A new nuclear war simulator program will allow users to see the extraordinary and far-reaching consequences of global nuclear war. Developed by German engineer Ivan Stepanov, Nuclear War Simulator will be available to the public in early 2020. The program will let users plan not just a single nuclear detonation, but hundreds of them all around the world, simulating how a nuclear war might break out from smaller regional conflicts. In an interview with Motherboard, Stepanov said the goal of the project was to'raise awareness of the dangers that nuclear weapons present to our society.' The program presents an interactive 3D model of the Earth, which users can rotate and zoom in and out of to locate targets.

NASA has selected the site for its asteroid sample collection mission from the four previously-proposed candidates after a year of study. The spinning-top-shaped asteroid, '101955 Bennu', is a 1,614 feet (492 m) wide near-Earth object with a cumulative 1-in-2,700 chance of hitting Earth from 2175–2199. The chosen primary sample site -- dubbed'Nightingale' -- is located in a young crater high up in the asteroid's northern hemisphere. The Origins, Spectral Interpretation, Resource Identification, Security, Regolith Explorer -- or OSIRIS-Rex -- craft has been analysing Bennu since December 2018. If successful in its mission, OSIRIS-Rex will be the first US spacecraft to return samples of an asteroid to the Earth for analysis. For NASA researchers, Bennu will act like a time-capsule from the birth of the solar system, containing information on its formation and evolution.

The Chief Justice of India may have cleared the air on India's courts using artificial intelligence (AI) in their decision-making process, but some concerns remain. In a measured gesture, CJI SA Bobde clarified a statement he reportedly made a few weeks ago at an event, saying that using technologies such as AI could help deliver justice swiftly. The CJI had also said that such technologies would help streamline hearing of cases while enabling better court management. The statement made many experts raise concerns over the potential abuse of AI in the judicial decision-making processes -- an issue that's fodder for heated debates across the globe now -- including former CJI RM Lodha, who at an event in Nagpur last week, expressed concerns over the use of AI in court proceedings. Today, the explosive growth in machine learning and AI seems so enticing that almost everyone, from businesses to governments to savvy individuals, is tempted to try these emerging technologies in services and processes they deem fit and in requirement of some automatic processes.

This article was submitted in response to the call for ideas issued by the co-chairs of the National Security Commission on Artificial Intelligence, Eric Schmidt and Robert Work. It addresses the fourth question (part a.) which asks what international norms for artificial intelligence should the United States lead in developing, and whether it is possible to create mechanisms for the development and enforcement of AI norms. In 1953, President Dwight Eisenhower asked the world to join him in building a framework for "Atoms for Peace." He made the case for a global agreement to prevent the spread of nuclear weapons while also sharing the peaceful uses of nuclear technology for power, agriculture, and medicine. No one would argue the program completely prevented the spread of weapons technology: India and Pakistan used technology gained through Atoms for Peace in their nascent nuclear weapons programs.

Obviously, cybersecurity is necessary for all computer systems – even those that don't use artificial intelligence. However, AI makes the need much greater. Artificial intelligence enables computers to make decisions that impact humans without the involvement of humans. Therefore, the human impact of hacking an AI-enabled system could be much more devastating than a typical non-AI system. It was recently reported that AI systems can modify MRI results to trick doctors and other AI systems into thinking that people who have cancer are healthy.

In this study, machine learning models were constructed to predict whether judgments made by the European Court of Human Rights (ECHR) would lead to a violation of an Article in the Convention on Human Rights. The problem is framed as a binary classification task where a judgment can lead to a "violation" or "non-violation" of a particular Article. Using auto-sklearn, an automated algorithm selection package, models were constructed for 12 Articles in the Convention. To train these models, textual features were obtained from the ECHR Judgment documents using N-grams, word embeddings and paragraph embeddings. Additional documents, from the ECHR, were incorporated into the models through the creation of a word embedding (echr2vec) and a doc2vec model. The features obtained using the echr2vec embedding provided the highest cross-validation accuracy for 5 of the Articles. The overall test accuracy, across the 12 Articles, was 68.83%. As far as we could tell, this is the first estimate of the accuracy of such machine learning models using a realistic test set. This provides an important benchmark for future work. As a baseline, a simple heuristic of always predicting the most common outcome in the past was used. The heuristic achieved an overall test accuracy of 86.68% which is 29.7% higher than the models. Again, this was seemingly the first study that included such a heuristic with which to compare model results. The higher accuracy achieved by the heuristic highlights the importance of including such a baseline.

Driver vigilance estimation is an important task for transportation safety. Wearable and portable brain-computer interface devices provide a powerful means for real-time monitoring of the vigilance level of drivers, thus help with avoiding distracted or impaired driving. In this paper, we propose a novel multimodal architecture for in-vehicle vigilance estimation from Electroencephalogram and Electrooculogram. To tackle this problem and other issues associated with multimodal biological signal analysis, we propose an architecture composed of a capsule attention mechanism following a deep Long Short-Term Memory (LSTM) network. Our model learns both temporal and hierarchical/spatial dependencies in the data through the LSTM and capsule feature representation layers. To better explore the discriminative ability of the learned representations, we study the effect of the proposed capsule attention mechanism including the number of dynamic routing iterations as well as other parameters.

In the past few years, generative models like Generative Adversarial Networks (GANs) have dramatically advanced our ability to represent and parameterize high-dimensional, non-linear image manifolds. As a result, they have been widely adopted across a variety of applications, ranging from challenging inverse problems like image completion, to being used as a prior in problems such as anomaly detection and adversarial defense. A recurring theme in many of these applications is the notion of projecting an image observation onto the manifold that is inferred by the generator. In this context, Projected Gradient Descent (PGD) has been the most popular approach, which essentially searches for a latent representation with the goal of minimizing discrepancy between a generated image and the given observation. However, PGD is an extremely brittle optimization technique that fails to identify the right projection when the observation is corrupted, even by a small amount. Unfortunately, such corruptions are common in the real world, for example arbitrary images with unknown crops, rotations, missing pixels, or other kinds of distribution shifts requiring a more robust projection technique. In this paper we propose corruption-mimicking, a new strategy that utilizes a surrogate network to approximate the unknown corruption directly at test time, without the need for additional supervision or data augmentation. The proposed projection technique significantly improves the robustness of PGD under a wide variety of corruptions, thereby enabling a more effective use of GANs in real-world applications. More importantly, we show that our approach produces state-of-the-art performance in several GAN-based applications -- anomaly detection, domain adaptation, and adversarial defense, that rely on an accurate projection.

Systems incorporating Artificial Intelligence (AI) and machine learning (ML) techniques are increasingly used to guide decision-making in the healthcare sector. While AI-based systems provide powerful and promising results with regard to their classification and prediction accuracy (e.g., in differentiating between different disorders in human gait), most share a central limitation, namely their black-box character. Understanding which features classification models learn, whether they are meaningful and consequently whether their decisions are trustworthy is difficult and often impossible to comprehend. This severely hampers their applicability as decision-support systems in clinical practice. There is a strong need for AI-based systems to provide transparency and justification of predictions, which are necessary also for ethical and legal compliance. As a consequence, in recent years the field of explainable AI (XAI) has gained increasing importance. The primary aim of this article is to investigate whether XAI methods can enhance transparency, explainability and interpretability of predictions in automated clinical gait classification. We utilize a dataset comprising bilateral three-dimensional ground reaction force measurements from 132 patients with different lower-body gait disorders and 62 healthy controls. In our experiments, we included several gait classification tasks, employed a representative set of classification methods, and a well-established XAI method - Layer-wise Relevance Propagation - to explain decisions at the signal (input) level. The presented approach exemplifies how XAI can be used to understand and interpret state-of-the-art ML models trained for gait classification tasks, and shows that the features that are considered relevant for machine learning models can be attributed to meaningful and clinically relevant biomechanical gait characteristics.

We consider the problem of clustering datasets in the presence of arbitrary outliers. Traditional clustering algorithms such as k-means and spectral clustering are known to perform poorly for datasets contaminated with even a small number of outliers. In this paper, we develop a provably robust spectral clustering algorithm that applies a simple rounding scheme to denoise a Gaussian kernel matrix built from the data points, and uses vanilla spectral clustering to recover the cluster labels of data points. We analyze the performance of our algorithm under the assumption that the "good" inlier data points are generated from a mixture of sub-gaussians, while the "noisy" outlier points can come from any arbitrary probability distribution. For this general class of models, we show that the asymptotic mis-classification error decays at an exponential rate in the signal-to-noise ratio, provided the number of outliers are a small fraction of the inlier points. Surprisingly, the derived error bound matches with the best-known bound for semidefinite programs (SDPs) under the same setting without outliers. We conduct extensive experiments on a variety of simulated and real-world datasets to demonstrate that our algorithm is less sensitive to outliers compared to other state-of-the-art algorithms proposed in the literature, in terms of both accuracy as well as scalability.