Sussing out water potability in the field is sometimes nigh impossible, depending on the remoteness of the region. The United Nations estimates that 2 million tons of sewage and industrial and agricultural waste are discharged into the world's water supply each day. And while a range of chemical kits can test for bacterial risks, not all are equally thorough. Even with recent technological advances, water pollution leads to roughly 9 million premature deaths a year and 16% of all deaths worldwide. This motivated a pair of researchers at the Thomas Jefferson High School for Science and Technology and the Department of Computer Science at the University of Maryland to investigate an AI-powered Android app capable of detecting water impurity, which they describe in a newly published paper on the preprint server Arxiv.org

Question Is a convolutional neural network able to extract prognostic information from chest radiographs? Findings In this prognostic study of data from 2 randomized clinical trials (Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial [n 10 464] and National Lung Screening Trial [n 5493]), a convolutional neural network identified persons at high risk of long-term mortality based on their chest radiographs, even with adjustment for the radiologists' diagnostic findings and standard risk factors. Meaning Individuals at high risk of mortality based on chest radiography may benefit from prevention, screening, and lifestyle interventions. Importance Chest radiography is the most common diagnostic imaging test in medicine and may also provide information about longevity and prognosis. Objective To develop and test a convolutional neural network (CNN) (named CXR-risk) to predict long-term mortality, including noncancer death, from chest radiographs. Design, Setting, and Participants In this prognostic study, CXR-risk CNN development (n 41 856) and testing (n 10 464) used data from the screening radiography arm of the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) (n 52 320), a community cohort of asymptomatic nonsmokers and smokers (aged 55-74 years) enrolled at 10 US sites from November 8, 1993, through July 2, 2001. External testing used data from the screening radiography arm of the National Lung Screening Trial (NLST) (n 5493), a community cohort of heavy smokers (aged 55-74 years) enrolled at 21 US sites from August 2002, through April 2004. Data analysis was performed from January 1, 2018, to May 23, 2019. Exposure Deep learning CXR-risk score (very low, low, moderate, high, and very high) based on CNN analysis of the enrollment radiograph.

China has been often touted as the fastest emerging hub for AI development, even surpassing the superpowers such as the USA in the emerging tech. Chinese companies and government are taking the analytics and AI play quite seriously, bringing newer and favourable policies around its adoption. Numbers suggest that in 2018, 60 per cent of total global AI investments poured into China with investments from VCs, private equity and the Chinese government. Not just the companies but educational institutes are taking AI seriously as many schools are teaching AI courses to make its citizens AI-ready. There is no doubt that China has been serious about its AI strategy, but is its power and supremacy in artificial intelligence real or exaggerated?

Artificial intelligence has been touted by some in the security community as the silver bullet in malware detection. Its proponents say it's superior to traditional antivirus since it can catch new variants and never-before-seen malware--think zero-day exploits--that are the Achilles heel of antivirus. One of its biggest proponents is the security firm BlackBerry Cylance, which has staked its business model on the artificial intelligence engine in its endpoint PROTECT detection system, which the company says has the ability to detect new malicious files two years before their authors even create them. But researchers in Australia say they've found a way to subvert the machine-learning algorithm in PROTECT and cause it to falsely tag already known malware as "goodware." The method doesn't involve altering the malicious code, as hackers generally do to evade detection.

Fast forward 50 years and the processing power of the Apollo Guidance Computer (AGC) that took those men to the surface of the moon is now in your pocket, multiple times over-- in fact, an iPhone 6 could be used to guide 120 million Apollo 11 spacecraft to the moon, all at the same time. This factoid doesn't really do justice to the brilliance of the AGC. Given Moore's law, you could pick anything computational and say that 50 years later, there will exist a machine that can run it 2²⁵ faster. It was people like Margaret Hamilton, the lead for the software team who coded the AGC, who chose not to see current hardware limitations as a barrier, but instead as a challenge. She used the resource available to her at the time to achieve the unthinkable.

The financial services industry has seen an explosive growth in Artificial Intelligence (AI) to supplement, and often supplant, existing processes both customer-facing and internal. Given the potential created by rapid advancements in AI sophistication and functionality, more and more financial services firms are leveraging the technology to deploy new use cases for improved decision-making processes – particularly in the areas of anti-money laundering, fraud prevention, risk management, and lending. While the first wave of AI was generally focused on automating manually-intensive and repetitive tasks, banks are now turning to machine learning systems (ML) to uncover more dynamic ways of interpreting their vast swaths of customer data. Whereas AI, at a fundamental level, permits a machine to imitate intelligent human behavior, ML is a specific application (or subset) of AI that enables systems automatically to learn and improve – e.g., reduce errors or maximize the likelihood that their predictions will be true – without being explicitly programmed to make such adjustments. This development has an exciting potential to expand the products available to underbanked communities and improve services and customer experience as a whole.

In this paper, we propose a new first-order gradient-based algorithm to train deep neural networks. We first introduce the sign operation of stochastic gradients (as in sign-based methods, e.g., SIGN-SGD) into ADAM, which is called as signADAM. Moreover, in order to make the rate of fitting each feature closer, we define a confidence function to distinguish different components of gradients and apply it to our algorithm. It can generate more sparse gradients than existing algorithms do. We call this new algorithm signADAM++. In particular, both our algorithms are easy to implement and can speed up training of various deep neural networks. The motivation of signADAM++ is preferably learning features from the most different samples by updating large and useful gradients regardless of useless information in stochastic gradients. We also establish theoretical convergence guarantees for our algorithms. Empirical results on various datasets and models show that our algorithms yield much better performance than many state-of-the-art algorithms including SIGN-SGD, SIGNUM and ADAM. We also analyze the performance from multiple perspectives including the loss landscape and develop an adaptive method to further improve generalization. The source code is available at https://github.com/DongWanginxdu/signADAM-Learn-by-Confidence.

Value-based argumentation enhances a classical abstract argumentation graph - in which arguments are modelled as nodes connected by directed arrows called attacks - with labels on arguments, called values, and an ordering on values, called audience, to provide a more fine-grained justification of the attack relation. With more than one agent facing such an argumentation problem, agents may differ in their ranking of values. When needing to reach a collective view, such agents face a dilemma between two equally justifiable approaches: aggregating their views at the level of values, or aggregating their attack relations, remaining therefore at the level of the graphs. We explore the strenghts and limitations of both approaches, employing techniques from preference aggregation and graph aggregation, and propose a third possibility aggregating rankings extracted from given attack relations.

Floating centroid method (FCM) offers an efficient way to solve a fixed-centroid problem for the neural network classifiers. However, evolutionary computation as its optimization method restrains the FCM to achieve satisfactory performance for different neural network structures, because of the high computational complexity and inefficiency. Traditional gradient-based methods have been extensively adopted to optimize the neural network classifiers. In this study, a gradient-based floating centroid (GDFC) method is introduced to address the fixed centroid problem for the neural network classifiers optimized by gradient-based methods. Furthermore, a new loss function for optimizing GDFC is introduced. The experimental results display that GDFC obtains promising classification performance than the comparison methods on the benchmark datasets.

One of the major problems in natural language processing (NLP) is the word sense disambiguation (WSD) problem. It is t he task of computationally identifying the right sense of a polysemou s word based on its context. Resolving the WSD problem boosts the accurac y of many NLP focused algorithms such as text classification and machi ne translation. In this paper, we introduce a new supervised algorithm for WSD, that is based on Kernel PCA and Semantic Diffusion Kernel, whi ch is called Diffusion Kernel PCA (DKPCA). DKPCA grasps the semant ic similarities within terms, and it is based on PCA. These prop erties enable us to perform feature extraction and dimension reducti on guided by semantic similarities and within the algorithm. Our empiri cal results on SensEval data demonstrate that DKPCA achieves higher or ver y close accuracy results compared to SVM and KPCA with various well-known kernels when the labeled data ratio is meager. Considering t he scarcity of labeled data, whereas large quantities of unlabeled text ual data are easily accessible, these are highly encouraging first resul ts to develop DKPCA further.