Hallucinations are (almost) all you Need
Jhave Johnston explains how fundamental research in science is being transformed by a practice predominantly associated with the arts: namely hallucinations.
Hovedinnhold
This rapid overview of key scientific AI examples (that covers a year loosely defined as starting with the release of GPT-4 on March 14th, 2023) is framed by the hypothesis that fundamental research in science is being transformed by a practice predominantly associated with the arts: namely hallucinations. Hallucinations in people are conventionally associated with mental illness, drugs, and/or genius. Hallucinations in AI (mostly in large language models) have been critiqued as net-negatives: contributing to disinformation, bias, post-truth, deep-fakes, collapse of democracy, copyright theft, etc… Yet at the same time, AI hallucinations (of proteins/crystals/algorithms/circuits etc) pruned down to the feasible, are contributing to a revolutionary acceleration of scientific discoveries in numeric-algorithmic optimizations, AI hardware accelerators, reward mechanism design, non-invasive brain sensors, drug discovery, sustainable deep-tech materials, autonomous lab robotics, neuromorphic organoid computing, and mathematical reasoning. In both art and science, hallucinations are almost enough: without the pruning down to the plausible, there is just a sprawl of potentiality.