We evaluate AI-assisted generative capabilities on fundamental numerical kernels in high-performance computing (HPC), including AXPY, GEMV, GEMM, SpMV, Jacobi Stencil, and CG. We test the generated kernel codes for a variety of language-supported programming models, including (1) C++ (e.g., OpenMP [including offload], OpenACC, Kokkos, SyCL, CUDA, and HIP), (2) Fortran (e.g., OpenMP [including offload] and OpenACC), (3) Python (e.g., numba, Numba, cuPy, and pyCUDA), and (4) Julia (e.g., Threads, CUDA.jl, AMDGPU.jl, and KernelAbstractions.jl). We use the GitHub Copilot capabilities powered by OpenAI Codex available in Visual Studio Code as of April 2023 to generate a vast amount of implementations given simple + + prompt variants. To quantify and compare the results, we propose a proficiency metric around the initial 10 suggestions given for each prompt. Results suggest that the OpenAI Codex outputs for C++ correlate with the adoption and maturity of programming models. For example, OpenMP and CUDA score really high, whereas HIP is still lacking. We found that prompts from either a targeted language such as Fortran or the more general-purpose Python can benefit from adding code keywords, while Julia prompts perform acceptably well for its mature programming models (e.g., Threads and CUDA.jl). We expect for these benchmarks to provide a point of reference for each programming model's community. Overall, understanding the convergence of large language models, AI, and HPC is crucial due to its rapidly evolving nature and how it is redefining human-computer interactions.

Email: glee@yonsei.ac.kr ABSTRACT This study explores the potential of generative artificial intelligence (AI) models, specifically OpenAI's generative pre-trained transformer (GPT) series, when integrated with building information modeling (BIM) tools as an interactive design assistant for architectural design. The research involves the development and implementation of three key components: 1) BIM2XML, a component that translates BIM data into extensible markup language (XML) format; 2) Generative AI-enabled Interactive Architectural design (GAIA), a component that refines the input design in XML by identifying designer intent, relevant objects, and their attributes, using pretrained language models; and 3) XML2BIM, a component that converts AI-generated XML data back into a BIM tool. This study validated the proposed approach through a case study involving design detailing, using the GPT series and Revit. Our findings demonstrate the effectiveness of state-of-the-art language models in facilitating dynamic collaboration between architects and AI systems, highlighting the potential for further advancements. INTRODUCTION The architectural design process is complex, demanding the incorporation of diverse ideas, constraints, and stakeholders to produce innovative and functional solutions.

The recent release of large language model (LLM) based chatbots, such as ChatGPT, has attracted significant attention on foundation models. It is widely believed that foundation models will serve as the fundamental building blocks for future AI systems. As foundation models are in their early stages, the design of foundation model based systems has not yet been systematically explored. There is little understanding about the impact of introducing foundation models in software architecture. Therefore, in this paper, we propose a taxonomy of foundation model based systems, which classifies and compares the characteristics of foundation models and design options of foundation model based systems. Our taxonomy comprises three categories: foundation model pretraining and fine-tuning, architecture design of foundation model based systems, and responsible-AI-by-design. This taxonomy provides concrete guidance for making major design decisions when designing foundation model based systems and highlights trade-offs arising from design decisions.

Advances in deep generative models shed light on de novo molecule generation with desired properties. However, molecule generation targeted for dual protein targets still faces formidable challenges including protein 3D structure data requisition for model training, auto-regressive sampling, and model generalization for unseen targets. Here, we proposed DiffDTM, a novel conditional structure-free deep generative model based on a diffusion model for dual targets based molecule generation to address the above issues. Specifically, DiffDTM receives protein sequences and molecular graphs as inputs instead of protein and molecular conformations and incorporates an information fusion module to achieve conditional generation in a one-shot manner. We have conducted comprehensive multi-view experiments to demonstrate that DiffDTM can generate drug-like, synthesis-accessible, novel, and high-binding affinity molecules targeting specific dual proteins, outperforming the state-of-the-art (SOTA) models in terms of multiple evaluation metrics. Furthermore, we utilized DiffDTM to generate molecules towards dopamine receptor D2 and 5-hydroxytryptamine receptor 1A as new antipsychotics. The experimental results indicate that DiffDTM can be easily plugged into unseen dual targets to generate bioactive molecules, addressing the issues of requiring insufficient active molecule data for training as well as the need to retrain when encountering new targets.

The US government is asking qualified members of the public for help in figuring out how to seize opportunities and overcome challenges associated with generative AI. Gina Raimondo, the US Secretary of Commerce, has announced that the National Institute of Standards and Technology (NIST) is launching a public working group for AI technologies that can generate content, including text, images, videos, music and code. The group will also help the agency develop key guidance that organizations can follow to address risks brought by generative AI tech. According to the agency, the group will be composed of volunteers with technical expertise from the private and public sectors and will work together via a collaborative online workspace. To start with, the group will gather input on how the NIST AI Risk Management Framework -- the framework the agency developed to "better manage risks to individuals, organizations and society associated with artificial intelligence" -- may be used to support the development of generative AI tech.

Digital minister Taro Kono is eager to promote the active use of generative artificial intelligence by central government staffers, expecting "a big benefit" from it. The use of AI will provide "a great benefit at central government workplaces as long as learning data is handled carefully," Kono said in a recent interview. Kono said office work would be done far more easily by utilizing AI, which can automatically generate texts and images. For example, by inputting the full text of a government white paper into an AI-powered program, officials would be able to easily create a summary or presentation slides, Kono said. This could be due to a conflict with your ad-blocking or security software.

Generative artificial intelligence (AI) has captured considerable popular attention recently. ChatGPT and DALL-E have given members of the general public opportunities to use AI systems to generate text and image outputs for fun and a wide range of other purposes. Google and Meta have announced their intentions to launch similar AI systems soon.

If ChatGPT and generative AI live up to even a tenth of the hype surrounding them, wide-scale job losses might seem inevitable. But new economic data shows that the last big leap in AI did not coincide with a reduction of jobs in affected industries--despite widespread fears of rapid replacement at the time. In a new research paper, economists looked at the job market across a number of European countries between 2011 and 2019. That's the period during which the AI technique deep learning emerged as a powerful way to automate tasks like transcribing speech, analyzing images, and making algorithmic recommendations for social feeds and ecommerce sites. Back then, deep learning was widely expected to have a broad and swift impact on employment.

Europe's focus on business practices of American tech titans is evident in the agenda for Breton's trip. The morning after the stress test, he will host a launch event for the European Union's San Francisco office, a physical foothold for regulators in the tech industry's backyard. He'll also meet with a host of tech executives shaping the future of AI, including Meta chief executive Mark Zuckerberg, chip maker Nvidia chief executive Jensen Huang and OpenAI chief executive Sam Altman. During those meetings, he plans to discuss a new "AI Pact," a voluntary pledge to ensure the responsible development of AI until the AI Act takes effect. Google chief executive Sundar Pichai agreed to take the pledge recently, Breton said.

The education ministry plans to implement new guidelines allowing elementary, junior high and high schools limited use of generative artificial intelligence, such as ChatGPT, to help formulate ideas to facilitate classroom discussions, among other uses, sources close to the matter said Thursday. However, the draft guidelines say generative AI should not be used in exams that measure students' academic performance or be used freely by students without them knowing the tendencies and limitations of the technology. It also said its use to create poems or haiku or in artistic activities without careful consideration is inappropriate. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites. If this does not resolve the issue or you are unable to add the domains to your allowlist, please see this FAQ.