Anyone with a wet finger in the air has by now heard that Google is facing an identity crisis because of its links to the American military. To crudely summarise, Google chose not to renew its "Project Maven" contract to provide artificial intelligence (A.I) capabilities to the U.S. Department of Defense after employee dissent reached a boiling point. This is an issue for Google, as the "Do No Evil" company is currently in an arm-wrestling match with Amazon and Microsoft for some juicy Cloud and A.I government contracts worth around $10B. Rejecting such work would deprive Google of a potentially huge business; in fact, Amazon recently advertised its image recognition software "Rekognition for defense", and Microsoft has touted the fact that its cloud technology is currently used to handle classified information within every branch of the American military. Nevertheless, the nature of the company's culture means that proceeding with big defence contracts could drive A.I experts away from Google.

Former Google CEO Eric Schmidt called on the US government to fast track development of emerging technology including artificial intelligence (AI) to …

The European Union (EU) should ban biometric mass surveillance tools such as facial recognition when it lays out its plans to regulate artificial intelligence (AI), a coalition of privacy advocates have claimed. The coalition, which includes Reclaim Your Face, European Digital Rights (EDRi), Privacy International, and numerous other non-profits, has made its demands official by launching a petition aimed at pressuring the EU to reconsider its stance on surveillance using biometric technology. The petition warns of numerous potential outcomes of not regulating the technology, such as employers monitoring facial expressions of job candidates in order to decide if they're fit for the position, or insurance companies increasing premiums based on dress codes. The coalition listed various examples of uses of biometric mass surveillance in EU member states including France and Serbia, which had violated EU data protection law as well as "unduly restricted people's rights including their privacy, right to free speech, right to protest and not to be discriminated against". According to EDRi member Linus Neumann, biometrics used in mass surveillance bring "'internet-style' omnipresent tracking to the offline world", leading to the eradication of "the few remaining refuges of privacy".

The U.S. Postal Service will finally get new high-tech mail delivery trucks. The agency said Tuesday that it awarded a 10-year multi-billion dollar contract to Wisconsin-based Oshkosh Defense to replace it's aging fleet of vehicles. The new fleet will replace and expand the existing more than 230,000 vehicles – among them approximately 190,000 delivery trucks – including many that have been in service for 30 years. The deal calls for the postal service to order between 50,000 to 165,000 new delivery trucks featuring 360-degree cameras, advanced braking, with front- and rear-collision avoidance system that includes visual, audio warning and automatic braking. What car was Tiger Woods driving in accident?:Golf

Safety, efficacy, speed and costs must all be prioritized and balanced in the delivery of life-changing therapies to patients. A drug that's quickly and cost-efficiently delivered to market, but isn't effective and safe is unacceptable. An effective, safe drug that doesn't get to patients in time to save lives has failed those who needed it most. When it comes to patient health and safety, there can be no compromises. Fortunately, in a world with abundant data and advanced analytics, we have more tools than ever before to optimize this balance for the betterment of patient safety and outcomes.

The AI in cybersecurity market is poised to reach USD 40 billion by 2026. Today, the experts are ready to consider Artificial Intelligence (AI) as the future of cybersecurity although it's hard to evaluate the promises against challenges posed by AI - as a key element in the internet security. If the AI offers considerable advantages, AI can also be a threat for cyber systems.

Making decisions in complex environments is a key challenge in artificial intelligence (AI). Situations involving multiple decision makers are particularly complex, leading to computational intractability of principled solution methods. A body of work in AI has tried to mitigate this problem by trying to distill interaction to its essence: how does the policy of one agent influence another agent? If we can find more compact representations of such influence, this can help us deal with the complexity, for instance by searching the space of influences rather than the space of policies. However, so far these notions of influence have been restricted in their applicability to special cases of interaction. In this paper we formalize influence-based abstraction (IBA), which facilitates the elimination of latent state factors without any loss in value, for a very general class of problems described as factored partially observable stochastic games (fPOSGs). On the one hand, this generalizes existing descriptions of influence, and thus can serve as the foundation for improvements in scalability and other insights in decision making in complex multiagent settings. On the other hand, since the presence of other agents can be seen as a generalization of single agent settings, our formulation of IBA also provides a sufficient statistic for decision making under abstraction for a single agent. We also give a detailed discussion of the relations to such previous works, identifying new insights and interpretations of these approaches. In these ways, this paper deepens our understanding of abstraction in a wide range of sequential decision making settings, providing the basis for new approaches and algorithms for a large class of problems.

Reduced-order dynamical models play a central role in developing our understanding of predictability of climate irrespective of whether we are dealing with the actual climate system or surrogate climate-models. In this context, the Linear-Inverse-Modeling (LIM) approach, by capturing a few essential interactions between dynamical components of the full system, has proven valuable in providing insights into predictability of the full system. We demonstrate that Reservoir Computing (RC), a form of learning suitable for systems with chaotic dynamics, provides an alternative nonlinear approach that improves on the predictive skill of the LIM approach. We do this in the example setting of predicting sea-surface-temperature in the North Atlantic in the pre-industrial control simulation of a popular earth system model, the Community-Earth-System-Model so that we can compare the performance of the new RC based approach with the traditional LIM approach both when learning data is plentiful and when such data is more limited. The improved predictive skill of the RC approach over a wide range of conditions -- larger number of retained EOF coefficients, extending well into the limited data regime, etc. -- suggests that this machine-learning technique may have a use in climate predictability studies. While the possibility of developing a climate emulator -- the ability to continue the evolution of the system on the attractor long after failing to be able to track the reference trajectory -- is demonstrated in the Lorenz-63 system, it is suggested that further development of the RC approach may permit such uses of the new approach in more realistic predictability studies.

This work considers variational Bayesian inference as an inexpensive and scalable alternative to a fully Bayesian approach in the context of sparsity-promoting priors. In particular, the priors considered arise from scale mixtures of Normal distributions with a generalized inverse Gaussian mixing distribution. This includes the variational Bayesian LASSO as an inexpensive and scalable alternative to the Bayesian LASSO introduced in [56]. It also includes priors which more strongly promote sparsity. For linear models the method requires only the iterative solution of deterministic least squares problems. Furthermore, for $n\rightarrow \infty$ data points and p unknown covariates the method can be implemented exactly online with a cost of O(p$^3$) in computation and O(p$^2$) in memory. For large p an approximation is able to achieve promising results for a cost of O(p) in both computation and memory. Strategies for hyper-parameter tuning are also considered. The method is implemented for real and simulated data. It is shown that the performance in terms of variable selection and uncertainty quantification of the variational Bayesian LASSO can be comparable to the Bayesian LASSO for problems which are tractable with that method, and for a fraction of the cost. The present method comfortably handles n = p = 131,073 on a laptop in minutes, and n = 10$^5$, p = 10$^6$ overnight.

Learning composable policies for environments with complex rules and tasks is a challenging problem. We introduce a hierarchical reinforcement learning framework called the Logical Options Framework (LOF) that learns policies that are satisfying, optimal, and composable. LOF efficiently learns policies that satisfy tasks by representing the task as an automaton and integrating it into learning and planning. We provide and prove conditions under which LOF will learn satisfying, optimal policies. And lastly, we show how LOF's learned policies can be composed to satisfy unseen tasks with only 10-50 retraining steps. We evaluate LOF on four tasks in discrete and continuous domains, including a 3D pick-and-place environment.