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Machine Learning

Machine Learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
196 episodes
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Practical AI Practical AI #184

Cloning voices with Coqui

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2022-07-12T15:10:00Z #ai +2 🎧 18,568

Coqui is a speech technology startup that making huge waves in terms of their contributions to open source speech technology, open access models and data, and compelling voice cloning functionality. Josh Meyer from Coqui joins us in this episode to discuss cloning voices that have emotion, fostering open source, and how creators are using AI tech.

Practical AI Practical AI #183

AI's role in reprogramming immunity

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2022-06-28T19:00:00Z #ai +2 🎧 18,780

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.

Practical AI Practical AI #182

Machine learning in your database

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2022-06-22T14:45:00Z #ai +2 🎧 20,135

While scaling up machine learning at Instacart, Montana Low and Lev Kokotov discovered just how much you can do with the Postgres database. They are building on that work with PostgresML, an extension to the database that lets you train and deploy models to make online predictions using only SQL. This is super practical discussion that you don’t want to miss!

Practical AI Practical AI #180

Generalist models & Iceman's voice

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2022-06-07T15:15:00Z #fully-connected +2 🎧 19,549

In this “fully connected” episode of the podcast, we catch up on some recent developments in the AI world, including a new model from DeepMind called Gato. This generalist model can play video games, caption images, respond to chat messages, control robot arms, and much more. We also discuss the use of AI in the entertainment industry (e.g., in new Top Gun movie).

Practical AI Practical AI #178

Active learning & endangered languages

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2022-05-17T16:45:00Z #ai +2 🎧 19,580

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 🎧 20,908

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 🎧 21,743

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 #166

Exploring deep reinforcement learning

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2022-02-01T20:00:00Z #ai +3 🎧 24,055

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.

Go Time Go Time #213

AI-driven development in Go

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2022-01-20T17:00:00Z #go +3 🎧 21,080

Alexey Palazhchenko joins Natalie to discuss the implications of GitHub’s Copilot on code generation. Go’s design lends itself nicely to computer generated authoring: thanks to go fmt, there’s already only one Go style. This means AI-generated code will be consistent and seamless. Its focus on simplicity & readability make it tailor made for this new approach to software creation. Where might this take us?

Practical AI Practical AI #164

Democratizing ML for speech

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2022-01-19T15:30:00Z #ai +2 🎧 21,953

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 🎧 22,675

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 🎧 25,840

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 🎧 20,216

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.

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