UX and the application in the world of generative AI and foundation models;
The skillset and toolkit needed to be an LLM and AI guru;
What they’re up to at answer.ai to democratize LLMs and foundation models.


LINKS



The livestream on YouTube
Zindi, the largest professional network for data scientists in Africa
A new old kind of R&D lab: Announcing Answer.AI
Why and how I’m shifting focus to LLMs by Johno Whitaker
Applying AI to Immune Cell Networks by Rachel Thomas
Replicate -- a cool place to explore GenAI models, among other things
Hands-On Generative AI with Transformers and Diffusion Models
Johno on Twitter
Hugo on Twitter
Vanishing Gradients on Twitter
SciPy 2024 CFP
Escaping Generative AI Walled Gardens with Omoju Miller, a Vanishing Gradients Livestream

Hugo speaks with Allen Downey, a curriculum designer at Brilliant, Professor Emeritus at Olin College, and the author of Think Python, Think Bayes, Think Stats, and other computer science and data science books. In 2019-20 he was a Visiting Professor at Harvard University. He previously taught at Wellesley College and Colby College and was a Visiting Scientist at Google. He is also the author of the upcoming book Probably Overthinking It!


They discuss Allen's new book and the key statistical and data skills we all need to navigate an increasingly data-driven and algorithmic world. The goal was to dive deep into the statistical paradoxes and fallacies that get in the way of using data to make informed decisions.


For example, when it was reported in 2021 that “in the United Kingdom, 70-plus percent of the people who die now from COVID are fully vaccinated,” this was correct but the implication was entirely wrong. Their conversation jumps into many such concrete examples to get to the bottom of using data for more than “lies, damned lies, and statistics.” They cover



Information and misinformation around pandemics and the base rate fallacy;
The tools we need to comprehend the small probabilities of high-risk events such as stock market crashes, earthquakes, and more;
The many definitions of algorithmic fairness, why they can't all be met at once, and what we can do about it;
Public health, the need for robust causal inference, and variations on Berkson’s paradox, such as the low-birthweight paradox: an influential paper found that that the mortality rate for children of smokers is lower for low-birthweight babies;
Why none of us are normal in any sense of the word, both in physical and psychological measurements;
The Inspection paradox, which shows up in the criminal justice system and distorts our perception of prison sentences and the risk of repeat offenders.


LINKS



The livestream on YouTube
Allen Downey on Github
Allen's new book Probably Overthinking It!
Allen on Twitter
Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions by Mitchell et al.

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

Hugo speaks with Hamel Husain, a machine learning engineer who loves building machine learning infrastructure and tools 👷. Hamel leads and contributes to many popular open-source machine learning projects. He also has extensive experience (20+ years) as a machine learning engineer across various industries, including large tech companies like Airbnb and GitHub. At GitHub, he led CodeSearchNet, a large language model for semantic search that was a precursor to CoPilot. Hamel is the founder of Parlance-Labs, a research and consultancy focused on LLMs.


They talk about generative AI, large language models, the business value they can generate, and how to get started.


They delve into



Where Hamel is seeing the most business interest in LLMs (spoiler: the answer isn’t only tech);
Common misconceptions about LLMs;
The skills you need to work with LLMs and GenAI models;
Tools and techniques, such as fine-tuning, RAGs, LoRA, hardware, and more!
Vendor APIs vs OSS models.


LINKS



Our upcoming livestream LLMs, OpenAI Dev Day, and the Existential Crisis for Machine Learning Engineering with Jeremy Howard (Fast.ai), Shreya Shankar (UC Berkeley), and Hamel Husain (Parlance Labs): Sign up for free!
Our recent livestream Data and DevOps Tools for Evaluating and Productionizing LLMs with Hamel and Emil Sedgh, Lead AI engineer at Rechat -- in it, we showcase an actual industrial use case that Hamel and Emil are working on with Rechat, a real estate CRM, taking you through LLM workflows and tools.
Extended Guide: Instruction-tune Llama 2 by Philipp Schmid
The livestream recoding of this episode!
Hamel on twitter