AI is making disinformation harder to spot—but we’ve found a new way to catch it
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Unlock the full reading · $5.50 →Researchers developed a system to detect AI-generated disinformation by analyzing conversational flow rather than word choice. Instead of identifying whether text was written by AI, the system detects when comments derail discussions using red herrings, non sequiturs, and topic shifts—achieving 77% accuracy by comparing actual responses against AI-generated relevant replies.
Teaching:
• Use the red herring concept when students deflect from difficult sensations in poses by intellectualizing or storytelling—bring attention back to the actual physical experience
• Notice when internal dialogue during practice derails from breath and sensation into planning or judgment—teach recognition of conversational patterns within the mind
• Frame adjustment cues as keeping the conversation between student and pose on-topic rather than letting compensatory patterns redirect the dialogue
• Teach students to distinguish between natural evolution of their practice and derailing distractions that undermine the method
Writing seeds:
• Essay on practice as conversation between body and attention where the mind's job is staying on-topic not solving problems
• Shala Daily post comparing disinformation detection to noticing when we derail our own practice with stories about why we can't do something
• Piece on how the Ashtanga sequence itself is an AI-like system that generates appropriate responses—when we override it we're often introducing red herrings
• Short post on systems literacy as learning to recognize when our internal dialogue shifts from observation to manipulation
Idea map:
• Detection through conversational flow mirrors how practice reveals truth through attention to relational patterns not content
• The shift from word-level to conversation-level analysis parallels moving from pose mechanics to understanding practice as dynamic system
• Early-warning system requiring human judgment reflects how practice develops sensitivity but still needs discernment
• Systems literacy means recognizing derailment patterns in both information ecosystems and our own embodied experience
Source: https://phys.org/news/2026-08-ai-disinformation-harder-weve.html
Teaching:
• Use the red herring concept when students deflect from difficult sensations in poses by intellectualizing or storytelling—bring attention back to the actual physical experience
• Notice when internal dialogue during practice derails from breath and sensation into planning or judgment—teach recognition of conversational patterns within the mind
• Frame adjustment cues as keeping the conversation between student and pose on-topic rather than letting compensatory patterns redirect the dialogue
• Teach students to distinguish between natural evolution of their practice and derailing distractions that undermine the method
Writing seeds:
• Essay on practice as conversation between body and attention where the mind's job is staying on-topic not solving problems
• Shala Daily post comparing disinformation detection to noticing when we derail our own practice with stories about why we can't do something
• Piece on how the Ashtanga sequence itself is an AI-like system that generates appropriate responses—when we override it we're often introducing red herrings
• Short post on systems literacy as learning to recognize when our internal dialogue shifts from observation to manipulation
Idea map:
• Detection through conversational flow mirrors how practice reveals truth through attention to relational patterns not content
• The shift from word-level to conversation-level analysis parallels moving from pose mechanics to understanding practice as dynamic system
• Early-warning system requiring human judgment reflects how practice develops sensitivity but still needs discernment
• Systems literacy means recognizing derailment patterns in both information ecosystems and our own embodied experience
Source: https://phys.org/news/2026-08-ai-disinformation-harder-weve.html
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