How APIs work APIs follow a request-response model. A client application sends a request to an API endpoint (a specific URL), and the server processes the request and returns a response—typically in JSON or XML format. A typical API call: Client sends request: GET https://api.example.com/users/123 Server processes: Looks up user 123 in the database Server…
Definition A subset of machine learning that uses artificial neural networks with many layers — typically three or more hidden layers — to learn hierarchical representations of data. Each layer extracts increasingly abstract features from the input, allowing the model to discover complex patterns without hand-engineered feature extraction. ‘Deep’ refers to the network’s depth, not…
Generative AI creates new content — text, images, video, code, audio — by learning patterns from training data. Learn how transformers, diffusion models, and other architectures power modern AI systems.
The show-your-work analogy Think of chain-of-thought like a math teacher asking students to show their work, not just the final answer. A student who writes “42” gets partial credit at best. A student who shows each calculation step—identifying the problem, breaking it into parts, solving each one, and combining the results—demonstrates real understanding and catches…
Sensory Inc.’s antitrust lawsuit against Google exposes the contractual lock-in that limits your smart home to three AI choices. A July 2026 ruling lets the case proceed to discovery.
AI alignment is the field of AI safety focused on ensuring AI systems behave consistently with human values, intentions, and goals.
Agent governance is the system of policies, controls, and oversight structures that determine what autonomous AI agents can do, how they are monitored, and who is accountable when they act.
Agent compliance is the set of regulatory, legal, and governance obligations that organizations must meet when developing, deploying, or operating autonomous AI agents—covering risk classification, human oversight, audit trails, incident reporting, and accountability for agent actions.
Natural Language Processing (NLP) is the field of AI focused on enabling machines to understand, interpret, and generate human language, from rule-based systems to modern LLMs.
Responsible AI is a set of principles and practices guiding the ethical design, development, and deployment of AI systems to ensure fairness, transparency, accountability, and safety.