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Three former OpenAI employees say they have been fired due to their efforts to ensure the safety of artificial intelligence. The trio warned of a "chilling" effect on other staff concerned about the frontier technology's risks. "I believe we were fired for prioritizing safety over the near-term interests of OpenAI as a corporation," Balesni said in a post on X. Korbak expressed a similar sentiment, expressing his belief that he had been terminated for raising concerns that OpenAI was "losing the ability to monitor what AI agents think, one of our best tools for catching when they misbehave". In an open letter published in tandem with their social media posts, Balesni, Korbak and Wang expressed concerns that their dismissal would make their former colleagues "afraid to speak and operate in ways that, until last week, were an integral part of working at OpenAI". "We could raise safety concerns and disagree openly, and were encouraged to draw on the expertise of independent safety organizations," they said in the letter. "This is part of what made OpenAI special, and why we are immensely proud to have been part of the team."

Things to Do in L.A. This is read by an automated voice. Please report any issues or inconsistencies here . See more from the L.A. Times in Google Search. California lawmakers have passed legislation that will make it easier for government agencies to protect themselves from the rising use of artificial intelligence for public comment, records requests and other forms of civic engagement.

This position paper argues that real-time generative AI has the potential to become the next wave of addictive digital media, creating a new class of digital content akin to "digital heroin" with severe implications for mental health and youth development. By shortening the content-generation feedback loop to mere seconds, these advanced models will soon be able to hyper-personalize outputs on the fly. When paired with misaligned incentives (e.g., maximizing user engagement), this will fuel unprecedented compulsive consumption patterns with far-reaching consequences for mental health, cognitive development, and social stability. Drawing on interdisciplinary research, from clinical observations of social media addiction to neuroscientific studies of dopamine-driven feedback, we illustrate how real-time tailored content generation may erode user autonomy, foment emotional distress, and disproportionately endanger vulnerable groups, such as adolescents. Due to the rapid advancement of generative AI and its potential to induce severe addictionlike effects, we call for strong government oversight akin to existing controls on addictive substances, particularly for minors. We further urge the machine learning community to act proactively by establishing robust design guidelines, collaborating with public health experts, and supporting targeted policy measures to ensure responsible and ethical deployment, rather than paving the way for another wave of unregulated digital dependence.

From media platforms to chatbots, algorithms shape how people interact, learn, and discover information. Such interactions between users and an algorithm often unfold over multiple steps, during which strategic users can guide the algorithm to better align with their true interests by selectively engaging with content. However, users frequently exhibit inconsistent preferences: they may spend considerable time on content that offers little long-term value, inadvertently signaling that such content is desirable. Focusing on the user side, this raises a key question: what does it take for such users to align the algorithm with their true interests? To investigate these dynamics, we model the user's decision process as split between a rational "system 2" that decides whether to engage and an impulsive "system 1" that determines how long engagement lasts.

Recent advances in diffusion models have dramatically improved image fidelity and diversity. However, aligning these models with nuanced human preferences -such as aesthetics, engagement, and subjective appeal remains a key challenge due to the scarcity of large-scale human annotations. Collecting such data is both expensive and limited in diversity. To address this, we leverage the reasoning capabilities of vision-language models (VLMs) and propose Self-Play Reward Optimization (SPRO), a scalable, annotation-free training framework based on multimodal self-play. SPRO learns to jointly align prompt and image generation with human preferences by iteratively generating, evaluating, and learning to refine outputs using synthetic reward signals such as aesthetics and human engagement.

Recent advances in diffusion models have dramatically improved image fidelity and diversity. However, aligning these models with nuanced human preferences -such as aesthetics, engagement, and subjective appeal remains a key challenge due to the scarcity of large-scale human annotations. Collecting such data is both expensive and limited in diversity. To address this, we leverage the reasoning capabilities of vision-language models (VLMs) and propose Self-Play Reward Optimization (SPRO), a scalable, annotation-free training framework based on multimodal self-play. SPRO learns to jointly align prompt and image generation with human preferences by iteratively generating, evaluating, and learning to refine outputs using synthetic reward signals such as aesthetics and human engagement.

The danger is no longer just misinformation. Thanks to AI, it's now possible to manufacture fake realities at scale. Deepfake videos, cloned voices, and bogus news stories are dissolving the line between what's real and what's not faster than society can adapt.

The systems behind them--and the reasons we keep passing around war memes as entertainment--are more serious. As ceasefire announcements between the US and Iran --and separately between Israel and Lebanon --dominated headlines over the past two weeks, they also prompted a look back at how war spread online: through memes. There were jokes about conscription. Captions about getting drafted, but at least with a Bluetooth device. The song "Bazooka" went viral, with users lip-syncing to "Rest in peace my granny, she got hit by a bazooka."

Machine learning competitions (MLCs) play a pivotal role in advancing artificial intelligence (AI) by fostering innovation, skill development, and practical problem-solving. This study provides a comprehensive analysis of major competition platforms such as Kaggle and Zindi, examining their workflows, evaluation methodologies, and reward structures. It further assesses competition quality, participant expertise, and global reach, with particular attention to demographic trends among top-performing competitors. By exploring the motivations of competition hosts, this paper underscores the significant role of MLCs in shaping AI development, promoting collaboration, and driving impactful technological progress. Furthermore, by combining literature synthesis with platform-level data analysis and practitioner insights a comprehensive understanding of the MLC ecosystem is provided. Moreover, the paper demonstrates that MLCs function at the intersection of academic research and industrial application, fostering the exchange of knowledge, data, and practical methodologies across domains. Their strong ties to open-source communities further promote collaboration, reproducibility, and continuous innovation within the broader ML ecosystem. By shaping research priorities, informing industry standards, and enabling large-scale crowdsourced problem-solving, these competitions play a key role in the ongoing evolution of AI. The study provides insights relevant to researchers, practitioners, and competition organizers, and includes an examination of the future trajectory and sustained influence of MLCs on AI development.