Author Archives: Joseph Jacobs

Machine Learning, meet Computer Vision

Computer vision, the field of building computer algorithms to automatically understand the contents of images, grew out of AI and cognitive neuroscience around the 1960s. “Solving” vision was famously set as a summer project at MIT in 1966, but it quickly became apparent that it might take a little longer! The general image understanding task remains elusive 50 years later, but the field is thriving. Dramatic progress has been made, and vision algorithms have started to reach a broad audience, with particular commercial successes including interactive segmentation available as the “Remove Background” feature in Microsoft Office, image search, face detection and alignment, and human motion capture for Kinect. Almost certainly the main reason for this recent surge of progress has been the rapid uptake of machine learning ML over the last 15 or 20 years.

This first post in a two-part series will explore some of the challenges of computer vision and touch on the powerful ML technique of decision forests for pixel-wise classification.

Read the full article by Jamie Shotton, Antonio Criminisi and Sebastian Nowozin on TechNet Blogs – Machine Learning.

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A Brief History of Machine Learning

Since the initial standpoint of science, technology and AI, scientists following Blaise Pascal and Von Leibniz ponder about a machine that is intellectually capable as much as humans. Famous writers like Jules Verne , Frank Baum (Wizard of OZ), Marry Shelly (Frankenstein), George Lucas (Star Wars) dreamed artificial beings resembling human behaviors or even more, swamp humanized skills in different contexts.

Machine Learning is one of the important lanes of AI which is very spicy hot subject in the research or industry. Companies, universities devote many resources to advance their knowledge. Recent advances in the field propel very solid results for different tasks, comparable to human performance (98.98% at Traffic Signs – higher than human-).

Here I would like to share a crude timeline of Machine Learning and sign some of the milestones by no means complete. In addition, you should add “up to my knowledge” to beginning of any argument in the text.

Read the full article on Eren Golge’s Blog.