In these spaces of high dimensionality, feature selection is a way to exclude those irrelevant and redundant features, whose presence might complicate the task of knowledge discovery. In classification tasks, a feature is considered irrelevant if it contains no information about the class and therefore it is not necessary at all for the predictive task. Besides, it is widely accepted that two features are redundant if their values are correlated. There are several well known measures that compare features and determine their importance, such as the symmetrical uncertainty (SU)[2]. SU is a measure based on information that uses entropy and conditional entropy values to determine the correlation between pairs of features.

Udacity's mission is to democratize education. Focused on self-empowerment through learning, Udacity is making innovative technologies such as self-driving cars available to a global community of aspiring technologists, while also enabling learners at all levels to skill up with essentials like programming, web and app development. If you love a challenge, and truly want to make a difference in the world, read on! A Student Experience Lead is a educational strategist and program manager in one, with a strong pedagogical sense. You should take pride in ensuring that the students in your Nanodegree program receive the best possible learning experience.

Brazil has a big electricity theft problem. But an AI algorithm tested on several million of the country's households shows promise as a tool for helping cut this out. It could also offer insights for electricity suppliers elsewhere seeking to do a better job of reading your meter. Who is responsible for the theft in Brazil isn't always clear – sending meter readers to check whether meters and overhead cabling have been tampered with is dangerous work, says Adrian Grilli of the Joint Radio Company in London, which does telecommunications for global energy companies. Electricity theft is hardly limited to Brazil: some countries see as much as 40 per cent of their supply siphoned off largely by users who have tampered with meters.

However, for the most part, these events are not new. Our planet has always been a dangerous place to live (climate change or no). Despots and regimes have, throughout history, posed a threat to humanity. While it's easy to ignore the dangers that these pose and leave it to the scientists or politicians to fix, we all may be about to enter uncharted waters with a new threat to our species…. In July this year, Facebook had to temporarily suspend its Artificial Intelligence (AI) research when two of its'chatbots' (Alice and Bob) began communicating with each other in a language they developed, independent of their programmers.

A large, now extinct frog called Beelzebufo that lived about 68 million years ago in Madagascar would have been capable of eating small dinosaurs, researchers have found. The discovery came after researchers measured the bite force of South American horned frogs, known as Pacman frogs because of their round shape and large mouth. The study found that these frogs have similar bite forces to those of mammalian predators, and the extinct frog may have had a bite as strong as that of a wolf or female tiger. A large, extinct frog called Beelzebufo that lived 68 million years ago would have been capable of eating small dinosaurs. The study, published in the journal Scientific Reports, analyzed the bite force of South American horned frogs from the living genus Ceratophrys, which are thought to be very similar to the extinct Beelzebufo frog.

Content-dense news report important factual information about an event in direct, succinct manner. Information seeking applications such as information extraction, question answering and summarization normally assume all text they deal with is content-dense. Here we empirically test this assumption on news articles from the business, U.S. international relations, sports and science journalism domains. Our findings clearly indicate that about half of the news texts in our study are in fact not content-dense and motivate the development of a supervised content-density detector. We heuristically label a large training corpus for the task and train a two-layer classifying model based on lexical and unlexicalized syntactic features. On manually annotated data, we compare the performance of domain-specific classifiers, trained on data only from a given news domain and a general classifier in which data from all four domains is pooled together. Our annotation and prediction experiments demonstrate that the concept of content density varies depending on the domain and that naive annotators provide judgement biased toward the stereotypical domain label. Domain-specific classifiers are more accurate for domains in which content-dense texts are typically fewer. Domain independent classifiers reproduce better naive crowdsourced judgements. Classification prediction is high across all conditions, around 80%.

The AGM paradigm of belief change studies the dynamics of belief states in light of new information. Finding, or even approximating, those beliefs that are dependent on or relevant to a change is valuable because, for example, it can narrow the set of beliefs considered during belief change operations. A strong intuition in this area is captured by Gärdenforss preservation criterion (GPC), which suggests that formulas independent of a belief change should remain intact. GPC thus allows one to build dependence relations that are linked with belief change. Such dependence relations can in turn be used as a theoretical benchmark against which to evaluate other approximate dependence or relevance relations. Fariñas and Herzig axiomatize a dependence relation with respect to a belief set, and, based on GPC, they characterize the correspondence between AGM contraction functions and dependence relations. In this paper, we introduce base dependence as a relation between formulas with respect to a belief base, and prove a more general characterization that shows the correspondence between kernel contraction and base dependence. At this level of generalization, different types of base dependence emerge, which we show to be a result of possible redundancy in the belief base. We further show that one of these relations that emerge, strong base dependence, is parallel to saturated kernel contraction. We then prove that our latter characterization is a reversible generalization of Fariñas and Herzigs characterization. That is, in the special case when the underlying belief base is deductively closed (i.e., it is a belief set), strong base dependence reduces to dependence, and so do their respective characterizations. Finally, an intriguing feature of Fariñas and Herzigs formalism is that it meets other criteria for dependence, namely, Keyness conjunction criterion for dependence (CCD) and Gärdenforss conjunction criterion for independence (CCI). We prove that our base dependence formalism also meets these criteria. Even more interestingly, we offer a more specific criterion that implies both CCD and CCI, and show our base dependence formalism also meets this new criterion.

We propose a protocol to perform generalized quantum reinforcement learning with quantum technologies. At variance with recent results on quantum reinforcement learning with superconducting circuits [L. Lamata, Sci. Rep. 7, 1609 (2017)], in our current protocol coherent feedback during the learning process is not required, enabling its implementation in a wide variety of quantum systems. We consider diverse possible scenarios for an agent, an environment, and a register that connects them, involving multiqubit and multilevel systems, as well as open-system dynamics. We finally propose possible implementations of this protocol in trapped ions and superconducting circuits. The field of quantum reinforcement learning with quantum technologies will enable enhanced quantum control, as well as more efficient machine learning calculations.

In the future, doctors will be able to diagnose your illness before even meeting you. Recent applications of machine learning with big data are able to predict diseases--such as Alzheimer's and diabetes--with incredible accuracy, years before the onset of symptoms. To assess the likelihood of a patient developing a certain condition, physicians have traditionally relied on risk calculators such as this one. These use basic patient info such as age, weight and blood pressure to quantify the probability of occurrence of the disease in the future. A risk calculator is a simple tool that works using an equation based on the data of one or a few studies; in fact, doctors used these mathematical tools on paper back in the day, simply solving for X after filling the blanks with a patient's data.

Seventeen startups from around the world will showcase their digital innovations this year at Oracle OpenWorld, displaying everything from intelligent contact hubs for customer engagement to an innovation-and talent-sourcing platform to a maintenance application that uses deep learning to predict when industrial equipment is about to malfunction. Participating startups were chosen from the first global class of the Oracle Startup Cloud Accelerator program, which gives select technology and technology-powered startups six months of mentoring and other kinds of support--including engagement opportunities with Oracle's more than 400,000 global customers. Thousands of applicants across more than eight countries applied for admission to the program, with judges from Oracle and industry executives scoring each company on the strength of its management team, its use of technology, and market traction. Startups get ready to pitch at Oracle's Startup Cloud Accelerator in Bristol, England. England-based GRAKN.AI is one of the 17 program members traveling to Oracle OpenWorld for the event, October 1 to 5 in San Francisco.