In a business where shaving off a mile per day per driver can result in savings of up to $50 million per year, UPS has plenty of incentive to incorporate technology to drive efficiencies in every area of its operations. Here are just a few of the ways UPS uses big data and artificial intelligence (AI) to prepare for the 4th Industrial Revolution. UPS was founded in 1907 and has a history of embracing change and evolving as new technologies arise. It's the use of big data and artificial intelligence that allows the company to operate its global logistics network in more than 220 countries and territories. On an average day, there are typically 96,000 UPS vehicles on the road handling 19 million packages.

Stretchable plant wearables and smart tags dropped by drones aim to help give farming a big data makeover. The relatively cheap technologies for mass monitoring of individual plants across large greenhouses or crop fields could get field tests in three countries starting in 2019. The idea came from researchers at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia with expertise in flexible electronics. After talking with colleagues who were cultivating genetically engineered plants in greenhouses, they recognized the need for inexpensive sensors that could be deployed en masse and report on individual plant conditions. Their early offerings include a stretchable sensor for measuring micrometer-level changes in plant growth and a "PlantCopter" temperature and humidity sensor designed to be dropped from a drone and corkscrew its way through the air for a gradual descent.

The CIA is reportedly expanding its armed drone programme in Africa and will start using a military base in the Nigerien desert to carry out raids on areas where ISIL and al-Qaeda are believed to operate. The New York Times reported on Monday that a secret military base in Dirkou, about 250km south of the Libyan border, will soon begin deploying armed drones in an apparent loosening of Obama-era limits on US raids outside conventional warzones. According to the Times, the Pentagon has already carried out five drone raids in Libya this year, including one two weeks ago. While the drones are currently being flown out of bases in Sicily and Niamey, Niger's capital, armed drones "would almost certainly" be deployed from Dirkou "in the near future". The Times added that one of its journalists said he saw "gray aircraft - about the size of Predator drones, which are 27 feet long - flying at least three times over six days in early August".

A key opponent of high-tech, automated weapons known as'killer robots' is blaming countries like the U.S. and Russia for blocking consensus at a U.N.-backed conference, where most countries wanted to ensure that humans stay at the controls of lethal machines. Coordinator Mary Wareham of the Campaign to Stop Killer Robots spoke Monday after experts from dozens of countries agreed before dawn Saturday at the U.N. in Geneva on 10 'possible guiding principles' about such'Lethal Automated Weapons Systems.' Point 2 said: 'Human responsibility for decisions on the use of weapons systems must be retained since accountability cannot be transferred to machines.' Killer robots must be banned to prevent unlawful killings, injuries and other violations of human rights'before it's too late', according to Amnesty International. Wareham said such language wasn't binding, adding that'it's time to start laying down some rules now.' Members of the LAWS conference will meet again in November. Last week Amnesty International said killer robots must be banned to prevent unlawful killings, injuries and other violations of human rights'before it's too late', as the talks kicked off.

Robots could teach themselves to treat other forms of life – including humans – as less valuable than themselves, new research claims. Experts say prejudice towards others does not require a high level of cognitive ability and could easily be exhibited by artificially intelligent machines. These machines could teach each other the value of excluding others from outside their immediate group. The latest findings are based on computer simulations of how AIs, or virtual agents, form a group and interact with each other. Robot could teach themselves to be treat other forms of life - including humans - as less valuable than themselves, new research suggests.

Before he died, eccentric philosopher Jeremy Bentham asked that his body be stuffed and wheeled out at parties to help his friends with their grief. But the social reformer didn't stop there, bequeathing 26 memorial rings to those he knew and admired, featuring his bust in silhouette and strands of his hair. Now scientists are on the hunt for Bentham's rings, of which just six have been found since his death in 1832. The philosopher commissioned the rings a decade before he passed, leaving them in his will and testament to a list that included famous politicians, journalists, fellow philosophers and several of his servants. Researchers say the missing artefacts could be spread across the globe, after one was found in a jewellery shop in New Orleans.

Case Law has a significant impact on the proceedings of legal cases. Therefore, the information that can be obtained from previous court cases is valuable to lawyers and other legal officials when performing their duties. This paper describes a methodology of applying discourse relations between sentences when processing text documents related to the legal domain. In this study, we developed a mechanism to classify the relationships that can be observed among sentences in transcripts of United States court cases. First, we defined relationship types that can be observed between sentences in court case transcripts. Then we classified pairs of sentences according to the relationship type by combining a machine learning model and a rule-based approach. The results obtained through our system were evaluated using human judges. To the best of our knowledge, this is the first study where discourse relationships between sentences have been used to determine relationships among sentences in legal court case transcripts.

Graph-based semi-supervised learning (SSL) is an important learning problem where the goal is to assign labels to initially unlabeled nodes in a graph. Graph Convolutional Networks (GCNs) have recently been shown to be effective for graph-based SSL problems. GCNs inherently assume existence of pairwise relationships in the graph-structured data. However, in many real-world problems, relationships go beyond pairwise connections and hence are more complex. Hypergraphs provide a natural modeling tool to capture such complex relationships. In this work, we explore the use of GCNs for hypergraph-based SSL. In particular, we propose HyperGCN, an SSL method which uses a layer-wise propagation rule for convolutional neural networks operating directly on hypergraphs. To the best of our knowledge, this is the first principled adaptation of GCNs to hypergraphs. HyperGCN is able to encode both the hypergraph structure and hypernode features in an effective manner. Through detailed experimentation, we demonstrate HyperGCN's effectiveness at hypergraph-based SSL.

Many questions in Data Science are fundamentally causal in that our objective is to learn the effect of some exposure (randomized or not) on an outcome interest. Even studies that are seemingly non-causal (e.g. prediction or prevalence estimation) have causal elements, such as differential censoring or measurement. As a result, we, as Data Scientists, need to consider the underlying causal mechanisms that gave rise to the data, rather than simply the pattern or association observed in the data. In this work, we review the "Causal Roadmap", a formal framework to augment our traditional statistical analyses in an effort to answer the causal questions driving our research. Specific steps of the Roadmap include clearly stating the scientific question, defining of the causal model, translating the scientific question into a causal parameter, assessing the assumptions needed to translate the causal parameter into a statistical estimand, implementation of statistical estimators including parametric and semi-parametric methods, and interpretation of our findings. Throughout we focus on the effect of an exposure occurring at a single time point and provide extensions to more advanced settings.

Graph-structured data arise in wide applications, such as computer vision, bioinformatics, and social networks. Quantifying similarities among graphs is a fundamental problem. In this paper, we develop a framework for computing graph kernels, based on return probabilities of random walks. The advantages of our proposed kernels are that they can effectively exploit various node attributes, while being scalable to large datasets. We conduct extensive graph classification experiments to evaluate our graph kernels. The experimental results show that our graph kernels significantly outperform existing state-of-the-art approaches in both accuracy and computational efficiency.