The Next Rembrandt is a multi-million dollar project, funded in part by a Big Bank and Microsoft, which trained AI to mimic the style of the great master in order to paint a mediocre original painting (left). In this article, J. Augustus Bacigalupi, Òscar Castro Garcia and I show how complex and sophisticated even the most primitive forms of life are as they sense and respond to their worlds. Artificial Intelligence, in comparison, is slow-witted, boring, and completely unable to get puns or jokes, much less to make art. We caution against anyone who might argue that current AI can begin to replace human judgement in, for instance, medicine or education. We also offer a means by which machine sensors might be designed so that they are a little bit closer the abilities of slime mold. Click to download: Living systems are smarter bots: Slime mold semiosis versus AI symbol manipulation
Although machines may be good at mimicking, they are not currently able, as organisms are, to act creatively. We offer an understanding of the emergent qualities of biological sign processing in terms of generalization, association, and encryption. We use slime mold as a model of minimal cognition and compare it to deep-learning video game bots, which some claim have evolved beyond their merely quantitative algorithms. We find that these discrete Turing machine bots are not able to make productive, yet unanticipated, “errors”—necessary for biological learning—which, based on the physicality of signs, their relatively similar shapes, and relative physical positions spatially and temporally, lead to emergent effects and make learning and evolution possible. In organisms, stochastic resonance at the local level can be leveraged for self-organization at the global level. We contrast all this to the symbolic processing of today’s machine learning, whereby each logic node and memory state is discrete. Computer codes are produced by external operators, whereas biological symbols are evolved through an internal encryption process.