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Despite operating at the cutting edge of AI research, hes fairly conservative about his estimates for when well reach the holy grail of artificial general intelligence (AGI) (basically meaning that an algorithm can operate with the same level of mental dexterity as a human). A number of problems remain, he says. Not only in matching the competencies of human reasoning, but also, even when you have this powerful tool, how do you align it effectively, verifiably and safely with what society and what individuals users want out of it? Its the meeting the messy world element: researchers like Bloxwich are still grappling with how to create effective tests for these algorithms and unleashing them, Kohli argues, is a number of scientific steps beyond that. To give an example, if you have an AI system which might be used for healthcare, right? How do you make it interpretable? And who should be able to interpret it's reasoning? Is it the doctors, is it the designers or is it the patients? And there are different forms of interpretability what might be obvious to a clinician, or a machine learning person, might not be obvious to a patient. Making sure that these systems are deployed safely in the real world is a whole research problem in its own right.

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What next for AI revolution? Inside Google DeepMind, the world's biggest AI company - Evening Standard