A preliminary test in only eight volunteers suggests the first coronavirus vaccine to be tested in people seems to be safe and can stimulate an immune response against the virus. Antibodies generated by the volunteers were able to stop the virus from replicating in human cells in the laboratory and the levels of antibodies in their blood were similar to those previously detected in recovered covid-19 patients. Tal Zaks of Moderna, the US firm developing the vaccine, said that if the next stages go well, it could be widely available by the end of this year or early next year. The US stock market was up sharply today following the announcement. However, it remains to be seen if such speedy testing and manufacturing of a vaccine is really possible – no vaccine has ever been produced in less than five years. Meanwhile, a trial of another vaccine, developed by researchers at the University of Oxford found it wasn't able to stop six rhesus macaque monkeys from becoming infected with the ...

SparkCognition, a leading industrial artificial intelligence (AI) company, is pleased to announce that Japanese AI and Fintech company, MILIZE Co., Ltd. will offer Japanese financial institutions fraud detection and anti-money laundering solutions. These solutions will be built using the automated machine learning software of SparkCognition. With the enormous increase of online payment, internet banking, and QR code payments, illegal use of credit cards is on the rise. However, there are not many Japanese companies that have introduced advanced solutions for fraud detection that currently exist internationally. In addition, financial authorities and institutions around the world are expected to report strengthened measures against money laundering in August 2020. As a result, taking these steps against money laundering has become an urgent management issue in Japanese financial institutions.

Machine Learning, AI, Deep Learning are buzz words being heard daily on TV, in workplaces, at gatherings, etc. Maybe you're a little bit embarrassed to ask what's Machine Learning or AI, or maybe you have the wrong understanding of Machine Learning. Either way that's okay because this article serves as an introduction to Machine Learning, I wrote it in a Q&A format so it becomes easy to follow and understand. Machine Learning is a subset of Artificial Intelligence (AI) and it's about writing software codes to enables computers (or machines in general) to get better at a given task on their own without human intervention. Some people argue that Machine Learning is a fancy way to say "Statistical Analysis" which is the science of collecting data and uncovering patterns and trends. Either way, think about all the data being generated daily and how people try to make sense of it to make their lives better, that's Machine Learning.

Alana'CityStyleBot' is giving high street and independent fashion retailers an alternative virtual shop front to serve customers post COVID-19. Launched in February 2020, Cork Start Up Alana is an innovative Fashion and Beauty platform for consumers to purchase curated fashion looks and beauty products. Alana is powered by Artificial Intelligence meaning that it learns to recommend styles/brands that will suit each customer's unique taste. Alana suggests clothes from high street retailers and independent boutiques with a same day delivery service making the whole highstreet a virtual shopping center – one checkout – one delivery charge of €3.99. Alana helps retailers compete with major brands who have an established ecommerce foothold. According to ACI Worldwide there is a 74% growth in the average transaction volumes due to a dramatic rise in online retail this March in comparison to March 2019.

Explainable Artificial Intelligence (or xAI) has become an important research topic in the fields of Machine Learning and Deep Learning. In this paper, we propose a Genetic Programming (GP) based approach, named Genetic Programming Explainer (GPX), to the problem of explaining decisions computed by AI systems. The method generates a noise set located in the neighborhood of the point of interest, whose prediction should be explained, and fits a local explanation model for the analyzed sample. The tree structure generated by GPX provides a comprehensible analytical, possibly non-linear, symbolic expression which reflects the local behavior of the complex model. We considered three machine learning techniques that can be recognized as complex black-box models: Random Forest, Deep Neural Network and Support Vector Machine in twenty data sets for regression and classifications problems. Our results indicate that the GPX is able to produce more accurate understanding of complex models than the state of the art. The results validate the proposed approach as a novel way to deploy GP to improve interpretability.

In this paper we demonstrate methods for reliable and efficient training of discrete representation using Vector-Quantized Variational Auto-Encoder models (VQ-VAEs). Discrete latent variable models have been shown to learn nontrivial representations of speech, applicable to unsupervised voice conversion and reaching state-of-the-art performance on unit discovery tasks. For unsupervised representation learning, they became viable alternatives to continuous latent variable models such as the Variational Auto-Encoder (VAE). However, training deep discrete variable models is challenging, due to the inherent non-differentiability of the discretization operation. In this paper we focus on VQ-VAE, a state-of-the-art discrete bottleneck model shown to perform on par with its continuous counterparts. It quantizes encoder outputs with on-line $k$-means clustering. We show that the codebook learning can suffer from poor initialization and non-stationarity of clustered encoder outputs. We demonstrate that these can be successfully overcome by increasing the learning rate for the codebook and periodic date-dependent codeword re-initialization. As a result, we achieve more robust training across different tasks, and significantly increase the usage of latent codewords even for large codebooks. This has practical benefit, for instance, in unsupervised representation learning, where large codebooks may lead to disentanglement of latent representations.

Traceability is a fundamental component of the modern software development process that helps to ensure properly functioning, secure programs. Due to the high cost of manually establishing trace links, researchers have developed automated approaches that draw relationships between pairs of textual software artifacts using similarity measures. However, the effectiveness of such techniques are often limited as they only utilize a single measure of artifact similarity and cannot simultaneously model (implicit and explicit) relationships across groups of diverse development artifacts. In this paper, we illustrate how these limitations can be overcome through the use of a tailored probabilistic model. To this end, we design and implement a HierarchiCal PrObabilistic Model for SoftwarE Traceability (Comet) that is able to infer candidate trace links. Comet is capable of modeling relationships between artifacts by combining the complementary observational prowess of multiple measures of textual similarity. Additionally, our model can holistically incorporate information from a diverse set of sources, including developer feedback and transitive (often implicit) relationships among groups of software artifacts, to improve inference accuracy. We conduct a comprehensive empirical evaluation of Comet that illustrates an improvement over a set of optimally configured baselines of $\approx$14% in the best case and $\approx$5% across all subjects in terms of average precision. The comparative effectiveness of Comet in practice, where optimal configuration is typically not possible, is likely to be higher. Finally, we illustrate Comets potential for practical applicability in a survey with developers from Cisco Systems who used a prototype Comet Jenkins plugin.

Keywords Studios PLC (LON:KWS) supplies a range of technical services to computer games developers and publishers. Some of the services it provides include art services, software engineering, audio services, functionality quality assurance (QA), localisation (enabling games to be published in several languages), localisation QA, and player support. Among its clients are Sega, Nintendo, Google, Microsoft and Warner Bros. Established in 1998 it now has studios in more than 42 locations in 20 countries across four continents. Keywords employs a buy-and-build strategy and has been expanding rapidly since its first acquisition in 2014. The list of what Keywords owns is long and will most likely to continue to grow as the company aims to make six small bolt-on acquisitions each year and one or two larger purchases.

Despite the advancement of ASR, however, most publicly trained from these speech data generally show poor recognition available call-based speech corpora such as Switchboard performance when applied to domain-specific tasks due to the are old-fashioned. Also, most existing call corpora are in English differences in their data distribution and vocabularies. In particular, and mainly focus on open domain dialog or general scenarios AICC requires an accurate ASR model to ensure the precise such as audiobooks. Here we introduce a new large-scale intent classification or slot extraction [9] from user natural Korean call-based speech corpus under a goal-oriented dialog language utterances.

Coronavirus Disease 2019 (COVID-19) is a rapidly emerging respiratory disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Due to the rapid human-to-human transmission of SARS-CoV-2, many healthcare systems are at risk of exceeding their healthcare capacities, in particular in terms of SARS-CoV-2 tests, hospital and intensive care unit (ICU) beds and mechanical ventilators. Predictive algorithms could potentially ease the strain on healthcare systems by identifying those who are most likely to receive a positive SARS-CoV-2 test, be hospitalised or admitted to the ICU. Here, we study clinical predictive models that estimate, using machine learning and based on routinely collected clinical data, which patients are likely to receive a positive SARS-CoV-2 test, require hospitalisation or intensive care. To evaluate the predictive performance of our models, we perform a retrospective evaluation on clinical and blood analysis data from a cohort of 5644 patients. Our experimental results indicate that our predictive models identify (i) patients that test positive for SARS-CoV-2 a priori at a sensitivity of 75% (95% CI: 67%, 81%) and a specificity of 49% (95% CI: 46%, 51%), (ii) SARS-CoV-2 positive patients that require hospitalisation with 0.92 AUC (95% CI: 0.81, 0.98), and (iii) SARS-CoV-2 positive patients that require critical care with 0.98 AUC (95% CI: 0.95, 1.00). In addition, we determine which clinical features are predictive to what degree for each of the aforementioned clinical tasks. Our results indicate that predictive models trained on routinely collected clinical data could be used to predict clinical pathways for COVID-19, and therefore help inform care and prioritise resources.