Autonomous vehicles - whether self-driving cars, long haul trucks or door-to-door delivery bots, rely heavily on Supervised Machine Learning models. These models require huge amounts of annotated data and power core functionalities like - Lane detection, pedestrian detection, traffic-light detection etc.
Training data for Autonomous Vehicles can be tricky to get right as it needs to cover Edge cases.
The healthcare industry is slowly adopting to the use of AI. From detecting anomalies in X-rays and MRIs to performing gene sequencing and drug development, huge volumes of labeled data can transform a field that is ripe for disruption.
Medical Image annotation tools need to be developed with the healthcare industry in mind, as the security and performance standards, as well as data formats can be quite different from other use-cases.
It is estimated that by 2035, AI-powered technologies could increase labor productivity by up to 40% across 16 industries, including manufacturing. From automated assembly lines, to defect detection and workplace security monitoring, AI is changing the way manufacturing is perceived in the industry.
Banking and Insurance
From using AI for fraud detection to automatic cheque verfication, banking is already transforming its very core. Another promising area is insurance where AI is being used for automatically detecting the level of damage on vehicles.
Another area that is ripe for disruption through the use of AI. Livestock management, disease detection in crops, weed detection are just some of the uses of AI in agriculture.
The COVID crisis highlighted how retail can use AI to monitor footfall. AI can also be used for analysing visitor sentiment, counting shopping carts and autonomous checkout.