Neural networks in the context of "Diffusion"

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⭐ Core Definition: Neural networks

A neural network is a group of interconnected units called neurons that send signals to one another. Neurons can be either biological cells or mathematical models. While individual neurons are simple, many of them together in a network can perform complex tasks. There are two main types of neural networks.

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👉 Neural networks in the context of Diffusion

Diffusion is the net movement of anything (for example, atoms, ions, molecules, energy) generally from a region of higher concentration to a region of lower concentration. Diffusion is driven by a gradient in Gibbs free energy or chemical potential. It is possible to diffuse "uphill" from a region of lower concentration to a region of higher concentration, as in spinodal decomposition. Diffusion is a stochastic process due to the inherent randomness of the diffusing entity and can be used to model many real-life stochastic scenarios. Therefore, diffusion and the corresponding mathematical models are used in several fields beyond physics, such as statistics, probability theory, information theory, neural networks, finance, and marketing.

The concept of diffusion is widely used in many fields, including physics (particle diffusion), chemistry, biology, sociology, economics, statistics, data science, and finance (diffusion of people, ideas, data and price values). The central idea of diffusion, however, is common to all of these: a substance or collection undergoing diffusion spreads out from a point or location at which there is a higher concentration of that substance or collection.

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Neural networks in the context of Generative artificial intelligence

Generative artificial intelligence (Generative AI, or GenAI) is a subfield of artificial intelligence that uses generative models to produce text, images, videos, audio, software code or other forms of data. These models learn the underlying patterns and structures of their training data and use them to produce new data based on the input, which often comes in the form of natural language prompts.

Generative AI tools have become more common since the AI boom in the 2020s. This boom was made possible by improvements in transformer-based deep neural networks, particularly large language models (LLMs). Major tools include chatbots such as ChatGPT, Copilot, Gemini, Claude, Grok, and DeepSeek; text-to-image models such as Stable Diffusion, Midjourney, and DALL-E; and text-to-video models such as Veo and Sora. Technology companies developing generative AI include OpenAI, xAI, Anthropic, Meta AI, Microsoft, Google, Mistral AI, DeepSeek, Baidu and Yandex.

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Neural networks in the context of Amorphous computing

Amorphous computing refers to computational systems that use very large numbers of identical, parallel processors each having limited computational ability and local interactions. The term amorphous computing was coined at MIT in 1996 in a paper entitled "Amorphous Computing Manifesto" by Abelson, Knight, Sussman, et al.

Examples of naturally occurring amorphous computations can be found in many fields, such as developmental biology (the development of multicellular organisms from a single cell), molecular biology (the organization of sub-cellular compartments and intra-cell signaling), neural networks, and chemical engineering (non-equilibrium systems). The study of amorphous computation is hardware agnostic—it is not concerned with the physical substrate (biological, electronic, nanotech, etc.) but rather with the characterization of amorphous algorithms as abstractions with the goal of both understanding existing natural examples and engineering novel systems. Ultimately, this field extenuates to Computational Intelligence, as this computational technique is an extenuation of Artificial Intelligence (but more specifically Artificial General Intelligence) for developing Biological Computation.

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LINKED FROM (PARENTS)
← Generative artificial intelligence ← Diffusion ← Amorphous computing ← Diffuse
LINKS TO (CHILDREN)
Neurons → Cell (biology) → Mathematical model → Neuroscience → Biological neural network → Brain → Nervous system → Synapse → Machine learning → Neural network (machine learning) → Nonlinear function → Artificial intelligence →