Practical AI

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Making artificial intelligence practical, productive & accessible to everyone

Practical AI Practical AI #178

Active learning & endangered languages

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2022-05-17T16:45:00Z #ai +2 🎧 19,915

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 joins us to discuss those and other related questions. She also shares many of her personal experiences working with languages in low resource settings.

Practical AI Practical AI #176

MLOps is NOT Real

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2022-04-26T14:00:00Z #ai +2 🎧 21,198

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 trained and ready for deployment). We get into topics including optimization on various kinds of hardware and deployment of models at the edge.

Practical AI Practical AI #171

Clothing AI in a data fabric

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2022-03-16T13:40:00Z #ai +3 🎧 22,004

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.

Practical AI Practical AI #170

Creating a culture of innovation

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2022-03-08T19:35:00Z #ai +2 🎧 22,736

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 how to infuse product development with that innovation culture. He also offers suggestions for how to mitigate challenges and blockers.

Practical AI Practical AI #166

Exploring deep reinforcement learning

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2022-02-01T20:00:00Z #ai +3 🎧 24,387

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.

Practical AI Practical AI #164

Democratizing ML for speech

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2022-01-19T15:30:00Z #ai +2 🎧 22,185

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 to discuss two new speech datasets that are democratizing machine learning for speech via data scale and language/speaker diversity.

Practical AI Practical AI #163

Eliminate AI failures

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2022-01-11T18:00:00Z #ai +2 🎧 22,878

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 “firewall” around your AI models, it’s possible to secure your AI workflows and prevent model failure.

Practical AI Practical AI #161

OpenAI and Hugging Face tooling

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2021-12-14T20:00:00Z #ai +3 🎧 26,123

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 reinforcement learning environments). Finally, Daniel gives an update on how he is building out infrastructure for a new AI team.

Practical AI Practical AI #160

Friendly federated learning 🌼

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2021-12-07T16:15:00Z #ai +3 🎧 20,438

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 motivating discussion of ML, privacy, distributed training, and open source AI.

Practical AI Practical AI

Technology as a force for good

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2021-12-02T21:00:00Z #ai 🎧 19,836

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.

We hand picked this full-length episode to share with you because of its focus on using technology as a force for good, something we’re very passionate about. This episode features, Paula Goldman, Chief Ethical and Humane Use Officer at Salesforce, and the conversation touches on some interesting topics around the role tech companies play in society at large.

Practical AI Practical AI #158

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 large multilingual models and datasets. Specifically they dive into the T0, a series of natural language processing (NLP) AI models specifically trained for researching zero-shot multitask learning. Daniel provides a brief tour of the possible with the T0 family. They finish up with a couple of new learning resources.

Practical AI Practical AI #157

Analyzing the 2021 AI Index Report

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2021-11-10T20:00:00Z #fully-connected +3 🎧 20,657

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.

Practical AI Practical AI #156

Photonic computing for AI acceleration

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2021-11-02T20:00:00Z #ai +2 🎧 19,813

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.

Practical AI Practical AI #154

🌍 AI in Africa - Makerere AI Lab

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2021-10-19T20:15:00Z #ai +2 🎧 18,742

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 appropriate data/tools, and works with the end users to ensure that solutions create real value.

Practical AI Practical AI #153

Federated Learning 📱

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2021-10-12T18:20:00Z #ai +3 🎧 20,083

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.

Practical AI Practical AI #152

The mathematics of machine learning

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2021-10-05T20:15:00Z #ai +2 🎧 24,652

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!

Practical AI Practical AI #151

Balancing human intelligence with AI

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2021-09-28T21:20:00Z #ai +3 🎧 19,616

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 discuss how an organization can strike a good balance between human intelligence and AI. AI can’t solve everything and humans need to be in-the-loop with many AI solutions.

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