Advances in machine learning and neuroscience have helped make great strides in computer vision. But we still have a long way to go before we can build AI systems that see the world as we do.

In the past decades, advances in machine learning and neuroscience have helped make great strides in computer vision. But we still have a long way to go before we can build AI systems that see the ...

Natural Language Processing (NLP), computer vision, customer relationship management systems (CRM), financial fraud detection, autonomous vehicles, virtual assistants, etc. What is the demand for ...

The gap between AI and human ability is, perhaps, greater for machine vision algorithms than some other areas like voice recognition. The algorithms succeed when they are asked to recognize ...

Machine learning and computer vision systems rely heavily on large datasets for training and operation. Quality data allows ML and CV models to be accurate and make fewer mistakes.

Machine learning relies on huge amounts of “training data.” Such data is often compiled by humans via data labeling (many of those humans are not paid very well).Through this process, a ...

Researchers are still puzzling over how animal collectives behave, but recent advances in machine learning and computer vision are revolutionizing the possibilities of studying animal behavior ...

The Computer Vision and Machine Learning focus area builds on the pioneering work at UB in enabling AI innovation in language and vision analytic sub-systems and their application to the fields of ...