A study from MIT has used machine learning techniques to identify biased phrasing across around 100 of the largest and most influential news outlets in the US and beyond, including 83 of the most influential print news publications. It's a research effort that shows the way towards automated systems that could potentially auto-classify the political character of a publication, and give readers a deeper insight into the ethical stance of an outlet on topics that they may feel passionately about. The work centers on the way topics are addressed with particular phrasing, such as undocumented immigrant illegal Immigrant, fetus unborn baby, demonstrators anarchists. The project used Natural Language Processing (NLP) techniques to extract and classify such instances of'charged' language (on the assumption that apparently more'neutral' terms also represent a political stance) into a broad mapping that reveals left and right-leaning bias across over three million articles from around 100 news outlets, resulting in a navigable bias landscape of the publications in question. The paper comes from Samantha D'Alonzo and Max Tegmark at MIT's Department of Physics, and observes that a number of recent initiatives around'fact checking', in the wake of numerous'fake news' scandals, can be interpreted as disingenuous and serving the causes of particular interests.

Today, in the COVID-19 World, working from home has become an accepted corporate culture, giving rise to security challenges across industries. Enterprises are waking up to the importance of cybersecurity, with rising demand for cybersecurity services among businesses. With such a massive need for cybersecurity, many startups are working towards bringing artificial intelligence into the field and securing companies with their endpoint security. Sequretek is one such company that is known among the circles to use unconventional ways to detect security breaches and using AI to spot an attack from miles away and stop it before it can cause any real damage. Started in 2013 by Pankit Desai and Anand Naik, Sequretek is built on the foundation of'simplifying security' -- less complexity and driving down the cost of ownership.

The U.N. human rights chief is calling for a moratorium on the use of artificial intelligence technology that poses a serious risk to human rights, including face-scanning systems that track people in public spaces. Michelle Bachelet, the U.N. High Commissioner for Human Rights, also said Wednesday that countries should expressly ban AI applications which don't comply with international human rights law. Applications that should be prohibited include government "social scoring" systems that judge people based on their behavior and certain AI-based tools that categorize people into clusters such as by ethnicity or gender. AI-based technologies can be a force for good but they can also "have negative, even catastrophic, effects if they are used without sufficient regard to how they affect people's human rights," Bachelet said in a statement. Her comments came along with a new U.N. report that examines how countries and businesses have rushed into applying AI systems that affect people's lives and livelihoods without setting up proper safeguards to prevent discrimination and other harms.

Isaac Newton's groundbreaking scientific productivity while isolated from the spread of bubonic plague is legendary. University of California San Diego physicists can now claim a stake in the annals of pandemic-driven science. A team of UC San Diego researchers and colleagues at Purdue University have now simulated the foundation of new types of artificial intelligence computing devices that mimic brain functions, an achievement that resulted from the COVID-19 pandemic lockdown. By combining new supercomputing materials with specialized oxides, the researchers successfully demonstrated the backbone of networks of circuits and devices that mirror the connectivity of neurons and synapses in biologically based neural networks. Like biologically based systems (left), complex emergent behaviors--which arise when separate components are merged together in a coordinated system--also result from neuromorphic networks made up of quantum-materials-based devices (right).

Concerns that emerging technologies like artificial intelligence and automation could wipe out wide swaths of American jobs aren't backed up by data, according to a Sept. 13 report released by the nonprofit, nonpartisan Information Technology and Innovation Foundation. The report examines decades' worth of data from the U.S. Bureau of Labor Statistics across 10 industries--construction, leisure and hospitality, professional and business services, retail trade, transportation and warehousing, wholesale trade, financial activities, information, education and health services, and manufacturing. The report found rates of job loss in each industry were lower in the third quarter of 2020 than in 1995. The third quarter of 2020 represented a stabilization of the American job market following a significant spike in job losses due to the COVID-19 pandemic that reached as high as 45% in the leisure and hospital industries. According to the report, U.S. workers have about a 5.8% chance of losing their jobs across those industries in any given quarter, down from 7.3% in 1995. "The prevailing narrative of accelerating job loss due to new technology is just a myth," ITIF President Robert Atkinson, who co-authored the report, said in a statement.

To advance the development of science and technology, research proposals are submitted to open-court competitive programs developed by government agencies (e.g., NSF). Proposal classification is one of the most important tasks to achieve effective and fair review assignments. Proposal classification aims to classify a proposal into a length-variant sequence of labels. In this paper, we formulate the proposal classification problem into a hierarchical multi-label classification task. Although there are certain prior studies, proposal classification exhibit unique features: 1) the classification result of a proposal is in a hierarchical discipline structure with different levels of granularity; 2) proposals contain multiple types of documents; 3) domain experts can empirically provide partial labels that can be leveraged to improve task performances. In this paper, we focus on developing a new deep proposal classification framework to jointly model the three features. In particular, to sequentially generate labels, we leverage previously-generated labels to predict the label of next level; to integrate partial labels from experts, we use the embedding of these empirical partial labels to initialize the state of neural networks. Our model can automatically identify the best length of label sequence to stop next label prediction. Finally, we present extensive results to demonstrate that our method can jointly model partial labels, textual information, and semantic dependencies in label sequences, and, thus, achieve advanced performances.

The executive branch, or government, is typically not elected directly by the people, but rather formed by another elected body or person such as the parliament or the president. As a result, its members are not directly accountable to the people, individually or as a group. We consider a scenario in which the members of the government are elected directly by the people, and wish to achieve proportionality while doing so. We propose a formal model consisting of $k$ offices, each with its own disjoint set of candidates, and a set of voters who provide approval ballots for all offices. We wish to identify good aggregation rules that assign one candidate to each office. As using a simple majority vote for each office independently might result in disregarding minority preferences altogether, here we consider an adaptation of the greedy variant of Proportional Approval Voting (GreedyPAV) to our setting, and demonstrate -- through computer-based simulations -- how voting for all offices together using this rule overcomes this weakness. We note that the approach is applicable also to a party that employs direct democracy, where party members elect the party's representatives in a coalition government.

We are interested in understanding the underlying generation process for long sequences of symbolic events. To do so, we propose COSSU, an algorithm to mine small and meaningful sets of sequential rules. The rules are selected using an MDL-inspired criterion that favors compactness and relies on a novel rule-based encoding scheme for sequences. Our evaluation shows that COSSU can successfully retrieve relevant sets of closed sequential rules from a long sequence. Such rules constitute an interpretable model that exhibits competitive accuracy for the tasks of next-element prediction and classification.

Part of speech (POS) tagging is a familiar NLP task. State of the art taggers routinely achieve token-level accuracies of over 97% on news body text, evidence that the problem is well understood. However, the register of English news headlines, "headlinese", is very different from the register of long-form text, causing POS tagging models to underperform on headlines. In this work, we automatically annotate news headlines with POS tags by projecting predicted tags from corresponding sentences in news bodies. We train a multi-domain POS tagger on both long-form and headline text and show that joint training on both registers improves over training on just one or naively concatenating training sets. We evaluate on a newly-annotated corpus of over 5,248 English news headlines from the Google sentence compression corpus, and show that our model yields a 23% relative error reduction per token and 19% per headline. In addition, we demonstrate that better headline POS tags can improve the performance of a syntax-based open information extraction system. We make POSH, the POS-tagged Headline corpus, available to encourage research in improved NLP models for news headlines.

Physical adversarial attacks in object detection have attracted increasing attention. However, most previous works focus on hiding the objects from the detector by generating an individual adversarial patch, which only covers the planar part of the vehicle's surface and fails to attack the detector in physical scenarios for multi-view, long-distance and partially occluded objects. To bridge the gap between digital attacks and physical attacks, we exploit the full 3D vehicle surface to propose a robust Full-coverage Camouflage Attack (FCA) to fool detectors. Specifically, we first try rendering the non-planar camouflage texture over the full vehicle surface. To mimic the real-world environment conditions, we then introduce a transformation function to transfer the rendered camouflaged vehicle into a photo-realistic scenario. Finally, we design an efficient loss function to optimize the camouflage texture. Experiments show that the full-coverage camouflage attack can not only outperform state-of-the-art methods under various test cases but also generalize to different environments, vehicles, and object detectors.