Deep learning is a sub-category within machine learning and artificial intelligence. It is inspired by and based on the model of the human brain to create artificial neural networks for machines. Deep learning will allow machines and devices to function in some ways as humans do. Dr. Rodrigo Agundez of GoDataDriven is co-author of this article and very enthusiastic about the improvements that deep learning can offer. He's been involved in the data science and analysis field for some time, and is already working on implementing models for practical applications.

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."

Beth is a Senior Principal Technologist for Amazon Robotics. Beth has been Founder and CEO of several successful startups, most notably EXOS, Inc., which was venture capital backed and sold to Microsoft in 1996. Since then she has been involved in 30 start-ups in a variety of fields as a founder, investor, or advisor. She was an advisor and investor in Leap Frog and has been involved in entertainment and mobile companies. Beth is an acknowledged expert in VR, AR and the hand-device interface space and has been an expert in support of prior patent litigations.

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.

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.

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.

Methods for reasoning under uncertainty are a key building block of accurate and reliable machine learning systems. Bayesian methods provide a general framework to quantify uncertainty. However, because of model misspecification and the use of approximate inference, Bayesian uncertainty estimates are often inaccurate -- for example, a 90% credible interval may not contain the true outcome 90% of the time. Here, we propose a simple procedure for calibrating any regression algorithm; when applied to Bayesian and probabilistic models, it is guaranteed to produce calibrated uncertainty estimates given enough data. Our procedure is inspired by Platt scaling and extends previous work on classification. We evaluate this approach on Bayesian linear regression, feedforward, and recurrent neural networks, and find that it consistently outputs well-calibrated credible intervals while improving performance on time series forecasting and model-based reinforcement learning tasks.