Artificial intelligence (AI) -- defined by John McCarthy, one of the doyens of AI, as "the science and engineering of making intelligent machines" -- is slowly gaining relevance in the military domain. While commercial use of AI is widening, there are only three countries that are reported to be developing serious military AI technologies: the United States, China and Russia. AI promises a significant military advantage to a nation's offensive and defensive military capabilities. AI now has the capacity to be merged with sophisticated but untried, new weaponry, such as offensive cyber capabilities. This is an alarming development, as it has the potential to destabilize the balance of military power among the leading industrial nations.

Is AI the Silver Bullet of Cybersecurity? Two years ago, I talked about how we were in the early stages of the artificial intelligence revolution and how to evaluate AI in security products. Since then, AI research continues to blow minds, particularly with Generative Adversarial Networks (GAN), which are being used to clone voices, generate big chunks of coherent text, and even create creepy pictures of faces of people who don't exist. With all these cool developments making headlines, it's no wonder that people want to understand how AI works and how it can be applied to different industries like cyber security. Unfortunately, there's no such thing as a silver bullet in security, and you should run away from anyone who says they're selling one. Security will always be an arms race between attackers and defenders.

Identification of causal direction between a causal-effect pair from observed data has recently attracted much attention. Various methods based on functional causal models have been proposed to solve this problem, by assuming the causal process satisfies some (structural) constraints and showing that the reverse direction violates such constraints. The nonlinear additive noise model has been demonstrated to be effective for this purpose, but the model class is not transitive--even if each direct causal relation follows this model, indirect causal influences, which result from omitted intermediate causal variables and are frequently encountered in practice, do not necessarily follow the model constraints; as a consequence, the nonlinear additive noise model may fail to correctly discover causal direction. In this work, we propose a cascade nonlinear additive noise model to represent such causal influences--each direct causal relation follows the nonlinear additive noise model but we observe only the initial cause and final effect. We further propose a method to estimate the model, including the unmeasured intermediate variables, from data, under the variational auto-encoder framework. Our theoretical results show that with our model, causal direction is identifiable under suitable technical conditions on the data generation process. Simulation results illustrate the power of the proposed method in identifying indirect causal relations across various settings, and experimental results on real data suggest that the proposed model and method greatly extend the applicability of causal discovery based on functional causal models in nonlinear cases.

We introduce a lifelong language learning setup where a model needs to learn from a stream of text examples without any dataset identifier. We propose an episodic memory model that performs sparse experience replay and local adaptation to mitigate catastrophic forgetting in this setup. Experiments on text classification and question answering demonstrate the complementary benefits of sparse experience replay and local adaptation to allow the model to continuously learn from new datasets. We also show that the space complexity of the episodic memory module can be reduced significantly ( 50-90%) by randomly choosing which examples to store in memory with a minimal decrease in performance. We consider an episodic memory component as a crucial building block of general linguistic intelligence and see our model as a first step in that direction.

Autonomous vehicles require precise knowledge of their position and orientation in all weather and traffic conditions for path planning, perception, control, and general safe operation. Here we derive these requirements for autonomous vehicles based on first principles. We begin with the safety integrity level, defining the allowable probability of failure per hour of operation based on desired improvements on road safety today. This draws comparisons with the localization integrity levels required in aviation and rail where similar numbers are derived at 10^-8 probability of failure per hour of operation. We then define the geometry of the problem, where the aim is to maintain knowledge that the vehicle is within its lane and to determine what road level it is on. Longitudinal, lateral, and vertical localization error bounds (alert limits) and 95% accuracy requirements are derived based on US road geometry standards (lane width, curvature, and vertical clearance) and allowable vehicle dimensions. For passenger vehicles operating on freeway roads, the result is a required lateral error bound of 0.57 m (0.20 m, 95%), a longitudinal bound of 1.40 m (0.48 m, 95%), a vertical bound of 1.30 m (0.43 m, 95%), and an attitude bound in each direction of 1.50 deg (0.51 deg, 95%). On local streets, the road geometry makes requirements more stringent where lateral and longitudinal error bounds of 0.29 m (0.10 m, 95%) are needed with an orientation requirement of 0.50 deg (0.17 deg, 95%).

Graph Neural Networks (GNNs) have proven to be successful in many classification tasks, outperforming previous state-of-the-art methods in terms of accuracy. However, accuracy alone is not enough for high-stakes decision making. Decision makers want to know the likelihood that a specific GNN prediction is correct. For this purpose, obtaining calibrated models is essential. In this work, we perform an empirical evaluation of the calibration of state-of-the-art GNNs on multiple datasets. Our experiments show that GNNs can be calibrated in some datasets but also badly miscalibrated in others, and that state-of-the-art calibration methods are helpful but do not fix the problem.

BAE Systems is a technology partner with robotic process automation (RPA) leader, UiPath, in developing suites of software robots that its customers can use to automate high-volume, repetitive business processes. This press release features multimedia. BAE Systems is now a technology partner with robotic process automation leader, UiPath, to integrate machine learning capabilities into defense and intelligence community programs. "RPAs fuel machine learning tools by feeding them high volumes of structured data necessary for it to begin learning and improving automatically, without being programmed," said Don DeSanto, director of strategic partnerships for the BAE Systems Intelligence & Security sector. "Human-machine teaming is the future of technology, and RPAs serve as workforce multipliers that can be designed to automate many common tasks performed in organizations every day."

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Despite the ongoing trade war between the two countries, some Western experts have been trying to build bridges. This week, the World Economic Forum announced its own AI principles, developed in collaboration with academics, business leaders, and policymakers from the US, China, and other countries. One of the co-chairs of the WEF's new AI council is Kai-Fu Lee, a prominent AI investor based in Beijing, who previously helped establish both Microsoft's and Google's outposts in China. Lee says the WEF group discussed the fact that the Chinese principles seem very similar to those developed by Western countries and companies. "This makes us quite optimistic," he says.

Overcoming these hurdles will get easier over time -- each iteration reduces the challenges for the next one, and scaled roll-out will ultimately make this simply one tool amongst many. But the situation at the moment is analogous to the early days of the world wide web. To make change happen, many organisations turned to a Chief Digital Officer. Over time this became redundant as digital became something that every process had baked in. But, initially, they had a critical role: part prophet, part teacher, part strategist, part operator and part venture capitalist.