Essay by Michael Batty at Urban Analytics and City Sciences: “…The notion of the smart city of course conjures up these images of such an automated future. Much of our thinking about this future, certainly in the more popular press, is about everything ranging from the latest App on our smart phones to driverless cars while somewhat deeper concerns are about efficiency gains due to the automation of services ranging from transit to the delivery of energy. There is no doubt that routine and repetitive processes – algorithms if you like – are improving at an exponential rate in terms of the data they can process and the speed of execution, faithfully following Moore’s Law.
Pattern recognition techniques that lie at the basis of machine learning are highly routinized iterative schemes where the pattern in question – be it a signature, a face, the environment around a driverless car and so on – is computed as an elaborate averaging procedure which takes a series of elements of the pattern and weights them in such a way that the pattern can be reproduced perfectly by the combinations of elements of the original pattern and the weights. This is in essence the way neural networks work. When one says that they ‘learn’ and that the current focus is on ‘deep learning’, all that is meant is that with complex patterns and environments, many layers of neurons (elements of the pattern) are defined and the iterative procedures are run until there is a convergence with the pattern that is to be explained. Such processes are iterative, additive and not much more than sophisticated averaging but using machines that can operate virtually at the speed of light and thus process vast volumes of big data. When these kinds of algorithm can be run in real time and many already can be, then there is the prospect of many kinds of routine behaviour being displaced. It is in this sense that AI might herald in an era of truly disruptive processes. This according to Brynjolfsson and McAfee is beginning to happen as we reach the second half of the chess board.
The real issue in terms of AI involves problems that are peculiarly human. Much of our work is highly routinized and many of our daily actions and decisions are based on relatively straightforward patterns of stimulus and response. The big questions involve the extent to which those of our behaviours which are not straightforward can be automated. In fact, although machines are able to beat human players in many board games and there is now the prospect of machines beating the very machines that were originally designed to play against humans, the real power of AI may well come from collaboratives of man and machine, working together, rather than ever more powerful machines working by themselves. In the last 10 years, some of my editorials have tracked what is happening in the real-time city – the smart city as it is popularly called – which has become key to many new initiatives in cities. In fact, cities – particularly big cities, world cities – have become the flavour of the month but the focus has not been on their long-term evolution but on how we use them on a minute by minute to week by week basis.
Many of the patterns that define the smart city on these short-term cycles can be predicted using AI largely because they are highly routinized but even for highly routine patterns, there are limits on the extent to which we can explain them and reproduce them. Much advancement in AI within the smart city will come from automation of the routine, such as the use of energy, the delivery of location-based services, transit using information being fed to operators and travellers in real time and so on. I think we will see some quite impressive advances in these areas in the next decade and beyond. But the key issue in urban planning is not just this short term but the long term and it is here that the prospects for AI are more problematic….(More)”.