Search results
Results from the WOW.Com Content Network
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 ]
Dennett identifies three key features of an algorithm: Substrate neutrality : an algorithm relies on its logical structure. Thus, the particular form in which an algorithm is manifested is not important (Dennett's example is long division: it works equally well on paper, on parchment, on a computer screen, or using neon lights or in skywriting).
Using artificial neural networks requires an understanding of their characteristics. Choice of model: This depends on the data representation and the application. Model parameters include the number, type, and connectedness of network layers, as well as the size of each and the connection type (full, pooling, etc. ).
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
Generative artificial intelligence (generative AI, GenAI, [1] or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data. [ 2 ] [ 3 ] [ 4 ] These models learn the underlying patterns and structures of their training data and use them to produce new data [ 5 ] [ 6 ] based on ...
Pronounced "A-star". A graph traversal and pathfinding algorithm which is used in many fields of computer science due to its completeness, optimality, and optimal efficiency. abductive logic programming (ALP) A high-level knowledge-representation framework that can be used to solve problems declaratively based on abductive reasoning. It extends normal logic programming by allowing some ...
The fundamental concept of algorithmic learning theory is learning in the limit: as the number of data points increases, a learning algorithm should converge to a correct hypothesis on every possible data sequence consistent with the problem space.
Automated decision-making involves using data as input to be analyzed within a process, model, or algorithm or for learning and generating new models. [7] ADM systems may use and connect a wide range of data types and sources depending on the goals and contexts of the system, for example, sensor data for self-driving cars and robotics, identity data for security systems, demographic and ...