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Federated analytics across heterogeneous stores: Google Cloud study Author: Sonu Kumar Singh (Senior Consultant — Cloud & AI Solutions Architecture, Capgemini US LLC) Professional Credential: Member, IEEE (Membership # 102728576) | ORCID: 0009-0002-9180-4946 Abstract Cloud data platforms are no longer used only for rep...
Deep learning models can achieve high performances in medical image analysis on tasks such as lesion detection (CT/MRI) and grading of tissue sections (histopathology), however, the black-box nature hinders the understanding of decision rationales to clinicians. This opacity engenders suspicion, regulatory obstacles (e...
This chapter discusses Decentralized Learning Management Systems (DLMS) as a novel method of contemporary education by combining blockchain, federated learning, and distributed technologies. It analyzes the architectural design, scalability issues, and implementation obstacles of DLMS, and stresses their benefits over...
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Federated analytics across heterogeneous stores: Google Cloud study Author: Sonu Kumar Singh (Senior Consultant — Cloud & AI Solutions Architecture, Capgemini US LLC) Professional Credential: Member, IEEE (Membership # 102728576) | ORCID: 0009-0002-9180-4946 Abstract Cloud data platforms are no longer used only for rep...
Federated analytics across heterogeneous stores: Cross-cloud comparative study Author: Sonu Kumar Singh (Senior Consultant — Cloud & AI Solutions Architecture, Capgemini US LLC) Professional Credential: Member, IEEE (Membership # 102728576) | ORCID: 0009-0002-9180-4946 Abstract Cloud data platforms are no longer used o...
The rapid development of contemporary technologies tends to digitalize all services across various public and private institutions, where most services are also provided on mobile devices due to their great practical usability by people in everyday life. The integration of Blockchain Technology (BT) and Artificial Inte...
The development of smart classroom technology hastened the implementation of data-driven and personalized learning systems, with the significant concern of data privacy, security, and trust. In this chapter, the author introduces a combined system of Federated Learning (FL) and blockchain to implement decentralized and...
Osteoporosis is a major global health burden, with considerable morbidity, mortality, and healthcare costs. Artificial intelligence (AI) has opened new avenues for osteoporosis screening, bone mineral density quantification, fracture-risk prediction, and clinical decision support, but progress remains fragmented across...
Sustainability has become one of the defining scientific and societal challenges of the twenty-first century. Climate change, rapid urbanization, population growth, increasing energy demand, and declining natural resources require decision-making that is continuous, adaptive, and supported by reliable environmental int...
With the rapid growth of digital technologies, the traditional education system is transforming into intelligent and decentralized learning environments that support personalized and adaptive learning experiences. Conventional educational platforms often face challenges such as scalability, learner engagement, data own...
Federated learning must accommodate statistical heterogeneity, costly client participation, and distinct fairness objectives for model performance and participation frequency. We introduce Fair Bayesian Stackelberg Federated Learning (FBS-FL), in which a server maintains beliefs over private client cost types and selec...
A new machine-learning approach broke records for weather forecasting for an important time frame in emergency planning.
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
Learn more about our approach to safety and security for Gemini 4 Argon.
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
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