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The regulation of artificial intelligence is the development of public sector policies and laws for promoting and regulating AI; it is therefore related to the broader regulation of algorithms. [322] The regulatory and policy landscape for AI is an emerging issue in jurisdictions globally. [ 323 ]
The book's chapters span from classical AI topics like searching algorithms and first-order logic, propositional logic and probabilistic reasoning to advanced topics such as multi-agent systems, constraint satisfaction problems, optimization problems, artificial neural networks, deep learning, reinforcement learning, and computer vision. [7]
Automated planning and scheduling, sometimes denoted as simply AI planning, [1] is a branch of artificial intelligence that concerns the realization of strategies or action sequences, typically for execution by intelligent agents, autonomous robots and unmanned vehicles.
A probabilistic algorithm's behavior depends on a random number generator called by the algorithm. These are subdivided into Las Vegas algorithms , for which (like concurrent algorithms) all runs must produce correct output, and Monte Carlo algorithms which are allowed to fail or produce incorrect results with low probability.
Artificial intelligence arms race – competition between two or more states to have its military forces equipped with the best "artificial intelligence" (AI). Lethal autonomous weapon; Military robot; Unmanned combat aerial vehicle; Mitigating risks: AI safety; AI control problem
Machine learning (ML) is a subfield of artificial intelligence within computer science that evolved from the study of pattern recognition and computational learning theory. [1] In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". [2]
Algorithmic information theory principally studies complexity measures on strings (or other data structures).Because most mathematical objects can be described in terms of strings, or as the limit of a sequence of strings, it can be used to study a wide variety of mathematical objects, including integers.
In computer science, empirical algorithmics (or experimental algorithmics) is the practice of using empirical methods to study the behavior of algorithms.The practice combines algorithm development and experimentation: algorithms are not just designed, but also implemented and tested in a variety of situations.