Computers will have developed ‘common sense’ within a decade and we could be counting them among our friends not long afterwards, one of the world’s leading AI scientists has predicted. Professor Geoff Hinton was hired by Google two years ago to help develop intelligent operating systems.
He said that the company is on the brink of developing algorithms with the capacity for logic and natural conversation. The researcher told the Guardian said that Google is working on a new type of algorithm designed to encode thoughts as sequences of numbers, something he described as “thought vectors”.
Although the work is at an early stage, he said there is a plausible path from the current software to a more sophisticated version that would have something approaching human-like capacity for reasoning and logic. “Basically, they’ll have common sense.”
The idea that thoughts can be captured and distilled down to cold sequences of digits is controversial, Hinton said. “There’ll be a lot of people who argue against it, who say you can’t capture a thought like that,” he added. “But there’s no reason why not. I think you can capture a thought by a vector.”
Hinton, who is due to give a talk at the Royal Society in London on Friday, believes that the “thought vector” approach will help crack two of the central challenges in artificial intelligence: mastering natural, conversational language, and the ability to make leaps of logic.
He painted a picture of the near-future in which people will chat with their computers, not only to extract information, but for fun, reminiscent of the film, Her, in which Joaquin Phoenix falls in love with his intelligent operating system.
“It’s not that far-fetched,” Hinton said. “I don’t see why it shouldn’t be like a friend. I don’t see why you shouldn’t grow quite attached to them.” In the past two years, scientists have already made significant progress in overcoming this challenge.
Richard Socher, an artificial intelligence scientist at Stanford University, recently developed a program called NaSent that he taught to recognise human sentiment by training it on 12,000 sentences taken from the film review website Rotten Tomatoes.
Part of the initial motivation for developing “thought vectors” was to improve translation software, such as Google Translate, which currently uses dictionaries to translate individual words and searches through previously translated documents to find typical translations for phrases. Although these methods often provide the rough meaning, they are also prone to delivering nonsense and dubious grammar.
Thought vectors, Hinton explained, work at a higher level by extracting something closer to actual meaning.