Seeing in Higher Dimensions

'Seeing' is constructing useful models about spaces from sensors to understand and navigate them.   Predict current and future states. A good studey of the idea.

An Idea From Physics Helps AI See in Higher Dimensions
The laws of physics stay the same no matter one’s perspective. Now this idea is allowing computers to detect features in curved and higher-dimensional space.

The new deep learning techniques, which have shown promise in identifying lung tumors in CT scans more accurately than before, could someday lead to better medical diagnostics.

Olena Shmahalo/Quanta Magazine
John Pavlus  Contributing Writer

January 9, 2020

Computers can now drive cars, beat world champions at board games like chess and Go, and even write prose. The revolution in artificial intelligence stems in large part from the power of one particular kind of artificial neural network, whose design is inspired by the connected layers of neurons in the mammalian visual cortex. These “convolutional neural networks” (CNNs) have proved surprisingly adept at learning patterns in two-dimensional data — especially in computer vision tasks like recognizing handwritten words and objects in digital images.

But when applied to data sets without a built-in planar geometry — say, models of irregular shapes used in 3D computer animation, or the point clouds generated by self-driving cars to map their surroundings — this powerful machine learning architecture doesn’t work well. Around 2016, a new discipline called geometric deep learning emerged with the goal of lifting CNNs out of flatland.... "