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How AI Can be Used in Smart Cities: Applications Role & Challenge

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how ai can used in smart cities

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.   

Also Read: How AI Based Drone Works: Artificial Intelligence Drone Use Cases

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.

Also Read: How Can Artificial Intelligence Benefit Humans

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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Artificial Intelligence

Top 5 Applications of Image Annotation in Machine Learning & AI

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applications of image annotation

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.

Also Read: What Is Computer Vision In Machine Learning And AI: How It Works

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.

Also Read: The Main Purpose Of Image Annotations Is To Develop AI Model

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

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Types of Medical Diagnostic Imaging Analysis by Deep Learning AI

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types of medical diagnostic imaging

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.

AI in neuroimaging

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.      

Also Read: How AI in Medical Imaging Can Help in Diagnosis of Coronavirus

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.

ai in breast cancer diagnois

Also Read: How Does Google AI Detect Breast Cancer Better Than Radiologists

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.

ai in prostate cancer diagnosis

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. 

Also Read: How Does AI Detect Cancer in Lung Skin Prostate Breast and Ovary

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.

ai in kidney liver diagnosis

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.

Also Read: How AI Robotics is Used in Healthcare: Types of Medical Robotics

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.

AI in brain tumor detection

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.

dental image analysis

Also Read: How Machine Learning In Dentistry Can Improve The Dental Imaging Analysis

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.

AI in fracture detection

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.

Summing-up

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                 

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Artificial Intelligence

How AI Robotics is Used in Healthcare: Types of Medical Robotics

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How AI Robotics is Used in Healthcare

Robots are one of the first-born automated machines, develop to perform various actions helping humans to complete various daily or repetitive tasks in a short span of time. And now using machine learning, robotics developers are building AI robots that can better understand the various scenarios and work more efficiently.

From manufacturing to agriculture, AI robots are playing a big role in making the production process faster and cost-effective due to mass production achieving the economies of scale helping such industries, produce the goods more economically than others.         

Robotics in Healthcare

AI robots in healthcare are now playing a big role in providing an automated solution to medicine and other divisions in the industry. AI companies are now using big data and other useful data from the healthcare industry to train robots for different purposes.

Robotics in Healthcare

AI in robotics making such machines become more intelligent learned from the data and perform various crucial tasks without the help of humans. So, you need to know about such robots, how they are used in medical fields and what are the types of robotics used in the healthcare industry.

Types of Medical Robotics Used in Healthcare

Types of Medical Robotics

Robots for Transporting the Medical Supplies

Medical supplies and other essential items used in hospitals, or medical centers can be now supplied by these trained robots. Actually, robots can be developed with self-navigation technology, to move in the hospital premises to reach a patient’s room or other places without human help.

Watch Video: Robots Deliver Lifesaving Medical Supplies

AI robots are trained with a huge amount of machine learning training data with sensor fusion location technology to make the navigational capabilities of transportation robots more robust. Anolytics, is one of them, providing high-quality training data sets, to train the AI-based robots.

Also Read: What is Training and Testing Data in Machine Learning with Types

Robots Used for Sanitation and Disinfection

To avoid human contacts, and minimize the transmission of highly contagious disease like COVID-19 and other infections, robots are the best options to use for disinfecting or sanitizing the infected areas. It can simply, spray the chemicals and disinfect the large areas with high speed saving time and efforts.

Also Read: How AI is Used in Healthcare to Control the Coronavirus Disease

AI robots are used for disinfection and sanitation can be sanitized, as it is also exposed to contaminated areas, and become infected. While humans exposed to such diseased areas, it makes him sick or health complications. So, robots can be used for sanitization or disinfection at different locations.  

Robotics for Prescription Dispensing Systems 

AI robots can perform accurately at better speed, which is a very important aspect for the healthcare industry to provide accurate details or medication and work faster for the quick response and timely treatment to patients.

Similarly, an automated dispensing system with advanced features, are developed to point where robots can now handle powder, liquids and highly viscous materials with better speed and accuracy. Such robots can dispense many other items when installed at hospitals or medical centers.

Robots Used as Telepresence Physicians  

Telepresence physicians – a kind of virtual reality physician is also used to examine and treat patients from remote locations or rural areas. Robots with Telepresence can provide the feeling of getting consulted with a real doctor, making the two-way communication possible among them. 

Watch Video: How Telepresence Robots Works

However, in critical situations, specialists can be called, via robot to answers the questions and guide about the treatment from the remote locations. And such AI robots are also equipped with AI cameras and other features to examine the patients physically and provide a realistic diagnosis of patients.      

Robots to Work as Surgical Assistants 

AI-enabled robots can be developed to assist surgeons to perform various tasks. These are continually being developed, with more advanced 3DHD technology gives surgeons the spatial references that are mainly required to perform highly complex surgeries.

Also Read: How AI in Medical Imaging Can Help in Diagnosis of Coronavirus

Such robots are equipped with including more enhanced natural stereo visualization, combined with augmented reality. And these robots are trained with different types of training data through computer vision to make them understand the scenario and perform the right action accordingly.

How AI Robots are Trained?

The robotics developed on AIprinciples, can work or perform various functions without the help of humanswhen they are deployed to use in real-life. And to detect the various objectsand visualize the different situations, these robots are trained through computer vision technology.

And using computer vision technology, machine learning algorithms are trained to learn certain patterns and predict the results precisely learned from such data sets. The data used to train the computer vision-based AI models like robots contains the annotated images of the objects, that help machines to detect and recognize in different situations available in different shapes and sizes. 

And there are many data annotation companies, provides the image annotation solution for AI and machine learning models. It can annotate the images to provide the AI robotics training data with the best level of accuracy to make such machines work with the best accuracy.

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