In today's digital age, artificial intelligence (AI) has earned a name for itself as a futuristic agent that can lead us to a world powered by machines mimicking human intelligence. As days pass by, the use of AI technology is becoming a significant factor in business success, healthcare services, customer engagement, and more. Further, this technology is pushing the envelope of human capability in a manner that humans-machine coexistence will be viewed as a symbolic relationship in the coming days. And this means combining the cognitive, emotional strengths of humans with that of machines (computational capabilities, data analysis, pattern recognition, and next action determination based on statistics). Meanwhile, AI's big data, machine learning, and automation potential have forced policymakers and business leaders worldwide to plan for a future where AI is a core competency.

The year 2020 has inarguably been an unprecedented year for humanity. With a global pandemic upending people's lives, the cyber world has been no less affected. On the upside, the virus-enforced digital transition in nearly all aspects of our lives has created massive momentum and scale for the uptake of cyber technologies. However, the downside is the increased opportunities this creates for unethical hackers and cyber criminals. In this backdrop, how is the cyber security landscape going to unfold this year?

The Fox News Voter Analysis (FNVA), conducted in partnership with the Associated Press, provides a comprehensive look at voting behavior, opinions and preferences as America votes. It is based on surveys conducted in all 50 states by NORC at the University of Chicago, as well as actual voting results by county collected by The AP. The FNVA survey encompasses interviews with an estimated 140,000 registered voters and is conducted Oct. 26 to Nov. 3, and continues through the end of voting on Election Day. Both voters and nonvoters are interviewed to provide a full picture of the election, including why some Americans voted while others stayed at home. FNVA combines respondent interviews from three data sources: (1) a random sample of registered voters drawn from state voter files; (2) a sample of self-identified registered voters conducted using NORC's probability-based panel, which is designed to be representative of the U.S. population; and (3) a sample of self-identified registered voters selected from nonprobability online panels.

The U.S. Food and Drug Administration is alerting clinical laboratory staff and health care providers that false positive results can occur with antigen tests for the virus that causes COVID-19. In a letter to stakeholders, the FDA said Tuesday that while antigen tests can be used for the rapid detection of SARS-CoV-2, false positive results can occur, especially if users don't follow the instructions. "The FDA is aware of reports of false positive results associated with antigen tests used in nursing homes and other settings and continues to monitor and evaluate these reports and other available information about device safety and performance," the letter said. A Boston-area infectious disease expert said the antigen tests are good for large scale screening, when used properly, but must be followed up with more accurate testing. "If you are testing a population at low risk, it's fine to do these tests for screening," said Dr. Daniel Kuritzkes, chief of the Division of Infectious Diseases at Brigham and Women's Hospital.

SAN FRANCISCO – Twitter Inc. and Facebook Inc. on Tuesday suspended several recently created right-leaning news accounts posting information about voting in the hotly contested U.S. election for violating their policies. Twitter said the accounts had been suspended for violating its policy against "coordination," posting identical content while appearing independent or engaging in other covertly automated behavior. Facebook suspended them for inauthentic behavior. One of those suspended, SVNewsAlerts, had more 78,000 Twitter followers, after adding more than 10,000 in the past week. The account frequently warned of election-related unrest and highlighted issues with voting safety and reliability.

Washington – The U.S. State Department cleared the potential sale of four sophisticated U.S.-made aerial drones to Taiwan in a formal notification sent to Congress, the Pentagon said on Tuesday, the last step before finalizing a weapons sale that will further anger China. The $600 million deal would be the first such sale since U.S. policy on the export of sophisticated and closely guarded drone technology was loosened by the Trump administration. Reuters reported in recent weeks on the administration moving ahead with four other sales of sophisticated military equipment to Taiwan, with a total value of around $5 billion, as it ramps up pressure on China and concerns rise about Beijing's intentions toward the island. The U.S. State Department's formal notification gives Congress 30 days to object to any sales, which is unlikely given broad bipartisan support for the defense of Taiwan. The four MQ-9 SeaGuardian drones, made by General Atomic Aeronautical System, Inc. of San Diego, California, would come with associated ground stations, spares and training.

Learning to quantify (a.k.a.\ quantification) is a task concerned with training unbiased estimators of class prevalence via supervised learning. This task originated with the observation that "Classify and Count" (CC), the trivial method of obtaining class prevalence estimates, is often a biased estimator, and thus delivers suboptimal quantification accuracy; following this observation, several methods for learning to quantify have been proposed that have been shown to outperform CC. In this work we contend that previous works have failed to use properly optimised versions of CC. We thus reassess the real merits of CC (and its variants), and argue that, while still inferior to some cutting-edge methods, they deliver near-state-of-the-art accuracy once (a) hyperparameter optimisation is performed, and (b) this optimisation is performed by using a true quantification loss instead of a standard classification-based loss. Experiments on three publicly available binary sentiment classification datasets support these conclusions.

We provide a comprehensive analysis of the computational complexity of the outcome determination problem for the most important aggregation rules proposed in the literature on logic-based judgment aggregation. Judgment aggregation is a powerful and flexible framework for studying problems of collective decision making that has attracted interest in a range of disciplines, including Legal Theory, Philosophy, Economics, Political Science, and Artificial Intelligence. The problem of computing the outcome for a given list of individual judgments to be aggregated into a single collective judgment is the most fundamental algorithmic challenge arising in this context. Our analysis applies to several different variants of the basic framework of judgment aggregation that have been discussed in the literature, as well as to a new framework that encompasses all existing such frameworks in terms of expressive power and representational succinctness.

We consider settings where an allocation has to be chosen repeatedly, returns are unknown but can be learned, and decisions are subject to constraints. Our model covers two-sided and one-sided matching, even with complex constraints. We propose an approach based on Thompson sampling. Our main result is a prior-independent finite-sample bound on the expected regret for this algorithm. Although the number of allocations grows exponentially in the number of participants, the bound does not depend on this number. We illustrate the performance of our algorithm using data on refugee resettlement in the United States.

To the layman, the word-set of ethical AI is a misnomer. AI oftentimes still conjures visions of a dystopian future in which artificial intelligence runs rampant, dominating humankind. Thanks to modern-era entertainment in films such as 2001:A Space Odyssey (HAL 3000) or The Terminator, public perception of AI has been limited to these fictional depictions. So it should come as no surprise that when we talk about ethical AI people would assume its inverse involves robots, lasers, and a war to end humanity. In truth, the conversation around ethical AI typically boils down to the societal issues such as data collection, cyberattacks on critical infrastructure, and inherent bias in code.