Google was forced to turn off the image-generation capabilities of its latest AI model, Gemini, last week after complaints that it defaulted to depicting women and people of color when asked to create images of historical figures that were generally white and male, including vikings, popes, and German soldiers. The company publicly apologized and said it would do better. And Alphabet's CEO, Sundar Pichai, sent a mea culpa memo to staff on Wednesday. "I know that some of its responses have offended our users and shown bias," it reads. "To be clear, that's completely unacceptable, and we got it wrong."

Former President Trump is demanding that President Biden take a cognitive exam to prove his mental capacity for re-election. Trump made the remarks on Thursday via his proprietary social media platform, Truth Social. "Crooked Joe Biden must take a Cognitive Test. Maybe that way we would be able to find out why he makes such terrible decisions," Trump wrote. "I took two of them, and ACED them both (no mistakes!)." "All Presidents, or people wanting to become President, should mandatorily take this test!" the former president added.

The two primary fears around AI are that the information these systems produce is gibberish, and that it'll unjustly take jobs away from people who won't make such sloppy mistakes. But the UK's current government is actively promoting the use of AI to do the work normally done by civil servants, including drafting responses to parliamentary inquiries, the Financial Times reports. UK Deputy Prime Minister Oliver Dowden is set to unveil a "red box" tool that can allegedly absorb and summarize information from reputable sources, like the parliamentary record. A separate instrument is also being trialed that should work similarly but with individual responses to public consultations. While it's unclear how quickly the AI tool can perform this work, Dowden claims it takes three months with 25 civil servants.

It took Ralph Ellison seven years to write Invisible Man. It took J. D. Salinger about 10 to write The Catcher in the Rye. J. K. Rowling spent at least five years on the first Harry Potter book. Writing with the hope of publishing is always a leap of faith. Will you finish the project?

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A schoolboy has completed one of his largest portraits yet by using over 1,500 Rubik's Cubes to resemble the prime minister of the United Kingdom. Henil Soni is an 11-year-old from Harwich, Essex, England, who began his infatuation with the handheld puzzle when he was just five years old, according to SWNS, the British news service. Soni, who can now solve the well-known puzzle in mere seconds, is taking his talents to a larger scale by making portraits out of the colors on the cube.

Exclusive: 2024 presidential candidate Robert F. Kennedy, Jr. sits down with'The Story's' Martha MacCallum to discuss his election bid. Independent presidential candidate Robert F. Kennedy, Jr. called on President Biden Wednesday to show the American people he has the "cognitive capacity" and "mental acuity" to lead the nation for another four-year term. "I think he [Biden] needs to come out of the White House and show Americans that he has the cognitive capacity, to, and the mental acuity, to handle this job at probably the most challenging time now, at least in recent American history," RFK Jr. told "The Story." "We're facing issues that are existential. We're involved in two wars. We have AI coming down, which is going to change everything, and there's enormous dangers in it," he continued.

Artificial Intelligence (AI) requires new ways of evaluating national technology use and strategy for African nations. We conduct a survey of existing 'readiness' assessments both for general digital adoption and for AI policy in particular. We conclude that existing global readiness assessments do not fully capture African states' progress in AI readiness and lay the groundwork for how assessments can be better used for the African context. We consider the extent to which these indicators map to the African context and what these indicators miss in capturing African states' on-the-ground work in meeting AI capability. Through case studies of four African nations of diverse geographic and economic dimensions, we identify nuances missed by global assessments and offer high-level policy considerations for how states can best improve their AI readiness standards and prepare their societies to capture the benefits of AI.

Large Language Models (LLM) have dramatically surged in popularity over the recent years. Since the release of ChatGPT, LLMs - especially those with an accessible chat interface - have not only been used by experts, but are also becoming an increasingly common tool with significant benefits for laypeople. To that end, many commercial actors have already begun implementing LLMs in their operations, ranging from customer-facing chatbots to internal decision support systems [14, 6]. The fairness of AI algorithms, including LLMs, has been a pernicious issue, motivating a growing literature and community of AI ethics research [8]. Disparities across gender and race, among other attributes, have especially preoccupied this field [4], leading to efforts to include bias auditing as an important component of AI harm mitigation in policy discussions and regulatory frameworks [28]. Mitigating biases arising from the explicit use of race or gender in the prompt is comparatively straightforward.

This practice can lead to more rational decision-making that is not heavily influenced by specific opinions or positions [12, 22, 23]. As the Internet is a primary source of information for many people and the volume of online information is immense, effectively helping people consume and share information from diverse perspectives is necessary but challenging [57, 93]. Researchers have proposed various support methods for this, including the development and use of computer technology. In particular, artificial intelligence (AI)-based recommendation systems have been designed to support efficient information consumption by learning users' demographic characteristics or online activity patterns and providing tailored information based on their preferences [77]. Although computer technology plays an important role in enabling people to access and share online information, it should be noted that providing information solely based on individuals' preferences and tendencies can inadvertently contribute to the formation of echo chambers [77], a phenomenon where individuals are exposed primarily to the like-minded groups or information, leading to a reinforcement of shared narratives [28]. Research has shown that echo chambers can have many negative outcomes, including the creation and dissemination of biased information [77], increased susceptibility to fake news [8, 27], resistance towards accepting scientific evidence [63], and the adoption of unbalanced perspectives [36]. To prevent users from becoming polarized towards a specific political stance, many studies have proposed the use of computer-based tools designed to present information from diverse perspectives [31, 48, 53, 62].

Mortgages account for the largest portion of household debt in the United States, totaling around \$12 trillion nationwide. In times of financial hardship, alleviating mortgage burdens is essential for supporting affected households. The mortgage servicing industry plays a vital role in offering this assistance, yet there has been limited research modelling the complex relationship between households and servicers. To bridge this gap, we developed an agent-based model that explores household behavior and the effectiveness of relief measures during financial distress. Our model represents households as adaptive learning agents with realistic financial attributes. These households experience exogenous income shocks, which may influence their ability to make mortgage payments. Mortgage servicers provide relief options to these households, who then choose the most suitable relief based on their unique financial circumstances and individual preferences. We analyze the impact of various external shocks and the success of different mortgage relief strategies on specific borrower subgroups. Through this analysis, we show that our model can not only replicate real-world mortgage studies but also act as a tool for conducting a broad range of what-if scenario analyses. Our approach offers fine-grained insights that can inform the development of more effective and inclusive mortgage relief solutions.