Artificial Intelligence (AI) and Data Visualization can seem like an unlikely marriage. AI techniques often work as a black box: we cannot know how the AI has reached its conclusion. This can raise uncomfortable questions: think of a medical diagnosis, or the screening of job applicants: if we cannot see inside the black box, we can't know whether the AI made a serious mistake, or reflected our implicit bias. When the AI becomes a veil between us and the data it makes us uncomfortable and it takes our own intuition and insight out of the game. But AI can help us see the data, becoming a crucial help to our own analysis and judgement.

BACKGROUND AND PURPOSE: Alberta Stroke Program Early CT Score (ASPECTS) was devised as a systematic method to assess the extent of early ischemic change on noncontrast CT (NCCT) in patients with acute ischemic stroke (AIS). Our aim was to automate ASPECTS to objectively score NCCT of AIS patients. MATERIALS AND METHODS: We collected NCCT images with a 5-mm thickness of 257 patients with acute ischemic stroke ( 8 hours from onset to scans) followed by a diffusion-weighted imaging acquisition within 1 hour. Expert ASPECTS readings on DWI were used as ground truth. Texture features were extracted from each ASPECTS region of the 157 training patient images to train a random forest classifier. The unseen 100 testing patient images were used to evaluate the performance of the trained classifier.

The U.S. Department of Defense on Feb. 12 released its roadmap for artificial intelligence, and the most interesting thing about it might be what's missing from the report: The military is nowhere close to building a lethal weapon capable of thinking and acting on its own. As it turns out, the military applications of artificial intelligence today and in the foreseeable future are much more mundane. The Defense Department has several pilot projects in the works that focus on using AI to solve everyday problems such as floods, fires, and maintenance, said U.S. Air Force Lt. Gen. Jack Shanahan, who heads up the Pentagon's new Joint Artificial Intelligence Center. "We are nowhere close to the full autonomy question that most people seem to leap to a conclusion on when they think about DoD and AI," Shanahan said during a briefing Tuesday. It's not that Department of Defense hasn't given the idea of fully autonomous weapons much thought.

Pledging to focus the resources of the federal government to develop artificial intelligence that will enhance national and economic security and prosperity, President Donald J. Trump has signed an executive order to launch the American AI Initiative. The order is the second action the Trump administration has taken in relation to AI technologies. It follows an AI summit hosted by the White House in May 2018. Today's executive order marks another step towards advancing a technology that is being used to create self-driving cars, find cures for cancer, fight human trafficking, design better products, and offer consumers the thing they want to buy before they even know they want it. The executive order comes at a time when China is considered a competitive threat in AI advances.

Good AI talent is hard to find. The talent pool for anyone with deep expertise in modern artificial intelligence techniques is terribly thin. More and more companies are committing to data and artificial intelligence as their differentiator. The early adopters will quickly find difficulties in determining which data science expertise meets their needs. If you are not Google, Facebook, Netflix, Amazon, or Apple, good luck.

We present VERIFAI, a software toolkit for the formal design and analysis of systems that include artificial intelligence (AI) and machine learning (ML) components. VERIFAI particularly seeks to address challenges with applying formal methods to perception and ML components, including those based on neural networks, and to model and analyze system behavior in the presence of environment uncertainty. We describe the initial version of VERIFAI which centers on simulation guided by formal models and specifications. Several use cases are illustrated with examples, including temporal-logic falsification, model-based systematic fuzz testing, parameter synthesis, counterexample analysis, and data set augmentation.

In this paper, we propose a new stochastic algorithmic framework to solve stochastic composite nonconvex optimization problems that covers both finite-sum and expectation settings. Our algorithms rely on the SARAH estimator introduced in (Nguyen et al., 2017a) and consist of two steps: a proximal gradient step and an averaging step that are different from existing nonconvex proximal-type algorithms. The algorithms only require a smoothness assumption of the nonconvex objective term. In the finite-sum case, we show that our algorithm achieves optimal convergence rate by matching the lower-bound worst-case complexity, while in the expectation case, it attains the best-known convergence rate under only standard smoothness and bounded variance assumptions. One key step of our algorithms is a new constant step-size that helps to achieve desired convergence rate. Our step-size is much larger than existing methods including proximal SVRG schemes in the single sample case. We generalize our algorithm to mini-batches for both inner and outer loops, and adaptive step-sizes. We also specify the algorithm to the non-composite case that covers and dominates existing state-of-the-arts in terms of convergence rate. We test the proposed algorithms on two composite nonconvex optimization problems and feedforward neural networks using several well-known datasets.

The promise of ANNs to automatically discover and extract useful features/patterns from data without dwelling on domain expertise although seems highly promising but comes at the cost of high reliance on large amount of accurately labeled data, which is often hard to acquire and formulate especially in time-series domains like anomaly detection, natural disaster management, predictive maintenance and healthcare. As these networks completely rely on data and ignore a very important modality i.e. expert, they are unable to harvest any benefit from the expert knowledge, which in many cases is very useful. In this paper, we try to bridge the gap between these data driven and expert knowledge based systems by introducing a novel framework for incorporating expert knowledge into the network (KINN). Integrating expert knowledge into the network has three key advantages: (a) Reduction in the amount of data needed to train the model, (b) provision of a lower bound on the performance of the resulting classifier by obtaining the best of both worlds, and (c) improved convergence of model parameters (model converges in smaller number of epochs). Although experts are extremely good in solving different tasks, there are some trends and patterns, which are usually hidden only in the data. Therefore, KINN employs a novel residual knowledge incorporation scheme, which can automatically determine the quality of the predictions made by the expert and rectify it accordingly by learning the trends/patterns from data. Specifically, the method tries to use information contained in one modality to complement information missed by the other. We evaluated KINN on a real world traffic flow prediction problem. KINN significantly superseded performance of both the expert and as well as the base network (LSTM in this case) when evaluated in isolation, highlighting its superiority for the task.

Wind power, as an alternative to burning fossil fuels, is plentiful and renewable. Data-driven approaches are increasingly popular for inspecting the wind turbine failures. In this paper, we propose a novel classification-based anomaly detection system for icing detection of the wind turbine blades. We effectively combine the deep neural networks and wavelet transformation to identify such failures sequentially across the time. In the training phase, we present a wavelet based fully convolutional neural network (FCNN), namely WaveletFCNN, for the time series classification. We improve the original (FCNN) by augmenting features with the wavelet coefficients. WaveletFCNN outperforms the state-of-the-art FCNN for the univariate time series classification on the UCR time series archive benchmarks. In the detecting phase, we combine the sliding window and majority vote algorithms to provide the timely monitoring of the anomalies. The system has been successfully implemented on a real-world dataset from Goldwind Inc, where the classifier is trained on a multivariate time series dataset and the monitoring algorithm is implemented to capture the abnormal condition on signals from a wind farm.

Regularized autoencoders learn the latent codes, a structure with the regularization under the distribution, which enables them the capability to infer the latent codes given observations and generate new samples given the codes. However, they are sometimes ambiguous as they tend to produce reconstructions that are not necessarily faithful reproduction of the inputs. The main reason is to enforce the learned latent code distribution to match a prior distribution while the true distribution remains unknown. To improve the reconstruction quality and learn the latent space a manifold structure, this work present a novel approach using the adversarially approximated autoencoder (AAAE) to investigate the latent codes with adversarial approximation. Instead of regularizing the latent codes by penalizing on the distance between the distributions of the model and the target, AAAE learns the autoencoder flexibly and approximates the latent space with a simpler generator. The ratio is estimated using generative adversarial network (GAN) to enforce the similarity of the distributions. Additionally, the image space is regularized with an additional adversarial regularizer. The proposed approach unifies two deep generative models for both latent space inference and diverse generation. The learning scheme is realized without regularization on the latent codes, which also encourages faithful reconstruction. Extensive validation experiments on four real-world datasets demonstrate the superior performance of AAAE. In comparison to the state-of-the-art approaches, AAAE generates samples with better quality and shares the properties of regularized autoencoder with a nice latent manifold structure.