Practical AI
Daniel Whitenack and Chris Benson
300 episodes
Making artificial intelligence practical, productive & 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, GANs, MLOps, AIOps, LLMs & more).
More about the show
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!
Episodes page 5 of 6
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Active learning & endangered languages
Don’t all AI methods need a bunch of data to work? How could AI help document and revitalize endangered languages with “human-in-the-loop” or “active learning” methods? Sarah Moeller from the University of Florida…
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Learning the language of life
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…
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MLOps is NOT Real
We all hear a lot about MLOps these days, but where does MLOps end and DevOps begin? Our friend Luis from OctoML joins us in this episode to discuss treating AI/ML models as regular software components (once they are…
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🌍 AI in Africa - Agriculture
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…
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Quick, beautiful web UIs for ML apps
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,…
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It's been a BIG week in AI news 🗞
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…
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"Foundation" models
The term “foundation” model has been around since about the middle of last year when a research group at Stanford published the comprehensive report On the Opportunities and Risks of Foundation Models. The naming of…
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Clothing AI in a data fabric
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,…
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Creating a culture of innovation
Daniel and Chris talk with Lukas Egger, Head of Innovation Office and Strategic Projects at SAP Business Process Intelligence. Lukas describes what it takes to bring a culture of innovation into an organization, and…
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Deploying models (to tractors 🚜)
Alon from Greeneye and Moses from ClearML blew us away when they said that they are training 1000’s of models a year that get deployed to Kubernetes clusters on tractors. Yes… we said tractors, as in farming! This is a…
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One algorithm to rule them all?
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…
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🌍 AI in Africa - Voice & language tools
In the third of the “AI in Africa” spotlight episodes, we welcome Kathleen Siminyu, who is building Kiswahili voice tools at Mozilla. We had a great discussion with Kathleen about creating more diverse voice and…
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Exploring deep reinforcement learning
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…
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The world needs an AI superhero
From drug discovery at the Quebec AI Institute to improving capabilities with low-resourced languages at the Masakhane Research Foundation and Google AI, Bonaventure Dossou looks for opportunities to use his expertise…
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Democratizing ML for speech
You might know about MLPerf, a benchmark from MLCommons that measures how fast systems can train models to a target quality metric. However, MLCommons is working on so much more! David Kanter joins us in this episode…
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Eliminate AI failures
We have all seen how AI models fail, sometimes in spectacular ways. Yaron Singer joins us in this episode to discuss model vulnerabilities and automatic prevention of bad outcomes. By separating concerns and creating a…
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🌍 AI in Africa - Radiant Earth
In the second of the “AI in Africa” spotlight episodes, we welcome guests from Radiant Earth to talk about machine learning for earth observation. They give us a glimpse into their amazing data and tooling for working…
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OpenAI and Hugging Face tooling
The time has come! OpenAI’s API is now available with no waitlist. Chris and Daniel dig into the API and playground during this episode, and they also discuss some of the latest tool from Hugging Face (including new…
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Friendly federated learning 🌼
This episode is a follow up to our recent Fully Connected show discussing federated learning. In that previous discussion, we mentioned Flower (a “friendly” federated learning framework). Well, one of the creators of…
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Technology as a force for good
Here’s a bonus episode this week from our friends behind Me, Myself, and AI — a podcast on artificial intelligence and business, and produced by MIT Sloan Management Review and Boston Consulting Group. We partnered…
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AI-generated code with OpenAI Codex
Recently, GitHub released Copilot, which is an amazing AI pair programmer powered by OpenAI’s Codex model. In this episode, Natalie Pistunovich tells us all about Codex and helps us understand where it fits in our…
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Zero-shot multitask learning
In this Fully-Connected episode, Daniel and Chris ponder whether in-person AI conferences are on the verge of making a post-pandemic comeback. Then on to BigScience from Hugging Face, a year-long research workshop on…
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Analyzing the 2021 AI Index Report
Each year we discuss the latest insights from the Stanford Institute for Human-Centered Artificial Intelligence (HAI), and this year is no different. Daniel and Chris delve into key findings and discuss in this…
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Photonic computing for AI acceleration
There are a lot of people trying to innovate in the area of specialized AI hardware, but most of them are doing it with traditional transistors. Lightmatter is doing something totally different. They’re building…
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Eureka moments with natural language processing
When is the last time you had a eureka moment? Chris had a chat with Nicholas Mohnacky, CEO and Cofounder of bundleIQ, where they use natural language processing algorithms like GPT-3 to connect your Google GSuite with…
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🌍 AI in Africa - Makerere AI Lab
This is the first episode in a special series we are calling the “Spotlight on AI in Africa”. To kick things off, Joyce and Mutembesa from Makerere University’s AI Lab join us to talk about their amazing work in…
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Federated Learning 📱
Federated learning is increasingly practical for machine learning developers because of the challenges we face with model and data privacy. In this fully connected episode, Chris and Daniel dive into the topic and…
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The mathematics of machine learning
Tivadar Danka is an educator and content creator in the machine learning space, and he is writing a book to help practitioners go from high school mathematics to mathematics of neural networks. His explanations are…
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Balancing human intelligence with AI
Polarity Mapping is a framework to “help problems be solved in a realistic and multidimensional manner” (see here for more info). In this week’s fully connected episode, Chris and Daniel use this framework to help them…
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From notebooks to Netflix scale with Metaflow
As you start developing an AI/ML based solution, you quickly figure out that you need to run workflows. Not only that, you might need to run those workflows across various kinds of infrastructure (including GPUs) at…
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Trends in data labeling
Any AI play that lacks an underlying data strategy is doomed to fail, and a big part of any data strategy is labeling. Michael, from Label Studio, joins us in this episode to discuss how the industry’s perception of…
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Stellar inference speed via AutoNAS
Yonatan Geifman of Deci makes Daniel and Chris buckle up, and takes them on a tour of the ideas behind his amazing new inference platform. It enables AI developers to build, optimize, and deploy blazing-fast deep…
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Anaconda + Pyston and more
In this episode, Peter Wang from Anaconda joins us again to go over their latest “State of Data Science” survey. The updated results include some insights related to data science work during COVID along with other…
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Exploring a new AI lexicon
We’re back with another Fully Connected episode – Daniel and Chris dive into a series of articles called ‘A New AI Lexicon’ that collectively explore alternate narratives, positionalities, and understandings to the…
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NLP to help pregnant mothers in Kenya
In Kenya, 33% of maternal deaths are caused by delays in seeking care, and 55% of maternal deaths are caused by delays in action or inadequate care by providers. Jacaranda Health is employing NLP and dialogue system…
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SLICED - will you make the (data science) cut?
SLICED is like the TV Show Chopped but for data science. Competitors get a never-before-seen dataset and two-hours to code a solution to a prediction challenge. Meg and Nick, the SLICED show hosts, join us in this…
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AI is creating never before heard sounds! 🎵
AI is being used to transform the most personal instrument we have, our voice, into something that can be “played.” This is fascinating in and of itself, but Yotam Mann from Never Before Heard Sounds is doing so much…
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Building a data team
Inspired by a recent article from Erik Bernhardsson titled “Building a data team at a mid-stage startup: a short story”, Chris and Daniel discuss all things AI/data team building. They share some stories from their…
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Towards stability and robustness
9 out of 10 AI projects don’t end up creating value in production. Why? At least partly because these projects utilize unstable models and drifting data. In this episode, Roey from BeyondMinds gives us some insights on…
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From symbols to AI pair programmers 💻
How did we get from symbolic AI to deep learning models that help you write code (i.e., GitHub and OpenAI’s new Copilot)? That’s what Chris and Daniel discuss in this episode about the history and future of deep…
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Vector databases for machine learning
Pinecone is the first vector database for machine learning. Edo Liberty explains to Chris how vector similarity search works, and its advantages over traditional database approaches for machine learning. It enables one…
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Multi-GPU training is hard (without PyTorch Lightning)
William Falcon wants AI practitioners to spend more time on model development, and less time on engineering. PyTorch Lightning is a lightweight PyTorch wrapper for high-performance AI research that lets you train on…
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Learning to learn deep learning 📖
Chris and Daniel sit down to chat about some exciting new AI developments including wav2vec-u (an unsupervised speech recognition model) and meta-learning (a new book about “How To Learn Deep Learning And Thrive In The…
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The fastest way to build ML-powered apps
Tuhin Srivastava tells Daniel and Chris why BaseTen is the application development toolkit for data scientists. BaseTen’s goal is to make it simple to serve machine learning models, write custom business logic around…
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Elixir meets machine learning
Today we’re sharing a special crossover episode from The Changelog podcast here on Practical AI. Recently, Daniel Whitenack joined Jerod Santo to talk with José Valim, Elixir creator, about Numerical Elixir. This is…
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Apache TVM and OctoML
90% of AI / ML applications never make it to market, because fine tuning models for maximum performance across disparate ML software solutions and hardware backends requires a ton of manual labor and is…
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25 years of speech technology innovation
To say that Jeff Adams is a trailblazer when it comes to speech technology is an understatement. Along with many other notable accomplishments, his team at Amazon developed the Echo, Dash, and Fire TV changing our…
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Generating "hunches" using smart home data 🏠
Smart home data is complicated. There are all kinds of devices, and they are in many different combinations, geographies, configurations, etc. This complicated data situation is further exacerbated during a pandemic…
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Mapping the world
Ro Gupta from CARMERA teaches Daniel and Chris all about road intelligence. CARMERA maintains the maps that move the world, from HD maps for automated driving to consumer maps for human navigation. Sponsors: •…
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Data science for intuitive user experiences
Nhung Ho joins Daniel and Chris to discuss how data science creates insights into financial operations and economic conditions. They delve into topics ranging from predictive forecasting to aid small businesses, to…
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