AI-powered camera traps give Gabon wildlife rangers a new tool in the fight against poaching and biodiversity loss. With around 24 million hectares of forest, Gabon is a biodiversity hotspot, including hosting one of the largest populations of the critically endangered African forest elephant (Loxodonta cyclotis). Now, researchers from the University of Stirling, UK, are using a new kind of camera trap to help monitor and protect this and other species. Traditional monitoring systems use cameras deployed in the field, but the results are often collected and analysed months later. "This tells you where the animals were," says Robin Whytock, who led the team behind the new camera traps.

They call it Jenny; a robotic solution with Artificial Intelligence, AI capabilities. Jenny has come to alter the status quo, disrupting the traditional method of multi-level marketing. With its AI capabilities, Jenny eliminates the degrading methods of public sharing of printed literature and the beggarly system of selling supplements and other health-based products in the market, at bus stops and on the streets. Built by Strategic Business Techspace for Wealth Solution Dynasty, WSD, Jenny has given a new impetus to multi-level marketing and handed WSD the bragging right as the first tech-based MLM outfit. The chatbox AI solution is innovative and multitasks as a marketer and customer relations interface between WSD and its public. It is sitting on the company's social media handles- WhatsApp, Facebook, Instagram, Twitter and even on its website.

Necessity is the mother of invention, and Rwanda's government seems to understand this more than most with the launch of the Centre of the Fourth Industrial Revolution (C4IR). "With the advent of the Fourth Industrial Revolution and the rapid innovations witnessed during the Covid-19 pandemic, there is an increased urgency to develop digital and technological capacities to build more resilient systems for a healthier society and more sustainable economy," said Rwandan Minister of Information Communication Technology and Innovation Paula Ingabire. Ingabire made the comment in a media statement posted on the World Economic Forum's (WEF) website. Rwanda has launched its C4IR, saying it will "work with stakeholders around the world to design and pilot new approaches to technology governance that foster innovation in an inclusive and responsible manner". Some of the projects that the C4IR is already working on are the country's artificial intelligence (AI) policy and laws on the protection of personal data and privacy.

AI-powered camera traps are being used for more than just documenting and monitoring animals -- they have also been a crucial tool in protecting the local wildlife from poachers, such is the case in Gabon in Central Africa. Congo, and Congo Basin, in particular, offer incredible biodiversity with roughly 400 species of mammals and 1,000 species of birds that reside in the largest area of forest preserve -- 80% of Gabon is covered in forests -- out of all African nations, reports Appsilon. Out of these diverse species are endangered wildlife -- elephants, bonobos, lowland gorillas, and chimpanzees, which are at the forefront of the country's so-called "Green Gabon" movement. It seeks to develop sustainable logging while preserving wildlife, with the help of various tracking systems using satellite imagery as well as camera traps on the ground. To help maintain Gabon's biodiversity, researchers from the University of Stirling in the United Kingdom have begun using a new kind of camera trap.

As electronics get smaller and manufacturers come under greater pressure to improve efficiency and throughput, the traditional hand soldering method is no longer up to scratch. In 1896, a patent for electric heating apparatus, now commonly known as a soldering iron, was granted. The process of soldering has remained much the same since then. But, that's about to change. In this article, Nigel Smith, CEO of TM Robotics, international distributor of Shibaura Machine, formerly Toshiba Machine, industrial and soldering robots, explains three reasons why you should automate the soldering process.

Developing artificial learning systems that can understand and generate natural language has been one of the long-standing goals of artificial intelligence. Recent decades have witnessed an impressive progress on both of these problems, giving rise to a new family of approaches. Especially, the advances in deep learning over the past couple of years have led to neural approaches to natural language generation (NLG). These methods combine generative language learning techniques with neural-networks based frameworks. With a wide range of applications in natural language processing, neural NLG (NNLG) is a new and fast growing field of research. In this state-of-the-art report, we investigate the recent developments and applications of NNLG in its full extent from a multidimensional view, covering critical perspectives such as multimodality, multilinguality, controllability and learning strategies. We summarize the fundamental building blocks of NNLG approaches from these aspects and provide detailed reviews of commonly used preprocessing steps and basic neural architectures. This report also focuses on the seminal applications of these NNLG models such as machine translation, description generation, automatic speech recognition, abstractive summarization, text simplification, question answering and generation, and dialogue generation. Finally, we conclude with a thorough discussion of the described frameworks by pointing out some open research directions.

Conversational messaging platform Gupshup announced the acquisition of Active.Ai, a conversational AI platform used by banks and fintech firms. The sum was not disclosed. The acquisition aims to strengthen Gupshup's customer experience solutions for its banking, financial services and insurance (BFSI) customers. Headquartered in Singapore, Active.Ai serves BFSI customers across 43 countries with its Conversational Banking as a Service (CBaaS) platform. Active.Ai said it has enabled more than 300 million user interactions via voice, video and messaging, managed over 30 million service requests and fulfilled 50 million plus enquiries in aggregate, with 95 percent accuracy.

For many centuries, the goal of linguistics has been to capture this capacity by a formal description--a grammar--consisting of a systematic set of rules and/or principles that determine which sentences are part of a given language and which are not (Bod, 2013). Over the years, these formal grammars have taken many forms but common to them all is the assumption that they capture the idealized linguistic competence of a native speaker/hearer, independent of any memory limitations or other non-linguistic cognitive constraints (Chomsky, 1965; Miller, 2000). These abstract formal descriptions have come to play a foundational role in the language sciences, from linguistics, psycholinguistics, and neurolinguistics (Hauser et al., 2002; Pinker, 2003) to computer science, engineering, and machine learning (Klein and Manning, 2003; Dyer et al., 2016; Gómez-Rodríguez et al., 2018). Despite evidence that processing difficulty underpins the unacceptability of certain sentences (Morrill, 2010; Hawkins, 2004), the cognitive independence assumption that is a defining feature of linguistic competence has not been examined in a systematic way using the tools of formal grammar. It is therefore unclear whether these supposedly idealized descriptions of language are free of non-linguistic cognitive constraints, such as memory limitations.

The importance of accurately quantifying forecast uncertainty has motivated much recent research on probabilistic forecasting. In particular, a variety of deep learning approaches has been proposed, with forecast distributions obtained as output of neural networks. These neural network-based methods are often used in the form of an ensemble based on multiple model runs from different random initializations, resulting in a collection of forecast distributions that need to be aggregated into a final probabilistic prediction. With the aim of consolidating findings from the machine learning literature on ensemble methods and the statistical literature on forecast combination, we address the question of how to aggregate distribution forecasts based on such deep ensembles. Using theoretical arguments, simulation experiments and a case study on wind gust forecasting, we systematically compare probability- and quantile-based aggregation methods for three neural network-based approaches with different forecast distribution types as output. Our results show that combining forecast distributions can substantially improve the predictive performance. We propose a general quantile aggregation framework for deep ensembles that shows superior performance compared to a linear combination of the forecast densities. Finally, we investigate the effects of the ensemble size and derive recommendations of aggregating distribution forecasts from deep ensembles in practice.

Two centuries after Charles Darwin put the theory forward, a new study finally shows that birds living near the equator are more colourful. Scientists have used artificial intelligence (AI) to identify the amount of colour in photos of over 24,000 preserved birds from the Natural History Museum's collection. Tropical birds living near the equator are roughly 30 per cent more colourful than non-tropical birds living nearer the poles, the scientists found, but they don't know exactly why. The long-held theory, first suspected by Charles Darwin and other naturalists in the 18th and 19th centuries, hasn't been proven until now, the experts say. Research from the University of Sheffield found tropical birds living near the equator are roughly 30 per cent more colourful than non-tropical birds living nearer the poles.