A long way to go before Blade Runner
The hallmark of human intelligence is that it can do many things to a standard: we can read, walk, play games and many, many more things. AI platforms, however, are extremely specialised: AlphaGo can't play chess, and OpenAI is incapable of making a cup of tea.
Although some systems composite multiple techniques into a single one (smartphone cameras with face recognition and post-shot editing are a good example), these are not truly a multi-skilled AI.
"We can't keep glueing software together until we end up with an artificial brain," says Cook, "[but] will we ever need to?"
It's a good question. Frankly, we don't need robots that are as multi-talented as humans, like those in Blade Runner and Terminator. A system that could perform many different tasks to achieve a single outcome is certainly desirable (Cook's example is saving people from a burning building), but that AI would hardly need to know how to play lawn bowls.
"It's often easier to build a system that solves a specific problem rather than trying to build a general one… So one thing that will delay this a lot will be the simple fact that there often won't be an incentive to build something as generally talented as we are."
It's one of the hardest problems humanity is working on right now - it's the cold fusion of computer science
On the other hand, multi-talented AI is the sci-fi dream - and laziness is the father of invention. A robot that can walk the dog, cook dinner and clean the house without - to paraphrase Pratchett - stripping the plaster off the walls and making a furious cup of cat would certainly be a time-saver, and could be in the future now that technologists have both the vision and the money to put towards it.
"We may begin to see systems which can perform a lot of related but distinct tasks over the next decade. There's a few different ways to approach these problems on a structural level: some of them just involve more money and time, while others require the invention of new algorithms and AI techniques.
"It's one of the hardest problems humanity is working on right now, though - along with quantum computing, it's the cold fusion of computer science."
The future of gaming
There is no doubt that OpenAI's show matches were a powerful demonstration of a new approach to artificial intelligence: one that, with time, can challenge the best human players. Challenging a human doesn't equate to thinking like one, though.
We play games against other people to get the best of our opponents: to out-think them, as well as out-play them. "Human-like intelligence doesn't make an AI human in all the other ways that are important for games - being social, being emotional," says Cook.
"That's why we need to appreciate that game AI - all AI, really - has this theatrical element, this layer on top that's all about how the user or the player perceives the system. We don't want our AI to act like real human beings do, we need our AI to be actors: to understand the drama of a situation, what the player wants to achieve or avoid, and what they can do to help make that happen.
"That might involve playing a game really well, but that's just one small aspect of modern AI, and I think truly great game AI will need to embrace these ideas about storytelling and social interaction and modelling the player."
That is why OpenAI - despite its tactical approach in Dota 2 - is more of a demonstration about the future of AI in the real world, rather than gaming. Human-versus-machine contests like those at The International are entertaining and technologically impressive, but don't tell us much about how an AI compares to a human.
"It's useful, it's interesting, it's impressive," says Cook. "But the future of games will need a very different kind of AI."
Developments like OpenAI are useful because they represent a research milestone: a task that developers weren't good enough to accomplish before, but now can. Cook warns, however, "It's important that we remember that the journey is what matters, not the milestone at the end."
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