Practical AI: Machine Learning, Data Science

Practical AI: Machine Learning, Data Science

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himalaya
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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, GANs, MLOps, AIOps, and more). 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!
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In this Fully-Connected episode, Daniel and Chris explore DALL-E 2, the amazing new model from Open AI that generates incredibly detailed novel images from text captions for a wide range of concepts expressible in natural language. Along the way, they acknowledge that some folks in the larger AI community are suggesting that sophisticated models may be approaching sentience, but together they pour cold water on that notion. But they can’t seem to get away from DALL-E’s images of raccoons in space, and of course, who would want to?

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.

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.

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!

Could we create a digital human that processes data in a variety of modalities and detects emotions? Well, that’s exactly what NTT DATA Services is trying to do, and, in this episode, Theresa Kushner joins us to talk about their motivations, use cases, current systems, progress, and related ethical issues.

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).

Hugging Face is increasingly becomes the “hub” of AI innovation. In this episode, Merve Noyan joins us to dive into this hub in more detail. We discuss automation around model cards, reproducibility, and the new community features. If you are wanting to engage with the wider AI community, this is the show for you!

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.

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 about healthcare at the most fundamental level.

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.

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