How Can AI Redefine Security Measures In Future Smart Cities 

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The smart cities of the future will thrive on radical advances in technology like AI to ensure security architecture and civil governance. Whenever we think about a ‘smart city’, we visualize flying cars, data connectivity, robots, and digital management as the major drivers of convenience. All of these facilities incline towards facilitating a comfortable lifestyle. But, what about security? Have you ever noticed that cinema depicting older civilizations like Troy, the fall of a city discusses wars and other threats to the masses? Today, we live in a much safer era for humanity and contemporary movies also reflect these improved situations. The smart cities will further increase the security of its citizens by using modern AI-enabled security measures. Currently, half of the world’s population lives in urban areas and will grow with another 2.5 billion people by 2050. (McKinsey

Problems with the Current Infrastructure in Cities: Dire Threats to Human Life and Well Being

Today, we are facing a shortage of security mechanisms as the relatively small police forces can’t tackle threats in densely populated areas. Using surveillance cameras or metal detectors results in vague efforts leading to compromises with citizens’ safety. We can’t monitor every corner through just human controllers. In tourism-driven cities, threats like terrorism, organized crime, sexual offenses against women and children along with cyber threats also prove fatal for the economy. The overpopulated and geographically small areas makes it difficult to take any proactive measures to predict or even do damage control in such situations. The infrastructure cannot rely solely on human mediators. The capacity to monitor data, analyze threatful developments, and identify triggers of unwanted events limits human staff. Moreover; epidemics, natural calamities, and rioting too need instant attention across the globe. The shortage of resources and utilization rates are two major setbacks for administrations. 

Dive in deeper to learn how advanced AI-powered systems will address these problems to ensure a safe, dependable, and proactive environment for tomorrow’s generation.

Transportation Management

It is the most important aspect of any AI-powered security system in a smart city. AI algorithms shall use the traffic movement to divert the routes of vehicles and reduce traffic-related problems. It also uses the history of accident-prone zones to alert the drivers. The spacing between two cars can be successfully controlled by using their GPS coordinates to reduce the chances of accidents. Roads, being a national asset, their improved utilization will benefit the economy. AI solutions can also recognize abnormal driving through movement on road to trace driving under alcohol consumption and mental breakdowns. Traffic jams, accidents caused due to faulty roads, clearing areas for official purposes, and tracking criminals fleeing through roadways are also done automatically.

Video Processing and Crowd Surveillance

Software finds the indicators of a probable anti-social activity by processing the video footage of the public places. Human beings can prove to be inefficient to monitor crowded areas continuously. Artificial intelligence video analytics can segregate and identify potential threats on a real-time basis. Deep learning also enables the detection of escalating violence by analyzing the body language of mobs and for organized crime. Dispatching task squads immediately at incidences like pointing guns, knives, and mob fights turn easier. The cameras and voice sensors placed all around the town connect to a centralized monitoring network using 5G connectivity for low latencies. I think we can expect increased response rates along with better up crime registration.

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IoT-based Threat Detection Network 

The deployment of various sensors, actuators, and AI-enabled patrol robots improves security by leaps and bounds. They find the weapons and explosives by using the geometrical features and chemical reactivity in proximity to particular substances. Artificial intelligence also empowers the patrol robots to approach these instances for direct engagement with threatful persons. AI bots can also help dispose of IEDs with remote support. IoT devices act as an autonomous layer of movable safety mechanisms in the entire city. You can expect them independently neutralizing the criminals as per the situation. Clubbing them with anti-insurgent forces to contain the collateral damage to human life is immensely beneficial for us.

Cyber Analytics and Big Data Utilization

The above-mentioned network of devices and sensors will generate gigantic data. This will demand continuous analysis with the help of new Big Data Analytics tools along with AI for streamlined monitoring. Adding to it, the cyber crimes will also pose a serious threat since this information will be vulnerable to a cluster of stakeholders’ devices and networks. Smart cities are particularly endangered by malware attacks and exposing government machinery will lead to an infrastructural collapse. AI bridges the gap between BDA tools and the use of data science through machine learning. It can also build virtual models through the user, application, network, and repository data for predictive measures.

Interfacing Utilities, Social Media, and Security

We know that social media has played a pivotal role in tracking down suspicious activities. Proper mediation through the partnership between public social media networks, enterprise solutions like accounting solutions and payroll software for curbing money laundering are inevitable. This also addresses the concerns raised over our privacy. Sensitive information transmitting networks for industries like defense equipment and nuclear fuel shall are also brought under administrative surveillance. I think that the security of the city will also require interfacing utilities as their failure can cause backlashes on both the economic front along with law and order.

Insights

Open Government Partnership (OGP) is a multilateral program that also promotes AI for better governance in member nations.

Epidemic and Natural Calamity Control

The epidemics like swine flu, ebola virus, and measles have attracted the attention of the global community. AI-based healthcare systems can help in identifying and controlling the spread of these epidemics. In the case of a natural calamity, the AI patrol robots can speed up the process of finding the survivors and evacuating the areas rapidly. Decisions regarding the damage control measures improve with the use of cognitive technologies. They connect with all devices in the city to help the state machinery. Human mediators can’t channelize task forces in densely crowded localities without the support of AI systems.

Tracking High-Intensity Targets

Once a terror suspect infiltrates in the city territory or a fugitive is on the run, the AI face recognition systems can help hunt them down. Finding criminals based on dubious activities, their vehicles, and usage of communication tools like social media, messaging platforms, cellular networks, and the internet becomes efficient. Keeping an eye on the suspects and history sheeters on multiple levels is a major benefit of employing AI for constant monitoring. Tracing high-intensity targets by manhunts is inefficient on a large scale due to the scale of the personnel deployed for encompassing the entire periphery. It also alerts the criminals. AI-enabled tracing systems covertly perform the same process. 

Conclusion

The use of AI solutions as a part of state administration is a trending move made by the governments globally. Recently, the Indian government announced the National Centre for Artificial Intelligence as a part of its roadmap. Yet I feel that the potential for using AI is yet to be explored in entirety. The addition of new datasets and automation-based machine learning in these systems will open doors to more resilient functionalities. The future of our smart cities will be redefined by smart AI applications and we will embark on newer horizons of intelligent civil safety infrastructure.

Data Scientist: Rock the Tech World

It’s almost 2020! Are you a data Rockstar or a laggard?

IDC agrees to the fact that the global data, 33 zettabytes in 2018 is predicted to grow to 175 zettabytes by 2025. That’s like ten times bigger the amount of data seen in 2017.

Isn’t this an exciting analysis? 

Hold on! Are all the industries set for a digitally transformed future? 

A digital transformed future is an opportunity of historic proportions. The way data is consumed today changes the way we work, live, and play. Businesses across the globe are now using data to transform themselves to adapt to these changes, become agile, improve customer experience, and to introduce new business models. 

With the full dependency on online channels, connectivity with friends and family around the world has increased the consumption of data. Today, the entire economy is reliant on data. Without data, you’re lost. 

Leverage the benefits of the data era

At the outset, with not many big data industries to be found, we can still agree to the fact that the knowledge for data skills is still early for professionals in the big data realm.

  • Big data assisting the humanitarian aid 
    • Case study: During a disaster

Be it natural or conflict-driven – if the response is driven quickly, it minimizes problems that are predicted to happen. In such instances, big data could be of great help in helping improve the responses of the aid organizations. 

Data scientists can easily use analytics and algorithms to provide actionable insights that can be used during emergencies to identify patterns in the data that is generated by online connected devices or from other related sources. 

During a 2015 refugee crisis in Germany, the Sweden Migration Board saw 10,000 asylum seekers every week up from 2,500 asylum seekers they saw in a month. A critical situation where other organizations could have faced challenges in dealing with the problem. However, with the help of big data this agency could cope up with the challenges. The challenges were addressed by ensuring extra staff was hired and of securing housing started early. Big data was of aid to this agency, meaning since they were users of this the preprocessing technology for quite a long time, the predictions were given well ahead of time. 

Earlier the results were not easy to extract due to obstruction such as not finding the relevant data. However, now with the launch of open data initiatives, the process has become easy. 

  • Tapping into talents of data scientist 

The Defence Science and Technology Laboratory (Dstl) along with other government partners launched “The Data Science Challenge.” This is done to harness the skills of data science professionals, to check their capability of tackling real-world problems people face daily. 

The challenge is part of a wider program set out majorly in the Defence Innovation Initiative.

It is an open data science challenge that welcome entrants from all facets of background and specialization to demonstrate their skills. The challenge is to acknowledge that the best of minds need not necessarily be the ones that work for you. 

 

  • The challenge comprises of two competitions each offering an award of £40,000
  1. First competition – this analyzes the ability to analyze data that is in documents i.e. media reports. This helps the data scientist have a deeper understanding of a political situation like it occurs for those on the ground level and even for those assisting it from afar. 
  2. Second competition – the second test involves creating possible ways to detect and classify vehicles like buses, cars, and motorbikes easily from aerial imagery. A solution to be used for aiding the safe journey of vehicles going through conflict zones.  

What makes the data world significant?

In all aspects, the upshot of the paradigm shift is that data has become a critical influencer in businesses as well as our lives. Internet of things (IoT) and embedded devices are already pacing their way in boosting the big data world. 

Some great key findings based on research by IDC: –

  • 75% of the population that interacts with data is estimated to stay connected by 2025.
  • The number of embedded devices that can be found on driverless cars, manufacturing floors, and smart buildings is estimated to grow from less than one per person to more than four in the next decade. 
  • In 2025, the amount of real-time data created in the data sphere is estimated to be over 25% while IoT real-time data will be more than 95%. 

With the data science industry becoming the top-end of the pyramid, a certified data scientist plays an imperative role today. 

In recent times, it is seen that big data has emerged to be the célèbre in the tech industry, generating several job opportunities.

What do you consider yourself to be today? 

Defining a data scientist is tough and finding one is tougher!

 

AI For Advertisers: How Data Analytics Can Change The Maths Of Advertising?

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The task of understanding a customer’s journey and designing your marketing strategy accordingly can be difficult in this data-driven world. Today, the customer expresses their needs in myriad forms of requests.

Consumers express their needs and want attitudes, and values in various forms through search, comments, blogs, Tweets, “likes,” videos, and conversations and access such data across many channels like web, mobile, and face to face. Volume, variety, velocity and veracity of the data accumulated through these customer interactions are huge.

BigData and data analytics can be leveraged to understand several phases of the customer journey. There are risks involved in using Artificial Intelligence for the marketing data analysis of data breach and even manipulation. But, AI do have brighter prospects when it comes to marketing and advertiser applications.

As the CEO of a technology firm Chop Dawg and marketer, Joshua Davidson puts it, “AI-powered apps are going to be the future for us, and there are several industries that are ripe for this.” The mobile-first strategy of many enterprises has powered the use of AI for digital marketing and developing technologies and innovations to power industries with intelligent systems.

How AI and Machine learning are affecting customer journeys?

Any consumer journey begins with the recognition of a problem and then stages like initial consideration, active evaluation, purchase, and postpurchase come through up till the consumer journey is over. The need for identifying the purchasing and need patterns of the consumers and finding the buyer personas to strategize the marketing for them.

Need and Want Recognition:

Identifying a need is quite difficult as it is the most initial level of a consumer’s journey and it is more on the category level than at a brand level. Marketers and advertisers are relying on techniques like market research, web analytics, and data mining to build consumer profiles and buyer’s persona for understanding the needs and influencing the purchase of products. AI can help identify these wants and needs in real-time as the consumers usually express their needs and wants online and help build profiles more quickly.

AI technologies offered by several firms help in consumer profiling. Firms like Microsoft offers Azure that crunches billions of data points in seconds to determine the needs of consumers. It then personalizes web content on specific platforms in real-time to align with those status-updates. Consumer digital footprints are evolving through social media status updates, purchasing behavior, online comments and posts. Ai tends to update these profiles continuously through machine learning techniques.

Initial Consideration:

A key objective of advertising is to insert a brand into the consideration set of the consumers when they are looking for deliberate offerings. Advertising includes increasing the visibility of brands and emphasize on the key reasons for consideration. Advertisers currently use search optimization, paid search advertisements, organic search, or advertisement retargeting for finding the consideration and increase the probability of consumer consideration.

AI can leverage machine learning and data analytics to help with search, identify and rank functions of consumer consideration that can match the real-time considerations at any specific time. Take an example of Google Adwords, it analyzes the consumer data and helps advertisers make clearer distinctions between qualified and unqualified leads for better targeting.

Google uses AI to analyze the search-query data by considering, not only the keywords but also context words and phrases, consumer activity data and other BigData. Then, Google identifies valuable subsets of consumers and more accurate targeting.

Active Evaluation: 

When consumers narrow it down to a few choices of brands, advertisers need to insert trust and value among the consumers for brands. A common technique is to identify the higher purchase consumers and persuade them through persuasive content and advertisement. AI can support these tasks using some techniques:

Predictive Lead Scoring: Predictive lead scoring by leveraging machine learning techniques of predictive analytics to allow marketers to make accurate predictions related to the intent of purchase for consumers. A machine learning algorithm runs through a database of existing consumer data, then recognize trends and patterns and after processing the external data on consumer activities and interests, creates robust consumer profiles for advertisers.

Natural Language Generation: By leveraging the image, speech recognition and natural language generation, machine learning enables marketers to curate content while learning from the consumer behavior in real-time scenarios and adjusts the content according to the profiles on the fly.

Emotion AI: Marketers use emotion AI to understand consumer sentiment and feel about the brand in general. By tapping into the reviews, blogs or videos they understand the mood of customers. Marketers also use emotion AI to pretest advertisements before its release. The famous example of Kelloggs, which used emotion AI to help devise an advertising campaign for their cereal, eliminating the advertisement executions whenever the consumer engagement dropped.

Purchase: 

As the consumers decide which brands to choose and what it’s worth, advertising aims to move them out of the decision process and push for the purchase by reinforcing the value of the brand compared with its competition.

Advertisers can insert such value by emphasizing convenience and information about where to buy the product, how to buy the product and reassuring the value through warranties and guarantees. Many marketers also emphasize on rapid return policies and purchase incentives.

AI can completely change the purchase process through dynamic pricing, which encompasses real-time price adjustments on the basis of information such as demand and other consumer-behavior variables, seasonality, and competitor activities.

Post-Purchase: 

Aftersales services can be improved through intelligent systems using AI technologies and machine learning techniques. Marketers and advertisers can hire dedicated developers to design intelligent virtual agents or chatbots that can reinforce the value and performance of a brand among consumers.

Marketers can leverage an intelligent technique known as Propensity modeling to identify the most valuable customers on the basis of lifetime value, likelihood of reengagement, propensity to churn, and other key performance measures of interest. Then advertisers can personalize their communication with these customers on the basis of these data.

Conclusion:

AI has shifted the focus of advertisers and marketers towards the customer-first strategies and enhanced the heuristics of customer engagement. Machine learning and IoT(Internet of Things) has already changed the way customer interact with the brands and this transition has come at a time when advertisers and marketers are looking for new ways to tap into the customer mindset and buyer’s persona.

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