interactive machine learning
PulseAugur coverage of interactive machine learning — every cluster mentioning interactive machine learning across labs, papers, and developer communities, ranked by signal.
7 day(s) with sentiment data
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LLMs and human feedback enhance real-time power grid control
Researchers have developed a novel framework that integrates Large Language Models (LLMs) with deep reinforcement learning (DRL) to improve real-time optimal power flow (RT-OPF) in electrical grids. This approach incorp…
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AI agents need guardrails to prevent runaway loops and improve decision-making
Current AI systems often struggle with human-in-the-loop processes, leading to either excessive approval or uncontrolled agent behavior. In software development, agents may waste resources on repetitive error correction…
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Humans may be unknowingly influenced by AI language models
The concept of being a "human in the loop" when using language models is being re-examined. While users believe they are in control by prompting and directing the AI, they may also be unknowingly influenced and trained …
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MLOps strategies to overcome human-in-the-loop bottlenecks
This article discusses strategies for optimizing the human-in-the-loop process within MLOps, focusing on maintaining effective oversight as systems evolve. It explores techniques such as active learning and data augment…
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New I-HITL Protocol Aims to Strengthen AI Human Oversight
A new research paper introduces Inalienable Human-in-the-Loop (I-HITL), a protocol designed to ensure robust human oversight in AI systems, even when the system is compromised. The paper details experiments where fronti…
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Grasys optimizes AI-driven development with human-in-the-loop approach
Grasys is developing an AI-driven development approach that prioritizes human-in-the-loop optimization and adaptability to rapid industry changes. This methodology aims to find the best balance between human oversight a…
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AI agents require robust privacy controls and disclosure mechanisms
Several articles discuss the importance of privacy and control when using AI agents. One piece outlines disclosure rules in California, Utah, and Washington, suggesting a chat widget disclosure can suffice. Another arti…
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Human-in-the-Loop MLOps Balances Automation and Expertise
This article discusses the integration of human expertise into MLOps (Machine Learning Operations) to enhance machine learning models. It explores how a human-in-the-loop approach can improve accuracy, reliability, and …
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Human-in-the-loop workflows enable AI agents to pause and resume tasks
This article discusses the implementation of human-in-the-loop workflows within AI systems. It focuses on the necessity of allowing agents to pause, hand control to a human for input or edits, and then resume their task…
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AI oversight modes: Human-in-the-loop, on-the-loop, and out-of-the-loop defined
This article distinguishes between three modes of human interaction with AI systems: human-in-the-loop, human-on-the-loop, and out-of-the-loop. Human-in-the-loop requires explicit approval for each action, offering stro…
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AI roadmap for embedded software firms detailed in new research paper
A new research paper outlines a roadmap for organizations to transition into an AI-first structure, focusing on the specific challenges within embedded software development. The study, conducted with 40 participants inc…
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Workshop tackles online misinformation with LLMs and credible IR
The 6th Workshop on Reducing Online Misinformation through Credible Information Retrieval (ROMCIR 2026) aims to address the growing problem of information pollution, including fake news, deceptive reviews, and unverifie…
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AI in education: Students prefer human graders, new papers reveal
Two new research papers explore the use of AI in educational writing assessment, with a focus on student perspectives and human oversight. The first study, conducted in Saudi Arabia, found that students found AI feedbac…
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MaxKernel automates TPU kernel generation using multi-agent AI
Researchers have developed MaxKernel, a multi-agent system designed to automate the creation of high-performance custom kernels for Tensor Processing Units (TPUs). The system employs three distinct approaches: a Human-i…
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Human-in-the-loop: An AI Architecture, Not Just a Popup
This article argues that human-in-the-loop (HITL) should be integrated as a core architectural component in AI systems, rather than an afterthought or a simple confirmation step. The author suggests that current agent d…
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Human Traders Remain Crucial Despite Advances in AI Trading
The article discusses the evolving landscape of AI trading and its impact on financial markets. While AI can automate many tasks like data processing and pattern recognition, human traders remain essential. The piece hi…
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LingShu knowledge graph bridges Traditional Chinese Medicine and modern biomedicine
Researchers have developed LingShu, a large-scale knowledge graph designed to bridge Traditional Chinese Medicine (TCM) and modern biomedicine. This graph integrates diverse data sources, including clinical records and …
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LLM agents face 'sim-to-real' gap, mirroring RL challenges, says ASU professor
Hua Wei, an assistant professor at Arizona State University, argues that the current challenges faced by large language model (LLM) agents in real-world applications mirror the "sim-to-real" gap encountered in tradition…
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AI guidance framework helps parents personalize emergency preparedness for children
Researchers have developed a framework for human-mediated AI guidance, demonstrated through a system called Ready Together. This system supports parents in personalizing AI-generated emergency preparedness information f…
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LLM-generated code liability rests with humans, mirroring self-driving car rules
The legal responsibility for accidents involving self-driving cars currently falls on the human occupant, as the vehicle itself cannot be held liable. This principle is being extended to code generated by Large Language…