Ai neural network
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Advances in AI Neural Networks: A Comprehensive Overview
Introduction to Deep Neural Networks (DNNs)
Deep Neural Networks (DNNs) have become a cornerstone of artificial intelligence (AI) applications, excelling in fields such as computer vision, speech recognition, and robotics. Despite their high accuracy, DNNs are computationally intensive, necessitating efficient processing techniques to enhance energy efficiency and throughput without compromising accuracy or increasing hardware costs1.
Efficient Processing Techniques for DNNs
Recent advancements focus on optimizing DNN processing through hardware design changes and algorithmic co-designs. These techniques aim to reduce computational costs and improve performance metrics, making DNNs more viable for widespread deployment in AI systems1. Key trends include the development of specialized hardware platforms and architectures that support efficient DNN operations1.
Hierarchical Deep Learning Neural Networks (HiDeNN)
The Hierarchical Deep Learning Neural Network (HiDeNN) framework addresses complex computational science and engineering problems. HiDeNN demonstrates superior accuracy compared to traditional methods by optimizing nodal positions and capturing stress concentrations with coarse meshes. It also excels in multiscale analysis and discovering governing parameters from experimental data, showcasing its versatility and efficiency2.
Artificial Neural Networks (ANNs) in Computational Neuroscience
Artificial Neural Networks (ANNs) serve as theoretical models to understand neural information processing. These models range from rate-based to biologically plausible spiking neuron models, aiding computational neuroscientists in exploring neural mechanisms3. ANNs have also been applied in various scientific fields, including big data management and medical diagnosis, highlighting their broad utility4.
Data Processing and Network Topology in ANNs
ANNs, inspired by the human brain, have evolved from basic machine learning approaches to sophisticated deep learning models. Key elements include network topology, the number of nodes and hidden layers, learning rules, and activation functions. These components are crucial for understanding and developing effective ANN models5.
Neurosymbolic AI: Integrating Learning and Reasoning
Neurosymbolic AI combines neural network-based learning with symbolic knowledge representation and logical reasoning. This integration addresses concerns about trust, safety, interpretability, and accountability in AI systems. Neurosymbolic computing aims to create AI systems that are both robust and explainable, paving the way for the next wave of AI advancements6.
Edge AI and On-Demand DNN Inference
Edge AI leverages edge computing to enhance DNN inference, addressing the limitations of mobile devices and traditional cloud-assisted methods. The Edgent framework exemplifies this approach by partitioning computations between devices and edge resources, reducing latency and improving real-time performance. This method is particularly effective in dynamic network environments, ensuring optimal configurations for low-latency edge intelligence7.
Applications of Neural Networks in Various Domains
Neural networks have been applied in diverse areas, including power electronics and motor drives. These applications benefit from the non-linear mapping properties of neural networks, which are essential for pattern recognition and dynamic system emulation. Different topologies, such as feedforward and recurrent networks, cater to specific application needs8.
AI in Brain Disease Diagnosis and Treatment
AI, particularly ANNs, plays a significant role in brain disease diagnosis and treatment. AI algorithms analyze complex medical data, aiding in diagnosis, surgical planning, and outcome prediction. Despite their potential, challenges remain in data comprehensiveness and algorithm explainability, which are critical for practical AI applications in neuroscience9.
Graph Neural Networks (GNNs)
Graph Neural Networks (GNNs) have gained popularity due to their ability to process unstructured data types. GNNs share some conceptual similarities with traditional neural networks but also introduce novel elements that enhance their performance. Practical tutorials and examples help AI practitioners understand and apply GNNs effectively10.
Conclusion
The field of AI neural networks is rapidly evolving, with significant advancements in efficient processing, hierarchical frameworks, neurosymbolic integration, and edge computing. These developments are expanding the applicability and performance of neural networks across various domains, from computational neuroscience to real-time edge AI. As research continues, the focus on trust, safety, and explainability will be crucial for the next generation of AI systems.
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Most relevant research papers on this topic
Efficient Processing of Deep Neural Networks: A Tutorial and Survey
Efficient processing of deep neural networks is crucial for AI systems, with recent advances in hardware design and algorithm changes reducing computation cost without sacrificing accuracy.
Highly CitedHierarchical Deep Learning Neural Network (HiDeNN): An artificial intelligence (AI) framework for computational science and engineering
The Hierarchical Deep Learning Neural Network (HiDeNN) framework effectively solves challenging computational science and engineering problems with improved accuracy, efficiency, and versatility, demonstrating its versatility in diverse fields.
Highly CitedEditorial: Artificial Neural Networks as Models of Neural Information Processing
Artificial neural networks (ANNs) can aid in understanding neural information processing, aiding in AI and computational neuroscience research.
Highly CitedArtificial Neural Networks Applied in Civil Engineering
In recent years, artificial neural networks (ANN) and artificial intelligence (AI), in general, have garnered significant attention with respect to their applications in several scientific fields, varying from big data management to medical diagnosis [...]
Data Processing Using Artificial Neural Networks
Artificial neural networks (ANN) are a machine learning methodology that imitates the human brain and can be used to solve various data processing problems.
Neurosymbolic AI: the 3rd wave
Neurosymbolic AI, combining neural network-based learning with symbolic knowledge representation and logical reasoning, is key to addressing trust, safety, interpretability, and accountability in AI systems.
Highly CitedEdge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing
Edgent, a framework for device-edge synergy, effectively accelerates deep neural network inference on mobile devices with low-latency, improving user experience in 5G AI applications.
Highly CitedNeural network and applications
Neural networks can efficiently solve complex problems in power electronics and motor drive, with feedforward networks used for pattern recognition and recurrent networks for dynamical system emulations.
Artificial intelligence for brain diseases: A systematic review
AI has the potential to improve diagnosis, planning, and outcome prediction in brain diseases, but requires comprehensive data and explainable algorithms for practical use.
Systematic ReviewHighly Cited