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Building a Smart City from scratch – IV

NVIDIA Metropolis is the edge to cloud platform developed to support AI algorithm accessing the huge, dynamical, set of data created by video cameras in a city and make sense out of the images streaming in. Image credit: NVIDIA

4. Artificial intelligence (AI),

The amount of data created and harvested by sensors (IoT) in a city is huge, but what matters is to be able to make sense out of those data. Basically, once one has the data the following questions have to be answered (ordered by the value they produce):

  • What is going on?
  • Why is it happening?
  • What is likely to happen?
  • How can we steer the evolution?

Artificial Intelligent algorithms are an effective way of doing this, thanks to an ever increasing availability of processing power and dedicated platform to make data access practical, both at the edges and in the cloud. To get a general feeling of what can be done applying AI to city data you may start by taking a look at the animation created by NVIDIA focussing on video camera images.

Indeed, the area of image recognition is an ideal turf for AI. Nvidia has created a platform, Metropolis, to provide easy access to these data and has promoted a challenge, in collaboration with SJSU, for exploiting image detection in real time in a city, watch the clip below. According to IDC safety/security cameras, along with smart mobility and smart illumination make up 25% of the global spending in Smart Cities in 2019.

In London there are an estimated 500,000 video cameras (over 15,000 in the underground alone). This is huge but it is not very different from what is deployed in other cities. Besides, their number is growing and the advent of autonomous vehicles will add even (many) more cameras.  The problem is that most of the images captured are not for share and many systems are closed systems, i.e. they are not connected to the cloud. There are mostly regulatory hurdles to overcome, technology is not a main issue. However, technology (and architecture/open platform services) can provide the needed safeguards to let regulators allow the sharing of images, thus allowing their processing.

This is not the only problem associated to the use of security camera data. An even bigger one is privacy concern and societal acceptability.  In China there is a plan to use city’s images of people and AI to create a Universal Social Credit System. The Big Brother is watching you: did you paid the ticket on the bus, did you drop a piece of paper on the sidewalk, did you walk across the road on a red light…. Your social behaviour will be monitored and your social credit will grow or decrease….

At BleuTech Park the idea is to have all video cameras becoming sensors providing data that can be analysed.

Clearly AI is going to process all sorts of data, well beyond the ones produced by video cameras (these data requires very specific AI capabilities, i.e. image recognition), aiming at answering the four questions I listed at the beginning of this post.

There is also another, important, area where AI can help: creating awareness at the single citizen level. So many data are bound to be overwhelming and there is a need to re-shape them into a form that is easy to understand by the individual citizen. This is a very complex task that is likely to involve the Digital Twin of the city AND the Digital Twin of the individual citizen. The information provided by BleuTech is insufficient to know if there is a plan to use Digital Twins and AI in this area. It would make perfect sense…

About Roberto Saracco

Roberto Saracco fell in love with technology and its implications long time ago. His background is in math and computer science. Until April 2017 he led the EIT Digital Italian Node and then was head of the Industrial Doctoral School of EIT Digital up to September 2018. Previously, up to December 2011 he was the Director of the Telecom Italia Future Centre in Venice, looking at the interplay of technology evolution, economics and society. At the turn of the century he led a World Bank-Infodev project to stimulate entrepreneurship in Latin America. He is a senior member of IEEE where he leads the Industry Advisory Board within the Future Directions Committee and co-chairs the Digital Reality Initiative. He teaches a Master course on Technology Forecasting and Market impact at the University of Trento. He has published over 100 papers in journals and magazines and 14 books.