Halpern and Pearl introduced a definition of actual causality; Eiter and Lukasiewicz showed that computing whether X = x is a cause of Y = y is NP-complete in binary models (where all variables can take on only two values) and Σ^P_2 -complete in general models. In the final version of their paper, Halpern and Pearl slightly modified the definition of actual cause, in order to deal with problems pointed out by Hopkins and Pearl. As we show, this modification has a nontrivial impact on the complexity of computing whether {X} = {x} is a cause of Y = y. To characterize the complexity, a new family D_k^P , k = 1, 2, 3, . . ., of complexity classes is introduced, which generalises the class DP introduced by Papadimitriou and Yannakakis (DP is just D_1^P). We show that the complexity of computing causality under the updated definition is D_2^P -complete. Chockler and Halpern extended the definition of causality by introducing notions of responsibility and blame, and characterized the complexity of determining the degree of responsibility and blame using the original definition of causality. Here, we completely characterize the complexity using the updated definition of causality. In contrast to the results on causality, we show that moving to the updated definition does not result in a difference in the complexity of computing responsibility and blame.

Machine learning is essentially the sciences of playing with data. An adaptive data selection strategy, enabling to dynamically choose different data at various training stages, can reach a more effective model in a more efficient way. In this paper, we propose a deep reinforcement learning framework, which we call \emph{\textbf{N}eural \textbf{D}ata \textbf{F}ilter} (\textbf{NDF}), to explore automatic and adaptive data selection in the training process. In particular, NDF takes advantage of a deep neural network to adaptively select and filter important data instances from a sequential stream of training data, such that the future accumulative reward (e.g., the convergence speed) is maximized. In contrast to previous studies in data selection that is mainly based on heuristic strategies, NDF is quite generic and thus can be widely suitable for many machine learning tasks. Taking neural network training with stochastic gradient descent (SGD) as an example, comprehensive experiments with respect to various neural network modeling (e.g., multi-layer perceptron networks, convolutional neural networks and recurrent neural networks) and several applications (e.g., image classification and text understanding) demonstrate that NDF powered SGD can achieve comparable accuracy with standard SGD process by using less data and fewer iterations.

Boltzmann machines are physics informed generative models with wide applications in machine learning. They can learn the probability distribution from an input dataset and generate new samples accordingly. Applying them back to physics, the Boltzmann machines are ideal recommender systems to accelerate Monte Carlo simulation of physical systems due to their flexibility and effectiveness. More intriguingly, we show that the generative sampling of the Boltzmann Machines can even discover unknown cluster Monte Carlo algorithms. The creative power comes from the latent representation of the Boltzmann machines, which learn to mediate complex interactions and identify clusters of the physical system. We demonstrate these findings with concrete examples of the classical Ising model with and without four spin plaquette interactions. Our results endorse a fresh research paradigm where intelligent machines are designed to create or inspire human discovery of innovative algorithms.

The Huawei Watch 2 was revealed by the Chinese technology giant Sunday at Mobile World Congress in Barcelona. Huawei vows the 4G sport smartwatch will be "your perfect workout companion" in a trailer of the device promoted by the hastag #FreeYourSpirit. Users also have access to a workout data report. There a one-press button on the lower crown which allows users to start the workout app instantly, and also has quick training options with fat-burning run or cardio run modes. The device also supports offline music and has a built-in speaker.

In 1849 Austria sent unmanned, bomb-filled balloons to attack Venice. UAV innovations started in the early 1900s and originally focused on providing practice targets for training military personnel. UAV development during World War I: the Dayton-Wright Airplane Company invented a pilotless aerial torpedo that would explode at a preset time. The earliest attempt at a powered UAV was A. M. Low's "Aerial Target" in 1916. Nikola Tesla described a fleet of unmanned aerial combat vehicles in 1915.

Mady Delvaux wrote a report urging European politicians to enforce regulation around AI and robotics. European politicians have voted in favour of a controversial report calling for regulation on robots and artificial intelligence (AI). The vote, which took place in France on Thursday, was based on a report from the Legal Affairs Committee, which warned that there is a growing need for regulation to address increasingly autonomous robots and other forms of sophisticated AI. The report passed 396-to-123, with 85 abstentions. "MEP's (Members of the European Parliament) voted overwhelmingly in favour of the report," said a spokesperson for the European Parliament.

Add artificial intelligence (AI) into the equation--and more than a few apocalyptic movies about such learning-enabled machines taking over the human race--and the fear factor is ratcheted up a bit. Though ethical concerns are prevalent, executives across the globe say AI is nonetheless certain to develop further in their businesses. According to a study released this week by Infosys, 71 percent of the 1,600 senior business decision-makers surveyed say that the rise of AI in the workplace is inevitable, pointing to positive changes for business prospects, employees and society. The study--commissioned by Infosys and conducted by independent market researcher Vanson Bourne--set out to investigate the approach and attitudes that senior decision-makers in large organizations (at least 1,000 employees and $500 million in annual revenue) have toward AI technology and how they see the future application and development of AI in their industries. Although AI definitions can vary, it is generally considered as an activity traditionally performed through human intelligence that can now be done by a computer.

News concerning Artificial Intelligence (AI) abounds again. The progress with Deep Learning techniques are quite remarkable with such demonstrations of self-driving cars, Watson on Jeopardy, and beating human Go players. This rate of progress has led some notable scientists and business people to warn about the potential dangers of AI as it approaches a human level. Exascale computers are being considered that would approach what many believe is this level. However, there are many questions yet unanswered on how the human brain works, and specifically the hard problem of consciousness with its integrated subjective experiences.

Thanks to a Target ad accidentally posted online we now know of the existence of Shadow of War, sequel to 2014's excellent Middle-earth action game, Shadow of Mordor. The leak was spotted by a NeoGaf user and box-art was downloaded from Target's website before the leaked material was taken down. Warner Bros. was reportedly teasing a March 8th announcement for an unnamed game many assumed would be the next Batman: Arkham title. It's possible the announcement was for Shadow of War instead. I've reached out to the publisher for comment.

China set a record high in terms of venture capital (VC) investments in 2016, despite a global slowdown. The strong performance is expected to continue with artificial intelligence (AI) an additional focus for investors, finds latest KPMG analysis. Investment by VCs in China increased 19 percent year on year to USD31 billion in 2016, although deal volumes declined 42 percent to 300 from 513 a year earlier, according to Venture Pulse, KPMG's quarterly global report on VC trends. The strong performance is attributed to a number of mega-deals recorded early in the year. The report highlights that artificial intelligence and cognitive learning are poised to transform almost every aspect of people's lives.