Telefónica Open Future_, Telefónica's startup accelerator that helps the best entrepreneurs grow and build successful businesses, and PAPIs.io Artificial Intelligence (AI) has a track-record of improving the way we make decisions. So why not use it to decide which startups to invest in, and take advantage of all the startup data that is available? The AI Startup Battle, powered by PreSeries (a joint venture between BigML and Telefónica Open Future_), is a unique experience you don't want to miss, where you'll witness real-world and high-stakes AI. As an early stage startup, you will enjoy a great opportunity to secure seed investment, and get press coverage in one of the technology capitals of the world.

Thousands of miles from the drama of the 2016 Olympics in Rio de Janeiro, Brazil, one man has been quietly plotting his own competition; a previously impossible event melding human and machine. His name is Robert Reiner, and next month, in Zurich, Switzerland, he'll host the world's first Cybathlon, aka the "Cyborg Olympics." The competition, while focused on individuals with disabilities, isn't a me-too Paralympics. Instead, the Cybathlon will pit the world's most advanced bionic assistive technologies against each other in an obstacle course of everyday tasks. With the help of exoskeletons, state-of-the-art prosthetics and all-terrain wheel chairs, they have surpassed the realm of the strictly biological to become superhuman.

Sequential planning portfolios are very powerful in exploiting the complementary strength of different automated planners. The main challenge of a portfolio planner is to define which base planners to run, to assign the running time for each planner and to decide in what order they should be carried out to optimize a planning metric. Portfolio configurations are usually derived empirically from training benchmarks and remain fixed for an evaluation phase. In this work, we create a per-instance configurable portfolio, which is able to adapt itself to every planning task. The proposed system pre-selects a group of candidate planners using a Pareto-dominance filtering approach and then it decides which planners to include and the time assigned according to predictive models. These models estimate whether a base planner will be able to solve the given problem and, if so, how long it will take. We define different portfolio strategies to combine the knowledge generated by the models. The experimental evaluation shows that the resulting portfolios provide an improvement when compared with non-informed strategies. One of the proposed portfolios was the winner of the Sequential Satisficing Track of the International Planning Competition held in 2014.

Knowledge bases in the form of ontologies are receiving increasing attention as they allow to clearly represent both the available knowledge, which includes the knowledge in itself and the constraints imposed to it by the domain or the users. In particular, Datalog± ontologies are attractive because of their property of decidability and the possibility of dealing with the massive amounts of data in real world environments; however, as it is the case with many other ontological languages, their application in collaborative environments often lead to inconsistency related issues. In this paper we introduce the notion of incoherence regarding Datalog± ontologies, in terms of satisfiability of sets of constraints, and show how under specific conditions incoherence leads to inconsistent Datalog± ontologies. The main contribution of this work is a novel approach to restore both consistency and coherence in Datalog± ontologies. The proposed approach is based on kernel contraction and restoration is performed by the application of incision functions that select formulas to delete. Nevertheless, instead of working over minimal incoherent/inconsistent sets encountered in the ontologies, our operators produce incisions over non-minimal structures called clusters. We present a construction for consolidation operators, along with the properties expected to be satisfied by them. Finally, we establish the relation between the construction and the properties by means of a representation theorem. Although this proposal is presented for Datalog± ontologies consolidation, these operators can be applied to other types of ontological languages, such as Description Logics, making them apt to be used in collaborative environments like the Semantic Web.

HARI SREENIVASAN: Lionfish have voracious appetites that are upsetting coral reef ecosystems from Rhode Island to Venezuela. But a new nonprofit company has an unusual plan to restore balance to those environments before it's too late. In the latest edition of our online series "ScienceScope," science producer Nsikan Akpan has the scoop. NSIKAN AKPAN: The lionfish is an invasive species. In its native home of the Indo-Pacific, the lionfish is a fierce, unrelenting predator.

An "AI writing robot" produced up to 58 articles per day for a Chinese publication at the Olympic Games in Rio de Janeiro this month. The Xiaomingbot wrote reports for the news syndication service Toutiao, delivering news items within two minutes of events ending. During the two weeks of the Olympics, the robot reporter produced a total of 450 stories. Xiaomingbot is not the first artificial intelligence (AI) reporter, though the quantity of reports makes it arguably the most prolific. The articles--ranging from around 100 words to 821 articles--appear to have been well received by readers, though some comments seen by Quartz reportedly claimed the prose was "too robotic."

In this special guest feature, Cecilia Pizzurro, Senior Director, Strategic Data Projects at LOGICnow, discusses the convergence of data/machine learning and cybersecurity, and the idea that these two are playing off of each other in a more meaningful way than ever before. Cecilia leads a team of data scientists and software engineers in Cambridge (US) and Newcastle (UK). These teams use machine learning and big data analytics to find business value in the vast amount of customer data gathered from LOGICnow's products. She was also the co-founder and CTO of the The Dolomite Group, a South American mining consortium, pioneering machine learning and big data analyses to improve mining efficiency and reduce environmental impact in Peru. This company is currently finalizing its acquisition by a Chilean mining company.

Why can't computers watch the Earth from above and automatically map our roads, buildings, and trash heaps? Satellite operator DigitalGlobe is teaming up with Amazon, the venture arm of the CIA, and chipmaker Nvidia to try to make it happen. In a joint project, DigitalGlobe today released satellite imagery depicting the whole of Rio de Janeiro to a resolution of 50 centimeters. The outlines of 200,000 buildings inside the city's roughly 1,900 square kilometers have been manually marked on the photos. The SpaceNet data set, as it is called, is intended to spark efforts to train machine-learning algorithms to interpret high-resolution satellite photos by themselves.

A consortium of companies, including DigitalGlobe, CosmiQ Works and NVIDIA, today launched SpaceNet, an open-data initiative aimed at improving image analysis tools. The data are being hosted by Amazon Web Services as part of a partnership. With an increase in the number of CubeSats, high-resolution satellites and drones of every shape and size, we have accumulated petabytes of imagining data that can be processed with analytics to solve myriad problems. DigitalGlobe, which operates imaging satellites, has built out partnerships with companies like Facebook to target rural villages with internet access using photography as a guide. Satellite imaging has also been analyzed to help the Navy find Somali pirates, crowdsource the hunt for Malaysia Airlines flight 370 and identify deforestation zones.

It sounds like something out of a sci-fi film - an AI that constantly monitors the Earth, looks for unusual activity. However, CosmiQ Works, a division of the CIA's venture arm, has revealed SpaceNet, a project with Amazon, satellite mapping firm DigitalGlobe and chip firm Nvidia to train algorithms to work out what's happening on our planet. The project will create a giant online database of hi-res images that AIs will be able to use to teach themselves - and started with images of Rio during the Olympics. SpaceNet will launch with an initial contribution of DigitalGlobe multi-spectral satellite imagery and 200,000 curated building footprints across the city of Rio de Janeiro, Brazil. 'Each minute something is happening in the world,' said said Tony Frazier, Senior Vice President at DigitalGlobe.