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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]
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.
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.
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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Artificial intelligence engineering (or AI engineering) is a technical discipline that focuses on the design, development, and deployment of AI systems. AI engineering involves applying engineering principles and methodologies to create scalable, efficient, and reliable AI-based solutions.
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