The industrial world has been in the throes of digitization for well over a decade. Primarily through enterprise resource planning (ERP) and manufacturing execution systems (MES), critical planning, scheduling, warehousing, inventory management, and logistics processes have been automated and simplified. But these gains have been restricted to technology silos, supporting separate functions of the factory rather than improving the performance of the plant -- and its extended supply chain -- in a broader way. Those days may finally be in the past, as manufacturers now have a golden opportunity to take advantage of digitization's promised outsized benefits. The advent of complex smart sensors, artificial intelligence, big data pools, and robotics, combined with the vast connections of the cloud, is heralding a new era for manufacturers, marked by totally integrated factories that can rapidly tailor products to individual customer needs and respond instantly to shifting demands and trends.

GENEVA – Major U.S. trading partners including the European Union, China and Japan voiced deep concern at the World Trade Organization (WTO) on Tuesday about possible U.S. measures imposing additional duties on imported autos and parts. Japan, which along with Russia had initiated the discussion at the WTO Council on Trade in Goods, warned that such measures could trigger a spiral of countermeasures and result in the collapse of the rules-based multilateral trading system, an official who attended the meeting said. Over 40 WTO members, including the 28 countries of the European Union -- warned that the U.S. action could seriously disrupt the world market and threaten the WTO system, given the importance of cars to world trade. The United States has imposed tariffs on European steel and aluminum imports and is conducting another national security study that could lead to tariffs on imports of cars and car parts. Both sets of tariffs would be based on concerns about U.S. national security. U.S. President Donald Trump said on June 29 that the probe would be completed in three to four weeks.

Differential privacy comes equipped with multiple analytical tools for the design of private data analyses. One important tool is the so called "privacy amplification by subsampling" principle, which ensures that a differentially private mechanism run on a random subsample of a population provides higher privacy guarantees than when run on the entire population. Several instances of this principle have been studied for different random subsampling methods, each with an ad-hoc analysis. In this paper we present a general method that recovers and improves prior analyses, yields lower bounds and derives new instances of privacy amplification by subsampling. Our method leverages a characterization of differential privacy as a divergence which emerged in the program verification community. Furthermore, it introduces new tools, including advanced joint convexity and privacy profiles, which might be of independent interest.

Domestic Violence (DV) is considered as big social issue and there exists a strong relationship between DV and health impacts of the public. Existing research studies have focused on social media to track and analyse real world events like emerging trends, natural disasters, user sentiment analysis, political opinions, and health care. However there is less attention given on social welfare issues like DV and its impact on public health. Recently, the victims of DV turned to social media platforms to express their feelings in the form of posts and seek the social and emotional support, for sympathetic encouragement, to show compassion and empathy among public. But, it is difficult to mine the actionable knowledge from large conversational datasets from social media due to the characteristics of high dimensions, short, noisy, huge volume, high velocity, and so on. Hence, this paper will propose a novel framework to model and discover the various themes related to DV from the public domain. The proposed framework would possibly provide unprecedentedly valuable information to the public health researchers, national family health organizations, government and public with data enrichment and consolidation to improve the social welfare of the community. Thus provides actionable knowledge by monitoring and analysing continuous and rich user generated content.

By Karina Toledo Agência FAPESP – A platform that can diagnose several diseases with a high degree of precision using metabolic markers found in patients' blood has been developed by scientists at the University of Campinas (UNICAMP) in Brazil. The method combines mass spectrometry, which can identify tens of thousands of molecules present in blood serum, with an artificial intelligence algorithm capable of finding patterns associated with diseases of viral, bacterial, fungal and even genetic origin. The results have been published in Frontiers in Bioengineering and Biotechnology. "We used infection by Zika virus as a model to develop the platform and showed that in this case, diagnostic accuracy exceeded 95%. One of the main advantages is that the method doesn't lose sensitivity even if the virus mutates," said Melo's supervisor Rodrigo Ramos Catharino, principal investigator for the project.

The generalization properties of Gaussian processes depend heavily on the choice of kernel, and this choice remains a dark art. We present the Neural Kernel Network (NKN), a flexible family of kernels represented by a neural network. The NKN's architecture is based on the composition rules for kernels, so that each unit of the network corresponds to a valid kernel. It can compactly approximate compositional kernel structures such as those used by the Automatic Statistician (Lloyd et al., 2014), but because the architecture is differentiable, it is end-to-end trainable with gradientbased optimization. We show that the NKN is universal for the class of stationary kernels. Empirically we demonstrate NKN's pattern discovery and extrapolation abilities on several tasks that depend crucially on identifying the underlying structure, including time series and texture extrapolation, as well as Bayesian optimization.

This blog offers a deeper dive into the machine learning training process for performing automatic target detection. Samples of automatic target detection were recently presented at the Machine Learning: Automate Remote Sensing Analytics to Gain a Competitive Advantage webinar. Machine learning (ML) applications, from object recognition and caption generation, to automatic language translation and driverless cars, have increased dramatically over the last few years, powered mainly by the increase of computing power (using GPUs), reduced cost of storage, wider availability of training data, and development of new training techniques for the machine learning models. In the last five years, Harris Corporation has made a multi-million dollar investment into applying machine learning to solve customer challenges using remote sensing data. In response to the increased interest from our customers in evaluating how machine learning can solve their problems using geospatial data, I set out to train some of my coworkers on how to build a ML model to perform automatic feature detection on 2D overhead imagery.

The API provides pre-trained object detection models that have been trained on the COCO dataset. COCO dataset is a set of 90 commonly found objects. See image below of objects that are part of COCO dataset. In this case we care about classes -- persons and soccer ball which are both part of COCO dataset. The API also has a big set of models it supports. See table below for reference. The models have a trade off between speed and accuracy. Since I was interested in real time analysis, I chose SSDLite mobilenet v2. Once we identify the players using the object detection API, to predict which team they are in we can use OpenCV which is powerful library for image processing.

SAPPHIRE NOW took place on June 5-7 in Orlando with impressive numbers: more than 21,000 attendees from 102 different countries and 1,275 lectures, which was the SAP's main global event at the year. It was 3 days of much learning, where I had an opportunity to attend lectures, several demonstrations of products and applications, and meet interesting people. At the opening keynote of the event, called "The Next Move", SAP CEO Bill McDermott made the main announcements: the launch of SAP C/4 Hana and the SAP HANA Data Management Suite, the importance of SAP Leonardo, and also defined and listed what he considered the 10 main characteristics of an intelligent enterprise. McDermott commented on the importance of artificial intelligence to drive economic growth through the use of machines and the judgment of humans. "Great moments are born from great opportunities."

The 13th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2017) was held at the Snowbird Ski and Summer Resort in Little Cottonwod Canyon in the Wasatch Range of the Rock Mountains near Salt Lake County, Utah. Along with the main conference presentations, the meeting included two tutorials, three workshops, and invited keynotes. This report summarizes the main conference. It also includes contributions from the organizers of the three workshops.