Recent years have witnessed a rapid growth of deep generative models, with text-to-image models gaining significant attention from the public. However, existing models often generate images that do not align well with human preferences, such as awkward combinations of limbs and facial expressions. To address this issue, we collect a dataset of human choices on generated images from the Stable Foundation Discord channel. Our experiments demonstrate that current evaluation metrics for generative models do not correlate well with human choices. Thus, we train a human preference classifier with the collected dataset and derive a Human Preference Score (HPS) based on the classifier. Using HPS, we propose a simple yet effective method to adapt Stable Diffusion to better align with human preferences. Our experiments show that HPS outperforms CLIP in predicting human choices and has good generalization capability toward images generated from other models. By tuning Stable Diffusion with the guidance of HPS, the adapted model is able to generate images that are more preferred by human users. The project page is available here: https://tgxs002.github.io/align_sd_web/ .

Conservation laws are key theoretical and practical tools for understanding, characterizing, and modeling nonlinear dynamical systems. However, for many complex systems, the corresponding conserved quantities are difficult to identify, making it hard to analyze their dynamics and build stable predictive models. Current approaches for discovering conservation laws often depend on detailed dynamical information or rely on black box parametric deep learning methods. We instead reformulate this task as a manifold learning problem and propose a non-parametric approach for discovering conserved quantities. We test this new approach on a variety of physical systems and demonstrate that our method is able to both identify the number of conserved quantities and extract their values. Using tools from optimal transport theory and manifold learning, our proposed method provides a direct geometric approach to identifying conservation laws that is both robust and interpretable without requiring an explicit model of the system nor accurate time information.

The USCO agreed that the work was generated by an AI model that Thaler calls the Creativity Machine. He claimed that the USCO's "human authorship" requirement was unconstitutional. However, Howell indicated that Thaler's case wasn't an especially complex one, since he admitted that he wasn't involved in the creation of A Recent Entrance to Paradise. "In the absence of any human involvement in the creation of the work, the clear and straightforward answer is the one given by the [Federal] Register: No," Howell ruled. Thaler plans to appeal the decision.

Jared Genser in many ways fits a certain Washington, D.C., type. He wears navy suits and keeps his hair cut short. He graduated from a top law school, joined a large firm, and made partner at 40. Eventually, he became disenchanted with big law and started his own boutique practice with offices off--where else--Dupont Circle. What distinguishes Genser from the city's other 50-something lawyers is his unusual clientele: He represents high-value political prisoners.

When Elon Musk introduced the team behind his new artificial intelligence company xAI last month, the billionaire entrepreneur took a question from the rightwing media activist Alex Lorusso. ChatGPT had begun "editorializing the truth" by giving "weird answers like that there are more than two genders", Lorusso posited. Was that a driver behind Musk's decision to launch xAI, he wondered. "I do think there is significant danger in training AI to be politically correct, or in other words training AI to not say what it actually thinks is true," Musk replied. His own company's AI on the other hand, would be "maximally true" he had said earlier in the presentation.

Earlier this month, facing an increasingly precarious situation, the US Food and Drug Administration (FDA) and the Drug Enforcement Administration (DEA) joined forces to address the ongoing Adderall shortage. Technically, neither organization has the power to compel pharmaceutical companies to produce mixed amphetamine salts, but in the face of skyrocketing diagnoses for attention deficit hyperactivity disorder (ADHD) in the pandemic era of telemedicine, they wanted to reassure the public that they were looking into potential alternatives to stimulant medications. In a joint statement, the agencies acknowledged that while they were actively working with the pharmaceutical industry to address the shortages, the FDA did approve a "game based digital therapeutic" to address ADHD symptoms in children back in 2020. While it's unclear whether digital therapeutics can replace stimulants entirely (they probably can't), it is clear that people want options beyond amphetamines. And this summer, digital medicine company Akili Interactive dropped the first "over-the-counter" digital therapeutic for managing ADHD symptoms in adults, using the same technology underlying their previously FDA-approved prescription video game for kids.

The United Nations condemned a Russian missile attack on Ukraine's northern city of Chernihiv on Saturday morning, which killed seven people and injured dozens. Ukrainian President Volodymyr Zelenskyy promised a "tangible response" from Ukrainian forces to what he called a "heinous strike". The Institute for the Study of War said Ukrainian forces conducted offensive operations in western parts of the Zaporizhia region and made modest advances. Russian forces continued to launch offensive operations around the city of Kupiansk in the Kharkiv region but did not make any confirmed advances, it said. Kharkiv region's Governor Oleh Syniehubov posted on his Telegram channel that a man in his 40s was seriously injured this morning after Russian forces shelled Kupiansk.

The increasing attention given to AI Generated Content (AIGC) has brought a profound impact on various aspects of daily life, industrial manufacturing, and the academic sector. Recognizing the global trends and competitiveness in AIGC development, this study aims to analyze China's current status in the field. The investigation begins with an overview of the foundational technologies and current applications of AIGC. Subsequently, the study delves into the market status, policy landscape, and development trajectory of AIGC in China, utilizing keyword searches to identify relevant scholarly papers. Furthermore, the paper provides a comprehensive examination of AIGC products and their corresponding ecosystem, emphasizing the ecological construction of AIGC. Finally, this paper discusses the challenges and risks faced by the AIGC industry while presenting a forward-looking perspective on the industry's future based on competitive insights in AIGC.

The scarcity of task-labeled time-series benchmarks in the financial domain hinders progress in continual learning. Addressing this deficit would foster innovation in this area. Therefore, we present COB, Crude Oil Benchmark datasets. COB includes 30 years of asset prices that exhibit significant distribution shifts and optimally generates corresponding task (i.e., regime) labels based on these distribution shifts for the three most important crude oils in the world. Our contributions include creating real-world benchmark datasets by transforming asset price data into volatility proxies, fitting models using expectation-maximization (EM), generating contextual task labels that align with real-world events, and providing these labels as well as the general algorithm to the public. We show that the inclusion of these task labels universally improves performance on four continual learning algorithms, some state-of-the-art, over multiple forecasting horizons. We hope these benchmarks accelerate research in handling distribution shifts in real-world data, especially due to the global importance of the assets considered. We've made the (1) raw price data, (2) task labels generated by our approach, (3) and code for our algorithm available at https://oilpricebenchmarks.github.io.

Methane is one of the most potent greenhouse gases, and its short atmospheric half-life makes it a prime target to rapidly curb global warming. However, current methane emission monitoring techniques primarily rely on approximate emission factors or self-reporting, which have been shown to often dramatically underestimate emissions. Although initially designed to monitor surface properties, satellite multispectral data has recently emerged as a powerful method to analyze atmospheric content. However, the spectral resolution of multispectral instruments is poor, and methane measurements are typically very noisy. Methane data products are also sensitive to absorption by the surface and other atmospheric gases (water vapor in particular) and therefore provide noisy maps of potential methane plumes, that typically require extensive human analysis. Here, we show that the image recognition capabilities of deep learning methods can be leveraged to automatize the detection of methane leaks in Sentinel-2 satellite multispectral data, with dramatically reduced false positive rates compared with state-of-the-art multispectral methane data products, and without the need for a priori knowledge of potential leak sites. Our proposed approach paves the way for the automated, high-definition and high-frequency monitoring of point-source methane emissions across the world.