Distributed AI and Applications
- [Mariaberget, Stockholm, Sweden - Unspalsh]
- Overview
Distributed artificial intelligence (DAI) is a subfield of AI that uses multiple devices or systems to solve problems in a decentralized way.
DAI is an approach to solving complex learning, planning, and decision-making problems. It is highly parallel and thus able to exploit large-scale computation and spatial distribution of computing resources.
DAI's goal is to create a network of intelligent agents that can work together to accomplish tasks that would be too difficult for a single agent.
Here are some characteristics of DAI:
- Scalability and robustness: DAI distributes AI model computation and communication across multiple nodes, which improves scalability and robustness.
- Parallel processing: DAI uses parallel processing to efficiently handle complex tasks.
- Collaborative computing: DAI uses collaborative computing to achieve a common goal.
- Communication and information exchange: DAI agents communicate and exchange information with each other to make collective decisions.
DAI is related to the field of multi-agent systems, which also uses distributed autonomous agents to interact within an environment. It involves distributing the computation and communication of AI models across multiple nodes or devices to enhance scalability and robustness.
Challenges include managing communication overhead and ensuring synchronization while delivering improved performance, but the potential of decentralized AI lies in its ability to efficiently handle complex tasks through parallel processing and collaborative computing.
[More to come ...]
