Samuel Greengard is an author and journalist based in West Linn, OR.

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.

An underlying assumption in conventional multi-view learning algorithms is that all views can be simultaneously accessed. However, due to various factors when collecting and pre-processing data from different views, the streaming view setting, in which views arrive in a streaming manner, is becoming more common. By assuming that the subspaces of a multi-view model trained over past views are stable, here we fine tune their combination weights such that the well-trained multi-view model is compatible with new views. This largely overcomes the burden of learning new view functions and updating past view functions. We theoretically examine convergence issues and the influence of streaming views in the proposed algorithm. Experimental results on real-world datasets suggest that studying the streaming views problem in multi-view learning is significant and that the proposed algorithm can effectively handle streaming views in different applications.

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.

That's the reason why I was shocked by a piece of news that came out of London on January 27 this year. AlphaGo, a program created by Google subsidiary DeepMind, defeated the European Go champion, five games to nothing. Maybe you think that's no big deal. After all, it's almost 20 years since IBM's Deep Blue beat Kasparov at chess in 1997. Chess is about logic; Go involves imagination and intuition.

Through a significant expansion of its technical bench, Aarki has further reinforced its commitment to achieving market differentiation through the use of advanced technology. Specifically, Aarki has promoted Dr. Yumio Saneyoshi to senior vice president of product, and Mark Kalygulov to vice president of engineering. The company's proprietary mobile advertising platform - Aarki Encore - is widely recognized as the leading technology in the industry. This platform has received fillip through the addition of Dr. Saneyoshi to Aarki's technical bench. Kalygulov heads the company's engineering team and has been responsible for developing its wide range of software solutions.

The robots are taking over. This is what we've been told. None of us will have jobs soon. The bots will do it all. Me, too, I get the grief.

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.

Alphabet's Google unit, Ford, 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.