How do we unlock the true value of data? Matt Lovell, CTO of Centiq, argues why we need to be taking a different approach to data science… Nowadays, collecting data has never been easier and faster, but understanding its true meaning and value still remains a challenge. IBM estimates 90% of all the data existing in the world today has been generated in the last 24 months. For data-driven organisations such as the NHS, which has disparate trusts throughout Britain generating vast amounts of data, having the ability to harness this information and analyse it correctly to get the right insights has never been more important. In the technology profession, we may have, ironically, promoted Big Data as a panacea without truly and fully understanding the issues it can solve.

The portal is funded by the European FP7 project "Organic" and the University of Gent. The Fraunhofer Institute for Intelligent Analysis and Information Systems claims international patents for commercial exploits of the ESN architecture and learning principle.

We consider a Gaussian process model trained on few evaluations of an expensive-to-evaluate deterministic function and we study the problem of estimating a fixed excursion set of this function. We review the concept of conservative estimates, recently introduced in this framework, and, in particular, we focus on estimates based on Vorob'ev quantiles. We present a method that sequentially selects new evaluations of the function in order to reduce the uncertainty on such estimates. The sequential strategies are first benchmarked on artificial test cases generated from Gaussian process realizations in two and five dimensions, and then applied to two reliability engineering test cases.

Do you have a data-driven business idea? Would it be worth to test it? Would you like to make it fly? We at Tieto Data Incubator would like to give your data-driven idea possibility to success. We are joining Stockholm AI event 18th of Jan in Stockholm.

A few months into my time as an insurance claims adjuster, a customer called and said he thought his house was breaking apart. He'd heard what sounded like wood beams snapping in the basement, so he went downstairs and crept into the crawl space to investigate. While he was lying in the dark surrounded by cement, he told me, he started to panic. It brought him back to years earlier when, as a firefighter with the New York City Fire Department, he served as a first responder on September 11, crawling through the giant blocks of brick and mortar that had collapsed hours earlier. This revelation came 10 or 15 minutes into our conversation, after I'd gathered his basic information and logged the details of the case.

Nier: Automata should be on your games-to-watch list for 2017. I played the Nier: Automata demo the other day and was immediately smitten. The demo is a little over 30 minutes long, but in that half hour or so is a wealth of striking gameplay, clever use of perspective, and surprisingly deep world-building. Indeed, when it came to the game's terrific combat I only barely scratched the surface, largely button-mashing my way through until I began to figure out the more involved systems of combos and special moves available. As the human-looking robot 2B, you fight your way through an industrial area filled with hostile machines.

We're two days out from the tech-filled extravaganza that is the Las Vegas Consumer Electronic Show, an epic display of wealth, gadgetry and bling to an audience of 150,000 people. Drones have their own wing of the event now, and a number of companies were displaying drones aimed directly at consumers. Leaving aside the growing business market for these models, I've taken a look at some of the most interesting options in the consumer space. Some can be classified as toys, some as aerial selfie sticks, but if this market gets traction, it will greatly increase the public support for business usages, so it's a carrot-stick-carrot approach. Drones are big business, predicted to be worth billions in the next decade. Getting into this nascent industry is a smart idea, and the best way to make them accessible is to let flyers understand how they work.

Edited by Michael F. Goodchild, University of California, Santa Barbara, CA, and approved November 22, 2016 (received for review July 20, 2016) Ride-sharing services can provide not only a very personalized mobility experience but also ensure efficiency and sustainability via large-scale ride pooling. Large-scale ride-sharing requires mathematical models and algorithms that can match large groups of riders to a fleet of shared vehicles in real time, a task not fully addressed by current solutions. We present a highly scalable anytime optimal algorithm and experimentally validate its performance using New York City taxi data and a shared vehicle fleet with passenger capacities of up to ten. Our results show that 2,000 vehicles (15% of the taxi fleet) of capacity 10 or 3,000 of capacity 4 can serve 98% of the demand within a mean waiting time of 2.8 min and mean trip delay of 3.5 min. Ride-sharing services are transforming urban mobility by providing timely and convenient transportation to anybody, anywhere, and anytime. Current mathematical models, however, do not fully address the potential of ride-sharing. Recently, a large-scale study highlighted some of the benefits of car pooling but was limited to static routes with two riders per vehicle (optimally) or three (with heuristics).

Nobody wants their company's core competency to be managing data in a database. We feel the same way about AI." In many ways, Hammond represents a wave of entrepreneurs who are counting on the cloud to help them make AI less exotic and more accessible.

The machines haven't taken over. However, they are seeping their way into our lives, affecting how we live, work and entertain ourselves. From voice-powered personal assistants like Siri and Alexa, to more underlying and fundamental technologies such as behavioral algorithms, suggestive searches and autonomously-powered self-driving vehicles boasting powerful predictive capabilities, there are several examples and applications of artificial intellgience in use today. However, the technology is still in its infancy. What many companies are calling A.I. today, aren't necessarily so.