The heads of two prominent artificial intelligence firms came under public scrutiny this month for ties to far right organizations. A report by Matt Stroud at OneZero identified the founder and CEO of surveillance firm Banjo, Damien Patton, as a former member of the Dixie Knights of the Ku Klux Klan, who was charged with a hate crime for shooting at a synagogue in 1990. The report led the Utah Attorney General's office to suspend a contract worth at least $750,000 with the company, and reportedly the firm has also lost a $20.8 million contract with the state's Department of Public Safety. Only a few weeks earlier, Luke O'Brien at the Huffington Post uncovered that Clearview AI's founder, Cam-Hoan Ton-That, affiliated with far right extremists including former Breitbart writer Chuck Johnson, Pizzagate conspiracy theorist Mike Cernovich, and neo-Nazi hacker Andrew'weev' Auernheimer. Moreover, the reporters found evidence that Ton-That collaborated with Johnson and others in the development of Clearview AI's software.

New Delhi, Jan 11 (IANS) When it comes to programming languages in India, Python is most popular among the students for its role in Artificial Intelligence (AI) applications, data science, Machine Learning (ML) and data analytics, US-based online education company Coursera has said. Python dominated the top 10 list with courses like "Programming for Everybody", "Python Data Structures", "Python for Data Science and AI" and more. Python is also easy to get started with, offers a lot of flexibility and is versatile. "Its open source nature makes it easy to learn. A large number libraries for tasks like web development, text processing, calculations add to its appeal," the repor said.

One of the advantages we have today in the fight against coronavirus that wasn't as sophisticated in the SARS outbreak of 2003 is big data and the high level of technology available. China tapped into big data, machine learning, and other digital tools as the virus spread through the nation in order to track and contain the outbreak. The lessons learned there have continued to spread across the world as other countries fight the spread of the virus and use digital technology to develop real-time forecasts and arm healthcare professionals and government decision-makers with intel they can use to predict the impact of the coronavirus. China's Surveillance Infrastructure Used to Track Exposed People China's surveillance culture became useful in the country's response to COVID-19. Thermal scanners were installed in train stations to detect elevated body temperatures--a potential sign of infection.

When Covid-19 was at its height in China, doctors in the city of Wuhan were able to use artificial intelligence (AI) algorithms to scan the lungs of thousands of patients. The algorithm in question, developed by Axial AI, analyses CT imagery in seconds. It declares, for example, whether a patient has a high risk of viral pneumonia from coronavirus or not. A consortium of firms developed the AI in response to the coronavirus outbreak. They say it can show whether a patient's lungs have improved or worsened over time, when more CT scans are done for comparison.

A team of robots is helping researchers expedite the process of analyzing COVID-19 samples. The bots, developed by researchers at the UC Berkeley and UCSF Innovative Genomics Institute (IGI), are being used in a pop-up lab to automate nearly the entire testing process. As noted in a report from the Daily Californian, while one bot takes patient samples from inside to the lab and transfers them to plates with wells another bot conduts what's known as a quantitative polymerase chain reaction, or qPCR, test. According to a report from Forbes, researchers in charge of the team of robots, which have already begun testing samples, say that they're conducting tests on about 200 samples per day. Once the system is scaled up, they hope to reach 1,000 samples per day with a max capacity up to 3,000 tests per day if necessary.

Predictive models that accurately emulate complex scientific processes can achieve exponential speed-ups over numerical simulators or experiments, and at the same time provide surrogates for improving the subsequent analysis. Consequently, there is a recent surge in utilizing modern machine learning (ML) methods, such as deep neural networks, to build data-driven emulators. While the majority of existing efforts has focused on tailoring off-the-shelf ML solutions to better suit the scientific problem at hand, we study an often overlooked, yet important, problem of choosing loss functions to measure the discrepancy between observed data and the predictions from a model. Due to lack of better priors on the expected residual structure, in practice, simple choices such as the mean squared error and the mean absolute error are made. However, the inherent symmetric noise assumption made by these loss functions makes them inappropriate in cases where the data is heterogeneous or when the noise distribution is asymmetric. We propose Learn-by-Calibrating (LbC), a novel deep learning approach based on interval calibration for designing emulators in scientific applications, that are effective even with heterogeneous data and are robust to outliers. Using a large suite of use-cases, we show that LbC provides significant improvements in generalization error over widely-adopted loss function choices, achieves high-quality emulators even in small data regimes and more importantly, recovers the inherent noise structure without any explicit priors.

Deep learning methods have shown great success in several domains as they process a large amount of data efficiently, capable of solving complex classification, forecast, segmentation, and other tasks. However, they come with the inherent drawback of inexplicability limiting their applicability and trustworthiness. Although there exists work addressing this perspective, most of the existing approaches are limited to the image modality due to the intuitive and prominent concepts. Conversely, the concepts in the time-series domain are more complex and non-comprehensive but these and an explanation for the network decision are pivotal in critical domains like medical, financial, or industry. Addressing the need for an explainable approach, we propose a novel interpretable network scheme, designed to inherently use an explainable reasoning process inspired by the human cognition without the need of additional post-hoc explainability methods. Therefore, class-specific patches are used as they cover local concepts relevant to the classification to reveal similarities with samples of the same class. In addition, we introduce a novel loss concerning interpretability and accuracy that constraints P2ExNet to provide viable explanations of the data including relevant patches, their position, class similarities, and comparison methods without compromising accuracy. Analysis of the results on eight publicly available time-series datasets reveals that P2ExNet reaches comparable performance when compared to its counterparts while inherently providing understandable and traceable decisions.

Effective property prediction methods can help accelerate the search for COVID-19 antivirals either through accurate in-silico screens or by effectively guiding on-going at-scale experimental efforts. However, existing prediction tools have limited ability to accommodate scarce or fragmented training data currently available. In this paper, we introduce a novel approach to learn predictors that can generalize or extrapolate beyond the heterogeneous data. Our method builds on and extends recently proposed invariant risk minimization, adaptively forcing the predictor to avoid nuisance variation. We achieve this by continually exercising and manipulating latent representations of molecules to highlight undesirable variation to the predictor. To test the method we use a combination of three data sources: SARS-CoV-2 antiviral screening data, molecular fragments that bind to SARS-CoV-2 main protease and large screening data for SARS-CoV-1. Our predictor outperforms state-of-the-art transfer learning methods by significant margin. We also report the top 20 predictions of our model on Broad drug repurposing hub.

Deep neural networks have been shown to be susceptible to adversarial examples -- small, imperceptible changes constructed to cause mis-classification in otherwise highly accurate image classifiers. As a practical alternative, recent work proposed so-called adversarial patches: clearly visible, but adversarially crafted rectangular patches in images. These patches can easily be printed and applied in the physical world. While defenses against imperceptible adversarial examples have been studied extensively, robustness against adversarial patches is poorly understood. In this work, we first devise a practical approach to obtain adversarial patches while actively optimizing their location within the image. Then, we apply adversarial training on these location-optimized adversarial patches and demonstrate significantly improved robustness on CIFAR10 and GTSRB. Additionally, in contrast to adversarial training on imperceptible adversarial examples, our adversarial patch training does not reduce accuracy.

Harnessing data to discover the underlying governing laws or equations that describe the behavior of complex physical systems can significantly advance our modeling, simulation and understanding of such systems in various science and engineering disciplines. Recent advances in sparse identification show encouraging success in distilling closed-form governing equations from data for a wide range of nonlinear dynamical systems. However, the fundamental bottleneck of this approach lies in the robustness and scalability with respect to data scarcity and noise. This work introduces a novel physics-informed deep learning framework to discover governing partial differential equations (PDEs) from scarce and noisy data for nonlinear spatiotemporal systems. In particular, this approach seamlessly integrates the strengths of deep neural networks for rich representation learning, automatic differentiation and sparse regression to approximate the solution of system variables, compute essential derivatives, as well as identify the key derivative terms and parameters that form the structure and explicit expression of the PDEs. The efficacy and robustness of this method are demonstrated on discovering a variety of PDE systems with different levels of data scarcity and noise. The resulting computational framework shows the potential for closed-form model discovery in practical applications where large and accurate datasets are intractable to capture.