In an Intelligence Committee hearing with representatives from Google, Apple, and Meta on Wednesday, senators stressed that foreign influence is far from a solved problem. Top officials from Google, Apple, and Meta testified Wednesday before the United States Senate Intelligence Committee about each of their company's ongoing efforts to identify and disrupt foreign influence campaigns ahead of the country's November elections . The hearing, chaired by Senator Mark Warner of Virginia, served largely to impress upon the companies the need for more extensive safeguards against the disinformation campaigns being funded by foreign entities with an eye on influencing US politics. "This is really our effort to try to urge you guys to do more. To alert the public that this has not gone away," Warner said.

Kyle Jensen, the director of Arizona State University's writing programs, is gearing up for the fall semester. The responsibility is enormous: Each year, 23,000 students take writing courses under his oversight. The teachers' work is even harder today than it was a few years ago, thanks to AI tools that can generate competent college papers in a matter of seconds. A mere week after ChatGPT appeared in November 2022, The Atlantic declared that "The College Essay Is Dead." Two school years later, Jensen is done with mourning and ready to move on.

On Wednesday, members of the US Senate Intelligence Committee questioned senior national security officials on how they plan to respond to attacks on voting infrastructure and attempts to influence the election using deepfakes, generative AI, and misinformation. While everyone in the room appeared to agree on what the threats are, senators expressed concern about how exactly government agencies would respond. In a wide-ranging session, director of national intelligence Avril Haines, Cybersecurity and Infrastructure Security Agency director Jen Easterly, and FBI executive assistant director Larissa Knapp focused especially on the wide availability of increasingly sophisticated AI tools that make it easier for more people to create convincing and deceptive fake videos and audio. Senators pressed them on what they would do if one of those AI-generated fakes went viral in the heat of a presidential election. "I don't think I have a clearer understanding of who's in charge and how we would respond," said Marco Rubio, a senator from Florida and vice chair of the committee.

Large pretrained language models (LLMs) have shown surprising In-Context Learning (ICL) ability. An important application in deploying large language models is to augment LLMs with a private database for some specific task. The main problem with this promising commercial use is that LLMs have been shown to memorize their training data and their prompt data are vulnerable to membership inference attacks (MIA) and prompt leaking attacks. In order to deal with this problem, we treat LLMs as untrusted in privacy and propose a locally differentially private framework of in-context learning (LDP-ICL) in the settings where labels are sensitive. Considering the mechanisms of in-context learning in Transformers by gradient descent, we provide an analysis of the trade-off between privacy and utility in such LDP-ICL for classification. Moreover, we apply LDP-ICL to the discrete distribution estimation problem. In the end, we perform several experiments to demonstrate our analysis results.

Who's at risk of mass job losses caused by the AI revolution - blue or white collar workers? Most experts agree that artificial intelligence will completely upend the American workforce (there are already signs it's doing so in the tech industry). But there have been conflicting reports about who is more at risk, lower-wage workers or middle-management types. A report by the think-tank McKinsey Global estimated Americans on the lowest wages are up to 14 times more likely to be replaced by AI those on the highest. But a separate JP Morgan report predicted'mass-scale white-collar job realignment' this decade.

The UK should concentrate on setting global standards for artificial intelligence testing instead of trying to carry out all the vetting itself, according to a company assisting the government's AI Safety Institute. Marc Warner, the chief executive of Faculty AI, said the newly established institute could end up "on the hook" for scrutinising an array of AI models – the technology that underpins chatbots like ChatGPT – owing to the government's world-leading work in AI safety. Rishi Sunak announced the formation of the AI Safety Institute (AISI) last year ahead of the global AI safety summit, which secured a commitment from big tech companies to cooperate with the EU and 10 countries, including the UK, US, France and Japan, on testing advanced AI models before and after their deployment. The UK has a prominent role in the agreement because of its advanced work on AI safety, underlined by the establishment of the institute. Warner, whose London-based company has contracts with the UK institute that include helping it test AI models on whether they can be prompted to breach their own safety guidelines, said the institute should be a world leader in setting test standards.

The problem with Bluey is there's not enough of it. Even with 151 seven-minute-long episodes of the popular children's animated show out there, parents of toddlers still desperately wait for Australia's Ludo Studio to release another season. The only way to get more Bluey more quickly is if they create their own stories starring the Brisbane-based family of blue heeler dogs. The London-based developer and father used OpenAI's latest tool, customizable bots called GPTs, to create a story generator for his young daughter. The bot, which he calls Bluey-GPT, begins each session by asking people their name, age, and a bit about their day, then churns out personalized tales starring Bluey and her sister Bingo.

Differentially private GNNs (Graph Neural Networks) have been recently studied to provide high accuracy in various tasks on graph data while strongly protecting user privacy. In particular, a recent study proposes an algorithm to protect each user's feature vector in an attributed graph with LDP (Local Differential Privacy), a strong privacy notion without a trusted third party. However, this algorithm does not protect edges (friendships) in a social graph, hence cannot protect user privacy in unattributed graphs. How to provide strong privacy with high accuracy in unattributed graphs remains open. In this paper, we propose a novel LDP algorithm called the DPRR (Degree-Preserving Randomized Response) to provide LDP for edges in GNNs. Our DPRR preserves each user's degree hence a graph structure while providing edge LDP. Technically, our DPRR uses Warner's RR (Randomized Response) and strategic edge sampling, where each user's sampling probability is automatically tuned using the Laplacian mechanism to preserve the degree information under edge LDP. We also propose a privacy budget allocation method to make the noise in both Warner's RR and the Laplacian mechanism small. We focus on graph classification as a task of GNNs and evaluate the DPRR using three social graph datasets. Our experimental results show that the DPRR significantly outperforms three baselines and provides accuracy close to a non-private algorithm in all datasets with a reasonable privacy budget, e.g., epsilon=1.

You can see where this is headed. A writing assignment asks students to compare and contrast feminist themes in Jane Eyre and Wuthering Heights. Yup, it can do that. A political science exam requires short-essay responses to questions around the rise and fall of the Soviet Union. So what does this all mean for teaching?

'Christmas with the Chosen: The Messengers' will take place in movie theaters beginning December 1. A recent Gallup poll reveals that more Americans will celebrate a secular Christmas than ever before. For many of us, Christmas is little more than an opportunity to play Santa with our kids. As a 35-year-old, atheist homicide detective, my Christmas celebrations were largely focused on my children as well. But I eventually investigated the birth and life of Jesus and began to observe the holiday differently.