In 1993, the state of computer vision was primarily focused on geometry, lacking the machine learning techniques that are prevalent today. In the early 90s, Bayesian statistics began to influence computer vision, enabling better tracking of objects in images and identifying the most probable paths, especially in crowded environments.

In my research, we are keen on analyzing moving images to extract geometric objects and understand motion, which is crucial for applications like self-driving cars. Geometric algebra serves as a unifying language, simplifying complex computations in fields like computer vision, making it easier to work with concepts like rotations and translations without confusion.

The motivation for using parameter estimation in image processing has shifted towards machine learning, which has generated more interest in this area.

The introduction of large neural networks has allowed us to solve complex problems in vision, speech, and language, shifting focus from traditional handcrafted approaches to machine learning.

Geometric algebra provides a mathematical framework that allows us to work with lines in computer vision, which is more complex than working with points.

Machine learning emphasizes the task of making machines learn from little initial knowledge to improve over time, with deep learning techniques utilizing neural networks being particularly effective in recent years.

Deep learning applications at Google include Google Photos, which effectively utilizes computer vision to understand the content of images.

Using geometric algebra provides a clear origin point for calculations, eliminating the confusion of multiple coordinate systems and making it easier to handle rotations and translations in computer vision.

Self-supervised learning aims to reduce human supervision by allowing machines to learn from unannotated data, which has been successful in natural language processing and is increasingly applied in computer vision.