269 episodes
Practical AI: Machine Learning, Data Science Changelog Media
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- Technology
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4.5 • 14 Ratings
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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). 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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Private, open source chat UIs
We recently gathered some Practical AI listeners for a live webinar with Danny from LibreChat to discuss the future of private, open source chat UIs. During the discussion we hear about the motivations behind LibreChat, why enterprise users are hosting their own chat UIs, and how Danny (and the LibreChat community) is creating amazing features (like RAG and plugins).
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Mamba & Jamba
First there was Mamba… now there is Jamba from AI21. This is a model that combines the best non-transformer goodness of Mamba with good ‘ol attention layers. This results in a highly performant and efficient model that AI21 has open sourced! We hear all about it (along with a variety of other LLM things) from AI21’s co-founder Yoav.
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Udio & the age of multi-modal AI
2024 promises to be the year of multi-modal AI, and we are already seeing some amazing things. In this “fully connected” episode, Chris and Daniel explore the new Udio product/service for generating music. Then they dig into the differences between recent multi-modal efforts and more “traditional” ways of combining data modalities.
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RAG continues to rise
Daniel & Chris delight in conversation with “the funniest guy in AI”, Demetrios Brinkmann. Together they explore the results of the MLOps Community’s latest survey. They also preview the upcoming AI Quality Conference.
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Should kids still learn to code?
In this fully connected episode, Daniel & Chris discuss NVIDIA GTC keynote comments from CEO Jensen Huang about teaching kids to code. Then they dive into the notion of “community” in the AI world, before discussing challenges in the adoption of generative AI by non-technical people. They finish by addressing the evolving balance between generative AI interfaces and search engines.
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AI vs software devs
Daniel and Chris are out this week, so we’re bringing you conversations all about AI’s complicated relationship to software developers from other Changelog pods: JS Party, Go Time & The Changelog.
Customer Reviews
Treasure find
With so many podcasts out there when you decide to listen, this is honestly one of the most interesting find. My new Addiction ! Thanks for sharing knowledge not only on practical AI but on other channels as well.
Learnt a lot from this podcast
[tl;dr - I like this podcast] I started listening to Practical AI on my commute. The hosts make it their job to be inclusive of beginners (like me) and break down complex topics into simple to understand conversations. The topics are open-ended. There is a wide variety of guests. Guests I particularly liked to hear from were spaCy, HuggingFace and NVIDIA because we actually use that stuff. I like listening to the Fully Connected episodes as well. This podcast does not get into discussing things like inner working details of GANs, etc because a podcast is neither meant to nor is suitable for deep technical explanations. They instead treat complex topics in a conversational manner. I use this podcast to not only learn but also with catch up industry developments without wasting time on sifting through hundreds of look-alike sites
Nothing “Practical” going on here
I heard the first episode and found the proposition to be enticing to stick around - practical advice with software devs angle in mind. But over a few episodes it turned out to be just a high level discussion with companies in the space with very little to offer on practical specifics of going from zero to building ML stack. The interview area is already saturated with TWiML&AI, DataFramed, O’Reilly Data and many others.
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