AI Fundamentals

CS50's Introduction to Artificial Intelligence with Python

Harvard's rigorous introduction to AI algorithms and concepts using Python. Covers search, optimization, machine learning, neural networks, and NLP. The definitive first technical AI course for engineers entering the field from adjacent disciplines.

AI for Everyone

Andrew Ng's non-technical introduction to AI designed for business leaders. Covers what AI can and cannot do, how to build an AI strategy, and how to work with an AI team. The clearest entry point for C-suite executives setting AI direction.

MIT 6.S191: Introduction to Deep Learning

MIT's annual intensive on deep learning methods — covering neural networks, CNNs, RNNs, generative models, and reinforcement learning. Freely available with lab notebooks. The standard benchmark for engineering teams evaluating deep learning literacy.

The Hundred-Page Machine Learning Book

The most concise and complete introduction to machine learning for technical professionals — covering supervised and unsupervised learning, neural networks, and practical deployment considerations in under 200 pages. A reliable desk reference.

Prediction Machines: The Simple Economics of Artificial Intelligence

The Rotman School economists' framework for understanding AI as a prediction-cost reduction technology. The clearest mental model for business leaders deciding where AI creates value versus where it creates risk in regulated enterprise contexts.

AI Superpowers: China, Silicon Valley, and the New World Order

Kai-Fu Lee's analysis of the global AI race and its economic implications. Required reading for executives in regulated industries who need to understand the strategic dimensions of AI adoption — not just its technical mechanics.

MIT 6.S191: Introduction to Deep Learning (2024)

MIT's full lecture series on deep learning — covering CNNs, RNNs, generative models, and reinforcement learning with full lab notebooks available. Free, annual, and maintained at the frontier of the field by MIT researchers.

Intro to Large Language Models

Andrej Karpathy's 1-hour introduction to LLMs for technical audiences — covering tokenization, the transformer architecture, RLHF, and the security implications of prompt injection. The clearest technical overview of how modern LLMs actually work.

IBM Technology AI Explainer Series

IBM Technology's channel of authoritative AI explainers — covering generative AI, LLMs, RAG, AI governance, and enterprise deployment patterns. Short-form (5-15 min) videos designed for busy executives and technical audiences in regulated industries.

Import AI 456: RSI and economic growth; radical optionality for AI regulation; and a neural computer

Musk v. Altman week 3: Musk and Altman traded blows over each other’s credibility. Now the jury will pick a side.

Solving virtual machine puzzles: How AI is optimizing cloud computing

Beyond the Model — Why Responsible AI Must Address Workforce Impact

Anthropic will pay xAI $1.25B per month for compute

Elements of AI

The University of Helsinki's free AI literacy program — completed by over 1 million people across 170 countries. A practical tool for organizations launching enterprise-wide AI literacy initiatives without requiring prior technical knowledge.

Google AI Studio

Google's free development environment for experimenting with Gemini models — covering prompt engineering, structured outputs, and multimodal AI. The fastest way to evaluate Google's frontier models for specific regulated-industry use cases.

ChatGPT (OpenAI)

OpenAI's widely-used AI assistant. The de facto standard for evaluating LLM capabilities in enterprise contexts. Enterprise tier provides data privacy controls required for regulated industry use. Essential benchmark for any AI capability assessment.