Small changes in prompts can create large changes in the output behavior of generative AI models. Add to that the confusion around proper evaluation of LLM applications, and you have a recipe for confusion and frustration. Raza and the Humanloop team have been diving into these problems, and, in this episode, Raza helps us understand how non-technical prompt engineers can productively collaborate with technical software engineers while building AI-driven apps.

Shopify recently released a Hugging Face space demonstrating very impressive results for replacing background scenes in product imagery. In this episode, we hear the backstory technical details about this work from Shopify’s Russ Maschmeyer. Along the way we discuss how to come up with clever AI solutions (without training your own model).

We are on the other side of “big data” hype, but what is the future of analytics and how does AI fit in? Till and Adithya from MotherDuck join us to discuss why DuckDB is taking the analytics and AI world by storm. We dive into what makes DuckDB, a free, in-process SQL OLAP database management system, unique including its ability to execute lighting fast analytics queries against a variety of data sources, even on your laptop! Along the way we dig into the intersections with AI, such as text-to-sql, vector search, and AI-driven SQL query correction.

Daniel & Chris sit down with Denis Yarats, Co-founder & CTO at Perplexity, to discuss Perplexity’s sophisticated AI-driven answer engine. Denis outlines some of the deficiencies in search engines, and how Perplexity’s approach to information retrieval improves on traditional search engine systems, with a focus on accuracy and validation of the information provided.

In the midst of the demos & discussion about OpenAI’s GPT-4o voice assistant, Kyutai swooped in to release the first real-time AI voice assistant model and a pretty slick demo (Moshi). Chris & Daniel discuss what this more open approach to a voice assistant might catalyze. They also discuss recent changes to Gartner’s ranking of GenAI on their hype cycle.

It seems like all we hear about are the great use cases for GenAI, but where should you NOT be using the technology? On this episode Chris and Daniel share their hot takes and bad use cases. Some may surprise you!

Why is ML is so poorly adopted in small organizations (hint: it’s not because they don’t have enough data)? In this episode, Kirsten Lum from Storytellers shares the patterns she has seen in small orgs that lead to a successful ML practice. We discuss how the job of a ML Engineer/Data Scientist is different in that environment and how end-to-end project management is key to adoption.

Daniel and Chris explore the intersection of Kaggle and real-world data science in this illuminating conversation with Christof Henkel, Senior Deep Learning Data Scientist at NVIDIA and Kaggle Grandmaster. Christof offers a very lucid explanation into how participation in Kaggle can positively impact a data scientist’s skill and career aspirations. He also shared some of his insights and approach to maximizing AI productivity uses GPU-accelerated tools like RAPIDS and DALI.

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.

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).

In this enlightening episode, we delve deeper than the usual buzz surrounding AI’s perils, focusing instead on the tangible problems emerging from the use of machine learning algorithms across Europe. We explore “suspicion machines” — systems that assign scores to welfare program participants, estimating their likelihood of committing fraud. Join us as Justin and Gabriel share insights from their thorough investigation, which involved gaining access to one of these models and meticulously analyzing its behavior.

This week, Chris is joined by Gregory Richardson, Vice President and Global Advisory CISO at BlackBerry, and Ismael Valenzuela, Vice President of Threat Research & Intelligence at BlackBerry. They address how AI is changing the threat landscape, why human defenders remain a key part of our cyber defenses, and the explain the AI standoff between cyber threat actors and cyber defenders.

Daniel & Chris conduct a retrospective analysis of the recent OpenAI debacle in which CEO Sam Altman was sacked by the OpenAI board, only to return days later with a new supportive board. The events and people involved are discussed from start to finish along with the potential impact of these events on the AI industry.

With the recent proliferation of generative AI models (from OpenAI, co:here, Anthropic, etc.), practitioners are racing to come up with best practices around prompting, grounding, and control of outputs.

Chris and Daniel take a deep dive into the kinds of behavior we are seeing with this latest wave of models (both good and bad) and what leads to that behavior. They also dig into some prompting and integration tips.

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.

While everyone is super hyped about generative AI, computer vision researchers have been working in the background on significant advancements in deep learning architectures. YOLOv9 was just released with some noteworthy advancements relevant to parameter efficient models. In this episode, Chris and Daniel dig into the details and also discuss advancements in parameter efficient LLMs, such as Microsofts 1-Bit LLMs and Qualcomm’s new AI Hub.

Daniel & Chris explore the advantages of vector databases with Roie Schwaber-Cohen of Pinecone. Roie starts with a very lucid explanation of why you need a vector database in your machine learning pipeline, and then goes on to discuss Pinecone’s vector database, designed to facilitate efficient storage, retrieval, and management of vector data.

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.

Elham Tabassi, the Chief AI Advisor at the U.S. National Institute of Standards & Technology (NIST), joins Chris for an enlightening discussion about the path towards trustworthy AI. Together they explore NIST’s ‘AI Risk Management Framework’ (AI RMF) within the context of the White House’s ‘Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence’.

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.

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.

Chris & Daniel explore AI in national security with Lt. General Jack Shanahan (USAF, Ret.). The conversation reflects Jack’s unique background as the only senior U.S. military officer responsible for standing up and leading two organizations in the United States Department of Defense (DoD) dedicated to fielding artificial intelligence capabilities: Project Maven and the DoD Joint AI Center (JAIC).

Together, Jack, Daniel & Chris dive into the fascinating details of Jack’s recent written testimony to the U.S. Senate’s AI Insight Forum on National Security, in which he provides the U.S. government with thoughtful guidance on how to achieve the best path forward with artificial intelligence.

Neural search and chat-based search are all the rage right now. However, You.com has been innovating in these topics long before ChatGPT. In this episode, Bryan McCann from You.com shares insights related to our mental model of Large Language Model (LLM) interactions and practical tips related to integrating LLMs into production systems.

Practical AI is a weekly podcast that’s marking artificial intelligence practical, productive, and accessible to everyone. If world of AI affects your daily life, this show is for you.

From the practitioner wanting to keep up with the latest tools & trends…

(clip from episode #68)

To the AI curious trying to understand the concepts at play and their implications on our lives…

(clip from episode #39)

Expert hosts Chris Benson and Daniel Whitenack are here to keep you fully-connected with the world of machine learning and data science.

Please listen to a recent episode that interests you and subscribe today. We’d love to have you as a listener!

Runway is an applied AI research company shaping the next era of art, entertainment & human creativity. Chris sat down with Runway co-founder / CTO, Anastasis Germanidis, to discuss their rise and how it’s defining the future of the creative landscape with its text & image to video models. We hope you find Anastasis’s founder story as inspiring as Chris did.

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.

What can art historians and computer scientists learn from one another? Actually, a lot! Amanda Wasielewski joins us to talk about how she discovered that computer scientists working on computer vision were actually acting like rogue art historians and how art historians have found machine learning to be a valuable tool for research, fraud detection, and cataloguing. We also discuss the rise of generative AI and how we this technology might cause us to ask new questions like: “What makes a photograph a photograph?”

This last week has been a big week for AI news. BigScience is training a huge language model (while the world watches), and NVIDIA announced their latest “Hopper” GPUs. Chris and Daniel discuss these and other topics on this fully connected episode!

According to Solana Larsen: “Too often, it feels like we have lost control of the internet to the interests of Big Tech, Big Data — and now Big AI.” In the latest season of Mozilla’s IRL podcast (edited by Solana), a number of stories are featured to highlight the trailblazers who are reclaiming power over AI to put people first. We discuss some of those stories along with the issues that they surface.

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 into this inspiring, nonpartisan conversation with Rep. Beyer about his decision to dive into the deep end of the AI pool & his leadership in bringing that expertise to Capitol Hill.