The Directorate of Science Technology and Innovations (DSTI) and the United Nations Development Program (UNDP) have signed a Memorandum of Understanding (MoU) to continue collaboration on applied artificial intelligence for governance, entrepreneurship, and social good. The MoU signed in Freetown last week provides a framework of cooperation and collaboration for both institutions to contribute to the successful implementation of the National Innovation and Digital Strategy (NIDS), especially in areas of common interest. In October 2019, the UNDP Country Lab also known as the Accelerator Lab for Sierra Leone was launched to examine and explore emerging untapped resources to speedup national SDG performance. The UNDP Accelerator Labs are a network of 60 labs serving 78 countries with the collective aim of finding new evidence-based approaches to problem-solving with the use of artificial intelligence, testing, mapping, and experimentation. "DSTI and UNDP have been engaging since Day 1. However, this particular agreement focuses on how we can continue to make significant inroads in the implementation of the National Innovation and Digital Strategy," said Dr. Moinina David Sengeh.

Phoronix is the leading technology website for Linux hardware reviews, open-source news, Linux benchmarks, open-source benchmarks, and computer hardware tests. Africa Animation, VFX & Video Games Industry Report 2020-2025 – AI, ML & Deep Learning are Being Leveraged to Drive Hyper-Personalisation for Video Games – ResearchAndMarkets.com AI still doesn't have the common sense to understand human language

On Wednesday, Microsoft Corp. announced AI for Health, a new $40 million, five-year program and part of the AI for Good initiative, that will leverage artificial intelligence (AI) technology to empower researchers and organizations addressing some of the world's toughest challenges in health. "Artificial intelligence has the potential to solve some of humanity's greatest challenges, like improving the health of communities around the world," said Brad Smith, president, Microsoft. "We know that putting this powerful technology into the hands of experts tackling this problem can accelerate new solutions and improve access for underserved populations. That's why we created AI for Health." In a new era of tech intensity, in which technology is reshaping every organization and becoming embedded in the fabric of every aspect of our lives, digital advances will continue to reshape our world in profound ways.

This paper introduces the Sylvester graphical lasso (SyGlasso) that captures multiway dependencies present in tensor-valued data. The model is based on the Sylvester equation that defines a generative model. The proposed model complements the tensor graphical lasso (Greenewald et al., 2019) that imposes a Kronecker sum model for the inverse covariance matrix by providing an alternative Kronecker sum model that is generative and interpretable. A nodewise regression approach is adopted for estimating the conditional independence relationships among variables. The statistical convergence of the method is established, and empirical studies are provided to demonstrate the recovery of meaningful conditional dependency graphs. We apply the SyGlasso to an electroencephalography (EEG) study to compare the brain connectivity of alcoholic and nonalcoholic subjects. We demonstrate that our model can simultaneously estimate both the brain connectivity and its temporal dependencies.

Granger causality is a fundamental technique for causal inference in time series data, commonly used in the social and biological sciences. Typical operationalizations of Granger causality make a strong assumption that every time point of the effect time series is influenced by a combination of other time series with a fixed time delay. The assumption of fixed time delay also exists in Transfer Entropy, which is considered to be a non-linear version of Granger causality. However, the assumption of the fixed time delay does not hold in many applications, such as collective behavior, financial markets, and many natural phenomena. To address this issue, we develop variable-lag Granger causality and Transfer Entropy, generalizations of both Granger causality and Transfer Entropy that relax the assumption of the fixed time delay and allows causes to influence effects with arbitrary time delays. In addition, we propose a method for inferring both variable-lag Granger causality and Transfer Entropy relations. We demonstrate our approach on an application for studying coordinated collective behavior and other real-world casual-inference datasets and show that our proposed approaches perform better than several existing methods in both simulated and real-world datasets. Our approach can be applied in any domain of time series analysis. The software of this work is available in the R package: VLTimeSeriesCausality.

In the space of a single year, South African agritech enterprise Aerobotics has won numerous awards and made strategic inroads into the massively competitive US agriculture industry. Propelled by world-leading technology, the South African success story is poised to mushroom into a truly global data and analytics software company serving the entire agriculture value chain. Aerobotics, which as little as a year ago was nominated as one of South Africa's most exciting startups, turns imagery into actionable data so that any issues on the farm, or elsewhere in the value chain, can be identified and resolved before they become problems. In essence, Aerobotics exposes what the naked eye cannot see in order to solve problems and make accurate projections, translating into improved yields and profitability. The company's CEO, James Paterson, says the business is ready to build on its highly successful launch in the US and strategically drop further roots and extend services in numerous regions around the world.

The University of Bristol (UoB) has partnered with Bristol Zoological Society (BZS) to develop a trailblazing approach to wildlife conservation, harnessing the power of machine learning and drone technology to transform wildlife conservation around the world. Backed by the Cabot Institute for the Environment, BZS and EPSRC's CASCADE grant, a team of researchers travelled to Cameroon in December last year to test a number of drones, sensor technologies and deployment techniques to monitor the critically endangered Kordofan giraffe populations in Bénoué National Park. "There has been significant and drastic decline recently of larger mammals in the park and it is vital that accurate measurements of populations can be established to guide our conservation actions," said Dr Gráinne McCabe, head of field conservation and science at BZS. "Bénoué National Park is very difficult to patrol on foot and large parts are virtually inaccessible, presenting a huge challenge for wildlife monitoring. What's more, the giraffe are very well camouflaged and often found in small, transient groups," said Dr Caspian Johnson, conservation science lecturer at BZS. Striving to uncover the best method for airborne wildlife monitoring, BZS reached out to Dr Matt Watson from the UoB's School of Earth Sciences, and Dr Tom Richardson from the University's Aerospace Department, as well as a member of the Bristol Robotics Laboratory (BRL). The team forged successful collaborations using drones to monitor and measure volcanic emissions to create a system for wildlife monitoring.

NAIROBI, Jan 31 (Thomson Reuters Foundation) - Ugandan doctors are giving new mothers artificial intelligence-enabled devices to remotely monitor their health in a first-of-its-kind study aiming to curb thousands of preventable maternal deaths across Africa, medics and developers said. Doctors at Mbarara Hospital in western Uganda will give devices to more than 1,000 women who have undergone caesarean section births to wear on their upper arms at all times. Algorithms detect at-risk cases and alert doctors. Joseph Ngonzi from Mbarara University of Science and Technology, which is conducting the study, said it would help "improve monitoring in a resource-constrained environment". The World Health Organization says almost 300,000 women worldwide die annually from preventable causes related to pregnancy and childbirth - that's more than 800 women every day.

Drug discovery is the preliminary step in the process of a novel drug identification and its therapeutic target. Artificial intelligence (AI) is commonly used in the healthcare industry for drug discovery. Artificial intelligence technology has the ability to recognize drug targets, and play a significant role in drug design, discovery, identification and screening of molecules instantly and effectively. Drug discovery or new drug target are being estimated based on potency, bioavailability, efficacy, and toxicity. The AI for drug discovery market is expected to grow during the forecast period due to the increasing number of cross-industry partnerships & collaborations, a significant growth in venture capital investments, rise in importance of drug discovery and increase in funding of the R&D activities for the use of AI technology in the field of drug discovery. However, limited awareness, unwillingness among medical practitioners to adopt AI-based technologies, unclear regulatory guidelines for medical software and lack of interoperability among AI solutions offered by different vendors are likely to hamper the growth of the market in the forecast period.

The below excerpt showcases the distinctiveness and acumen of a holistic AI company – TransOrg Analytics that is consistently striving to roll out intelligent and scalable solutions for the betterment of its customers. TransOrg Analytics is an award-winning player in'Analytics and Advisory' space. Founded in 2009, TransOrg is headquartered in Gurugram, India with a global presence in the US, UK, Singapore, India and the Middle East. Its global clientele includes Fortune 500 companies and industry leaders in sectors like Banking, Financial Services, Insurance, Telecom, Hospitality, CPG, Retail, E-commerce, Travel & Aviation. TransOrg has a strong team of over 80 high-performing Data Scientists, Data Engineers, Visualization experts from top schools and leadership with strong academic credentials and collective work experience of over 100 years with reputed organizations.