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Machine learning with artificial neural networks has recently transformed many scientific fields by introducing new data analysis and information processing techniques. Despite these advancements, efficient implementation of machine learning on conventional computers remains challenging due to speed and power constraints. Optical computing schemes have quickly emerged as the leading candidate for replacing their electronic counterparts as the backbone for artificial neural networks. Some early integrated photonic neural network (IPNN) techniques have already been fast-tracked to industrial technologies. This review article focuses on the next generation of optical neural networks (ONNs), which can perform machine learning algorithms directly in free space. We have aptly named this class of neural network model the free space optical neural network (FSONN). We systematically compare FSONNs, IPNNs, and the traditional machine learning models with regard to their fundamental principles, forward propagation model, and training process. We survey several broad classes of FSONNs and categorize them based on the technology used in their hidden layers. These technologies include 3D printed layers, dielectric and plasmonic metasurface layers, and spatial light modulators. Finally, we summarize the current state of FSONN research and provide a roadmap for its future development. 

Abstract The identification of Chiral molecules is essential in pharmaceutical and food science. However, conventional methods are complex and cost‐prohibitive. This study introduces a sustainable method using hydroxypropyl cellulose (HPC) gel to identify amino acids enantiomers, such as phenylalanine and alanine, through visible light. By integrating the structural color properties of HPC, this research demonstrates the HPC gel's capability to distinguish L (Levo)‐phenylalanine (L‐Phe), D (Dextro)‐phenylalanine (D‐Phe), and DL (racemic mixture)‐phenylalanine (DL‐Phe) supplemented with visible circular dichroism (CD) spectra or hydrochloric acid (HCl) as visual indicators. Similar chiral sensing results are observed with D‐alanine, L‐alanine, and DL‐alanine. Unlike traditional UV‐based detection requiring expensive equipment, this approach simplifies the process while maintaining sensitivity. Varying phenylalanine concentrations altered the CD response without disrupting the gel's helical structure, and color changes in response to HCl addition facilitated visual identification of enantiomers. Furthermore, adding various salts generates colorful HPC/Phe gels, demonstrating their suitability for 3D printing. Meanwhile, the HPC gels remained functional for three months, indicating long‐term stability. These advancements are significant for pharmaceutical and biotechnological industries, facilitating efficient low‐concentration chirality detection (0.2 wt.%). Continued development and refinement of this technology are expected to expand its applications and improve analytical capabilities for future chirality‐related studies and photonic gel 3D printing.