Artificial intelligence

Artificial intelligence (also known by its acronym, AI; hereinafter, AI).

Related concepts

• Algorithm. A formula or set of rules for performing a task. In AI, the algorithm tells the machine how to go about finding answers to a question or solutions to a problem.
• Analogical reasoning. Solving problems by using analogies, by comparing to past experiences.
• Artificial intelligence (AI). A field of computer science dedicated to the study of computer software making intelligent decisions, reasoning, and problem solving.
• Artificial neural network (ANN). A learning concept based on the biological neural networks present in the brains of animals. Based on the activity of neurons, ANNs are used to solve tasks that would be too difficult for traditional methods of programming.
• Autonomous. Autonomy is the ability to act independently of a ruling body. In AI, a machine or vehicle is referred to as autonomous if it doesn't require input from a human operator to function properly.
• Backpropagation. Short for "backward propagation of errors," backpropagation is a way of training neural networks based on a known, desired output for specific sample case.
• Backward chaining. A method in which machines work backward from the desired goal, or output, to determine if there is any data or evidence to support those goals or outputs.
• Case-based reasoning (CBR). An approach to knowledge-based problem solving that uses the solutions of a past, similar problem (case) to solve an existing problem.
• Data mining. The process by which patterns are discovered within large sets of data with the goal of extracting useful information from it.
• Deep learning. A subset of machine learning that uses specialized algorithms to model and understand complex structures and relationships among data and datasets.
• Forward chaining. A situation where an AI system must work "forward" from a problem to find a solution. Using a rule-based system, the AI would determine which "if" rules it would apply to the problem.
• Heuristics. These are rules drawn from experience used to solve a problem more quickly than traditional problem-solving methods in AI. While faster, a heuristic approach typically is less optimal than the classic methods it replaces.
• Inductive reasoning. In AI, inductive reasoning uses evidence and data to create statements and rules.
• Machine learning. A field of AI focused on getting machines to act without being programmed to do so. Machines "learn" from patterns they recognize and adjust their behavior accordingly.
• Natural language processing (NLP). The ability of computers to understand, or process natural human languages and derive meaning from them. NLP typically involves machine interpretation of text or speech recognition.
• Planning. A branch of AI dealing with planned sequences or strategies to be performed by an AI-powered machine. Things such as actions to take, variable to account for, and duration of performance are accounted for.
• Pruning. The use of a search algorithm to cut off undesirable solutions to a problem in an AI system. It reduces the number of decisions that can be made by the AI system.
• Strong AI. An area of AI development that is working toward the goal of making AI systems that are as useful and skilled as the human mind.
• Turing test. A test developed by Alan Turing that tests the ability of a machine to mimic human behavior. The test involves a human evaluator who undertakes natural language conversations with another human and a machine and rates the conversations.
• Weak AI. Also known as narrow AI, weak AI refers to a non-sentient computer system that operates within a predetermined range of skills and usually focuses on a singular task or small set of tasks. Most AI in use today is weak AI.
• Weights. The connection strength between units, or nodes, in a neural network. These weights can be adjusted in a process called learning.

Related lectures

• Enterprise Intelligence Quarter.