Once Drones Get Artificial Intelligence, They'll Rule the World Once Drones Get Artificial Intelligence, They'll Rule the World

Tech giant Microsoft and large equipment manufacturer Liebherr are collaborating on a new generation of smart refrigerators that would help in shopping and planning meals with intelligent food management. "As part of the Liebherr household appliances division's digital initiative, the duo would develop'SmartDeviceBox' -- a communication module which fits into refrigerators and freezers -- connecting them to the internet," T.J. Hazen, Principal Data Scientist Manager at Microsoft, wrote. The system, which would utilize the same machine learning technology used in Microsoft's artificial intelligence (AI) assistant Cortana, is designed to have a long lifecycle. With this technology, modular units can be integrated and upgraded at any time in existing SmartDevice-ready appliances to create value and comfort for customers through new digital features and solutions, the post on Microsoft blog said. With the refrigerators, stored groceries can be monitored using internal cameras and object recognition technology.

Dramatic success in machine learning has led to a torrent of Artificial Intelligence (AI) applications. Continued advances promise to produce autonomous systems that will perceive, learn, decide, and act on their own. However, the effectiveness of these systems is limited by the machine's current inability to explain their decisions and actions to human users. The Department of Defense is facing challenges that demand more intelligent, autonomous, and symbiotic systems. Explainable AI--especially explainable machine learning--will be essential if future warfighters are to understand, appropriately trust, and effectively manage an emerging generation of artificially intelligent machine partners.

People walk past a LG Electronics logo during the Mobile World Congress in Barcelona, Spain, February 25, 2016. SEOUL South Korea's LG Electronics Inc said on Sunday it will aggressively invest in robots, seeking to capitalize on advancing artificial intelligence that may eventually lead to sophisticated machines performing everyday human tasks. LG, in a statement, said its appliances division is preparing the firm's entry into the robotics industry with the aim to develop products that will work closely with home appliances products such as refrigerators, washers and air conditioning units. "We will prepare for the future by aggressively investing in smart home, robots and key components and strengthen the home appliances business's capabilities," said Jo Seung-jin, head of LG's appliances business, in the statement. Advances in fields such as artificial intelligence and wireless communications are allowing for more sophisticated machines that can talk to each other via the internet and perform more complex tasks.

Google's AI technology may be capable of putting on a trippy art show -- thanks to its neural network-powered DeepDream computer vision program -- but when Google turned its machine learning technology to the world of fashion via its new experiment Project Muze, the results were less than compelling. Designed in partnership with European e-commerce company Zalando, Project Muze is an attempt at building a neural network capable of making creative decisions resulting in virtual fashions that will be transformed into real-life clothing. The project is based on Google's open-source platform TensorFlow and consists of a neural network -- meaning, an algorithm that's modeled on the human brain -- and a set of aesthetic parameters, the company explains. Google showed the neural network things like color, texture and style preferences from more than 600 fashion experts to train it. It then learned to connect those preferences back to people with similar interests.

Which is impossible: a computer needs information or facts to make decisions. Unfortunately, 'facts' are often skewed by humans for personal or political purposes. Saying such, however, is to your advantage. If the computer is wrong, you are guiltless. If correct, you benefit as the creator of the computer.

Suppose your learning algorithm has the following form: 1. learn a transformation of the always-present attributes, 2. learn a prediction rule as a function of the transformed features. Alternatively, if you're doing variable selection, you can think of choosing the relevant variables as the transformation). You can use additional information that is only present in the training data to assist with the first step. For example, if a feature transformation is useful for predicting not only whether the email was opened or not, but also the other training-only features, then it is likely more meaningful than a transformation that is only useful for predicting whether the email was opened. One way you might exploit this intuition algorithmically is to just apply your algorithm to learn a predictor for all of the features that you don't have in the testing data (i.e., a single vector-valued predictor that predicts if the email was opened or not, together with all of the other training-only attributes).

Give computer two pictures and it will redraw the first one with the style of the second one. The original Style Transfer requires lots of computational resources, this is simpler but faster version. From left to right, the first column is the 16x16 input image, the second one is what you would get from a standard bicubic interpolation, the third is the output generated by the neural net, and on the right is the ground truth. Tell computer how flower should looks like and it will draw it.

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