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.

In a recent study published in the journal Nature, researchers developed and evaluated an approach that used machine-learning algorithms to analyze mobile phone and satellite data to estimate poverty. They aimed to optimize the'Novissi' flagship emergency social assistance program in Togo, West Africa, providing subsistence cash relief to those most affected by COVID-19. Study: Machine learning and phone data can improve targeting of humanitarian aid. The coronavirus disease 2019 (COVID-19) pandemic has had devastating consequences in low- and lower-middle-income countries (LMICs). The living standards of the most economically vulnerable individuals have further worsened with a transition toward extreme poverty.

USM Business Systems has posted a manifold of articles on the emergence and benefits of Artificial Intelligence (AI) technology. From the travel, healthcare, and e-commerce to banking, finance, and entertainment sectors, AI technologies have grabbed the highest priority. Businesses across the globe have a strong belief that revolutionizing AI technology assists them in automating services, reaching the audience, delivering better customer experiences, and generating a strong sales pipeline. In this article, we would like to give you a detailed guide on how AI technology is adopting by industries and what benefits the brands are enjoying by implementing AI in mobile apps. Artificial Intelligence technology is increasingly adopting for mobile apps development.

The deep learning revolution incited by the 2012 Alexnet paper has been transformative for the field of computer vision. Many problems which were severely limited using classical solutions are now seeing unprecedented success. The rapid proliferation of deep learning methods has led to a sharp increase in their use in consumer and embedded applications. One consequence of consumer and embedded applications is lossy multimedia compression which is required to engineer the efficient storage and transmission of data in these real-world scenarios. As such, there has been increased interest in a deep learning solution for multimedia compression which would allow for higher compression ratios and increased visual quality. The deep learning approach to multimedia compression, so called Learned Multimedia Compression, involves computing a compressed representation of an image or video using a deep network for the encoder and the decoder. While these techniques have enjoyed impressive academic success, their industry adoption has been essentially non-existent. Classical compression techniques like JPEG and MPEG are too entrenched in modern computing to be easily replaced. This dissertation takes an orthogonal approach and leverages deep learning to improve the compression fidelity of these classical algorithms. This allows the incredible advances in deep learning to be used for multimedia compression without threatening the ubiquity of the classical methods. The key insight of this work is that methods which are motivated by first principles, i.e., the underlying engineering decisions that were made when the compression algorithms were developed, are more effective than general methods. By encoding prior knowledge into the design of the algorithm, the flexibility, performance, and/or accuracy are improved at the cost of generality...

To help the company and its customers gain value from this data deluge, the Dell IT organization manages a massive data lake and a world-class set of tools for data analytics, machine learning, deep learning and artificial intelligence. At the heart of this data environment is a Greenplum database, a massively parallel data platform for structured data analytics, machine learning and AI. In a typical use case, this raw data gets parsed in Hadoop into a structured format, and then that structured data gets pumped into the Greenplum database, so business and IT users can consume it in analytics applications. The data is used by Dell Technologies employees and customers in the Americas, Europe, the Middle East, Asia and other geographic regions, according to Darryl Smith, chief data platform architect and distinguished engineer at Dell Technologies. For the full story, see the Dell Technologies case study "Analytics and AI in a massive data lake."