Through the cooperative research and development agreement, scientists at the IU School of Informatics and Computing and at the Naval Surface Warfare Center, Crane Division will work together to transform existing military sensor technology through machine learning and artificial intelligence. The agreement was signed at the base, which is near Crane, Indiana. The lead researchers on the project are Sriraam Natarajan, associate professor in the IU School of Informatics and Computing at IU Bloomington, and Robert Cruise, chief scientist for the Special Warfare and Expeditionary Systems Department at NSWC Crane. The agreement is part of a larger effort at the IU School of Informatics and Computing to foster partnerships with NSWC Crane, a major economic driver in southern Indiana and one of the largest naval bases in the country. "Artificial intelligence, machine learning and human-computer interaction are three areas of interest to the researchers at Crane, and also areas of great strength at our school," said Martina Barnas, assistant dean for research and director of research collaborations at the IU School of Informatics and Computing.

Apple Inc. is set to join the Partnership on AI, an artificial intelligence research group that includes Amazon.com Apple's admission into the group could be announced as soon as this week, according to people familiar with the situation. Representatives at Apple and the Partnership on AI declined to comment. When the nonprofit organization was announced in September, it anticipated gaining additional members. Apple, Twitter Inc., Intel Corp. and China's Baidu Inc. were among noticeable absentees at the time.

At Continental, we're working to make mobility clean, intelligent and safe. Continental Digital Services France (CDSF) is a new entity dedicated to take up this innovation challenge in a unique way, achieving a fusion between on board and collective intelligence in our collaborative platform. With one of our Lighthouse project – eHorizon – we want to provide connected and autonomous vehicles with the ability to "see" beyond the reach of its sensors (and obstacles, corners, etc). By "crowdsensing" data from all vehicles, our platform can generate smarter insights and enable both on and off board systems to make better decisions. We're also working on imagining, developing and deploying new mobility services that will seamlessly be integrated in people's overall experience with digital services in their personal, social or professional lives, especially in smart cities.

Ever since 2007 with the explosion in the use of parallel hardware, the field of machine learning has become more exciting and more promising. It seems that the dream of true AI is finally just around the corner. Certainly, there are many companies that are starting to rely heavily on AI for their products. These include companies in search like Facebook, Google, as well as retailers and multimedia companies like Amazon and Netflix. But more recently many others in the health-care and cyber security industries are also interested in what AI and machine learning can do for them. Some of these technologies such as Tensorflow (which came about around 2015) are new and not widely understood.

Reading Data Science – Machine Learning vs Rules Based Systems by Karthik Guruswamy Data Scientist at Teradata got me thinking about how machine learning is replacing rule-based systems in how we manage and use enterprise content, knowledge, and ideas. These types of rules are extremely easy to understand and easy to code. But, Karthik points out, things quickly get out of hand: "When a system gets operationalized, one starts with 100 scenarios with 100 rules to handle it. As time goes by we encounter more and more exceptions and start making more rules to keep exceptions under control. Think US Tax Code – things get unwieldy and cumbersome over time."

Forbes "30 under 30″ list celebrates the achievements of 30 game changers in 20 different industries -- 600 in total -- of the brightest young entrepreneurs and innovators who are challenging conventional wisdom. The list highlights some interesting trends in robotics, AI, intelligence energy storage, and automation. Connected/smart consumer goods, advanced manufacturing, and artificial intelligence are big ticket items for 2017. Forbes also unveiled its second annual "30 Under 30 Europe" list, featuring 300 young innovators, covering 10 categories. The entire list can be viewed here.

Google is on its way to bringing artificial intelligence and machine learning tools to computer Raspberry Pi. The cheap tiny computer is often used in schools and the maker community for programming devices. Google has set up its very own survey and is asking makers what smart tools would be the "most helpful". In the past Google, has developed a wide range of tools for machine learning such as internet of things devices, wearables, robotics and home automation. Eben Upton, founder of the Raspberry Pi Foundation, told the BBC: "It's fantastic to see Google getting closer to the maker community."

Most of these concepts should hold deep meaning for you: Hyperparameter optimization, Hilbert space, BFGS, Spectral clustering, EM, Multinomial logistic regression Superb communication skills. This is often added to job requirements as an afterthought. You'll need to explain yourself and your methods. Excellent data analysis & visualization skills using tools like R or Python Orbital Insight is a fast growing provider of geospatial data and geo -analytic services to commercial, nonprofit and public sector clients across the globe. Leveraging advancements in computer vision and cloud computing, Orbital Insight is turning millions of images into a big picture understanding of the world that is quantitatively grounded in observation, creating unprecedented transparency, and empowering global decision makers with new sources of objective, quantified socio -economic insights.

Autonomous cars, personal assistants, and robots that give yoga lessons. For better or worse, the world of AI has arrived. In the beginning of January, businesses and journalists of all sorts converged on Las Vegas for the annual CES conference for consumer electronics and consumer technologies. While many of the products showcased and announced during the four-day hullabaloo aren't of particular interest to us--8k TVs and virtual reality headsets, for example--we were intrigued by the significant presence of artificial intelligence products this year. It permeated just about every type of product, and, for many businesses, is at their forefront of their efforts moving into 2017 and beyond.

Learning to reinforcement learn by Jane X Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z Leibo, Remi Munos, Charles Blundell, Dharshan Kumaran, Matt Botvinick In recent years deep reinforcement learning (RL) systems have attained superhuman performance in a number of challenging task domains. However, a major limitation of such applications is their demand for massive amounts of training data. A critical present objective is thus to develop deep RL methods that can adapt rapidly to new tasks. In the present work we introduce a novel approach to this challenge, which we refer to as deep meta-reinforcement learning. Previous work has shown that recurrent networks can support meta-learning in a fully supervised context.