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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." [3]
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.
She is the founder of the Distributed Artificial Intelligence Research Institute (DAIR). In December 2020, public controversy erupted over the circumstances surrounding Gebru's departure from Google , where she was technical co-lead of the Ethical Artificial Intelligence Team.
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]
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.
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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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