José Valim joins Jerod to talk all about what’s new in Livebook – the Elixir-based interactive code notebook he’s been working on the last few years.

José made a big bet when he decided to bring machine learning to Elixir. That bet is now paying off with amazing new capabilities such as building and deploying a Whisper-based chat app to Hugging Face in just 15 minutes.

José demoed that and much more during Livebook’s first-ever launch week. Let’s get into it.

Daniel and Chris explore the intersection of Kaggle and real-world data science in this illuminating conversation with Christof Henkel, Senior Deep Learning Data Scientist at NVIDIA and Kaggle Grandmaster. Christof offers a very lucid explanation into how participation in Kaggle can positively impact a data scientist’s skill and career aspirations. He also shared some of his insights and approach to maximizing AI productivity uses GPU-accelerated tools like RAPIDS and DALI.

Robin Linacre:

SQL should be the first option considered for new data engineering work. It’s robust, fast, future-proof and testable. With a bit of care, it’s clear and readable. A new SQL engine - DuckDB - makes SQL competitive with other high performance dataframe libraries, making SQL a good candidate for data of all sizes.

You can make a similar argument for SQL that Gary Bernhardt made for Vim. Here’s Gary on Vim, run your own s/Vim/SQL/g filter on this as you read it:

…just for me, 15 years; at the beginning of that time, TextMate was just becoming popular. Then it was Sublime Text was cool. Then Atom was cool. Then VS Code was cool. A lot of people switched between two of those, three of those, maybe all four of those, and that whole time I was just getting better and better and better at Vim… And you multiply that out by the length of a career, you use Vim for 40 years - you’re gonna be so good at it by the end, and it’s still gonna be totally relevant, I think.

Jeremia Kimelman:

Datasette is an open source tool that takes an SQLite database and gives you an out-of-the-box, web-based UI built specifically for exploring data. Need an example? Here’s a database of all of Motley Fool’s earning transcripts that I used to look for talk of their California campaign activity. And here’s a bunch of other examples of Datasette from the official site.

And the thing is: I love Datasette. It recently turned 5 years old and I wanted to write down the thing that makes it an absolutely delightful data hammer.

Guillem Ballesteros:

I have reached Vim nirvana with my latest setup. I can finally bring all the advantages of working within Jupyter to my favorite text editor. You get the code cells and interactive development with a fine-tuned editor and plain text files which can be put through linters and code formatters.

He goes on to share the plugins and config that make the nirvana happen.

A series of 8 free data puzzles to be released daily starting on December 18th. Created by Saul Pwanson (author of VisiData) as an analogue to the popular Advent of Code, but for data nerds who want to find answers in a dataset, rather than hack on algorithms.

In this special episode, we interview some of the sponsors and teams from a recent case competition organized by Purdue University, Microsoft, INFORMS, and SIL International. 170+ teams from across the US and Canada participated in the competition, which challenged students to create AI-driven systems to caption images in three languages (Thai, Kyrgyz, and Hausa).

Online platforms and their users are susceptible to a barrage of threats – from disinformation to extremism to terror. Daniel and Chris chat with Matar Haller, VP of Data at ActiveFence, a leader in identifying online harm – is using a combination of AI technology and leading subject matter experts to provide Trust & Safety teams with precise, real-time data, in-depth intelligence, and automated tools to protect users and ensure safe online experiences.

People are starting to wake up to the fact that they have control and ownership over their data, and governments are moving quickly to legislate these rights. John K. Thompson has written a new book on the topic that is a must read! We talk about the new book in this episode along with how practitioners should be thinking about data exchanges, privacy, trust, and synthetic data.

Chris sits down with Ankur Goyal to talk about DocQuery, Impira’s new open source ML model. DocQuery lets you ask questions about semi-structured data (like invoices) and unstructured documents (like contracts) using Large Language Models (LLMs). Ankur illustrates many of the ways DocQuery can help people tame documents, and references Chris’s real life tasks as a non-profit director to demonstrate that DocQuery is indeed practical AI.

It’s one thing to gather some labels for your data. It’s another thing to integrate data labeling into your workflows and infrastructure in a scalable, secure, and useful way. Mark from Xelex joins us to talk through some of what he has learned after helping companies scale their data annotation efforts. We get into workflow management, labeling instructions, team dynamics, and quality assessment. This is a super practical episode!

In this Fully-Connected episode, Daniel and Chris discuss concerns of privacy in the face of ever-improving AI / ML technologies. Evaluating AI’s impact on privacy from various angles, they note that ethical AI practitioners and data scientists have an enormous burden, given that much of the general population may not understand the implications of the data privacy decisions of everyday life.

This intentionally thought-provoking conversation advocates consideration and action from each listener when it comes to evaluating how their own activities either protect or violate the privacy of those whom they impact.

Chip Huyen:

As machine learning moves towards real-time, streaming technology is becoming increasingly important for data scientists. Like many people coming from a machine learning background, I used to dread streaming. In our recent survey, almost half of the data scientists we asked said they would like to move from batch prediction to online prediction but can’t because streaming is hard, both technically and operationally…

Over the last year, working with a co-founder who’s super deep into streaming, I’ve learned that streaming can be quite intuitive. This post is an attempt to rephrase what I’ve learned.

Every year Mozilla releases an Internet Health Report that combines research and stories exploring what it means for the internet to be healthy. This year’s report is focused on AI. In this episode, Solana and Bridget from Mozilla join us to discuss the power dynamics of AI and the current state of AI worldwide. They highlight concerning trends in the application of this transformational technology along with positive signs of change.

Sean Moriarity:

A common argument against using Nx for a new machine learning project is its perceived lack of a library/support for some common task that is available in Python. In this post, I’ll do my best to highlight areas where this is not the case, and compare and contrast Elixir projects with their Python equivalents. Additionally, I’ll discuss areas where the Elixir ecosystem still comes up short, and using Nx for a new project might not be the best idea.

Sean is a prominent member of the Elixir community, so that’s the perspective on display here, but it’s a thorough and well-reasoned comparison. He concludes:

While there are still many gaps in the Elixir ecosystem, the progress over the last year has been rapid. Almost every library I’ve mentioned in this post is less than two years old, and I suspect there will be many more projects to fill some of the gaps I’ve mentioned in the coming months.

Drausin Wulsin, Director of ML at Immunai, joins Daniel & Chris to talk about the role of AI in immunotherapy, and why it is proving to be the foremost approach in fighting cancer, autoimmune disease, and infectious diseases.

The large amount of high dimensional biological data that is available today, combined with advanced machine learning techniques, creates unique opportunities to push the boundaries of what is possible in biology.

To that end, Immunai has built the largest immune database called AMICA that contains tens of millions of cells. The company uses cutting-edge transfer learning techniques to transfer knowledge across different cell types, studies, and even species.

Hugging Face is increasingly becomes the “hub” of AI innovation. In this episode, Merve Noyan joins us to dive into this hub in more detail. We discuss automation around model cards, reproducibility, and the new community features. If you are wanting to engage with the wider AI community, this is the show for you!

AI is discovering new drugs. Sound like science fiction? Not at Absci! Sean and Joshua join us to discuss their AI-driven pipeline for drug discovery. We discuss the tech along with how it might change how we think about healthcare at the most fundamental level.

In the fourth “AI in Africa” spotlight episode, we welcome Leonida Mutuku and Godliver Owomugisha, two experts in applying advanced technology in agriculture. We had a great discussion about ending poverty, hunger, and inequality in Africa via AI innovation. The discussion touches on open data, relevant models, ethics, and more.

Abubakar Abid joins Daniel and Chris for a tour of Gradio and tells them about the project joining Hugging Face. What’s Gradio? The fastest way to demo your machine learning model with a friendly web interface, allowing non-technical users to access, use, and give feedback on models.

This last week has been a big week for AI news. BigScience is training a huge language model (while the world watches), and NVIDIA announced their latest “Hopper” GPUs. Chris and Daniel discuss these and other topics on this fully connected episode!

What happens when your data operations grow to Internet-scale? How do thousands or millions of data producers and consumers efficiently, effectively, and productively interact with each other? How are varying formats, protocols, security levels, performance criteria, and use-case specific characteristics meshed into one unified data fabric? Chris and Daniel explore these questions in this illuminating and Fully-Connected discussion that brings this new data technology into the light.

From MIT researchers who have an AI system that rapidly predicts how two proteins will attach, to Facebook’s first high-performance self-supervised algorithm that works for speech, vision, and text, Daniel and Chris survey the AI landscape for notable milestones in the application of AI in industry and research.

It’s still in closed beta, but this looks like a really cool environment for data scientists and other folks who code to accomplish other goals vs code as craft. One cool thing you can do is take your Jupyter notebooks and convert them to PyFlow graphs (and vice versa).

In addition to being a Developer Advocate at Hugging Face, Thomas Simonini is building next-gen AI in games that can talk and have smart interactions with the player using Deep Reinforcement Learning (DRL) and Natural Language Processing (NLP). He also created a Deep Reinforcement Learning course that takes a DRL beginner to from zero to hero. Natalie and Chris explore what’s involved, and what the implications are, with a focus on the development path of the new AI data scientist.