Spinal cord injuries alter someone's life forever, and they can happen to anyone. When someone suffers a serious spinal cord injury, they most typically become paralyzed from that point down, or up. The reason behind this devastating paralysis is because the human body isn't able to regenerate severed nerve fibers. The brain loses its signal to alert muscles to move, and paralysis occurs. However, with the help of artificial intelligence (AI) and new technologies, the medical field is working toward helping patients with spinal cord injuries to move again.

Automation Anywhere, a global leader in Robotic Process Automation (RPA), announced today that it has launched the world's first purely web-based, cloud-native Digital Workforce platform, Automation Anywhere Enterprise A2019. Enterprise A2019 is now available both on-premise and in any public, private or hybrid cloud, delivering RPA-as-a-Service seamlessly to any user and any business through any delivery channel anywhere in the world. The game-changing platform, now available in more than 14 languages, dramatically reduces cost and infrastructure barriers to RPA adoption. The company's flagship platform – inspired by the ways in which humans work – now includes more than 175 new features across 40 different product capabilities to simplify business automation. It also incorporates feedback from more than 3,000 customers worldwide and thousands of hours of research and development to bring automation one step closer to recognizing the $100 billion market opportunity industry analysts have predicted1.

Facebook has achieved a major milestone in artificial intelligence (AI) thanks to one of its systems beating six professional poker players at no-limit Texas hold'em. The Pluribus AI defeated renowned players including Darren Elias, who holds the record for most World Poker Tour titles. Beating poker pros has been a major challenge for AI researchers, as the best players need to be good at bluffing and unpredictable. "Playing a six-player game rather than head-to-head requires fundamental changes in how the AI develops its playing strategy," said Noam Brown, a research scientist at Facebook AI. "We're elated with its performance and believe some of Pluribus's playing strategies might even change the way pros play the game." The breakthrough comes two years after an AI algorithm developed by Google-owned DeepMind helped a computer beat a human champion at the notoriously complicated board game Go for the first time.

Elon Musk has been ridiculed for claiming he's on the brink of perfecting a fleet of self-driving taxis after Tesla owners reported that their cars crash on summon mode. The CEO of Tesla says that the fleet will be ready by the end of next year, but that has been called into question after the release of Tesla's Smart Summon technology. Raj Rajkumar, from Carnegie Mellon University, says that the California company's new feature is'far from perfection' and he can'only laugh' at Musk's timeline. Many Tesla owners using the summon function, which calls their car to them without anyone in, have experienced several close calls and nasty fender benders. The release of Tesla's Smart Summon technology has CEO Elon Musk promising a fleet of self-driving taxis by the end of next year.

NASA's InSight rover has provided the American space agency with weather reports, images and other interesting findings on Mars – but has struggled to probe its surface. In nearly eight months, the land rover has only dug through 14 inches of the red planet's surface, even though it was engineered to reach at least 16 feet to study how heat escapes from the interior. This blunder has come down to InSight's'mole' heat probe's inability to keep its footing in the soil – NASA believes the device is just bouncing in place. In nearly eight months, the land rover has only dug through 14 inches of the red planet's surface, even though it was engineered to reach at least 16 feet in order to study how heat escapes from the interior InSight, NASA's $1 billion rover, made landing on Mars in November 2018 after traveling through space for seven months. And although it has been a key player in the Mars mission, it has failed to explore the planet's interior.

We model a piece of text of human language telling a story by means of the quantum structure describing a Bose gas in a state close to a Bose-Einstein condensate near absolute zero temperature. For this we introduce energy levels for the words (concepts) used in the story and we also introduce the new notion of 'cogniton' as the quantum of human thought. Words (concepts) are then cognitons in different energy states as it is the case for photons in different energy states, or states of different radiative frequency, when the considered boson gas is that of the quanta of the electromagnetic field. We show that Bose-Einstein statistics delivers a very good model for these pieces of texts telling stories, both for short stories and for long stories of the size of novels. We analyze an unexpected connection with Zipf's law in human language, the Zipf ranking relating to the energy levels of the words, and the Bose-Einstein graph coinciding with the Zipf graph. We investigate the issue of 'identity and indistinguishability' from this new perspective and conjecture that the way one can easily understand how two of 'the same concepts' are 'absolutely identical and indistinguishable' in human language is also the way in which quantum particles are absolutely identical and indistinguishable in physical reality, providing in this way new evidence for our conceptuality interpretation of quantum theory.

Trained DNN models are increasingly adopted as integral parts of software systems. However, they are often over-confident, especially in practical operation domains where slight divergence from their training data almost always exists. To minimize the loss due to inaccurate confidence, operational calibration, i.e., calibrating the confidence function of a DNN classifier against its operation domain, becomes a necessary debugging step in the engineering of the whole system. Operational calibration is difficult considering the limited budget of labeling operation data and the weak interpretability of DNN models. We propose a Bayesian approach to operational calibration that gradually corrects the confidence given by the model under calibration with a small number of labeled operational data deliberately selected from a larger set of unlabeled operational data. Exploiting the locality of the learned representation of the DNN model and modeling the calibration as Gaussian Process Regression, the approach achieves impressive efficacy and efficiency. Comprehensive experiments with various practical data sets and DNN models show that it significantly outperformed alternative methods, and in some difficult tasks it eliminated about 71% to 97% high-confidence errors with only about 10% of the minimal amount of labeled operation data needed for practical learning techniques to barely work.

For embodied agents to infer representations of the underlying 3D physical world they inhabit, they should efficiently combine multisensory cues from numerous trials, e.g., by looking at and touching objects. Despite its importance, multisensory 3D scene representation learning has received less attention compared to the unimodal setting. In this paper, we propose the Generative Multisensory Network (GMN) for learning latent representations of 3D scenes which are partially observable through multiple sensory modalities. We also introduce a novel method, called the Amortized Product-of-Experts, to improve the computational efficiency and the robustness to unseen combinations of modalities at test time. Experimental results demonstrate that the proposed model can efficiently infer robust modality-invariant 3D-scene representations from arbitrary combinations of modalities and perform accurate cross-modal generation. To perform this exploration, we also develop the Multisensory Embodied 3D-Scene Environment (MESE).

We derive an efficient method to perform clustering of nodes in Gaussian graphical models directly from sample data. Nodes are clustered based on the similarity of their network neighborhoods, with edge weights defined by partial correlations. In the limited-data scenario, where the covariance matrix would be rank-deficient, we are able to make use of matrix factors, and never need to estimate the actual covariance or precision matrix. We demonstrate the method on functional MRI data from the Human Connectome Project. A matlab implementation of the algorithm is provided.

Controlled interacting particle systems such as the ensemble Kalman filter (EnKF) and the feedback particle filter (FPF) are numerical algorithms to approximate the solution of the nonlinear filtering problem in continuous time. The distinguishing feature of these algorithms is that the Bayesian update step is implemented using a feedback control law. It has been noted in the literature that the control law is not unique. This is the main problem addressed in this paper. To obtain a unique control law, the filtering problem is formulated here as an optimal transportation problem. An explicit formula for the (mean-field type) optimal control law is derived in the linear Gaussian setting. Comparisons are made with the control laws for different types of EnKF algorithms described in the literature. Via empirical approximation of the mean-field control law, a finite-$N$ controlled interacting particle algorithm is obtained. For this algorithm, the equations for empirical mean and covariance are derived and shown to be identical to the Kalman filter. This allows strong conclusions on convergence and error properties based on the classical filter stability theory for the Kalman filter. It is shown that, under certain technical conditions, the mean squared error (m.s.e.) converges to zero even with a finite number of particles. A detailed propagation of chaos analysis is carried out for the finite-$N$ algorithm. The analysis is used to prove weak convergence of the empirical distribution as $N\rightarrow\infty$. For a certain simplified filtering problem, analytical comparison of the m.s.e. with the importance sampling-based algorithms is described. The analysis helps explain the favorable scaling properties of the control-based algorithms reported in several numerical studies in recent literature.