Market predictions and expectations are inherently uncertain and actual results may differ materially from those contained in this press release or the report. Please refer to the full report for a complete understanding of the assumptions underlying the report's conclusions and the methodologies used to create the report. Neither Navigant Research nor Navigant undertakes any obligation to update any of the information contained in this press release or the report.

Pileup involves the contamination of the energy distribution arising from the primary collision of interest (leading vertex) by radiation from soft collisions (pileup). We develop a new technique for removing this contamination using machine learning and convolutional neural networks. The network takes as input the energy distribution of charged leading vertex particles, charged pileup particles, and all neutral particles and outputs the energy distribution of particles coming from leading vertex alone. The PUMML algorithm performs remarkably well at eliminating pileup distortion on a wide range of simple and complex jet observables. We test the robustness of the algorithm in a number of ways and discuss how the network can be trained directly on data.

The two biggest societal challenges for the twenty-first century are also the biggest opportunities – automation and climate change. The epitaph of fossil fuels with its dark cloud burning a hole in the ozone layer is giving way to a rise of solar and wind farms worldwide. Servicing these plantations are fleets of robots and drones, providing greater possibilities of expanding CleanTech to the most remote regions of the planet. As 2017 comes to end, the solar industry for the first time in ten years has plateaued due to the proposed budget cuts by the Trump administration. Solar has had quite a run with an average annual growth rate of more than 65% for the past decade promoted largely by federal subsidies.

The nation could be shaped by geopolitics, momentum from robust economic growth, and a host of new leaders eager to implement new policy.

Digital transformation is no longer a fuzzy buzzword in industry, rather it is now a well understood and a credible approach to achieving business value. With increasing maturation of transformative technologies, it's becoming a lot easier for organizations to chart their approach and digital transformation journeys.

Each new year provides the opportunity for reflection upon how far we have come and how far we still have to go, on both a personal and societal level. It was also the year that it took you at least a few minutes to realise the customer agent answering your queries in that little chat-box wasn't human, when you picked up a VR headset from your local toyshop for the price of a pizza, when you found yourself in far too many political arguments around the water-cooler, and when you began seriously questioning whether a computer might someday take your job -- maybe for the second time that year. We will see continuing tensions within and between countries, as 20th century nationalist sentiments push resentfully against 21st century supranational integration. There will be moments when it feels like only technology can save us, followed by events which remind us of how perilous our inventions can be when we still barely understand them. The following is not investment or professional advice of any kind, and is intended only to promote discussion and reflection on some of the rising trends and ideas of our time.

Optimization of high-dimensional black-box functions is an extremely challenging problem. While Bayesian optimization has emerged as a popular approach for optimizing black-box functions, its applicability has been limited to low-dimensional problems due to its computational and statistical challenges arising from high-dimensional settings. In this paper, we propose to tackle these challenges by (1) assuming a latent additive structure in the function and inferring it properly for more efficient and effective BO, and (2) performing multiple evaluations in parallel to reduce the number of iterations required by the method. Our novel approach learns the latent structure with Gibbs sampling and constructs batched queries using determinantal point processes. Experimental validations on both synthetic and real-world functions demonstrate that the proposed method outperforms the existing state-of-the-art approaches.

With our astonishing production of data on this planet every single second, Stephen Dorney asks if we are underestimating the sheer size and what will it mean for consumers, business and regulation. Most of us in the course of our day-to-day personal and entertainment usage don't really think about where our data comes from or how we're creating it. But what about those who have to know the where and how? This data creation causes a headache for the likes of data managers and analytical teams. How do they keep up with the sheer amounts of 1s and 0s coming at them every single second?

When humans are finally ready to relocate civilization to Mars, they won't be able to do it alone. They'll need trusted specialists with encyclopedic knowledge, composure under pressure, and extreme endurance--droids like Justin. Built by the German space agency DLR, such humanoid bots are being groomed to build the first martian habitat for humans. Engineers have been refining Justin's physical abilities for a decade; the mech can handle tools, shoot and upload photos, catch flying objects, and navigate obstacles. Now, thanks to new AI upgrades, Justin can think for itself.

The smart city challenges are not a challenge for AI…but for us'. From the day one, human civilisation has always tried to seek out ways that could make our life better and better each day by overcoming the challenges that come by. We always look for new ideas, innovations, and strategies that could augment our existence as effectively as possible – as they say, the sky's the limit. And even with artificial intelligence, it's the same – smart city challenges are easy for AI to be accomplished but how it does accomplish is also important. The path chosen to reach the destination is more important than the destination itself.