Safe deployment of time-series classifiers for real-world applications relies on the ability to detect the data which is not generated from the same distribution as training data. This task is referred to as out-of-distribution (OOD) detection. We consider the novel problem of OOD detection for the time-series domain. We discuss the unique challenges posed by time-series data and explain why prior methods from the image domain will perform poorly. Motivated by these challenges, this paper proposes a novel {\em Seasonal Ratio Scoring (SRS)} approach. SRS consists of three key algorithmic steps. First, each input is decomposed into class-wise semantic component and remainder. Second, this decomposition is employed to estimate the class-wise conditional likelihoods of the input and remainder using deep generative models. The seasonal ratio score is computed from these estimates. Third, a threshold interval is identified from the in-distribution data to detect OOD examples. Experiments on diverse real-world benchmarks demonstrate that the SRS method is well-suited for time-series OOD detection when compared to baseline methods. Open-source code for SRS method is provided at https://github.com/tahabelkhouja/SRS

Feedforward neural network (FFN) and two specific types of recurrent neural network, long short-term memory (LSTM) and gated recurrent unit (GRU), are used for modeling US recessions in the period from 1967 to 2021. The estimated models are then employed to conduct real-time predictions of the Great Recession and the Covid-19 recession in US. Their predictive performances are compared to those of the traditional linear models, the logistic regression model both with and without the ridge penalty. The out-of-sample performance suggests the application of LSTM and GRU in the area of recession forecasting, especially for the long-term forecasting tasks. They outperform other types of models across 5 forecasting horizons with respect to different types of statistical performance metrics. Shapley additive explanations (SHAP) method is applied to the fitted GRUs across different forecasting horizons to gain insight into the feature importance. The evaluation of predictor importance differs between the GRU and ridge logistic regression models, as reflected in the variable order determined by SHAP values. When considering the top 5 predictors, key indicators such as the S\&P 500 index, real GDP, and private residential fixed investment consistently appear for short-term forecasts (up to 3 months). In contrast, for longer-term predictions (6 months or more), the term spread and producer price index become more prominent. These findings are supported by both local interpretable model-agnostic explanations (LIME) and marginal effects.

Biases with respect to socially-salient attributes of individuals have been well documented in evaluation processes used in settings such as admissions and hiring. We view such an evaluation process as a transformation of a distribution of the true utility of an individual for a task to an observed distribution and model it as a solution to a loss minimization problem subject to an information constraint. Our model has two parameters that have been identified as factors leading to biases: the resource-information trade-off parameter in the information constraint and the risk-averseness parameter in the loss function. We characterize the distributions that arise from our model and study the effect of the parameters on the observed distribution. The outputs of our model enrich the class of distributions that can be used to capture variation across groups in the observed evaluations. We empirically validate our model by fitting real-world datasets and use it to study the effect of interventions in a downstream selection task. These results contribute to an understanding of the emergence of bias in evaluation processes and provide tools to guide the deployment of interventions to mitigate biases.

Deadly bioweapons, automated cybersecurity attacks, powerful AI models escaping human control. Those are just some of the potential threats posed by artificial intelligence, according to a new UK government report. It was released to help set the agenda for an international summit on AI safety to be hosted by the UK next week. The report was compiled with input from leading AI companies such as Google's DeepMind unit and multiple UK government departments, including intelligence agencies. Joe White, the UK's technology envoy to the US, says the summit provides an opportunity to bring countries and leading AI companies together to better understand the risks posed by the technology.

Artificial intelligence brings new dangers to society that must be addressed "head on", the prime minister will warn on Thursday, as the government admitted it could not rule out the technology posing an existential threat. Rishi Sunak will refer to the "new opportunities" for economic growth offered by powerful AI systems but will also acknowledge they bring "new dangers" including risks of increased cybercrime, disinformation and upheaval to jobs. In a speech delivered as the UK government prepares to host global politicians, tech executives and experts at an AI safety summit in Bletchley Park next week, Sunak is expected to call for honesty about the risks posed by the technology. "The responsible thing for me to do is to address those fears head on, giving you the peace of mind that we will keep you safe, while making sure you and your children have all the opportunities for a better future that AI can bring," Sunak will say. "Doing the right thing, not the easy thing, means being honest with people about the risks from these technologies."

U.S. senators and technology experts met for the second of Senate Majority Leader Chuck Schumer's AI Insight Forums Oct. 24. Among the 21 invitees were venture capitalists, academics, civil rights campaigners, and industry figures. The discussion at the second Insight Forum, which was closed to the public, focused on how AI could enable innovation, and the innovation required to ensure that AI progress is safe, according to a press release from Schumer's office. In the previous forum, attended by the CEOs of most of the large tech companies, Schumer asked who agreed that some sort of legislation would be required. This time, he asked for a show of hands to see who agreed whether significant federal funding would be required to support AI innovation. Again, all hands were raised, according to Suresh Venkatasubramanian, a professor of data science and computer science at Brown University, who attended the forum.

The Biden Administration is reportedly set to unveil a broad executive order on artificial intelligence next week. According to The Washington Post, the White House's "sweeping order" would use the federal government's purchasing power to enforce requirements on AI models before government agencies can use them. The order is reportedly scheduled for Monday, October 30, two days before an international AI Safety Summit in the UK. The order will allegedly require advanced AI models to undergo a series of assessments before federal agencies can adopt them. In addition, it would ease immigration for highly skilled workers, which was heavily restricted during the Trump administration.

The U.S. Marine Corps is testing a new breed of robotic canine that can do much more than fetch and could possibly be headed to the battlefield. The Marines hope that these four-legged robotic dogs will enhance the mobility and safety of their soldiers in the future. CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH SECURITY ALERTS, QUICK VIDEO TIPS, TECH REVIEWS, AND EASY HOW-TO'S TO MAKE YOU SMARTER The Unitree Go1 robot dog, nicknamed the GOAT (Grounded Open-Air Transport) by the Marines, is a four-legged machine that has a built-in AI system. It can be outfitted to carry an infantry anti-armor rocket launcher on its back. It can also be equipped with a forward-facing GoPro camera, multiple rails for extra cameras, aiming lasers, and other essential gear.

The sweeping order would leverage the U.S. government's role as a top technology customer by requiring advanced AI models to undergo assessments before they can be used by federal workers, according to three people involved in discussions about the order. The lengthy action would ease barriers to immigration for highly-skilled workers, an attempt to boost the United States' technological edge. Federal government agencies -- including the Defense Department, Energy Department and intelligence agencies -- would be required to run assessments to determine how they might incorporate AI into their agencies work, with a focus on bolstering national cyber defenses.

American aircraft carrier USS Gerald R. Ford is seen from the air anchored in Italy in the Gulf of Trieste. The USS Gerald R. Ford is the largest warship in the world. MOVE OVER, MAVERICK: OPINION: Navy finds perfect wingman for carrier pilots. BREAST CANCER BREAKTHROUGH: AI predicts one-third of cases prior to diagnosis in mammography study. AI hallucinations are the category of content that may be inaccurate, nonsensical or even harmful due to AI models drawing information from outdated or incorrect data sets.