We introduce a grey-box adversarial attack and defence framework for sentiment classification. We address the issues of differentiability, label preservation and input reconstruction for adversarial attack and defence in one unified framework. Our results show that once trained, the attacking model is capable of generating high-quality adversarial examples substantially faster (one order of magnitude less in time) than state-of-the-art attacking methods. These examples also preserve the original sentiment according to human evaluation. Additionally, our framework produces an improved classifier that is robust in defending against multiple adversarial attacking methods. Code is available at: https://github.com/ibm-aur-nlp/adv-def-text-dist.

Rosenblatt led the design of a computer to implement this idea and tried to train it to recognize the differences between males and females in photos. "the embryo of an electronic computer that [the Navy] expects will be able to walk, talk, see, write, reproduce itself and be conscious of its existence."

Daniel E Ho and colleagues explore the legal implications of using artificial intelligence in the response to covid-19 and call for more robust evaluation frameworks Numerous proposals, prototypes, and models have emerged for using artificial intelligence (AI) and machine learning to predict individual risk related to covid-19. In the United States, for instance, the Department of Veterans Affairs uses individualised risk scores to allocate medical resources to people with covid-19,1 and prisons have sought to detect symptoms by processing inmates’ phone calls.2 Further tools, such as vulnerability predictions for individuals3 and voice based detection of infection,4 are on the horizon. But use of AI for such purposes has given rise to questions about legality. When a state or federal government seeks to use AI models to predict an individual’s risk of covid-19, the key legal questions will ultimately turn on how effective the models are and how much they burden legal interests. We focus on two of the most salient legal concerns under US law: privacy and discrimination. Challenges on privacy or discrimination grounds might appear in a variety of contexts, including challenges to regulatory decisions, tort actions, or lawsuits under health privacy laws. We argue that the basic need to balance benefits against burdens runs through all of these legal regimes. Governments implementing risk scoring tools must show that their tools produce valid, reliable predictions and burden individuals’ civil liberties no more than necessary. In evaluating the legality of public health use of algorithms, courts will likely also probe how the output of these tools is used to shape policies and programs. But showing that a model performs well and does not exceedingly burden privacy and other interests are essential preconditions for lawful deployment. ### Privacy law Government intrudes on privacy when it forces people to reveal what …

The Food and Drug Administration this week gave emergency-use authorization to a "machine learning-based" device that will reportedly work to detect COVID even in cases in which no immediate symptoms are evident. The device, manufactured by Tiger Tech Solutions, "identifies certain biomarkers that may be indicative of SARS-CoV-2 infection … in asymptomatic individuals over the age of 5," the FDA said in a press release. The device works by reading signals of a patient's blood flow using an armband. "The sensors first obtain pulsatile signals from blood flow over a period of three to five minutes," the FDA said. "Once the measurement is completed," the statement continued.

The increasing adoption of automation, artificial intelligence (AI), and other technologies suggests that the role of humans in the economy will shrink drastically, wiping out millions of jobs in the process. COVID-19 accelerated this effect in 2020 and will likely boost digitization, and perhaps establish it permanently, in some areas. However, the real picture is more nuanced: though these technologies will eliminate some jobs, they will create many others. Governments, companies, and individuals all need to understand these shifts when they plan for the future. BCG recently collaborated with Faethm, a firm specializing in AI and analytics, to study the potential impact of various technologies on jobs in three countries: the US, Germany, and Australia.

Whether it's police brutality, the disproportionate over-exposure of racial minorities to COVID-19 or persistent discrimination in the labour market, Europe is "waking up" to structural racism. Amid the hardships of the pandemic and the environmental crisis, new technological threats are arising. One challenge will be to contest the ways in which emerging technologies, like Artificial Intelligence (AI), reinforce existing forms of discrimination. From predictive policing systems that disproportionately score racialised communities with a higher "risk" of future criminality, all the way to the deployment of facial recognition technologies that consistently mis-identify people of colour, we see how so called "neutral" technologies are secretly harming marginalised communities. The use of data-driven systems to surveil and provide a logic to discrimination is not novel.

On July 7, 1958, several men gathered around a machine inside the offices of the United States Weather Bureau in Washington, D.C., about fifteen blocks west of the White House. As wide as a kitchen refrigerator, twice as deep, and nearly as tall, the machine was just one piece of a mainframe computer that fanned across the room like a multipiece furniture set. It was encased in silvery plastic, reflecting the light from above, and the front panel held row after row of small round lightbulbs, red square buttons, and thick plastic switches, some white and some gray. Normally, this $2 million machine ran calculations for the Weather Bureau, the forerunner of the National Weather Service, but on this day, it was on loan to the U.S. Navy and a twenty-nine-year-old Cornell University professor named Frank Rosenblatt. As a newspaper reporter looked on, Rosenblatt and his Navy cohorts fed two white cards into the machine, one marked with a small square on the left, the other marked on the right.

Beijing – China and the United States will set up a joint working group on climate change, China's official Xinhua News Agency said, in a potentially positive takeaway from what was an unusually rancorous high-level meeting. The top Chinese and U.S. diplomats, in their first meeting of Joe Biden's presidency on Thursday and Friday, publicly rebuked each other's policies at the start of what Washington called "tough and direct" talks in Alaska. But the Chinese delegation said after the meeting the two sides were "committed to enhancing communication and cooperation in the field of climate change," Xinhua said on Saturday. They would also hold talks to facilitate the activities of diplomats and consular missions, "as well as on issues related to media reporters in the spirit of reciprocity and mutual benefit," the report said. The U.S. Embassy in Beijing did not immediately respond to an email seeking comment on Sunday.

This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved 2nd and 1st place, respectively. We also discuss CoSTAR's demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states). We discuss various components of the NeBula framework, including: (i) geometric and semantic environment mapping; (ii) a multi-modal positioning system; (iii) traversability analysis and local planning; (iv) global motion planning and exploration behavior; (i) risk-aware mission planning; (vi) networking and decentralized reasoning; and (vii) learning-enabled adaptation. We discuss the performance of NeBula on several robot types (e.g. wheeled, legged, flying), in various environments. We discuss the specific results and lessons learned from fielding this solution in the challenging courses of the DARPA Subterranean Challenge competition.

Various robustness evaluation methodologies from different perspectives have been proposed for different natural language processing (NLP) tasks. These methods have often focused on either universal or task-specific generalization capabilities. In this work, we propose a multilingual robustness evaluation platform for NLP tasks (TextFlint) that incorporates universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analysis. TextFlint enables practitioners to automatically evaluate their models from all aspects or to customize their evaluations as desired with just a few lines of code. To guarantee user acceptability, all the text transformations are linguistically based, and we provide a human evaluation for each one. TextFlint generates complete analytical reports as well as targeted augmented data to address the shortcomings of the model's robustness. To validate TextFlint's utility, we performed large-scale empirical evaluations (over 67,000 evaluations) on state-of-the-art deep learning models, classic supervised methods, and real-world systems. Almost all models showed significant performance degradation, including a decline of more than 50% of BERT's prediction accuracy on tasks such as aspect-level sentiment classification, named entity recognition, and natural language inference. Therefore, we call for the robustness to be included in the model evaluation, so as to promote the healthy development of NLP technology.