Vanishing Gradients

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

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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 (https://youtube.com/live/-6o5-3cP0ik?feature=share)
How S&P Global is making markets more transparent with NLP, spaCy and Prodigy (https://explosion.ai/blog/sp-global-commodities)
A practical guide to human-in-the-loop distillation (https://explosion.ai/blog/human-in-the-loop-distillation)
Laws of Tech: Commoditize Your Complement (https://gwern.net/complement)
spaCy: Industrial-Strength Natural Language Processing (https://spacy.io/)
LLMs with spaCy (https://spacy.io/usage/large-language-models)
Explosion, building developer tools for AI, Machine Learning and Natural Language Processing (https://explosion.ai/)
Back to our roots: Company update and future plans, by Matt and Ines (https://explosion.ai/blog/back-to-our-roots-company-update)
Matt's detailed blog post: back to our roots (https://honnibal.dev/blog/back-to-our-roots)
Ines on twitter (https://x.com/_inesmontani)
Matt on twitter (https://x.com/honnibal)
Vanishing Gradients on Twitter (https://twitter.com/vanishingdata)
Hugo on Twitter (https://twitter.com/hugobowne)
Check out and subcribe to our lu.ma calendar (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk) for upcoming livestreams!

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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 (https://youtube.com/live/hDmnwtjktsc?feature=share)
Educational resources from Dan and Hamel's LLM course (https://parlance-labs.com/education/)
Upwork Study Finds Employee Workloads Rising Despite Increased C-Suite Investment in Artificial Intelligence (https://investors.upwork.com/news-releases/news-release-details/upwork-study-finds-employee-workloads-rising-despite-increased-c)
Episode 29: Lessons from a Year of Building with LLMs (Part 1) (https://vanishinggradients.fireside.fm/29)
Episode 30: Lessons from a Year of Building with LLMs (Part 2) (https://vanishinggradients.fireside.fm/30)
Dan's demo: Creating Physical Products with Generative AI (https://youtu.be/U5J5RUOuMkI?si=_7cYLYOU1iwweQeO)
Build Great AI, Dan's boutique consulting firm helping clients be successful with large language models (https://buildgreat.ai/)
Parlance Labs, Hamel's Practical consulting that improves your AI (https://parlance-labs.com/)
Hamel on Twitter (https://x.com/HamelHusain)
Dan on Twitter (https://x.com/dan_s_becker)
Vanishing Gradients on Twitter (https://twitter.com/vanishingdata)
Hugo on Twitter (https://twitter.com/hugobowne)

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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.
In this episode, we dive deep into the world of LLMs and the critical challenges of building reliable AI pipelines. We'll explore:
The fascinating journey from classic machine learning to the current LLM revolution
Why Shreya believes most ML problems are actually data management issues
The concept of "data flywheels" for LLM applications and how to implement them
The intriguing world of evaluating AI systems - who validates the validators?
Shreya's work on SPADE and EvalGen, innovative tools for synthesizing data quality assertions and aligning LLM evaluations with human preferences
The importance of human-in-the-loop processes in AI development
The future of low-code and no-code tools in the AI landscape
We'll also touch on the potential pitfalls of over-relying on LLMs, the concept of "Habsburg AI," and how to avoid disappearing up our own proverbial arseholes in the world of recursive AI processes.
Whether you're a seasoned AI practitioner, a curious data scientist, or someone interested in the human side of AI development, this conversation offers valuable insights into building more robust, reliable, and human-centered AI systems.
LINKS
The livestream on YouTube (https://youtube.com/live/hKV6xSJZkB0?feature=share)
Shreya's website (https://www.sh-reya.com/)
Shreya on Twitter (https://x.com/sh_reya)
Data Flywheels for LLM Applications (https://www.sh-reya.com/blog/ai-engineering-flywheel/)
SPADE: Synthesizing Data Quality Assertions for Large Language Model Pipelines (https://arxiv.org/abs/2401.03038)
What We’ve Learned From A Year of Building with LLMs (https://applied-llms.org/)
Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferences (https://arxiv.org/abs/2404.12272)
Operationalizing Machine Learning: An Interview Study (https://arxiv.org/abs/2209.09125)
Vanishing Gradients on Twitter (https://twitter.com/vanishingdata)
Hugo on Twitter (https://twitter.com/hugobowne)
In the podcast, Hugo also mentioned that this was the 5th time he and Shreya chatted publicly. which is wild!
If you want to dive deep into Shreya's work and related topics through their chats, you can check them all out here:
Outerbounds' Fireside Chat: Operationalizing ML -- Patterns and Pain Points from MLOps Practitioners (https://www.youtube.com/watch?v=7zB6ESFto_U)
The Past, Present, and Future of Generative AI (https://youtu.be/q0A9CdGWXqc?si=XmaUnQmZiXL2eagS)
LLMs, OpenAI Dev Day, and the Existential Crisis for Machine Learning Engineering (https://www.youtube.com/live/MTJHvgJtynU?si=Ncjqn5YuFBemvOJ0)
Lessons from a Year of Building with LLMs (https://youtube.com/live/c0gcsprsFig?feature=share)
Check out and subcribe to our lu.ma calendar (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk) for upcoming livestreams!

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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.
In this episode, they dive deep into rethinking established methods in data science, machine learning, and AI. We explore Vincent's principled approach to the field, including:
The critical importance of exposing yourself to real-world problems before applying ML solutions
Framing problems correctly and understanding the data generating process
The power of visualization and human intuition in data analysis
Questioning whether algorithms truly meet the actual problem at hand
The value of simple, interpretable models and when to consider more complex approaches
The importance of UI and user experience in data science tools
Strategies for preventing algorithmic failures by rethinking evaluation metrics and data quality
The potential and limitations of LLMs in the current data science landscape
The benefits of open-source collaboration and knowledge sharing in the community
Throughout the conversation, Vincent illustrates these principles with vivid, real-world examples from his extensive experience in the field. They also discuss Vincent's thoughts on the future of data science and his call to action for more knowledge sharing in the community through blogging and open dialogue.
LINKS
The livestream on YouTube (https://youtube.com/live/-CD66CI1pEo?feature=share)
Vincent's blog (https://koaning.io/)
CalmCode (https://calmcode.io/)
scikit-lego (https://koaning.github.io/scikit-lego/)
Vincent's book Data Science Fiction (WIP) (https://calmcode.io/book)
The Deon Checklist, an ethics checklist for data scientists (https://deon.drivendata.org/)
Of oaths and checklists, by DJ Patil, Hilary Mason and Mike Loukides (https://www.oreilly.com/radar/of-oaths-and-checklists/)
Vincent's Getting Started with NLP and spaCy Course course on Talk Python (https://training.talkpython.fm/courses/getting-started-with-spacy)
Vincent on twitter (https://x.com/fishnets88)
:probabl. on twitter (https://x.com/probabl_ai)
Vincent's PyData Amsterdam Keynote "Natural Intelligence is All You Need [tm]" (https://www.youtube.com/watch?v=C9p7suS-NGk)
Vincent's PyData Amsterdam 2019 talk: The profession of solving (the wrong problem) (https://www.youtube.com/watch?v=kYMfE9u-lMo)
Vanishing Gradients on Twitter (https://twitter.com/vanishingdata)
Hugo on Twitter (https://twitter.com/hugobowne)
Check out and subcribe to our lu.ma calendar (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk) for upcoming livestreams!

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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.
These five guests, along with Jason Liu who couldn't join us, have spent the past year building real-world applications with Large Language Models (LLMs). They've distilled their experiences into a report of 42 lessons across operational, strategic, and tactical dimensions (https://applied-llms.org/), and they're here to share their insights.
We’ve split this roundtable into 2 episodes and, in this second episode, we'll explore:
An inside look at building end-to-end systems with LLMs;
The experimentation mindset: Why it's the key to successful AI products;
Building trust in AI: Strategies for getting stakeholders on board;
The art of data examination: Why looking at your data is more crucial than ever;
Evaluation strategies that separate the pros from the amateurs.
Although we're focusing on LLMs, many of these insights apply broadly to data science, machine learning, and product development, more generally.
LINKS
The livestream on YouTube (https://www.youtube.com/live/c0gcsprsFig)
The Report: What We’ve Learned From A Year of Building with LLMs (https://applied-llms.org/)
About the Guests/Authors (https://applied-llms.org/about.html)

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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.
These five guests, along with Jason Liu who couldn't join us, have spent the past year building real-world applications with Large Language Models (LLMs). They've distilled their experiences into a report of 42 lessons across operational, strategic, and tactical dimensions (https://applied-llms.org/), and they're here to share their insights.
We’ve split this roundtable into 2 episodes and, in this first episode, we'll explore:
The critical role of evaluation and monitoring in LLM applications and why they're non-negotiable, including "evals" - short for evaluations, which are automated tests for assessing LLM performance and output quality;
Why data literacy is your secret weapon in the AI landscape;
The fine-tuning dilemma: when to do it and when to skip it;
Real-world lessons from building LLM applications that textbooks won't teach you;
The evolving role of data scientists and AI engineers in the age of AI.
Although we're focusing on LLMs, many of these insights apply broadly to data science, machine learning, and product development, more generally.
LINKS
The livestream on YouTube (https://www.youtube.com/live/c0gcsprsFig)
The Report: What We’ve Learned From A Year of Building with LLMs (https://applied-llms.org/)
About the Guests/Authors (https://applied-llms.org/about.html)

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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.
What's super cool is that Alan and the Rasa team have been doing this type of thing for over a decade, giving them a wealth of wisdom on how to effectively incorporate LLMs into chatbots - and how not to. For example, if you want a chatbot that takes specific and important actions like transferring money, do you want to fully entrust the conversation to one big LLM like ChatGPT, or secure what the LLMs can do inside key business logic?
In this episode, they also dive into the history of conversational AI and explore how the advent of LLMs is reshaping the field. Alan shares his perspective on how supervised learning has failed us in some ways and discusses what he sees as the most overrated and underrated aspects of LLMs.
Alan offers advice for those looking to work with LLMs and conversational AI, emphasizing the importance of not sleeping on proven techniques and looking beyond the latest hype. In a live demo, he showcases Rasa's Calm (Conversational AI with Language Models), which allows developers to define business logic declaratively and separate it from the LLM, enabling reliable execution of conversational flows.
LINKS
The livestream on YouTube (https://www.youtube.com/live/kMFBYC2pB30?si=yV5sGq1iuC47LBSi)
Alan's Rasa CALM Demo: Building Conversational AI with LLMs (https://youtu.be/4UnxaJ-GcT0?si=6uLY3GD5DkOmWiBW)
Alan on twitter.com (https://x.com/alanmnichol)
Rasa (https://rasa.com/)
CALM, an LLM-native approach to building reliable conversational AI (https://rasa.com/docs/rasa-pro/calm/)
Task-Oriented Dialogue with In-Context Learning (https://arxiv.org/abs/2402.12234)
'We don’t know how to build conversational software yet' by Alan Nicol (https://medium.com/rasa-blog/we-don-t-know-how-to-build-conversational-software-yet-a18301db0e4b)
Vanishing Gradients on Twitter (https://twitter.com/vanishingdata)
Hugo on Twitter (https://twitter.com/hugobowne)
Upcoming Livestreams
Lessons from a Year of Building with LLMs (https://lu.ma/e8huz3s6?utm_source=vgan)
VALIDATING THE VALIDATORS with Shreya Shanker (https://lu.ma/zz3qic45?utm_source=vgan)

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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.
They talk about how Jason approaches consulting companies across many industries, including construction and sales, in building production LLM apps, his playbook for getting ML and AI up and running to build and maintain such apps, and the future of tooling to do so.
They take an inverted thinking approach, envisaging all the failure modes that would result in building terrible AI systems, and then figure out how to avoid such pitfalls.
LINKS
The livestream on YouTube (https://youtube.com/live/USTG6sQlB6s?feature=share)
Jason's website (https://jxnl.co/)
PyDdantic is all you need, Jason's Keynote at AI Engineer Summit, 2023 (https://youtu.be/yj-wSRJwrrc?si=JIGhN0mx0i50dUR9)
How to build a terrible RAG system by Jason (https://jxnl.co/writing/2024/01/07/inverted-thinking-rag/)
To express interest in Jason's Systematically improving RAG Applications course (https://q7gjsgfstrp.typeform.com/ragcourse?typeform-source=vg)
Vanishing Gradients on Twitter (https://twitter.com/vanishingdata)
Hugo on Twitter (https://twitter.com/hugobowne)
Upcoming Livestreams
Good Riddance to Supervised Learning with Alan Nichol (CTO and co-founder, Rasa) (https://lu.ma/gphzzyyn?utm_source=vgj)
Lessons from a Year of Building with LLMs (https://lu.ma/e8huz3s6?utm_source=vgj)

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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 (https://youtube.com/live/qL4JY6Y5pmA)
Sebastian's website (https://sebastianraschka.com/)
Machine Learning Q and AI: 30 Essential Questions and Answers on Machine Learning and AI by Sebastian (https://nostarch.com/machine-learning-q-and-ai)
Build a Large Language Model (From Scratch) by Sebastian (https://www.manning.com/books/build-a-large-language-model-from-scratch)
PyTorch Lightning (https://lightning.ai/docs/pytorch/stable/)
Lightning Fabric (https://lightning.ai/docs/fabric/stable/)
LitGPT (https://github.com/Lightning-AI/litgpt)
Sebastian's notebook for finetuning GPT-2 for spam classification! (https://github.com/rasbt/LLMs-from-scratch/blob/main/ch06/01_main-chapter-code/ch06.ipynb)
The end of fine-tuning: Jeremy Howard on the Latent Space Podcast (https://www.latent.space/p/fastai)
Our next livestream: How to Build Terrible AI Systems with Jason Liu (https://lu.ma/terrible-ai-systems?utm_source=vg)
Vanishing Gradients on Twitter (https://twitter.com/vanishingdata)
Hugo on Twitter (https://twitter.com/hugobowne)

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