If you grew up obsessed with gaming, them you were probably told by various relatives that you could never make a living playing video games all day. Yet while that might once have been true, there is now a growing industry of professional gamers for hire making serious money with their hard-earned skills. Marko Uslinkovski is a 36-year-old professional gamer from North Macedonia who makes a living beating games for people who don't have time to do it themselves. With a team of 50 'boosters' Marko told MailOnline his company, Captain Carry, can turnover between 30,000 to 50,000 in a good month. Marko told MailOnline: 'These new games are extremely difficult, so we're like the last ditch effort for people that are borderline giving up.' Marko Uslinkovski (pictured) is a 36-year-old professional gamer from North Macedonia who makes a living beating games for people who don't have time to do it themselves If you grew up obsessed with gaming, then you were probably told by various relatives that you'd never make a living playing video games all day (stock image) Like so many who end up with a life-long passion for video games, Marko was hooked from his very first taste.

Alexander Felfernig is a full professor at the Graz University of Technology (Austria) since March 2009 and received his PhD in Computer Science from the University of Klagenfurt. He directs the Applied Software Engineering (ASE) research group. His research interests include configuration systems, recommender systems, model-based diagnosis, software requirements engineering, different aspects of human decision making, and knowledge acquisition methods. In these areas, he is engaged in national research projects as well as in a couple of European Union projects. Alexander Felfernig has published numerous papers in renowned international conferences and journals (e.g., AI Magazine, Artificial Intelligence, IEEE Transactions on Engineering Management, IEEE Intelligent Systems, Journal of Electronic Commerce) and is a co-author of the book on "Recommender Systems" published by Cambridge University Press.

Just like a ray of light, a wireless signal bounces off of certain objects and surfaces. Different materials affect the wireless signal in different ways. Human bodies, therefore, cause particular changes when reflecting wireless signals. Using this property, MIT researchers from the Computer Science and Artificial Intelligence Laboratory (CSAIL) designed a wireless system that captures reflections off of humans and collects health and behavioral data. The system, dubbed Marko, broadcasts radio-frequency (RF) signals that bounce off people in motion and return with specific changes. Specially-designed algorithms then analyze the reflected signals and associate them with specific people.

We live in a world of wireless signals flowing around us and bouncing off our bodies. MIT researchers are now leveraging those signal reflections to provide scientists and caregivers with valuable insights into people's behavior and health. The system, called Marko, transmits a low-power radio-frequency (RF) signal into an environment. The signal will return to the system with certain changes if it has bounced off a moving human. Novel algorithms then analyze those changed reflections and associate them with specific individuals.

Quotage: "We are still decades from Star Trek-style artificial general intelligence that could pass the Turing test or outperform humans on a gamut of unrelated cognitive tasks. In the meantime, a promising compromise would be the ability to automate model selection and tuning based on the problem and available data, and then select the best options from a portfolio of deep learning software each designed for different applications." Thus the theme of Marko's useful offering, which gets into the practicalities of "automated algorithm selection," where the proper algorithm for a narrower use case is machine-determined. I can see why Marko argues that helping companies sort the right algorithms could make up for lack of internal data science expertise. More APIs and "metadata taxonomies" are needed.

The arrival of artificial intelligence and its ilk -- cognitive computing, deep machine learning -- has felt like a vague distant future state for so long that it's tempting to think it's still decades away from practicable implementation at the point of care. And while many use cases today are admittedly still the exception rather than the norm, some examples are emerging to make major healthcare providers take note. Regenstrief Institute and Indiana University School of Informatics and Computing, for instance, recently examined open source algorithms and machine learning tools in public health reporting: The tools bested human reviewers in detecting cancer using pathology reports and did so faster than people. Indeed, more and more leading health systems are looking at ways to harness the power of AI, cognitive computing and machine learning. "Our initial application of deep learning convinced me that these methods have great value to healthcare," said Andy Schuetz, a senior data scientist at Sutter Health's Research Development and Dissemination Group.

The basic unit of meaning on the Semantic Web is the RDF statement, or triple, which combines a distinct subject, predicate and object to make a definite assertion about the world. A set of triples constitutes a graph, to which they give a collective meaning. It is upon this simple foundation that the rich, complex knowledge structures of the Semantic Web are built. Yet the very expressiveness of RDF, by inviting comparison with real-world knowledge, highlights a fundamental shortcoming, in that RDF is limited to statements of absolute fact, independent of the context in which a statement is asserted. This is in stark contrast with the thoroughly context-sensitive nature of human thought. The model presented here provides a particularly simple means of contextualizing an RDF triple by associating it with related statements in the same graph. This approach, in combination with a notion of graph similarity, is sufficient to select only those statements from an RDF graph which are subjectively most relevant to the context of the requesting process.

A semantic network is a directed labeled graph (Sowa, 1991). The thesis of this article is that the state of a computing machine, its low-level instructions, and the executing program can be represented as a semantic network. The computational model that is presented can be instantiated using any semantic network representation. However, given the existence of the Resource Description Framework (RDF) (Manola & Miller, 2004) and the popular Web Ontology Language (OWL) (McGuinness & Harmelen, 2004), this article presents the theory and the application in terms of these constructs. The computing model that is proposed is perhaps simple in theory, but in application, requires a relatively strong background in the computer sciences.

The emerging Web of Data utilizes the web infrastructure to represent and interrelate data. The foundational standards of the Web of Data include the Uniform Resource Identifier (URI) and the Resource Description Framework (RDF). URIs are used to identify resources and RDF is used to relate resources. While RDF has been posited as a logic language designed specifically for knowledge representation and reasoning, it is more generally useful if it can conveniently support other models of computing. In order to realize the Web of Data as a general-purpose medium for storing and processing the world's data, it is necessary to separate RDF from its logic language legacy and frame it simply as a data model. Moreover, there is significant advantage in seeing the Semantic Web as a particular interpretation of the Web of Data that is focused specifically on knowledge representation and reasoning. By doing so, other interpretations of the Web of Data are exposed that realize RDF in different capacities and in support of different computing models.

Since the Turing test was first proposed by Alan Turing in 1950, the primary goal of artificial intelligence has been predicated on the ability for computers to imitate human behavior. However, the majority of uses for the computer can be said to fall outside the domain of human abilities and it is exactly outside of this domain where computers have demonstrated their greatest contribution to intelligence. Another goal for artificial intelligence is one that is not predicated on human mimicry, but instead, on human amplification. This article surveys various systems that contribute to the advancement of human and social intelligence. The alleged shortcut to knowledge, which is faith, is only a short-circuit destroying the mind.