Can we avoid wars at the crossroads of history? This question has been pursued by individuals, scholars, policymakers, and organizations throughout human history. In this research, we attempt to answer the question based on the recent advances of Artificial Intelligence (AI) and Large Language Models (LLMs). We propose \textbf{WarAgent}, an LLM-powered multi-agent AI system, to simulate the participating countries, their decisions, and the consequences, in historical international conflicts, including the World War I (WWI), the World War II (WWII), and the Warring States Period (WSP) in Ancient China. By evaluating the simulation effectiveness, we examine the advancements and limitations of cutting-edge AI systems' abilities in studying complex collective human behaviors such as international conflicts under diverse settings. In these simulations, the emergent interactions among agents also offer a novel perspective for examining the triggers and conditions that lead to war. Our findings offer data-driven and AI-augmented insights that can redefine how we approach conflict resolution and peacekeeping strategies. The implications stretch beyond historical analysis, offering a blueprint for using AI to understand human history and possibly prevent future international conflicts. Code and data are available at \url{https://github.com/agiresearch/WarAgent}.

The development of large language models (LLMs) capable of following instructions and engaging in conversational interactions sparked increased interest in their utilization across various support tools. We investigate the utility of modern LLMs in assisting professional writers via an empirical user study (n=30). The design of our collaborative writing interface is grounded in the cognitive process model of writing that views writing as a goal-oriented thinking process encompassing non-linear cognitive activities: planning, translating, and reviewing. Participants are asked to submit a post-completion survey to provide feedback on the potential and pitfalls of LLMs as writing collaborators. Upon analyzing the writer-LLM interactions, we find that while writers seek LLM's help across all three types of cognitive activities, they find LLMs more helpful in translation and reviewing. Our findings from analyzing both the interactions and the survey responses highlight future research directions in creative writing assistance using LLMs.

The rise of Generative Artificial Intelligence systems ("AI systems") has created unprecedented social engagement. AI code generation systems provide responses (output) to questions or requests by accessing the vast library of open-source code created by developers over the past few decades. However, they do so by allegedly stealing the open-source code stored in virtual libraries, known as repositories. This Article focuses on how this happens and whether there is a solution that protects innovation and avoids years of litigation. We also touch upon the array of issues raised by the relationship between AI and copyright. Looking ahead, we propose the following: (a) immediate changes to the licenses for open-source code created by developers that will limit access and/or use of any open-source code to humans only; (b) we suggest revisions to the Massachusetts Institute of Technology ("MIT") license so that AI systems are required to procure appropriate licenses from open-source code developers, which we believe will harmonize standards and build social consensus for the benefit of all of humanity, rather than promote profit-driven centers of innovation; (c) we call for urgent legislative action to protect the future of AI systems while also promoting innovation; and (d) we propose a shift in the burden of proof to AI systems in obfuscation cases.

Even before the drone strike that killed three U.S. service members in Jordan on Sunday, the Biden administration was planning for a moment just like this, debating how it might strike back in ways that would deter Iran's proxy forces and send a message that Tehran would not miss. But the options range from the unsatisfying to the highly risky. Mr. Biden could order strikes on the proxy forces, a major escalation of the whack-a-mole attacks it has conducted in recent weeks in Syria, Iraq and Yemen. So far, those attacks have put a dent into the abilities of the Iranian-backed groups that have mounted more than 160 attacks. But they have failed, as Mr. Biden himself noted 10 days ago, to deter those groups.

There have been at least 160 attacks on U.S. troops in the Middle East since mid-October, following this weekend's attack on a base in Jordan near the Syrian border that left three American soldiers dead and dozens of others injured, U.S. officials said. Defense Secretary Lloyd Austin addressed Sunday's attack and vowed the U.S. would "take all necessary actions" to keep U.S. troops in the region safe. "Let me start with my outrage and sorrow for the deaths of three brave U.S. troops in Jordan and for the other troops who were wounded," Austin said. He added, "The president and I will not tolerate attack on U.S. forces. And we will take all necessary actions to defend the U.S. and our troops."

The Biden Administration will start implementing a new requirement for the developers of major artificial intelligence systems to disclose their safety test results to the government. The White House AI Council is scheduled to meet Monday to review progress made on the executive order that President Joe Biden signed three months ago to manage the fast-evolving technology. Read More: Why Biden's AI Executive Order Only Goes So Far Chief among the 90-day goals from the order was a mandate under the Defense Production Act that AI companies share vital information with the Commerce Department, including safety tests. Ben Buchanan, the White House special adviser on AI, said in an interview that the government wants "to know AI systems are safe before they're released to the public -- the president has been very clear that companies need to meet that bar." The software companies are committed to a set of categories for the safety tests, but companies do not yet have to comply with a common standard on the tests.

Former U.S. Navy SEAL Jonathan Gilliam joined'Fox & Friends First' to discuss what he sees as the'common denominator' to the Biden administration's response to attacks in the Middle East and how it will serve as a'litmus test' in 2024. A U.S. official confirmed to Fox News the drone from an Iranian proxy that killed 3 American service members in Jordan and injured others got past the air defenses for Tower 22 because it was mistaken for a U.S. drone expected to return to the base at the same time. The Wall Street Journal initially reported on this development on Monday. A U.S. official confirmed the information to Fox News. President Biden has vowed to take action against Iranian-backed militants in the Middle East after the drone attack at Tower 22, a post in Jordan near Syria's border, over the weekend.

The United States Department of Justice has failed to convince a group of US lawmakers that state and local police agencies aren't awarded federal grants to buy AI-based "policing" tools known to be inaccurate, if not prone to exacerbating biases long observed in US police forces. Seven members of Congress wrote in a letter to the DOJ, first obtained by WIRED, that the information they pried loose from the agency had only served to inflame their concerns about the DOJ's police grant program. Nothing in its responses so far, the lawmakers said, indicates the government has bothered to investigate whether departments awarded grants bought discriminatory policing software. "We urge you to halt all Department of Justice grants for predictive policing systems until the DOJ can ensure that grant recipients will not use such systems in ways that have a discriminatory impact," the letter reads. The Justice Department previously acknowledged that it had not kept track of whether police departments were using the funding, awarded under the Edward Byrne Memorial Justice Assistance Grant Program, to purchase so-called predictive policing tools.

Social media platform X has blocked searches for one of the world's most popular personalties, Taylor Swift, after explicit artificial intelligence images of the singer-songwriter went viral. The deepfakes flooded several social media sites from Reddit to Facebook. This has renewed calls to strengthen legislation around AI, particularly when it is misused for sexual harassment. Here's what you need to know about the Swift episode and legality around deepfakes. On Wednesday, AI-generated, sexually explicit images began circulating on social media sites, particularly gaining traction on X.

Amazon has dropped its planned 1.4bn ( 1.1bn) acquisition of the Roomba maker iRobot, amid EUopposition to the deal. The e-commerce company will pay a 94m break fee to iRobot, which immediately announced plans to axe 31% of its workforce – or 350 employees – and the departure of its chief executive. The Wall Street Journal had reported on 18 January that the EU's executive arm was preparing to block the deal and had informed Amazon of its proposed view. Amazon and iRobot said in a joint statement the takeover had "no path to regulatory approval in the European Union, preventing Amazon and iRobot from moving forward together". David Zapolsky, the Amazon general counsel, said: "Undue and disproportionate regulatory hurdles discourage entrepreneurs, who should be able to see acquisition as one path to success, and that hurts both consumers and competition – the very things that regulators say they're trying to protect."