Blogs

Artificial Intelligence (AI) needs just one thing to thrive—data. But raw data is not on its own enough to allow machines to be intelligent. Data needs to be registered, labelled and contextualized for AI models to understand and predict. This​‍​‌‍​‍‌ is the way the data is given meaning or labeled in the system. It is... Continue Reading

The perception of autonomous cars is achieved by using a combination of highly sophisticated sensors and machine learning algorithms. LiDAR (light detection and ranging) is one of the sensors, playing a major role in helping normal vehicles to detect objects, measure distance, and get them across safely. But LiDAR’s real no-fluff power exists in its... Continue Reading

Electric vehicles (EVs) are not a far-off vision of the future; they’re going mainstream around the world at an unprecedented pace. AI and data annotation are driving auto manufacturing and the electrification boom. As car manufacturers and tech giants continue to push the boundaries of sustainable mobility, AI and data annotation are proving to be... Continue Reading

The race of autonomous vehicles (AV) has remade the face of the global automotive industry. Market studies foresee that the self-driving market will be valued at a few hundred billion dollars by 2030, and as such major contributors like Tesla, Waymo, and Nvidia are putting heavy capital into both Advanced Driver-Assistance Systems (ADAS) and full... Continue Reading

Data annotation is arguably the most important aspect of the development of AI and ML models. For example, if the task is to train a self-driving car to recognize road signs or to guide a chatbot in predicting human intention, properly labeled data is what makes an intelligent system work. Yet the process isn’t as... Continue Reading