The 2020 Call for Code Global Challenge has expanded its focus to tackle the effects of COVID-19. Technology solutions can help reduce the impact this pandemic has on our daily lives and the world. COVID-19, which is caused by the novel corona virus, has revealed the limits of the systems we take for granted in a very short period of time. Whether it's the massive increase in demand for information during a time of crisis, educating children when schools are closed, or helping communities best distribute limited resources, technology has a pivotal role to play. Through Call for Code, you can see your idea deployed by a global partner ecosystem.
With the digital world of data becoming the focal point of discussions and innovation, there is unparalleled hype over what it takes to be a digital enterprise in this day and age. Data sits at the center of the digital revolution, and companies that have determined the best possible way to extract meaning out of data are well on their way to glory. An organization takes its first steps into the digital world of change when it realizes and utilizes the importance of cloud based technologies like AI and IoT. These services are used to better manage data and to generate the best possible insights from it on a real-time basis. The insights generated from your data through cloud based services like IoT and AI can help improve business processes, automate tasks, design new products and manage operations in an efficient manner.
Artificial intelligence is improving the ability of healthcare providers to effectively respond to the coronavirus pandemic – allowing for faster diagnoses and speedy dissemination of trusted information as well as detecting fraudulent insurance claims and accurately evaluating patient data in real time. SoftBank-backed AI company Automation Anywhere is offering free healthcare bots to help the industry manage increased workloads due to the outbreak. "Bots are software that will be configured within the company's system in 24 to 48 hours. They can keep a track of infected people, analyse data, find new trends and perform clerical tasks," Milan Sheth, the company's executive vice president for India, the Middle East and Africa, told The National. Collaborating with one of its technology partners in Macau, Automation Anywhere has developed a global positioning system-enabled dashboard that shows local statistics, sites of infection, hospital wait times, local availability of masks and other useful information which is updated every few minutes.
Avaloq and Personetics are entering into a strategic partnership to connect Avaloq's global banking clients with Personetic's AI-powered personalized engagement solutions. Personetics, a global provider of data-driven and personalized engagement solutions for bank customers, is joining the Avaloq.one This partnership gives Avaloq's banking clients access to Personetics Engage, a business solution that offers deep analysis of customers' financial data in real-time, understands their financial behaviour, anticipates their needs and acts accordingly on their behalf. Personetics Engage provides data-driven day-to-day personalized insights, financial advice and wellness programmes, tailored to mass market, affluent as well as small business customers. Personetics Engage is supported by a rich set of tools used to help banks create Personalization IP for market differentiation while delivering significant business impact.
Personetics, the leading global provider of data-driven and personalized engagement solutions powered by artificial intelligence (AI) for bank customers, is joining the Avaloq.one This partnership gives Avaloq's banking clients access to Personetics Engage, a business solution that offers deep analysis of customers' financial data in real-time, understands their financial behaviour, anticipates their needs and acts accordingly on their behalf. Personetics Engage provides data-driven day-to-day personalized insights, financial advice and wellness programmes, tailored to mass market, affluent as well as small business customers. Personetics Engage is supported by a rich set of tools used to help banks create Personalization IP for market differentiation while delivering significant business impact. This is the latest international addition to the growing list of selected fintechs on Avaloq.one, a platform that helps banks and wealth managers to connect to a suite of pre-screened, pre-integrated fintech solutions and to third-party services that complement their existing core banking solutions and help drive new customer experiences.
We're officially a month into 2020 and the new decade is well underway. So much so, it is worth reflecting back as it jolted our eyes open and set the stage for what's to come. To sum it up in a word, data. Data, data everywhere – how to get it, how to use it, how to see it. Everywhere you looked there were analytics dashboards.
Z Advanced Computing, Inc. (ZAC), the pioneer startup on Explainable-AI (Artificial Intelligence) (XAI), is developing its Smart Home product line through a paid-pilot for Smart Appliances for BSH Home Appliances (a subsidiary of the Bosch Group, originally a joint venture between Bosch and Siemens), the largest manufacturer of home appliances in Europe and one of the largest in the world. ZAC just successfully finished its Phase 1 of the pilot program. "Our cognitive-based algorithm is more robust, resilient, consistent, and reproducible, with a higher accuracy, than Convolutional Neural Nets or GANs, which others are using now. It also requires much smaller number of training samples, compared to CNNs, which is a huge advantage," said Dr. Saied Tadayon, CTO of ZAC. "We did the entire work on a regular laptop, for both training and recognition, without any dedicated GPU. So, our computing requirement is much smaller than a typical Neural Net, which requires a dedicated GPU," continued Dr. Bijan Tadayon, CEO of ZAC.
Air pollution kills an estimated seven million people every year and cities around the world are being forced to take action to do what they can to lower the risk to inhabitants. A team of Loughborough University computer scientists believe their AI system has the potential to provide new insight into the environmental factors that have significant impacts on air pollution levels. In particular it focuses on the amount of'PM2.5' In 2013, a study involving 312,944 people in nine European countries revealed that there was no safe level of particulates. PM2.5 particulates were found to be particularly deadly, blamed for a 36 per cent increase in lung cancer per 10 μg/m3 as they can penetrate deep into the lungs.
Imagine being scared to breathe the air around you. An unusual concept for us here in the UK, but it is a genuine concern for communities all over the world with air pollution killing an estimated seven million people every year. A team of Loughborough University computer scientists are hoping to help eradicate this fear with a new artificial intelligence (AI) system they have developed that can predict air pollution levels hours in advance. The technology is novel for a number of reasons, one being that it has the potential to provide new insight into the environmental factors that have significant impacts on air pollution levels. Professor Qinggang Meng and Dr. Baihua Li are leading the project which is focused on using AI to predict PM2.5--particulate matter of less than 2.5 microns (10-6 m) in diameter--that is often characterized as reduced visibility in cities and hazy-looking air when levels are high.
In this paper, we develop an asymptotically exact characterization of the SLOPE solution under Gaussian random designs through solving the SLOPE problem using approximate message passing (AMP). This algorithmic approach allows us to approximate the SLOPE solution via the much more amenable AMP iterates. Moreover, we prove that the AMP iterates converge to the SLOPE solution in an asymptotic sense, and numerical simulations show that the convergence is surprisingly fast. Our proof rests on a novel technique that specifically leverages the SLOPE problem. In contrast to prior literature, our work not only yields an asymptotically sharp analysis but also offers an algorithmic, flexible, and constructive approach to understanding the SLOPE problem.