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Research Shows Bright Spot for Optical Computing Through Artificial Intelligence

8 Dec '20 | By Maxim Batalin
Research Shows Bright Spot for Optical Computing Through Artificial Intelligence
Artistic depiction of how AI is used to virtually stain human tissue using optical microscopy.

Optical computing, once a hot research topic a couple of decades ago, has emerged again as a promising technology — this time backed by artificial intelligence.

Aydogan Ozcan, the Volgenau Professor for Engineering Innovation at UCLA Samueli School of Engineering, and colleagues have outlined in a Nature article recent advances in artificial intelligence and their impact on visual computing applications. The emerging research area suggests that AI inference based on incoming light moving through an optical device can play a key role in new visual-computing technologies and capabilities that require little or no power to run. The article’s co-authors are researchers from Stanford University, the Massachusetts Institute of Technology, École Polytechnique Fédérale de Lausanne in Switzerland, Sorbonne University in France and the University of Münster in Germany.

According to the authors, optical computing, which uses photons instead of electrons to perform computations, has shown potential over the years. However, limited applications and technological hurdles led to a decline in enthusiasm from its heyday in the 1980s to waning interest in the 1990s.

While several advances were made in the ensuing decades in developing optical computing platforms, challenges still remain for the technology to evolve into a practical, general-use system. According to the researchers, however, one bright spot has emerged in more recent years.

Beginning in the 2010s, the major success of deep neural networks — a type of artificial intelligence commonly known as deep learning that uses a series of layers and nodes to process information — has offered a vehicle for emerging applications in optical computing. Some familiar commercial products that could utilize deep-learning technology include autonomous cars, robotic vision, smart homes, remote sensing and medical imaging. AI-based optical systems in these applications could enhance the capabilities of a regular electronic computer by using information in the incoming light to rapidly analyze objects and their surroundings. Such hybrid computing systems could combine the speed and parallelism of optical computing with the flexibility and maturity of electronic computing. A big test remains in making such systems energy efficient without compromising performance.

Ozcan has led groundbreaking research in developing an optical neural network in 2018 that can instantly process and identify objects without needing external energy except incoming light, and a follow-up study showing major improvements to the concept. He has also spearheaded efforts on using artificial intelligence in medical imaging, such as building comprehensive 3D images from 2D images of living cells and tissues, and transforming low-resolution microscopic images into dramatically higher resolution and more detailed ones. These concepts could lay the foundation for a “thinking microscope” as depicted in the Nature article.

Ozcan holds faculty appointments at UCLA in electrical and computer engineering, and bioengineering. He is also the associate director of California NanoSystems Institute (CNSI) and an HHMI Professor with the Howard Hughes Medical Institute.

Area of application: Dentistry , Dermatology, Gastroenterology, Gynecology and Obstetrics, Human genetics, Infectious disease and antibiotic resistance, Internal medicine and general medicine, Laboratory and environmental medicine, Neurology, Neurosurgery, Oncology, Ophthalmology Diagnostics and Imaging, Otorhinolaryngology, Pathology, Pediatrics and Neonatology, Pharmacology, Pulmonology, Reproductive Medicine, Rheumatology, Surgery, Urology and Nephrology, Medicine, other applications, Cellular biotechnology, Drug delivery, Molecular diagnostics, Pharmaceuticals (development, production, monitoring), Therapeutics, Tissue enginieering, Health, other applications, Biochemistry, Cell Biology, Developmental biology, Ecology, Genetics, Human biology, Microbiology, Molecular biology, Physiology, Biology, other applications, Other Areas of Application
Methods and Techniques: Endoscopy, General microscopy (white light, confocal, bright field, dark field, phase contrast, DIC etc.), Linear and non-linear fluorescence imaging (confocal LSM, multi-photon, STED, PALM, STORM, SIM, FRET, FRAP, FLIM, etc.), Linear and non-linear vibrational microscopy / imaging (IR, confocal Raman, CARS, SRS etc.), Near-field microscopy (SNOM, AFM, STM, etc.), Optical Coherence Tomography (OCT ), Operating microscopy, Photoacoustic imaging (PAI, MSOT), Polarimetry, Probe and sensor development, Terahertz imaging, Thermography, Microscopy/Imaging, other methods and techniques, ATR / FTIR Spectroscopy, Coherent Back Scattering CBS, Diffuse Optics, Dynamic Light Scattering DLS, Ellipsometry, Fluorescence spectroscopy, Photoluminescence, Reflectance, Terahertz spectroscopy, UV / VIS spectroscopy, Vibrational spectroscopy (Raman, Infrared), Spectroscopy, other methods and techniques, fs LASIC, Laser lithotripsy, Laser Surgery, Laser-based thermotherapy, LASIK, LIBS, Photo-coagulation, Photodynamic therapy, Photonic therapeutic approaches, other methods and techniques, Biochips, Bioassays, High-throughput screening, Micro-array technologies, Point-of-care, other methods and techniques, Big data, Chemometrics, Image analysis, Image processing, Digitalization, other methods and techniques, CCD and CMOS sensors and cameras, Fiber optical illumination, Functionalized fibres, Implant manufacturing, Laser , Laser-induced microdissection and catapulting of cells, Laser micromanipulation, Microstructure Fibers, Objectives, Optical clearing, Optical tweezers, Tissue separation / laser scalpels, Enabling Technologies, other methods and techniques, Other Methods and Techniques

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