
Making artificial intelligence practical, productive, and 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, etc). 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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Pausing to think about scikit-learn & OpenAI o1
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."
Cybersecurity in the GenAI age
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.
AI is more than GenAI
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.
Metrics Driven Development
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.
Threat modeling LLM apps
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.
Only as good as the data
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.
Gaudi processors & Intel's AI portfolio
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.
Broccoli AI at its best 🥦
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.
Hyperventilating over the Gartner AI Hype Cycle
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.
The first real-time voice assistant
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.
Vectoring in on Pinecone
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.
Stanford's AI Index Report 2024
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.
Apple Intelligence & Advanced RAG
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.
The perplexities of information retrieval
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.
Using edge models to find sensitive data
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.