A Friday report from The Washington Post warned that small New York City landlords are "at their breaking point" under Mayor Zohran Mamdani's housing policies.

Utilizing global information to reduce the complexity of imperfect-information games has also been investigated in some works. In their implementation, the value network of the agent can observe the full information about the game state, including those that are hidden from the policy. They argue that such a training style improves training performance. Moreover, in Suphx [15], a strong Mahjong AI system, they used a similar method namely oracle guiding. Particularly, in the beginning of the training stage, all global information is utilized; then, as the training goes, the additional information would be dropped out slowly to none, and only the information that the agent is allowed to observe is reserved in the subsequent training stage.

As a challenging multi-player card game, DouDizhu has recently drawn much attention for analyzing competition and collaboration in imperfect-information games. In this paper, we propose PerfectDou, a state-of-the-art DouDizhu AI system that dominates the game, in an actor-critic framework with a proposed technique named perfect information distillation.

Utilizing global information to reduce the complexity of imperfect-information games has also been investigated in some works. In their implementation, the value network of the agent can observe the full information about the game state, including those that are hidden from the policy. They argue that such a training style improves training performance. Moreover, in Suphx [15], a strong Mahjong AI system, they used a similar method namely oracle guiding. Particularly, in the beginning of the training stage, all global information is utilized; then, as the training goes, the additional information would be dropped out slowly to none, and only the information that the agent is allowed to observe is reserved in the subsequent training stage.

As a challenging multi-player card game, DouDizhu has recently drawn much attention for analyzing competition and collaboration in imperfect-information games. In this paper, we propose PerfectDou, a state-of-the-art DouDizhu AI system that dominates the game, in an actor-critic framework with a proposed technique named perfect information distillation.

One way social groups are marginalized in discourse is that the narratives told about them often default to a narrow, stereotyped range of topics. In contrast, default groups are allowed the full complexity of human existence. We describe the constrained representations of queer people in LLM generations in terms of harmful representations, narrow representations, and discursive othering and formulate hypotheses to test for these phenomena. Our results show that LLMs are significantly limited in their portrayals of queer personas.

President Donald Trump and Israeli Prime Minister Benjamin Netanyahu meet over dinner. Fox News' "Antisemitism Exposed" newsletter brings you stories on the rising anti-Jewish prejudice across the U.S. and the world. TOP STORY: Israeli Prime Minister Benjamin Netanyahu has sent a letter to the Nobel Prize Committee to nominate President Donald Trump for the peace prize. "He forged the Abraham Accords. He's forging peace as we speak, in one country and one region after the other," Netanyahu said at a White House meeting.

Access to legal information is fundamental to access to justice. Yet accessibility refers not only to making legal documents available to the public, but also rendering legal information comprehensible to them. A vexing problem in bringing legal information to the public is how to turn formal legal documents such as legislation and judgments, which are often highly technical, to easily navigable and comprehensible knowledge to those without legal education. In this study, we formulate a three-step approach for bringing legal knowledge to laypersons, tackling the issues of navigability and comprehensibility. First, we translate selected sections of the law into snippets (called CLIC-pages), each being a small piece of article that focuses on explaining certain technical legal concept in layperson's terms. Second, we construct a Legal Question Bank (LQB), which is a collection of legal questions whose answers can be found in the CLIC-pages. Third, we design an interactive CLIC Recommender (CRec). Given a user's verbal description of a legal situation that requires a legal solution, CRec interprets the user's input and shortlists questions from the question bank that are most likely relevant to the given legal situation and recommends their corresponding CLIC pages where relevant legal knowledge can be found. In this paper we focus on the technical aspects of creating an LQB. We show how large-scale pre-trained language models, such as GPT-3, can be used to generate legal questions. We compare machine-generated questions (MGQs) against human-composed questions (HCQs) and find that MGQs are more scalable, cost-effective, and more diversified, while HCQs are more precise. We also show a prototype of CRec and illustrate through an example how our 3-step approach effectively brings relevant legal knowledge to the public.

A Maryland court has ordered a blogger known as "Ziz", who leads a cult-like group connected to six killings, to be held without bail. The blogger, Jack LaSota, 34, of Berkeley, California, was arrested Sunday along with Michelle Zajko, 32, of Media, Pennsylvania, and Daniel Blank, 26, of Sacramento, California. The Zizians, as the group are known after their apparent leader, have been tied to the killing of a United States Border Patrol agent David Maland last month near the Canadian border, as well as five other killings in three states. LaSota, Zajko and Blank were arrested in Frostburg, Maryland, on Sunday afternoon. The judge in the case ordered LaSota to be held without bail, citing concerns about her being a flight risk and a danger to public safety.

Artificial intelligence for card games has long been a popular topic in AI research. In recent years, complex card games like Mahjong and Texas Hold'em have been solved, with corresponding AI programs reaching the level of human experts. However, the game of Dou Di Zhu presents significant challenges due to its vast state/action space and unique characteristics involving reasoning about competition and cooperation, making the game extremely difficult to solve.The RL model DouZero, trained using the Deep Monte Carlo algorithm framework, has shown excellent performance in DouDiZhu. However, there are differences between its simplified game environment and the actual Dou Di Zhu environment, and its performance is still a considerable distance from that of human experts. This paper modifies the Deep Monte Carlo algorithm framework by using reinforcement learning to obtain a neural network that simultaneously estimates win rates and expectations. The action space is pruned using expectations, and strategies are generated based on win rates. This RL model is trained in a realistic DouDiZhu environment and achieves a state-of-the-art level among publicly available models.