Debate over the existence versus nonexistence of trends in the stellar activity-rotation diagrams continues. Application of modern time series analysis tools to study the mean cycle periods in chromospheric activity index is lacking. We develop such models, based on Gaussian processes, for one-dimensional time series and apply it to the extended Mount Wilson Ca H&K sample. Our main aim is to study how the previously commonly used assumption of strict harmonicity of the stellar cycles affects the results. We introduce three methods of different complexity, starting with the simple harmonic model and followed by Gaussian Process models with periodic and quasi-periodic covariance functions. We confirm the existence of two populations in the activity-period diagram. We find only one significant trend in the inactive population, namely that the cycle periods get shorter with increasing rotation. This is in contrast with earlier studies, that postulate the existence of trends in both of the populations. In terms of rotation to cycle period ratio, our data is consistent with only two activity branches such that the active branch merges together with the transitional one. The retrieved stellar cycles are uniformly distributed over the R'HK activity index, indicating that the operation of stellar large-scale dynamos carries smoothly over the Vaughan-Preston gap. At around the solar activity index, however, indications of a disruption in the cyclic dynamo action are seen. Our study shows that stellar cycle estimates depend significantly on the model applied. Such model-dependent aspects include the improper treatment of linear trends and too simple assumptions of the noise variance model. Assumption of strict harmonicity can result in the appearance of double cyclicities that seem more likely to be explained by the quasi-periodicity of the cycles.

The report, headed "Sizing the prize", establishes that for many businesses to grow, they need to make strategic investments into artificial intelligence technology. This is based on the premise that to stimulate consumer demand, product enhancements are required. Here artificial intelligence platforms can drive greater product variety, such as increased personalization, attractiveness and affordability of products over time. The emphasis upon personalization reflects a drift towards niche products, tailored to the specific consumers and a move away from mass production models in some sectors. In total, PwC calculated that artificial intelligence could contribute up to $15.7 trillion to the global economy by 2030.

Machine learning algorithms require training data -- and a lot of it -- to get the models working correctly. But more training data alone doesn't necessarily make for smarter algorithms, according to Tolga Kurtoglu, CEO at PARC, a research and development company spun out Xerox in 2002. Looking to establish accountability across disparate project teams? Trying to automate processes or allow for lean methodology support? Hoping to enable business consequence modeling or real-time reporting?

The U.S. automaker says it'll deploy its self-driving vehicles on Lyft's network within four years. As Fred Katayama reports, this expands Lyft's partnerships beyond Google and GM.

Before scientists can effectively capture and deploy fusion energy, they must learn to predict major disruptions that can halt fusion reactions and damage the walls of doughnut-shaped fusion devices called tokamaks. Timely prediction of disruptions, the sudden loss of control of the hot, charged plasma that fuels the reactions, will be vital to triggering steps to avoid or mitigate such large-scale events. Today, researchers at the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) and Princeton University are employing artificial intelligence to improve predictive capability. Researchers led by William Tang, a PPPL physicist and a lecturer with the rank of professor in astrophysical sciences at Princeton, are developing the code for predictions for ITER, the international experiment under construction in France to demonstrate the practicality of fusion energy. The new predictive software, called the Fusion Recurrent Neural Network (FRNN) code, is a form of "deep learning" -- a newer and more powerful version of modern machine learning software, an application of artificial intelligence.

At its core, machine learning studies the construction of algorithms and learns from them to make predictions on data by building models from sample inputs. If we further break it down, machine learning borrows heavily from computational statistics (prediction modeling using computers) and mathematical optimization, which provides methods, theory and application data to those models. In essence, it creates its own data models based on algorithms and then uses them to predict defined patterns within a range of data sets. Machine-learning algorithms can be broken down into five types: supervised, unsupervised, semi-supervised, active, and reinforcement, all of which act just like they sound. Supervised algorithms are programmed and implemented by humans to provide both input and output as well as furnishing feedback on predictive accuracy during training.

Tesla just announced it's newest, largest supercharger stations in the US. LOUISVILLE -- UPS Inc. is making a big investment in all-electric big rigs by placing an order for 125 of the sleek new semi trucks from Tesla. Published reports put the cost of the trucks at between $150,000 and $200,000 each and that the UPS order is the largest to date for a vehicle unveiled in November. That could set the package and freight delivery company with its main air hub in Louisville back as much as $25 million. Tesla expects to begin producing the vehicles in 2019.

File photo - An airplane flies over a drone during the Polar Bear Plunge on Coney Island in the Brooklyn borough of New York Jan. 1, 2015. While it seems unlikely that everyday drone hobbyists would want to make a beeline for their nearest nuclear facility to grab some aerial shots, the Federal Aviation Administration (FAA) has nevertheless announced a ban on drone flights over such locations in the U.S., namely: As you can see, they're mainly labs, while the Hanford Site, for example, is a mostly decommissioned nuclear production complex. Another of those listed, the Pantex Site, is an active nuclear weapons assembly and dismantlement plant. The restrictions, which come into force on December 29, have been put in place "to address concerns about unauthorized drone operations over seven Department of Energy (DOE) facilities," the FAA confirmed on its website. It added that "operators who violate the airspace restrictions may be subject to enforcement action, including potential civil penalties and criminal charges."

Japanese robotics firm tmsuk has unveiled a new piggyback-style rideable designed to make life easier for wheelchair users. The Rodem electric wheelchair is positioned in a way that allows a person to pull their body straight onto the seat, simplifying the process of moving from a bed or sofa onto the wheelchair. Rodem users can even control the robotic chair with a smartphone, to drive their wheelchair right up to them when it's needed, or park it out of the way when it isn't. Japanese robotics firm tmsuk has unveiled a new piggyback-style rideable designed to make life easier for wheelchair users. The ¥980,000 (roughly US$8,700) robotic wheelchair weighs about 110 kilograms (242lbs), and can reach a maximum speed of about 6 km per hour (3.7mph).

A major concern for our rights as consumers is the way that machines direct us according to their interests and not ours. Experts such as Dr Jonathan Cave warn about the growing influence of software machines on our lives. Cave says that software machines will make use of what they know about us to present information to us which may not be to our advantage. Because the search engines that we have used know a certain amount about us and our previous buying decisions, they are keen to exploit that by turning us into a buyer of something, by a process known as'filter bubbles' – a feedback loop where recommendations only reinforce existing patterns. As Dr Rupp states'if you are not paying then you are not the customer'. Thus if you are not paying for an internet technology such as Google or Facebook it is not acting in your interests, but rather in the interests of the customers who are paying to present information to you.