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We build learning systems that model relationships between signals, hold context over time, and support decisions in clinical, wearable, and population-scale settings.
We connect fragmented signals across people, devices, records, and conversations into representations that support grounded reasoning and calibrated action.
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We work where the quality of an action depends on understanding the surrounding system, not merely answering a single question.
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Papers, talks, and challenge reports across affective computing, wearable AI, computational biology, clinical forecasting, epidemiology, and audio.
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Work in progress that organizations can join directly, rather than wait to read about. Each one takes a small number of partners at a time.
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{{ p.cta }}Placements and shows, filed separately from papers because they are judged on different terms.
Multi-turn egocentric conversation, judged in two model-size divisions. Our 1.3B model placed in the Small track; a larger variant placed in the Large track.
Public leaderboard →Audiovisual interactive exhibition.
See the work at brwn.art →{{ t.focus }}
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We believe capable systems should understand more of the situation around a decision, make their uncertainty legible, and expand rather than obscure human agency.
“The future of AI is not a better autocomplete. It is better orientation inside a living, changing world.”
Everyday Intelligence is an independent research and product studio. The work spans clinical prediction, wearable and ambient sensing, population health, affective computing, and enterprise operations, and it is expanding into new domains with the partners who bring them.
Short technical writing between papers. Roughly monthly. Where a note has a paper behind it, both are linked.
We take on client engagements, pilots, and build work, and we are open to research collaborations alongside them. Tell us what you are trying to predict, what you already tried, and what a good answer would change.
hello@everydayintelligence.org →