Text-to-Image (T2I) has witnessed significant advancements, demonstrating superior performance for various generative tasks. However, the presence of stereotypes in T2I introduces harmful biases that require urgent attention as the T2I technology becomes more prominent.Previous work for stereotype mitigation mainly concentrated on mitigating stereotypes engendered with individual objects within images, which failed to address stereotypes engendered by the association of multiple objects, referred to as . For example, mentioning ''black people'' and ''houses'' separately in prompts may not exhibit stereotypes. Nevertheless, when these two objects are associated in prompts, the association of ''black people'' with ''poorer houses'' becomes more pronounced. To tackle this issue, we propose a novel framework, MAS, to Mitigate Association-engendered Stereotypes.

Amazon's Used Over 350 AI Shots in Season 2. Its Creator Isn't Sorry The show, which follows David's ascent to King of Israel, used four times as much AI this season, including for many of its battle scenes. A dusty visual overlay partially obscures crowds of men in the desert, sword-fighting in armor and on horseback. With some wardrobe tweaks, this scene could look like something out of or . But showrunner Jon Erwin says he didn't have the budget to bring these scenes to life. Instead, he used AI .

On May 16, 2023, Sam Altman appeared before a subcommittee of the Senate Judiciary. The title of the hearing was "Oversight of AI." The session was a lovefest, with both Altman and the senators celebrating what Altman called AI's "printing press moment"--and acknowledging that the US needed strong laws to avoid its pitfalls. "We think that regulatory intervention by governments will be critical to mitigate the risks of increasingly powerful models," he said. The legislators hung on Altman's every word as he gushed about how smart laws could allow AI to flourish--but only within firm guidelines that both lawmakers and AI builders deemed vital at that moment.

Parliament on Wednesday enacted a bill to establish a new law that will promote the development of artificial intelligence while addressing risks associated with the technology. The bill cleared the House of Councilors, the upper chamber, by a majority vote with support from the Liberal Democratic Party-led ruling bloc and opposition parties including the Constitutional Democratic Party of Japan and Nippon Ishin no Kai. The measure had been adopted by the House of Representatives, the lower chamber, in April. To address mounting concerns over the spread of false and erroneous information generated by AI tools, the new law includes a provision to allow the government to disclose the names of malicious businesses in the event of crime using AI. If a serious incident that infringes on citizens' rights and interests occurs, the government will conduct investigations, advise and instruct related business operators, provide information to the public and take other necessary actions.

Texas Rep. Jasmine Crockett attacked President Donald Trump's West Point address on MSNBC and called it proof of his unfitness as commander in chief. Rep. Jasmine Crockett, D-Texas, appears to be leaning in on her rising political stardom this week, briefly sharing what appeared to be a fan-made song that referred to the Democratic firebrand as the "leader of the future." "Jasmine Crockett, she rises with the dawn. Fighting for justice, her light will never be gone," the song went. Infectious with passion, she'll never bow down.

But opting out is impossible to do without AI transparency. The plan is a charter for theft, since creatives would have no idea who is taking what, when and from whom. When the government stoops to a preferred outcome that undermines the moral right to your work and income, you might reasonably be angered. As Elton John said last weekend: "The government have no right to do this to my songs. They have no right to do it to anybody's songs, or anybody's prose."

In an interview on BBC One's Sunday with Laura Kuenssberg programme, John said the government was on course to "rob young people of their legacy and their income", adding: "It's a criminal offence, I think. The government are just being absolute losers, and I'm very angry about it." Last week, Kyle was accused of being too close to big tech after analysis showed a sharp increase in his department's meetings with companies such as Google, Amazon, Apple and Meta since Labour won the election last July. John referred to a similar amendment that received peers' support last week, only to be removed by the government in the Commons, in a tit-for-tat process that threatens to mire the data bill. "It's criminal, in that I feel incredibly betrayed: the House of Lords did a vote, and it was more than two to one in our favour, the government just looked at it as if to say: 'Hmmm, well the old people … like me can afford it," said John.

While recent works (e.g. o1, DeepSeek R1) have demonstrated great promise of using long Chain-of-Thought (CoT) to improve reasoning capabilities of language models, scaling it up during test-time is challenging due to inefficient memory usage -- intermediate computations accumulate indefinitely in context even no longer needed for future thoughts. We propose PENCIL, which incorporates a reduction mechanism into the autoregressive generation process, allowing the model to recursively clean up intermediate thoughts based on patterns learned from training. With this reduction mechanism, PENCIL significantly reduces the maximal context length required during generation, and thus can generate longer thoughts with limited memory, solving larger-scale problems given more thinking time. For example, we demonstrate PENCIL achieves 97\% accuracy on the challenging Einstein's puzzle -- a task even large models like GPT-4 struggle with -- using only a small 25M-parameter transformer with 2048 context length. Theoretically, we prove PENCIL can perform universal space-efficient computation by simulating Turing machines with optimal time and space complexity, and thus can solve arbitrary computational tasks that would otherwise be intractable given context window constraints.

Analysis of parliamentary speeches and political-party manifestos has become an integral area of computational study of political texts. While speeches have been overwhelmingly analysed using unsupervised methods, a large corpus of manifestos with by-statement political-stance labels has been created by the participants of the MARPOR project. It has been recently shown that these labels can be predicted by a neural model; however, the current approach relies on provided statement boundaries, limiting out-of-domain applicability. In this work, we propose and test a range of unified split-and-label frameworks -- based on linear-chain CRFs, fine-tuned text-to-text models, and the combination of in-context learning with constrained decoding -- that can be used to jointly segment and classify statements from raw textual data. We show that our approaches achieve competitive accuracy when applied to raw text of political manifestos, and then demonstrate the research potential of our method by applying it to the records of the UK House of Commons and tracing the political trajectories of four major parties in the last three decades.

Fox News senior congressional correspondent Chad Pergram joins'Fox News Live' to explain how he prepares to report on Congress for the upcoming year. Every two years, the period between the November election and when the new Congress begins is often the busiest swath of time for covering Congress. Reporters are trying to figure out who won their elections and who lost. The existing Congress is back, attempting to prevent a government shutdown and often plowing through a landscape of other major legislation. There are often leadership elections.