The website of the PRIN 2017 project “Photonic Extreme Learning Machine” is online !
Nuove Direzioni numero 62, Nov-Dic 2020, pag. 75 “L’intelligenza delle onde”
Messaggero, 5 October 2020
Optical neural networks process information at the speed of light and are energetically efficient. Photonic artificial intelligence allows speech recognition, image classification, and Ising machines. Modern machine learning paradigms, as extreme learning machines, reveal that disordered and biological materials may realize optical neural networks with thousands of nodes trained only at the input and at the readout. May we use living matter for machine learning? Here, we employ living three-dimensional tumor brain models to demonstrate a random optical learning machine (ROM) for the investigation of glioblastoma. The tumor spheroid act as a computational reservoir. The ROM detects cancer morphodynamics by laser-induced hyperthermia, quantifies chemotherapy, and cell metabolism. The ROM is a sensitive noninvasive smart probe for cytotoxicity assay and enables real-time investigation of tumor dynamics. We hence design and demonstrate a novel bio-hardware for optical computing and the study of light/complex matter interaction.
Selected as Editor’s Highlights – Communications Physics 2020
In a paper published in Physical Review Letters, with title
we study artificial neural networks with nonlinear waves as a computing reservoir. We discuss universality and the conditions to learn a dataset in terms of output channels and nonlinearity. A feed-forward three-layered model, with an encoding input layer, a wave layer, and a decoding readout, behaves as a conventional neural network in approximating mathematical functions, real-world datasets, and universal Boolean gates.
The rank of the transmission matrix has a fundamental role in assessing the learning abilities of the wave.
For a given set of training points, a threshold nonlinearity for universal interpolation exists. When considering the nonlinear Schrödinger equation, the use of highly nonlinear regimes implies that solitons, rogue, and shock waves do have a leading role in training and computing. Our results may enable the realization of novel machine learning devices by using diverse physical systems, as nonlinear optics, hydrodynamics, polaritonics, and Bose-Einstein condensates. The application of these concepts to photonics opens the way to
a large class of accelerators and new computational paradigms. In complex wave systems, as multimodal fibers, integrated optical circuits, random, topological devices, and metasurfaces, nonlinear waves can be employed to perform computation and solve complex combinatorial optimization.
The paper was selected as Editors’Suggestion and Featured in Physics