Adversarial examples are carefully constructed modifications to an input that completely change the output of a classifier but are imperceptible to humans. Despite these successful attacks for continuous data (such as image and audio samples), generating adversarial examples for discrete structures such as text has proven significantly more challenging. In this paper we formulate the attacks with discrete input on a set function as an optimization task. We prove that this set function is submodular for some popular neural network text classifiers under simplifying assumption. This finding guarantees a $1-1/e$ approximation factor for attacks that use the greedy algorithm. Meanwhile, we show how to use the gradient of the attacked classifier to guide the greedy search. Empirical studies with our proposed optimization scheme show significantly improved attack ability and efficiency, on three different text classification tasks over various baselines. We also use a joint sentence and word paraphrasing technique to maintain the original semantics and syntax of the text. This is validated by a human subject evaluation in subjective metrics on the quality and semantic coherence of our generated adversarial text.

Adversarial examples are important for understanding the behavior of neural models, and can improve their robustness through adversarial training. Recent work in natural language processing generated adversarial examples by assuming white-box access to the attacked model, and optimizing the input directly against it (Ebrahimi et al., 2018). In this work, we show that the knowledge implicit in the optimization procedure can be distilled into another more efficient neural network. We train a model to emulate the behavior of a white-box attack and show that it generalizes well across examples. Moreover, it reduces adversarial example generation time by 19x-39x. We also show that our approach transfers to a black-box setting, by attacking The Google Perspective API and exposing its vulnerability. Our attack flips the API-predicted label in 42\% of the generated examples, while humans maintain high-accuracy in predicting the gold label.

Turing Award winners (from left to right) Yoshua Bengio, Yann LeCun, and Geoffrey Hinton at the ReWork Deep Learning Summit, Montreal, October 2017. AI "Sputnik moment" (say it in Chinese*) is at hand China is overtaking the US not just in the sheer volume of AI research papers submitted and published, but also in the production of high-impact papers as measured by the top 50%, top 10%, and top 1% most-cited papers. "By projecting current trends, we see that China is likely to have more top-10% papers by 2020 and more top-1% papers by 2025" (Allen Institute for Artificial Intelligence). Cisco attributes the decline to their increased confidence that "migrating to the cloud will improve protection efforts, while apparently decreasing reliance on less proven technologies such as artificial intelligence" (Cisco). Nearly 90% of IT leaders see their use of AI/ML increasing in the future and 41% look for technology that is powered by AI, a top factor in their purchasing decisions.

Singer John Legend is the newest voice of the Google Assistant, the tech company announced Wednesday. Google Assistant users, get ready to have the silky voice of John Legend in your home. The tech company on Wednesday announced the 40-year-old singer as the voice of its virtual assistant for a limited time in the U.S. Legend's voice will be able to give users the weather forecast, sing happy birthday and tell jokes. His voice will also tell you a few things about the singer's personal life, including about his wife, Chrissy Teigen. There will also be a few Easter eggs.

An artificial intelligence has gone to the top of its class after passing an English exam. Though it can't beat more able human students, it achieved the best mark yet for a machine. Hai Zhao at Shanghai Jiao Tong University in China and his colleagues trained their AI on more than 25,000 English reading comprehension tests. Each contained a 200 to 300-word story followed by a series of related multiple-choice questions.

Artificial Intelligence (AI) has been a game changer for global businesses, opening doors to innumerable possibilities. With the integration of AI in businesses, the global economy is excepted to grow exponentially in the coming years. Although the introduction of AI into business strategies is considered a revolutionary idea, what most business executives struggle with is the proper application of AI throughout their organization in such a way that it generates maximum ROI and value. This gives rise of several questions, "How do we educate our staff about AIs? How can we acquire AI-trained employees? What is the most suitable AI strategy for our business? How do we certify our AI is trustworthy? Will there be new privacy and cybersecurity threats to deal with?".

This paper reviews recent advances in the field of optimization under uncertainty via a modern data lens, highlights key research challenges and promise of data-driven optimization that organically integrates machine learning and mathematical programming for decision-making under uncertainty, and identifies potential research opportunities. A brief review of classical mathematical programming techniques for hedging against uncertainty is first presented, along with their wide spectrum of applications in Process Systems Engineering. A comprehensive review and classification of the relevant publications on data-driven distributionally robust optimization, data-driven chance constrained program, data-driven robust optimization, and data-driven scenario-based optimization is then presented. This paper also identifies fertile avenues for future research that focuses on a closed-loop data-driven optimization framework, which allows the feedback from mathematical programming to machine learning, as well as scenario-based optimization leveraging the power of deep learning techniques. Perspectives on online learning-based data-driven multistage optimization with a learning-while-optimizing scheme is presented.

Due to their capacity to condense the spatiotemporal structure of a data set in a format amenable for human interpretation, forecasting, and anomaly detection, causality graphs are routinely estimated in social sciences, natural sciences, and engineering. A popular approach to mathematically formalize causality is based on vector autoregressive (VAR) models, which constitutes an alternative to the well-known but usually intractable Granger causality. Relying on such a VAR causality notion, this paper develops two algorithms with complementary benefits to track time-varying causality graphs in an online fashion. Despite using data in a sequential fashion, both algorithms are shown to asymptotically attain the same average performance as a batch estimator with all data available at once. Moreover, their constant complexity per update renders these algorithms appealing for big-data scenarios. Theoretical and experimental performance analysis support the merits of the proposed algorithms. Remarkably, no probabilistic models or stationarity assumptions need to be introduced, which endows the developed algorithms with considerable generality

We claim that it is possible to have artificial software agents for which their actions and the world they inhabit have first-person or intrinsic meanings. The first-person or intrinsic meaning of an entity to a system is defined as its relation with the system's goals and capabilities, given the properties of the environment in which it operates. Therefore, for a system to develop first-person meanings, it must see itself as a goal-directed actor, facing limitations and opportunities dictated by its own capabilities, and by the properties of the environment. The first part of the paper discusses this claim in the context of arguments against and proposals addressing the development of computer programs with first-person meanings. A set of definitions is also presented, most importantly the concepts of cold and phenomenal first-person meanings. The second part of the paper presents preliminary proposals and achievements, resulting of actual software implementations, within a research approach that aims to develop software agents that intrinsically understand their actions and what happens to them. As a result, an agent with no a priori notion of its goals and capabilities, and of the properties of its environment acquires all these notions by observing itself in action. The cold first-person meanings of the agent's actions and of what happens to it are defined using these acquired notions. Although not solving the full problem of first-person meanings, the proposed approach and preliminary results allow us some confidence to address the problems yet to be considered, in particular the phenomenal aspect of first-person meanings.

Game-based benchmarks have been playing an essential role in the development of Artificial Intelligence (AI) techniques. Providing diverse challenges is crucial to push research toward innovation and understanding in modern techniques. Rinascimento provides a parameterised partially-observable multiplayer card-based board game, these parameters can easily modify the rules, objectives and items in the game. We describe the framework in all its features and the game-playing challenge providing baseline game-playing AIs and analysis of their skills. We reserve to agents' hyper-parameter tuning a central role in the experiments highlighting how it can heavily influence the performance. The base-line agents contain several additional contribution to Statistical Forward Planning algorithms.