Andrey Rzhaksinskiy’s Post

What advancements are enabling faster and more energy-efficient AI computations? Researchers at MIT have developed a photonic processor that uses light to perform deep neural network operations on a chip, enabling ultrafast AI computations with extreme energy efficiency. This innovation opens the door to high-speed processors capable of real-time learning, significantly enhancing the performance of AI systems. The use of photonics in computing represents a substantial leap forward in processing capabilities, potentially transforming various applications that rely on rapid data analysis and machine learning. #AI #Photonics #MIT #MachineLearning Read More: 🔗https://buff.ly/3CVyX5c

If you couldn't catch the Arrow Electronics webinar, no need to fret – we've got you covered! In the webinar, we'll be exploring the capabilities of in-sensor AI with Neuton.AI neural networks, focusing on the integration of Neuton.AI neural networks into the STMicroelectronics ISPU sensor. During this enlightening session, you'll discover: 🔸 The significance of in-sensor AI innovation 🔸 Advantages of in-sensor AI architecture 🔸 Real-world applications 🔸 A real-time demonstration 🔸 A step-by-step tutorial on creating and deploying in-sensor AI solutions using the Neuton.AI platform. Sit back, relax, and enjoy the presentation! 😎 George Dickey Aditi Shivkar #webinar #tinyml  #aibasedsolutions  #ondeviceai  #ml  #insensorai  #smartsensors #remotecontrol  #gesturerecognition  #aifeatures  #MEMS  #Sensors #Future #Innovation #Technology #smartring #arrowelectronics #STMicroelectronics #neutonai

Unlock Visual Data with CNNs 🤖📸 Convolutional Neural Networks (CNNs) are the backbone of tasks like image recognition, object detection, and computer vision. ✨ Applications: Self-driving cars 🚗 Medical imaging 🩺 Face recognition 📸 Start exploring CNNs today and transform the way we see data! 🚀 📞 Tel: +1-929-672-1814 📧 Email: info@genai-training.com 🌐 Website: www.genai-training.com #DeepLearning #CNN #AI #MachineLearning #DataScience #Technology

Synchron is revolutionizing implantable brain-computer interface (BCI) technology with NVIDIA Holoscan. By integrating real-time AI with edge computing, this collaboration aims to transform neuroprosthetics and digital interaction, delivering faster, more intuitive neural processing and scalable brain-language models. These advancements hold the potential to enhance autonomy for BCI users and were unveiled today at the J.P. Morgan Healthcare Conference 2025. ➡️ Read the Full Story: https://lnkd.in/eTvrq9iX #BCI #Innovation #AI #Neurotech

Spatially varying nanophotonic neural networks for high-performance optical computing As artificial intelligence reshapes computational capabilities, the energy demands of today’s powerful AI models are straining traditional electronic architectures. Optical neural networks (ONNs), which process information using photons rather than electrons, present a promising alternative for ultra-fast, energy-efficient computing. Yet, achieving the recognition accuracy of electronic neural networks has remained challenging for previous optical designs. In an innovative study, Wei et al. unveil a spatially varying nanophotonic neural network (SVN3) that performs deep learning computations directly within a camera lens, integrating optical processing into a compact, energy-efficient structure. The breakthrough lies in embedding parallel optical computation within the camera’s optics: a metalens array functions as spatially varying, large-kernel convolutions, capturing complex visual information from various angles. This design enables SVN3 to perform convolutional neural network (CNN) computations in the optical domain, drastically reducing electronic processing requirements to a lightweight back-end of just 2,000 parameters. By reparameterizing its optical layer, SVN3 emulates large kernel convolutions through a decomposition into smaller, manageable elements. The network achieves a CIFAR-10 classification accuracy of 72.76%, outperforming AlexNet on the same dataset. The SVN3 design also generalizes well to other tasks, such as semantic segmentation on PASCAL VOC, highlighting its versatility for diverse, real-world applications. Paper: https://lnkd.in/dXH4PC2n Preprint: https://lnkd.in/dT8VAp5n #Nanophotonics #OpticalComputing #Photonics #NeuralNetworks #DeepLearning #AIInnovation #AIforScience #EnergyEfficientComputing #NextGenAI #MachineLearning #OpticalTechnology #Nanotechnology #ArtificialIntelligence #ComputationalImaging #FutureOfAI #TechInnovation

🔬 The Neural Network Revolution: Pushing the Boundaries of What's Possible The landscape of artificial intelligence is evolving at breakneck speed, and neural networks are leading this transformation. As someone deeply fascinated by AI's potential, I've been tracking some groundbreaking developments that are reshaping our world. Here are some remarkable advancements that caught my attention: 1. Multimodal Foundation Models These sophisticated networks can now seamlessly process text, images, and audio simultaneously, enabling more natural human-AI interactions. Imagine medical diagnostic systems that can analyze patient symptoms, medical imaging, and clinical notes all at once. 2. Neural Architecture Search (NAS) AI is now designing AI! Advanced algorithms can automatically discover optimal neural network architectures, often outperforming human-designed models while requiring significantly less computational power. 3. Neuromorphic Computing By mimicking the brain's neural structure, these specialized hardware implementations are achieving unprecedented energy efficiency - running complex neural networks using just a fraction of traditional power requirements. The most exciting part? These aren't just laboratory curiosities. They're driving real-world applications - from personalized medicine and climate modeling to autonomous vehicles and scientific discovery. 🤔 My take: We're witnessing the emergence of a new paradigm where neural networks aren't just tools for automation, but partners in innovation. The key will be ensuring these advances benefit humanity as a whole. What are your thoughts on these developments? Which application area excites you the most? #ArtificialIntelligence #DeepLearning #NeuralNetworks #Innovation #TechTrends #FutureOfAI #MachineLearning #DataScience

Check out my new review paper published in Advanced Materials! Thanks to everyone in this work! The new era of intelligent materials and neuromorphic AI is imminent! https://lnkd.in/eBZUKcC5

🌟 That AR filter making you look like a puppy? Or your phone unlocking when it sees your face? That’s the magic of CNNs (Convolutional Neural Networks) at work! Think of a CNN as your brain's visual system on silicon. Just like you scan a photo piece by piece to spot details, CNNs process images through layers of digital filters, each looking for specific patterns. Fun fact: CNNs were inspired by the human visual cortex! In 1959, neuroscientists Hubel and Wiesel discovered how brain cells in the visual cortex respond to specific patterns and orientations. This groundbreaking research influenced the design of CNNs. 🧪 Why do they matter now? - Powering facial recognition on your phone - Helping doctors spot anomalies in medical scans - Enabling self-driving cars to "see" the road - Making those cool AI art generators possible From selfies to cancer detection, CNNs are quietly revolutionizing how machines understand our visual world! 🚀 #AI #MachineLearning #Technology #Innovation

'This is a marriage of #AI and quantum': New technology gives AI the power to feel surfaces for the 1st time Combining quantum science with machine learning has led to a model that can accurately measure how surfaces feel to the touch. https://buff.ly/3OsFg2z #Innovation

Now on #NaturePhotonics: a fully integrated optical neural network on a single chip: https://lnkd.in/dM-WCsa6 As deep neural networks continue to transform industries, the limitations of traditional CMOS electronics, particularly in energy consumption and throughput, are becoming more apparent. Saumil Bandyopadhyay et. al tackle this challenge head-on by leveraging photonic integrated circuits to achieve unprecedented performance in optical processing. In this work, they use a scalable photonic integrated circuit integrating coherent optical processors for both *matrix algebra* and *nonlinear activation* functions. They demonstrate a deep neural network processing (6 neurons, 3 layers) with a latency of just 410 ps, utilizeing processing at the speed of light. Morover, they implement backpropagation-free training with 92.5% accuracy on a six-class vowel classification task—comparable to digital systems but orders of magnitude faster. This work opens new pathways for ultrafast optical computing in real-time applications, from high-speed communications to advanced sensing and AI. Thank you National Science Foundation (NSF), Research Laboratory of Electronics at MIT, and colabroators from Nokia and Periplous. #Photonics #ArtificialIntelligence #DeepLearning #NaturePhotonics #NeuralNetworks #Innovation