By now, most of us are used to the idea of rapid, even accelerating, technological change, particularly where information technologies are concerned. Indeed, as consumers, we helped the process along considerably. We love the convenience of mobile phones, and the lure of social-media platforms such as Facebook, even if, as we access these services, we find that bits and pieces of our digital selves become strewn all over the internet.
More and more tasks are being automated. Computers (under human supervision) already fly planes and sail ships. They are rapidly learning how to drive cars. Automated factories make many of our consumer goods. If you enter (or return to) Australia with an eligible e-passport, a computer will scan your face, compare it with your passport photo and, if the two match up, let you in. The “internet of things” beckons; there seems to be an “app” for everything. We are invited to make our homes smarter and our lives more convenient by using programs that interface with our home-based systems and appliances to switch the lights on and off, defrost the fridge and vacuum the carpet.
With the demise of the local car industry and the decline of manufacturing, the services sector is expected to pick up the slack for job seekers. But robots are taking over certain roles once deemed human-only.
Clever though they are, these programs represent more-or-less familiar applications of computer-based processing power. With artificial intelligence, though, computers are poised to conquer skills that we like to think of as uniquely human: the ability to extract patterns and solve problems by analysing data, to plan and undertake tasks, to learn from our own experience and that of others, and to deploy complex forms of reasoning.
The quest for AI has engaged computer scientists for decades. Until very recently, though, AI’s initial promise had failed to materialise. The recent revival of the field came as a result of breakthrough advances in machine intelligence and, specifically, machine learning. It was found that, by using neural networks (interlinked processing points) to implement mathematically specified procedures or algorithms, machines could, through many iterations, progressively improve on their performance – in other words, they could learn. Machine intelligence in general and machine learning in particular are now the fastest-growing components of AI.
The achievements have been impressive. It is now 20 years since IBM’s Deep Blue program, using traditional computational approaches, beat Garry Kasparov, the world’s best chess player. With machine-learning techniques, computers have conquered even more complex games such as Go, a strategy-based game with an enormous range of possible moves. In 2016, Google’s Alpha Go program beat Lee Sedol, the world’s best Go player, in a four-game match.
Allan Dafoe, of Oxford University’s future humanities institute, says AI is already at the point where it can transform almost every industry, from agriculture to health and medicine, from energy systems to security and the military. With sufficient data, computing power and an appropriate algorithm, machines can be used to come up with solutions that are not only commercially useful but, in some cases, novel and even innovative.
Should we be worried? Commentators as diverse as the late Stephen Hawking and development economist Muhammad Yunus have issued dire warnings about machine intelligence. Unless we learn how to control AI, they argue, we risk finding ourselves replaced by machines far more intelligent than we are. The fear is that not only will humans be redundant in this brave new world, but the machines will find us completely useless and eliminate us.
If these fears are realistic, then governments clearly need to impose some sort of ethical and values-based framework around this work. But are our regulatory and governance techniques up to the task? When, in Australia, we have struggled to regulate our financial services industry, how on earth will governments anywhere manage a field as rapidly changing and complex as machine intelligence?
Governments often seem to play catch-up when it comes to new technologies. Privacy legislation is enormously difficult to enforce when technologies effortlessly span national boundaries. It is difficult for legislators even to know what is going on in relation to new applications developed inside large companies such as Facebook. On the other hand, governments are hardly IT ingenues. The public sector provided the demand-pull that underwrote the success of many high-tech firms. The US government, in particular, has facilitated the growth of many companies in cybersecurity and other fields.
Governments have been in the information business for a very long time. As William the Conqueror knew when he ordered his Domesday Book to be compiled in 1085, you can’t tax people successfully unless you know something about them. Spending of tax-generated funds is impossible without good IT. In Australia, governments have developed and successfully managed very large databases in health and human services.
The governance of all this data is subject to privacy considerations, sometimes even at the expense of information-sharing between agencies. The evidence we have is that, while some people worry a lot about privacy, most of us are prepared to trust government with our information. In 2016, the Australian Bureau of Statistics announced that, for the first time, it would retain the names and addresses it collected during the course of the 2016 population census. It was widely expected (at least by the media) that many citizens would withhold their names and addresses when they returned their forms. In the end, very few did.
But these are government agencies operating outside the security field. The so-called “deep state” holds information about citizens that could readily be misused. Moreover, private-sector profit is driving much of the current AI surge (although, in many cases, it is the thrill of new knowledge and understanding, too). We must assume that criminals are working out ways to exploit these possibilities, too.
If we want values such as equity, transparency, privacy and safety to govern what happens, old-fashioned regulation will not do the job. We need the developers of these technologies to co-produce the values we require, which implies some sort of effective partnership between the state and the private sector.
Could policy development be the basis for this kind of partnership? At the moment, machine intelligence works best on problems for which relevant data is available, and the objective is relatively easy to specify. As it develops, and particularly if governments are prepared to share their own data sets, machine intelligence could become important in addressing problems such as climate change, where we have data and an overall objective, but not much idea as to how to get there.
Machine intelligence might even help with problems where objectives are much harder to specify. What, for example, does good urban planning look like? We can crunch data from many different cities, and come up with an answer that could, in theory, go well beyond even the most advanced human-based modelling. When we don’t know what we don’t know, machines could be very useful indeed. Nor do we know, until we try, how useful the vast troves of information held by governments might be.
Perhaps, too, the jobs threat is not as extreme as we fear. Experience shows that humans are very good at finding things to do. And there might not be as many existing jobs at risk as we suppose. I am convinced, for example, that no robot could ever replace road workers – just think of the fantastical patterns of dug-up gravel and dirt they produce, the machines artfully arranged by the roadside or being driven, very slowly, up and down, even when all the signs are there, and there is absolutely no one around. How do we get a robot, even one capable of learning by itself, to do all that?
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