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Illuminating AI: The Transformative Potential of Neuromorphic Optical Neural Networks

Unite.AI

Artificial intelligence (AI) has become a fundamental component of modern society, reshaping everything from daily tasks to complex sectors such as healthcare and global communications. As AI technology progresses, the intricacy of neural networks increases, creating a substantial need for more computational power and energy.

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AI trends in 2023: Graph Neural Networks

AssemblyAI

While AI systems like ChatGPT or Diffusion models for Generative AI have been in the limelight in the past months, Graph Neural Networks (GNN) have been rapidly advancing. And why do Graph Neural Networks matter in 2023? What is the current role of GNNs in the broader AI research landscape?

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A Brain-Inspired Learning Algorithm Enables Metaplasticity in Artificial and Spiking Neural Networks

Marktechpost

Credit assignment in neural networks for correcting global output mistakes has been determined using many synaptic plasticity rules in natural neural networks. Methods of biological neuromodulation have inspired several plasticity algorithms in models of neural networks.

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Harvard Neuroscientists and Google DeepMind Create Artificial Brain in Virtual Rat

Unite.AI

The Harvard researchers worked closely with the DeepMind team to build a biomechanically realistic digital model of a rat. The neural network was trained to use inverse dynamics models, which are believed to be employed by our brains for guiding movement.

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Rethinking Neural Network Efficiency: Beyond Parameter Counting to Practical Data Fitting

Marktechpost

Neural networks, despite their theoretical capability to fit training sets with as many samples as they have parameters, often fall short in practice due to limitations in training procedures. Convolutional networks, while more parameter-efficient than MLPs and ViTs, do not fully leverage their potential on randomly labeled data.

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Apple Researchers Unveil DeepPCR: A Novel Machine Learning Algorithm that Parallelizes Typically Sequential Operations in Order to Speed Up Inference and Training of Neural Networks

Marktechpost

Complex tasks like text or picture synthesis, segmentation, and classification are being successfully handled with the help of neural networks. However, it can take days or weeks to obtain adequate results from neural network training due to its computing demands. If you like our work, you will love our newsletter.

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New Neural Model Enables AI-to-AI Linguistic Communication

Unite.AI

The capacity for an AI to intuitively grasp a task from minimal instruction and then articulate its understanding has remained elusive. This gap in AI capabilities highlights the limitations of existing models. These networks emulate the way human neurons transmit electrical signals, processing information through interconnected nodes.