More than 2,400 researchers, scientists, engineers, entrepreneurs and others have signed a pledge – organised by the Future of Life Institute (FLI) – promising not to develop lethal autonomous weapons. In addition to many prominent individuals, the list of signatories also includes over 160 AI-related firms and organisations from around the world – such as Google DeepMind, XPRIZE Foundation, University College London, the European Association for AI (EurAI), Swedish AI Society (SAIS), ClearPath Robotics and OTTO Motors. The pledge is being announced today at the annual International Joint Conference on Artificial Intelligence (IJCAI) in Sweden, which draws over 5,000 of the world's leading AI researchers. Artificial intelligence (AI) is poised to play an increasing role in military systems. There is an urgent opportunity and necessity for citizens, policymakers, and leaders to distinguish between acceptable and unacceptable uses of AI.

A Cambridge-based start-up believes machine learning software is the key to autonomous vehicles and Wayve is developing machine learning algorithms for autonomous vehicles. Wayve, which includes the chief scientist at Uber amongst its investors, believes the industry has been doing too much hand-engineering and too little machine learning. The firm is hiring for positions in its Cambridge-based headquarters. "The missing piece of the self-driving puzzle is intelligent algorithms, not more sensors, rules and maps. Humans have a fascinating ability to perform complex tasks in the real world, because our brains allow us to learn quickly and transfer knowledge across our many experiences. We want to give our vehicles better brains, not more hardware."

Industries are investing aggressively in artificial intelligence (AI) projects to drive efficiency for better business performance. International Data Corporation predicts that AI spending will achieve a compound annual growth rate (CAGR) of 46.2% from 2016 growing to become a $52.2 billion industry by 2021. AI can significantly improve business operations by leveraging the tremendous amount of data generated by sensors monitoring the production and movement of products using IoT. The end result is AIIOT, which is the merging of AI and IoT to manage inventory, logistics, and suppliers with a higher level of awareness and precision. The supply chain is one area that can benefit the most from streamlining since it has a direct influence on profitability and customer satisfaction.

Despite its status as a core part of contemporary physics, there is no consensus among physicists or philosophers of physics on the question of what, if anything, the empirical success of quantum theory is telling us about the physical world. This gives rise to the collection of philosophical issues known as "the interpretation of quantum mechanics". One should not be misled by this terminology into thinking that what we have is an uninterpreted mathematical formalism with no connection to the physical world. Rather, there is a common core of interpretation that consists of recipes for calculating probabilities of outcomes of experiments performed on systems subjected to certain state preparation procedures. What are often referred to as different "interpretations" of quantum mechanics differ on what, if anything, is added to the common core. Arguably, two of the major approaches, hidden-variables theories and collapse theories, involve formulation of physical theories distinct from standard quantum mechanics; this renders the terminology of "interpretation" even more inappropriate. Much of the philosophical literature connected with quantum theory centers on the problem of whether we should construe the theory, or a suitable extension or revision of it, in realist terms, and, if so, how this should be done. Various approaches to the "Measurement Problem" propose differing answers to these questions. There are, however, other questions of philosophical interest. These include the bearing of quantum nonlocality on our understanding of spacetime structure and causality, the question of the ontological character of quantum states, the implications of quantum mechanics for information theory, and the task of situating quantum theory with respect to other theories, both actual and hypothetical.

They define middle-aged workers as those between the ages of 26 and 55, and older workers as those over the age of 55. They find that countries that are undergoing more rapid aging, meaning that they are experiencing a greater proportional decrease in the number of middle-aged workers relative to older workers, invest significantly more in robotics. They are more likely to develop new technologies and manufacture robots, and to deploy these robots in production. Population aging can explain almost 40 percent of the country-to-country variation in the adoption of industrial robots. The researchers estimate that a 10 percentage point increase in the ratio of the number of middle-aged to older workers is associated with 0.9 more robots per thousand workers.

Typically, engineers want to get bugs out of their creations. Not so for the U.K. engineering firm (not the famed carmaker) Rolls-Royce -- it's looking for a way to get bugs into the aircraft engines it builds. They're tiny robots modeled after the cockroach. On Tuesday, Rolls-Royce shared the latest developments in its research into cockroach-like robots at the Farnborough International Airshow. Rolls-Royce believes these tiny insect-inspired robots will save engineers time by serving as their eyes and hands within the tight confines of an airplane's engine.

DUBLIN--(BUSINESS WIRE)--The "Artificial Intelligence (AI) - Global Strategic Business Report" report has been added to ResearchAndMarkets.com's offering. The report provides separate comprehensive analytics for the US, Canada, Japan, Europe, Asia-Pacific, and Rest of World. Annual estimates and forecasts are provided for the period 2015 through 2024. Market data and analytics are derived from primary and secondary research.

White matter hyperintensity (WMH) is commonly found in elder individuals and appears to be associated with brain diseases. U-net is a convolutional network that has been widely used for biomedical image segmentation. Recently, U-net has been successfully applied to WMH segmentation. Random initialization is usally used to initialize the model weights in the U-net. However, the model may coverage to different local optima with different randomly initialized weights. We find a combination of thresholding and averaging the outputs of U-nets with different random initializations can largely improve the WMH segmentation accuracy. Based on this observation, we propose a post-processing technique concerning the way how averaging and thresholding are conducted. Specifically, we first transfer the score maps from three U-nets to binary masks via thresholding and then average those binary masks to obtain the final WMH segmentation. Both quantitative analysis (via the Dice similarity coefficient) and qualitative analysis (via visual examinations) reveal the superior performance of the proposed method. This post-processing technique is independent of the model used. As such, it can also be applied to situations where other deep learning models are employed, especially when random initialization is adopted and pre-training is unavailable.

This paper examines to what degree current deep learning architectures for image caption generation capture spatial language. On the basis of the evaluation of examples of generated captions from the literature we argue that systems capture what objects are in the image data but not where these objects are located: the captions generated by these systems are the output of a language model conditioned on the output of an object detector that cannot capture fine-grained location information. Although language models provide useful knowledge for image captions, we argue that deep learning image captioning architectures should also model geometric relations between objects.

Industrial Revolution (4IR) (Xing and Marwala, 2017), many countries (Shah et al., 2015; Ding and Li, 2015) are setting out an overarching goal of building/securing an "innovation-driven" economy. As innovation emphasizes the implementation of ideas, creativity is typically regarded as the first stage of innovation in which generating ideas becomes the dominant focus (Tang and Werner, 2017; Amabile, 1996; Mumford and Gustafson, 1988; Rank et al., 2004; West, 2002). In other words, if creativity is absent, innovation could be just luck. Though creativity can be generally understood as the capability of producing original and novel work or knowledge, the universal definition of creativity remains rather controversial, mainly due to its complex nature (Tang and Werner, 2017; Hernández-Romero, 2017). But putting it informally, by famous innovator Steve Jobs in 1995, we can think creativity like this way (Sanchez-Burks et al., 2015): "Creative people [are] able to connect experiences they've had and synthesize new things."