Labour would use artificial intelligence to help those looking for work prepare their CVs, find jobs and receive payments faster, according to the party's shadow work and pensions secretary. Jonathan Ashworth told the Guardian he thought the Department for Work and Pensions was wasting millions of pounds by not using cutting-edge technology, even as the party also says AI could also cause massive disruption to the jobs market. Both Ashworth and Lucy Powell, the shadow digital secretary, are making speeches on Tuesday about AI as the party hones its policies concerning one of the fastest-moving areas in the technology industry. But while Ashworth will talk up the potential benefits of the technology for public services, Powell will say it can leave workers disempowered and excluded. Ashworth will say AI could make as big a difference to job-seeking as when the Blair government set up Jobcentre Plus in 2002.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. An unidentified armed group attacked a U.N. peacekeeping patrol Monday in the Central African Republic, killing a peacekeeper from Rwanda, the United Nations said. U.N. spokesman Stephane Dujarric said initial reports indicated the U.N. patrol returned fire and killed three of the assailants. The attack happened as the peacekeepers were providing a protective presence around the town of Sam-Ouandja, in the Haute Kotto prefecture in the Central African Republic's east, Dujarric said. Peacekeepers were deployed to Sam-Ouandja last week in response to an attack on the town by an armed group, which fled after the peacekeepers intervened, he said.

There is a sense of uneasiness when the screen lights up. Excitement, yes, because you're being shown a new way to fight a war, having gained access to a perspective until now closed to human perception. But also modesty because the action is down below, a thousand miles below, and all the courage and suffering of the battle are so distant as to almost lose their human meaning. In a recent visit to Palantir's offices in London, I was able to witness first-hand how the firm's superior data technology really works. I have not been able to stop thinking about the experience ever since.

In August 2021, forest range officer Remya Raghavan caught three people carrying wild boar meat in the Wayanad forest of Kerala, a state in southern India. Possessing wild animal meat is a crime under the country's 1972 Wildlife Protection Act, so Raghavan entered all the details of the crime--location, witnesses, names of the accused, items seized, and section of the forest--in a mobile application. Just like that, the case was officially registered in the app-based system, which signaled that it needed to be taken to court. The app Raghavan used is called HAWK, or Hostile Activity Watch Kernel, and it appears to be the first such digital intelligence gathering system for wildlife crime in India. It helps officers like Raghavan centralize and share information on forest and wildlife crimes in real time.

Harvey Castro talks about how AI could be used in cold cases and the symbiotic relationship between AI and a detective. All 100 senators are invited to sit for a classified briefing this week on artificial intelligence and its effects on global and national security, Senate Majority Leader Chuck Schumer announced in a weekend letter to colleagues. Schumer pointed out that it will be the first session of its kind, as Congress works to get ahead of the rapidly advancing technology. "This Tuesday we will have a classified all-senators briefing with the Department of Defense and Intelligence Community to learn how we're using and investing in AI to protect our national security and learn what our adversaries are doing in AI," the New York Democrat wrote on Sunday. LAWMAKERS RATTLED BY AI-LAUNCHED NUKES, DEMAND'HUMAN CONTROL' IN DEFENSE POLICY BILL The Senate is getting a classified briefing on artificial intelligence and national security on Tuesday.

China has been on the receiving end of semiconductor export restrictions for years. On July 3, the Chinese Ministry of Commerce announced that the export of gallium and germanium, two elements used in producing chips, solar panels, and fiber optics, will soon be heavily restricted. Exports of the materials will need to be approved by the government, and Western companies that rely on them could have a hard time securing a supply. Even though the news immediately sent the price of gallium and germanium up, the curbs are not likely to hit the US as hard as American export restrictions have hit China. Zeyi Yang, our China reporter, explains why.

President Joe Biden listens as British Prime Minister Rishi Sunak speaks during a news conference in the East Room of the White House in Washington, on Thursday, June 8. SIGN OF THE TIMES - US Postal Service just raised the price of sending a letter. AI ON THE MENU - How artificial intelligence is targeting meat in the name of climate change. TERROR THREAT SLAIN - US CENTCOM says it killed ISIS leader in Syria airstrike, no civilian casualties. LOOSE CANNON - Biden lets American military info slip during live interview, sparking backlash.

RABUPURA, India – Their love affair across one of the world's most heavily guarded borders had begun on the virtual battlefields of a video game, where players bond over having one another's back against bloody enemy ambushes to become the last survivors. But when Seema Ghulam Haider, 27, a married Pakistani Muslim, sneaked into India with her four children to be with Sachin Meena, 22, a Hindu man, their time together was brief. About two months after they started secretly living in the same neighborhood outside New Delhi, the couple ran into difficulties with the Indian authorities. This week, Haider and her children were arrested on charges of having illegally entered India. Meena and his father were also arrested, on charges that amount to little short of conspiring to shelter an enemy.

Although deep learning has achieved remarkable success in various scientific machine learning applications, its black-box nature poses concerns regarding interpretability and generalization capabilities beyond the training data. Interpretability is crucial and often desired in modeling physical systems. Moreover, acquiring extensive datasets that encompass the entire range of input features is challenging in many physics-based learning tasks, leading to increased errors when encountering out-of-distribution (OOD) data. In this work, motivated by the field of functional data analysis (FDA), we propose generalized functional linear models as an interpretable surrogate for a trained deep learning model. We demonstrate that our model could be trained either based on a trained neural network (post-hoc interpretation) or directly from training data (interpretable operator learning). A library of generalized functional linear models with different kernel functions is considered and sparse regression is used to discover an interpretable surrogate model that could be analytically presented. We present test cases in solid mechanics, fluid mechanics, and transport. Our results demonstrate that our model can achieve comparable accuracy to deep learning and can improve OOD generalization while providing more transparency and interpretability. Our study underscores the significance of interpretability in scientific machine learning and showcases the potential of functional linear models as a tool for interpreting and generalizing deep learning.

Given a language model (LM), maximum probability is a poor decoding objective for open-ended generation, because it produces short and repetitive text. On the other hand, sampling can often produce incoherent text that drifts from the original topics. We propose contrastive decoding (CD), a reliable decoding approach that optimizes a contrastive objective subject to a plausibility constraint. The contrastive objective returns the difference between the likelihood under a large LM (called the expert, e.g. OPT-13B) and a small LM (called the amateur, e.g. OPT-125M), and the constraint ensures that the outputs are plausible. CD is inspired by the fact that the failures of larger LMs (e.g., repetition, incoherence) are even more prevalent in smaller LMs, and that this difference signals which texts should be preferred. CD requires zero additional training, and produces higher quality text than decoding from the larger LM alone. It also works across model scales (OPT-13B and GPT2-1.5B) and significantly outperforms four strong decoding algorithms (e.g., nucleus, top-k) in automatic and human evaluations across wikipedia, news and story domains.