GPU-based Private Information Retrieval for On-Device Machine Learning Inference | Maximilian Lam, Jeff Johnson, Wenjie Xiong, Kiwan Maeng, Udit Gupta, Minsoo Rhu, Hsien-Hsin S. Lee, Vijay Janapa Reddi, Gu-Yeon Wei, David Brooks, Edward Suh | Computer science, Information Retrieval, Machine learning, nVidia, nVidia V100, Security

New advancements in data science often spark dire predictions about how powerful new technologies will transform the world. Yet, as writer Stephen Shankland reminds us, technologies like Open AI's new Chat GPT (short for chat-based Generative Pretrained Transformer) are created by humans. Chat GPT is a chatbot that is "trained with human assistance to deliver more useful, better dialog." The people assisting that training--those who create the models and assemble the data used to train chatbots--make a difference in the technologies that will go on to shape our lives. Computer scientist Joy Buolamwini, an early critic of racial bias in facial recognition software, said technology should "be more attuned to the people who use it and the people it's used on."

OpenMP Advisor | Alok Mishra, Abid M. Malik, Meifeng Lin, Barbara Chapman | AMD Radeon Instinct Mi50, ATI, Benchmarking, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce RTX 2080, OpenMP, Tesla A100, Tesla K80, Tesla V100

SPOT is a quadruped robot "dog" from the Boston Dynamics company. It can be difficult for rescue personnel to reach an injured person in inaccessible terrain in time to provide necessary aid. It is probable that autonomous drones and quadruped robot "dogs" will become our'friends in need' in the future. But we are currently far from achieving full autonomy for these robotic systems. Consequently, well-functioning collaboration between human and machine is crucial. A moment later, a yellow quadruped robot makes its way across the well-manicured lawns of Gränsö Manor.

Flexible metadata pipelines are crucial for supporting the FAIR data principles. Despite this need, researchers seldom report their approaches for identifying metadata standards and protocols that support optimal flexibility. This paper reports on an initiative targeting the development of a flexible metadata pipeline for a collection containing over 300,000 digital fish specimen images, harvested from multiple data repositories and fish collections. The images and their associated metadata are being used for AI-related scientific research involving automated species identification, segmentation and trait extraction. The paper provides contextual background, followed by the presentation of a four-phased approach involving: 1. Assessment of the Problem, 2. Investigation of Solutions, 3. Implementation, and 4. Refinement. The work is part of the NSF Harnessing the Data Revolution, Biology Guided Neural Networks (NSF/HDR-BGNN) project and the HDR Imageomics Institute. An RDF graph prototype pipeline is presented, followed by a discussion of research implications and conclusion summarizing the results.

An office worker answers it and hears his boss, in a panic, tell him that she forgot to transfer money to the new contractor before she left for the day and needs him to do it. She gives him the wire transfer information, and with the money transferred, the crisis has been averted. The worker sits back in his chair, takes a deep breath, and watches as his boss walks in the door. The voice on the other end of the call was not his boss. The voice he heard was that of an audio deepfake, a machine-generated audio sample designed to sound exactly like his boss.

Advanced cyberinfrastructure (CI) is critical to science and engineering (S&E) research. For example, over the past two years, CI resources (including those provided by the COVID-19 HPC Consortiuma) enabled research that dramatically accelerated efforts to understand, respond to, and mitigate near- and longer-term impacts of the novel coronavirus disease 2019 (COVID-19) pandemic.b Computer-based epidemiology models informed public policy in the U.S., and in countries throughout the world, and newly studied transmission models for the virus have been used to forecast resource availability and mortality stratified by age group at the county level.c Artificial intelligence and machine learning approaches accelerated drug screening to find candidate medicines from trillions of possible chemical compounds,d and differential gene expressions among COVID-19 patient populations have been analyzed with important implications for treatment planning.e Structural modeling of the virus has led to new insights, speeding the development of vaccines and antigens.

It takes real intelligence and plenty of collaborative muscle to harness the potential of artificial intelligence. Most of us can barely grasp the concept of human-made machines learning how to process and analyze enormous amounts of data, then using that mass of information to understand things at new scales and in new combinations, delivering useful insights that our brains would never be able to produce on their own. Now University of Delaware Prof. Rudolf Eigenmann, interim chair of the Department of Computer and Information Sciences and professor of electrical and computer engineering, is playing a critical role in a new $20 million National Science Foundation-supported project designed to expand access to artificial intelligence. AI for the masses, you might call it. The project, called the NSF AI Institute for Intelligent Cyberinfrastructure with Computational Learning in the Environment (ICICLE), is one of 11 new National Artificial Intelligence Research Institutes the NSF announced recently. It is the second year of such investment by NSF.

Best papers of the workshop, after further revisions and independent reviews, will be considered for publication in a special issue of a renowned journal. By this holistic view we encounter a variety of challenges along the AI modeling cycle and software system engineering lifecycle as outlined in the figure below such as: • theory-practice gap in machine learning with impact on stability, reproducibility or integrity due to limitations of nowadays theoretical foundations in statistical learning theory or lack of control of high-dimensionality effects of deep learning; • facing computational constraints, e.g. All submissions will be peer-reviewed by, at least, 3 reviewers and judged on the basis of originality, contribution to the field, technical and presentation quality, and relevance to the workshop. Short papers are meant for timely discussion and feedback at the workshop. Papers are accepted with the understanding that at least one author will register for the conference to present the paper.

The aim of the TextAD research project at Linköping University is to better understand different types of reading difficulties. This knowledge can be used to develop digital tools that automatically adapt texts to the needs of readers. For many people, reading can be difficult, and some people need texts adapted to their ability. This group is very diverse, and the aspects of reading that cause problems differ from one individual to another. The project will investigate how three groups aged 13-17 years (pupils with dyslexia, pupils with intellectual disabilities, and typical readers) understand texts that present factual information.