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DILIP VERMA, REGIONAL VP, INDIA // JANUARY 23, 2018
Before we delve too deeply into just how this can happen in cognitive cities, we first need to ground our discussion in a more solid definition of what a cognitive city really is. Wikipedia’s article on the subject provides a good starting point by defining a cognitive city as one which learns through the constant engagement of its citizens and advanced technologies. Together, these two features enable the cognitive city to become more efficient and resilient by enabling information exchange within the city.
Safe cities protect their residents. Smart cities collect data from their residents and technologies in order improve safety as well as the efficiency of public services. Cognitive cities can effectively use that data for improving the health, safety, well-being, and prosperity of its citizens, because the whole city is involved in the gathering, sharing, and use of the data. In cognitive cities, data now flows not only from the citizens to and from city management (as in smart cities), but also from citizen to citizen, and citizen to system. Not only does the whole city generate and consume information, but the whole city learns continuously and adapts as the city learns from it.
The cognition in a cognitive city happens not only in an administrative office or in a control center, but in and across the complex web of systems which comprise an urban area: social networks, local industries, transportation networks, utility systems, communications infrastructure and services, non-profit organizations, and political parties and movements. In a truly cognitive city, all members in this complex ecosystem are able to not only disperse data but also store memories, like how your brain records memories by connecting individual synapses.
With that adaptability comes the resiliency necessary to take on the list of the current urban challenges we cataloged in the first article of this series – rapid growth, limited space and resources, crime prevention – while at the same time surviving and managing rarer crises like earthquakes, floods, and other disasters.
So how do we build this cognitive city of the future?
The first step is to fully realize the potential of a smart city because cognitive cities build on their foundation. In practical terms, this includes constant citizen interaction via multiple touchpoints: in-person at government offices, over the phone with city personnel, and online through social media, city government websites, online chat, email, and mobile apps. Of those, perhaps social media is the technology which has the best potential to quickly foster the harvesting of information from citizens and the citizen to citizen interaction we mentioned above, and which is an indispensable part of a cognitive city. In addition, the rapidity of social media communications naturally lends itself to crowdsourced feedback.
Once that feedback comes in through social, other tech can step in. Predictive analytics, natural language processing, and cloud computing can not only help analyze citizen sentiment, cross reference citizens data with systems and sensors but to also proactively respond to behavioral patterns and deviations in them. For example, if there’s a sudden surge of tweets which favorably mention the hashtag of the local hockey team by city residents, the city administration may want to allocate more police to directing traffic on the streets around the rink during the next home game, in order to accommodate the throngs of fans who’ve come out to see their team. That city could then also put out advance warnings of traffic restrictions special parking changes on Twitter, tagging those tweets with the same hashtag, thus contributing relevant and useful information back to those same fans.
In that simple example, we see the essence of the smart city: seamlessly knitting together communication technology (Twitter) with how city personnel (the police) are deployed to make existing infrastructure (the roads around the rink) function more efficiently.
Once those tools are in place, the cognitive city can emerge. Cognitive cities learn from the data over time, and that learning will require more sophisticated tools for finding correlations between different data streams, trends within each of them, and instantly detecting anomalies in them and in the complex city system. Machine learning and the cloud will be key enablers in this transition from smart to cognitive.
A cognitive city would be able to learn from our above example, and generalize from the individual data points to valuable lessons which can then be used to make predictions of road traffic conditions and the number of police personnel required for any major sporting event, in any neighborhood, given historical data and real-time social media trending. This fundamentally shifts all urban operations away from situation management and towards real-time adaptation.
In the first post in this series, we saw how advanced technology alone is unable to make a city safe or smart. In our second installment, we discussed why: city residents are more than consumers, they’re citizens, and thus tech must incorporate and augment existing legal and civic mechanisms. In this final post, we demonstrated how cognitive cities use extensive data analytics to make the city smarter over time, and one of the ways this intelligence shows is in predictive, proactive adaptations (as opposed to reactive, rushed crisis management).
Even though smart cities are still maturing, cognitive cities will be the future of efficient, connected urban areas which employ technology and human intelligence to foster community, innovation, and prosperity. Both elements are necessary because tech alone can’t make a city safe, nor are city residents mere consumers of goods and services. Constant citizen engagement, ubiquitous data collection, and sophisticated analytics can combine to produce the best kind of cognitive city: the kind someone would actually want to live in.
IFTACH DRORI, DIGITAL MARKETING MANAGER, QOGNIFY // FEBRUARY 07, 2018
The ultimate goal of a smart city is to improve quality of life by using technology. In a recent article in Public Sector Executive, author Eddie Copeland talks about how governments should be focused on addressing urban challenges. Of course, safety and security are always urban challenges, because they are always primary objectives.
According to Copeland, what makes a city smart isn’t the technology, but what it can do to better the lives of those living and visiting. The real value of a smart city is what it enables: a place where people and business can thrive, prosper and enjoy life.
A defining trait of smart cities is their interconnectedness. Unfortunately, the more connectivity there is, the less privacy we’ve got. This situation has been referred to as the “cost of luxury”. Minimizing that cost becomes an imperative so that the benefits of a smart city outweigh the potential vulnerabilities.
The potential risk of cyber breaches extends beyond those associated with personal privacy. Think of the consequences of a hacked utility or smart app that controls traffic flow; those could easily result in business disruption and potential physical harm to citizens. For example, the ShotSpotter smart city application initiative in New York City is a “gunfire detection system that can detect different types of weaponry as it is being fired.” The nefarious hacking of a system like this could have dangerous results.
A critical element of cybersecurity is physical security – they are interdependent. You’ve got to protect physical access to cyber assets in order for them to be secure. That makes many of the elements of a safe city necessary for not only the actual operation of smart city applications but also for their cybersecurity.
Moreover, as concerns of personal privacy increase, city governments and smart city app vendors will need to be able to demonstrate that maximum cyber (and thus physical) protection is in place.
So much of the technology that enables smart city apps are from or used in safe city solutions: video, sensors, analytics, information management software, communication tools and more. Based on the two reasons discussed above – having a strategic smart city strategy and ensuring privacy and cybersecurity – it simply doesn’t make sense not to leverage safe city technology as a foundation for smart city applications.
As in any new field, buzz words will keep coming out. However, when you strip out all the hype, the main objective of any smart city initiative is the value it provides citizens and governments. And there simply is no value to what could be the smartest application without it being based on a foundation of personal and public safety and security.
Call them safe, smart or safe smart cities, at the end of the day it’s all about making technology work to improve aspects of our lives – and safety and security are always first and foremost.
HAGAR LEV, MARKETING DIRECTOR, QOGNIFY // FEBRUARY 14, 2018
IFTACH DRORI, DIGITAL MARKETING MANAGER, QOGNIFY // FEBRUARY 19, 2018
There are a few issues that will need to be addressed prior to seeing the mass adoption that has been predicted.
While the market is working on improving the battery life of a drone, currently, the average commercial drone has about a 25-minute flight time capacity. For certain applications, this is fine. Firefighters effectively use drones to gain a birds-eye view of the fire they’re battling to great benefit.
For applications such as guard duty or perimeter protection, which require 24/7 capacity, short battery life poses an issue.
Like with all technology that matures, the cost of drones is on a downward trend. The issue is not necessarily with the cost of a single drone, it’s the number of drones needed in order to effectively carry out many of its potential applications.
As mentioned above, a drone could be very effective in perimeter protection. In theory, you could overcome the battery-life issue by rotating a fleet of drones, but this would be expensive, especially if manned guards are still necessary.
Like a lot of technology, the origin of drones comes from the military with their invention of UAVs (unmanned aerial aircraft). Developed for battlefield use, as its name implies, a drone is an aircraft. As such, there are regulatory considerations when using drones for security or commercial purposes, including aviation; but also because of their capabilities, governance concerning consumer data protection and privacy also must be addressed.
While governments and industry are pushing forward to apply relevant regulations in order not to stifle this important technology, until that time, the market is seeking clarity prior to moving ahead with full-scale adoption.
Here’s how a civilian drone almost shut down Gatwick Airport last July:
As mentioned, firefighters are already using this technology to gain a perspective they otherwise wouldn’t have, making their efforts more effective. Drones are also being used detect movement and armed with video monitoring capabilities can transmit real-time footage of what is happening at the scene. Drones are here to stay.
The market is rapidly figuring out how to expand the use of drones for the security applications mentioned above. With global powerhouses like Amazon investing in drone technology for their own commercial use, the security market will certainly benefit from the attention and resources being put forth by other industries. In the meantime, security vendors and users of the technology are working together to leverage drones within the existing parameters.
ANDREW SELDON, GUEST BLOGGER // MARCH 05, 2018
You can’t step out of the door these days without hearing about Artificial Intelligence (AI) and how companies are using it to change the world of security. this is a short article, we will simply use AI as a holdall to refer to Artificial Intelligence, Deep Learning, and Machine Learning, although these are really distinct, yet connected disciplines.
The problem with all this talk is that it’s easy to be caught up in the hype without understanding what AI actually is or the benefits it is supposed to bring. I would suggest we have yet to see real AI at work in the security industry.
From traditional line crossing to the far more complex facial recognition systems out there, as well as the ability to differentiate between humans and animals or inanimate objects, there are some impressive products on the market (and a few not quite as impressive).
But is this AI? The short answer is ‘sort of yes’, and no. The advances in software that led to these features are impressive, but there is much more to come.
Real AI is built on masses of data that is analyzed and categorized (or ‘sliced and diced’ in old Business Intelligence parlance). More importantly, real AI learns from the data it analyses and can make predictions and inferences based on that analysis.
Video analytics today offers the ability to detect when a human is approaching a restricted area and raise an alert. Some vendors offer solutions that can tell the difference between animals and a human, and a dog, for example, won’t generate an alarm. But what if there is heavy rainfall or mist so that even a thermal camera can’t generate images the system can clearly identify as human?
This is where AI comes into the picture, so to speak. As a simple example: by using the historic data as a learning experience, the system would be able to infer that although it can’t clearly identify the object moving into a restricted area as human, it seems to move on two legs and therefore can be classified as human and this warrants raising the alarm.
Similarly, take facial recognition in a retail environment. It’s easy (assuming you have the right equipment) to build up a database of faces and to denote certain faces as VIP guests and others as unwanted guests. What an AI system will do is take that facial database, combine it with other information from the retail environment, and provide the security team with actionable information.
Another simple example. The retail system will alert security that face ‘X’ has entered the premises and that on the past six occasions X was around, someone had their handbag stolen and X left the premises within 10 minutes of the theft. This is not to say X is a thief or that there is video or any other evidence of wrongdoing, but it says there is a correlation between face X and a security event. With this information, perhaps a security operative should be sent to hang around near X, or perhaps the control room should keep an eye on him/her through virtual surveillance.
AI goes far beyond security, of course. Businesses can use the information for operational, human resources, marketing, and other purposes as well, with direct results observable on the bottom line.
The key here is data. Not simply video surveillance data, but any and all data, whether structured or unstructured and the ability to gain access to it easily in a format the system can read and analyze, with the ultimate goal of making connections between seemingly unrelated events.
A real AI system will use multiple sources of data to predict probable events or to infer outcomes based on the patterns it detects in data. It’s far more than simply crossing a line or recognizing a face, it’s what you do with information that defines intelligence – artificial or not.
DILIP VERMA, REGIONAL VP, INDIA // MARCH 26, 2018
Almost all cities are currently looking into or have already taken steps to become safe cities. A few have even begun making the transition to becoming smart cities. The next stage in this urban evolution is the so-called “cognitive” city. Even though the concept of cognitive cities is still in its infancy stage, at the end of our recent 3-part series, we identified two key ingredients: advanced data analytics of large volumes and multiple types of data, and an adaptability which drives resiliency and continuous improvement. Although the advent of truly cognitive cities is well into the future, when they arrive, we’ll be able to distinguish them from both safe and smart cities:
The key to a cognitive city is city-wide cognition of the underlying need, rather than the leveraging of a specific tech or platform. Meaning, to be able and rapidly adopt technologies and practices, thus giving the city a natural protection against vendor lock-in. Smart cities, by contrast, tend to approach ICT as just another utility, like water and sewage, electricity, or garbage collection, which are often operated as regulated monopolies, with long contract periods and expensive financial and legal hurdles in place that prevent adopting another service provider or utility.
Cognitive cities, therefore, are much better positioned to thrive in the face of significant challenges like clean water scarcity, climate change, and providing functional and efficient mass transportation at mega-city scales.
Cognitive cities hold the promise of being more resilient than other urban areas which haven’t made that transition.
In our next post, we’ll take a closer look at how a cognitive city of the future serves its citizens, examine new challenges, and offer an interim view or where we are currently in terms of cognitive cities becoming a reality.
DILIP VERMA, REGIONAL VP, INDIA // APRIL 09, 2018
In our previous post, we discussed that, although we don’t exactly know what a cognitive city will look like, we do, however, have a general idea of how it would behave: a cognitive city will be more resilient, adaptable, and efficient than both smart and safe cities, and would not only improve security, safety, and city operations but also to more generally improve the lives of the city’s residents.
In today’s post, we’re going to explain how the focus beyond the technology itself is key for cities to transition from being smart to becoming cognitive.
One of the reasons why a firmer concept of cognitive cities remains so elusive is because the idea of smart cities hasn’t been fully realized. In the opening installment of our recent three-post series on the transition of smart cities to cognitive ones, we hinted at one of the reasons why:
“There is one major caveat to smart city solutions: the data tends to flow in one direction from what are ultimately surveillance devices to government officials… leading to tensions between personal privacy and government goals of safety and higher efficiency”.
This tension is not the only root of a lot of the pushback and criticism of smart city initiatives, including very tech-savvy media outlets.
Here are two others:
Does social-media driven transition to cognitive cities serve ALL citizens? Even with wireless broadband connectivity becoming cheaper and faster, and sensor and processing technologies getting more affordable, it is likely to be limited to certain parts of the population, for at least some time. Consider this article from Wired which describes an ambitious smart city project, backed by Google, in Toronto as likely to only serve technologically-capable millennials, and ignore the other citizens such as the elderly, the disabled, and the poor.
Data doesn’t analyze itself: Another reason why smart cities have yet to widely appear is the fact that people with real data science, statistical, and programming skills are required for a city’s data to work for its people. Again, a very non-technical roadblock arises, as a recent article from the same publication points out, which is the challenge of hiring knowledgeable people who can actually “separate the data wheat from the data chaff”.
Cities trying to mature from safe to truly smart will encounter these and other constraining realities, but it’s just as important to remember that even cities which will have become cognitive will also encounter these same realities.
The advantage of a cognitive city lies in the fact that data collection won’t be limited to electronic sensors, information sharing will occur over more than just copper wires or strands of glass fiber, and decision making will be distributed over residents, civic groups, elected officials, and other stakeholders.
All of a city’s fluid, often competing, and overlapping constituencies and systems must sense, adapt, learn, and remember together. This is a key insight from our previous post: collective and individual responses to change and challenges become ingrained habits, a key mechanism behind memory formation in a cognitive city. Those habits – namely citizens’ interaction with ICT systems and each other – can become the means by which the next challenge is met.
Objective data can provide a lot of insight into the practical ramifications of any decision a city makes. Data combined with advanced predictive analytics can help us more intelligently allocate limited resources. Big data, cloud, social, IoT, and machine learning can help make a city smart, but much more is needed to make it wise.
The technologies for enabling this transition are already here. However, the maturity of a cognitive city depends on a lot more than employing the latest tech or popular platform to improve safety, city operations, and the general well-being of the city. More time and patience are needed. They are needed for such technologies to become available and used by more sectors of society. They are needed in order for community values to be taken into account when considering how and to what extent a city should make use of the technologies for a safer and smoother city life for all.
The technologies and practices to move the cognitive city from the conceptual stage to an actual being are progressing, as are the social processes described in this article. We are sure to keep a watchful eye and share our insights.
DILIP VERMA, REGIONAL VP, INDIA // APRIL 23, 2018
All over the world, safe city initiatives are popping up. Today’s abundance of captured data, connectivity, analytics, and computing power have made the once futuristic concept of a smart city an increasingly common reality. But perhaps it is the smart safe city that has been its biggest enabler as it has allowed us to apply, test and prove the basic principles of the concept.
As we’ve discussed in a previous blog post, a safe city is a core element of a smart city. There is no more fundamental and important public issue than security and safety. And it is with this understanding that India has approached its increasing global domination in this arena.
In June 2015, Prime Minister Narendra Modi announced the nation’s “100 Smart Cities Mission”. India’s government has approved a total of 15 billion US dollars towards the effort, which includes the development 100 smart cities. The initiative is true to the principals of a smart city, as funds are distributed by a competition-based method, wherein citizens are integral in the planning and interpretation of ‘smartness’.
An excellent example of this is from Nanded City, in the Maharashtra state. The city’s leadership determined that in order to monitor the entire city, as they wanted, they would need an innovative approach. And thus, the C-Cube project was conceived. The integrated command, control and communication center, is powered by Qognify’s Safe City solution that includes Situator, (PSIM/Situation Management solution), Video Management and Analytics.
It’s the innovative use of technology has made Nanded a benchmark smart city in India. With 24/7 monitoring of the city, situation and disaster management, predictive and prescriptive guidance, the city has experienced a significant improvement in safety, security, and operations.
In Kohlapur, another city in India, the need was more specific. A heavy influx of religious tourism created increasing safety and security issues. The city sought to mitigate the risk of that is associated with these types of spikes in the population. Through the use of Qognify’s Video Management and Video Analytics, law enforcement and city management have been able to monitor, manage and prevent unfolding events.
The Control Room at Kolhapur
While Kohlapur is an ancient city, Navi Mumbai is a new planned township, designed to handle the population overflow from Mumbai. Without any limiting restrictions posed by existing infrastructure, city planners and leaders were able to design a ground-up smart safe city solution with Qognify technology.
The solution monitors all the critical points within the city such as public transportation, schools, heavily traveled traffic junctions, city entrances and exits, open-air markets, and utility infrastructure and more. Additionally, by integrating third-party systems and sensors, the city has a complete operational view of everything that is taking place or potentially unfolding.
While India has embraced the smart and safe city concept as a nation, cities all over the world are doing tremendously innovative initiatives of their own. San Francisco named the Greenest City in the U.S. in 2011, declared a goal of achieving zero waste by 2020 and carbon-free by 2030. They intend to meet those objectives through a range of smart initiatives that include things like making building operations more efficient, reducing energy use, streamlining waste management systems, and improving transportation systems.
Chicago has declared it wants to become ‘the most data-driven government in the world’. One of the initiatives they’re using to get there is called the Array of Things or AoT project. Mounted on traffic signal poles will be sensors that will measure everything from temperature and carbon monoxide to ambient sound intensity and pedestrian and vehicle traffic. The collection of all of this data will be used to improve quality of life in a variety of different ways – making Chicago healthier and more livable among other things.
Smart and safe cities are no longer a trend, but future of our urban areas. We’re just discovering the many different applications and forms this may take, but one thing that seems to characterize them all is their intention and purpose.
IFTACH DRORI, DIGITAL MARKETING MANAGER, QOGNIFY // MAY 09, 2018
How Situator mitigates a common incident for the utility industry