The applications of Artificial Intelligence will continue to explode in the new decade ahead, and data annotation solutions will support that journey. Over 86% of respondents of a PwC survey say that AI will be a “mainstream technology” at their company in 2021. Here are some of the biggest ways in which AI will drive innovation across industries.
1. Construction and Maintenance of Utilities Using Aerial Imagery
Corridor mapping is an important step during the planning and construction of roadways, railways, and oil and gas pipelines, among other applications. For a long-term perspective, it is necessary to conduct a thorough feasibility study of the terrain where the project is to be constructed. LiDAR drones provide this information by helping build three-dimensional (3D) elevation models of the surveyed area. AI systems are also being used to inspect and monitor these utilities assets and infrastructure elements, once they are constructed and operational.
Data Annotation: Annotation of LiDAR data captured by drones & generation of 3D models, annotation of drone videos and images to identify objects or defects
2. Accurate CT Scan Analysis
Human error is a problem in CT scan analysis. Artificial intelligence will bring to market many new categories of CT scan analysis and show major improvements in speed, accuracy and costs associated with the analysis. i.e. AI can detect pneumonia caused by COVID-19 in chest CT scans or in the case of IVT treatment, AI can perform embryo classification with an accuracy higher and faster than embryologists can perform.
Data Annotation: Labeling of CT scans
3. Virtual patient monitoring
Remote monitoring sensors are being leveraged to accelerate the identification of COVID-19-positive patients and predict symptom and disease severity in patients, healthcare workers, and other at-risk individuals in critical service sectors.
Data Annotation: Key Point Annotation, Facial recognition, Gesture recognition for Sensors, data extraction from patient wearables
4. RPA In Accounting
From invoicing to accounts receivable, Robotic Process Automation (RPA) can speed up the accounting process, keep it error-“clean”, and, consequently, keep customers happy and build lifetime relationships. Accounting comprises several rule-based, structured processes that make it an idea use case for RPA. This helps streamline repetitive tasks, enable form automation, and also monitor suspicious activity.
Data Annotation: Data extraction, verification
5. Crowd Monitoring in Urban Spaces
The range of the applications of crowd monitoring in the COVID-era, including safety monitoring, disaster management, and traffic monitoring has encouraged researchers to develop models for crowd monitoring and associated tasks such as counting, density estimation, tracking, scene understanding, localization and behavior detection. Among these, the crowd counting and density estimation are important tasks and represent fundamental building blocks for several other applications.
Data Annotation: Human head detection, density mapping, crowd behavior detection in video surveillance
6. Advanced Drug Development
Through Machine Learning algorithms, drug development is being improved by advancing the search for chemical and biological interactions. This will help bring new pharmaceuticals to market quicker.
Huge volumes of data from sources such as research papers, patents, clinical trials and patient records are taken into account for drug discovery. This generates billions of known and inferred relationships between biological entities such as genes, symptoms, diseases, proteins, tissues, species and candidate drugs.
Data Annotation: Natural Language Processing to recognize entities, attributes, and understand relationships between factors
7. Connected Cars With Increased Security And Infotainment Features
IoT is now everywhere and the car is an extension of the consumer environment. The car will need to communicate with the customer’s personal digital device and have telemetry integrated into its architecture. Wi-Fi has enabled the integration of telematics system whereby a vehicle tracking device is installed, allowing a car owner to keep an eagle eye view on its vehicle even from remote locations, and the collection of telemetry data. Powerful Wi-Fi capabilities have also given rise to smart infotainment systems where owners can connect different equipment like music systems and GPS in a car with their smartphone and operate them remotely.
Data annotation: Audio transcription, entity recognition, intent and conversation analysis, video annotation, driver facial expression recognition
8. AI-powered Cybersecurity Applications On the Rise
Machine Learning is increasingly finding its way into cybersecurity systems for both corporate systems and home security. The AI technology can be employed to help identify threats, including variants of earlier threats. AI-powered cybersecurity tools also can collect data from a company’s own transactional systems, communications networks, digital activity and websites, as well as from external public sources, and utilize AI algorithms to recognize patterns and identify threatening activity – such as detecting suspicious IP addresses and potential data breaches.
Data Annotation: Data Extraction
9. IoT in Agriculture
Internet of Things (IoT) devices in farms are proving extremely beneficial for farmers. The number of wireless connections used in global agricultural production is predicted to reach 27.4 million by 2021, according to new figures released by Berg Insight. IoT technology using sensors helps farmers monitor temperature, humidity, irrigation, fertilization and more during the planting and growing process. IoT systems on industrial farms can track at least 30 different factors and assist farmers in maximizing output. Machine telematics data can proactively diagnose potential problems. IoT software and machine learning can together regulate the LED lights and nutrient distribution in controlled environments.
Data Annotation: Semantic segmentation, data extraction
10. Property & Damage Inspection Simplified
Companies are increasingly using drones or satellite imagery to appraise residential properties for insurance purposes, both to evaluate risk and set premiums, as well as to assess damage in claims applications. AI models are also being trained using these geospatial datasets to recognize and evaluate properties, further improving accuracy and shortening processing time for the customer.
Data annotation: Polygon annotation, bounding boxes, semantic segmentation
