The experimental data is used to train complex-weighted models with a range of regularisation approaches.

An optical fibre technology can help chips communicate with each other at the speed of light, enabling them to transmit 80 times as much information as they could using traditional electrical connections. That could significantly speed up the training times required for large artificial intelligence models – from months to weeks – while also reducing the energy and emissions costs for data centres. Most advanced computer chips still communicate using electrical signals carried over copper wires. But as the tech industry races to train large AI models – a process that requires networks of AI superchips to transfer huge amounts of data – companies are eager to link chips using the light-speed communication of fibre optics. This technology isn't new: the internet already relies on undersea fibre-optic cables stretching thousands of kilometres between continents.

The development of miniature joint angle sensors is a crucial factor for the successful utilisation of various robotic applications in the healthcare and many other industries. This includes applications such as continuum robots used in minimally invasive surgery (MIS), prosthetics, wearable flexible devices, and many more [1]. Joint angle sensing in these applications, or more broadly, shape sensing, is required to accurately actuate and measure tip position and curvatures made by these robotic devices. To do this, a number of miniaturised joint angle sensors have been developed for integration into these applications, uti-lising various sensor types. Some examples include inertial, stretch, and FBG-based sensors [2].

In this paper, we present a new computer-controlled weaving technology that enables the fabrication of woven structures in the shape of given 3D surfaces by using threads in non-traditional materials with high bending-stiffness, allowing for multiple applications with the resultant woven fabrics. A new weaving machine and a new manufacturing process are developed to realize the function of 3D surface weaving by the principle of short-row shaping. A computational solution is investigated to convert input 3D freeform surfaces into the corresponding weaving operations (indicated as W-code) to guide the operation of this system. A variety of examples using cotton threads, conductive threads and optical fibres are fabricated by our prototype system to demonstrate its functionality.

The accumulation of nonlinear effects in an optical fibre is often seen as a source of significant impairment for the propagating light signals, but the same effects, when properly managed, can provide a remarkable tool to tailor the temporal and spectral content of the signals. Indeed, depending on the regime of dispersion of the fibre and the frequency chirp, an initial pulse can be significantly expanded or compressed in the time or frequency domain, or it can be reshaped into advanced temporal waveforms such as parabolic, rectangular and triangular shapes [1]. Yet, due to the typically wide range of degrees of freedom involved, predicting the behaviour of nonlinear pulse shaping by numerical integration of the nonlinear Schrödinger equation (NLSE) or its extensions may be computationally demanding, especially when dealing with inverse-mapping problems. Recently, we have successfully introduced the use of the machine-learning (ML) method of artificial neural networks (NNs) as an efficient tool for complementing or substituting the NLSE in the modelling of nonlinear pulse shaping [2-5] or for predicting the generation of optical supercontinua [6, 7]. Fibre nonlinearity does not only affect the propagation of ultrashort pulses.

A touch-sensitive glove made from stretchable fibre-optic sensors could be used in robotics, sport and medicine. "We made a sensor that can sense haptic interactions, in the same way that our own skin sensors interact with [the] environment," says Hedan Bai at Cornell University in Ithaca, New York. Bai and her team created the glove using optical fibres made from thin elastomeric polyurethane cables that transmit light from an LED. The light is interrupted when the cables are bent, stretched or put under pressure. The team dyed parts of the fibres with different colours, meaning that as they are distorted, the colour of light coming out of the fibres changes.

A synthetic mesh could give robots a sense of touch that is delicate as the skin on out backs, researchers have claimed. The material forms a linked sensory network similar to that of a biological nervous system -- one that could help robots feel their interactions with the environment. The lattice is made of flexible polyurethane that contains stretchable optical fibres with sensors than can detect how the fibres are being deformed. The device -- a sort-of stretchable optical lace -- was developed by roboticists Patricia Xu and Rob Shepherd of Cornell University and colleagues. 'We want to have a way to measure stresses and strains for highly deformable objects, and we want to do it using the hardware itself, not vision,' said Professor Shepherd.

Artificial intelligence (AI) was centre stage at the World Economic Forum in China. At least 20 of the 56 companies selected for the organisation's Technology Pioneers programme are using AI in some way with applications ranging from autonomous vehicles to advertising technology. A branch of AI, machine learning, is dedicated to the ability of a machine to learn something without having to be programmed for that specific thing. It enables computers to improve their performance automatically over time by being fed data and information in the form of observations and real-world interactions – like a toddler learning about the world around them. Answering whether the animal in a photo is a cat or a dog, spotting obstacles in front of a self-driving car, spam mail detection, and speech recognition of a YouTube video to generate captions are just a few examples out of a plethora of predictive machine learning models.

We use complex-weighted, deep networks to invert the effects of multimode optical fibre distortion of a coherent input image. We generated experimental data based on collections of optical fibre responses to greyscale input images generated with coherent light, by measuring only image amplitude (not amplitude and phase as is typical) at the output of \SI{1}{\metre} and \SI{10}{\metre} long, \SI{105}{\micro\metre} diameter multimode fibre. This data is made available as the {\it Optical fibre inverse problem} Benchmark collection. The experimental data is used to train complex-weighted models with a range of regularisation approaches. A {\it unitary regularisation} approach for complex-weighted networks is proposed which performs well in robustly inverting the fibre transmission matrix, which fits well with the physical theory. A key benefit of the unitary constraint is that it allows us to learn a forward unitary model and analytically invert it to solve the inverse problem. We demonstrate this approach, and show how it can improve performance by incorporating knowledge of the phase shift induced by the spatial light modulator.

We use complex-weighted, deep networks to invert the effects of multimode optical fibre distortion of a coherent input image. We generated experimental data based on collections of optical fibre responses to greyscale input images generated with coherent light, by measuring only image amplitude (not amplitude and phase as is typical) at the output of \SI{1}{\metre} and \SI{10}{\metre} long, \SI{105}{\micro\metre} diameter multimode fibre. This data is made available as the {\it Optical fibre inverse problem} Benchmark collection. The experimental data is used to train complex-weighted models with a range of regularisation approaches. A {\it unitary regularisation} approach for complex-weighted networks is proposed which performs well in robustly inverting the fibre transmission matrix, which fits well with the physical theory. A key benefit of the unitary constraint is that it allows us to learn a forward unitary model and analytically invert it to solve the inverse problem. We demonstrate this approach, and show how it can improve performance by incorporating knowledge of the phase shift induced by the spatial light modulator.