We live in a world where we are constantly in contact with Artificial Intelligence, perhaps without even being aware. We live in a world where we are constantly in contact with Artificial Intelligence, perhaps without even being aware. It may not seem that way due to the stigma that Hollywood has put into our mind about what exactly Artificial Intelligence is (killer robots, omniscient software, etc.) but it's really a lot simpler than that. John McCarthy (2007) defined Artificial Intelligence as the science and engineering of making intelligent [having the computational ability to achieve goals in the world] machines. Right now, the main way in which these machines "learn" is through rote learning (trail and error) and drawing inferences. It is widely believed that "AI [artificial intelligence] will drive the human race" (Prime Minister Navendra Modi) and there is not true evidence for or against the contrary, but it is widely accepted that A.I. does and will have a extreme influence on day to day life.

Usually, every month we write an article about the best and most promising AI research papers from that month. In addition to that, we list fifteen AI articles we have found amazing that month. This collection of articles should give you an overview of what happened that month in the AI industry both from technical, business and from an ethical perspective. Are you afraid that AI might take your job? Make sure you are the one who is building it.

A new commission has been formed by Oxford University to advise world leaders on effective ways to use Artificial Intelligence (AI) and machine learning in public administration and governance. The Oxford Commission on AI and Good Governance (OxCAIGG) will bring together academics, technology experts and policymakers to analyse the AI implementation and procurement challenges faced by governments around the world. Led by the Oxford Internet Institute, the Commission will make recommendations on how AI–related tools can be adapted and adopted by policymakers for good governance now and in the near future. The new Commission's inaugural thinkpiece, "Four Principles for Integrating AI & Good Governance" by Lisa-Maria Neudert and Philip Howard examines the procurement and use of AI by government and public agencies. The report outlines four significant challenges relating to AI development and application that need to be overcome for AI to be put to work for good governance and leverage it as a'force for good' in government responses to the COVID-19 pandemic.

Faced with a congressional mandate to test its GPS system for cyber vulnerabilities, the Air Force commissioned a digital replica of the satellites and then asked contractors to hack the system. The use of "digital twins" is expanding from modelling in conventional simulators to include testing of emerging technologies and systems, predicting engine performance, or training automated systems to fly a plane. With GPS, Booz Allen Hamilton built the SatSim twin for Lockheed Martin's Block IIR GPS satellite for the Air Force Space and Missile Systems Center (SMC), in El Segundo, Calif. "The satellite itself was on orbit," BAH Vice President Kevin Coggins told Air Force Magazine. "So we built this digital model … and then we went looking for vulnerabilities. We did [penetration] testing and we saw what we could discover."

In partnership with the Harvard Global Health Institute, Google today released the COVID-19 Public Forecasts, a set of models that provide projections of COVID-19 cases, deaths, ICU utilization, ventilator availability, and other metrics over the next 14 days for U.S. counties and states. The models are trained on public data such as those from Johns Hopkins University, Descartes Labs, and the United States Census Bureau, and Google says they'll continue to be updated with guidance from its collaborators at Harvard. The COVID-19 Public Forecasts are intended to serve as a resource for first responders in health care, the public sector, and other affected organizations preparing for what lies ahead, Google says. They allow for targeted testing and public health interventions on a county-by-county basis, in theory enhancing the ability of those who use them to respond to the rapidly evolving COVID-19 pandemic. For example, health care providers could incorporate the forecasted number of cases as a datapoint in resource planning for PPE, staffing, and scheduling.

Events are occurrences in specific locations, time, and semantics that nontrivially impact either our society or the nature, such as civil unrest, system failures, and epidemics. It is highly desirable to be able to anticipate the occurrence of such events in advance in order to reduce the potential social upheaval and damage caused. Event prediction, which has traditionally been prohibitively challenging, is now becoming a viable option in the big data era and is thus experiencing rapid growth. There is a large amount of existing work that focuses on addressing the challenges involved, including heterogeneous multi-faceted outputs, complex dependencies, and streaming data feeds. Most existing event prediction methods were initially designed to deal with specific application domains, though the techniques and evaluation procedures utilized are usually generalizable across different domains. However, it is imperative yet difficult to cross-reference the techniques across different domains, given the absence of a comprehensive literature survey for event prediction. This paper aims to provide a systematic and comprehensive survey of the technologies, applications, and evaluations of event prediction in the big data era. First, systematic categorization and summary of existing techniques are presented, which facilitate domain experts' searches for suitable techniques and help model developers consolidate their research at the frontiers. Then, comprehensive categorization and summary of major application domains are provided. Evaluation metrics and procedures are summarized and standardized to unify the understanding of model performance among stakeholders, model developers, and domain experts in various application domains. Finally, open problems and future directions for this promising and important domain are elucidated and discussed.

Whether to give rights to artificial intelligence (AI) and robots has been a sensitive topic since the European Parliament proposed advanced robots could be granted "electronic personalities." Numerous scholars who favor or disfavor its feasibility have participated in the debate. This paper presents an experiment (N=1270) that 1) collects online users' first impressions of 11 possible rights that could be granted to autonomous electronic agents of the future and 2) examines whether debunking common misconceptions on the proposal modifies one's stance toward the issue. The results indicate that even though online users mainly disfavor AI and robot rights, they are supportive of protecting electronic agents from cruelty (i.e., favor the right against cruel treatment). Furthermore, people's perceptions became more positive when given information about rights-bearing non-human entities or myth-refuting statements. The style used to introduce AI and robot rights significantly affected how the participants perceived the proposal, similar to the way metaphors function in creating laws. For robustness, we repeated the experiment over a more representative sample of U.S. residents (N=164) and found that perceptions gathered from online users and those by the general population are similar.

Following the recent enthusiasm for Machine Learning (ML) methods and widespread availability of big data, macroeconomic forecasting research gradually evolved further and further away from the traditional tightly specified OLS regression. Rather, nonparametric non-linearity and regularization of many forms are slowly taking the center stage, largely because they can provide sizable forecasting gains with respect to traditional methods (see, among others, Kim and Swanson (2018); Medeiros et al. (2019); Goulet Coulombe et al. (2020); Goulet Coulombe (2020a)). In such environments, different linear transformations of the informational set X can change the prediction and taking first differences may not be the optimal transformation for many predictors, despite the fact that it guarantees viable frequentist inference. For instance, in penalized regression problems - like Lasso or Ridge, different rotations of X imply different priors on β in the original regressor space.

Deep Learning models hold state-of-the-art performance in many fields, but their vulnerability to adversarial examples poses a threat to their ubiquitous deployment in practical settings. Additionally, adversarial inputs generated on one classifier have been shown to transfer to other classifiers trained on similar data, which makes the attacks possible even if the model parameters are not revealed to the adversary. This property of transferability has not yet been systematically studied, leading to a gap in our understanding of robustness of neural networks to adversarial inputs. In this work, we study the effect of network architecture, initialization, input, weight and activation quantization on transferability. Our experiments reveal that transferability is significantly hampered by input quantization and architectural mismatch between source and target, is unaffected by initialization and is architecture-dependent for both weight and activation quantization. To quantify transferability, we propose a simple metric, which is a function of the attack strength. We demonstrate the utility of the proposed metric in designing a methodology to build ensembles with improved adversarial robustness. Finally, we show that an ensemble consisting of carefully chosen input quantized networks achieves better adversarial robustness than would otherwise be possible with a single network.

Discussions about the application of artificial intelligence (AI) in healthcare often span multiple areas, most commonly about making more accurate diagnoses, identifying at-risk populations, and better understanding how individual patients will respond to medicines and treatment protocols. To date, there has been relatively little discussion about practical applications of AI to improve medication management across the care continuum, an area this article will address. What's the first thing that comes to mind when someone mentions prescription drugs in the United States? In poll after poll, the high and rising costs of medications are American voters' top healthcare-related issue. This concern is well founded.