Search results
Results from the WOW.Com Content Network
Distributed Artificial Intelligence (DAI) is an approach to solving complex learning, planning, and decision-making problems.It is embarrassingly parallel, thus able to exploit large scale computation and spatial distribution of computing resources.
Diagram of a Federated Learning protocol with smartphones training a global AI model. Federated learning (also known as collaborative learning) is a machine learning technique focusing on settings in which multiple entities (often referred to as clients) collaboratively train a model while ensuring that their data remains decentralized. [1]
What a Web server is to the Internet, a model server is to AI. Where a Web server receives an HTTP request and returns data about a Web site, a model server receives data, and returns a decision or prediction about that data: e.g. sent an image, a model server might return a label for that image, identifying faces or animals in photographs.
The Distributed Artificial Intelligence Research Institute (or DAIR Institute) is a research institute founded by Timnit Gebru in December 2021. [ 1 ] [ 2 ] The institute announced itself as "an independent, community-rooted institute set to counter Big Tech’s pervasive influence on the research, development and deployment of AI."
Simple reflex agent Learning agent. A multi-agent system (MAS or "self-organized system") is a computerized system composed of multiple interacting intelligent agents. [1] Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. [2]
Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices.
The notion of parallel intelligence has its roots in the interdisciplinary fields of cognitive science, AI, and collective intelligence. It draws inspiration from the observation that human intelligence, when combined with AI technologies, can lead to superior performance in problem-solving tasks compared to either intelligence alone.
General purpose AI; human performance modeling; learning (including explanation-based learning) John E. Laird, Clare Bates Congdon, Mazin Assanie, Nate Derbinsky and Joseph Xu; Division of Computer Science and Engineering, University of Michigan, Ann Arbor, Michigan, USA