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Council Post: 16 Experts Predict The Tech Trends That Will Dominate Industry In 2022

#artificialintelligence

Tech trends can change on a dime--for example, the Covid-19 pandemic caused many companies to reactively switch their tech focus to enabling and supporting remote work. Still, industry watchers generally have insight into likely upcoming developments--they named cybersecurity and the Internet of Things as trends to watch in 2021, and there's no doubt these topics have generated plenty of headlines this year. The tech experts of Forbes Technology Council have their own predictions about the technologies and trends they believe will dominate not only the tech industry but business in general in the year ahead. From continued upgrades to artificial intelligence, voice search and battery technology to the rise of "citizen developers," here are their predictions and reasons for new and continuing tech trends in 2022. Members of Forbes Technology Council share their predictions for the tech trends that will dominate industry in 2022.


Artificial Intellgence -- Application in Life Sciences and Beyond. The Upper Rhine Artificial Intelligence Symposium UR-AI 2021

arXiv.org Artificial Intelligence

The TriRhenaTech alliance presents the accepted papers of the 'Upper-Rhine Artificial Intelligence Symposium' held on October 27th 2021 in Kaiserslautern, Germany. Topics of the conference are applications of Artificial Intellgence in life sciences, intelligent systems, industry 4.0, mobility and others. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, Offenburg and Trier, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.


Review of Low-Voltage Load Forecasting: Methods, Applications, and Recommendations

arXiv.org Machine Learning

The increased digitalisation and monitoring of the energy system opens up numerous opportunities % and solutions which can help to decarbonise the energy system. Applications on low voltage (LV), localised networks, such as community energy markets and smart storage will facilitate decarbonisation, but they will require advanced control and management. Reliable forecasting will be a necessary component of many of these systems to anticipate key features and uncertainties. Despite this urgent need, there has not yet been an extensive investigation into the current state-of-the-art of low voltage level forecasts, other than at the smart meter level. This paper aims to provide a comprehensive overview of the landscape, current approaches, core applications, challenges and recommendations. Another aim of this paper is to facilitate the continued improvement and advancement in this area. To this end, the paper also surveys some of the most relevant and promising trends. It establishes an open, community-driven list of the known LV level open datasets to encourage further research and development.