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PODCAST

Practical AI

Practical AI LLC

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

technology

All Episodes

44:38

Federated learning in production (part 1)

Practical AI ·

2025/05/30

In this first of a two part series of episodes on federated learning, we dive into the evolving world of federated learning and distributed AI frameworks with Patrick Foley from Intel. We explore how frameworks like OpenFL and Flower are enabling secure, collaborative model training across silos, especially in sensitive fields like healthcare. The conversation touches on real-world use cases, the challenges of distributed ML/AI experiments, and why privacy-preserving techniques may become

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47:27

Email like a superhuman

Practical AI ·

2025/05/17

Loïc Houssier, Head of Engineering at Superhuman, joins us to discuss how AI and LLMs are reshaping the email experience. They discuss challenges like prompt and infrastructure optimization. Loïc also shares how a deep focus on user experience and real human workflows is key to building AI tools people actually love to use. Featuring:Loïc Houssier – LinkedInChris Benson – Website, GitHub, LinkedIn, XDaniel Whitenack – Website, GitHub, XLinks:SuperhumanReferral Code for a Free

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42:23

Model Context Protocol Deep Dive

Practical AI ·

2025/05/08

In this episode, Daniel and Chris unpack the Model Context Protocol (MCP), a rising standard for enabling agentic AI interactions with external systems, APIs, and data sources. They explore how MCP supports interoperability, community contributions, and a rapidly developing ecosystem of AI integrations. The conversation also highlights some real-world tooling such as FastAPI-MCP.Featuring:Chris Benson – Website, GitHub, LinkedIn, XDaniel Whitenack – Website, GitHub, XLinks:Protocol

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42:58

Seeing beyond the scan in neuroimaging

Practical AI ·

2025/04/30

In this episode, we explore the intersection of AI, machine learning, and healthcare through the lens of neuroimaging and epilepsy diagnosis. Dr. Gavin Winston shares insights from his work using MRI data and machine learning to uncover subtle abnormalities in brain function. We discuss the cultural and ethical barriers to AI adoption in medicine, how predictive data analysis could transform the diagnostic workflow, and what the future holds for medical imaging in a world increasingly

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42:04

Open source AI to tackle your backlog

Practical AI ·

2025/04/17

Vibe coding, agentic workflows, and AI-assisted pull requests? In this episode, Daniel and Chris chat with Robert Brennan and Graham Neubig of All Hands AI about how AI is transforming software development—from senior engineer productivity to open source agents that address GitHub issues. They dive into trust, tooling, collaboration, and what it means to build software in the era of AI agents. Whether you're coding from your laptop or your phone on a morning walk, the future is hands-free

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42:52

Orchestrating agents, APIs, and MCP servers

Practical AI ·

2025/04/14

In this episode, Daniel sits down with Pavel Veller, EPAM’s Chief Technologist, to explore the practical challenges of orchestrating many AI agents and managing connections to disparate systems/tools. Pavel shares insights from his hands-on work with agentic architectures and internal tools like "DIAL". Pavel also helps us understand things like MCP servers and why connecting assistants via APIs is easy—but making them useful is hard. Featuring:Pavel Veller – LinkedIn, XDaniel Whitenack –

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43:24

Software and hardware acceleration with Groq

Practical AI ·

2025/04/02

How do you enable AI acceleration (at both the hardware and software layers) that stays ahead of rapid industry shifts? In this episode, Dhananjay Singh from Groq dives into the evolving landscape of AI inference and acceleration. We explore how Groq optimizes the serving layer, adapts to industry shifts, and supports emerging model architectures. Featuring:Dhananjay Singh – LinkedIn, XChris Benson – Website, GitHub, LinkedIn, XDaniel Whitenack – Website, GitHub, XLinks:GroqSponsors:Augment

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50:04

AI-assisted coding with GitHub's COO

Practical AI ·

2025/03/21

Kyle Daigle, COO of GitHub, joins the hosts to discuss the evolving role of AI in software development, GitHub Copilot’s impact, and the challenges of AI-assisted coding. The conversation covers licensing concerns, ethical considerations, and how developers can navigate these complexities. Kyle also shares his vision for ambient AI, which seamlessly integrates into workflows to enhance productivity and innovation, shaping the future of developer tools. Featuring:Kyle Daigle – LinkedInChris

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43:38

Optimizing for efficiency with IBM’s Granite

Practical AI ·

2025/03/14

We often judge AI models by leaderboard scores, but what if efficiency matters more? Kate Soule from IBM joins us to discuss how Granite AI is rethinking AI at the edge—breaking tasks into smaller, efficient components and co-designing models with hardware. She also shares why AI should prioritize efficiency frontiers over incremental benchmark gains and how seamless model routing can optimize performance. Featuring:Kate Soule – LinkedInChris Benson – Website, GitHub, LinkedIn, XDaniel

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45:02

Tool calling and agents

Practical AI ·

2025/02/14

It seems like everyone is uses the term "agent" differently these days. In this episode, Chris and Daniel dig into the details of tool calling and its connection to agents. They help clarify how LLMs can "talk to" and "interact with" other systems like databases, APIs, web apps, etc. Along the way they share related learning resources.

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50:49

Deep-dive into DeepSeek

Practical AI ·

2025/01/31

There is crazy hype and a lot of confusion related to DeepSeek's latest model DeepSeek R1. The products provided by DeepSeek (their version of a ChatGPT-like app) has exploded in popularity. However, ties to China have raised privacy and geopolitical concerns. In this episode, Chris and Daniel cut through the hype to talk about the model, privacy implications, running DeepSeek models securely, and what this signals for open models in 2025.

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45:11

Video generation with realistic motion

Practical AI ·

2025/01/23

We seem to be experiencing a surge of video generation tools, models, and applications. However, video generation models generally struggle with some basic physics, like realistic walking motion. This leaves some generated videos lacking true motion with disappointing, simplistic panning camera views. Genmo is focused on the motion side of video generation and has released some of the best open models. Paras joins us to discuss video generation and their journey at Genmo.

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50:05

Full-duplex, real-time dialogue with Kyutai

Practical AI ·

2024/12/04

Kyutai, an open science research lab, made headlines over the summer when they released their real-time speech-to-speech AI assistant (beating OpenAI to market with their teased GPT-driven speech-to-speech functionality). Alex from Kyutai joins us in this episode to discuss the research lab, their recent Moshi models, and what might be coming next from the lab. Along the way we discuss small models and the AI ecosystem in France.

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53:12

Clones, commerce & campaigns

Practical AI ·

2024/11/29

Chris and Daniel dive into what Trump’s impending second term could mean for AI companies, model developers, and regulators, unpacking the potential shifts in policy and innovation. Next, they discuss the latest models, like Qwen, that blur the performance gap between open and closed systems. Finally, they explore new AI tools for meeting clones and AI-driven commerce, sparking a conversation about the balance between digital convenience and fostering genuine human connections.

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40:05

AI is more than GenAI

Practical AI ·

2024/09/05

GenAI is often what people think of when someone mentions AI. However, AI is much more. In this episode, Daniel breaks down a history of developments in data science, machine learning, AI, and GenAI in this episode to give listeners a better mental model. Don't miss this one if you are wanting to understand the AI ecosystem holistically and how models, embeddings, data, prompts, etc. all fit together.

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45:08

Apple Intelligence & Advanced RAG

Practical AI ·

2024/06/25

Daniel & Chris engage in an impromptu discussion of the state of AI in the enterprise. Then they dive into the recent Apple Intelligence announcement to explore its implications. Finally, Daniel leads a deep dive into a new topic - Advanced RAG - covering everything you need to know to be practical & productive.

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38:30

Using edge models to find sensitive data

Practical AI ·

2024/06/13

We’ve all heard about breaches of privacy and leaks of private health information (PHI). For healthcare providers and those storing this data, knowing where all the sensitive data is stored is non-trivial. Ramin, from Tausight, joins us to discuss how they have deploy edge AI models to help company search through billions of records for PHI.

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35:37

Rise of the AI PC & local LLMs

Practical AI ·

2024/06/04

We’ve seen a rise in interest recently and a number of major announcements related to local LLMs and AI PCs. NVIDIA, Apple, and Intel are getting into this along with models like the Phi family from Microsoft. In this episode, we dig into local AI tooling, frameworks, and optimizations to help you navigate this AI niche, and we talk about how this might impact AI adoption in the longer term.

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40:50

AI in the U.S. Congress

Practical AI ·

2024/05/29

At the age of 72, U.S. Representative Don Beyer of Virginia enrolled at GMU to pursue a Master’s degree in C.S. with a concentration in Machine Learning. Rep. Beyer is Vice Chair of the bipartisan Artificial Intelligence Caucus & Vice Chair of the NDC’s AI Working Group. He is the author of the AI Foundation Model Transparency Act & a lead cosponsor of the CREATE AI Act, the Federal Artificial Intelligence Risk Management Act & the Artificial Intelligence Environmental Impacts Act. We hope you tune

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43:30

First impressions of GPT-4o

Practical AI ·

2024/05/22

Daniel & Chris share their first impressions of OpenAI’s newest LLM: GPT-4o and Daniel tries to bring the model into the conversation with humorously mixed results. Together, they explore the implications of Omni’s new feature set - the speed, the voice interface, and the new multimodal capabilities.

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311 results

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