Virtus AllianzGI Artificial Intelligence & Technology Opportunities Fund (NYSE: AIO), a diversified closed-end fund, today announced that it will institute a managed distribution plan effective with the distribution payable February 1, 2022. Coincident with the adoption of the plan, the Fund will raise its monthly distribution to $0.15 per share, a 20% increase from the distribution of $0.125 per share payable January 3, 2022. The increased distribution represents an annualized distribution rate of 6.7% based on the closing market price of $26.88 on December 16, 2021. The Fund is undertaking these actions as part of its ongoing efforts to enhance shareholder value by both seeking to provide a more attractive distribution rate and furthering its efforts to reduce the current discount to net asset value at which its shares currently trade. The Board also approved a change in the Fund's fiscal year end from February 28 to January 31, effective January 31, 2022.

The use of statistical methods to estimate or predict the behavior of a phenomenon in the future has been common practice in many disciplines such as health care, trading, auto insurance and customer relationship management. The goal of these methods is not necessarily the prediction of the exact outcome, but a determination of the likelihood of different outcomes, since in that case one can prepare accordingly. Machine learning has further empowered such methods, allowing them to become even more accurate by using more data and additional computation. Predictive models require lots of data, which can be problematic since storing such data is expensive and transferring such data can create a significant load on networks. What if there was a method that would allow for the training of predictive models without the need to transfer needed data in its original raw form.

LONDON--(BUSINESS WIRE)--Deep Knowledge Analytics, Big Innovation Centre and Innovation Eye launch'Artificial Intelligence in the UK: Industry landscape overview in 2021', the most comprehensive industry mapping made to date, profiling, categorising and analysing over 3,600 private and public sector entities across 20 sectors and 50 locations in the UK. The open-access report and IT platform, covers the latest developments in technology and innovation, best ranking companies and investors hubs, AI and COVID-19, policy and ethics, AI challenges and opportunities for the UK. Additionally, it profiles the top 100 UK AI experts and hubs, including think tanks, tech-hubs and doctoral training centres. The UK remains first in Europe and ranks third globally, behind the USA and China in developing AI technologies with a reported £9 billion investment growth for the industry between 2019 and 2021. The UK government continues to foster growth and industry development through initiatives in 5 key areas - human capital, lab to market developments, networking, regulation and infrastructure.

Utilidata, an industry leading grid-edge software company, announced today that it is developing a software-defined smart grid chip in collaboration with NVIDIA. The chip will be powered by NVIDIA's AI platform and embedded in smart meters to enhance grid resiliency, integrate distributed energy resources (DERs) -- including solar, storage, and electric vehicles (EVs) -- and accelerate the transition to a decarbonized grid. The U.S. Department of Energy's (DOE's) National Renewable Energy Laboratory (NREL) will be among the first to test the software-defined smart grid chip as a way to scale and commercialize the lab's Real-Time Optimal Power Flow (RT-OPF) technology, with support from the Solar Energy Technologies Office Technology Commercialization Fund. Originally developed with funding from DOE's Advanced Research Projects – Energy (ARPA-E) program, RT-OPF enables highly localized load control to seamlessly integrate an increasing number of DERs while ensuring stable and efficient grid operations. "To date, the scalability and commercial potential of technologies like RT-OPF have been limited by single-use hardware solutions," said Santosh Veda, Group Manager for Grid Automation and Controls at NREL. "By developing a smart grid chip that can be embedded in one of the most ubiquitous utility assets – the smart meter – this approach will potential enable wider adoption and commercialization of the technology and redefine the role of edge computing for DER integration and resiliency. Enhanced situational awareness and visibility from this approach will greatly benefit both the end customers and the utility."

Continual brings together second time founders Tristan Zajonc and Tyler Kohn who previously built and sold machine learning infrastructure startups.

A pilot project being conducted at Audi's Neckarsulm site is using artificial intelligence to control the quality of spot welds in high-volume production. The solution is being advanced together with Siemens and Amazon Web Services as part of the Volkswagen Group's Industrial Cloud, and is set to be rolled out at other locations. Around 5,300 spot welds are required to join the parts that make up the body of an Audi A6. Up until now, production staff use ultrasound to manually monitor the quality of resistance spot welding (abbreviated WPS in German) processes on the basis of random analyses. However, experts from the fields of manufacturing, innovation management, digitalization planning, and IT are currently testing a much smarter way of determining the quality of spot welds at the Neckarsulm site.

After forming a partnership with Walmart in July to reoutfit the retailer's distribution network with a fleet of fully autonomous robots, Symbotic has announced plans to become a publicly traded company early next year. Yesterday, the robotics and automation firm announced it will go public via a special acquisition company (SPAC), courtesy of a merger with SoftBank Investment Advisers' SVF Investment Corp 3 (SVFC). Once the merger is finalised in the first half of 2022, the combined company will operate under the name Symbotic and trade on the Nasdaq under the ticker symbol SYM. Both Walmart and Symbotic declined comment following a series of phone calls and emails from Capital.com. In the company release issued on Tuesday, Symbotic chair and CEO Rick Cohen said, "Now is the time to take Symbotic to the next level."

In the article the authors describe a new field of study converging AI, basic research, and medicine referred to as Longevity Medicine. Another definition for Longevity Medicine is the preventative and restorative medicine enabled by the deep aging clocks and artificial intelligence. The article was authored by Alex Zhavoronkov, the founder and chief longevity officer of Deep Longevity, a computer scientist with a PhD in biophysics, Evelyne Yehudit Bischof, a practicing medical doctor trained in the top European and the US medical schools actively engaged in aging research and gerooncology at the University Hospital Basel in Switzerland, and at Shanghai University of Medicine and Health Sciences, and one of the most prolific scientists and entrepreneurs in artificial intelligence, Kai-Fu Lee. The traditional approach to medicine is to treat diseases. However, scientists estimate (Cutler and Mattson, 2006) that complete elimination of cancer would result in only 2.3 year increase in life expectancy in the US at birth and 1.3 year gain at age 65.

Microsoft announced today that 12 new languages and dialects have been added to Translator. These additions mean that the service can now translate between more than 100 languages and dialects, making information in text and documents accessible to 5.66 billion people worldwide. "One hundred languages is a good milestone for us to achieve our ambition for everyone to be able to communicate regardless of the language they speak," said Xuedong Huang, Microsoft technical fellow and Azure AI chief technology officer. Translator today covers the world's most spoken languages including English, Chinese, Hindi, Arabic and Spanish. In recent years, advances in AI technology have allowed the company to grow its language library with low-resource and endangered languages, such as Inuktitut, a dialect of Inuktut that is spoken by about 40,000 Inuit in Canada.

Lightning is one of the most destructive forces of nature, as in 2020 when it sparked the massive California Lightning Complex fires, but it remains hard to predict. A new study led by the University of Washington shows that machine learning -- computer algorithms that improve themselves without direct programming by humans -- can be used to improve lightning forecasts. Better lightning forecasts could help to prepare for potential wildfires, improve safety warnings for lightning and create more accurate long-range climate models. "The best subjects for machine learning are things that we don't fully understand. And what is something in the atmospheric sciences field that remains poorly understood? Lightning," said Daehyun Kim, a UW associate professor of atmospheric sciences.