machine learning
Scientific study of algorithms and statistical models that computer systems use to perform tasks without explicit instructions.
- Developer
- Arthur Samuel
- Follows
- pattern recognition, computational learning theory
- Type of
- computer science, artificial intelligence
- Instance of
- academic discipline, certification, finance, field of study, field of study, learning approach, branch of computer science
Connected
Linked from
- OpenAIAmerican artificial intelligence research organization
- AnthropicAmerican artificial intelligence corporation
- DataikuAI and data analytics software company
- humainSaudi company owned by the Public Investment Fund that focuses on artificial intelligence
- Reflection AIAmerican artificial intelligence company developing open-weight foundation models
- Aliceisraeli-American cybersecurity firm
- PhotomyneIsraeli technology company
- Geoffrey HintonBritish-Canadian computer scientist and psychologist
- John HopfieldAmerican scientist (born 1933)
- Lila TretikovRussian–American engineer, manager and former executive director of the Wikimedia Foundation
- Katie BoumanAmerican engineer and computer scientist
- Daphne KollerIsraeli-American computer scientist
- Andrej KarpathySlovakian-Canadian AI researcher (born 1986)
- Ilya SutskeverCanadian-Israeli AI researcher
- Richard S. SuttonCanadian computer scientist
- Andrew BartoProfessor of computer science
- Ian GoodfellowAmerican scientist, author
- Vladimir VapnikRussian-American mathematician (born 1936)
- Jeff DeanAmerican computer scientist
- Wojciech ZarembaPolish mathematician and computer scientist
- Sara van de GeerDutch statistician
- Sebastian ThrunGerman-American entrepreneur (born 1967)
- Stanley OsherAmerican mathematician
- Christopher M. Bishopscientist at Microsoft Research Cambridge and Professor of Computer Science at the University of Edinburgh
More concepts & methods
- deep learningspecialized form of machine learning that uses artificial neural networks to identify patterns in data
- genetic algorithmcompetitive algorithm for searching a problem space
- Altitude, depth and rangeFelix Rieseberg’s framework for ambition with AI models: speed repeats work faster, altitude targets outcomes, depth uses expert instruments and range combines disciplines.
- Outcome-based promptingAsking an AI agent to pursue a specified change for a user or system, with evidence and boundaries, instead of merely producing a named deliverable.
- AI outside judgeAn acceptance check outside an agent’s self-assessment, such as a deterministic test, held-out evaluation, measurement or separately briefed reviewer.
- Multidisciplinary agent reviewA workflow in which specialist passes apply distinct methods and produce concrete evidence, rather than merely imitate expert personas.
Sources
- Wikidata Q2539 (structured facts, CC0)
- Wikipedia