I recall Nicky Hockley's keynote for the Reform Symposium 2013. She argued that the future is found in the present, and that many of the top science fiction films feature technology that already is in existence. She showed several images of recent movies such as Ellysium, I, Robot and Avatar to emphasise her point. Technology of the future is already here - we just haven't seen it released on the general public yet. The blockbuster science fiction movie Minority Report featured gestural computing, targeted advertising through biometric data scanning and augmented reality technologies. All of these were possible at the time the movie was being produced, and have been for some time. The director and production team consulted with researchers who showed them the possibilities. It won't be long before costs of such devices come down, and they become pervasive. Gestural computing for example, has been with us for a few years in the guise of games consoles such as the 360 Kinnect system manufactured by Microsoft.
In the late 1960s and early 1970s, Star Trek was just emerging as a popular new TV science fiction series. Kirk and Spock could walk up to a door and it would automatically open for them, and they could talk into personal, handheld communicators, and others could hold conversations with them. These technologies are now common place in the Western Industrial World, and we don't think twice about them. Other Star Trek technologies are also becoming common, including medical scanners (tricorder), video conferencing, touch screen tablets and even 3D printers (replicators). One technology that caught my eye was the universal translator. With it, Captain Picard and his crew could talk to any alien in real time, and could be understood perfectly. Along with faster-than-light travel and teleportation, it seemed like the only impossible dream remaining. Until now.
This week I read an article that documented the recent partnership between futurist Ray Kurzweil and Google's Larry Page. It seems they have teamed up to investigate how the Search Engine giant's massive server fleet and computational power can be harnessed to emulate a virtual human brain. They are calling it Deep Learning - a form of machine intelligence - and the project is already at an advanced stage of development. As the 'machine' is programmed, and supplied with vast amounts of connections at multiple layers of processing, and is exposed to massive amounts of stimulus material, it begins to 'think' and 'perceive' for itself. It has learnt to determine shapes and identify specific objects from among billions of images. Here's an excerpt from an article by Robert Hof in Forbes which documents the outcome of the collaboration between Google and Kurzweil:
In October [2013], Microsoft chief research officer Rick Rashid wowed attendees at a lecture in China with a demonstration of speech software that transcribed his spoken words into English text with an error rate of 7 percent, translated them into Chinese-language text, and then simulated his own voice uttering them in Mandarin. That same month, a team of three graduate students and two professors won a contest held by Merck to identify molecules that could lead to new drugs. The group used deep learning to zero in on the molecules most likely to bind to their targets.
The implications of this for education, business, commerce and a whole host of other sectors of society is ... immense. If we are all suddenly able to converse naturally, and in real time in any language, the world is going to change, and change radically. What will become of language teaching? Will we need it any more? Will translation services become redundant? Or will we still see people paying to learn foreign languages? What will happen to the social and cultural divides that currently separate us across the globe? Will they remain, or will they dissipate over time as we begin to come to terms with this new technology? Will such a universal translation tool become available to all, or will the social gulfs be amplified because of a new digital divide?
Photo by Richard Greenhill and Hugo Elias on Wikimedia Commons
Deep learning by Steve Wheeler is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.
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