Last Week in AI News #64 Subscribe for future emails here! Mini Briefs OpenAI begins publicly tracking AI model efficiency In recent discussions on the progress of AI, a great deal of focus has been placed on model efficiency, which refers to “reducing the compute needed to train a model to perform a specific capability.” While methods such as deep neural networks and neural architecture search have produced great improvements according to metrics like accuracy, they use vast amounts of compute. This raises a number of issues, including the environmental impacts of using so much compute power and the inequitable access to the compute needed to reproduce research that employs these methods.
Skynet Today Last Week in AI News #64
Skynet Today Last Week in AI News #64
Skynet Today Last Week in AI News #64
Last Week in AI News #64 Subscribe for future emails here! Mini Briefs OpenAI begins publicly tracking AI model efficiency In recent discussions on the progress of AI, a great deal of focus has been placed on model efficiency, which refers to “reducing the compute needed to train a model to perform a specific capability.” While methods such as deep neural networks and neural architecture search have produced great improvements according to metrics like accuracy, they use vast amounts of compute. This raises a number of issues, including the environmental impacts of using so much compute power and the inequitable access to the compute needed to reproduce research that employs these methods.