AI is the greatest IT disruption of our time, promising to transform society and industry. I've never seen a technology with as much potential to boost the security, health and prosperity of our country. It's estimated to make an economic impact measured in trillions of dollars. The U.S. federal government has been moving quickly, especially this past year, to help advance our nation's adoption of this transformative technology. From the White House to agency leaders and department heads in dozens of federal organizations, the government is acutely aware of the competitive international environment, with more than 35 countries that have already announced AI strategies.

Machine learning (ML) in the representation of molecular-orbital-based (MOB) features has been shown to be an accurate and transferable approach to the prediction of post-Hartree-Fock correlation energies. Previous applications of MOB-ML employed Gaussian Process Regression (GPR), which provides good prediction accuracy with small training sets; however, the cost of GPR training scales cubically with the amount of data and becomes a computational bottleneck for large training sets. In the current work, we address this problem by introducing a clustering/regression/classification implementation of MOB-ML. In a first step, regression clustering (RC) is used to partition the training data to best fit an ensemble of linear regression (LR) models; in a second step, each cluster is regressed independently, using either LR or GPR; and in a third step, a random forest classifier (RFC) is trained for the prediction of cluster assignments based on MOB feature values. Upon inspection, RC is found to recapitulate chemically intuitive groupings of the frontier molecular orbitals, and the combined RC/LR/RFC and RC/GPR/RFC implementations of MOB-ML are found to provide good prediction accuracy with greatly reduced wall-clock training times. For a dataset of thermalized geometries of 7211 organic molecules of up to seven heavy atoms, both implementations reach chemical accuracy (1 kcal/mol error) with only 300 training molecules, while providing 35000-fold and 4500-fold reductions in the wall-clock training time, respectively, compared to MOB-ML without clustering. The resulting models are also demonstrated to retain transferability for the prediction of large-molecule energies with only small-molecule training data. Finally, it is shown that capping the number of training datapoints per cluster leads to further improvements in prediction accuracy with negligible increases in wall-clock training time.

Employing deep neural networks as natural image priors to solve inverse problems either requires large amounts of data to sufficiently train expressive generative models or can succeed with no data via untrained neural networks. However, very few works have considered how to interpolate between these no- to high-data regimes. In particular, how can one use the availability of a small amount of data (even $5-25$ examples) to one's advantage in solving these inverse problems and can a system's performance increase as the amount of data increases as well? In this work, we consider solving linear inverse problems when given a small number of examples of images that are drawn from the same distribution as the image of interest. Comparing to untrained neural networks that use no data, we show how one can pre-train a neural network with a few given examples to improve reconstruction results in compressed sensing and semantic image recovery problems such as colorization. Our approach leads to improved reconstruction as the amount of available data increases and is on par with fully trained generative models, while requiring less than $1 \%$ of the data needed to train a generative model.

We propose a novel adaptive empirical Bayesian (AEB) method for sparse deep learning, where the sparsity is ensured via a class of self-adaptive spike-and-slab priors. The proposed method works by alternatively sampling from an adaptive hierarchical posterior distribution using stochastic gradient Markov Chain Monte Carlo (MCMC) and smoothly optimizing the hyperparameters using stochastic approximation (SA). We further prove the convergence of the proposed method to the asymptotically correct distribution under mild conditions. Empirical applications of the proposed method lead to the state-of-the-art performance on MNIST and Fashion MNIST with shallow convolutional neural networks (CNN) and the state-of-the-art compression performance on CIFAR10 with Residual Networks. The proposed method also improves resistance to adversarial attacks.

In the last couple of years, several adversarial attack methods based on different threat models have been proposed for the image classification problem. Most existing defenses consider additive threat models in which sample perturbations have bounded L_p norms. These defenses, however, can be vulnerable against adversarial attacks under non-additive threat models. An example of an attack method based on a non-additive threat model is the Wasserstein adversarial attack proposed by Wong et al. (2019), where the distance between an image and its adversarial example is determined by the Wasserstein metric ("earth-mover distance") between their normalized pixel intensities. Until now, there has been no certifiable defense against this type of attack. In this work, we propose the first defense with certified robustness against Wasserstein Adversarial attacks using randomized smoothing. We develop this certificate by considering the space of possible flows between images, and representing this space such that Wasserstein distance between images is upper-bounded by L_1 distance in this flow-space. We can then apply existing randomized smoothing certificates for the L_1 metric. In MNIST and CIFAR-10 datasets, we find that our proposed defense is also practically effective, demonstrating significantly improved accuracy under Wasserstein adversarial attack compared to unprotected models.

Adversarial attacks and defenses are currently active areas of research for the deep learning community. A recent review paper divided the defense approaches into three categories; gradient masking, robust optimization, and adversarial example detection. We divide gradient masking and robust optimization differently: (1) increasing intra-class compactness and inter-class separation of the feature vectors improves adversarial robustness, and (2) marginalization or removal of non-robust image features also improves adversarial robustness. By reframing these topics differently, we provide a fresh perspective that provides insight into the underlying factors that enable training more robust networks and can help inspire novel solutions. In addition, there are several papers in the literature of adversarial defenses that claim there is a cost for adversarial robustness, or a trade-off between robustness and accuracy but, under this proposed taxonomy, we hypothesis that this is not universal. We follow up on our taxonomy with several challenges to the deep learning research community that builds on the connections and insights in this paper.

A Nobel Peace prize winner has warned against robots making life-and-death decision on the battlefield, as it is'unethical and immoral' and can never be undone. Jody Williams made the statement at the United Nations in New York City after the US military announced its project the uses AI to make decisions on what human soldiers should target and destroy. Williams also pointed out the difficulty of holding those involved accountable for certain war crimes, as there will be a programmer, manufacturer, commander and the machine itself involved in the act. Jody Williams (right) has warned against robots making life-and-death decision on the battlefield, as it is'unethical and immoral' and'can never be undone'. She was accompanied with fellow activists Liz O'Sullivan (left) and Mary Wareham (center) Williams won the prestigious accolade in 1997 after leading efforts to ban landmines and is now an advocate with the'Campaign To Stop Killer Robots'.

Increasingly, AI is adopted by our banks and our bosses, by our cars and our courts. Across the board, implicit bias remains a significant and complex problem. Several examples have become emblematic of the ways in which implicit bias can channel AI in a prejudiced direction. The Nikon camera that kept asking whether Taiwanese American blogger Joz Wang and her family members were "blinking" while they were taking photographs, for instance, or the time when Google Photos tagged two black friends as "gorillas." Or take the example of Google search results.

Google is taking packages into the air to customers' homes. The first drone home deliveries of packages from Walgreens have started from Wing, the Alphabet subsidiary. Wing recently received an expanded Air Carrier Certificate from the Federal Aviation Administration allowing the first commercial air delivery service by drone directly to homes in the U.S. The FAA permissions are the first allowing multiple pilots to oversee multiple unmanned aircraft making commercial deliveries to the general public simultaneously. Collaborating with Federal Express and Virginia retailer Sugar Magnolia, Wing began delivering over-the-counter medication, gifts and snacks to residents of Christiansburg, Virginia. FedEx completed the first scheduled ecommerce drone delivery on Friday, essentially beginning the connection of retailers to last-mile drone delivery services.

Artificial Intelligence – Where might technology take us in 20 years, moderated by Sean Gourley (CEO Primer AI) on Day 2 of #TIDESprint. Three panelists: Dr Feras A. Batarseh (George Mason University), Tony Reeves (Microsoft) and Ben Snively (Amazon) provide a personal perspective on AI and where the technology could take us in the long term, before going into military implications, opportunities and risks and the technological impact of AI.