The holy grail of AI is natural language processing (NLP). Teaching machines to accurately and reliably understand and generate human language ushers in a revolution with boundaries that are hard to envision.
We’ve all seen the headlines. It’s big news when an AI system fails or backfires, and it’s an awful black eye for the organization the headlines point to. Most of the time these headlines can be traced back to issues with the AI model’s training data.
Numerous industries use satellite and aerial imagery to apply machine learning to business and social problem sets. This is a particular strength for DDD given our experience.
Machine learning (ML) and AI have dramatically changed the way many businesses across the globe work. As ML and AI continue to evolve, one of the biggest challenges is to ensure the quality of the data utilized by your systems.
For machine learning to work, your system needs properly labeled data. Without it, your ML model may not recognize patterns, which it needs to make decisions or perform its functions.
If the data quality of the raw data is high and the training data sampling is done well the models shouldn’t vary a lot.
Today’s OCR is an application of computer vision that enables machines to find and extract text embedded in images.
Digital Divide Data (DDD) continues to expand its end-to-end data capabilities for Autonomous Systems across land, air, sea, and space. Our latest solution set is targeted towards supporting Autonomous Fleet Operations including Human in the Loop (HiTL) data solutions to drive forward the safety of ADAS systems.
