U.S. government funding in artificial intelligence has fallen short and the country needs to invest in research, train an AI-ready workforce and apply the technology to national security missions, an independent government-commissioned panel said in an interim report on Monday. The National Security Commission on Artificial Intelligence (NSCAI) said it believes the U.S. government still confronts enormous work before it can transition AI from "a promising technological novelty into a mature technology integrated into core national security missions." The commission thinks an allied effort on AI in the realm of national security is important, Robert Work, vice chairman of the NSCAI and a former deputy secretary of defense, told reporters. The NSCAI has spoken with Japan, Canada, the United Kingdom, Australia and the European Union, Work said. China is investing more than the United States in artificial intelligence, said the report, which referred to the Asian nation more than 50 times. "China takes advantage of the openness of U.S. society in numerous ways – some legal, some not – to transfer AI know-how," the report said.

We construct realistic equity option market simulators based on generative adversarial networks (GANs). We consider recurrent and temporal convolutional architectures, and assess the impact of state compression. Option market simulators are highly relevant because they allow us to extend the limited real-world data sets available for the training and evaluation of option trading strategies. We show that network-based generators outperform classical methods on a range of benchmark metrics, and adversarial training achieves the best performance. Our work demonstrates for the first time that GANs can be successfully applied to the task of generating multivariate financial time series.

Traditional first-order logic (FOL) reasoning systems usually rely on manual heuristics for proof guidance. We propose TRAIL: a system that learns to perform proof guidance using reinforcement learning. A key design principle of our system is that it is general enough to allow transfer to problems in different domains that do not share the same vocabulary of the training set. To do so, we developed a novel representation of the internal state of a prover in terms of clauses and inference actions, and a novel neural-based attention mechanism to learn interactions between clauses. We demonstrate that this approach enables the system to generalize from training to test data across domains with different vocabularies, suggesting that the neural architecture in TRAIL is well suited for representing and processing of logical formalisms.

Plan Jericho has introduced Ai-Search – an artificial intelligence (Ai) prototype – to transform airborne search and rescue. The prototype came about after Air Commodore Darren Goldie challenged Jericho to find a way of using a detector on an aircraft to enhance search and rescue (SAR). Plan Jericho's Ai lead Wing Commander Michael Gan said Jericho saw the opportunity to use Ai to augment and enhance SAR. "The idea was to train a machine-learning algorithm and Ai sensors to complement existing visual search techniques. Our vision was to give any aircraft and other Defence platforms, including unmanned aerial systems, a low-cost, improvised SAR capability," Wing Commander Gan said.

An Art, Technology, and Culture Colloquium, co-sponsored by the Center for New Music and Audio Technologies and CITRIS People and Robots (CPAR), presented with Berkeley Arts Design as part of Arts Design Mondays. As we continue to develop social robots designed for connectedness, we struggle with paradoxes related to authenticity, transience, and replication. In this talk, I will attempt to link together 15 years of experience designing social robots with 100-year-old texts on transience, replication, and the fear of dying. Can there be meaningful relationships with robots who do not suffer natural decay? What would our families look like if we all choose to buy identical robotic family members?

QUT researchers have applied artificial intelligence (AI) deep learning techniques to develop a more accurate and detailed method for analysing images of the back of the eye to help clinicians better detect and track eye diseases, such as glaucoma and aged-related macular degeneration. Their findings have been published in Nature Scientific Reports. Study lead author QUT Senior Research Fellow Dr David Alonso-Caneiro, from the Faculty of Health School of Optometry and Vision Science, said the team had explored a range of state-of-the-art deep learning techniques to analyse Optical Coherence Tomography (OCT) images. OCT is a common instrument used by optometrists and ophthalmologists. It takes cross-sectional images of the eye which show different tissue layers.

Google's plan to monitor home activities "crosses a moral boundary" that "needlessly encourages a conflict between science and ethics," says Australia's chief science adviser Alan Finkel. Earlier this month Google obtained a patent on the use of an array of sensors and cameras to monitor home life, and claims the technology has the capability to see the title of the book you're reading in bed. The system could also electronically lock doors and turn off running taps. Speaking at an artificial intelligence (AI) summit at Monash University, Melbourne on Thursday, Finkel likened this to a complete stranger offering you unlimited furniture and non-stick frying pans, in exchange for sitting in your bedroom for the next fortnight to observe your behaviour. "We are repulsed by this prospect not because of its unfamiliarity, but because we innately feel that it violates fundamental principles we rightfully hold dear," Finkel said.

We propose a probabilistic framework for modelling and exploring the latent structure of relational data. Given feature information for the nodes in a network, the scalable deep generative relational model (SDREM) builds a deep network architecture that can approximate potential nonlinear mappings between nodes' feature information and the nodes' latent representations. Our contribution is two-fold: (1) We incorporate high-order neighbourhood structure information to generate the latent representations at each node, which vary smoothly over the network. (2) Due to the Dirichlet random variable structure of the latent representations, we introduce a novel data augmentation trick which permits efficient Gibbs sampling. The SDREM can be used for large sparse networks as its computational cost scales with the number of positive links. We demonstrate its competitive performance through improved link prediction performance on a range of real-world datasets.

Large-scale trends in urban crime and global terrorism are well-predicted by socio-economic drivers, but focused, event-level predictions have had limited success. Standard machine learning approaches are promising, but lack interpretability, are generally interpolative, and ineffective for precise future interventions with costly and wasteful false positives. Here, we are introducing Granger Network inference as a new forecasting approach for individual infractions with demonstrated performance far surpassing past results, yet transparent enough to validate and extend social theory. Considering the problem of predicting crime in the City of Chicago, we achieve an average AUC of ~90\% for events predicted a week in advance within spatial tiles approximately $1000$ ft across. Instead of pre-supposing that crimes unfold across contiguous spaces akin to diffusive systems, we learn the local transport rules from data. As our key insights, we uncover indications of suburban bias -- how law-enforcement response is modulated by socio-economic contexts with disproportionately negative impacts in the inner city -- and how the dynamics of violent and property crimes co-evolve and constrain each other -- lending quantitative support to controversial pro-active policing policies. To demonstrate broad applicability to spatio-temporal phenomena, we analyze terror attacks in the middle-east in the recent past, and achieve an AUC of ~80% for predictions made a week in advance, and within spatial tiles measuring approximately 120 miles across. We conclude that while crime operates near an equilibrium quickly dissipating perturbations, terrorism does not. Indeed terrorism aims to destabilize social order, as shown by its dynamics being susceptible to run-away increases in event rates under small perturbations.

In variational inference, the complicated target is approximated by a closest member relative to the Kullback-Leibler (KL) divergence in a pre-specified family of tractable densities. In many large-scale machine learning applications including clustering problems [11, 32], image classification [25, 27] and topic models [21, 7], variational inference can be orders of magnitude faster than the traditional sampling based approaches such as Markov Chain Monte Carlo (MCMC). In particular, by turning the integration, or sampling, problem into an optimization problem, variational inference can take advantage of modern optimization tools such as stochastic optimization techniques [20, 17] and distributed optimization architecture [1, 8] for further improving its efficiency. Among various approximating schemes, mean-field approximation is the most common type of variational inference that is conceptually simple, implementation-wise easy and particularly suitable for problems involving large numbers of latent variables. The word "mean-field" is originated from the mean-field theory in physics where despite complex interactions among many particles in a many (infinite) body system, all interactions to any one particle can be approximated by a single averaged effect from a "mean-field". In variational inference, by restricting the approximating family of the mean-field to be all density functions that are fully factorized over (blocks of) unknown variables, the associated optimization problem of finding a closest weih2@illinois.edu