Artificial Neural Network

An Artificial Neural Network (ANN) is the formal term for the layered, parameterized models that power deep learning. The name distinguishes them from biological neural networks, though the underlying inspiration is the same. ANNs come in many architectures — feedforward networks for tabular prediction, convolutional networks (CNNs) for images, recurrent networks (RNNs and LSTMs) for sequences, and transformers for almost everything modern. Today's ANNs range from small classifiers used in fraud detection to massive transformers like GPT-4 and Gemini that contain hundreds of billions of parameters. They are trained on GPUs or specialized AI accelerators like Google's TPUs and Nvidia's H100s. AI governance programs treat each deployed ANN as an AI asset requiring inventory, model documentation, and AI compliance review. Mastering this AI term is essential for any responsible AI or AI risk management initiative, especially as regulatory frameworks like the EU AI Act formalize obligations for neural-network-based systems.

Centralpoint Inventories Every ANN in Your Portfolio: Oxcyon's Centralpoint AI Governance Platform tracks every neural-network-powered system across cloud and on-prem deployments. It is model-agnostic across OpenAI, Gemini, Llama, and embedded models, meters consumption, and keeps prompts and skills on-premise. Multiple chatbots can be added to any web property with one JavaScript line.


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