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  2. AIhub

    Collection of Interviews from the 2026 AAAI / ACM SIGAI Doctoral Consortium

    AIhub has compiled a series of interviews from the 2026 AAAI/SIGAI Doctoral Consortium. The interviewed doctoral students come from institutions such as NTU, the Hong Kong University of Science and Technology, IIT Hyderabad, Oregon State University, ShanghaiTech University, Singapore Management University, Johns Hopkins University, McGill University, and Mila. Their research topics cover multimodal learning and embodied intelligence, reinforcement learning-driven management of gig transportation labor, interpretability and concept learning, causal decision-making systems, networked multi-agent systems, formal verification for autonomous driving, resource-constrained image generation, theory of mind, theoretical foundations of large language models, time-series anomaly detection, multiplicity in responsible AI, and improving the transparency and trustworthiness of AI systems.

    AIhub has compiled a series of interviews from the 2026 AAAI/SIGAI Doctoral Consortium. The interviewed doctoral students come from institutions such as NTU, the Hong Kong University of Science and Technology, IIT Hyderabad, Oregon State University, ShanghaiTech University, Singapore Management University, Johns Hopkins University, McGill University, and Mila. Their research topics cover multimodal learning and embodied intelligence, reinforcement learning-driven management of gig transportation

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    #ai-vertical#Industry news

Wednesday

  2. IEEE Spectrum

    IEEE Spectrum White Paper: How Edge AI Moves Smart Cities Toward Cognitive Cities

    IEEE Spectrum has released a white paper describing how edge AI and physical AI can move urban infrastructure from passive monitoring to sensing, reasoning, and acting in real time. According to the white paper, edge AI enables low-latency decision-making, keeps sensitive data within city boundaries, and reduces reliance on centralized cloud; computer vision, multimodal sensing, and generative and agentic systems can work together to support public safety, mobility, and infrastructure management. The paper also describes a layered edge architecture that distributes monitoring, planning, and action tasks across the far edge, regional, and cloud layers.

    IEEE Spectrum has released a white paper describing how edge AI and physical AI can move urban infrastructure from passive monitoring to sensing, reasoning, and acting in real time. According to the white paper, edge AI enables low-latency decision-making, keeps sensitive data within city boundaries, and reduces reliance on centralized cloud; computer vision, multimodal sensing, and generative and agentic systems can work together to support public safety, mobility, and infrastructure management

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    #hot-signal#Industry news#English text is being prepared.#Multimodal

Tuesday