Telecom companies have celebrated 20 years in the Romanian market and what a period it has been. From clunky mobile phones dubbed "bricks" and the squeaky sound of slow dial-up connections we have moved to extremely powerful devices that are processing impressive amounts of data in complete silence. Moving from little over half a million users in 1998 to more than 26 million in the first half of 2017 proved an impressive feat for telecom players, which have made constant investments in technology. Their next milestone will be the deployment of the new 5G wireless infrastructure, which should happen locally by 2020. Through such investments, autonomous driving will edge one step closer to reality in large cities.

In this post, I will go through the steps required for building a three layer neural network. I'll go through a problem and explain you the process along with the most important concepts along the way. A farmer in Italy was having a problem with his labelling machine: it mixed up the labels of three wine cultivars. Now he has 178 bottles left, and nobody knows which cultivar made them! To help this poor man, we will build a classifier that recognizes the wine based on 13 attributes of the wine.

One of the biggest issues facing the introduction of self-driving cars is where blamed is placed if one is involved in an accident. Does the responsibility lie with the vehicle owner, the car manufacturer or the firm that's developed the autonomous driving software? This and more will be decided in the next three years, with the Government launching a new legal review to prepare the country for driverless cars hitting UK roads. Minsters have commissioned the study in order to'ensure the UK remains one of the best places in the world to develop, test and drive self-driving vehicles'. Roads minister Jesse Norman yesterday announced the start of the review by the Law Commission of England and Wales and the Scottish Law Commission that will examine any legal obstacles that might restrict the widespread introduction of self-driving vehicles and highlight the need for regulatory reforms.

Business Finland has granted 678,000 euros to a team lead by Aalto University's Jussi Rintanen for the commercialisation of a new information system technology based on artificial intelligence. Rintanen wants not only to automate the development of large information systems but also to integrate all parts of software development into a single functioning whole. Information systems projects in health care and in public sector administration, for instance, are highly labour intensive. The whole information system market in Finland is worth about two billion euros annually, and worldwide the figure is about 200 billion. Information systems projects with overruns in time and costs, or projects that cannot be completed at all, suffer from the same basic problem: a massive amount of routine programming work whose management is extremely difficult.

Marketing Engineering Center CUHK 20,742 views IBM Watson Machine Learning: Score a Predictive Model Built with IBM SPSS Modeler - Duration: 5:31.

Chris Boos has a mission: empowering human potential, freeing up time for creativity and innovative thinking through artificial intelligence (AI). Since then, Chris has led Arago to become a key partner and driver for the established economy, positioning Arago's AI HIRO as a platform for companies to reinvent their business models in the digital age. As CEO of Arago, Chris Boos vision is to transform traditional companies into future-proof, AI-enabled enterprises – regardless of their respective industry. Arago's international client base includes IT service providers, banks and companies in the telecommunications, steel and metals indusries. But his ambitions go far beyond: a strong believer in integrating machine reasoning and machine learning, Chris is constantly challenging current thinking on AI.

Predictive motion planning is the key to achieve energy-efficient driving, which is one of the main benefits of automated driving. Researchers have been studying the planning of velocity trajectories, a simpler form of motion planning, for over a decade now and many different methods are available. Dynamic programming has shown to be the most common choice due to its numerical background and ability to include nonlinear constraints and models. Although planning of an optimal trajectory is done in a systematic way, dynamic programming does not use any knowledge about the considered problem to guide the exploration and therefore explores all possible trajectories. A* is a search algorithm which enables using knowledge about the problem to guide the exploration to the most promising solutions first. Knowledge has to be represented in a form of a heuristic function, which gives an optimistic estimate of cost for transitioning to the final state, which is not a straightforward task. This paper presents a novel heuristics incorporating air drag and auxiliary power as well as operational costs of the vehicle, besides kinetic and potential energy and rolling resistance known in the literature. Furthermore, optimal cruising velocity, which depends on vehicle aerodynamic properties and auxiliary power, is derived. Results are compared for different variants of heuristic functions and dynamic programming as well.

Machine learning can impact people with legal or ethical consequences when it is used to automate decisions in areas such as insurance, lending, hiring, and predictive policing. In many of these scenarios, previous decisions have been made that are unfairly biased against certain subpopulations, for example those of a particular race, gender, or sexual orientation. Since this past data may be biased, machine learning predictors must account for this to avoid perpetuating or creating discriminatory practices. In this paper, we develop a framework for modeling fairness using tools from causal inference. Our definition of counterfactual fairness captures the intuition that a decision is fair towards an individual if it is the same in (a) the actual world and (b) a counterfactual world where the individual belonged to a different demographic group. We demonstrate our framework on a real-world problem of fair prediction of success in law school.

Graphs are fundamental data structures which concisely capture the relational structure in many important real-world domains, such as knowledge graphs, physical and social interactions, language, and chemistry. Here we introduce a powerful new approach for learning generative models over graphs, which can capture both their structure and attributes. Our approach uses graph neural networks to express probabilistic dependencies among a graph's nodes and edges, and can, in principle, learn distributions over any arbitrary graph. In a series of experiments our results show that once trained, our models can generate good quality samples of both synthetic graphs as well as real molecular graphs, both unconditionally and conditioned on data. Compared to baselines that do not use graph-structured representations, our models often perform far better. We also explore key challenges of learning generative models of graphs, such as how to handle symmetries and ordering of elements during the graph generation process, and offer possible solutions. Our work is the first and most general approach for learning generative models over arbitrary graphs, and opens new directions for moving away from restrictions of vector- and sequence-like knowledge representations, toward more expressive and flexible relational data structures.

Many loss functions in representation learning are invariant under a continuous symmetry transformation. As an example, consider word embeddings (Mikolov et al., 2013b), where the loss remains unchanged if we simultaneously rotate all word and context embedding vectors. We show that representation learning models with a continuous symmetry and a quadratic Markovian time series prior possess so-called Goldstone modes. These are low cost deviations from the optimum which slow down convergence of gradient descent. We use tools from gauge theory in physics to design an optimization algorithm that solves the slow convergence problem. Our algorithm leads to a fast decay of Goldstone modes, to orders of magnitude faster convergence, and to more interpretable representations, as we show for dynamic extensions of matrix factorization and word embedding models. We present an example application, translating modern words into historic language using a shared representation space.