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Practical AI: Machine Learning, Data Science

Technology

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,Read more

Popular episodes

Friendly federated learning 🌼

Dec 7 • 46:35

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 Flower, Daniel Beutel, agreed to join us on the show to discuss the project (and federated learning more broadly)! The result is a really interesting and moti...

Technology as a force for good

Dec 2 • 25:36

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 with them to help promote their awesome podcast....

AI-generated code with OpenAI Codex

Nov 30 • 46:37

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 development workflow. We also discuss MLOps and how AI is influencing software engineering more generally....

Zero-shot multitask learning

Nov 24 • 46:19

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 large multilingual models and datasets. Specifically they dive into the T0, a series of natural language processing (NLP) AI models specifically trained for...

Analyzing the 2021 AI Index Report

Nov 10 • 46:14

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 Fully-Connected episode. They also check out a study called ‘Delphi: Towards Machine Ethics and Norms’, about how to integrate ethics and morals into AI models....

Photonic computing for AI acceleration

Nov 2 • 44:21

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 photonic computers that are more power efficient and faster for AI inference. Nick Harris joins us in this episode to bring us up to speed on all the details....

Eureka moments with natural language processing

Oct 26 • 36:35

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 other personal data sources to find deeper connections, go beyond the obvious, and create eureka moments....

🌍 AI in Africa - Makerere AI Lab

Oct 19 • 43:18

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 computer vision, natural language processing, and data collection. Their lab seeks out problems that matter in African communities, pairs those problems with appro...

Federated Learning 📱

Oct 12 • 45:17

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 dissect the ideas behind federated learning, practicalities of implementing decentralized training, and current uses of the technique....

The mathematics of machine learning

Oct 5 • 38:03

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 lucid and easy to understand. You have never had such a fun and interesting conversation about calculus, linear algebra, and probability theory before!...

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