01
AI Foundations
Understand what AI is, how systems learn, where they fail, and how to judge claims without hype.
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Five outcome-based courses
Each course includes explicit outcomes, mapped competencies, practice activities, and an applied capstone.
02
Responsible AI User
Use AI productively while protecting privacy, checking outputs, and preserving human accountability.
03
AI at Work
Evaluate workplace use cases, run safe pilots, measure value, and communicate changes responsibly.
04
AI Policy & Society
Analyze AI systems through rights, equity, governance, safety, and public-interest outcomes.
05
Building with AI Systems
Understand language models, retrieval, agents, evaluation, cost, and deployment safeguards through practical system design.
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FundamentalsCore concepts: how AI works at a high level.84 guides Language AILLMs, NLP, ChatGPT, prompting, and text understanding.155 guides Visual AIImage generation, computer vision, video, and design.133 guides Audio AIVoice, music, podcasting, and audio-first AI.117 guides CompaniesProfiles of OpenAI, Anthropic, Google, Meta, and more.121 guides ApplicationsHow AI shows up in coding, search, agents, and daily life.111 guides IndustriesAI in healthcare, finance, education, energy, and more.91 guides TechnicalChips, fine-tuning, RAG, vectors, and AI infrastructure.177 guides SocietyBias, privacy, copyright, safety, and the AI future.30 guides
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Language AI
Medusa Decoding Heads
Medusa is a speculative-decoding method that bolts several extra prediction 'heads' onto a language model so it can guess multiple future tokens at once.
Language AIMixture of Depths
Mixture of Depths (MoD) lets a transformer spend different amounts of compute on different tokens, routing only the 'important' tokens through each layer's…
Language AIMamba and Selective State Spaces
Mamba is a sequence model built on state space models (SSMs) that processes text in linear time, offering a fast alternative to the Transformer's quadratic…
Language AIRWKV Linear Attention
RWKV is an architecture that trains like a Transformer but runs inference like a recurrent network, giving linear-time, constant-memory generation.
Language AISparse Attention Patterns
Sparse attention makes Transformers cheaper by letting each token attend to only a carefully chosen subset of other tokens rather than all of them.
IndustriesAI in Water Management
AI helps utilities detect pipe leaks, predict demand, and optimize treatment so cities waste less water and energy.
IndustriesAI in Space and Satellites
AI lets spacecraft navigate, analyze imagery, and make decisions without waiting for distant ground commands.
IndustriesAI in Wildlife Conservation
AI helps conservationists count animals, identify individuals, and catch poachers by automatically analyzing camera-trap photos, audio, and satellite imagery.
TechnicalQ-Learning
Q-Learning is a reinforcement learning algorithm that teaches an agent which actions pay off best by gradually learning the value of each move through trial…
TechnicalActor-Critic Methods
Actor-Critic methods combine two learners: an 'actor' that chooses actions and a 'critic' that judges how good those actions were.
TechnicalMonte Carlo Tree Search
Monte Carlo Tree Search (MCTS) is a planning algorithm that decides the best move by selectively building a search tree and simulating many possible futures.
IndustriesAI in Emergency Medicine and Triage
AI helps emergency departments and ambulance services decide who needs care first and fastest, flagging the sickest patients before a clinician can see them.
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