Making artificial intelligence practical, productive, and accessible to everyone. Practical AI is a show in which technology professionals, business people, students, enthusiasts, and expert guests engage in lively discussions about Artificial Intelligence and related topics (Machine Learning, Deep Learning, Neural Networks, etc). The focus is on productive implementations and real-world scenarios that are accessible to everyone. If you want to keep up with the latest advances in AI, while keeping one foot in the real world, then this is the show for you!
GPU dev environments that just work
Creating and sharing reproducible development environments for AI experiments and production systems is a huge pain. You have all sorts of weird dependencies, and then you have to deal with GPUs and NVIDIA drivers on top of all that! brev.dev is attempting to mitigate this pain and create delightful GPU dev environments. Now that sounds practical!
Machine learning at small organizations
Why is ML is so poorly adopted in small organizations (hint: it’s not because they don’t have enough data)? In this episode, Kirsten Lum from Storytellers shares the patterns she has seen in small orgs that lead to a successful ML practice. We discuss how the job of a ML Engineer/Data Scientist is different in that environment and how end-to-end project management is key to adoption.
ChatGPT goes prime time!
Daniel and Chris do a deep dive into OpenAI’s ChatGPT, which is the first LLM to enjoy direct mass adoption by folks outside the AI world. They discuss how it works, its effect on the world, ramifications of its adoption, and what we may expect in the future as these types of models continue to evolve.
NLP research by & for local communities
While at EMNLP 2022, Daniel got a chance to sit down with an amazing group of researchers creating NLP technology that actually works for their local language communities. Just Zwennicker (Universiteit van Amsterdam) discusses his work on a machine translation system for Sranan Tongo, a creole language that is spoken in Suriname. Andiswa Bukula (SADiLaR), Rooweither Mabuya (SADiLaR), and Bonaventure Dossou (Lanfrica, Mila) discuss their work with Masakhane to strengthen and spur NLP research in African languages, for Africans, by Africans. The group emphasized the need for more linguistically diverse NLP systems that work in scenarios of data scarcity, non-Latin scripts, rich morphology, etc. You don’t want to miss this one!
SOTA machine translation at Unbabel
José and Ricardo joined Daniel at EMNLP 2022 to discuss state-of-the-art machine translation, the WMT shared tasks, and quality estimation. Among other things, they talk about Unbabel’s innovations in quality estimation including COMET, a neural framework for training multilingual machine translation (MT) evaluation models.
AI competitions & cloud resources
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).
Copilot lawsuits & Galactica "science"
There are some big AI-related controversies swirling, and it’s time we talk about them. A lawsuit has been filed against GitHub, Microsoft, and OpenAI related to Copilot code suggestions, and many people have been disturbed by the output of Meta AI’s Galactica model. Does Copilot violate open source licenses? Does Galactica output dangerous science-related content? In this episode, we dive into the controversies and risks, and we discuss the benefits of these technologies.
Protecting us with the Database of Evil
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.
Hybrid computing with quantum processors
It’s been a while since we’ve touched on quantum computing. It’s time for an update! This week we talk with Yonatan from Quantum Machines about real progress being made in the practical construction of hybrid computing centers with a mix of classical processors, GPUs, and quantum processors. Quantum Machines is building both hardware and software to help control, program, and integrate quantum processors within a hybrid computing environment.
The practicalities of releasing models
Recently Chris and Daniel briefly discussed the Open RAIL-M licensing and model releases on Hugging Face. In this episode, Daniel follows up on this topic based on some recent practical experience. Also included is a discussion about graph neural networks, message passing, and tweaking synthesized voices!
AI adoption in large, well-established companies
This panel discussion was recorded at a recent event hosted by a company, Aryballe, that we previously featured on the podcast (#120). We got a chance to discuss the AI-driven technology transforming the order/fragrance industries, and we went down the rabbit hole discussing how this technology is being adopted at large, well-established companies.
Data for All
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. Thomson 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.
What's up, DocQuery?
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.
Production data labeling workflows
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!
Evaluating models without test data
WeightWatcher, created by Charles Martin, is an open source diagnostic tool for analyzing Neural Networks without training or even test data! Charles joins us in this episode to discuss the tool and how it fills certain gaps in current model evaluation workflows. Along the way, we discuss statistical methods from physics and a variety of practical ways to modify your training runs.