The Genesis GV80 luxury SUV has a lot riding on its broad shoulders. The BMW X5 and Mercedes-Benz GLE rival carries the future of Hyundai's fledgling luxury offshoot in Australia and beyond. Expected to arrive locally mid-year, it joins the new Genesis G70 and G80 sedan duo to fill an important void for Hyundai's luxury brand. Its bold exterior design cues, including a bluff grille, 22-inch wheels and split LED lights will appear on its more conservative four-door cousins in the near future. The interior brings a choice of five or seven seats in the South Korean home market, along with a whopping 14.5-inch digital display stacked with technology to rival Europe's best.

Researchers at IIT Kharagpur have evolved an artificial intelligence-aided method to automate reading of legal judgements. A research team at the institute's Department of Computer Science and Engineering has developed two deep neural models to understand the rhetorical roles of sentences in a legal case judgement, an IIT KGP statement said here. This could be unique in India where the country uses a Common Law system that prioritises the doctrine of legal precedence over statutory law and where legal documents are often written in an unstructured way, a member of the team said. "Taking 50 judgments from the Supreme Court of India, we have segmented these by first labelling sentences with the help of three senior law students from IIT Kharagpur's Rajiv Gandhi School of Intellectual Property Law," Saptarshi Ghosh, professor of the Department of Computer Science and Engineering, who is leading the research team, said. "We then performed extensive analysis of the human- assigned labels, and developed a high quality gold standard corpus to train the machine to carry out the task," Ghosh said.

School of the Built Environment, Oxford Brookes University, Oxford OX3 0BP, UK; a. mosavi@brookes.ac.uk Abstract: Prediction models in mobility and transportation maintenance systems have been dramatically improved through using machine learning methods . The traditional road inspecti on systems based on the pavement condition index (PCI) are often associated with the critical safety, energy and cost issues. Alternatively, t he proposed models utilize surface deflection data from falling weight deflectometer (FWD) test s to predict the PC I. Machine learning methods are the single multi - layer perceptron (MLP) and radial basis function (RBF) neural networks as well their hybrids, i.e., L eve nberg - M arquardt (MLP - LM), scaled conjugate gradient (MLP - SCG), imperialist competitive (RBF - ICA), and g enetic algorithms (RBF - GA). Furthermore, the committee machine intelligent systems (CMIS) method was adopted to combine the results and improve the accur acy of the modeling. The results of the analysis have been verified through using four criteria of aver age percent relative error (APRE), average absolute percent relative error (AAPRE), root mean square error (RMSE), and standard error (SD). The CMIS mode l outperforms other models with the promising results of APRE 2.3303, AAPRE 11.6768, RMSE 12.0056, and SD 0.0210. Introduction In road transportation, pavement plays a vital role as th e part of the road that is in direct contact with vehicles . U sers' judgment about the quality of road service is primarily predicated upon pavement conditions. The Maintena nce, Rehabilitation, and Reconstruction (MR&R) program of pavement network is a multidimensional decision - making process that takes into account several consideration s.

Artificial intelligence is already ubiquitous, and is increasingly being used to autonomously make ever more consequential decisions. However, there has been relatively little research into the consequences for equity of the use of narrow AI and automated decision systems in medicine and public health. A narrative review using a hermeneutic approach was undertaken to explore current and future uses of AI in medicine and public health, issues that have emerged, and longer-term implications for population health. Accounts in the literature reveal a tremendous expectation on AI to transform medical and public health practices, especially regarding precision medicine and precision public health. Automated decisions being made about disease detection, diagnosis, treatment, and health funding allocation have significant consequences for individual and population health and wellbeing. Meanwhile, it is evident that issues of bias, incontestability, and erosion of privacy have emerged in sensitive domains where narrow AI and automated decision systems are in common use. As the use of automated decision systems expands, it is probable that these same issues will manifest widely in medicine and public health applications. Bias, incontestability, and erosion of privacy are mechanisms by which existing social, economic and health disparities are perpetuated and amplified. The implication is that there is a significant risk that use of automated decision systems in health will exacerbate existing population health inequities. The industrial scale and rapidity with which automated decision systems can be applied to whole populations heightens the risk to population health equity. There is a need therefore to design and implement automated decision systems with care, monitor their impact over time, and develop capacities to respond to issues as they emerge.

This dissertation explores the integration of learning and analogy-making through the development of a computer program, called Analogator, that learns to make analogies by example. By "seeing" many different analogy problems, along with possible solutions, Analogator gradually develops an ability to make new analogies. That is, it learns to make analogies by analogy. This approach stands in contrast to most existing research on analogy-making, in which typically the a priori existence of analogical mechanisms within a model is assumed. The present research extends standard connectionist methodologies by developing a specialized associative training procedure for a recurrent network architecture. The network is trained to divide input scenes (or situations) into appropriate figure and ground components. Seeing one scene in terms of a particular figure and ground provides the context for seeing another in an analogous fashion. After training, the model is able to make new analogies between novel situations. Analogator has much in common with lower-level perceptual models of categorization and recognition; it thus serves as a unifying framework encompassing both high-level analogical learning and low-level perception. This approach is compared and contrasted with other computational models of analogy-making. The model's training and generalization performance is examined, and limitations are discussed.

Anomaly detection aims to recognize samples with anomalous and unusual patterns with respect to a set of normal data, which is significant for numerous domain applications, e.g. in industrial inspection, medical imaging, and security enforcement. There are two key research challenges associated with existing anomaly detention approaches: (1) many of them perform well on low-dimensional problems however the performance on high-dimensional instances is limited, such as images; (2) many of them depend on often still rely on traditional supervised approaches and manual engineering of features, while the topic has not been fully explored yet using modern deep learning approaches, even when the well-label samples are limited. In this paper, we propose a One-for-all Image Anomaly Detection system (OIAD) based on disentangled learning using only clean samples. Our key insight is that the impact of small perturbation on the latent representation can be bounded for normal samples while anomaly images are usually outside such bounded intervals, called structure consistency. We implement this idea and evaluate its performance for anomaly detention. Our experiments with three datasets show that OIAD can detect over $90\%$ of anomalies while maintaining a high low false alarm rate. It can also detect suspicious samples from samples labeled as clean, coincided with what humans would deem unusual.

Newark Venture Partners hosted a full house at its biannual Demo Day, for its 7th NVP Labs class, at the Audible Innovation Cathedral. The event featured presentations from founders of the graduating companies, Botmock, Brahmin Solutions, Galaxy.AI, MindRight Health, omniX, Speak2 Software and SpeechKit. Other featured speakers included Don Katz, Founder and Executive Chairman of Audible, Newark Assemblywoman Eliana Pintor Marin, and Wole Coaxum, Founder and CEO of MoCaFi (an NVP portfolio company) who paid tribute to Dr. Martin Luther King, Jr.'s birthday. Don Katz, Founder and Executive Chairman of Audible said, "Everyone loves a comeback story and Newark has a great one, including Newark Venture Partners, which is an internationally acknowledged phenomenon that has exceeded all of my founder expectations. When I recently visited NVP labs I was dazzled by one impassioned founder, team, and company after another. Now it is time to double down on NVP's measurable success."

KHARAGPUR: Researchers at IIT Kharagpur have evolved an Artificial Intelligence-aided method to automate the reading of legal case judgments, the premier institute said in a statement on Friday. The researchers from IIT Kharagpur's Computer Science and Engineering department have developed two deep neural models to understand the rhetorical roles of sentences in a legal case judgment, which could prove phenomenal in India where AI is yet to sufficiently penetrate the legal field. The country uses a Common Law system that prioritises the doctrine of legal precedent over statutory law, and where legal documents are often written in an unstructured way. "Taking 50 judgments from the Supreme Court of India, we segmented these by first labelling sentences with the help of three senior law students from IIT Kharagpur's Rajiv Gandhi School of Intellectual Property Law, then performing extensive analysis of the human-assigned labels and developing a high quality gold standard corpus to train the machine to carry out the task," explained research lead Professor Saptarshi Ghosh. Unlike earlier attempts which required substantial human intervention, the neural methods used by Ghosh's team enables automatic learning of the features, given sufficient amount of data, and can be used across multiple legal domains.

In October 2018, months after a brief reveal, Amazon brought Whisper Mode to select third- and first-party Alexa devices. It expanded the feature to all locales in November 2019, such that all smart home appliances powered by Alexa -- the company's virtual assistant -- now respond to whispered speech by whispering back. Amazon was a bit light on the technical details initially, save that Whisper Mode uses a neural network -- layers of mathematical functions loosely modeled after the human brain's neurons -- to distinguish among normal and whispered words. But in an academic paper appearing in the January 2020 issue of the journal IEEE Signal Processing Letters and an accompanying blog post, it detailed the research that led to the expansion. The principal challenge was converting normal speech into whispered speech while maintaining naturalness and speaker identity, explained Marius Cotescu, an applied scientist in Amazon's text-to-speech research group.

Data augmentation is essential for medical research to increase the size of training datasets and achieve better results. In this work, we experiment three GAN architectures with different loss functions to generate new brain MRIs. The results show the importance of hyperparameter tuning and the use of mini-batch similarity layer in the Discriminator and gradient penalty in the loss function to achieve convergence with high quality and realism. Moreover, huge computation time is needed to generate indistinguishable images from the original dataset.