The machine refused to move to a position that had a decisive short-term advantage - showing a very human sense of danger," said chess grandmaster Garry Kasparov after losing game two of his famous 1997 rematch with IBM's supercomputer Deep Blue.

Kasparov would go on lose the series with Deep Blue by a half point, and would later question IBM's use of human experts in between games, but it was his choice of words in the aftermath of game two that has raised the most enduring questions.

If a computer could mimic human emotions and intuitions, defeating one of the greatest chess minds of all time in the process, what else could machines achieve?

At the heart of Deep Blue's victory was its evaluation function - a complex algorithm that measured the 'goodness' of a given chess position and was capable of evaluating 200 million moves per second.

While in 1997 algorithms were the sole domain of computer science whizzes, today they are helping to solve problems in business and science that were seemingly intractable just a few years ago.

Take medical diagnostics. Hospitals around the world are increasingly using algorithms to help diagnose conditions and identify patients who might be at risk for certain diseases.

By combining patients' historic health data with the wealth of information in electronic medical databases and online textbooks, doctors are able to identify more effective treatment plans.

AI is also helping to restore mobility for quadriplegics, with wheelchairs capable of being controlled by thoughts, and AI may yet bring sight back to the blind. In America, the FDA recently approved a first-generation retinal implant, which transmits images wirelessly to a microelectrode implanted on a patient's damaged eye.

The march of AI doesn't stop at medical diagnostics and solutions. In manufacturing, intelligent machines are regularly deployed to help optimise what resources get allocated on the production line, while in Iraq and Afghanistan the PackBot by iRobot has performed thousands of bomb disposals, making the job of a soldier safer in the process.

AI is also helping to make human lives easier. Apps such as RedLaser and BuyVia are helping consumers compare prices by simply scanning the barcode of the product they are interested in on their mobile device, while Apple and Android's smartphone assistants are capable of responding to thousands of voice-activated user requests.

For those who don't like driving, a future in which cars drive themselves may not be a thing of fantasy. Google recently unveiled a prototype of a driverless car with no steering wheel or pedals and a top speed of 25mph.

Although many of these advances are proving to be life-changing for individuals and have improved business productivity as a whole, technological change is rarely pain free.

While it is difficult to extricate the effects of AI from other macroeconomic effects, there is increasing evidence that AI is destroying jobs faster than it is creating them.

Erik Brynjolfsson and Andrew McAfee, academics at MIT, argue that advances in AI are behind the sluggish employment growth American has experienced in the last 10 to 15 years, noting that since 2000 productivity has continued to rise robustly, but employment growth has stagnated.

"People are falling behind because technology is advancing so fast and our skills and organisations aren't keeping up," they say.

Oxford University academics take a more pessimistic view of the effects of AI on the labour market, arguing that nearly half of all American jobs could be automated in a decade or two.

"While computerisation has been historically confined to routine tasks, algorithms for big data are now readily substituting labour in a wide range of non-routine cognitive tasks," say Carl Frey and Michael Osborne.

One such non-routine job potentially under threat is that of the board director. In the first appointment of its kind, a Japanese venture capital firm Deep Knowledge recently named an algorithm to its board of directors.

Dmitry Kaminskiy, a senior partner at the firm, says that the machine can "automate due diligence and use historical data-sets to uncover trends that are not immediately obvious to humans".

Journalists aren't off limits either. In June of this year, The Associated Press announced that the majority of US corporate earnings stories for its business news report will eventually be produced through a computer program that is able to create a 300-word article.

If algorithms moonlighting as journalists and board directors sounds ambitious, consider the aims of RoboCup, an annual robotics competition founded in 1997:

"By the middle of the 21st century, a team of fully autonomous humanoid robot soccer players shall win a soccer game, complying with the official rules of FIFA, against the winner of the most recent World Cup."

Could intelligent machines really take the place of the world's best soccer players? It may seem preposterous now, but then so did the thought of Garry Kasparov being defeated by an intelligent machine in 1997.

Given its disruptive effects, it's somewhat unsurprising that AI has experienced a backlash from certain sections of society.

Various survivalist and anarchist groups have stated their intention to halt the development of AI, with one New Mexico group going so far as to detonate a letter bomb at the Monterrey Institute of Technology in 2011.

In the academic community there is also fear. In a recent opinion piece, Stephen Hawking and several scientist co-authors warned: "One can imagine such technology outsmarting financial markets, out-inventing human researchers, out-manipulating human leaders, and developing weapons we cannot even understand. Whereas the short-term impact of AI depends on who controls it, the long-term impact depends on whether it can be controlled at all."

While these concerns have led to calls for swift and effective regulation to limit the effects of AI on the workforce, and also calls for protective rights for robots, the consensus in the scientific community is that AI is a long way from making the kind of intuitive leaps that come naturally to humans.

As Douglas Hofstadter, Professor of Cognitive Science at Indian University makes clear: "Deep Blue plays very good chess - so what? Does that tell you something about how we play chess?"

For now at least, the answer is no.

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