"Seagulls," said Andy Stanford-Clark, excitedly. In fact, you can totally ignore them." Stanford-Clark, the chief technology officer for IBM in the U.K. and Ireland, was exuding nervous energy. It was the afternoon before the morning when, at 4 a.m. British Summer Time, IBM's Mayflower Autonomous Ship -- a crewless, fully autonomous trimaran piloted entirely by IBM's A.I., and built by non-profit ocean research company ProMare -- was set to commence its voyage from Plymouth, England. to Cape Cod, Massachusetts. And now, after countless tests and hundreds of thousands of hours of simulation training, it was about to set sail for real. Stanford-Clark was running through the potential risks. Seagulls, he pointed out, were something of a false alarm. From an image-recognition perspective, they were a challenge because they had a tendency of getting right up in the camera lens so that they looked like enormous winged obstacles that needed to be avoided at all costs. But they had a tendency ...

Operational forecasting centers are investing in decadal (1-10 year) forecast systems to support long-term decision making for a more climate-resilient society. One method that has previously been employed is the Dynamic Mode Decomposition (DMD) algorithm - also known as the Linear Inverse Model - which fits linear dynamical models to data. While the DMD usually approximates non-linear terms in the true dynamics as a linear system with random noise, we investigate an extension to the DMD that explicitly represents the non-linear terms as a neural network. Our weight initialization allows the network to produce sensible results before training and then improve the prediction after training as data becomes available. In this short paper, we evaluate the proposed architecture for simulating global sea surface temperatures and compare the results with the standard DMD and seasonal forecasts produced by the state-of-the-art dynamical model, CFSv2.

Artificial intelligence (AI) has quickened its progress in 2021. A new administration is in place in the US and the talk is about a major push for Green Technology and the need to stimulate next generation infrastructure including AI and 5G to generate economic recovery with David Knight forecasting that 5G has the potential - the potential - to drive GDP growth of 40% or more by 2030. The Biden administration has stated that it will boost spending in emerging technologies that includes AI and 5G to $300Bn over a four year period. On the other side of the Atlantic Ocean, the EU have announced a Green Deal and also need to consider the European AI policy to develop next generation companies that will drive economic growth and employment. It may well be that the EU and US (alongside Canada and other allies) will seek ways to work together on issues such as 5G policy and infrastructure development. The UK will be hosting COP 26 and has also made noises about AI and 5G development.

Avatars: In this year's inaugural Grands Prix, the locations for which are yet to be revealed, 'telerobotic avatars' named'The Aviators' will sit in the'octocopter' race car The craft sports eight rotor blades surrounding a central carbon-fibre cockpit and is capable of going from 0-62 miles per hour in 2.8 seconds. Lewis Hamilton in a Mercedes F1 car would be able to do the same in around 2.6 seconds

Artificial intelligence (AI) has quickened its progress in 2021. A new administration is in place in the US and the talk is about a major push for Green Technology and the need to stimulate next generation infrastructure including AI and 5G to generate economic recovery with David Knight forecasting that 5G has the potential - the potential - to drive GDP growth of 40% or more by 2030. The Biden administration has stated that it will boost spending in emerging technologies that includes AI and 5G to $300Bn over a four year period. On the other side of the Atlantic Ocean, the EU have announced a Green Deal and also need to consider the European AI policy to develop next generation companies that will drive economic growth and employment. It may well be that the EU and US (alongside Canada and other allies) will seek ways to work together on issues such as 5G policy and infrastructure development. The UK will be hosting COP 26 and has also made noises about AI and 5G development.

Another ship called the Mayflower is set to make its way across the Atlantic Ocean this week, but it won't be carrying English pilgrims -- or any people -- at all. When the Mayflower Autonomous Ship leaves its home port in Plymouth, England to attempt the world's first fully autonomous transatlantic voyage, it will have a highly trained "captain" and a "navigator" versed in the rules of avoiding collisions at sea on board, both controlled by artificial intelligence (AI). The ship's AI captain was developed by Marine AI and is guided by an expert system based on IBM technologies, including automation software widely used by the financial sector. The technology could someday help crewed vessels navigate difficult situations and facilitate low-cost exploration of the oceans that cover 70 percent of the Earth's surface. Over its roughly three-week trip, the Mayflower sea drone will sail through the Isles of Scilly and over the site of the lost Titanic to land in Plymouth, Massachusetts, as the colonists on the first Mayflower did more than 400 years ago.

"If we told clinicians, 'we will use advanced math to help you improve care,' they would probably be fine with it. But the term'artificial intelligence' raises natural skepticism about what it really means." "First, do no harm" is a promise many of us make when becoming clinicians. To me it means that, for as long as I practice medicine, I must carefully weigh the risks and benefits of my decisions for patients. This principle underpins a healthy skepticism from some clinicians when learning about new approaches and technologies, such as artificial intelligence (AI), that claim to improve patient care.

Neural Processes (NPs) are a family of conditional generative models that are able to model a distribution over functions, in a way that allows them to perform predictions at test time conditioned on a number of context points. A recent addition to this family, Convolutional Conditional Neural Processes (ConvCNP), have shown remarkable improvement in performance over prior art, but we find that they sometimes struggle to generalize when applied to time series data. In particular, they are not robust to distribution shifts and fail to extrapolate observed patterns into the future. By incorporating a Gaussian Process into the model, we are able to remedy this and at the same time improve performance within distribution. As an added benefit, the Gaussian Process reintroduces the possibility to sample from the model, a key feature of other members in the NP family.

Razer has partnered with marine waste cleaning startup ClearBot to advance the use of AI and robotics to reduce ocean pollution. The pair announced their partnership in celebration of World Oceans Day and is part of Razer's 10-year #GoGreenWithRazer campaign that will see the company make green investments to support environment- and sustainability-focused startups. "We are extremely happy to have the opportunity to work with a startup focused on saving the environment. ClearBot's unique AI and advanced machine learning technology will enable and empower governments and organisations around the world to broaden their sustainability efforts. We urge other innovative startups to reach out to Razer for collaboration opportunities as we strive to make the world a safer place for future generations."

Many single-target regression problems require estimates of uncertainty along with the point predictions. Probabilistic regression algorithms are well-suited for these tasks. However, the options are much more limited when the prediction target is multivariate and a joint measure of uncertainty is required. For example, in predicting a 2D velocity vector a joint uncertainty would quantify the probability of any vector in the plane, which would be more expressive than two separate uncertainties on the x- and y- components. To enable joint probabilistic regression, we propose a Natural Gradient Boosting (NGBoost) approach based on nonparametrically modeling the conditional parameters of the multivariate predictive distribution. Our method is robust, works out-of-the-box without extensive tuning, is modular with respect to the assumed target distribution, and performs competitively in comparison to existing approaches. We demonstrate these claims in simulation and with a case study predicting two-dimensional oceanographic velocity data. An implementation of our method is available at https://github.com/stanfordmlgroup/ngboost.