Recent progress in generative AI, especially diffusion models, has demonstrated significant utility in text-to-image synthesis. Particularly in healthcare, these models offer immense potential in generating synthetic datasets and training medical students. However, despite these strong performances, it remains uncertain if the image generation quality is consistent across different demographic subgroups. To address this critical concern, we present the first comprehensive study on the fairness of medical text-to-image diffusion models. Our extensive evaluations of the popular Stable Diffusion model reveal significant disparities across gender, race, and ethnicity. To mitigate these biases, we introduce FairDiffusion, an equity-aware latent diffusion model that enhances fairness in both image generation quality as well as the semantic correlation of clinical features. In addition, we also design and curate FairGenMed, the first dataset for studying the fairness of medical generative models. Complementing this effort, we further evaluate FairDiffusion on two widely-used external medical datasets: HAM10000 (dermatoscopic images) and CheXpert (chest X-rays) to demonstrate FairDiffusion's effectiveness in addressing fairness concerns across diverse medical imaging modalities. Together, FairDiffusion and FairGenMed significantly advance research in fair generative learning, promoting equitable benefits of generative AI in healthcare.

OpenAI has laid out a plan to revamp its corporate structure next year, saying it would create a public benefit corporation to manage its growing business and ease the restrictions imposed by its current non-profit parent. Rumors have swirled that OpenAI was in the process of shifting to a largely for-profit company, but this is the first time it has detailed the proposal publicly. Under the proposed structure, the public benefit corporation, which is a for-profit corporate entity, will run and control OpenAI's operations and business, while the non-profit will hire a leadership team and staff for charitable initiatives in sectors such as healthcare, education and science. This new structure will give the for-profit arm of OpenAI much more control. In a blogpost, the company said it is "a stronger non-profit supported by the for-profit's success".

Following months of speculation, OpenAI has finally shared how it plans to become a for-profit company. In a blog post penned by its board of directors, OpenAI said Thursday it plans to transform its for-profit arm into a Public Benefit Corporation sometime in 2025. PBCs or B Corps are for-profit organizations that attempt to balance the interests of their stakeholders while making a positive impact on society. "As we enter 2025, we will have to become more than a lab and a startup -- we have to become an enduring company," OpenAI said, adding that many of its competitors are registered as PBCs, including Anthropic and even Elon Musk's own xAI. "[The move] would enable us to raise the necessary capital with conventional terms like others in this space."

If you or someone you know is having thoughts of suicide, please contact the Suicide & Crisis Lifeline at 988 or 1-800-273-TALK (8255). Balaji's death on November 26 was ruled a suicide, and Fox News Digital previously reported that the San Francisco Police Department found no evidence of foul play. But the 26-year-old's mother is urging police to reopen their investigation, saying it "doesn't look like a normal situation." Bereaved mother Poornima Ramarao told Business Insider that a private autopsy commissioned by Balaji's family and completed in early December produced concerning results. Now, they are working with an attorney to urge the department to conduct a "proper investigation."

Differential equations (DEs) are crucial for modeling the evolution of natural or engineered systems. Traditionally, the parameters in DEs are adjusted to fit data from system observations. However, in fields such as politics, economics, and biology, available data are often independently collected at distinct time points from different subjects (i.e., repeated cross-sectional (RCS) data). Conventional optimization techniques struggle to accurately estimate DE parameters when RCS data exhibit various heterogeneities, leading to a significant loss of information. To address this issue, we propose a new estimation method called the emulator-informed deep-generative model (EIDGM), designed to handle RCS data. Specifically, EIDGM integrates a physics-informed neural network-based emulator that immediately generates DE solutions and a Wasserstein generative adversarial network-based parameter generator that can effectively mimic the RCS data. We evaluated EIDGM on exponential growth, logistic population models, and the Lorenz system, demonstrating its superior ability to accurately capture parameter distributions. Additionally, we applied EIDGM to an experimental dataset of Amyloid beta 40 and beta 42, successfully capturing diverse parameter distribution shapes. This shows that EIDGM can be applied to model a wide range of systems and extended to uncover the operating principles of systems based on limited data.

The preliminary spacecraft trajectory design phase can be posed as a parameterized global search problem for optimal spacecraft trajectories. At each stage of the preliminary design, the mission objectives, requirements, and constraints may change, resulting in variations of the global search problem parameters. Parameters may also change to represent increased modeling fidelity. The aim at any stage of the preliminary design is to solve for a large set of high quality spacecraft trajectories with diverse, or similarly qualitatively different, features. High quality is naturally defined by the value of a solution's objective value relative to the best known. Examples of qualitatively different features may include trajectories that have a different number of revolutions around a central body, a different number or sequence of gravity assist flybys, solutions that avoid radiation belts or other hazards, or solutions that depart the original or target orbital planes. The benefit of having different qualitative solutions is that it allows mission designers to trade different priorities in their design and reflects the fact that not all relevant objectives and constraints can be incorporated into the optimal spacecraft trajectory problem so early or readily in the design phase (i.e., without prior knowledge of what is relevant and when designing at a quick cadence). In the simplest of cases, a mission designer's past experience may be sufficient to guide them in finding a high quality set of solutions.

In the construction sector, workers often endure prolonged periods of high-intensity physical work and prolonged use of tools, resulting in injuries and illnesses primarily linked to postural ergonomic risks, a longstanding predominant health concern. To mitigate these risks, researchers have applied various technological methods to identify the ergonomic risks that construction workers face. However, traditional ergonomic risk assessment (ERA) techniques do not offer interactive feedback. The rapidly developing vision-language models (VLMs), capable of generating textual descriptions or answering questions about ergonomic risks based on image inputs, have not yet received widespread attention. This research introduces an interactive visual query system tailored to assess the postural ergonomic risks of construction workers. The system's capabilities include visual question answering (VQA), which responds to visual queries regarding workers' exposure to postural ergonomic risks, and image captioning (IC), which generates textual descriptions of these risks from images. Additionally, this study proposes a dataset designed for training and testing such methodologies. Systematic testing indicates that the VQA functionality delivers an accuracy of 96.5%. Moreover, evaluations using nine metrics for IC and assessments from human experts indicate that the proposed approach surpasses the performance of a method using the same architecture trained solely on generic datasets. This study sets a new direction for future developments in interactive ERA using generative artificial intelligence (AI) technologies. Keywords: Generative Artificial Intelligence; Vision-Language Model; Large language model; Ergonomic Risk Assessment; Construction Safety 1 Introduction Prompt and effective identification and mitigation of workplace hazards are essential for maintaining safety, health, and productivity within the work environment. In the construction industry, workers are often subject to conditions that require awkward body postures, repetitive motions, and intense physical effort, which can detrimentally impact their health [1]. Such conditions in construction tasks usually lead to the emergence of work-related musculoskeletal disorders (WMSDs). Statistics from the United States Bureau of Labor Statistics show that the construction industry's injuries and illnesses caused by WMSDs ranked fifth among all industries. Moreover, in the same year, WMSDs represented 30% of all occupational injuries and illnesses [1]. According to the Association of Workers' Compensation Boards of Canada, the manufacturing and construction sectors reported the second and third-highest rates of losttime injury claims in 2021, representing 13.6% and 10.4% of claims, respectively [2]. European Agency for Safety and Health at Work indicated that the construction and manufacturing sectors reported the highest sick leave rates due to WMSDs [3].

Since the release of iOS 18.2 on December 11, ChatGPT integration has been an integral part of Apple Intelligence. Provided you own a recent iPhone, iPad or Mac, you can access OpenAI's chatbot directly from your device, with no need to go through the ChatGPT app or web client. ChatGPT is a generative AI chatbot created by OpenAI and powered by a large language machine-learning model. In addition to the capability to interact with people using natural language, ChatGPT can search the web, solve complex math and coding problems, as well as generate text, images and audio. As of the writing of this article, the current version of ChatGPT is based on OpenAI's GPT-4o and 4o mini models.

In the spring, the US saw what was likely its first AI candidate. In a brief campaign for the mayor of Wyoming, virtual integrated citizen (VIC), a ChatGPT-based bot created by real human Victor Miller, promised to govern entirely by AI. At the outset of 2024, many suggested that even if not winning office, generative AI would play a pivotal role in--and pose significant risks to--democratic elections, as more than 2 billion people voted in more than 60 countries. But now, experts and analysts have changed their tune, saying that generative AI likely had little to no effect at all. So were all those prognostications that 2024 would be the AI election year wrong?

"This is a powerful technology that will sweep through American offices with potentially very significant geographic implications," said Mark Muro, a senior fellow at the Brookings Institution, where he studies the regional effects of technology and government policy. "We need to think about what's coming down the pike." At issue is a new and rapidly growing breed of the technology known as generative A.I., which can quickly draft business reports, write software and answer questions, often with human-level skill. Already, predictions abound that generative A.I. will displace workers in call centers, software developers and business analysts. That pattern of technology disruption has happened before.