And one definitely does not think of gesture recognition running on a homebrew breadboard version of a 6502 machine, and yet that’s exactly what [Nick Bild] has accomplished.

Most image recognition algorithms require lots of labeled pictures. This new approach eliminates the need for most of the labeling.

But neural network-based image recognition algorithms are still far from perfect, and according to a pair of recent papers these algorithms can be tricked pretty easily.

In The Optical Society's journal for high-impact research, Optica, the researchers report teaching a type of machine learning algorithm known as a deep neural network to recognize images of ...

To quantify progress, the researchers chose a benchmark image recognition algorithm (AlexNet) from 2012 and tracked how much computing power newer algorithms took to match or exceed the benchmark.

These machine learning systems are then fed pictures from ImageNet and its ilk, proprietary images (aka Google Photos) or other sources (like anonymized, indexed clinical records).

A new AI study describes a machine learning algorithm that was able to classify schizophrenia, based on brain images, with 87 percent accuracy.

This essay on the lessons we learned about deep learning systems and gender recognition is one part of a three-part examination of issues relating to machine vision technology.