Video Library

Is AI just ChatGPT? And where did all this come from? In this video, we'll cut through the hype to explain how AI, machine learning, deep learning, and generative AI relate to one another—and why the distinction matters for the work you do.

What does it mean to be AI-fluent? More importantly, how does fluency differ from basic AI literacy? In this video, we’ll break down the fundamentals of AI literacy and explain why moving toward fluency matters.

How many AI tools are there, really? And which ones matter for your work? In this video, we'll take a tour of the AI tool landscape, from foundation models to specialized industry solutions, so you know what's in the toolbox.

Which parts of your work should AI do, and which should stay with you? In this video, we'll look at how to map AI's strengths against the stakes of a task, so you delegate deliberately instead of by default.

What does AI fluency actually look like in practice? And how do you know which parts of it you're missing? In this video, we'll introduce the four dimensions of our AI fluency framework and the observable behaviors that sit underneath each one.

How does AI actually get things wrong? And could you spot it if it happened in your work? In this video, we'll walk through twelve common failure modes, from hallucination to context rot, so you can predict, catch, and prevent them.

How confident are you that AI makes you faster and better at your job? And how would you know if it didn't? In this video, we'll look at the gap between what people believe about their AI use and what actually happens—and why confidence is a poor guide to capability.

What exactly are you asking the AI to do? In this video, we'll focus on the task—the specific job that connects your business problem to the right data and method—and introduce the most common ones, from classification to clustering to sentiment analysis.

What's the difference between a spreadsheet and a photo, as far as AI is concerned? In this video, we'll compare structured and unstructured data side by side and show why the distinction dictates which AI model you should use.

How do you get an LLM to answer questions about data it was never trained on? And what makes an AI agent different from a chatbot? In this video, we'll move beyond single prompts to the two most important generative AI archetypes: RAG and agents.

Why do so many AI projects go wrong before a model is even chosen? In this video, we'll explain why understanding your data comes first: the qualities it needs, the three broad structures it takes, and how that shapes everything downstream.

How does a computer learn to understand language? In this video, we'll dive into the foundations of generative AI: language modeling, the pre-training and fine-tuning process, and the fast-moving ecosystem of closed and open-source models that resulted.

What is a model, really? And how did "input" become "prompt"? In this video, we'll explain what an AI model does in the simplest terms, how generative AI changed the picture, and the questions to ask before trusting any model with your task.

Should AI run the whole task, or just part of it? And how do you decide? In this video, we'll introduce the delegation dial—four settings for how much control you hand to AI, calibrated to the stakes of the work.

What's actually happening inside an AI model? In this video, we'll go under the hood to match the four common data types with the methods built for them: decision trees and boosting for tables, transformers for text, CNNs for images, and graph neural networks for networks.

Did that 30-minute AI task really take 30 minutes? Or did it quietly turn into four days of checking and re-prompting? In this video, we'll look at the hidden cost of verifying AI output and how to factor it into what you delegate.