I am a Machine Learning Engineer, Deep Learning Engineer and even an Indie Game Developer with a Major in Compilers and a Master's degree in Artificial Intelligence from University Politehnica of Bucharest. I am passionate about Games and Artificial Intelligence. I love to give life to A.I. agents in my project or my friend's projects and I want to teach you too.

A Deutsche Telekom official says artificial intelligence (AI) will be needed to break smart city data out of its silos. As reported by Mobile World Live, thoughts about smart city evolution appeared on the blog of Claudia Nemat, the Deutsche Telekom (DT) board member who oversees Technology & Innovation Europe. In her piece, Nemat described current smart city practices as trapping data from sensors in an isolated "landlocked lake." But as sensors proliferate at blinding speed in global smart cities, the rivers of data being produced will turn into oceans. This will be complicated by the multiple digital platforms running simultaneously in each city.

The next time you open the door to find the pizza you ordered (via an app of course), you may not find a delivery person standing on your doorstep. Expect to see a drone dropping off your dinner. And perhaps that dinner was by prepped, at least in part, by robots. Eventually, some of your pizza's toppings may be lab grown-- but in the meantime, even those fresh-off-the-farm ingredients have a good chance of reaching your fork via robotics. Today, automation is shaping up to be our generation's food revolution.

A Deutsche Telekom official says artificial intelligence (AI) will be needed to break smart city data out of its silos. As reported by Mobile World Live, thoughts about smart city evolution appeared on the blog of Claudia Nemat, the Deutsche Telekom (DT) board member who oversees Technology & Innovation Europe. In her piece, Nemat described current smart city practices as trapping data from sensors in an isolated "landlocked lake." But as sensors proliferate at blinding speed in global smart cities, the rivers of data being produced will turn into oceans. This will be complicated by the multiple digital platforms running simultaneously in each city.

Romanian startup Grobyk is organizing ready-to-use, trending information from your chosen sources, using Artificial Intelligence algorithms. Their 3 words pitch is: Instant Relevant Research. Content marketers keep up with a lot of sources, from social media, blogs, news site and so on. Search engine trends are misleading. What's relevant are social media shares.

Do you fancy a technical talk to kick off this week? The AI Machine Learning community gets back at TechHub Bucharest for an interesting presentation. Join them on Monday, June 6, to find out more about the AlphaGo model and the machine learning algorithms it employs. During this meetup you'll get an in-depth presentation of the AlphaGo model and the machine learning algorithms it employs. AlphaGo is the artificial Go player developed by DeepMind, the first one to win against a professional human Go player.

Textual complexity is widely used to assess the difficulty of reading materials and writing quality in student essays. At a lexical level, word complexity can represent a building block for creating a comprehensive model of lexical networks that adequately estimates learners’ understanding. In order to best capture how lexical associations are created between related concepts, we propose automated indices of word complexity based on Age of Exposure (AoE). AOE indices computationally model the lexical learning process as a function of a learner's experience with language. This study describes a proof of concept based on the on a large-scale learning corpus (i.e., TASA). The results indicate that AoE indices yield strong associations with human ratings of age of acquisition, word frequency, entropy, and human lexical response latencies providing evidence of convergent validity.

Automatic annotation of documents with controlled vocabulary terms (descriptors) from a conceptual thesaurus is not only useful for document indexing and retrieval. The mapping of texts onto the same thesaurus furthermore allows to establish links between similar documents. This is also a substantial requirement of the Semantic Web. This paper presents an almost language-independent system that maps documents written in different languages onto the same multilingual conceptual thesaurus, EUROVOC. Conceptual thesauri differ from Natural Language Thesauri in that they consist of relatively small controlled lists of words or phrases with a rather abstract meaning. To automatically identify which thesaurus descriptors describe the contents of a document best, we developed a statistical, associative system that is trained on texts that have previously been indexed manually. In addition to describing the large number of empirically optimised parameters of the fully functional application, we present the performance of the software according to a human evaluation by professional indexers.

Later in the school, the focus turned to ontologies, which is where the true power of the semantic web lies. EUROLAN lecturers treated its potential in terms of what the topic of ontology development it might--and might not--bring to us in the future. This year's and how great its impact will really start somewhere, somehow, even if school was organized by the Faculty be. Although it is not yet clear what emerges is a variety of ontological of Computer Science at the A. I. Cuza whether the current vision of the semantic stores from which to choose. University of Iasi, the Research Institute web will indeed reach its expectations, The EUROLAN summer school also for Artificial Intelligence at the there are more and more included a workshop on ontologies Romanian Academy in Bucharest, opinions that it represents a major and information extraction, a student and the Department of Computer technological step that will permanently workshop on applied natural Science at Vassar College.

The 2002 Trading Agent Competition (TAC) presented a challenging market game in the domain of travel shopping. One of the pivotal issues in this domain is uncertainty about hotel prices, which have a significant influence on the relative cost of alternative trip schedules. Thus, virtually all participants employ some method for predicting hotel prices. We survey approaches employed in the tournament, finding that agents apply an interesting diversity of techniques, taking into account differing sources of evidence bearing on prices. Based on data provided by entrants on their agents' actual predictions in the TAC-02 finals and semifinals, we analyze the relative efficacy of these approaches. The results show that taking into account game-specific information about flight prices is a major distinguishing factor. Machine learning methods effectively induce the relationship between flight and hotel prices from game data, and a purely analytical approach based on competitive equilibrium analysis achieves equal accuracy with no historical data. Employing a new measure of prediction quality, we relate absolute accuracy to bottom-line performance in the game.