When we started DeepMind in 2010, there was far less interest in the field of AI than there is today. To accelerate the field, we took an interdisciplinary approach, bringing together new ideas and advances in machine learning, neuroscience, engineering, mathematics, simulation and computing infrastructure, along with new ways of organising scientific endeavour. Intelligence allows us to learn, imagine, cooperate, create, comm...
When we started DeepMind in 2010, there was far less interest in the field of AI than there is today. To accelerate the field, we took an interdisciplinary approach, bringing together new ideas and advances in machine learning, neuroscience, engineering, mathematics, simulation and computing infrastructure, along with new ways of organising scientific endeavour. Intelligence allows us to learn, imagine, cooperate, create, communicate, and so much more. By better understanding different aspects of intelligence, we can use this knowledge as inspiration to build novel computer systems that learn to find solutions to difficult problems.
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