When industry observers reference "smart manufacturing," Industry 4.0 or the Industrial Internet of Things (IIoT), it is often related to connecting plant floor equipment and analyzing Big Data in an effort to increase quality and efficiency on the production line or in the supply chain. But sometimes it is the most simplistic applications that make the biggest difference. Enter Otto Motors, a start-up division of Clearpath Robotics, which is focused exclusively on intelligent self-driving vehicles (SDVs) for material transport in factory and distribution facilities. The company and its products, including the Otto 100 for light-load material transport and the Otto 1500 for heavy-load transport, were unveiled this week at the International Manufacturing Technology Show (IMTS) in Chicago. The company launched in April of this year to focus on bringing modular SDVs that use onboard sensors to navigate the facility.

June 1, 2016, The Internet of Things topped the target list for developers working with artificial intelligence across a wide spectrum of technologies including machine learning, neural networks, deep learning, and pattern recognition, according to Evans Data's just released Global Development Survey. While targets for these technologies remain fragmented, IoT was the top target for all of them and in most cases the only target with a double digit response. Non-computer related professional, scientific and technical services was cited second as a target for the above disciplines, and was first in the category of Natural Language Processing. "All the related disciplines that are commonly lumped together as artificial intelligence are being stimulated by the burgeoning growth of Internet of Things," said Janel Garvin, CEO of Evans Data. "These technologies are being incorporated very rapidly into the design and development process across a host of industries, and types of applications, but it's IoT that is the strongest driver."

Machine learning used to take place behind the scenes: Amazon mined your clicks and purchases for recommendations, Google mined your searches for ad placement, and Facebook mined your social network to choose which posts to show you. But now machine learning is on the front pages of newspapers, and the subject of heated debate. Learning algorithms drive cars, translate speech, and win at Jeopardy! What can and can't they do? Are they the beginning of the end of privacy, work, even the human race?

Posted by Sara Robinson, Developer Advocate I've been playing around with Google's machine learning tools recently, especially image recognition. By far, my favorite feature of the Cloud Vision API is landmark detection: you send the API a picture of a popular (or obscure) landmark and it returns JSON with the name of the landmark, its latitude / longitude coordinates, a bounding box indicating where the landmark was found in the image, and even more landmark metadata. To see the Vision API in action and understand how it works behind the scenes, let's look at some landmarks from around the world. First, let's see what a JSON response looks like. We'll start with this image of the Eiffel Tower: If at first glance you thought this was a picture of the Eiffel Tower in Paris, you were wrong (don't worry, I was too).

Currently, the two largest companies in legal data-driven research are LexisNexis and Westlaw. They have databases that contain huge numbers of case details, and often serve as the default starting point for legal researchers. However, they are not a resource for running advanced analytical tools. Others are filling this gap in the market though. Brainspace, for example, is applying more analytically driven tools to unstructured data in ways that companies have previously not been able to do, and one application that it's been used for successfully is legal files.

A new personalized search engine helps you explore what you would look like with brown hair, curly hair or in a different time period. Ira Kemelmacher-Shlizerman, a computer vision researcher at University of Washington, developed the image recognition software using a TITAN X GPU and the cuDNN-accelerated Caffe deep learning framework to train the models and for inference. Ira presented her paper at this week's SIGGRAPH 2016 and the search engine will be publicly available later this year. Dreambit is also able to predict what a child might look like when they are forty years old or with red hair, black hair, or even a shaved head. "It's hard to recognize someone by just looking at a face, because we as humans are so biased towards hairstyles and hair colors," said Kemelmacher-Shlizerman. "With missing children, people often dye their hair or change the style so age-progressing just their face isn't enough. This is a first step in trying to imagine how a missing person's appearance might change over time."

The Amazon Echo, a voice-controlled virtual assistant, is seen at it's product launch for Britain and Germany in London, Britain, September 14, 2016. What's all the fuss about the voice-activated home speaker that Amazon is due to release in the UK and Germany in late September? This gadget has been available in the US for over a year and has proven a minor hit, with sales estimates between 1.6m and 3m. But these figures belie the potential impact this kind of artificial intelligence device could have on our lives in the near future. Echo doesn't just let you switch on your music by voice command. It's the first of what will be several types of smart home appliances that work beyond simple tasks like playing music or turning on a light.

Five companies have been the first to sign up with Grip to provide integrated business event networking apps via the API including Eventfuel, ITM Mobile, Attendease, Goomeo and Loopd. Grip's artificial intelligence powered matchmaking platform, which is currently taking the events world by storm, can now integrate with any event app or website through REST APIs. This makes it possible for event businesses to easily integrate Grip into their products. Tim Groot, CEO of Grip explains: "By launching our API, we have widened the scope of our unique event matchmaking solution so that it can be used via a company's own website or existing digital apps. It provides a seamless and simple solution for companies keen to use our matchmaking engine in the optimum way and who want to integrate it with their existing solutions."

Online shopping has the potential to be so much smarter. If you stumble across a rug or lamp you like in a photo, shouldn't it be easier to track down where to buy it? One home design website is aiming to do just that, with the help of artificial intelligence. Palo Alto-based Houzz is a platform for people who want to remodel or redesign their homes and are looking for inspiration. Consumers and design professionals alike can upload photos of their completed projects, where they can then tag specific furniture and accessories offered by merchants Houzz partners with--letting other users easily buy any product they see and love.