There are two ways we make AI accessible for real-world applications. First, we deliver annotated data - the food that teaches perception algorithms to recognize the world around them. On the other hand we apply machine learning models ourselves to automate the annotation process.
Why is automation needed? AI training and testing requires huge amounts of labeled data to be of use. In autonomous driving perception algorithms in particular, we are talking about billions of data that have to be annotated.
It’s not feasible to do it manually, it would take too long and would be too expensive. Our automation engine for data annotation enables our customers to cut costs and accelerate the training and testing of autonomous driving technology.
We always place great emphasis on quality and scalability. UAI is committed to continuously improve the quality of our products and processes by utilizing the feedback of its employees and customers.