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.

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 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.

We are at GenAI saturation, so let’s talk about scikit-learn, a long time favorite for data scientists building classifiers, time series analyzers, dimensionality reducers, and more! Scikit-learn is deployed across industry and driving a significant portion of the “AI” that is actually in production. :probabl is a new kind of company that is stewarding this project along with a variety of other open source projects. Yann Lechelle and Guillaume Lemaitre share some of the vision behind the company and talk about the future of scikit-learn!

It can be frustrating to get an AI application working amazingly well 80% of the time and failing miserably the other 20%. How can you close the gap and create something that you rely on? Chris and Daniel talk through this process, behavior testing, and the flow from prototype to production in this episode. They also talk a bit about the apparent slow down in the release of frontier models.

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.

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’.

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.

Workflow orchestration has always been a pain for data scientists, but this is exacerbated in these AI hype days by agentic workflows executing arbitrary (not pre-defined) workflows with a variety of failure modes. Adam from Prefect joins us to talk through their open source Python library for orchestration and visibility into python-based pipelines. Along the way, he introduces us to things like Marvin, their AI engineering framework, and ControlFlow, their agent workflow system.

As Argilla puts it: “Data quality is what makes or breaks AI.” However, what exactly does this mean and how can AI team probably collaborate with domain experts towards improved data quality? David Berenstein & Ben Burtenshaw, who are building Argilla & Distilabel at Hugging Face, join us to dig into these topics along with synthetic data generation & AI-generated labeling / feedback.

We are constantly hearing about disillusionment as it relates to AI. Some of that is probably valid, but Mike Lewis, an AI architect from Cincinnati, has proven that he can consistently get LLM and GenAI apps to the point of real enterprise value (even with the Big Cos of the world). In this episode, Mike joins us to share some stories from the AI trenches & highlight what it takes (practically) to show what is possible, doable & scalable with AI.

Seems like we are hearing a lot about GraphRAG these days, but there are lots of questions: what is it, is it hype, what is practical? One of our all time favorite podcast friends, Prashanth Rao, joins us to dig into this topic beyond the hype. Prashanth gives us a bit of background and practical use cases for GraphRAG and graph data.

Recently the company stewarding the open source library scikit-learn announced their seed funding. Also, OpenAI released “o1” with new behavior in which it pauses to “think” about complex tasks. Chris and Daniel take some time to do their own thinking about o1 and the contrast to the scikit-learn ecosystem, which has the goal to promote “data science that you own.”

Dinis Cruz drops by to chat about cybersecurity for generative AI and large language models. In addition to discussing The Cyber Boardroom, Dinis also delves into cybersecurity efforts at OWASP and that organization’s Top 10 for LLMs and Generative AI Apps.

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.

How do you systematically measure, optimize, and improve the performance of LLM applications (like those powered by RAG or tool use)? Ragas is an open source effort that has been trying to answer this question comprehensively, and they are promoting a “Metrics Driven Development” approach. Shahul from Ragas joins us to discuss Ragas in this episode, and we dig into specific metrics, the difference between benchmarking models and evaluating LLM apps, generating synthetic test data and more.

If you have questions at the intersection of Cybersecurity and AI, you need to know Donato at WithSecure! Donato has been threat modeling AI applications and seriously applying those models in his day-to-day work. He joins us in this episode to discuss his LLM application security canvas, prompt injections, alignment, and more.

You might have heard that “AI is only as good as the data.” What does that mean and what data are we talking about? Chris and Daniel dig into that topic in the episode exploring the categories of data that you might encounter working in AI (for training, testing, fine-tuning, benchmarks, etc.). They also discuss the latest developments in AI regulation with the EU’s AI Act coming into force.

There is an increasing desire for and effort towards GPU alternatives for AI workloads and an ability to run GenAI models on CPUs. Ben and Greg from Intel join us in this episode to help us understand Intel’s strategy as it related to AI along with related projects, hardware, and developer communities. We dig into Intel’s Gaudi processors, open source collaborations with Hugging Face, and AI on CPU/Xeon processors.

We discussed “🥦 Broccoli AI” a couple weeks ago, which is the kind of AI that is actually good/healthy for a real world business. Bengsoon Chuah, a data scientist working in the energy sector, joins us to discuss developing and deploying NLP pipelines in that environment. We talk about good/healthy ways of introducing AI in a company that uses on-prem infrastructure, has few data science professionals, and operates in high risk environments.

This week Daniel & Chris hang with repeat guest and good friend Demetrios Brinkmann of the MLOps Community. Together they review, debate, and poke fun at the 2024 Gartner Hype Cycle chart for Artificial Intelligence. You are invited to join them in this light-hearted fun conversation about the state of hype in artificial intelligence.

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.

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.

We’ve had representatives from Stanford’s Institute for Human-Centered Artificial Intelligence (HAI) on the show in the past, but we were super excited to talk through their 2024 AI Index Report after such a crazy year in AI! Nestor from HAI joins us in this episode to talk about some of the main takeaways including how AI makes workers more productive, the US is increasing regulations sharply, and industry continues to dominate frontier AI research.

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.

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.

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.

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.

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.

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.