Approach may be particularly useful in form that initially affects mouth and throat AI voice analysis shows potential in detecting early signs of ALS, particularly the bulbar-onset form affecting the mouth and throat. Bulbar-onset ALS carries a poorer prognosis, making early diagnosis particularly important. Machine learning models, such as XGBoost, distinguished ALS patients from healthy

Assistant Professor of Computer Science Stephanie Gil Stephanie Gil, Assistant Professor of Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), researches how robots, drones, autonomous vehicles, and other physical agents can make decisions, coordinate with one another, and act effectively in complex real-world environments. Much of her recent work

Service officials are improving data and network infrastructure to support AI-enabled operations and information sharing. The Marine Corps and Coast Guard are investing in artificial intelligence and machine learning infrastructure to enhance operational capabilities as part of broader War Department efforts to build an AI-enabled force. Each service is taking a different approach to measuring

a lot about agentic AI, and the principle is simple: let the model decide. For a general-purpose assistant, that is fine: let the agent try, watch what it does. For an enterprise RAG process, it is dangerous. The answers feed real decisions, so we have to know every step and control the flow between the

This blog post is the first of a four-part series called The Economics of Agent Optimization which shares the strategies, capabilities, and proof points to help you optimize agent costs and run AI as a managed investment system on Microsoft Foundry. The AI conversation in most enterprises has moved from the whiteboard to the budget

DeepAdapter: a generalisable algorithm integrating self-supervised learning and unsupervised domain adaptation for robust retinopathy of prematurity screening Jiaman Zhao1,2, http://orcid.org/0009-0009-7533-5642Longhui Li1, Zhenzhe Lin1, Mingjie Luo1, Dongyuan Yun1,2, Jingyi Wen1, Lixue Liu1, Jianqiao Li3, Fabao Xu3, Suzhen Xie4, Meirong Wei5, Baohai Liu6, http://orcid.org/0000-0002-4853-2474Zhenzhen Liu1, Duoru Lin1, http://orcid.org/0000-0003-4672-9721Haotian Lin1,7 1 Sun Yat-sen University, Zhongshan Ophthalmic Center, State

I was also thinking about the future of technology and looking for an area that would remain highly valuable, even as AI continues to change the way ...

Deep Origin has recently announced a groundbreaking advance in virtual screening, as detailed in a preprint published on bioRxiv. The company has developed a novel framework called DODock, which, when combined with DOScore, maintains predictive accuracy on new biological targets, outperforming conventional machine-learning models. "Virtual screening has been widely used for more than 40 years