The Brazilian government has published the country's artificial intelligence (AI) strategy to guide actions around research, innovation and the development of related technologies to tackle the country's greatest challenges, as well as ethics. The publication of the strategy follows a process of over a year since the launch of the consultation to gather input for the plan in late 2019, after a period of engagement with AI consulting firms and an international benchmarking process. According to the Brazilian government, the consultation lasted until March 2020 and more than 1,000 contributions were received. According to the Brazilian minister of Science, Technology and Innovation, Marcos Pontes, the publication is "the fulfillment of a dream" and a big step for Brazil, since the government considers AI is as "essential" for the development of many other technologies, such as innovations around the Internet of Things approach. Pontes also noted the Brazilian government is also making progress around the national AI research center network.

In Yeshiva University's engineering-focused M.S. in Artificial Intelligence (AI), offered by the Katz School of Science and Health, students will learn the key skills most valued in today's marketplace, including machine learning and deep neural networks, along with cutting-edge technologies such as reinforcement learning, voice recognition and generation, and image recognition and generation. In the program's project-based courses, students will build systems, models and algorithms using the best available artificial intelligence design patterns and engineering principles, all done in the heart of Manhattan, a global epicenter for artificial intelligence work and research. Prof. Andrew Catlin is the program director for the AI program, with a background as a data scientist and production systems developer who has worked with such major clients as Fidelity Investments; Smart Money; Donaldson, Lufkin and Jenrette; Manufacturers Hanover Trust; and the National Football League. He is also a founder of multiple tech startups, including Hudson Technology and Metrics Reporting. He teaches graduate courses in recommender systems, natural language processing and neural networks, among others.

The European Union is considering banning the use of artificial intelligence for a number of purposes, including mass surveillance and social credit scores. This is according to a leaked proposal that is circulating online, first reported by Politico, ahead of an official announcement expected next week. If the draft proposal is adopted, it would see the EU take a strong stance on certain applications of AI, setting it apart from the US and China. Some use cases would be policed in a manner similar to the EU's regulation of digital privacy under GDPR legislation. Member states, for example, would be required to set up assessment boards to test and validate high-risk AI systems.

A European Union plan to regulate artificial intelligence could see companies that break proposed rules on mass surveillance and discrimination fined millions of euros. Draft legislation, leaked ahead of its official release later this month, suggests the EU is attempting to find a "third way" on AI regulation, between the free market US and authoritarian China. The draft rules represent an outright ban on AI designed to manipulate people "to their detriment", carry out indiscriminate surveillance or calculate "social scores". Much of the wording is currently vague enough that it could cover the entire advertising industry or nothing at all. In any case, the military and any agency ensuring public security are exempt.

Nasa's InSight Mars lander is currently trying to endure the abrasive Martian environment, as it sits on the Red Planet conserving power as its solar panels get covered in dust. InSight was designed to be powered by solar energy, gathered through dual two-meter panels. It was always expected that the panels would reduce their power output as time went on and dust landed on them, but would still have enough to last throughout the two-year mission. Unfortunately, not all has gone to plan. Despite InSight landing in Elysium Planitia, a windswept area of Mars that gets lots of sunlight, none of the passing dust devils (funnel-like chimneys of hot air) have been close enough to clean the panels.

In 1962, when astronaut John Glenn was preparing for an orbital mission, mathematician Katherine Johnson was called by the US space agency Nasa for an important task: calculating trajectories. According to Nasa's website, the complexity of the orbital flight, which would make Glenn the first American to orbit Earth, required the construction of a communications network that would link tracking stations around the world to computers in Washington, Florida and Bermuda. These computers were programmed with orbital equations that would control the trajectory of Glenn's Friendship 7 spacecraft. But these machines were also prone to glitches. So, Glenn asked engineers to enlist Johnson to run the same numbers through the same equations programmed into the computer, but by hand, on a desktop mechanical calculating machine.

We propose the first general-purpose gradient-based attack against transformer models. Instead of searching for a single adversarial example, we search for a distribution of adversarial examples parameterized by a continuous-valued matrix, hence enabling gradient-based optimization. We empirically demonstrate that our white-box attack attains state-of-the-art attack performance on a variety of natural language tasks. Furthermore, we show that a powerful black-box transfer attack, enabled by sampling from the adversarial distribution, matches or exceeds existing methods, while only requiring hard-label outputs.

Topological data analysis (TDA) studies the shape patterns of data. Persistent homology (PH) is a widely used method in TDA that summarizes homological features of data at multiple scales and stores this in persistence diagrams (PDs). As TDA is commonly used in the analysis of high dimensional data sets, a sufficiently large amount of PDs that allow performing statistical analysis is typically unavailable or requires inordinate computational resources. In this paper, we propose random persistence diagram generation (RPDG), a method that generates a sequence of random PDs from the ones produced by the data. RPDG is underpinned (i) by a parametric model based on pairwise interacting point processes for inference of persistence diagrams and (ii) by a reversible jump Markov chain Monte Carlo (RJ-MCMC) algorithm for generating samples of PDs. The parametric model combines a Dirichlet partition to capture spatial homogeneity of the location of points in a PD and a step function to capture the pairwise interaction between them. The RJ-MCMC algorithm incorporates trans-dimensional addition and removal of points and same-dimensional relocation of points across samples of PDs. The efficacy of RPDG is demonstrated via an example and a detailed comparison with other existing methods is presented.

Topic modeling algorithms traditionally model topics as list of weighted terms. These topic models can be used effectively to classify texts or to support text mining tasks such as text summarization or fact extraction. The general procedure relies on statistical analysis of term frequencies. The focus of this work is on the implementation of the knowledge-based topic modelling services in a KNIME workflow. A brief description and evaluation of the DBPedia-based enrichment approach and the comparative evaluation of enriched topic models will be outlined based on our previous work. DBpedia-Spotlight is used to identify entities in the input text and information from DBpedia is used to extend these entities. We provide a workflow developed in KNIME implementing this approach and perform a result comparison of topic modeling supported by knowledge base information to traditional LDA. This topic modeling approach allows semantic interpretation both by algorithms and by humans.

Natural language processing (NLP) research combines the study of universal principles, through basic science, with applied science targeting specific use cases and settings. However, the process of exchange between basic NLP and applications is often assumed to emerge naturally, resulting in many innovations going unapplied and many important questions left unstudied. We describe a new paradigm of Translational NLP, which aims to structure and facilitate the processes by which basic and applied NLP research inform one another. Translational NLP thus presents a third research paradigm, focused on understanding the challenges posed by application needs and how these challenges can drive innovation in basic science and technology design. We show that many significant advances in NLP research have emerged from the intersection of basic principles with application needs, and present a conceptual framework outlining the stakeholders and key questions in translational research. Our framework provides a roadmap for developing Translational NLP as a dedicated research area, and identifies general translational principles to facilitate exchange between basic and applied research.