As per the World Economic Forum’s job report, algorithms and intelligent machines are expected to create 133 million new roles globally while displacing around 75 million by 2022 – which is a total net gain of 58 million jobs, not killing them.
While, Gartner estimates AI will create 2.3 million new jobs in 2020 while eliminating 1.8 million positions. And according to Dun & Bradstreet 2019 report, 40% of organizations are adding more jobs, thanks to adopting AI, whereas only 8% are cutting jobs because of implementing such new technology into their operations and management.
However, according to Oxford academic duo Carl Benedikt Frey and Michael Osborne estimates published in the document “The Future of Employment: How Susceptible Are Jobs to Computerization?”, by the end of 2030 47% of American jobs are at high risk.
Artificial Intelligence (AI) based developments are on rapid speed – From self-driving cars, to robots removing weeds from farm fields or managing the inventory in assembly lines, warehouse or virtual assistant apps assisting people in solving their queries and computers able to detect cancer accurately is because of AI applied science.
Artificial Intelligence and machine learning oriented automation system will create, eliminate or change job roles, and how much is the question of discussion among the economic advisories and job data experts.
AI Creating New Jobs Rapidly for Highly-Skilled Professionals
Yes, its true AI is creating new job opportunities for highly-skilled professionals like software engineers, AI or machine learning engineer, data analyst, data scientists, digital communicators and online specialists. As these professionals are playing a key role in AI developments using their cognitive analytical and coding skills.
And as per the tops job sites and recruitment consultants, AI job postings as a percentage of overall job postings at on such websites rapidly increased and reached nearly doubled in the last two years. While searching the jobs in AI fields increased just 15 percent showing a huge gap between demand and availability of such professionals.
Reuters Graphic Showing Gap Between AI Job Openings and Job Seekers
This demand and supply gap between job openings and job seekers, is also pushing the pay scale bar of these professionals. As per the industry experts, the average salaries for AI-related jobs advertised by the companies on career sites rose 11% between October 2017 and September 2018 to $123,069 annually.
Will AI create jobs for low-skilled workers or not?
Highly-Skilled engineers and other professionals can easily find the jobs in AI fields, but the question arises here is that – automation and artificial intelligence will make low-skilled jobs disappear compare to these specialized and highly knowledgeable workers.
As per the job data in various countries, it is apparently visible that low-skilled workers are losing their job and becoming unemployed with lesser opportunists in job market due to rise of AI-oriented developments and implementation around the world.
Though, AI and automation have great scope of replacing repetitive and predictable cognitive and physical tasks. But there is a hidden side of AI and machine learning and that is rarely discussed by the experts, even most of the people not aware how AI is creating jobs not killing them even for low-skilled workers with great opportunities.
AI Creating New Jobs for Low-Skilled Workers
Data is the fuel requires to automated the AI and most of the machine learning algorithms need to be trained with huge volume of data sets. And training the machines with labeled datasets comes under the process of supervised learning.
In fact, a computer can take decisions or inferences, but only when you show enough examples with the respective solutions to individual problems. You can teach a neural network to recognize pictures of a car by feeding the network with thousands of images of a car while specifying every time to the algorithm.
The more pictures of cars you give, it will learn better and becomes more helpful in recognizing the images at a faster speed. And these AI and machine learning training data need to be annotated by someone – off course by humans.
Labeled Data Required for AI and Machine Learning
Humans-are-in-the-loop everywhere, in AI and ML development huge quantity of dataset, is required. And to generate the labeled data, thousands of human working hours required to annotate each image manually with precision.
AI and ML are escalating into vital fields, like healthcare and medical care. Automated pattern recognizing software is used in radiology, pathology, cardiology, oncology and even psychiatry helping doctors to detect different kind of diseases timely.
Medical imagining files like X-rays, CT Scans, MRI, ECGs and Ultrasound are manually annotated with the help of image annotation services highlighting pathological signals to doctors. And similarly, various other NLP based data for speech recognition are also highly in demand among the AI and ML developers for various industries.
A New Assembly Line for Low-skilled Workers
Though, there are various companies has developed software tools that are utilized by humans to annotate the different types of images. While many organizations have internal employees and many of them outsource the manual data labeling to others.
And such outsourcing is most probably done to underdeveloped or developing nations like India, China or African nations where the cost of labor is comparatively low. And these annotators are working in a big team, even some of the companies are working with more than 50,000 people, drawing from a pool of more than a million of annotators working worldwide in the day and night shifts producing the huge quantity annotated images.
Low-skilled workers basically work in manufacturing companies. But now companies become smarter implementing an autonomous or robotic system to perform the repetitive tasks with better speed and efficiency which were earlier performed by humans.
But right now in the age of rapidly growing AI development, data annotation is new assembly line providing the new opportunity to such workers. And these new types of jobs would not exist without machine learning algorithms which are at the revolutionary stage.
Data labeling is much more different from working in manufacturing assembly lines where workers perform physically exhausting demanding tasks. While in labeling data they need to more engaged in more cognitive tasks that are performed just sitting at one place on a chair in front of computers, it’s also repetitive but safe from machineries.
Data Annotation Doesn’t Require High-end Computer Skills
However, data annotation is not an easy job, as it requires training and meticulous attention to perform each task. You have to draw polygon or bounding box annotation around the various types of objects in an image or need to pinpoint the landmarks using the mouse and keyboards.
And while doing this you need to ensure the accuracy, because the quality of the data set is very important for the success of machine learning algorithm. In various industries like self-driving or autonomous driving, fallacious training data can be a cause of death due to crash or accidents becomes the prime reasons for fail of AI projects.
Though, annotating the data is a time-consuming task, but it is very essential to teach the machines how to perceive various situations while running on the road and take precise decisions accordingly to avoid such disasters and providing the safe driving.
Job Opportunities for Both – Highly-skilled and Low-skilled
Envisaging the high volume characteristics of the tasks data annotation for AI creating jobs with great opportunity for low-skilled workers, especially for people living in developing or undeveloped countries where the job market is very low for unskilled group.
Many companies in such countries like Cogito are hiring the undergraduates, or fresh graduates and unskilled people training them creating jobs at large scale helping and improving the socio-economic situation of the entire country.
Thanks to AI, not only highly-skilled professionals, but unskilled people are now also finding the jobs easily with satisfying pay scale fulfilling their basic needs. Further with more developments in unexploited fields AI and ML will create more jobs for low-skilled workers which accounts for a major population of many countries around the world.
Is Data Annotation Job Good for them?
The other side of this story is that, instead of learning more skill-based knowledge, such employees stuck in low-skilled jobs, which is economically not good for their long-term growth and developments.
But companies and organizations, in an attempt to operate in the market with hyper-competitive pricing with higher margins, are keeping annotators salary tremendously low as much as below $1 per hour which is below minimum wage.
Actually, in the digital era, such organizations are adopting this line of business which also sponsoring a new kind of slavery in the digital era. And the hunger of data annotation is so big, that in short-term this kind approach will be monetarily more rewarding for the companies compare to other lines of business.
While from the long-term perspective, it will affect the whole economy, as it will determine high employee churn rate, bad quality in the output, and negative impact on various communities.
Such companies, not only upsetting social norms by exploiting workers and running the business unethically, but this kind of unfair business practice is also harmful to the whole industry across the world will also impact the AI sector.
Though, AI is already a controversial technology, due to lots of societal, ethical and moral concerns associated with its developments. And this kind of dirty games by corporates can cost them when it will affect the entire sector with cascading effects.
In the nutshell, the human race of automation with AI is eliminating and creating millions of jobs worldwide in various sectors. And it is also not necessarily true that all the jobs created by AI are only for high-skilled and knowledgeable professionals but low-skilled workers are also getting new opportunities with multiple job options.
Although, the rate at which such jobs are created might not match the rate at which other low-skilled positions are disappearing globally. AI is at the growing stage, and the availability of annotated data and the need to access such data sets will grow exponentially over the next years, means a steep rise in demand for data annotators.
Top 5 Applications of Image Annotation in Machine Learning & AI
At the time of developing the AI models through machine learning (ML) first and most important thing you need, relevant training data sets, which can only help the algorithms understand the scenario through new data or seeing the objects and predict when used in real-life making various tasks autonomous.
In the visual perception based AI model, you need images, containing the objects that we see in our real life. And to make the object of interest recognizable to such models the images need to be annotated with the right techniques. And image annotation is the process, used to create such annotated images. The applications of image annotation in machine learning and AI is substantial in terms of model success.
What is Image Annotation?
So, right here we will discuss the applications of the image annotation, but before we proceed, we need to review the definition of image annotation and its use in the AI industry. Image annotation is the process of making the object of interest detectable and recognizable to machines.
And to make such objects recognizable in the images, they are annotated with added metadata for the description of the object. And when a huge amount of similar data is feed into the model, it becomes trained enough to recognize the objects when new data is presented in real-life situations.
5 APPLICATIONS OF IMAGE ANNOTATION
Annotated images are mainly used to make the machine learn how to detect the different types of objects. But as per the AI model functions, ML algorithms compatibility and use in the various industries, image annotation applications also differ that all about we will discuss here below with the annotation types.
Detection of Object of Interest
The most important application of image annotation is detecting the objects in the images. In an image, there are multiple things, or you can say objects, but every object would be not required to get noticed by the machines. But the object of interest need to be get detected, and the image annotation technique is applied to annotate and make such objects detectable through computer vision technology.
Recognition of Types of Objects
After detecting the object, it is also important to recognize what types of objects it is, humans, animals or non-living objects like vehicles, street poles and other man-made objects visible in the natural environment. Here again image annotation helps to recognize the objects in the images.
Though, object detection and recognition runs simultaneously, and while annotating the objects in various cases, the notes or metadata is added to describe the attributes and nature of the object, so that machine can easily recognize such things and store the information for the future references.
Classification of Different Objects
It is not necessary all objects in an image belong to the same category, if a dog is visible with man, it needs to be classified or categorized to differentiate both of them. Classification of the objects in the images is another important application of image annotation used in machine learning training.
Along with image classification, the localization of objects is also done through image annotation practice. In image annotation, there are multiple techniques, used to annotate the objects and classified into the different categories helping the visual perception based AI model detect and categorize the objects.
Segmentation of Object in the Single Class
Just like object classification, objects in the single class need to be segmented to make it more clear about the object, its category, position and its attributes. Semantic segmentation image annotation is used to annotate the objects with each pixel in the image belongs to a single class.
The main applications of image annotation are to make the AI model or machine learning algorithm learn with more accuracy about objects in the images. For semantic segmentation, image annotation is basically applied for deep learning-based AI models to give precise results in various scenarios.
Recognizing the Humans Faces & Poses
AI cameras in smartphones or security surveillance are now able to recognize the face of humans. And do you how it became possible in AI world? Thanks to image annotation, that makes the humans face recognizable through computer vision with the ability to identify the person from the database and discriminate them among the huge crowd from the security surveillance system perspective.
In image annotation for face recognition algorithms, the faces of humans are annotated from one point to another point measuring the dimension of the face and its various points like chin, ears eyes, nose and mouth. And these facial landmarks are annotated and provided to the image classification system. Hence, image annotation is playing another important role in recognizing the people from their faces.
TYPES OF IMAGE ANNOTATION
I hope you got to know the applications of image annotation in the world of AI and machine learning. Now you should know what are the types of image annotations used to create the machine learning training datasets for deep learning-based AI models? And we will also discuss here the application of different types of image annotation into various industries, fields and sectors with uses cases of AI-based models.
Bounding Box Annotation to Easily Detect the Objects
Bounding box annotation is one of the most popular techniques used to detect the objects in the images. The object of interest are annotated either in a rectangular or square shape to make the object recognizable to machines through computer vision. All types of AI models like self-driving cars, robots, autonomous flying objects and AI security cameras relying on data created by bounding box annotation.
Semantic Segmentation to Localize Objects in Single Class
To recognize, classify and segment the objects in the single class, semantic image segmentation is used to annotate the objects for more accurate detection by machines. It is actually, the process of diving the images into multiple segments of an object having the different semantic definitions. Autonomous vehicles and drones, need such training data to improve the performance of the AI model.
3D Point Cloud Annotation to Detect the Minor Objects
The image annotation applications not only include object detection or recognition, but even can also measure or estimate the types and dimensions of the object. 3D point cloud annotation is the technique that helps to make such objects detectable to machines through computer vision. Self-driving cars are the use case, where training data sets are created through 3D point cloud annotation. This image annotation helps to detect the object with additional attributes including lane and sideways path detection.
Landmark Annotation to Detect Human Faces & Gestures
Landmark annotation is another type of image annotation technique used to detect human faces. AI models like AI cameras in security surveillance, smartphones and other devices can detect the human faces and recognize the gestures and various human possess. Landmarking is also used in sports analytics to analyze the human possess performed while playing outdoor games. Cogito provides the landmark point annotation with the next-level of accuracy for precise detection of human faces or their poses.
3D Cuboid Annotation to Detect the Object with Dimension
Detecting the dimensions of the object is also important for AI models to get a more accurate measurement of various objects. The 2D images are annotated with capturing all the dimensions visible in the image to build a ground truth dataset for 3D perception on the objects of interest. Again autonomous vehicles, AI robots and visual perception models used to detect the indoor objects like carton boxes with the dimension need such annotated images, created through 3D cuboid annotation.
Polygon Annotation to Detect Asymmetrical Shaped Objects
Similarly, polygon annotation is used to annotate the objects that are in irregular shapes. Coarse or asymmetrical objects can be made recognizable through the polygon image annotation technique. Mainly road marking or other objects are annotated for the self-driving cars. And autonomous flying objects like drones, viewing the objects from Ariel view can detect or recognize such things when trained with training data sets created through polygon annotation for precise object detection.
Polyline/Splines/Line Annotation for Lane or Path Detection
Lines, Polylines and Splines are all similar types of image annotations used to create the training data sets allowing computer vision systems to consider the divisions between important regions of an image. The boundaries, annotating lines or splines are useful to detect lanes for self-driving cars. Road surface marking that are indicating the instructions of driving on the road need to also make understandable to autonomous cars. Polyline annotation that divides one region from another region.
The right applications of image annotation are possible when you use the right tools and techniques to create high-quality training data sets for machine learning. And Cogito is the industry leader in human-powered image annotation services with the best level of accuracy for different AI models or use cases. Working with a team of well-trained and experienced annotators, it can produce the machine learning training data sets for healthcare, agriculture, retail, automotive, drones and robotics.
This article was originally written for cogitotech.com
Types of Medical Diagnostic Imaging Analysis by Deep Learning AI
Artificial intelligence (AI) and machine learning have enough potential to make various tasks in the healthcare industry possible with accurate performance. Patient’s timely disease diagnosis and the right decision is an important part of hospitals to improve the overall healthcare system.
Medical imaging is playing a vital role in diagnosing the various types of diseases among patients across the healthcare system. MRI, CT Scan, Ultrasound and X-Rays are the key medical images use to diagnosis the disease, which is usually performed manually by the specialist doctors called Radiologists.
But now, Artificial intelligence in medical diagnosis is helping radiologists to use such images and detect the maladies with an acceptable level of accuracy. And now AI is capable enough to detect the different types of critical diseases with more level of accuracy.
To make the AI model, learn and detect, similar medical images are used that are annotated by the experienced radiologist to make the affected areas recognizable to machines through computer vision. So, today we will discuss here types of medical diagnostic imaging used to train the machine learning models and what are the data annotation techniques to create such medical imaging training data for AI.
Types of Diseases Diagnosed with AI in Medical Imaging
Detecting the Neurological Abnormalities
AI in neuroimaging is now detecting brain-related injuries, blood clotting and other neurological disorders with a high level of accuracy. A team of Radiologists annotate and highlight such brain-related disorders and helping the algorithms learn from the same.
Once the machine learning algorithms, get fully trained to diagnose and predict the brain-related problems, it can be used on the place of radiologist making the medical imaging diagnosis process faster and more correct for timely detection of brain diseases.
Semantic segmentation and polygon annotation are the leading techniques used to label the medical imaging training data for brain-related abnormalities.
Screening of the Common Cancers
Cancer – one of the most common and deadly diseases, people are fighting to survive globally. Cancer is more life-threatening if diagnosed at last or critical stage of development. At this level, it becomes difficult for the oncologist to save the patients from dying.
Breast cancer and prostate cancer are the most common types of cancers found in women and men respectively taking their lives due to lack of timely detection. In breast cancer, microcalcification is the key tissue sometimes difficult to conclusively identify as it could be either malignant or benign.
And if the diagnosis was false positives it could lead to unnecessary invasive testing or treatment, while missed malignancies could result in delayed diagnoses and worse outcomes for the patients. Similarly, AI deep learning” can detect the prostate and other types of cancers with accurate results.
Diagnosing the Infections in Kidney & Liver
AI is widely used and getting increasingly popular in the medical imaging of the liver, including radiology, ultrasound, and nuclear medicine. In liver medical imaging, physicians usually detect, characterize, and monitor diseases by assessing liver medical images visually.
However, sometimes, such visual assessment, done by experts or experienced radiologist doctors, may be biased due to personal experiences and inaccuracy. While on the other hand, AI can make a quantitative assessment by recognizing imaging information automatically instead of such qualitative reasoning as more accurate and reproductive imaging diagnosis.
Whereas, AI applications in kidney disease are currently no significant, but the potential of AI in the management of kidney disease is well recognized by clinicians, hence the role of AI in kidney disease diagnosis will improve the diagnosis process in the future.
Acute kidney problems like severe infections and stones can be detected with an AI-enabled medical imaging process. Basically, the function of AI in kidney disease mainly focuses on four aspects: Alerting systems, Diagnostic assistance, Guiding treatment and evaluating prognosis.
And with well-organized and annotated training a machine learning models can predict the kidney failure possibilities among the patients suffering from kidney-related problems. To detect the kidney stones, bounding box annotation is used to make such ailments recognizable to machines.
Brain Tumor Detection with High Accuracy
Just like cancers, tumors are also life-threatening disease, especially if it is developed in the brain. And AI in brain tumor detection is also possible with a precise medical imaging technique. As per the various research and studies, in brain tumor detection, the AI-based diagnosis was 94.6% accurate, compared with 93.9% for the pathologist-based interpretation.
Semantic segmentation technique is used to create such medical imaging data used for machine learning for brain tumor diagnosis. And as per the new studies, a novel method of combining advanced optical imaging with an artificial intelligence algorithm produces an accurate, real-time intraoperative diagnosis of brain tumors.
Machine Learning for AI in Dental Imaging Analysis
AI in dentistry for dental image analysis is playing an important role to find out the conditions of teeth helping doctors to recommend the right treatment. Using the dental image analysis, AI models can detect the teeth related problems including damaged teeth, uneven teeth structure or cavities and other maladies inside the roots of the teeth.
In dentistry, the affected or damaged teeth conditions are outlined in the X-Ray images by an experienced radiologist, once such data sets get ready, it is used in ML. The ML algorithm learns from varied types of annotated dental x-rays, and learn from such source data, that is further used to detect when shown such x-rays and predict the teeth condition.
Semantic segmentation is one of them used for detection, classification and segmentation of objects (teeth) in dentistry. Highly experienced dentist cum radiologist, examine the medical images or x-rays and annotate the affected areas with accuracy. Such a practice can create a huge amount of training data sets for machine learning in dentistry.
Detecting the Bone Fractures and Musculoskeletal Injuries
The invisible bone fractures and other hidden musculoskeletal injuries can be fatal if undetected or untreated for a long time. Hip fractures and bone injuries in elderly persons are more critical due to reductions in mobility and associated hospitalizations.
But medical imaging with machine learning, AI in fracture detection can diagnose the bone ruptures with the next-level of accuracy. X-ray imaging provides images of the body’s internal structures and when the fractured areas are annotated, it becomes delectable to machines through computer vision algorithm training with the huge amount of similar machine learning training data sets feed into the model.
AI in radiology is going to play a big role in diagnosing the various types of diseases including the critical maladies with a high level of accuracy. And further, with more improved or high-quality medical imaging data the diagnosis process and prediction accuracy will be better making the medical treatment and healthcare procedure more efficient and effective.
And Anolytics is one of the leading data annotation companies, providing the machine learning training data sets for AI developments in different industries like automotive, retail, agriculture and healthcare, etc. It is also offering a medical image dataset to train the AI models for diagnosing the various types of diseases with a high level of accuracy at every stage.
This article was originally written for anolytics.ai
How AI Can be Used in Smart Cities: Applications Role & Challenge
As per the international organization reports, more than fifty percent population globally living in urban areas, and by 2050, more than two-third population will live in cities providing big investment opportunities of tech development companies.
As per the IDC report, by 2021, the spending on smart cities likely to reach more than $130 billion to make the urban areas and cities more livable with advanced infrastructural facilities and society management systems.
AI in smart cities is going to play a big role in making urbanization smarter aim to be sustainable growth making the cities equipped with advanced features to live, walk, shop and enjoy a safe and more convenient life in such an environment.
Actually, while developing cities to make smart, several challenges likeadministration, sanitation, traffic congestion, securitysurveillance, parking management and many more that AI can helpto provide a sustainable solution to habitants.
USE OR APPLICATIONS OF AI IN SMART CITIES
The use of artificial intelligence in smart cities can be life-changing if implemented in the right spaces. There are multiple zones in cities or in urban development where AI can be used to improve the performance and efficiency of the system.
Advance Security Camera & Surveillance System
AI-enabled cameras and sensors can keep an eye on the surroundings to enhance the security level in the city’s neighborhoods. Such cameras can recognize the people and their faces or track the unusual activities done by them in restricted areas.
Watch Video: How AI-enabled Security Cameras Helping Police Monitor Illegal Activities in the Cities
The high-resolution AI cameras can track the movement of all registered vehicles and can monitor crowd density and cleanliness of public spaces round-the-clock. And thanks to the historic data available from different departments of the city, police can predict category and intensity of crimes monitoring all such activities in a particular area.
Vehicle Parking and Traffic Management System
In cities, most of the people have their own cars and also there is a huge movement of commercial vehicles to transport people as well as goods. So, parking of such vehicles and traffic management is another space where AI can help.
Watch Video: How to AI is Solving the Car Parking Problems in the Cities
Using the road surface sensors or CCTV cameras incorporated into parking spots allow cities to create real-time parking and traffic maps, helping drivers to save their time by avoiding waiting to find an empty space to move smoothly or be in traffic.
AI-supported traffic sensor systems can use cameras to collect real-time data of vehicles on road, and send it to a control center, which collates the data fed from other points and adjusts the signal timings to ensure smooth flow of vehicles.
Smart transportation also includes the public sector, and thanks to AI, there are lots of opportunities for improvement of public transit. And now cab services like Uber are also using AI to give a better riding experience to its customers.
Autonomous Flying Objects for Ariel View Monitoring
AI-enabled drones or autonomous flying similar objects can be used to monitor the inner-city and houses or other concerning areas. The in-built cameras in drones help to provide the real-time visuals of the different locations where humans cannot reach easily or quickly helping the administration and security departments to take timely actions.
Autonomous flying drones can track humans, monitor traffic movement and provide the 2D aerial view imagery mapping for better urbanization of cities. It can be used for advanced security and surveillance for security departments and crime squad.
Watch Video: How Autonomous Flying Drones used for Ariel View Monitoring
Face Detection Cameras and Movement for Public Safety
AI in face recognition technology is capable to detect the different people from their faces disclosing their personal identities. AI in security cameras or drones can recognize the human faces and match with the database to trace his identity and authenticate the person entering the cities, societies and other restricted areas.
Watch Video: How AI Cameras used for Face Detection & Public Movement
Landmarking annotation is the technique used to train the AI-based model in the face recognition system. And to make cities smart, such high-level of face detection technology is necessary to make the surroundings more safe and private.
Smart Waste and Disposal Management System
People living in cities produce a huge amount of waste that is another challenge for urban management to find the right way of managing garbage and keep the environment clean to maintain the hygiene level in society. AI-enabled cameras can detect trash thrown on the street and recognize the types of garbage for categorization.
Watch Video: How AI-enabled Smart Waste Management System Works
But AI-enabled installing sensors on waste bins can make the waste collection more efficient. Authorities can receive notifications when the bins are about to be filled and ensure reducing operational costs by eliminating unnecessary pickups, providing dynamic collection routes, and schedules for optimization of waste management.
Better Governance and Planning Management
While developing the cities or urban townships, AI or machine learning techniques can be used to map land use across time to generate crucial insights using the satellite imagery and aerial view 2D or 3D images of geographical areas.
The algorithms trained through machine learning can analyze satellite images for city planning and development with scope to adjust the formation based on changing data, areas prone to calamities like flooding, earthquake and storms. Such real-time as well as historical data, can continuously be monitored to enable better governance.
BENEFITS OF SMART CITY DEVELOPMENT
The integration of AI in smart cities has multiple benefits for humans as well as the environment. From an eco-friendly environment to sustainable development, AI in smart cities comes with multiple types of advantages for everyone discussed below.
Positive Impact on the Environmental
One of the best benefits of smart city development is that there will CO2 emission and it would be the main driver behind the development of smart and sustainable cities. And it would be possible due to energy-efficient and eco-friendly, waste and traffic management, which helps to curb pollution and makes the environment less polluted.
Optimized Energy & Water Management
Similarly, in smart cities, the power generating grid and smart water management are leading factors that help to produce energy with less pollution. Such an approach also helps to get clean drinking water keep our environment clean.
More Accessible Transportation System
In smart cities, clean and efficient transportation of goods, services are essential for the people. In the hope of optimizing mobility, many cities are turning to smart technologies to ease traffic congestion and provide users with real-time updates.
Advance Security and Safety in the Public
The safety of the people in the cities is at topmost priority in such cities. The AI-enabled development of smart cities allowing municipalities to better monitor their citizen’s thanks to CCTV cameras with facial recognition. Furthermore, AI cameras are also equipped with motion and smoke detectors for better security surveillance.
AI SMART CITY CHALLENGE
Achieving the AI in smart cities is not a simple task, as there are multiple challenges in making the cities smart. Apart from lack of funds, lack of technological edge and other things discussed below make it difficult to develop such cities.
Infrastructure and Costing
Most of the devices, equipment, systems, and machines installed in smart cities use sensor technology to gather and analyze information such as rush hour stats, gather a huge amount of data from AI cameras or other security systems or air quality or crime rates. The implementation of these sensors requires a sophisticated and costly infrastructure.
Security and Privacy Concerns
Another challenge in developing such metropolises is security and privacy of people living in such cities. As the data of monitoring people or watching their activities gathered can be regarded as a challenge as the use of IoT and sensor technology increases.
Besides, the threat of cyber-attacks is a critical issue for smart cities. Hence, to avoid concerns about data use, smart cities need to involve their citizens. Awareness, education, and transparency on the purpose of data collection are crucial to make the community feel that they are truly taking part in making their city more sustainable.
Risk of Socialization
While developing such cities, inclusive urbanization must be a priority to deal with the increasing vulnerability of poor and slum populations. That is why we need to ensure that no population is excluded from smart city data collection and use. Covering all age, gender, class and income group of people from society is necessary.
How Do Smart Cities Aim to be Sustainable?
AI in smart cities development can play a crucial role in urban planning, development and management with the an advance security systems, traffic monitoring and disposal management to make the societies more secured and livable with access and control of their home and other activities making their living experience more enjoyable and comfortable.
And to develop the AI-enabled systems for smart city development, machine learning and AI companies need a huge amount of training data for smart cities to train the models like drones, AI security camera and face recognition system to work with accuracy and provide the correct information making the smart cities really smart for sustainable growth.
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