Jensen Sun developed Geoweaver, a system that uses machine learning for earth science data. Today's Earth scientists are spending less time standing in fields collecting soil samples, and more time behind a computer screen. Most geoscience data is automatically collected by sensors and satellites. The big challenge is making sense of all that data so that scientists can get back to what they do best: Observing the world, asking questions, conducting experiments, and finding evidence. Scientists use large, publicly available datasets from government programs such as NASA, NOAA, and USGS (that's the National Aeronautics and Space Administration, the National Oceanic and Atmospheric Administration, and US Geological Survey, in non-acronym speak).

The construction and infrastructure sector is often criticised for not clearly and in an accessible way illustrating the tangible benefits of its work to citizens. In my experience this is a charge that could be laid at the door of construction's growing number of digital advocates, who come together at conferences and workshops and are so excited by the tech that they forget about its impact on those whose lives it affects the most. So, it was a breath of fresh air to hear Ayesha Khanna, co-founder and CEO of ADDO AI, an artificial intelligence advisory firm and incubator, speaking at the Bentley Year in Infrastructure conference in Singapore, say passionately "It's never the right way to start with the technology – you should always start with the problem." Khanna repeatedly urged her audience to focus on citizens and the benefits of digital technology on real people's lives and making them better for longer. Khanna has been a strategic advisor on artificial intelligence, smart cities and fintech to a number of clients such as SMRT, Singapore's largest public transport company, SOMPO, Japan's largest insurance firm and Smart Dubai, the government agency tasked to transform Dubai into a leading smart city.

In the digitally driven world, there are endless numbers of solutions made available for addressing every type of threat. FREMONT, CA: The enterprises are welcoming more devices to the corporate network but are facing a cybersecurity challenge attack as constant threats are getting widespread. The panorama of data breaches, loss of essential data, or network crashes forces the enterprises to try out security measures. Nevertheless, they also force them to create cybersecurity strategies to guard the digital assets and level up with hackers and cybercriminals. Artificial intelligence (AI) has been a technology that is now primarily leveraged by enterprises as they realize that cyber threats have been a lot to manage without advanced technology.

Debra Fahey will be joining the company as Senior Vice President, Global Head of Delivery & Operations, and Michael Fox will be joining as Senior Vice President of Product Management. Together, these leaders bring over 40 years of experience in the fields of technology, business analytics, innovation strategy, and software development to ElectrifAi's growing team of skilled professionals. "I am proud to welcome Debra and Michael as the newest additions to ElectrifAi's deep executive leadership team," said CEO, Edward Scott. "Their wealth of knowledge and invaluable experience in the fields of delivery, operations, and product management will be vital in their new roles advancing ElectrifAi's industry-leading Ai and ML products. I am confident Ms. Fahey and Mr. Fox will add value to our expanding global leadership team and continue to strengthen our expertise within the Ai and ML technology, innovation, and delivery of solutions."

DUBAI, UAE – The United States of America and the United Arab Emirates reaffirm their shared commitment to a strong bilateral relationship within the framework of the U.S.-UAE Strategic Energy Dialogue first established in 2010 and reiterated in 2017. U.S. Secretary of Energy Rick Perry and UAE Minister of State for Artificial Intelligence Omar bin Sultan Al-Olama met to exchange views on the responsible use of Artificial Intelligence in improving grid resilience, increasing energy exploration and environmental sustainability, optimizing transportation and enabling smarter cities, improving water resource management, and in the discovery of new materials and compounds. They identified opportunities for DOE's Artificial Intelligence & Technologies Office and the Dubai Futures Foundation to hold further discussions, and agreed to explore the potential to expand the U.S.-UAE Strategic Dialogue to include cooperation on areas of mutual interest in AI. The parties also reiterated the importance of addressing energy security challenges through public and private sector partnerships and investment to support the research, development and deployment of all forms of energy and technologies.

Calibration error is commonly adopted for evaluating the quality of uncertainty estimators in deep neural networks. In this paper, we argue that such a metric is highly beneficial for training predictive models, even when we do not explicitly measure the uncertainties. This is conceptually similar to heteroscedastic neural networks that produce variance estimates for each prediction, with the key difference that we do not place a Gaussian prior on the predictions. We propose a novel algorithm that performs simultaneous interval estimation for different calibration levels and effectively leverages the intervals to refine the mean estimates. Our results show that, our approach is consistently superior to existing regularization strategies in deep regression models. Finally, we propose to augment partial dependence plots, a model-agnostic interpretability tool, with expected prediction intervals to reveal interesting dependencies between data and the target.

We analyze the practices of reservoir computing in the framework of statistical learning theory. In particular, we derive finite sample upper bounds for the generalization error committed by specific families of reservoir computing systems when processing discrete-time inputs under various hypotheses on their dependence structure. Non-asymptotic bounds are explicitly written down in terms of the multivariate Rademacher complexities of the reservoir systems and the weak dependence structure of the signals that are being handled. This allows, in particular, to determine the minimal number of observations needed in order to guarantee a prescribed estimation accuracy with high probability for a given reservoir family. At the same time, the asymptotic behavior of the devised bounds guarantees the consistency of the empirical risk minimization procedure for various hypothesis classes of reservoir functionals.

Cybersecurity is perhaps the single greatest threat to any organization today. While hardly a new challenge, the proliferation of systems, data, cloud technologies, apps, devices and distributed endpoints has only exacerbated cybersecurity threats. Organizations must work harder than ever to safeguard their assets and customers. This goes beyond automating reactive measures. It now requires infosec professionals to work toward proactive detection to preemptively avoid or thwart threats.

For private companies and government agencies trying to track peoples' movements, technology is making the task increasingly easy. Facial recognition and analysis are becoming increasingly popular surveillance tools – the technology was rolled out in airports across the world this summer as a tool for verifying flyers' identity, and is widely used by police departments for tracking suspected criminals. Privacy-minded activists and lawmakers are now hitting back at facial recognition. The technology has been banned for law-enforcement purposes across California, and a similar bill is being weighed in Massachusetts. Meanwhile, artists and researchers have begun to develop clothes designed to thwart algorithms that detect human faces.

Ray Kurzweil's The Singularity is Near peaked my interest when he posited his reasoning for why there is likely no intelligent life elsewhere in the universe. By a mere matter of odds, most of us assume (likely myself included) that there simply must be some kind of super-intelligent species "out there somewhere." One of the many postulations made (the book is more than worth reading), is that species might – at the point of attaining a certain degree of capacity or intelligence – destroy themselves. Could be bombs, could be nanotechnologies, could be super-intelligent computers – but something batters them back to the stone age – or worse. In thinking recently on topics related to ethical enhancement and human enhancement in general, I came to the notion that this "self-extermination theory" might pan out in some other interesting and less considered ways.