Michael Abramov

Defining image annotation  Image annotation gives machine learning models information about what a given image is showing. In most cases this information is applied to datasets by human annotators who highlight and label relevant parts of images in accordance with the needs of machine learning developers. Image annotation allows researchers

Three types of image annotation can be used to train your computer vision model: image classification, object detection, and segmentation. Image classification and object detection are fairly straightforward — the former involves labeling the object featured in an image while the latter takes the same process a step further by introducing

Today’s cutting edge computer vision based AI models are required to operate within an increasingly complex, dynamic and fast changing world. In order to reliably function within moving, real-time environments AI systems require training with high-quality annotated video data. To create this data annotators use digital platforms to apply

Computer vision based AI models are being deployed in military contexts. The use of AI in weapon systems is a complex and controversial area, the practical and moral implications of which are still being debated by thought leaders and policy makers. However, AI powered military technology is increasingly being harnessed

“Poor data quality is enemy number one to the widespread, profitable use of machine learning.” – Tomas C. Redman, Harvard Business Review, If Your Data Is Bad, Your Machine Learning Tools Are Useless The most exciting, innovative machine learning projects in development today are reliant on access to quality data. Inaccuracies

While learning from experience is natural for the majority of organisms—even plants and bacteria—designing machines with the same ability requires creativity, experimentation, and persistence. And yet the potential for machine learning is limitless.   A number of the most anticipated applications for machine learning have sprung from the subfield