interactive machine learning
PulseAugur coverage of interactive machine learning — every cluster mentioning interactive machine learning across labs, papers, and developer communities, ranked by signal.
3 day(s) with sentiment data
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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…
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LLM framework enhances accuracy in identifying adverse drug events
A new research paper details a human-in-the-loop framework utilizing a retrieval-augmented, multi-agent large language model (LLM) to identify cutaneous immune-related adverse events (cirAEs) from clinical notes. This L…
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Human-In-The-Loop AI faces scalability and fatigue challenges
This article explores the challenges of integrating humans into AI systems, a process known as Human-In-The-Loop (HITL). While humans are crucial for tasks like labeling, tuning, and validation, their involvement presen…
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Google and Anthropic adopt human-in-the-loop for AI agent safety
The human-in-the-loop pattern is being adopted by major AI players like Google and Anthropic to enhance agent safety. This approach integrates checkpoints into AI workflows, pausing the agent to allow human review, appr…
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Human-in-the-Loop ML for Safer Autonomous Vehicles Explored
A new arXiv paper explores the integration of Human-in-the-Loop Machine Learning (HITL-ML) techniques to enhance the safety and ethical considerations of autonomous vehicles (AVs). The paper details how human input, thr…
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New framework uses AI for comprehensive cardiac CT analysis · 2 sources tracked
Researchers have developed a unified framework for cardiac CT segmentation and phenotyping, combining a human-in-the-loop annotation process with a self-supervised foundation model. This approach, pre-trained on 60,000 …
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AI to generate bug acceptance criteria from incomplete data
An AI is being tasked with generating acceptance criteria for software bugs based on incomplete information provided by an admin team. This approach is raising concerns that the AI, despite the lack of detail, will prod…
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Human-in-the-loop system boosts AI agent reliability via voice calls
Omar Sanseviero has developed a human-in-the-loop system to enhance the reliability of agentic loops, particularly for tasks involving Claude and Codex. This system leverages voice agents and a dedicated MCP server from…
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AI Concepts Demystified Through Inbox Automation With Claude
The author explains how automating their inbox with Claude provided a practical understanding of several modern AI concepts. By using Claude to manage sponsorship emails, the author gained insights into Large Language M…
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Human oversight in AI safety often fails due to automation bias
Human oversight in AI safety is often ineffective because it creates a false sense of security without genuinely preventing errors. While approval gates can reduce the number of problematic actions proposed by AI, human…
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Reddit user seeks local speech annotation tools for AI model tuning
A user on the r/MachineLearning subreddit is seeking recommendations for speech annotation tools. They are specifically looking for platforms that support a human-in-the-loop process, enabling automatic transcription fo…