Aug 21, 2018 (Heraldkeeper via COMTEX) -- New York, August 22, 2018: Artificial intelligence (AI) Robots is arguably the foremost exciting field in artificial intelligence. It's definitely the foremost controversial: everyone agrees that a mechanism will add a production line, however there is not any consensus on whether a robot will ever be intelligent. Factors like the growing adoption of customer-centric marketing methods, increased use of social media for advertising, and increase in demand for virtual assistants are conducive to the expansion of the AI in promoting market. The Artificial Intelligence (AI) Robots Market is expected to exceed more than US$ 12 Billion by 2024 at a CAGR of 29% in the given forecast period. The Artificial Intelligence (AI) Robots Market is segmented on the lines of its application, offering, robot type and regional.

When consequential decisions are informed by algorithmic input, individuals may feel compelled to alter their behavior in order to gain a system's approval. Previous models of agent responsiveness, termed "strategic manipulation," have analyzed the interaction between a learner and agents in a world where all agents are equally able to manipulate their features in an attempt to "trick" a published classifier. In cases of real world classification, however, an agent's ability to adapt to an algorithm, is not simply a function of her personal interest in receiving a positive classification, but is bound up in a complex web of social factors that affect her ability to pursue certain action responses. In this paper, we adapt models of strategic manipulation to better capture dynamics that may arise in a setting of social inequality wherein candidate groups face different costs to manipulation. We find that whenever one group's costs are higher than the other's, the learner's equilibrium strategy exhibits an inequality-reinforcing phenomenon wherein the learner erroneously admits some members of the advantaged group, while erroneously excluding some members of the disadvantaged group. We also consider the effects of potential interventions in which a learner can subsidize members of the disadvantaged group, lowering their costs in order to improve her own classification performance. Here we encounter a paradoxical result: there exist cases in which providing a subsidy improves only the learner's utility while actually making both candidate groups worse-off--even the group receiving the subsidy. Our results reveal the potentially adverse social ramifications of deploying tools that attempt to evaluate an individual's "quality" when agents' capacities to adaptively respond differ.

A minimum dominating set in a graph is a minimum set of vertices such that every vertex of the graph either belongs to it, or is adjacent to one vertex of this set. This mathematical object is of high relevance in a number of applications related to social networks analysis, design of wireless networks, coding theory, and data mining, among many others. When vertex weights are given, minimizing the total weight of the dominating set gives rise to a problem variant known as the minimum weight dominating set problem. To solve this problem, we introduce a hybrid matheuristic combining a tabu search with an integer programming solver. The latter is used to solve subproblems in which only a fraction of the decision variables, selected relatively to the search history, are left free while the others are fixed. Moreover, we introduce an adaptive penalty to promote the exploration of intermediate infeasible solutions during the search, enhance the algorithm with perturbations and node elimination procedures, and exploit richer neighborhood classes. Extensive experimental analyses on a variety of instance classes demonstrate the good performance of the algorithm, and the contribution of each component in the success of the search is analyzed.

During just its third day in action, a facial recognition system used by Washington Dulles International Airport (IAD) caught its first imposter. While that's a clear win for proponents of the tech, it might also be major blow to the privacy of the average airline passenger. On Monday, 14 airports in the U.S. launched a pilot program to test the effectiveness of a biometric scanning system during the security and boarding processes. Passengers simply stand in front of a camera that takes their photo. The system then compares that photo to the one on the person's passport to confirm their identity.

It's been more than a century since Spanish neuroanatomist Santiago Ramón y Cajal won the Nobel Prize for illustrating the way neurons allow you to walk, talk, think, and be. In the intervening hundred years, modern neuroscience hasn't progressed that much in how it distinguishes one kind of neuron from another. Sure, the microscopes are better, but brain cells are still primarily defined by two labor-intensive characteristics: how they look and how they fire. Which is why neuroscientists around the world are rushing to adopt new, more nuanced ways to characterize neurons. Sequencing technologies, for one, can reveal how cells with the same exact DNA turn their genes on or off in unique ways--and these methods are beginning to reveal that the brain is a more diverse forest of bristling nodes and branching energies than even Ramón y Cajal could have imagined.

Killer robots must be banned to prevent unlawful killings, injuries and other violations of human rights'before it's too late', according to Amnesty International. The human rights non-profit is calling upon the United Nations to place tough new restraints on the development of autonomous weapon systems ahead of key negotiations in Geneva this week. The development of automated weapons, which can pick out and eliminate targets without input from a human being, has proliferated over the past decade. Countries including the UK, France, Israel and the US are known to be developing the technology for use in military and police operations. Amnesty International argues humans should remain'at the core of critical decisions' on the use of deadly force, such as the selection and engagement of targets.

After just three days using its new cutting-edge facial comparison biometric system, US customs intercepted an imposter posing as a French citizen trying to enter America. The 26-year-old man who was travelling from Sao Paulo, Brazil, last week became the first person to be caught out by the new technology, which is currently being tested at 14 international US airports. After the system alerted to a facial discrepancy, a search of the passenger revealed the man had concealed a Republic of Congo identification card in his shoe. He was deported without charge. US Customs and Border Protection hope the facial recognition software will detect terrorists and criminals before they can enter the US.

Exponential and concentration inequalities are corner stones of machine learning theory. The first distribution-free bounds on the Empirical Risk Minimiser (ERM), proven by Vapnik and Cervnonenkis in the early 70s, are based on Hoeffding's inequality, see Vapnik (1998). Model selection techniques rely heavily on concentration inequalities (Massart (2007)). We defer the reader to Boucheron et al. (2013) for an overview on concentration inequalities. However, all the results in these references are in the case of i.i.d random variables. Many extensions of Hoeffding and Bernstein's inequalities were proposed for dependent observations: see Catoni (2003); Bertail and Clémençon (2010); Joulin and Ollivier (2010); Dedecker and Fan (2015); Fan et al. (2018) under

We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total). The dialogs involve two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2) a teacher who answers the questions by providing short excerpts from the text. QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context, as we show in a detailed qualitative evaluation. We also report results for a number of reference models, including a recently state-of-the-art reading comprehension architecture extended to model dialog context. Our best model underperforms humans by 20 F1, suggesting that there is significant room for future work on this data. Dataset, baseline, and leaderboard available at http://quac.ai.

A number of techniques have been proposed for aspect discovery using part of speech tagging (Hu and Liu, 2004), syntactic parsing (Lu et al., 2009), clustering (Mei et al., 2007; Titov and McDonald, 2008b), data mining (Ku et al., 2006), and information extraction (Popescu and Etzioni, 2005). Various lexicon and rule-based methods (Hu and Liu, 2004; Ku et al., 2006; Blair-Goldensohn et al., 2008) have been adopted for sentiment prediction together with a few learning approaches (Lu et al., 2009; Pappas and Popescu-Belis, 2017; Angelidis and Lapata, 2018). As for the summaries, a common format involves a list of aspects and the number of positive and negative opinions for each (Hu and Liu, 2004). While this format gives an overall idea of people's opinion, reading the actual text might be necessary to gain a better understanding of specific details. Textual summaries are created following mostly extractive methods (but see Ganesan et al. 2010 for an abstractive approach), and various formats ranging from lists of words (Popescu and Etzioni, 2005), to phrases (Lu et al., 2009), and sentences (Mei et al., 2007; Blair-Goldensohn et al., 2008; Lerman et al., 2009; Wang and Ling, 2016). In this paper, we present a neural framework for opinion extraction from product reviews. We follow the standard architecture for aspect-based summarization, while taking advantage of the success of neural network models in learning continuous features without recourse to preprocessing tools or linguistic annotations.