On 18 February, Internal Market, Industry, Entrepreneurship and SMEs Commissioner Elżbieta Bieńkowska participated in the Competitiveness Council sessions on the impact of artificial intelligence (AI) on European industry and progress made on the development of national AI strategies, which should be in place by mid-2019. The Council will adopt conclusions on the Co-ordinated Plan to boost AI, presented by the Commission in December 2018 and prepared together with the Member States. The discussion builds on the European Council meeting from 13-14 December 2018 where EU leaders agreed on the need for the Single Market to evolve so that it fully embraces the digital transformation. In spring, the independent expert group on artificial intelligence will present ethics guidelines for trustworthy AI and their policy and investment recommendations on how to strengthen Europe's competitiveness in AI. Research, Science and Innovation Commissioner Carlos Moedas, will participate in today's (19 February) discussion on the next EU programme for research and innovation, Horizon Europe.

PRESIDENT TRUMP'S recently announced artificial intelligence initiative does not include the word "China." But that country's progress in the race to machine-learning supremacy has prompted calls for the United States to start running faster. Though Mr. Trump's plan is light on actual planning, the Pentagon released its own report last week that provides more detail. Taken together, the documents are promising. Mr. Trump's order directs agencies to assess their spending, reprioritize existing funds toward artificial intelligence and consider that priority in their upcoming budget proposals.

When the first human explorers head for Mars, they're likely to have a non-human judging their performance and tweaking their interpersonal relationships when necessary. NASA and outside researchers are already working on artificial intelligence agents to monitor how future long-duration space crews interact, sort of like the holographic doctor on "Star Trek: Voyager." But there'll also be a need for the human touch -- in the form of crew members who could serve the roles of social directors or easygoing jokesters. That's the upshot of research initiatives discussed over the weekend here at the annual meeting of the American Association for the Advancement of Science. Using AI to assess astronauts' mental state is the focus of a NASA program known as Human Capabilities Assessments for Autonomous Missions, or H-CAAM, said Tom Williams, a researcher at NASA's Johnson Space Center who concentrates on human factors and performance for the space agency's Human Research Program.

Prime Minister Narendra Modi's government will leverage latest tools and insights drawn from Artificial Intelligence to improve the quality of governance for more than 1.34 billion Indian citizens. The government will also update the legal and regulatory framework to better protect the privacy and ownership of citizen's data, said commerce minister Suresh Prabhu. Addressing a conference on AI in New Delhi on Monday, Suresh Prabhu said that India was transporting more data than the cumulative data transported by both US and China. Without naming any company, the minister said the top six companies were using this massive amount of data for value addition and monetisation. "AI is the technology of today and the one who masters it will rule the world," said Suresh Prabhu, adding that every country is developing an AI strategy and India too is working on developing a strategy for the use of AI for the common good.

There are lots of reasons why colonizing space seems compelling. The popular astronomer Neil deGrasse Tyson argues that it would stimulate the economy and inspire the next generation of scientists. Elon Musk, who founded SpaceX, argues that "there is a strong humanitarian argument for making life multiplanetary…to safeguard the existence of humanity in the event that something catastrophic were to happen." And the late astrophysicist Stephen Hawking has conjectured that if humanity fails to colonize space within 100 years, we could face extinction. To be sure, humanity will eventually need to escape Earth to survive, since the sun will make the planet uninhabitable in about 1 billion years.

At a high level, the American AI Initiative seems to be headed in the right direction. We absolutely need a holistic approach that considers all the various areas that are critical to building innovative AI solutions. This seems to be an underlying concept of the Initiative, as the executive order places priority on making data available across government agencies, allocating cloud computing resources to support AI R&D and training the workforce. Commitment to AI innovation is critical to maintaining our leadership position in technology with the increasing level of global AI competition. We know that China, France and the U.K. have invested and committed billions already to their own AI initiatives.

In this paper, we consider batch supervised learning where an adversary is allowed to corrupt instances with arbitrarily large noise. The adversary is allowed to corrupt any $l$ features in each instance and the adversary can change their values in any way. This noise is introduced on test instances and the algorithm receives no label feedback for these instances. We provide several subspace voting techniques that can be used to transform existing algorithms and prove data-dependent performance bounds in this setting. The key insight to our results is that we set our parameters so that a significant fraction of the voting hypotheses do not contain corrupt features and, for many real world problems, these uncorrupt hypotheses are sufficient to achieve high accuracy. We empirically validate our approach on several datasets including three new datasets that deal with side channel electromagnetic information.

Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations. GCNs derive inspiration primarily from recent deep learning approaches, and as a result, may inherit unnecessary complexity and redundant computation. In this paper, we reduce this excess complexity through successively removing nonlinearities and collapsing weight matrices between consecutive layers. We theoretically analyze the resulting linear model and show that it corresponds to a fixed low-pass filter followed by a linear classifier. Notably, our experimental evaluation demonstrates that these simplifications do not negatively impact accuracy in many downstream applications. Moreover, the resulting model scales to larger datasets, is naturally interpretable, and yields up to two orders of magnitude speedup over FastGCN.

Despite being popularly used in many application domains, neural network models have been found to be vulnerable to adversarial examples, examples formed by applying imperceptible perturbation on legitimate examples from the datasets. Adversarial examples can pose potential risks on safety and security of real-world applications. However, existing defense approaches are still vulnerable to adversarial examples, especially in a white-box attack scenario. To address this problem, we propose a new defense approach, named MULDEF, based on robustness diversity. Our approach consists of (1) a general defense framework based on multiple models and (2) a technique for generating these multiple models to achieve high defense capability. In particular, given a target model to defend, our framework includes multiple models (constructed from the target model) to form a model family. The model family is designed to achieve robustness diversity (i.e., an adversarial example successfully attacking one model cannot succeed in attacking other models in the family). At runtime, a model is randomly selected from the family to be applied on each input example. Our general framework can inspire rich future research to construct a desirable model family achieving higher robustness diversity. Our evaluation results show that MULDEF (with only up to 5 models in the family) can already substantially improve the target model's accuracy on adversarial examples by 35-74% in a white-box attack scenario, while maintaining similar accuracy on legitimate examples as the target model.

Humans use language to collectively execute complex strategies in addition to using it as a referential tool for referring to physical entities. While existing approaches that study the emergence of language in settings where the language mainly acts as a referential tool, in this paper, we study the role of emergent languages in discovering and implementing strategies in a multi-agent setting. The agents in our setup are connected via a network and are allowed to exchange messages in the form of sequences of discrete symbols. We formulate the problem as a voting game, where two candidate agents are contesting in an election and their goal is to convince the population members (other agents) in the network to vote for them by sending them messages. We use neural networks to parameterize the policies followed by agents in the game. We investigate the effect of choosing different training objectives and strategies for agents in the game and make observations about the emergent language in each case. To the best of our knowledge this is the first work that explores emergence of language for discovering and implementing strategies in a setting where agents are connected via an underlying network.