For Rolls-Royce, the world's second largest manufacturer of aero engines and a company with a distinguished history of pioneering R&D, technology strategy is all about the play-off between optimising existing products and simultaneously leading the charge on developing the low carbon power systems of the future. "The most pressing issue is how to get the right balance between new technology-led opportunities and existing product evolution," says the firm's chief technology officer, Paul Stein. "People will still be buying gas turbines [conventional aero engines] for the next 40 or 50 years, so we have to make sure we keep those products competitive for the long term. But we also have to free up as many resources as we can for driving productivity and for investing in the new." Stein explains how technologies such as digitisation and AI are already paying big dividends in design and operational efficiency.

"Please think forward to the year 2030. Analysts expect that people will become even more dependent on networked artificial intelligence (AI) in complex digital systems. Some say we will continue on the historic arc of augmenting our lives with mostly positive results as we widely implement these networked tools. Some say our increasing dependence on these AI and related systems is likely to lead to widespread difficulties. Our question: By 2030, do you think it is most likely that advancing AI and related technology systems will enhance human capacities and empower them? That is, most of the time, will most people be better off than they are today? Or is it most likely that advancing AI and related technology systems will lessen human autonomy and agency to such an extent that most people will not be better off than the way things are today? Please explain why you chose the answer you did and sketch out a vision of how the human-machine/AI collaboration will function in 2030.

This article is adapted from GreenBiz's newsletter, VERGE Weekly, running Wednesdays. If you think your organization has a challenging energy consumption profile, try running a network of cold storage facilities -- with most sites in not-so-cold locations, such as California and Georgia. One of the more intriguing examples of smart power management I've come across in recent months is an initiative under way at Lineage Logistics, which runs more than 200 warehouses across North America, Europe and Asia. It handles food for more than 2,500 customers, which are all businesses themselves, such as grocery stories and food services organizations. Each of the Lineage buildings -- an average of 1 million square feet in size -- basically stores about the same amount of food that you'd find in 770,000 home freezers.

We propose $\tt RandUCB$, a bandit strategy that uses theoretically derived confidence intervals similar to upper confidence bound (UCB) algorithms, but akin to Thompson sampling (TS), uses randomization to trade off exploration and exploitation. In the $K$-armed bandit setting, we show that there are infinitely many variants of $\tt RandUCB$, all of which achieve the minimax-optimal $\widetilde{O}(\sqrt{K T})$ regret after $T$ rounds. Moreover, in a specific multi-armed bandit setting, we show that both UCB and TS can be recovered as special cases of $\tt RandUCB.$ For structured bandits, where each arm is associated with a $d$-dimensional feature vector and rewards are distributed according to a linear or generalized linear model, we prove that $\tt RandUCB$ achieves the minimax-optimal $\widetilde{O}(d \sqrt{T})$ regret even in the case of infinite arms. We demonstrate the practical effectiveness of $\tt RandUCB$ with experiments in both the multi-armed and structured bandit settings. Our results illustrate that $\tt RandUCB$ matches the empirical performance of TS while obtaining the theoretically optimal regret bounds of UCB algorithms, thus achieving the best of both worlds.

Deep learning techniques have been shown to be extremely effective for various classification and regression problems, but quantifying the uncertainty of their predictions and separating them into the epistemic and aleatoric fractions is still considered challenging. In oil and gas exploration projects, tools consisting of seismic, sonic, magnetic resonance, resistivity, dielectric and/or nuclear sensors are sent downhole through boreholes to probe the earth's rock and fluid properties. The measurements from these tools are used to build reservoir models that are subsequently used for estimation and optimization of hydrocarbon production. Machine learning algorithms are often used to estimate the rock and fluid properties from the measured downhole data. Quantifying uncertainties of these properties is crucial for rock and fluid evaluation and subsequent reservoir optimization and production decisions. These machine learning algorithms are often trained on a "ground-truth" or core database. During the inference phase which involves application of these algorithms to field data, it is critical that the machine learning algorithm flag data as out of distribution from new geologies that the model was not trained upon. It is also highly important to be sensitive to heteroscedastic aleatoric noise in the feature space arising from the combination of tool and geological conditions. Understanding the source of the uncertainty and reducing them is key to designing intelligent tools and applications such as automated log interpretation answer products for exploration and field development. In this paper we describe a methodology consisting of a system of dual networks comprising of the combination of a Bayesian Neural Network (BNN) and an Artificial Neural Network (ANN) addressing this challenge for geophysical applications.

Massive power outages won't save California, Twitter misused your two-factor authentication data, and scientists now know where lightning strikes twice (as much as anywhere else). Here's the news you need to know, in two minutes or less. Want to receive this two-minute roundup as an email every weekday? Power shutoffs can't save California from wildfire hell On Wednesday night, PG&E started shuting off power for hundreds of thousands of California residents in an effort to prevent wildfires during a high-wind period. Though this may be necessary as a stopgap, shutoffs won't save California from wildfires entirely.

It was a major international merger, and the clock was ticking. The CFO had to determine if the framework and operating model they developed would ensure the pending merger and acquisition (M&A) is a success. This scenario is an indication of the CFO's changing charter. Tacking onto their traditional focus of managing financial health, compliance and risk, the modern CFO's responsibilities is focused on growth and efficiencies, identified through new business models and revenue streams. CFOs now play an increasingly pivotal role in strategy formulation and execution, including M&As, which have grown exponentially over the last two years.

Imagine you're taking an online business class -- the kind where you watch video lectures and then answer questions at the end. But this isn't a normal class, and you're not just watching the lectures: They're watching you back. Every time the facial recognition system decides that you look bored, distracted, or tuned out, it makes a note. And after each lecture, it only asks you about content from those moments. This isn't a hypothetical system; it's a real one deployed by a company called Nestor.

When I was a young boy, I was very ill. My immune system was not working as it should. I felt sick after meeting other children, so I had to live in isolation. I was coughing so severely that on a few occasions, I almost fainted. My loving parents tried everything to help me, consulted every health practitioner. I was prescribed strong medications that left me numb and without energy.

In management, business, economics, scien ce, engineering, and research domains, L arge Scale Global Optimization (LSGO) plays a predominant and vital role. Though LSGO is applied in many of the application domains, it is a very troublesome and a perverse task . The Congress o n Evolutionary Comp utation (CEC) began a n LSGO competition to come up with algorithms with a bunch of standard benchmark unconstrained LS GO functions . Therefore, in this paper, we propos e a hybrid meta - heuristic algorithm, which combines a n I mproved and M odified Harmony Search (IMHS), along with a M odified Differential Evolution (MDE) with an alternate selection strategy . Harmony Search (HS) does the job of exploration and exploitation, and Differe ntial Evolution does the job of giving a perturbation to the exploration of IMHS, as harmony search suffers from being stuck at the basin of local optimal . To judge the performance of the suggested algorithm, we compare the proposed algorithm with ten excellent met a - heuristic algorithms on fifteen LSGO benchmark functions, which have 1000 continuous decision variables, of the CEC 2013 LSGO special session . The experimental results consistently show that our proposed hybrid meta - heuristic performs statistically on par with some algorithms in a few problems, while it turned out to be the best in a couple of problems.