Photography company Getty Images is accusing Google of scraping images from third party websites and encouraging piracy, adding a new wrinkle to the Mountain View, Calif.'s ongoing legal battles in Europe. Google first introduced the feature in Jan. 2013. Previously, the search engine only displayed tiny thumbnails of images. In a statement released to TIME ahead of the filing, Getty argues that since image consumption is immediate, "there is little impetus to view the image on the original source site" once it's seen in high resolution on Google. The complaint comes less than a week after the European Union's antitrust commission charged Google with using unfair practices to promote its own services on Android devices.

The research on human cognition has recently benefited from the use of the mathematical formalism of quantum theory in Hilbert space. However, cognitive situations exist which indicate that the Hilbert space structure, and the associated Born rule, would be insufficient to provide a satisfactory modeling of the collected data, so that one needs to go beyond Hilbert space. In Part I of this paper we follow this direction and present a general tension-reduction (GTR) model, in the ambit of an operational and realistic framework for human cognition. In this Part II we apply this non-Hilbertian quantum-like model to faithfully reproduce the probabilities of the 'Clinton/Gore' and 'Rose/Jackson' experiments on question order effects. We also explain why the GTR-model is needed if one wants to deal, in a fully consistent way, with response replicability and unpacking effects.

The formalism of quantum theory in Hilbert space has been applied with success to the modeling and explanation of several cognitive phenomena, whereas traditional cognitive approaches were problematical. However, this 'quantum cognition paradigm' was recently challenged by its proven impossibility to simultaneously model 'question order effects' and 'response replicability'. In Part I of this paper we describe sequential dichotomic measurements within an operational and realistic framework for human cognition elaborated by ourselves, and represent them in a quantum-like 'extended Bloch representation' where the Born rule of quantum probability does not necessarily hold. In Part II we apply this mathematical framework to successfully model question order effects, response replicability and unpacking effects, thus opening the way toward quantum cognition beyond Hilbert space.

Background: In cognitive neuroscience the potential of Deep Neural Networks (DNNs) for solving complex classification tasks is yet to be fully exploited. The most limiting factor is that DNNs as notorious 'black boxes' do not provide insight into neurophysiological phenomena underlying a decision. Layer-wise Relevance Propagation (LRP) has been introduced as a novel method to explain individual network decisions. New Method: We propose the application of DNNs with LRP for the first time for EEG data analysis. Through LRP the single-trial DNN decisions are transformed into heatmaps indicating each data point's relevance for the outcome of the decision. Results: DNN achieves classification accuracies comparable to those of CSP-LDA. In subjects with low performance subject-to-subject transfer of trained DNNs can improve the results. The single-trial LRP heatmaps reveal neurophysiologically plausible patterns, resembling CSP-derived scalp maps. Critically, while CSP patterns represent class-wise aggregated information, LRP heatmaps pinpoint neural patterns to single time points in single trials. Comparison with Existing Method(s): We compare the classification performance of DNNs to that of linear CSP-LDA on two data sets related to motor-imaginery BCI. Conclusion: We have demonstrated that DNN is a powerful non-linear tool for EEG analysis. With LRP a new quality of high-resolution assessment of neural activity can be reached. LRP is a potential remedy for the lack of interpretability of DNNs that has limited their utility in neuroscientific applications. The extreme specificity of the LRP-derived heatmaps opens up new avenues for investigating neural activity underlying complex perception or decision-related processes.

Drawing a sample from a discrete distribution is one of the building components for Monte Carlo methods. Like other sampling algorithms, discrete sampling suffers from the high computational burden in large-scale inference problems. We study the problem of sampling a discrete random variable with a high degree of dependency that is typical in large-scale Bayesian inference and graphical models, and propose an efficient approximate solution with a subsampling approach. We make a novel connection between the discrete sampling and Multi-Armed Bandits problems with a finite reward population and provide three algorithms with theoretical guarantees. Empirical evaluations show the robustness and efficiency of the approximate algorithms in both synthetic and real-world large-scale problems.

In this paper, we discuss software design issues related to the development of parallel computational intelligence algorithms on multi-core CPUs, using the new Java 8 functional programming features. In particular, we focus on probabilistic graphical models (PGMs) and present the parallelisation of a collection of algorithms that deal with inference and learning of PGMs from data. Namely, maximum likelihood estimation, importance sampling, and greedy search for solving combinatorial optimisation problems. Through these concrete examples, we tackle the problem of defining efficient data structures for PGMs and parallel processing of same-size batches of data sets using Java 8 features. We also provide straightforward techniques to code parallel algorithms that seamlessly exploit multi-core processors. The experimental analysis, carried out using our open source AMIDST (Analysis of MassIve Data STreams) Java toolbox, shows the merits of the proposed solutions.

In a recent survey in the UK, one in four young people agreed that they would date a robot. Try looking at the idealized view in the first video from the 2015 movie Ex Machina, but then check out the more realistic actual robot in the second (linked).

Virtual reality and artificial intelligence (AI) are becoming powerful tools needed to revolutionize education. Experts suggest the use of technology, with the integration of the ever-evolving cyber tools, will unify education and research environment and network. Despite the threat of artificial intelligence to rise up against humans and destroy humanity within decades, AI continues to prove its usefulness to mankind. Recently, artificial intelligence makes headlines for having a potential to provide solutions to various global issues such as poaching, illegal logging, cyber-\attacks, in aiding cancer diagnosis and in education. The rise of technology has changed the way students communicate and entertain themselves.

Alphabet Inc's Google unit, Ford Motor Co, the ride-sharing service Uber and two other companies said on Tuesday they are forming a coalition to push for federal action to help speed self-driving cars to market. Sweden-based Volvo Cars, which is owned by China's Zhejiang Geely Holding Group Co, and Uber rival Lyft also are part of the Self-Driving Coalition for Safer Streets. The group said in a statement it will "work with lawmakers, regulators and the public to realize the safety and societal benefits of self-driving vehicles." The coalition said David Strickland, the former top official of the U.S. National Highway Traffic Safety Administration (NHTSA), the top U.S. auto safety agency that is writing new guidance on self-driving cars, will be the coalition's counsel and spokesman. "The best path for this innovation is to have one clear set of federal standards and the coalition will work with policymakers to find the right solutions that will facilitate the deployment of self-driving vehicles," Strickland said in the statement.

Robin Li, head of China's web giant Baidu, unveils the firm's intelligent digital assistant, Duer. When Andrew Ng joined Google from Stanford University in 2011, he was among a trickle of artificial-intelligence (AI) experts in academia taking up roles in industry. Five years later, demand for expertise in AI is booming -- and a torrent of researchers is following Ng's lead. The laboratories of tech titans Google, Microsoft, Facebook, IBM and Baidu (China's web-services giant) are stuffed with ex-university scientists, drawn to private firms' superior computing resources and salaries. "Some people in academia blame me for starting part of this," says Ng, who in 2014 moved again to become chief scientist at Baidu, working at the company's research lab in California's Silicon Valley.