Modern statistical learning algorithms are capable of amazing flexibility, but struggle with interpretability. One possible solution is sparsity: making inference such that many of the parameters are estimated as being identically 0, which may be imposed through the use of nonsmooth penalties such as the $\ell_1$ penalty. However, the $\ell_1$ penalty introduces significant bias when high sparsity is desired. In this article, we retain the $\ell_1$ penalty, but define learnable penalty weights $\lambda_p$ endowed with hyperpriors. We start the article by investigating the optimization problem this poses, developing a proximal operator associated with the $\ell_1$ norm. We then study the theoretical properties of this variable-coefficient $\ell_1$ penalty in the context of penalized likelihood. Next, we investigate application of this penalty to Variational Bayes, developing a model we call the Sparse Bayesian Lasso which allows for behavior qualitatively like Lasso regression to be applied to arbitrary variational models. In simulation studies, this gives us the Uncertainty Quantification and low bias properties of simulation-based approaches with an order of magnitude less computation. Finally, we apply our methodology to a Bayesian lagged spatiotemporal regression model of internal displacement that occurred during the Iraqi Civil War of 2013-2017.

Solar-powered balloons floating in the stratosphere have recorded low-frequency sounds of mysterious origin. "When we started flying balloons years ago, we didn't really know what we'd hear," says Daniel Bowman at Sandia National Laboratories in New Mexico. "We learned how to identify sounds from explosions, meteor crashes, aircraft, thunderstorms and cities. But virtually every time we send balloons up, we find sounds that we cannot identify." Bowman and his colleagues measured infrasound signals – sounds with a frequency so low they are inaudible to human ears – using solar-powered balloons floating 20 kilometres high.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Authorities around the world are racing to draw up rules for artificial intelligence, including in the European Union, where draft legislation faced a pivotal moment on Thursday. A European Parliament committee voted to strengthen the flagship legislative proposal as it heads toward passage, part of a yearslong effort by Brussels to draw up guardrails for artificial intelligence. Those efforts have taken on more urgency as the rapid advances of chatbots like ChatGPT highlight benefits the emerging technology can bring -- and the new perils it poses.

Fake AI pictures and videos will be nearly impossible to discern from real images as the technology behind deepfakes advances, University of California, Berkeley professor says. In a nearly unanimous vote, Minnesota Senate lawmakers passed a bill Wednesday that would criminalize people who non-consensually share deepfake sexual images of others, and people who share deepfakes to hurt a political candidate or influence an election. Deepfakes are videos and images that have been digitally created or altered with artificial intelligence or machine learning. Deepfake pornography and political misinformation have been created with the technology since it first began spreading across the internet several years ago. That technology is easier to use now than ever before.

I have been studying the whole range of issues/opportunities in the commercial roll out of robotics for many years now, and I've spoken at a number of conferences about the best way for us to look at regulating robotics. In the process I've found that my guidelines most closely match the EPSRC Principles of Robotics, although I provide additional focus on potential solutions. And I'm calling it the 5 Laws of Robotics because it's so hard to avoid Asimov's Laws of Robotics in the public perception of what needs to be done. The first most obvious point about these "5 Laws of Robotics" should be that I'm not suggesting actual laws, and neither actually was Asimov with his famous 3 Laws (technically 4 of them). Asimov proposed something that was hardwired or hardcoded into the existence of robots, and of course that didn't work perfectly, which gave him the material for his books.

PsychoGenics CEO Emer Leahy of Paramus, New Jersey, explains how the first potential AI-discovered treatment for schizophrenia was developed through machine learning. Fox News Digital spoke with her. Artificial intelligence already has a lengthy track record in the field of weather prediction, where it has helped prognosticators make faster, more accurate forecasts for nearly three decades. But now, AI has the potential to take the next step when it comes to predicting sun, rain, wind and snow by doing the work on its own, without using various models that human forecasters have relied on for generations. Hendrik Tolman, senior adviser for advanced modeling systems at the National Weather Service, told Fox News Digital this possibility is now on the horizon and is actively being explored.

Fox News correspondent Mark Meredith has the latest on ChatGPT on'Special Report.' With AI large language models like ChatGPT being developed around the globe, countries have raced to regulate AI. Some have drafted strict laws on the technology, while others lack regulatory oversight. China and the EU have received particular attention, as they have created detailed, yet divergent, AI regulations. In both, the government plays a large role.

Fox News contributor Joe Concha joins'Fox & Friends First' to discuss Elon Musk's warning that artificial intelligence could threaten elections and his concerns on the declining birth rate. The United States government uses artificial intelligence in the military, intelligence, and law enforcement to help mitigate potential threats. However, the use of machine learning technology largely remains unregulated by the government, although year-on-year spending on AI government contracts continues to increase. Read below to find out how AI can potentially transform government agencies and impact US democracy. WHAT ARE THE DANGERS OF AI? FIND OUT WHY PEOPLE ARE AFRAID OF ARTIFICIAL INTELLIGENCE The Federal Bureau of Investigations, the top law enforcement agency in the United States, has used artificial intelligence to assist in crime prevention and intelligence gathering.

We're now seeing the Democrats say the quiet part out loud when it comes to artificial intelligence: they want to control AI development for political purposes. These efforts are not just going to further divide the country, but they will kneecap America's next decade of innovation. This assault on innovation is occurring at both the executive and legislative levels, where Democrats are using the novelty of AI to seize control over speech. Earlier this year, President Biden signed an executive order for agencies to "root out bias" by requiring diversity, equity and inclusion training for AI – ensuring any results are woke approved. This month, Vice President Kamala Harris is also getting in on the AI action by meeting with developers to ensure "equity" in AI.

This paper aims to quantitatively evaluate the performance of ChatGPT, an interactive large language model, on inter-sentential relations such as temporal relations, causal relations, and discourse relations. Given ChatGPT's promising performance across various tasks, we conduct extensive evaluations on the whole test sets of 13 datasets, including temporal and causal relations, PDTB2.0-based and dialogue-based discourse relations, and downstream applications on discourse understanding. To achieve reliable results, we adopt three tailored prompt templates for each task, including the zero-shot prompt template, zero-shot prompt engineering (PE) template, and in-context learning (ICL) prompt template, to establish the initial baseline scores for all popular sentence-pair relation classification tasks for the first time. We find that ChatGPT exhibits strong performance in detecting and reasoning about causal relations, while it may not be proficient in identifying the temporal order between two events. It can recognize most discourse relations with existing explicit discourse connectives, but the implicit discourse relation still remains a challenging task. Meanwhile, ChatGPT performs poorly in the dialogue discourse parsing task that requires structural understanding in a dialogue before being aware of the discourse relation.