Editor's note: You can find part one of this article here. Task: The task is to generate a ranked list of images which are semantically similar to the query image. We will split our dataset into two parts: training and evaluation. From each class, we will randomly sample 20 images and create an evaluation set out of it. The remaining images will be part of the training set.

Artificial Intelligence (AI) has become a trend that is here to stay at least in the foreseeable future. Many countries have started embracing this technology; notable among them is China. This article explores how China has harnessed AI in the fields of e-commerce, finance and health from a layman's perspective. AI has improved our lives in many ways, but there are still some controversial issues concerning its use. The first thing that comes to mind in the way China has been transformed by AI technology can be traced back to the year 2013.

Machine learning is impacting countless industries, from the recent discovery of a black hole to improving healthcare, we are just scratching the surface. The retail industry is a prime example. Retailers and manufacturers are racing to figure out how they can employ machine learning to target specific consumers, monitor trends, and discover new pricing models. While retailers and manufacturers are doubling down on new ways to target and sell to consumers, Jia Rui Ong, a two-time Nanodegree program graduate, and his team are employing machine learning to help you, the consumer, find the best price for the clothing you desire. We recently had a chance to sit down with Jia Rui Ong and his team at Yux to discuss their product, as well as, our newly updated Machine Learning Nanodegree program.

Human communication is multimodal and includes elements such as gesture and facial expression along with spoken language. Modern technology makes it feasible to capture all such aspects of communication in natural settings. As a result, similar to fields such as genetics, astronomy and neuroscience, scholars in areas such as linguistics and communication studies are on the verge of a data-driven revolution in their fields. These new approaches require analytical support from machine learning and artificial intelligence to develop tools to help process the vast data repositories. The Distributed Little Red Hen Lab project is an international team of interdisciplinary researchers building a large-scale infrastructure for data-driven multimodal communications research. In this paper, we describe a machine learning system developed to automatically annotate a large database of television program videos as part of this project. The annotations mark regions where people or speakers are on screen along with body part motions including head, hand and shoulder motion. We also annotate a specific class of gestures known as timeline gestures. An existing gesture annotation tool, ELAN, can be used with these annotations to quickly locate gestures of interest. Finally, we provide an update mechanism for the system based on human feedback. We empirically evaluate the accuracy of the system as well as present data from pilot human studies to show its effectiveness at aiding gesture scholars in their work.

Browsing and finding relevant information for Bangladeshi laws is a challenge faced by all law students and researchers in Bangladesh, and by citizens who want to learn about any legal procedure. Some law archives in Bangladesh are digitized, but lack proper tools to organize the data meaningfully. We present a text visualization tool that utilizes machine learning techniques to make the searching of laws quicker and easier. Using Doc2Vec to layout law article nodes, link mining techniques to visualize relevant citation networks, and named entity recognition to quickly find relevant sections in long law articles, our tool provides a faster and better search experience to the users. Qualitative feedback from law researchers, students, and government officials show promise for visually intuitive search tools in the context of governmental, legal, and constitutional data in developing countries, where digitized data does not necessarily pave the way towards an easy access to information.

At this moment in history, there are more satellites photographing Earth from orbit than just about anyone knows what to do with. Planet, Inc., has more than 150 orbiting cameras, each the size of a shoebox. And more startups are planning to launch their own. What should we do with all that imagery? How can we search it and process it?

Without a doubt, Bill Cunningham has an incredible ability for discerning clothing. One may wonder how he got that way. On top of being quite gifted, someone like Bill must have also taken notice of a lot of outfits throughout his 60-year career as a photographer. Assuming Bill works every day of the year (which isn't a bad approximation) and shoots 10 outfits an hour for 8 hours a day, that number is well over a million. Here's the motivating question: If we presented the same number of clothing images to an artificial neural network, can it learn to see the world of fashion like Bill Cunningham does? Said in a less sensationalized way, what we're proposing is training a neural network to recognize clothing from images and find us visually similar ones.