Ocean Energy

Nissan's Brain Wave Project Could Help You Drive by Reading Your Mind


As I sit down in Nissan's simulator, I prepare myself for the fact that a cohort of researchers could scrutinize my skills as a wheelman with more rigor than the most aggravating backseat driver. And, I accept that this process involves wearing what looks like a too-small, sideways bicycle helmet, which holds 11 electrodes poking through my hair.

Eco Marine Power To Study Use of Artificial Intelligence In Research Projects


To further enhance its research capabilities Eco Marine Power announced today that it will begin using the Neural Network Console provided by Sony Network Communications Inc., as part of a strategy to incorporate Artificial Intelligence (AI) into various ongoing ship related technology projects including the further development of the patented Aquarius MRE (Marine Renewable Energy) and EnergySail.

Higgs boson uncovered by quantum algorithm on D-Wave machine


Machine learning has returned with a vengeance. I still remember the dark days of the late '80s and '90s, when it was pretty clear that the current generation of machine-learning algorithms didn't seem to actually learn much of anything. Then big data arrived, computers became chess geniuses, conquered Go (twice), and started recommending sentences to judges. In most of these cases, the computer had sucked up vast reams of data and created models based on the correlations in the data. But this won't work when there aren't vast amounts of data available.

Emerging Technologies That Will Play A Part In Our Future - ETHOZ


In a slightly disconcerting vision of things that may come to pass, transmission of brain waves through the Internet to manipulate devices was demonstrated in a study in 2014. Synaptic Interface – Taking it a step further, images are directly projected into a viewer's brain. Kymogen Wave Energy Generator – A low cost, clean method of producing energy from the constant power of oceanic waves is the promise of the Kymogen Wave Energy Generator. Orbital Solar Energy Harvesters – Solar power is nothing new but the wireless power transmission of energy from a solar energy collecting satellite is ground breaking.

Understand the next wave of technology with this four-course package on AI (91% off)


You'll receive these courses in your bundle: Deep Learning: Convolutional Neural Networks in Python: Convolutional Neural Networks (CNNs) are the engine behind image recognition…find out how it works. Unsupervised Deep Learning in Python: Learn about encoders that process, then reassess information to find new connections to make computers even smarter. Natural Language Processing with Deep Learning in Python: Explore advanced natural language processing, the computer science and AI study that links computer and human languages. Natural Language Processing with Deep Learning in Python: Explore advanced natural language processing, the computer science and AI study that links computer and human languages.

PhD in Computer Science: Development of machine learning techniques for the modelling of the sea's surface shape from video observations, with the aim of improving the safety of maritime operations and the power output of wave energy converters at University of Exeter


The safety of critical maritime operations and the power output of wave energy converters can both be improved by measuring and predicting the shape and motion of sea waves. You will join a growing machine learning group at Exeter, working directly with Dr Jacqueline Christmas in collaboration with Prof Michael Belmont. For informal enquiries about the project, please contact Dr Jacqueline Christmas, J.T.Christmas@exeter.ac.uk. You should have strong programming skills, an aptitude for mathematics, and an enthusiasm for research into image processing and machine learning.

Sequential Design for Computer Experiments with a Flexible Bayesian Additive Model

arXiv.org Machine Learning

In computer experiments, a mathematical model implemented on a computer is used to represent complex physical phenomena. These models, known as computer simulators, enable experimental study of a virtual representation of the complex phenomena. Simulators can be thought of as complex functions that take many inputs and provide an output. Often these simulators are themselves expensive to compute, and may be approximated by "surrogate models" such as statistical regression models. In this paper we consider a new kind of surrogate model, a Bayesian ensemble of trees (Chipman et al. 2010), with the specific goal of learning enough about the simulator that a particular feature of the simulator can be estimated. We focus on identifying the simulator's global minimum. Utilizing the Bayesian version of the Expected Improvement criterion (Jones et al. 1998), we show that this ensemble is particularly effective when the simulator is ill-behaved, exhibiting nonstationarity or abrupt changes in the response. A number of illustrations of the approach are given, including a tidal power application.