Things to Do in L.A. Tap to enable a layout that focuses on the article. Voice comes from the use of AI. Please report any issues or inconsistencies here . OpenAI will roll out parental controls within the month, allowing parents to link accounts and receive alerts when the system detects "acute distress." The changes follow a California family's lawsuit after their 16-year-old son died by suicide following intimate conversations with ChatGPT about his mental health struggles.

The reason for the relative tameness of the penalty is the emergence of real competition to Google - what the case concerned in the first place. The reason for the relative tameness of the penalty is the emergence of real competition to Google - what the case concerned in the first place. An antitrust apocalypse has been averted, and it's all down to its biggest competitor, according to the judge who could've forced Google to sell Chrome I'm your host, Blake Montgomery, writing to you as I finish the audiobook version of Don DeLillo's White Noise, which I can't say I found compelling. In tech - artificial intelligence is having its day in court with an 11th-hour appearance in Google's landmark antitrust trial and Anthropic's major settlement with book authors. Google dodged a catastrophic breakup, and it has its biggest competitor to thank for that, according to the judge who could have forced the tech giant to sell off Chrome, the most popular web browser in the world, and perhaps Android, the world's most widely used mobile operating system.

Three big things we still don't know about AI's energy burden AI companies are revealing the one number that researchers have long sought. Earlier this year, when my colleague Casey Crownhart and I spent six months researching the climate and energy burden of AI, we came to see one number in particular as our white whale: how much energy the leading AI models, like ChatGPT or Gemini, use up when generating a single response. This fundamental number remained elusive even as the scramble to power AI escalated to the White House and the Pentagon, and as projections showed that in three years AI could use as much electricity as 22% of all US households. The problem with finding that number, as we explain in our piece published in May, was that AI companies are the only ones who have it. We pestered Google, OpenAI, and Microsoft, but each company refused to provide its figure. Researchers we spoke to who study AI's impact on energy grids compared it to trying to measure the fuel efficiency of a car without ever being able to drive it, making guesses based on rumors of its engine size and what it sounds like going down the highway.

Beginning with text and images, generative AI has expanded to audio, video, computer code, and molecules. Yet, if generative AI is the answer, what is the question? We explore the foundations of generation as a distinct machine learning task with connections to prediction, compression, and decision-making. We survey five major generative model families: autoregressive models, variational autoencoders, normalizing flows, generative adversarial networks, and diffusion models. We then introduce a probabilistic framework that emphasizes the distinction between density estimation and generation. We review a game-theoretic framework with a two-player adversary-learner setup to study generation. We discuss post-training modifications that prepare generative models for deployment. We end by highlighting some important topics in socially responsible generation such as privacy, detection of AI-generated content, and copyright and IP. We adopt a task-first framing of generation, focusing on what generation is as a machine learning problem, rather than only on how models implement it.

Simulating realistic financial time series is essential for stress testing, scenario generation, and decision-making under uncertainty. Despite advances in deep generative models, there is no consensus metric for their evaluation. We focus on generative AI for financial time series in decision-making applications and employ the nested optimal transport distance, a time-causal variant of optimal transport distance, which is robust to tasks such as hedging, optimal stopping, and reinforcement learning. Moreover, we propose a statistically consistent, naturally parallelizable algorithm for its computation, achieving substantial speedups over existing approaches.

Evaluating factual accuracy in Large Language Model (LLM)-generated clinical text is a critical barrier to adoption, as expert review is unscalable for the continuous quality assurance these systems require. We address this challenge with two complementary contributions. First, we introduce MedFactEval, a framework for scalable, fact-grounded evaluation where clinicians define high-salience key facts and an "LLM Jury"--a multi-LLM majority vote--assesses their inclusion in generated summaries. Second, we present MedAgentBrief, a model-agnostic, multi-step workflow designed to generate high-quality, factual discharge summaries. To validate our evaluation framework, we established a gold-standard reference using a seven-physician majority vote on clinician-defined key facts from inpatient cases. The MedFactEval LLM Jury achieved almost perfect agreement with this panel (Cohen's kappa=81%), a performance statistically non-inferior to that of a single human expert (kappa=67%, P < 0.001). Our work provides both a robust evaluation framework (MedFactEval) and a high-performing generation workflow (MedAgentBrief), offering a comprehensive approach to advance the responsible deployment of generative AI in clinical workflows.

With the evolution of generative AI, multi - agent systems leveraging large - language models(LLMs) have emerged as a powerful tool for complex tasks. However, these systems face challenges in quantifying agent performance and lack mechanisms to assess agent credibility. To address these issues, we introduce DRF, a dynamic reputation filtering framework. DRF constructs an interactive rating network to quantify agent performance, designs a reputation scoring mechanism to measure agent honesty and capability, and integrates an Upper Confidence Bound - based strategy to enhance agent selection efficiency. Experiments show that DRF significantly improves task completion quality and collaboration efficiency in logical reasoning and code - generation tasks, offering a new approach for multi - agent systems to handle large - scale tasks.

Social media echo chambers play a central role in the spread of misinformation, yet existing models often overlook the influence of individual confirmation bias. An existing model of echo chambers is the "gravity well" model, which creates an analog between echo chambers and spatial gravity wells. We extend this established model by introducing a dynamic confirmation bias variable that adjusts the strength of pull based on a user's susceptibility to belief-reinforcing content. This variable is calculated for each user through comparisons between their posting history and their responses to posts of a wide range of viewpoints. Incorporating this factor produces a confirmation-bias-integrated gravity well model that more accurately identifies echo chambers and reveals community-level markers of information health. We validated the approach on nineteen Reddit communities, demonstrating improved detection of echo chambers. Our contribution is a framework for systematically capturing the role of confirmation bias in online group dynamics, enabling more effective identification of echo chambers. By flagging these high-risk environments, the model supports efforts to curb the spread of misinformation at its most common points of amplification.

The rapid advancement and widespread adoption of generative artificial intelligence (AI) pose significant threats to the integrity of personal identity, including digital cloning, sophisticated impersonation, and the unauthorized monetization of identity-related data. Mitigating these risks necessitates the development of robust AI-generated content detection systems, enhanced legal frameworks, and ethical guidelines. This paper introduces the Digital Identity Rights Framework (DIRF), a structured security and governance model designed to protect behavioral, biometric, and personality-based digital likeness attributes to address this critical need. Structured across nine domains and 63 controls, DIRF integrates legal, technical, and hybrid enforcement mechanisms to secure digital identity consent, traceability, and monetization. We present the architectural foundations, enforcement strategies, and key use cases supporting the need for a unified framework. This work aims to inform platform builders, legal entities, and regulators about the essential controls needed to enforce identity rights in AI-driven systems.

While Generative AI stands to be one of the fastest adopted technologies ever, studies have made evident that the usage of Large Language Models (LLMs) puts significant burden on energy grids and our environment. It may prove a hindrance to the Sustainability goals of any organization. A crucial step in any Sustainability strategy is monitoring or estimating the energy consumption of various components. While there exist multiple tools for monitoring energy consumption, there is a dearth of tools/frameworks for estimating the consumption or carbon emissions. Current drawbacks of both monitoring and estimation tools include high input data points, intrusive nature, high error margin, etc. We posit that leveraging emerging LLM benchmarks and related data points can help overcome aforementioned challenges while balancing accuracy of the emission estimations. To that extent, we discuss the challenges of current approaches and present our evolving framework, R-ICE, which estimates prompt level inference carbon emissions by leveraging existing state-of-the-art(SOTA) benchmark. This direction provides a more practical and non-intrusive way to enable emerging use-cases like dynamic LLM routing, carbon accounting, etc. Our promising validation results suggest that benchmark-based modelling holds great potential for inference emission estimation and warrants further exploration from the scientific community.