Kevin Warwick is Emeritus Professor at Reading and Coventry Universities. Prior to that he was Deputy Vice Chancellor (Research) at Coventry University, England. His main research areas are artificial intelligence, biomedical systems, robotics and cyborgs. Due to his research as a self-experimenter he is frequently referred to as the world's first Cyborg. Kevin was born in Coventry, UK and left school to join British Telecom.

Farmers around the world are facing the urgent question of how to sustainably feed a global population expected to reach 9.7 billion by 2050 -- and the answer, in part, might be found nestled among the cornstalks and soybeans on a farm a short distance from Washington, D.C. The fields are outfitted with a network of high-tech sensors that could revolutionize how food is grown across the globe by putting data in the hands of farmers and scientists in ways unimaginable a few years ago. The sensors are part of a groundbreaking new partnership between Microsoft and the U.S. Department of Agriculture (USDA). The 7,000-acre farm at the USDA's Beltsville Agricultural Research Center in Maryland is using FarmBeats, a project that aims to harness data and artificial intelligence to help farmers cut costs, increase yields and sustainably grow crops that are more resilient to climate change. "We can't simply double our acreage to produce this food," says Dan Roberts, research leader at the Sustainable Agricultural Systems Research Laboratory, located at the Beltsville center.

--The increasing penetration level of energy generation from renewable sources is demanding for more accurate and reliable forecasting tools to support classic power grid operations (e.g., unit commitment, electricity market clearing or maintenance planning). For this purpose, many physical models have been employed, and more recently many statistical or machine learning algorithms, and data-driven methods in general, are becoming subject of intense research. While generally the power research community focuses on power forecasting at the level of single plants, in a short future horizon of time, in this time we are interested in aggregated macro-area power generation (i.e., in a territory of size greater than 100000 km Real data are used to validate the proposed forecasting methodology on a test set of several months. A. Motivations As the penetration level of Renewable Energy (RE) sources is growing worldwide to meet ever tightening sustainability goals [1], the intermittent and uncertain nature of RE is posing increasing challenges to efficiently manage a power grid, eventually endangering its own stability. In this context, the availability of accurate forecasts of power generation from RE may mitigate the impact of the increasing penetration level and improve the operation of power systems [2].

The C-130 Hercules is the most versatile aircraft in aviation history. From landing at the world's highest airstrip in the Himalayas to taking off and landing on an aircraft carrier in the middle of the Atlantic Ocean, the aircraft is celebrated for its unsurpassed versatility, performance and mission effectiveness. Today, 70 countries rely on the C-130 for search and rescue, peacekeeping, medical evacuations, scientific research, military operations, aerial refueling and humanitarian relief. More than 2,500 C-130s have been produced to date. The worldwide operational fleet includes legacy C-130 models as well as the current production variant – the C-130J Super Hercules.

At more than seven decades old, the United Nations has often been criticized for being too slow to respond to crises. But behind the scenes, a high-tech team is harnessing the power of big data and artificial intelligence to predict, monitor and respond to emergencies. CGTN's U.N. correspondent Liling Tan has an inside look at how U.N. Global Pulse is keeping the organization up to speed in the 21st century. Three blocks from the United Nations headquarters in New York, a veritable geek squad of data scientists, analysts and engineers are using big data and artificial intelligence for global good. Or, as U.N. Global Pulse's Director Robert Kirkpatrick puts it, "Our job is to help superheroes find out where people are in trouble, so they can rescue them."

The voyage is expected to take about 35 days and could prove that ships never really needed humans in the first place. They call Maxlimer a robot ship. But a more apt name could also be a ghost ship. Because if you came across it during one of its seafaring journeys, no humans would be onboard. SEE ALSO: Is This New Submarine the World's Best Aquatic War Machine?

Predicting the next position of movable objects has been a problem for at least the last three decades, referred to as trajectory prediction. In our days, the vast amounts of data being continuously produced add the big data dimension to the trajectory prediction problem, which we are trying to tackle by creating a {\lambda}-Architecture based analytics platform. This platform performs both batch and stream analytics tasks and then combines them to perform analytical tasks that cannot be performed by analyzing any of these layers by itself. The biggest benefit of this platform is its context agnostic trait, which allows us to use it for any use case, as long as a time-stamped geolocation stream is provided. The experimental results presented prove that each part of the {\lambda}-Architecture performs well at certain targets, making a combination of these parts a necessity in order to improve the overall accuracy and performance of the platform.

Many methods have been developed to represent knowledge graph data, which implicitly exploit low-rank latent structure in the data to encode known information and enable unknown facts to be inferred. To predict whether a relationship holds between entities, their embeddings are typically compared in the latent space following a relation-specific mapping. Whilst link prediction has steadily improved, the latent structure, and hence why such models capture semantic information, remains unexplained. We build on recent theoretical interpretation of word embeddings as a basis to consider an explicit structure for representations of relations between entities. For identifiable relation types, we are able to predict properties and justify the relative performance of leading knowledge graph representation methods, including their often overlooked ability to make independent predictions.

The federal government is proposing to establish an agency that will focus on robotics and Artificial Intelligence, the Minister of Science and Technology, Dr Ogbonnaya Onu has said. He disclosed this on Monday when he received a delegation of the Academy of Science in his office in Abuja. Dr Onu noted that when established, the agency will help to improve the quality of research in the nation's universities and industrial laboratories. He said the Academy of Science is well-placed to advise the federal government on issues bordering on Science, Technology and Innovation. The minister also said it was high time Nigeria takes its place among the leading nations of the world in terms of Science, Technology and Innovation.

Artificial Intelligence (AI) as a concept has been around since the development of computational devices, as early as the creation of Turing machines during World War II. The term itself was first coined by University of Washington professor John McCarthy in 1956, and now, 60 years later we see the actual commercialization of AI. Why now, and what has finally enabled this? AI is designed to process and interpret vast sums of data (aka Big Data), and while humanity has always generated a lot of data, the volumes in the last few years have spiked sharply. Right now we are generating 2.5 quintillion bytes of data, per day, and this number is just a preview.