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Study examines trust and fraud prevention in AI-enabled banking in Bangladesh
Research examines how AI tools influence trust and fraud prevention in Bangladesh’s banking.
UK NCSC publishes framework on adversarial attacks against AI systems
The UK’s National Cyber Security Centre has published a paper on adversarial attacks against machine learning and AI, setting out a framework for understanding attacks that target the operation of ML models. The paper introduces a common language intended to support awareness, threat modelling, and collaboration on AI security. The NCSC says ML systems present (See more)
Microsoft report highlights growing use of AI in healthcare systems
Global healthcare transformation is being driven by AI adoption, with institutions focusing on efficiency, data protection and improved patient outcomes through digital integration.
Brazil’s Ceará state introduces AI assistant for document review
The tool helps detect errors before submission.
New MIT research hub targets future of advanced computation
The new lab will explore hybrid systems combining AI and quantum computing, aiming to improve transparency, reliability and real-world deployment of advanced models.
Powerful Gemini update turns simple prompts into ready-to-use results
Google has added direct file creation to Gemini, allowing users to download or export structured outputs to Drive.
United Nations warns AI-driven advertising could deepen information crisis
Concerns are rising over AI transparency gaps in advertising systems, with risks including fraud, inefficiency and weakening trust in digital platforms and media environments.
China pushes AI self-reliance while expanding global cooperation
A national strategy that highlights China’s AI growth, safety controls and international collaboration in digital transformation
Crypto crackdown intensifies in Kazakhstan over illegal exchanges
A large-scale investigation into a shadow crypto network revealed over $224 million in illicit transactions, highlighting risks tied to unregulated platforms.
New federated learning approach highlights shift towards decentralised and privacy-preserving AI
Distributed machine learning is shifting towards systems that rely on collaboration between everyday devices rather than centralised computing infrastructure.
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