Ever thought about what it would look like to have a personal assistant with domain-specific knowledge discovery? At the speed of Information Retrieval and Generative AI collaboration, this could be a reality in 10 years. CCC will be hosting a workshop on, “Information Retrieval in the Age of Generative AI,” from July 19-20 in Washington, DC.  In today’s digital landscape, IR is undergoing a profound evolution, driven by the convergence of traditional search methods with advanced AI technologies.  Key among these technologies are generative AI models like GPT and diffusion models, which represent a significant leap forward in how information is both accessed and processed. These models not only excel […]

Are you curious about where computational advancements will be in four decades? The Computing Community Consortium’s (CCC) council member Bill Gropp and The Computing Research Association’s Widening Participation (CRA-WP) board member Nancy Amato along with four other faculty members from the University of Illinois at Urbana–Champaign (UIUC) shared their knowledge on how developments in computer science and robotics will impact society by 2067.  In an article from Illinois Alumni at the UIUC, these experts see a future of innovation and optimism despite possible challenges.  Computer Science In the future, Gropp explained that computing advancements won’t rely on faster hardware alone. Instead, compute speedups will depend on making processes more efficient […]

In another installment of CCC’s Weekly Computing News, we are sharing a technical tool to support the health and wellbeing of veterans and other vulnerable populations. AI-enabled support for veterans A team of researchers from Arizona State University, led by associate professors Nicole Roberts and Ming Zhao, is pioneering a project aimed at enhancing healthcare outcomes for aging veterans using artificial intelligence and edge computing technologies. Veterans, who often face social isolation and health challenges due to their unique life experiences, are the focus of this initiative. The project, supported by a grant from the National Science Foundation (NSF), aims to develop a specialized AI system called EdgeCare. This system […]

Imagine a situation where an AI system labels someone as high risk without taking into account important factors like their low income, family responsibilities, and parental duties. It’s crucial to realize that most defendants, especially those from marginalized backgrounds, aren’t likely to pose a serious threat to society. Additionally, past infractions, especially those that occurred years ago, should not be the sole basis for predicting future inmate behavior. Judges need to be careful about relying only on AI algorithms because they can’t fully understand the complexities of human life and may not consider all the relevant details. Human judgment, which considers all aspects of a person’s situation, should always guide […]

In another installment of CCC’s Weekly Computing News, we are highlighting a fascinating article by the University of Michigan News. This article explores how artificial intelligence is being used to develop tools that can understand what a dog’s bark means. Using AI to decode dog vocalizations Researchers at the University of Michigan, in collaboration with Mexico’s National Institute of Astrophysics, Optics and Electronics (INAOE), achieved a breakthrough in animal communication research by repurposing AI models originally trained for human speech analysis to understand dog barks. Led by CCC Council Member Rada Mihalcea, the team adapted the Wav2Vec2 machine-learning model to interpret a dataset of dog vocalizations collected by the INAOE.  […]

This week we discovered an interesting article from ACM Queue, a bimonthly magazine of the Association for Computing Machinery (ACM). This article, written by researchers Jinnan Guo, Peter Pietzuch, Andrew Paverd, and Kapil Vaswanin, explores how as the demand for trustworthy AI systems grows, the confluence of Federated Learning (FL) and Confidential Computing emerges as a promising solution.  Trustworthy AI Using Confidential Federated Learning The article emphasizes the crucial need to ensure the trustworthiness of AI systems, particularly in safeguarding personal information. It highlights two key methodologies, Federated Learning (FL) and Confidential Computing, as effective approaches to achieving this goal. While FL addresses privacy concerns by enabling collaborative model training […]