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Research#AI Ethics📝 BlogAnalyzed: Jan 3, 2026 06:25

What if AI becomes conscious and we never know

Published:Jan 1, 2026 02:23
1 min read
ScienceDaily AI

Analysis

This article discusses the philosophical challenges of determining AI consciousness. It highlights the difficulty in verifying consciousness and emphasizes the importance of sentience (the ability to feel) over mere consciousness from an ethical standpoint. The article suggests a cautious approach, advocating for uncertainty and skepticism regarding claims of conscious AI, due to potential harms.
Reference

“According to Dr. Tom McClelland, consciousness alone isn’t the ethical tipping point anyway; sentience, the capacity to feel good or bad, is what truly matters. He argues that claims of conscious AI are often more marketing than science, and that believing in machine minds too easily could cause real harm. The safest stance for now, he says, is honest uncertainty.”

PermalinkScienceDaily AI
Research#llm📝 BlogAnalyzed: Dec 27, 2025 05:31

Claude Code Practical Verification Day 27: Collaborating with AI on Long-Term Projects - Basic Design of WBS + /auto-exec

Published:Dec 26, 2025 22:00
1 min read
Zenn AI

Analysis

This article discusses how to effectively collaborate with AI, specifically Claude Code, on long-term projects. It highlights the limitations of relying solely on AI for such projects and emphasizes the importance of human-defined project structure, using a combination of WBS (Work Breakdown Structure) and /auto-exec commands. The author shares their experience of initially believing AI could handle everything but realizing that human guidance is crucial for AI to stay on track and avoid getting lost or deviating from the project's goals over extended periods. The article suggests a practical approach to AI-assisted project management.
Reference

“When you ask AI to "make something," single tasks go well. But for projects lasting weeks to months, the AI gets lost, stops, or loses direction. The combination of WBS + /auto-exec solves this problem.”

PermalinkZenn AI
Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 09:28

Seeing is Believing (and Predicting): Context-Aware Multi-Human Behavior Prediction with Vision Language Models

Published:Dec 17, 2025 20:44
1 min read
ArXiv

Analysis

This research explores the use of Vision Language Models (VLMs) for predicting multi-human behavior. The focus on context-awareness suggests an attempt to incorporate environmental and relational information into the prediction process, potentially leading to more accurate and nuanced predictions. The use of VLMs indicates an integration of visual and textual data for a more comprehensive understanding of human actions. The source being ArXiv suggests this is a preliminary research paper.
Reference

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PermalinkArXiv