Google researchers have created a machine learning system that adds color to black & white videos, and can also choose which specific objects, people and pets receive the color treatment. The technology is based on what’s called a convolutional neural network, which is architecturally suited for object tracking and video stabilization. Meanwhile, Nvidia has debuted an algorithm that slows down video, without the jitters, after it’s been captured, by using a neural network to create “in between” frames required for smooth motion. Continue reading Google, Nvidia Train Neural Networks to Post-Process Video

As predicted, artificial intelligence has been one of the most repeated phrases of CES 2017. It seems every other vendor here is slapping the “AI” label on its technology. So much so that it inspired us to take a (short) step back and look at what AI is in relation to machine learning. The reality is: there are still very few applications that can be legitimately labeled as artificial intelligence. Self-driving cars, DeepMind’s AlphaGo, Hanson Robotics’ Sophia robot, and to a lesser extent Alexa, Siri and the Google Assistant, are all AI applications. Most of the rest, and certainly most of what we’ve seen here at CES, are robust, well productized machine learning applications (usually built on neural network architectures), often marketed as AI. Continue reading CES 2017: Distinguishing Between Machine Learning and AI