How Deep Learning Can Give Birth to General Artificial Intelligence
From chessbots to chatbots and from search algorithms to industrial assembly machines – humanity has arrived at the age of machine intelligence; or at least machine work, if we take seriously a definition of intelligence as “the abilities to learn from experience, adapt to new situations, understand and handle abstract concepts, and use knowledge to manipulate one’s environment“ (Encyclopedia Britannica). While some machines like IBM’s Watson are astonishingly good at solving certain problems, a general artificial intelligence (AI) which would satisfy the definition above has yet to be created. AI research has been around since the mid-fifties, but the enormous scientific and technical challenges of building a “full AI” are still keeping researchers and programmers awake at night. Spearheading today’s research is a London-based start-up, DeepMind, which was founded in 2010 and bought by Google in 2014. Co-founder and CEO Demis Hassabis is a former child chess prodigy, video game creator, neuroscientist, and an artificial intelligence researcher whose vision is to “solve intelligence and use it to solve everything else”. With more than 150 brilliant machine learning experts, AI researchers and coders, Google DeepMind is now one of the most promising candidates for realizing this extremely ambitious goal. At Falling Walls, Demis provides rare insights into the organisation that is set out to create an “Apollo mission” for AI research.