AutoLab: Can an AI Replace Human Data Collectors?
By Purav Patel
Can an autonomous AI conduct experiments on real humans in a lab safely and efficiently? With Automated Laboratory (AutoLab), our open-source ubiquitous computing project, we will do just that. Imagine a Star Trek-like computer infused into a lab room. Participants enter the smart lab by activating an electronic lock, converse naturally with an AI during the study, and are compensated digitally. This will greatly speed up and standardize data collection in the health and social sciences. Currently, unpaid and overworked assistants must spend inordinate time and money to collect data. This results in human errors that waste tax dollars, lost time that could be spent on more creative tasks, and small datasets that limit generalizability to the larger population.
Typical human experiments in the socio-behavioral and health sciences span 1-2 hours and need one more hour to prep and manage data. For sample sizes of 50-200, this requires at least 100-600 hours for one study (~ $2,000-12,000 tax dollars annually). Not long ago, statistical/visualization software, word processors, and communication tools advanced science by standardizing tasks, reducing errors, increasing speed, and fostering more creative pursuits. Ubiquitous computing can do the same by collecting large datasets from human subjects in social and health science labs 24/7/365.
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