The future is dystopian: a world in which we humble humans will be replaced by fleets of slick automatons – mechanical menials destined to not only solder, weld and glue us out of jobs, but account, diagnose, and translate us out, too. Or, so goes a certain line of argument.
Certainly, there have been some heavyweight concerns voiced about the rise of artificial intelligence. Among them, by no lesser figures than those of physicist Stephen Hawking, Tesla/Space X chief Elon Musk and Microsoft’s Bill Gates. Of course, there are counterarguments too. Just as the Industrial Revolution sparked fears around the supplanting of man by machine (fears which lead some as far as destroying the new mechanical marvels: hence today’s use of the word ‘Luddite’ to denote those opposed to technological progress), all new vistas are likely to provoke both optimism and hesitance.
Leaving aside more extreme visions of armies of self-replicating nanobots and a workless future, AI – and talk of the next big thing – is, seemingly, everywhere. Some of the existing tech remains impressive: consider Amazon’s bright orange fleet of order-fulfilment Kiva robots, or at-your-service virtual assistants such as Apple’s Siri, Amazon’s Alexa or Microsoft’s Cortana.
In still another AI arena lies the computing power behind robo-vacuums or driverless cars. Then there are the more Kubrick-esque machines: neural network-based technologies capable of so-called ‘deep-learning’. Examples include Google’s DeepMind (whose AlphaGo succeeded at beating world champion Lee Sedol at the ancient game of Go), along with IBM’s multi-talented Watson.
The exponential rate of development in the field of machine processing has led many to believe that the future may, in fact, be now. Indeed, for certain applications, computers do a pretty good job at replicating – and indeed, in some instances, bettering – their human counterparts’ abilities.
There are other areas, however, in which the skills of machines remain far from their apogee. Among these are the areas in which analysis of human cognition is required – namely, understanding the subtleties inherent in both how people communicate and how they feel. The fields of social listening and opinion data analytics – essentially, areas in which sentiment meets data – are chief among them.
Unlike the rules of a game – take chess for example – human communication cannot mapped by simply encoding for a limited number of possible interactions. Nor are the ways in which we communicate always predictable or direct. Being socially embedded, we express ourselves differently in different contexts; moreover, norms of interaction vary by both place and time. Add to this humour, irony, sarcasm, allusion and the modelling universe expands drastically.
In search of insight
Both language itself – and the emotions we express using it – are highly complex. As such, it is unsurprising that pure machine processing remains not fully up to the task of interpreting the contents of the online landscapes in which the two interact, such as Facebook and Twitter.
To this end, the issue of so-called ‘topic discovery’ is the first challenge – honing on what Go to the full article.