Explore AI Learning Resources

It's easy to assume AI means generative AI, because that's what gets the attention. But generative AI is one specialized part of a much larger field, and different tasks call for different kinds of AI—recommendation engines, for instance, are not chatbots.

In this video, you'll trace a brief history of AI from the first neural network theories of the 1940s through the AI winters to the deep learning era and today's generative boom. You'll learn how the key terms nest inside one another and leave with a mental map you can use to place any AI tool you encounter.

While AI literacy and AI fluency are often conflated, they mean vastly different things in practice. Literacy is conceptual: knowing what AI is, distinguishing machine learning from generative AI, and understanding hallucinations. Fluency is operational: how you delegate, exercise judgment, and navigate risk.

In this video, you’ll learn what AI literacy and fluency truly are—and what becoming AI-fluent means for your career and daily work.

AI fluency is easier to recognize than to define. This framework breaks it into four dimensions that follow the lifecycle of any AI-assisted task: understanding AI before you start, working with AI as you delegate, evaluating AI once it produces something, and owning AI risk across the whole process.

Each dimension unpacks into sub-skills you can observe and develop: matching the right tool to the task, sizing the stakes, spotting irreversible decisions, checking facts and substance, and taking ownership of the outcome. In this video, you'll learn the full framework and how the pieces fit together.

Modern generative AI rests on a surprisingly simple idea. Feed a model billions of sentences and play games with it—hide words and make it fill in the blanks, or ask whether one sentence logically follows another. Do this at scale and the model learns grammar, context, and a good deal of world knowledge.

In this video, you'll learn how that pre-training stage works, how instruction fine-tuning turns a general model into one that follows commands, and how those two steps produced the explosion of models—closed-source and open-source—that now compete on capability. You'll also see where text fits in the wider world of image and multimodal generation.