  Artificial neural networks with internal dynamics exhibit remarkable
capability in processing information. Reservoir computing (RC) is a canonical
example that features rich computing expressivity and compatibility with
physical implementations for enhanced efficiency. Recently, a new RC paradigm
known as next generation reservoir computing (NGRC) further improves
expressivity but compromises its physical openness, posing challenges for
neuromorphic realizations. Here we demonstrate optical NGRC with large-scale
computations performed by light scattering through disordered media. In
contrast to conventional optical RC implementations, we drive our optical
reservoir directly with time-delayed inputs. Much like digital NGRC that relies
on polynomial features of delayed inputs, our optical reservoir also implicitly
generates these polynomial features for desired functionalities. By leveraging
the domain knowledge of the reservoir inputs, we show that the optical NGRC not
only predicts the short-term dynamics of the low-dimensional Lorenz63 and
large-scale Kuramoto-Sivashinsky chaotic time series, but also replicates their
long-term ergodic properties. Optical NGRC shows superiority in shorter
training length, increased interpretability and fewer hyperparameters compared
to conventional optical RC, while achieving state-of-the-art forecasting
performance. Given its scalability and versatility, our optical NGRC framework
also paves the way for other next generation physical reservoirs, new
applications and architectures in a broad sense.
