After a school shooting in Parkland, Florida left 17 people dead, RealNetworks decided to make its facial recognition technology available for free to schools across the US and Canada. If school officials could detect strangers on their campuses, they might be able to stop shooters before they got to a classroom. Anxious to keep children safe from gun violence, thousands of schools reached out with interest in the technology. Dozens started using SAFR, RealNetworks' facial recognition technology. From working with schools, RealNetworks, the streaming media company, says it's learned an important lesson: Facial recognition isn't likely an effective tool for preventing shootings.

I grew up during the 80s and 90s, the kind of kid who built his own computers late into the night and heard the dial-up modem's tones as my personal anthem. During this time of great promise in mankind's technological potential I remember watching early documentaries on artificial intelligence (AI). My experiences as a software programmer, patent-holding inventor and successful entrepreneur during the past three decades were fueled by this wide-eyed optimism of my youth. I still draw upon that feeling daily, but more than ever we need to see past it if we are to create the best possible future for AI, a technology I believe that will transform the world as we know it. As more people are connected online, it is more obvious than ever that such blind optimism is as anachronistic as a five and a quarter inch floppy.

January 25, 2020 - The AI revolution is upon us. How much is Croatia lagging behind, and are we going to do something about it? But even if we start those processes, where would we be in comparison to the rest of the world? What are other countries already doing and what should we be aware of? Fortunately, a fear of missing out is spreading around the globe or at least among some countries.

Massive bushfires have destroyed millions of acres in Australia over the past few months. RAND's Melissa Finucane, a community resilience expert who grew up in a remote region of New South Wales, has watched in anguish. Experiences from previous disasters have highlighted concrete steps that can help communities start to recover right away, she says. She also notes that rural Australians have "a special kind of resilience," with perspectives and wisdom from years of hard experience. But still, measuring the effects that fires and other disasters have on people's mental health, social, and economic needs remains a unique challenge.

The reproducing Stein kernel approach for post-hoc corrected sampling Liam Hodgkinson 1,, Robert Salomone 2,, and Fred Roosta 3,, † 1 Department of Statistics, UC Berkeley, Berkeley, CA, 94720, USA. Abstract: Stein importance sampling [42] is a widely applicable technique based on kernelized Stein discrepancy [43], which corrects the output of approximate sampling algorithms by reweighting the empirical distribution of the samples. A general analysis of this technique is conducted for the previously unconsidered setting where samples are obtained via the simulation of a Markov chain, and applies to an arbitrary underlying Polish space. We prove that Stein importance sampling yields consistent estimators for quantities related to a target distribution of interest by using samples obtained from a geometrically ergodic Markov chain with a possibly unknown invariant measure that differs from the desired target. The approach is shown to be valid under conditions that are satisfied for a large number of unadjusted samplers, and is capable of retaining consistency when data subsampling is used. Along the way, a universal theory of reproducing Stein kernels is established, which enables the construction of kernelized Stein discrepancy on general Polish spaces, and provides sufficient conditions for kernels to be convergence-determining on such spaces. These results are of independent interest for the development of future methodology based on kernelized Stein discrepancies. 1. Introduction Our problem of interest is the efficient computation of integrals with respect to some target probability measure π . Adopting the Monte Carlo approach, π is approximated by an empirical distribution formed from samples drawn according to π . However, in many problems of interest, it is not possible to simulate according to π exactly, and so further approximate methods must be used. Arguably the most widely employed and general approach is Markov Chain Monte Carlo (MCMC); successively drawing samples as a realization of a Markov chain. The dominant approach to MCMC involves the simulation of a process that is π -ergodic, often constructed by the Metropolis-Hastings algorithm from an underlying irreducible and aperiodic Markov chain [58]. However, there has been significant recent interest in so-called unadjusted MCMC approaches [14, 19, 29, 45]. A common strategy with these methods is the approximate numer-All authors are supported in part by the Australian Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), under Australian Research Council grant CE140100049. For the same computational effort, one can achieve substantially lower variance of estimates at the cost of incurring additional (asymptotic) bias. Despite poorer asymptotic guarantees [21], the ensuing Markov chains are often rapidly mixing, and perform particularly well in high dimensional settings [20].

Toyota Finance New Zealand Limited (TFNZ), a regional subsidiary of Japan-based automotive finance company Toyota Financial Services Corporation, has selected Ephesoft, a content capture and data discovery solutions provider, to accelerate its automotive loan application and settlement processing. Stephen Blay, General Manager Operations, Toyota Finance New Zealand said, "For a financial organization like TFNZ, the digital transformation of loan processes is most importantly a customer service initiative." He further added, "Ephesoft will enable us to integrate an innovative, cloud-enabled solution that uses machine learning for enhanced document and data management, ultimately making it simpler for our staff to help customers secure financing for a new vehicle. This partnership will also enable our loan operations team to shift their focus on manual data handling to strategic customer support." The approach is expected to serve as a best practice for financial service providers globally.

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The capability and spread of such systems have reached the point where they are beginning to touch much of everyday life. However, regulators grapple with how to deal with autonomous systems, for example how could we certify an Unmanned Aerial System for autonomous use in civilian airspace? We here analyse what is needed in order to provide verified reliable behaviour of an autonomous system, analyse what can be done as the state-of-the-art in automated verification, and propose a roadmap towards developing regulatory guidelines, including articulating challenges to researchers, to engineers, and to regulators. Case studies in seven distinct domains illustrate the article. Keywords: autonomous systems; certification; verification; Artificial Intelligence 1 Introduction Since the dawn of human history, humans have designed, implemented and adopted tools to make it easier to perform tasks, often improving efficiency, safety, or security.

Temenos AG, a Geneva-based banking software company, announced the launch of its banking-as-a-service platform in the U.S., which it says can help launch a digital banking platform go live in 90 days. The Tenemos SaaS platform is designed to offer a range services like digital onboarding, know-your-customer verification, personal financial management and support for artificial intelligence, chatbots, wearables and other technology, according to a press release. "With our new U.S. front-to-back SaaS product for digital banks, we will revolutionize the software banking landscape in the U.S., which is a highly strategic market for us," Max Chuard, CEO of Temenos, said in the release. Temenos has worked with some of the fastest growing challenger banks in the U.S, including Grasshopper and Varo Money, Volt Bank and Judo Bank in Australia and Leumi's Pepper in Israel, according to the release. Temenos also works with incumbent financial institutions, including Commerce Bank and Partners Federal Credit Union, in Burbank, California.

ICHEC, the national high-performance computing authority of Ireland, recently participated in the xView2 disaster recovery challenge run by the US Defense Innovation Unit and other Humanitarian Assistance and Disaster Recovery (HADR) organisations. Models developed during the challenge including those developed at ICHEC are currently being tested by agencies responding to the ongoing bushfires in Australia. XView2 Challenge is based on using high resolution imagery to see the details of specific damage conditions in overhead imagery of a disaster area. The challenge involved building AI models to locate and classify the severity of damage to buildings using pairs of pre and post disaster satellite images. Models like these allow those responding to disasters to rapidly assess the damage left in their wake, enabling more effective response efforts and potentially saving lives.