Robot cage fights are pretty sweet--who doesn't like seeing machines armed with spinning sawblades flamethrowers fight to the death--but medieval robot wars are so much more fun. Think about it: Hand-built robots catapulting rocks at castles, trying to breach their defenses. George R. R. Martin couldn't come up with something that crazy. But some 1,500 high school kids in Southern California did, coming together for an epic battle last month during the Los Angeles regional championship of the "Super Bowl of Smarts." Now, the thought of a robot war set in the Middle Ages probably brings to mind all the worst stereotypes of geeks and nerds and dweebs.

The rise of on-demand startups and other fast-growing businesses like online marketplaces has created a need for reliable ways to verify new hires and customers to make sure they are cut out for the task. Onfido, one of the group of startups building software-based solutions to do just that, has raised 25 million to meet that need. The Series B round was led by Idinvest Partners, with participation also from existing backers Wellington Partners and CrunchFund (founded by TC founder Michael Arrington), and brings the total raised by the startup to just over 30 million. It's not disclosing its valuation in this round, but as a marker, its competitor Checkr, which has raised just under 50 million, is valued between 250 million and 300 million. Onfido plans to use the capital in a couple of different ways.

Scientists have compiled the biggest known library of bat sounds in an effort to identify and conserve rare species. Mexico is home to many of the world's bats, but it also has one of the highest rates of extinction. International researchers recorded more than 4,500 calls from about half of Mexico's 130 bat species. The audio library allows bat calls to be identified automatically, helping to monitor any changes in biodiversity. Co-researcher, Prof Kate Jones, of UCL and Zoological Society London, said the ability to map bats was crucial for implementing effective conservation measures.

Major players in the tech industry including Google and Microsoft have bet big money on the rise of internet bots as a form of communication between man and machine. While not the eerily humanoid robots in sci-fi movies (yet), the advancement and refinement of the bots' mechanical psyche parallel to the development of robotic parts is one step closer to merging the two. That is if it hasn't happened already. Isaac Asimov was a prolific sci-fi writer and Professor of Biochemistry at Boston University and is regarded by many as the father of robotics. "The Three Laws of Robotics 1: A robot may not injure a human being or, through inaction, allow a human being to come to harm; 2: A robot must obey the orders given it by human beings except where such orders would conflict with the First Law; 3: A robot must protect its own existence as long as such protection does not conflict with the First or Second Law; The Zeroth Law: A robot may not harm humanity, or, by inaction, allow humanity to come to harm."?

2QBF is a special case of general quantified Boolean formulae (QBF). It is limited to just two quantification levels, i.e., to a form forall-exists. Despite this limitation it applies to a wide range of applications, e.g., to artificial intelligence, graph theory, synthesis, etc.. Recent research showed that CEGAR-based methods give a performance boost to QBF solving (e.g, compared to QDPLL). Conjunctive normal form (CNF) is a commonly accepted representation for both SAT and QBF problems; however, it does not reflect the circuit structure that might be present in the problem. Existing attempts of extracting this structure from CNF and using it in 2QBF context do not show advantages over CNF based 2QBF solvers. In this work we introduce a new workflow for 2QBF, containing a new semantic circuit extraction algorithm and a CEGAR-based 2QBF solver that uses circuit structure and is improved by a so-called "cofactor sharing'' heuristics. We evaluate the proposed methodology on a range of benchmarks and show the practicality of the new approach.

A major challenge in cyber-threat analysis is combining information from different sources to find the person or the group responsible for the cyber-attack. It is one of the most important technical and policy challenges in cyber-security. The lack of ground truth for an individual responsible for an attack has limited previous studies. In this paper, we overcome this limitation by building a dataset from the capture-the-flag event held at DEFCON, and propose an argumentation model based on a formal reasoning framework called DeLP (Defeasible Logic Programming) designed to aid an analyst in attributing a cyber-attack to an attacker. We build argumentation-based models from latent variables computed from the dataset to reduce the search space of culprits (attackers) that an analyst can use to identify the attacker. We show that reducing the search space in this manner significantly improves the performance of classification-based approaches to cyber-attribution.

We study the automatic reply of email business messages in Brazilian Portuguese. We present a novel corpus containing messages from a real application, and baseline categorization experiments using Naive Bayes and Support Vector Machines. We then discuss the effect of lemmatization and the role of part-of-speech tagging filtering on precision and recall. Support Vector Machines classification coupled with non-lemmatized selection of verbs and nouns, adjectives and adverbs was the best approach, with 87.3% maximum accuracy. Straightforward lemmatization in Portuguese led to the lowest classification results in the group, with 85.3% and 81.7% precision in SVM and Naive Bayes respectively. Thus, while lemmatization reduced precision and recall, part-of-speech filtering improved overall results.

In the field of General Game Playing the imperfectinformationgames present a special challenge for researchers.In general the search space is larger, and thelack of information requires a different decision makingtechnique. A simple Monte Carlo sampling using a particlefilter may serve for the simple games, but this soonfails when more complex games are played. The HyperPlaytechnique was one such ”simple” player, soonenhanced to HyperPlay-II capable of handling the mostcomplex of games. However, this technique is very resourcehungry and may not scale well for larger games.We explore the scalability of HyperPlay-II for a varietyof imperfect-information games and test some perfectinformationpruning techniques to see if they will improveefficiency.

Today I was invited to give a KeyNote Lecture about Artificial Intelligence in the beautiful city of Zaragoza, by Javier Khunel the CEO of the main business school there, media group Heraldo and CaixaBank, to a diverse audience of business owners, entrepreneurs, c level execs, intrapreneurs and many more, at a great venue The CaixaForum building. I have been in the field for the last 21 years, and the last ones as clear advocate of AI and Deep Learning, with a company in the field, and advising Emotiv Inc the Leader in Brain Computer Interfaces about Data Science and Artificial Intelligence. So it is fair to say that I play in a field I know very well. Anyway I always get my facts and figures up to date, and to my surprise I discover an amazing fact: the father of Artificial Intelligence according to the MIT Technology Review is from my own backyard so to speak, from the country I was born Spain. He develop a machine in 1912 called "El Ajedrecista" or "The Chess Player" a very limited precursor of IBM's Deep Blue, and the first true chess computer, but by all means a pioneer (electro mechanical) work in the Artificial Intelligence field, he also build an Algebraic Formula Machine and many other mostly unknown marvels.

It represents a particular pure quantum state of a specific isolated system of one or more particles. By choosing a specific system of coordinates, e.g.