Situ8ed, founded in December 2015, based in Grenoble in the heart of the French Alps, and hosted by INRIA in their Rhone Alpes research centre. We're using situation modelling techniques pioneered by one of our founders to recognize real-world situations for smartphone users. Key to our products is the use the latest signal processing and machine learning techniques. Key to our value proposition is the protection of confidential data. We're developing an SDK for application developers (for Android and IOS) with cloud-based services for managing the installed base.

There is no need to worry about whether robots might start taking our jobs. Three of the world's 10 largest employers are already replacing tens of thousands of their workers with robots: That is likely the tip of the iceberg. This deliberately scruffy chart from CSLA of the 10 largest global employers shows the world's biggest workforces shows the potential for axing workers in favour of machines: Of those 10, only the UK's National Health Service - with its massive army of doctors and nurses doing unrepetitive, unique tasks - looks like hostile territory for robots. The other nine are rich with rote, repetitive tasks that might be better performed by software. Foxconn's 60,000 robots are only a small fraction of its 1.3 million total workers.

The insurance industry has always used analytics and insights as a key competitive advantage, and there have been significant advances in growing analytics capabilities in recent years. However - analytics is set to take a huge leap forward. Machine learning and artificial intelligence has the potential to completely transform an entire insurance organization, but where and how can machine learning be practically applied by insurers? Insurance Nexus recently spoke with executive insurance leaders from AIG & Zurich Insurance to understand just that. Check out the full whitepaper, "Anything You Can Do, AI Can Do Better: Insurance Applications for Machine Learning" (1.fc-bi.com/LP

A key goal of computational personalized medicine is to systematically utilize genomic and other molecular features of samples to predict drug responses for a previously unseen sample. Such predictions are valuable for developing hypotheses for selecting therapies tailored for individual patients. This is especially valuable in oncology, where molecular and genetic heterogeneity of the cells has a major impact on the response. However, the prediction task is extremely challenging, raising the need for methods that can effectively model and predict drug responses. In this study, we propose a novel formulation of multi-task matrix factorization that allows selective data integration for predicting drug responses. To solve the modeling task, we extend the state-of-the-art kernelized Bayesian matrix factorization (KBMF) method with component-wise multiple kernel learning. In addition, our approach exploits the known pathway information in a novel and biologically meaningful fashion to learn the drug response associations. Our method quantitatively outperforms the state of the art on predicting drug responses in two publicly available cancer data sets as well as on a synthetic data set. In addition, we validated our model predictions with lab experiments using an in-house cancer cell line panel. We finally show the practical applicability of the proposed method by utilizing prior knowledge to infer pathway-drug response associations, opening up the opportunity for elucidating drug action mechanisms. We demonstrate that pathway-response associations can be learned by the proposed model for the well known EGFR and MEK inhibitors.

The Internet of Things (IoT), Artificial Intelligence (AI) and cloud computing are three technologies that are converging to disrupt nearly every industry. IoT refers to a connected network of objects embedded with technology that enables the collection and exchange of data. Cloud computing is the storing and retrieval of data, and accessing application programs via the Internet. Artificial Intelligence is the simulation of human intelligence by machines. We are currently in the midst of the rise of the first wave of this technological convergence.

A University of Surrey professor was'overwhelmed' after being appointed a CBE in the Queen's Birthday honours for his career and work in sociology and engineering. Professor Nigel Gilbert founded the Social and Computer Sciences research group in 1984 which focuses on applying social science to the design of artificial intelligence systems. He established the Centre for Research in Social Simulation in 1997 and it is still based at the Guildford university campus. Prof Gilbert said of being appointed a Commander of the Order of the British Empire: "It was very overwhelming. I received the letter about three weeks ago, a brown envelope from the cabinet office. "At first I thought it was a tax bill.

In a series of papers posted online in recent weeks, mathematicians have solved a problem about the pattern-matching card game Set that predates the game itself. The solution, whose simplicity has stunned mathematicians, is already leading to advances in other combinatorics problems. Invented in 1974, Set has a simple goal: to find special triples called "sets" within a deck of 81 cards. Each card displays a different design with four attributes--color (which can be red, purple or green), shape (oval, diamond or squiggle), shading (solid, striped or outlined) and number (one, two or three copies of the shape). In typical play, 12 cards are placed face-up and the players search for a set: three cards whose designs, for each attribute, are either all the same or all different.

U.S. drone operators had been stalking the baby-faced British terrorist for days with infrared cameras and other sensors before the order came to kill him. As night fell on April 25, a U.S. warplane dropped a guided-bomb that obliterated the SUV occupied by 23-year-old Raphael Saihou Hostey near Mosul, Islamic State's stronghold in Iraq. Hostey, a recruiter for the militants, was targeted by a U.S. military campaign that has singled out and killed more than 120 Islamic State leaders, commanders, propagandists, recruiters and other so-called high-value individuals so far this year, officials said. The leadership attacks have picked up recently due to intelligence collected by special operations teams on night raids, from captured militants, and from intercepts of emails, cellphones and other communications. The focus on Islamic State's command and control structure, including its recruitment and funding systems, has helped weaken the Sunni extremist group as Iraqi, Syrian and Kurdish forces press the militants on the battlefield.

App developers rely on user demographics to effectively target their audiences – but just how much information do your apps really reveal about you? According to a new study, it might be more than you think. Researchers analysed the app choices of thousands of Android users to determine the predictability of certain attributes, and found that apps can provide insight on your gender, age, and even income. Researchers analysed the app choices of thousands of Android users to determine the predictability of certain attributes, and found that apps can provide insight on your gender, age, and even income. In the paper, published to the journal arXiv, researchers with Verto Analytics in Finland and the Qatar Computing Research Institute created a model based on the demographic attributes and apps of 3,760 Android users.

A few weeks ago Emma Sinclair (@ES_Entrepeneur) asked me if I would write a piece about new trends in technology (applied to the real-estate industry) for the annual PROPS Lunch Magazine published to help Variety, a children's charity in the UK. How could I possibly say no? This piece that I wrote was printed in a magazine (pictured at the end of this post) that was handed out at the 25th Props Lunch. This event raised more than 380,000 from the real estate industry to buy wheelchairs for disabled or disadvantaged kids -- bringing the total raised to date to 9.5m. I'm so glad I could contribute to an event miles away in my own little way.