Materials design is an important component of modern science and technology, yet traditional approaches rely heavily on trial-and-error and can be inefficient. Computational techniques, enhanced by modern artificial intelligence (AI), have greatly accelerated the design of new materials. Among these approaches, inverse design has shown great promise in designing materials that meet specific property requirements. In this mini-review, we summarize key computational advancements for materials design over the past few decades. We follow the evolution of relevant materials design techniques, from high-throughput forward machine learning (ML) methods and evolutionary algorithms, to advanced AI strategies like reinforcement learning (RL) and deep generative models. We highlight the paradigm shift from conventional screening approaches to inverse generation driven by deep generative models. Finally, we discuss current challenges and future perspectives of materials inverse design. This review may serve as a brief guide to the approaches, progress, and outlook of designing future functional materials with technological relevance.

Face parsing is a fundamental task in computer vision, enabling applications such as identity verification, facial editing, and controllable image synthesis. However, existing face parsing models often lack fairness and robustness, leading to biased segmentation across demographic groups and errors under occlusions, noise, and domain shifts. These limitations affect downstream face synthesis, where segmentation biases can degrade generative model outputs. We propose a multi-objective learning framework that optimizes accuracy, fairness, and robustness in face parsing. Our approach introduces a homotopy-based loss function that dynamically adjusts the importance of these objectives during training. To evaluate its impact, we compare multi-objective and single-objective U-Net models in a GAN-based face synthesis pipeline (Pix2PixHD). Our results show that fairness-aware and robust segmentation improves photorealism and consistency in face generation. Additionally, we conduct preliminary experiments using ControlNet, a structured conditioning model for diffusion-based synthesis, to explore how segmentation quality influences guided image generation. Our findings demonstrate that multi-objective face parsing improves demographic consistency and robustness, leading to higher-quality GAN-based synthesis.

T he past century ha s witnessed incredible technological change . The many benefits and conveniences o f technology are accompanied by new complexities and human challenges that affect work, home, social, and civic realms. Th ere is a w idening gap "between a growing complexity of our own making and a lagging development of our own capacities" (Botkin et al., 1998) . Now, artificial intelligence promises to increase the rate of scientific discovery and innovation exponentially, creating new changes and p otential complexities to which humans must adapt (Friedman, 2017) . On the other hand, new AI tools, especially generative AI models, may help people to engage with the growing volume and complexity of information in their reasoning tasks such as decisionmaking and problem solving.

A United States federal judge has said that parts of Elon Musk's lawsuit against OpenAI to halt its conversion to a for-profit entity might go to trial, adding that the Tesla CEO will have to appear in court and testify. "Something is going to trial in this case," US District Judge Yvonne Gonzalez Rogers in Oakland, California, said early in the court session on Tuesday. "[Elon Musk will] sit on the stand, present it to a jury, and a jury will decide who is right." Rogers was considering Musk's recent request for a preliminary injunction to block OpenAI's conversion before going to trial, the latest move in a grudge match between the world's richest person and OpenAI CEO Sam Altman that is playing out publicly in court. The last time Rogers provided a preliminary injunction was in Epic Games's case against Apple in May 2021.

California State University on Tuesday unveiled what is believed to be among the largest and most ambitious efforts in higher education to champion artificial intelligence with an initiative to provide tools and training in the groundbreaking technology across the system's 23 campuses. With generative AI's ability to create new content learned from training data, CSU is working to ensure students in the nation's largest and most diverse public university system have equitable access to the technology. Nearly half of CSU's 450,000 students are low-income and about 30% are the first in their families to attend college. The university has enlisted Gov. Gavin Newsom's office and nearly a dozen leading tech companies -- including Microsoft, Meta, Nvidia, OpenAI, Intel, LinkedIn, Amazon Web Services and Alphabet -- to join academics on an advisory board to help identify AI skills needed in the California workforce and provide advice on how best to teach them. Industry partners will also provide internships and apprenticeships to give students real-world experience with AI on the job.

OpenAI's CEO Sam Altman has signed a deal with South Korean tech giant Kakao as his firm seeks new alliances after Chinese rival DeepSeek shook the global AI industry. Altman says the partnership, which lets Kakao use ChatGPT for its artificial intelligence services, will prioritise safety.

OpenAI said on Tuesday it will develop artificial intelligence products for South Korea with chat app operator Kakao, unveiling a second major alliance with a high-profile Asian partner this week. In a whirlwind tour through Asia, OpenAI CEO Sam Altman also announced a partnership with Japan's SoftBank on Monday and is, according to sources, scheduled to visit India on Wednesday where he is seeking to meet with Prime Minister Narendra Modi. Like SoftBank, Kakao said it would be using technology developed by the ChatGPT creator for its products.

An artificial intelligence-based chatbot will start answering questions from a Buddhist viewpoint in English in Bhutan, its developers including a Kyoto University professor said Monday. The team of the university and a startup initially developed a chatbot called Buddhabot in 2021 with the Japanese translation of the Sutta Nipata, considered to be the oldest collection of discourses of Buddha. Data on other classic collections were also incorporated into the AI Buddha later. In 2023, the team remodeled the Buddhabot using OpenAI's ChatGPT generative AI to create Buddhabot Plus, which adds interpretations and explanations to the discourses. The English version of Buddhabot Plus was completed last year following the Bhutanese government's request.

This technology has important applications in many challenges of the industry such as content moderation, tracing AI-generated content, and monitoring the usage of AI models. The contributions of this thesis include the development of new watermarking techniques for images, audio, and text. We first introduce methods for active moderation of images on social platforms. We then develop specific techniques for AI-generated content. We specifically demonstrate methods to adapt latent generative models to embed watermarks in all generated content, identify watermarked sections in speech, and improve watermarking in large language models with tests that ensure low false positive rates. Furthermore, we explore the use of digital watermarking to detect model misuse, including the detection of watermarks in language models fine-tuned on watermarked text, and introduce training-free watermarks for the weights of large transformers. Through these contributions, the thesis provides effective solutions for the challenges posed by the increasing use of generative AI models and the need for model monitoring and content moderation. It finally examines the challenges and limitations of watermarking techniques and discuss potential future directions for research in this area.

Art, as a universal language, can be interpreted in diverse ways, with artworks embodying profound meanings and nuances. The advent of Large Language Models (LLMs) and the availability of Multimodal Large Language Models (MLLMs) raise the question of how these transformative models can be used to assess and interpret the artistic elements of artworks. While research has been conducted in this domain, to the best of our knowledge, a deep and detailed understanding of the technical and expressive features of artworks using LLMs has not been explored. In this study, we investigate the automation of a formal art analysis framework to analyze a high-throughput number of artworks rapidly and examine how their patterns evolve over time. We explore how LLMs can decode artistic expressions, visual elements, composition, and techniques, revealing emerging patterns that develop across periods. Finally, we discuss the strengths and limitations of LLMs in this context, emphasizing their ability to process vast quantities of art-related data and generate insightful interpretations. Due to the exhaustive and granular nature of the results, we have developed interactive data visualizations, available online https://cognartive.github.io/, to enhance understanding and accessibility.