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.

Scientists mimicked the neural networks of the brain to map phytoplankton types in the Mediterranean Sea. A new study published in the Journal of Geophysical Research: Oceans presented a new method of classifying phytoplankton that relies on artificial intelligence clustering. Phytoplankton blanket surface waters of the world's oceans, and pigments in their cells absorb certain wavelengths of light, like the chlorophyll that gives plants their green color. In the Mediterranean Sea, where the latest study focused its efforts, an array of phytoplankton species bloom throughout the year. Past research has mined satellite images of ocean color in the Mediterranean for common pigments found in phytoplankton.

In today's multifaceted energy world, a growing number of prosumer assets are increasing the complexity of power grids. This is even more important in an ever-changing climate that more and more generates huge storms such as the Typhoon Lekima which caused 9.3 Billion in damage (5th Costliest known Pacific typhoons) and more than 90 deaths in the Philippines, Taiwan and China earlier this year, or the recent monstrous Category 5 Hurricane Dorian in the Atlantic Ocean. The director-general of the Bahamas Ministry of Tourism and Aviation, Joy Jibrilu, details the damage left in the aftermath from Hurricane Dorian and what the Bahamas will need to move forward especially on the infrastructures. This looks too similar to what we've seen in Porto Rico two years ago which suffered severe damage from the category 5 hurricane Maria. The blackout as a result of Maria has been identified as the largest in US history and the second-largest in world history.

The cylindrical vessel sports a futuristic design like a surfaced submarine, it's sleek hull sculpted to slice through waves with ease. But step on board and things are out of the ordinary. The living quarters have been removed. Cars may dominate today's discussion about the future of autonomous transportation but some of the world's largest maritime companies are betting big on autonomous shipping. Within the next decade, driverless ships like the one just described could be hauling cargo around the world.

The modern world is becoming increasingly technology driven. Many areas, such as healthcare, have been quick to realise the possibilities. AI and machine learning in oil & gas focused sectors has been slower to establish itself. This is largely because the industry has been slow to realise the potential. However this is slowly changing. Machine learning in oil & gas can be used to enhance the capabilities of this increasingly competitive sector. Not only can it help to streamline the workforce. The technology can also be used to optimise extraction and deliver accurate models. These benefits are just some of the reasons why machine learning in oil & gas is becoming increasingly important. Here are 10 ways that the impact of machine learning in oil & gas industries is being felt. One of the most noticeable impacts of machine learning in oil & gas focused industries is how it transforms discovery processes. Applications employing machine learning in oil & gas enable computers to quickly and accurately analyse huge amounts of data. This includes being able to sift precisely through signals and noise in seismic data.

This paper addresses the data-driven identification of latent dynamical representations of partially-observed systems, i.e., dynamical systems for which some components are never observed, with an emphasis on forecasting applications, including long-term asymptotic patterns. Whereas state-of-the-art data-driven approaches rely on delay embeddings and linear decompositions of the underlying operators, we introduce a framework based on the data-driven identification of an augmented state-space model using a neural-network-based representation. For a given training dataset, it amounts to jointly learn an ODE (Ordinary Differential Equation) representation in the latent space and reconstructing latent states. Through numerical experiments, we demonstrate the relevance of the proposed framework w.r.t. state-of-the-art approaches in terms of short-term forecasting performance and long-term behaviour. We further discuss how the proposed framework relates to Koopman operator theory and Takens' embedding theorem.