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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]
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]
BigScience Large Open-science Open-access Multilingual Language Model (BLOOM) [1] [2] is a 176-billion-parameter transformer-based autoregressive large language model (LLM). The model, as well as the code base and the data used to train it, are distributed under free licences. [ 3 ]
The workload during the model design and training phase depends significantly on whether the engineer is building the model from scratch or fine-tuning an existing one. When creating a model from scratch, AI engineers must design the entire architecture, selecting or developing algorithms and structures that are suited to the problem.
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.
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