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.

Abstract-- This paper presents a teleoperation system for controlling a redundant degree-of-freedom (DOF) robot manipulator using human arm gestures. We propose a GRU-based Variational Autoencoder (VAE) to learn a latent representation of the manipulator's configuration space, capturing its complex joint kinematics. A fully-connected neural network maps human arm configurations into this latent space, allowing the system to mimic and generate corresponding manipulator trajectories in real-time through the VAE decoder. Arrow shows the mapping relationship between the manipulator's For example, an operator can use as agriculture, healthcare medicine, warehousing, and manufacturing. Another have proliferated, such as for image generation and natural approach instead uses an external RGB and RGBD (depth) language generation, for example ChatGPT, Midjourney, camera to estimate the operator's 6-DOF hand pose [4], [5] and Dall-E, to name a few.

Deep generative models have recently shown success in solving complex engineering design problems where models predict solutions that address the design requirements specified as input. However, there remains a challenge in aligning such models for effective design exploration. For many design problems, finding a solution that meets all the requirements is infeasible. In such a case, engineers prefer to obtain a set of Pareto optimal solutions with respect to those requirements, but uniform sampling of generative models may not yield a useful Pareto front. To address this gap, we introduce a new framework for Pareto-front design exploration with simulation fine-tuned generative models. First, the framework adopts preference alignment methods developed for Large Language Models (LLMs) and showcases the first application in fine-tuning a generative model for engineering design. The important distinction here is that we use a simulator instead of humans to provide accurate and scalable feedback. Next, we propose epsilon-sampling, inspired by the epsilon-constraint method used for Pareto-front generation with classical optimization algorithms, to construct a high-quality Pareto front with the fine-tuned models. Our framework, named e-SimFT, is shown to produce better-quality Pareto fronts than existing multi-objective alignment methods.

In response to the success of proprietary Large Language Models (LLMs) such as OpenAI's GPT-4, there is a growing interest in developing open, non-proprietary LLMs and AI foundation models (AIFMs) for transparent use in academic, scientific, and non-commercial applications. Despite their inability to match the refined functionalities of their proprietary counterparts, open models hold immense potential to revolutionize healthcare applications. In this paper, we examine the prospects of open-source LLMs and AIFMs for developing healthcare applications and make two key contributions. Firstly, we present a comprehensive survey of the current state-of-the-art open-source healthcare LLMs and AIFMs and introduce a taxonomy of these open AIFMs, categorizing their utility across various healthcare tasks. Secondly, to evaluate the general-purpose applications of open LLMs in healthcare, we present a case study on personalized prescriptions. This task is particularly significant due to its critical role in delivering tailored, patient-specific medications that can greatly improve treatment outcomes. In addition, we compare the performance of open-source models with proprietary models in settings with and without Retrieval-Augmented Generation (RAG). Our findings suggest that, although less refined, open LLMs can achieve performance comparable to proprietary models when paired with grounding techniques such as RAG. Furthermore, to highlight the clinical significance of LLMs-empowered personalized prescriptions, we perform subjective assessment through an expert clinician. We also elaborate on ethical considerations and potential risks associated with the misuse of powerful LLMs and AIFMs, highlighting the need for a cautious and responsible implementation in healthcare.

Microsoft is indeed making access to OpenAI's 01 AI reasoning model completely free -- but there's a limitation and one which Microsoft is refusing to tell you about. Microsoft said Wednesday that it would provide access to OpenAI's o1 model, for free, to Copilot users as part of a toggle option called "Think Deeper." OpenAI uses the o1 model in its paid ChatGPT plans and charges 20/mo for "limited" access to the model, and unlimited access to it for a Pro plan costing 200/mo. However, Mustafa Suleyman, the chief of Microsoft's AI, said that the model would be "free and available," and "everywhere at no cost" -- potentially an enormous discount. Microsoft said Friday that there are limits to their new Think Deeper feature, which they're keeping mum about.