No current AI systems are conscious, but there are no obvious technical barriers to building ones that might be, according to an analysis grounded in neuroscientific theories of consciousness. The report surveys prominent theories—recurrent processing, global workspace, higher-order, predictive processing, and attention schema—and derives computational indicator properties from them. Applying these indicators to recent AI systems yields no evidence of consciousness, but the authors argue that future systems could potentially implement the necessary properties.
A survey of 249 participants, mostly in academia and about 40% experts in consciousness research, assessed views on the field's progress, funding, job opportunities, and scientific rigor. 78% of respondents said scientific research on consciousness has been making progress. However, most perceived obtaining funding and getting a job in consciousness research as more difficult than in other neuroscience subfields. Work in consciousness research was seen as less rigorous than other neuroscience subfields, but this perception was not linked to the perceived difficulty in funding and jobs. Global workspace theory was rated most promising overall (about 28%), while among non-experts integrated information theory (IIT) was most popular (about 22%).