associate professor of computing and information systems at Melbourne University, Tim Baldwin Facebook has formed a secretive research group rumoured to be working on the possibility of developing software that can identify emotions in text, recognise objects in photos and even predict the likely future behaviour of each of its 1 billion users. Google's chief engineer, Ray Kurzweil, recently told MIT Technology Review that he envisions a ''cybernetic friend'' based on deep learning that listens in on your phone conversations, reads your emails and tracks your every move so that it can tell you things you want to know even before you ask. Speaking at a Google language-processing workshop in China, Tim Baldwin, an associate professor of computing and information systems at the University of Melbourne, confirms that there have been significant advances in deep learning. ''The technologies, the techniques and theories have existed for a while,'' he says. ''Now there has been a coming together of hardware fast enough to train the models, a corporate boost to push it forward and teams formed to make it happen more quickly.''

Hod Lipson with actual modular cubes used to make a self-replicating robot and small toy cubes he uses to illustrate the mechanism. The neural network process uses layers of information-processing algorithms, or ''neural nodes'', stacked on top of each other. Each layer passes what it has learnt to the one above for further processing, providing an increasingly sophisticated result. Older image processing software, for example, examined images pixel by pixel, a system that required immensely complicated algorithms and produced uncertain outcomes. Neural networking applies sparse coding to images and looks for outlines rather than pixel patterns, allowing computers to identify them with greater accuracy. The same sparse coding approach, which breaks down information into simple forms to be interpreted and reassembled through successive layers of neural nodes in a way that mimics the operation of the human brain, can be used to process and understand any kind of data, and could produce results previously confined to science fiction. Many scientists, who once scoffed at the idea of a truly intelligent computer capable of making its own decisions, no longer do. For example, Hod Lipson, professor of engineering at New York's Cornell University, has built ''self-aware'' robots that use feedback from their limbs to learn to walk.

Lipson says his robots have the ability to learn, understand themselves and even self-replicate. As well, by watching and touching swinging pendulums for a day, one used its own basic algorithm to learn a formula that took humans thousands of years to discover: force equals mass times acceleration. On another front, Kurzweil believes a conscious machine capable of understanding complex natural language will be developed within the next 16 years. ''I've had a consistent date of 2029 for that vision,'' he told Wired magazine, ''and that doesn't just mean logical intelligence. It means emotional intelligence, being funny, getting the joke, being sexy, being loving, understanding human emotion. That's actually the most complex thing we do. That is what separates computers and humans today.'' However, Baldwin is not convinced. ''Frankly, there's a lot of hype around deep learning,'' he says. ''There's lots of exciting work to be done, but it [digital consciousness] is a long way off.

''We're giving machines instructions so they can learn for themselves how to do tasks, but it's still very one-dimensional. We have come a long way, but there's nothing that's going to directly produce a sentient machine, so it's not as if machines are about to take over the world. ''It's more like, looking back over our shoulders, we can say 'wow', we've actually come a reasonable way. We're opening up new frontiers and maybe one of those frontiers will be a thinking, feeling machine.'' The world's biggest artificial brain is one built by Andrew Ng at Stanford University, in California. It's a deep-learning monster with the equivalent of more than 11 billion neural connections, while requiring the comparatively small computational power of 16 servers with graphics processing units. Although Ng's effort is more than six times larger than the previous record holder, Google's Brain project, two researchers have pointed out that such neural network computers haven't even approached the intelligence of a rat. The human brain has about 100 trillion connections, and there is still that elusive word ''consciousness'' to grapple with. Teams at the cutting edge of deep learning are increasingly turning to neurologists and biologists to come up with an answer.

In the meantime, perhaps you should be nice to your computer, just in case.