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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.
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."
Horovod is a free and open-source software framework for distributed deep learning training using TensorFlow, Keras, PyTorch, and Apache MXNet. Horovod is hosted under the Linux Foundation AI (LF AI). [3] Horovod has the goal of improving the speed, scale, and resource allocation when training a machine learning model. [4]
Hanna now oversees research at Gebru's Distributed AI Research Institute. Her work centers on communities most affected by AI. "A near-term AI dystopia is one in which there's not a masterful AI ...
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.
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.
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]
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