CybAI news

Women make up 15% to 22% of data science professionals, research from the Boston Consulting Group shows. One of the women who is hoping to increase that number is Chisoo Lyons, the executive director of Women in Data Science Worldwide (WiDS) and a BigDATAwire 2024 Person to Watch. WiDS started in 2015 with a one-day technical conference at Stanford University to give women a voice in data science. Since then, the group has expanded significantly, and it now touches more than 150,000 participants across 160 countries via conferences, datathons, podcasts, upskilling workshops, its Next Gen outreach program, the WiDS Academy,…

arXiv:2411.14507v1 Announce Type: new Abstract: Generative Pre-trained Transformers (GPTs) have demonstrated remarkable performance across diverse domains through the extensive scaling of model parameters. Recent works observe the redundancy across the transformer blocks and develop compression methods by structured pruning of the unimportant blocks. However, such straightforward elimination will always provide irreversible performance degradation. In this paper, we propose FuseGPT, a novel methodology to recycle the pruned transformer blocks to further recover the model performance. Firstly we introduce a new importance detection metric, Macro Influence (MI), to detect the long-term influence of each transformer block by calculating their loss of information…

arXiv:2411.14505v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) are widely used for visual perception, understanding, and reasoning. However, long video processing and precise moment retrieval remain challenging due to LLMs' limited context size and coarse frame extraction. We propose the Large Language-and-Vision Assistant for Moment Retrieval (LLaVA-MR), which enables accurate moment retrieval and contextual grounding in videos using MLLMs. LLaVA-MR combines Dense Frame and Time Encoding (DFTE) for spatial-temporal feature extraction, Informative Frame Selection (IFS) for capturing brief visual and motion patterns, and Dynamic Token Compression (DTC) to manage LLM context limitations. Evaluations on benchmarks like Charades-STA and…

arXiv:2411.14461v1 Announce Type: new Abstract: Artificial Intelligence (AI) has become essential in modern healthcare, with large language models (LLMs) offering promising advances in clinical decision-making. Traditional model-based approaches, including those leveraging in-context demonstrations and those with specialized medical fine-tuning, have demonstrated strong performance in medical language processing but struggle with real-time adaptability, multi-step reasoning, and handling complex medical tasks. Agent-based AI systems address these limitations by incorporating reasoning traces, tool selection based on context, knowledge retrieval, and both short- and long-term memory. These additional features enable the medical AI agent to handle complex medical scenarios where decision-making should be built…