The filmmaker, who died in January, showed us what our world was becoming, and how we should respond. In the summer, the actress Natasha Lyonne relayed an anecdote about the late director David Lynch, in which he told her that A.I. in the creative arts would soon be as ubiquitous and indispensable as the pencil. Lyonne, who happens to be the co-founder of an A.I. studio, seemed to be implying that the revered filmmaker had offered his approval to the same nihilistic and destructive technology that recently enabled President Donald Trump to imagine himself as a king in a fighter jet dropping payloads of diarrhea on the people he's sworn to serve. In an interview with magazine in November, 2024, he said that, on the one hand, "the good side" of A.I. could be "important for moving forward in a beautiful way," and, on the other, "if money is the bottom line, there'd be a lot of sadness, and despair and horror." He added, "I'm hoping better times are coming." In January, amid the wildfires that ravaged Los Angeles, Lynch was evacuated from his home and died shortly thereafter, of complications from emphysema.

--In conflict, people use emotional expressions to shape their counterparts' thoughts, feelings, and actions. This paper explores whether automatic text emotion recognition offers insight into this influence in the context of dispute resolution. Prior work has shown the promise of such methods in negotiations; however, disputes evoke stronger emotions and different social processes. We use a large corpus of buyer-seller dispute dialogues to investigate how emotional expressions shape subjective and objective outcomes. We further demonstrate that large-language models yield considerably greater explanatory power than previous methods for emotion intensity annotation and better match the decisions of human annotators. Findings support existing theoretical models for how emotional expressions contribute to conflict escalation and resolution and suggest that agent-based systems could be useful in managing disputes by recognizing and potentially mitigating emotional escalation. Emotional expressions serve essential social functions in human relationships. They convey one's beliefs, desires, and intentions -- shaping the beliefs, desires, and intentions of interaction partners [1], [2]. People high in emotional intelligence achieve more success in navigating emotional relationships [3], and there exists growing interest in creating AI agents that understand and enact these social functions [4], [5]. Prior work suggests that emotionally-aware agents are suitable for a growing list of applications, including teaching people to convey emotions effectively [6], improving human-agent interaction [7], detecting and moderating toxic communication [8], and serving as methodological tools for studying human emotion [9]. This paper examines the capacity of agents to understand human emotional expressions in the context of text-based dispute resolution. Disputes arise when one party in a relationship (an individual, group, or nation) levies a claim that another party refuses to accept, thus threatening the future of the relationship [10].

As artificial intelligence (AI) improves, traditional alignment strategies may falter in the face of unpredictable self-improvement, hidden subgoals, and the sheer complexity of intelligent systems. Inspired by contemplative wisdom traditions, we show how four axiomatic principles can instil a resilient Wise World Model in AI systems. First, mindfulness enables self-monitoring and recalibration of emergent subgoals. Second, emptiness forestalls dogmatic goal fixation and relaxes rigid priors. Third, non-duality dissolves adversarial self-other boundaries. Fourth, boundless care motivates the universal reduction of suffering. We find that prompting AI to reflect on these principles improves performance on the AILuminate Benchmark (d=.96) and boosts cooperation and joint-reward on the Prisoner's Dilemma task (d=7+). We offer detailed implementation strategies at the level of architectures, constitutions, and reinforcement on chain-of-thought. For future systems, active inference may offer the self-organizing and dynamic coupling capabilities needed to enact Contemplative AI in embodied agents.

NVIDIA CEO and co-founder Jensen Huang commends President Donald Trump's A.I. agenda and outlines what the country's job future will look like on'Special Report.' Classical and character-based education may seem to some antiquated concepts in the new AI-driven world. However, two recent and prominent AI developments definitively prove the opposite to be true. Going back to our nation's founding, great minds were universal in the belief that the survival of the Republic depended on an educated and virtuous public. Now, if AI experts are to be believed, classical and character education is fundamental to the very survival of humanity.

We present KODIS, a dyadic dispute resolution corpus containing thousands of dialogues from over 75 countries. Motivated by a theoretical model of culture and conflict, participants engage in a typical customer service dispute designed by experts to evoke strong emotions and conflict. The corpus contains a rich set of dispositional, process, and outcome measures. The initial analysis supports theories of how anger expressions lead to escalatory spirals and highlights cultural differences in emotional expression. We make this corpus and data collection framework available to the community.

As large language models (LLMs) enter the mainstream, aligning them to foster constructive dialogue rather than exacerbate societal divisions is critical. Using an individualized and multicultural alignment dataset of over 7,500 conversations of individuals from 74 countries engaging with 21 LLMs, we examined how linguistic attributes linked to constructive interactions are reflected in human preference data used for training AI. We found that users consistently preferred well-reasoned and nuanced responses while rejecting those high in personal storytelling. However, users who believed that AI should reflect their values tended to place less preference on reasoning in LLM responses and more on curiosity. Encouragingly, we observed that users could set the tone for how constructive their conversation would be, as LLMs mirrored linguistic attributes, including toxicity, in user queries.

Artificial intelligence (AI) and large language models (LLMs) in healthcare require advanced clinical skills (CS), yet current benchmarks fail to evaluate these comprehensively. We introduce MedQA-CS, an AI-SCE framework inspired by medical education's Objective Structured Clinical Examinations (OSCEs), to address this gap. MedQA-CS evaluates LLMs through two instruction-following tasks, LLM-as-medical-student and LLM-as-CS-examiner, designed to reflect real clinical scenarios. Our contributions include developing MedQA-CS, a comprehensive evaluation framework with publicly available data and expert annotations, and providing the quantitative and qualitative assessment of LLMs as reliable judges in CS evaluation. Our experiments show that MedQA-CS is a more challenging benchmark for evaluating clinical skills than traditional multiple-choice QA benchmarks (e.g., MedQA). Combined with existing benchmarks, MedQA-CS enables a more comprehensive evaluation of LLMs' clinical capabilities for both open- and closed-source LLMs.

In the 1990s and early 2000s, technologists made the world a grand promise: new communications technologies would strengthen democracy, undermine authoritarianism, and lead to a new era of human flourishing. But today, few people would agree that the internet has lived up to that lofty goal. Today, on social media platforms, content tends to be ranked by how much engagement it receives. Over the last two decades politics, the media, and culture have all been reshaped to meet a single, overriding incentive: posts that provoke an emotional response often rise to the top. Efforts to improve the health of online spaces have long focused on content moderation, the practice of detecting and removing bad content.

It is important to note that Recent work in conversational AI has focused our argument applies to any use of empathetic AI on generating empathetic responses to users' (see also for example (Morris et al., 2018; De Carolis emotional states (e.g., Ide and Kawahara, 2022; et al., 2017)). What happens if the chatbot gets Svikhnushina et al., 2022; Zhu et al., 2022) as a way it right? There may be instances where a chatbot to increase or maintain engagement and rapport correctly identifies that a given situation is worthy with the user and to simulate intelligence. However, of praise and amplifies the pride of the user and these empathetic responses are problematic.

NO matter how intelligent machines will be, the human element is still needed when it comes to decisions involving law and judgments. But in the long run, using artificial intelligence (AI) in our justice system will help improve the quality of judgements and avoid lengthy and expensive litigation processes. While Sabah and Sarawak are using it now in courts, plans are still in the pipeline for the system to be applied in Peninsular Malaysia. "AI is used to assist us in better decision- making, as it amplifies our capacity and detects flaws at the same time. "However, it also has no element of emotion or compassion which can only come from a person.