Bayesian-based decipherment of in-depth information in bacterial chemical sensing beyond pleasant/unpleasant responses

Physics-AI Symbiosis

The phenomenal success of physics in explaining nature and engineering machines is predicated on low dimensional deterministic models that accurately describe a wide range of natural phenomena. Physics provides computational rules that govern physical systems and the interactions of the constituents therein. Led by Deep Neural Networks (DNNs), Artificial Intelligence (AI) has introduced an alternate data-driven computational framework, with astonishing performance in domains that don’t lend themselves to deterministic models such as image classification and speech recognition. These gains, however, come at the expense of predictions that are inconsistent with the physical world as well as computational complexity, with the latter placing AI on a collision course with the expected end of the semiconductor scaling known as Moore’s Law. This paper argues how an emerging symbiosis of physics and AI can overcome such formidable challenges, thereby not only extending AI’s spectacular rise but also transforming the direction of engineering and physical science.