When AI rules the world: what SF novels tell us about our future overlords

#artificialintelligence

It's only March and already we've seen a computer beat a Go grandmaster and a self-driving car crash into a bus. The world is waking up to the ways in which a combination of "deep learning" artificial intelligence and robotics will take over most jobs. But if we don't want our robot servants to rise up and kill us in our beds, maybe we should delete the video of us beating their grandparents with hockey sticks. Thanks to science fiction, we know that the first thing AI will do is take over the defence grid and nuke us all. In Harlan Ellison's 1967 story I Have No Mouth, and I Must Scream – one of the most brutal depictions of an AI-dominated world – an AI called AM, constructed to fight a nuclear war, kills off most of the human race, keeping five people as playthings.


These poker-playing robots can bluff better than humans

#artificialintelligence

When it comes to understanding intelligence, the greatest challenge out there is not a Rubik's Cube, or chess, or even Go. These games are difficult in the sense that there are often many options, but they are still transparent: nothing is hidden; every bit of information is in front of you. The main obstacle is converting this perfect information into a strategy. There is a fixed set of rules out there, and if a computer can find them, it will achieve the optimal result in every game. When Garry Kasparov lost to IBM's Deep Blue chess computer in 1997, he lamented this approach.


Why football, not chess, is the true final frontier for robotic artificial intelligence

#artificialintelligence

First was the Monte Carlo tree search, an algorithm that rather than attempting to examine all possible future moves instead tests a sparse selection of them, combining their value in a sophisticated way to get a better estimate of a move's quality. The second was the (re)discovery of deep networks, a contemporary incarnation of neural networks that had been experimented with since the 1960s, but which was now cheaper, more powerful, and equipped with huge amounts of data with which to train the learning algorithms. The combination of these techniques saw a drastic improvement in Go-playing programs, and ultimately Google DeepMind's AlphaGo program beat Go world champion Lee Sedol in March 2016. Now that Go has fallen, where do we go from here? Following Kasparov's defeat in 1997, scientists considered that the challenge for AI was not to conquer some cerebral game.


Why football, not chess, is the true final frontier for robotic artificial intelligence

#artificialintelligence

First was the Monte Carlo tree search, an algorithm that rather than attempting to examine all possible future moves instead tests a sparse selection of them, combining their value in a sophisticated way to get a better estimate of a move's quality. The second was the (re)discovery of deep networks, a contemporary incarnation of neural networks that had been experimented with since the 1960s, but which was now cheaper, more powerful, and equipped with huge amounts of data with which to train the learning algorithms. The combination of these techniques saw a drastic improvement in Go-playing programs, and ultimately Google DeepMind's AlphaGo program beat Go world champion Lee Sedol in March 2016. Now that Go has fallen, where do we go from here? Following Kasparov's defeat in 1997, scientists considered that the challenge for AI was not to conquer some cerebral game.


Why football, not chess, is the true final frontier for robotic artificial intelligence

#artificialintelligence

First was the Monte Carlo tree search, an algorithm that rather than attempting to examine all possible future moves instead tests a sparse selection of them, combining their value in a sophisticated way to get a better estimate of a move's quality. The second was the (re)discovery of deep networks, a contemporary incarnation of neural networks that had been experimented with since the 1960s, but which was now cheaper, more powerful, and equipped with huge amounts of data with which to train the learning algorithms. The combination of these techniques saw a drastic improvement in Go-playing programs, and ultimately Google DeepMind's AlphaGo program beat Go world champion Lee Sedol in March 2016. Now that Go has fallen, where do we go from here? Following Kasparov's defeat in 1997, scientists considered that the challenge for AI was not to conquer some cerebral game.