When is a second or third earbud tone worth it before a glance?
research/ambient-rechime
How many repeated earbud tones before a glance are worth their annoyance, by urgency, tone gap and masking, in the ambient-urgency utility model (stylised)
What it found
A stylised extension of the urgency model lets the assistant repeat a tone up to four times, 3 s apart, each noticed independently and cancelled once the wearer looks. Choosing the number of tones per context raises mean utility per message from 0.387 (plain glance or one tone) to 0.409; two tones give most of it, and the gain is nil for low urgency and 0.062 for high urgency (partly built in by the weights). Always repeating is barely better than one tone. A Monte Carlo check agrees with the closed form to 0.002. Tones are assumed independent and annoyance flat per tone, and every parameter is a round figure of mine: nothing is fitted or measured.
A figure from its own output
Paper, code and results
More in Ambient AI assistant
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ambient-urgencyutility model (stylised) - How many audited messages does an always-on assistant need before it can trust a noisy urgency label?How many audited messages an assistant needs to estimate a noisy urgency label's confusion matrix and prior well enough to act on it: about 50 at moderate accuracy, with smoothing helping at tiny audit sets (stylised)
- What does a noisy auditor do to an assistant that learns its urgency label’s reliability from audits?What a noisy human auditor does to an assistant that learns its urgency label's reliability from audits: taking audit labels as truth biases the estimate and loses 8% to 69% of the label's gain at a 0.9 to 0.7 auditor, and correcting for a known auditor accuracy recovers it (stylised)
- How many gold-labelled audits replace noisy ones when the auditor’s accuracy is unknown?How many gold-labelled audits replace noisy ones when the auditor's accuracy is unknown: 25 gold audits match 1000 noisy ones read as truth, and a joint EM using 10 gold plus 200 noisy audits nearly reaches the true-parameter policy (stylised)
- How should a fixed audit budget be split between gold-labelled and noisy audits?How to split a fixed audit budget between gold and noisy audits when gold costs 3 to 30 times more: all-noisy is worst or near-worst in all 12 cells, 25% on gold with a joint EM fit is within 0.0009 of the best, and all-gold loses when gold is expensive (stylised)
- Can an assistant learn its urgency label’s reliability from two noisy auditors, and what does a wrongly assumed auditor accuracy cost?Can the assistant learn its urgency label's reliability from two noisy auditors with no accuracy known (Dawid-Skene), and what does a wrong assumed auditor accuracy cost: over-trusting the auditor loses 31% of a 0.9 label's gain, and Dawid-Skene reaches within 0.0005 of the oracle at n = 1000 (stylised)
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Proper scoring & elicitation28Effort & peer prediction9Verification games & slashing19Detection & tolerances14Markets as routers13Prediction-market design9Paying contributors8Decentralised training systems23Digital twins & sim-to-real164LLM chip design loops40Robot manufacturing loops53Ambient AI assistant46Self-replicating manufacturing34Simulation-first design64Engineered clothing45Task-specific AI accelerators40Algorithmic chip-design loops42Open-source purpose-built robots44The software-engineering loop43Adversarial human imitation41Open-source residential construction41Autonomous multi-angle capture34Sensing, imaging and 3D31Energy supply and dense energy storage39Robot societies among people31Agentic co-design of low-cost robots27Laundry-hanging robots25Machine learning on qualitative data23Robots on the building site27Civilizations & group psychology1History & human sciences1
Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.