Automated surveillance is going to become increasingly common as companies and researchers find new ways to use machine learning to analyze live video footage. A new project from scientists in the UK and India shows one possible use for this technology: identifying violent behavior in crowds with the help of camera-equipped drones. In a paper titled "Eye in the Sky," the researchers describe their system. It uses a simple Parrot AR quadcopter (which costs around $200) to transmit video footage over a mobile internet connection for real-time analysis. An algorithm trained using deep learning estimates the poses of humans in the video and matches them to postures the researchers have designated as "violent."

The nonprofit MathandCoding, a San Francisco Bay Area-based organization that teaches coding to kids, June 11 announced it has expanded its offering to include machine learning and engineering workshops to its lessons. The organization as a whole has grown since being founded by three high school students to hold lessons at more than 25 libraries and community centers throughout the area. The success has led to co-founder Anika Cheerla saying the organization has ventured into physics, engineering and machine learning. MathandCoding started machine learning and AI for Girls initiative about a year ago to encourage middle and high school girls to learn machine learning and artificial intelligence, which Cheerla said are the future technologies sweeping all facets of life. "It was received with a lot of enthusiasm," the Indian American said in an email to India-West of the workshop, where students learn to use open databases to create models and do predications.

WASHINGTON – General Motors Co. warned on Friday that higher tariffs on imported vehicles under consideration by the Trump administration could cost jobs and lead to "a smaller GM" while isolating U.S. businesses from the global market. The administration in May launched an investigation into whether imported vehicles pose a national security threat, and U.S. President Donald Trump has repeatedly threatened to impose a 20 percent vehicle import tariff. The largest U.S. automaker said in comments filed with the U.S. Commerce Department that overly broad tariffs could "lead to a smaller GM, a reduced presence at home and abroad for this iconic American company, and risk less -- not more -- U.S. jobs." Higher tariffs could also hike vehicle prices and reduce sales, GM said. Its comments echoed those from two major U.S. auto trade groups on Wednesday, when they warned that tariffs of up to 25 percent on imported vehicles would cost hundreds of thousands of auto jobs, dramatically raise prices on vehicles and threaten industry spending on self-driving cars.

In 2014 Chinese President Xi Jinping called for a "robot revolution" in manufacturing. It's now under way and boosting productivity, but there are adverse consequences. After decades of growth, rising wages are consuming profits and pushing manufacturing to Southeast Asia. Shanghai's minimum monthly wage, for example, the highest in China, is 2,420 yuan (US$366.62), "They realize you cannot just compete with cheap labor. You have to elevate the manufacturing capabilities as a whole," said Jing Bing Zhang, research director of market intelligence and consulting firm IDC.

"We wanted to know whether an AI can be smart enough to discover the periodic table on its own, and our team showed that it can," said study leader Shou-Cheng Zhang, the J. G. Jackson and C. J. Wood Professor of Physics at Stanford's School of Humanities and Sciences. Zhang says the research, published in the June 25 issue of Proceedings of the National Academy of Sciences, is an important first step toward a more ambitious goal of his, which is designing a replacement to the Turing test – the current gold standard for gauging machine intelligence. In order for an AI to pass the Turing test, it must be capable of responding to written questions in ways that are indistinguishable from a human. But Zhang thinks the test is flawed because it is subjective. "Humans are the product of evolution and our minds are cluttered with all sorts of irrationalities. For an AI to pass the Turing test, it would need to reproduce all of our human irrationalities," Zhang said.

Scientists led by Daigo Shoji from the Earth-Life Science Institute (Tokyo Institute of Technology) have shown that a type of artificial intelligence called a convolutional neural network can be trained to categorize volcanic ash particle shapes. Because the shapes of volcanic particles are linked to the type of volcanic eruption, this categorization can provide information on eruptions and aid volcanic hazard mitigation efforts. Volcanic eruptions come in many forms, from the explosive eruptions of Iceland's Eyjafjallajökull in 2010, which disrupted European air travel for a week, to the Hawaiian Islands' relatively tranquil May 2018 lava flows. Likewise, these eruptions have different associated threats, from ash clouds to lava. Sometimes, the eruption mechanism (e.g., water and magma interaction) is not obvious, and needs to be carefully evaluated by volcanologists to determine future threats and responses.

We consider the problem of detecting norm violations in open multi-agent systems (MAS). We show how, using ideas from scrip systems, we can design mechanisms where the agents comprising the MAS are incentivised to monitor the actions of other agents for norm violations. The cost of providing the incentives is not borne by the MAS and does not come from fines charged for norm violations (fines may be impossible to levy in a system where agents are free to leave and rejoin again under a different identity). Instead, monitoring incentives come from (scrip) fees for accessing the services provided by the MAS. In some cases, perfect monitoring (and hence enforcement) can be achieved: no norms will be violated in equilibrium. In other cases, we show that, while it is impossible to achieve perfect enforcement, we can get arbitrarily close; we can make the probability of a norm violation in equilibrium arbitrarily small. We show using simulations that our theoretical results, which apply to systems with a large number of agents, hold for multi-agent systems with as few as 1000 agents--the system rapidly converges to the steady-state distribution of scrip tokens necessary to ensure monitoring and then remains close to the steady state.

Fashion preference is a fuzzy concept that depends on customer taste, prevailing norms in fashion product/style, henceforth used interchangeably, and a customer's perception of utility or fashionability, yet fashion e-retail relies on algorithmically generated search and recommendation systems that process structured data and images to best match customer preference. Retailers study tastes solely as a function of what sold vs what did not, and take it to represent customer preference. Such explicit modeling, however, belies the underlying user preference, which is a complicated interplay of preference and commercials such as brand, price point, promotions, other sale events, and competitor push/marketing. It is hard to infer a notion of utility or even customer preference by looking at sales data. In search and recommendation systems for fashion e-retail, customer preference is implicitly derived by user-user similarity or item-item similarity. In this work, we aim to derive a metric that separates the buying preferences of users from the commercials of the merchandise (price, promotions, etc). We extend our earlier work on explicit signals to gauge sellability or preference [5] with implicit signals from user behaviour.

Topic discovery has witnessed a significant growth as a field of data mining at large. In particular, time-evolving topic discovery, where the evolution of a topic is taken into account has been instrumental in understanding the historical context of an emerging topic in a dynamic corpus. Traditionally, time-evolving topic discovery has focused on this notion of time. However, especially in settings where content is contributed by a community or a crowd, an orthogonal notion of time is the one that pertains to the level of expertise of the content creator: the more experienced the creator, the more advanced the topic. In this paper, we propose a novel time-evolving topic discovery method which, in addition to the extracted topics, is able to identify the evolution of that topic over time, as well as the level of difficulty of that topic, as it is inferred by the level of expertise of its main contributors. Our method is based on a novel formulation of Constrained Coupled Matrix-Tensor Factorization, which adopts constraints well-motivated for, and, as we demonstrate, are essential for high-quality topic discovery. We qualitatively evaluate our approach using real data from the Physics and also Programming Stack Exchange forum, and we were able to identify topics of varying levels of difficulty which can be linked to external events, such as the announcement of gravitational waves by the LIGO lab in Physics forum. We provide a quantitative evaluation of our method by conducting a user study where experts were asked to judge the coherence and quality of the extracted topics. Finally, our proposed method has implications for automatic curriculum design using the extracted topics, where the notion of the level of difficulty is necessary for the proper modeling of prerequisites and advanced concepts.

New technologies have enabled the investigation of biology and human health at an unprecedented scale and in multiple dimensions. These dimensions include myriad properties describing genome, epigenome, transcriptome, microbiome, phenotype, and lifestyle. No single data type, however, can capture the complexity of all the factors relevant to understanding a phenomenon such as a disease. Integrative methods that combine data from multiple technologies have thus emerged as critical statistical and computational approaches. The key challenge in developing such approaches is the identification of effective models to provide a comprehensive and relevant systems view. An ideal method can answer a biological or medical question, identifying important features and predicting outcomes, by harnessing heterogeneous data across several dimensions of biological variation. In this Review, we describe the principles of data integration and discuss current methods and available implementations. We provide examples of successful data integration in biology and medicine. Finally, we discuss current challenges in biomedical integrative methods and our perspective on the future development of the field.