A new installation on display until November 27 brings artificial intelligence into London's Tate Britain for the first time. The winner of Tate's IK Prize 2016 -- an annual commission by Tate in association with Microsoft that awards "digital innovation" -- the work "Recognition" was created by Italian communications research center Fabrica. It uses AI to match a constantly updating stream of news images and photojournalism from Reuters with works from the Tate's extensive collections. Currently on display in a dedicated gallery at Tate Britain and on the website recognition.tate.org.uk, the program uses an algorithm that judges images based on four criteria: object recognition, facial recognition, composition, and by analyzing accompanying text data to match images by context. Through this, Fabrica is hoping to uncover "inspiring, insightful, humorous, and thought-provoking relationships," according to a statement.

I cannot think more than 2 hours without thinking how to introduce AI techniques into what I'm thinking about. The last time it happened was super interesting and stay with me to see how I used graph theories to crawl reddit and make a knowledge base about Magic the Gathering card relations. Long story short, I was browsing magiccardmarket.eu to check which cards to buy when I found a guy selling a 9 card for 6 . The card spiked over the week-end and I jumped on reddit to check out the reason. Is there a new deck using it?

The inference of biological networks is a highly relevant and challenging task in systems biology and integrative bioinformatics. Biological networks are graphs in which nodes represent genes or proteins, and a connection between them indicates some kind of biological relationship, e.g. regulatory or functional. The network inference is, in an essence, an attempt to reverse engineer the biological relationships from the high-throughput biological data [1]. Most biological network inference methods focus on the definition of gene regulatory networks, in which edges represent direct regulatory interactions between genes [2–4]. Far less effort has been put into the design of methods to build functional networks in which a connection indicates a functional relationship, e.g.

This post tries to demonstrates how to approximate a sequence of vectors using a recurrent neural networks, in particular I will be using the LSTM architecture, The complete code used for this post could be found here. Most of the examples I found in the internet apply the LSTM architecture to natural language processing problems, and I couldn't find an example where this architecture could be used to predict continuous values. So the task here is to predict a sequence of real numbers based on previous observations. The traditional neural networks architectures can't do this, this is why recurrent neural networks were made to address this issue, as they allow to store previous information to predict future event. First of all let's build our model, lstm_model, the model is a list of stacked lstm cells of different time steps followed by a dense layers.

Above the Trend Line: machine learning industry rumor central, is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items such as people movements, funding news, financial results, industry alignments, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz. Our intent is to provide our readers a one-stop source of late-breaking news to help keep you abreast of this fast-paced ecosystem. We're working hard on your behalf with our extensive vendor network to give you all the latest happenings. Be sure to Tweet Above the Trend Line articles using the hashtag: #abovethetrendline.

ANN ARBOR, Mich.--(BUSINESS WIRE)--The North American automation industry has set new records to begin 2016, according to the Association for Advancing Automation (A3), the industry's trade group. The advent of more automation in North American manufacturing promises increased efficiencies in production and the ability to compete globally. A3, whose mission is to advocate and promote automation technologies and ideas that transform the way business is done, tracks the robotics, machine vision and motion control markets for its three daughter associations, Robotic Industries Association (RIA), AIA - Advancing Vision Imaging, and Motion Control & Motor Association (MCMA). A3 is at its highest membership level ever, welcoming 119 new member companies year to date. A3, with its daughter organizations, now represents 965 member companies directly involved with robotics, vision, motion control and motors.

Artificial Intelligence (AI) or machine learning can be used to help improve the accuracy of the diagnosis in lung diseases, finds a study. Machine learning utilizes algorithms that can learn from and perform predictive data analysis. The team developed an algorithm process in addition to the routine lung function parameters and clinical variables of smoking history, body mass index (BMI) and age. Based on the pattern of both the clinical and lung function data, the algorithm makes a suggestion for the most likely diagnosis. "We have demonstrated that AI can provide us with a more accurate diagnosis. The algorithm can simulate the complex reasoning that a clinician uses to give their diagnosis, but in a more standardized and objective way so it removes any bias," said Wim Janssens from the University of Leuven in Belgium.

It certainly has been another long week in cybersecurity. First, news that a third party hacked a group allegedly connected to the NSA and made off with secret "hacking tools" rocked the industry. It was shortly followed by news of cyber attacks in the form of smart email "bombs" raining down upon the mailboxes of .gov News of new ransomware-as-a-service platforms added to the headache, as did news of two big PoS hacks where credit card information may have been stolen from retailers, hotels and restaurants. As summer wanes and stretches towards Labor Day, it will also be hard to forget the hack of the servers at the Democratic National Committee.

The technology-minded capital markets participants do love a good trend. If 2014 was centered around "IBOR-mania" and 2015 was the year that the blockchain exploded into the hyperbole stratosphere, then the major trend of 2016 will arguably be machine learning. It may sound like the sexy new foundation upon which so much of asset management's future can be built, but at heart, machine learning is a data efficiency play, one that many on the buy side are still unable to get right for a variety of reasons. While no one wants to get left behind when it comes to the application of new technology, in this case it really will be those firms that have come to grips with the questions posed by big data management that will have the head start in machine learning. One of the key distinctions to make on this subject is the differentiation between machine learning and artificial intelligence; the former is concerned with automating processes and associated troubleshooting, such as documentation completion or trade confirmation, while the latter mainly concerns the application of that automation--for example, sending out the right information to investors or clients based on historic trends.

The world's largest artificial intelligence body, the European Association for Artificial Intelligence (EurAI), has appointed UCC's Prof Barry O'Sullivan as its deputy president. Professor O'Sullivan is director for the Insight Centre for Data Analytics, and UCC's chair of Constraint Programming. He leads Insight's research group on optimisation and decision analytics, and is responsible for Insight's strategic engagement in European initiatives, such as Horizon 2020. Professor O'Sullivan has been involved in winning over 140 million in research funding, of which approximately 25 million has directly supported his research activities at UCC. EurAI has more than 4,500 members across more than 30 countries, making it the largest AI organisation in the world. The primary objective of the association is to promote research in, and study of, artificial intelligence, as well as representing the AI community externally.