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Changelog Media

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!

Practical AI: Machine Learning, Data Science

Today, Chris explores Shopify Magic and other AI offerings with Mike Tamir, Distinguished ML Engineer and Head of Machine Learning, and Matt Colyer, Director of Product Management for Sidekick. They talk about how Shopify uses generative AI and LLMs to enhance their products, and they take a deeper dive into Sidekick, a first-of-its-kind, AI-enabled commerce assistant that understands a merchant’s business (products, orders, customers) and has been trained to know all about Shopify.

Sidekick is an AI Shopify expert

Daniel and Chris groove with Jeff Smith, Founder and CEO at CHRP.ai. Jeff describes how CHRP anonymously analyzes emotional wellness data, derived from employees' music preferences, giving HR leaders actionable insights to improve productivity, retention, and overall morale. By monitoring key trends and identifying shifts in emotional health across teams, CHRP.ai enables proactive decisions to ensure employees feel supported and engaged.

Mozart to Megadeath at CHRP

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.

Video generation with realistic motion

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.

Deep-dive into DeepSeek

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.

Tool calling and agents

In this inaugural episode of Practical AI — Adam Stacoviak and Jerod Santo sit down with Daniel Whitenack and Chris Benson to discuss their experiences in Artificial Intelligence, Machine Learning, and Data Science and what they hope to accomplish as hosts of this podcast.

Meet your Practical AI hosts

Mat Ryer and David Hernandez joined Daniel and Chris to talk about MachineBox, building a company around AI, and democratizing AI.

Putting AI in a box at MachineBox

Amanda Ramcharan, Latifa Mrisho, and Peter McCloskey joined Daniel and Chris to talk about how Penn State University are collaborating to help African farmers increase their yields via a TensorFlow powered mobile app.

Helping African farmers with TensorFlow

Matthew Carroll and Andrew Burt of Immuta talked with Daniel and Chris about data management for AI, how data regulation will impact AI, and schooled them on the finer points of the General Data Protection Regulation (GDPR).

Data management, regulation, the future of AI

Andrew Vanderburg of UT Austin and Christ Shallue of Google Brain join us to talk about their deep learning collaboration, which involved searching through a crazy amount of space imagery to find new planets.

Tool calling and agents

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