Neural radiance fields (NeRFs) are advanced machine learning techniques that can generate three-dimensional (3D) representations of objects or environments from two-dimensional (2D) images. As these techniques can model complex real-world environments realistically and in detail, they could greatly support robotics research.

Most existing datasets and platforms for training NeRFs, however, are designed to be used offline, as they require the completion of a pose optimization step that significantly delays the creation of photo realistic representations. This has so far prevented most roboticists from using these techniques to test their algorithms on physical robots in real-time.

A research team at Stanford University recently introduced NerfBridge, a new open-source software package for training NeRF algorithms that could ultimately enable their use in online robotics experiments, This package, introduced in a paper pre-published on arXiv, is designed to effectively bridge ROS (the robot operating system), a renowned software library for robotics applications, and Nerfstudio, an open-source library designed to train NeRFs in real-time.

Meta has created an AI language model that (in a refreshing change of pace) isn’t a ChatGPT clone. The company’s Massively Multilingual Speech (MMS) project can recognize over 4,000 spoken languages and produce speech (text-to-speech) in over 1,100. Like most of its other publicly announced AI projects, Meta is open-sourcing MMS today to help preserve language diversity and encourage researchers to build on its foundation. “Today, we are publicly sharing our models and code so that others in the research community can build upon our work,” the company wrote. “Through this work, we hope to make a small contribution to preserve the incredible language diversity of the world.”

Speech recognition and text-to-speech models typically require training on thousands of hours of audio with accompanying transcription labels. (Labels are crucial to machine learning, allowing the algorithms to correctly categorize and “understand” the data.) But for languages that aren’t widely used in industrialized nations — many of which are in danger of disappearing in the coming decades — “this data simply does not exist,” as Meta puts it.

Meta used an unconventional approach to collecting audio data: tapping into audio recordings of translated religious texts. “We turned to religious texts, such as the Bible, that have been translated in many different languages and whose translations have been widely studied for text-based language translation research,” the company said. “These translations have publicly available audio recordings of people reading these texts in different languages.” Incorporating the unlabeled recordings of the Bible and similar texts, Meta’s researchers increased the model’s available languages to over 4,000.

AI tools based on artificial neural networks (ANNs) are being introduced in a growing number of settings, helping humans to tackle many problems faster and more efficiently. While most of these algorithms run on conventional digital devices and computers, electronic engineers have been exploring the potential of running them on alternative platforms, such as diffractive optical devices.

A research team led by Prof. Tie Jun Cui at Southeast University in China has recently developed a new programmable neural network based on a so-called spoof surface plasmon polariton (SSPP), which is a surface electromagnetic wave that propagates along planar interfaces. This newly proposed surface plasmonic neural network (SPNN) architecture, introduced in a paper in Nature Electronics, can detect and process microwaves, which could be useful for wireless communication and other technological applications.

“In digital hardware research for the implementation of artificial neural networks, optical neural networks and diffractive deep neural networks recently emerged as promising solutions,” Qian Ma, one of the researchers who carried out the study, told Tech Xplore. “Previous research focusing on optical neural networks showed that simultaneous high-level programmability and nonlinear computing can be difficult to achieve. Therefore, these ONN devices usually have been limited to specific tasks without programmability, or only applied for simple recognition tasks (i.e., linear problems).”