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  1. 1 day ago · Google DeepMind has now shared new research on JEST (Joint Example Selection Training), a way of training AI models that is 13 times faster and 10 times more power-efficient than current techniques. As the AI industry grows, so are concerns about the environmental impact of data centers required to train these sophisticated models.

  2. 1 day ago · Google DeepMind, Google's AI research lab, has published new research on training AI models that claims to greatly accelerate both training speed and energy efficiency by an order of magnitude ...

  3. 1 day ago · Google 's AI research lab, DeepMind, has developed a new AI training method called JEST (joint example selection), which is claimed to be 13 times faster and 10 times more energy-efficient than ...

  4. 1 day ago · In a bid to address these challenges, Google DeepMind has unveiled a groundbreaking new technique called Joint Example Selection (JEST). JEST represents a significant leap forward in AI training efficiency, promising to dramatically accelerate training speeds while drastically reducing energy consumption. This innovation has the potential to ...

  5. 5 days ago · A 2019 Google DeepMind project, for example, trained an AI model on weather forecasts and historical wind turbine data to predict the availability of wind power, helping to increase the value of...

  6. 1 day ago · “Soccer is a nice environment to study general reinforcement learning,” says team member Guy Lever. It requires planning, agility, exploration and competition. He, too, works at Google DeepMind in London. The toy size of the robots they trained allowed the team to run lots of experiments on their machines and learn from their mistakes.

  7. 4 days ago · Google Deepmind researchers have developed a method called JEST that makes training multimodal AI models for image and text processing more efficient by selecting subsets of data according to their joint learning ability. JEST uses two AI models - the model to be trained and a pre-trained reference model - to find out which data is particularly ...