Neural Network
A neural network is a machine-learning model composed of layers of interconnected nodes ("neurons") whose numerical weights are adjusted during training so the network maps inputs to desired outputs.
Full definition
Each neuron computes a weighted sum of its inputs, applies a non-linear activation function, and passes the result to the next layer. Training uses backpropagation and gradient descent to update the weights so the network's output gets closer to the target on the training data. Modern neural networks can have hundreds of billions of parameters; GPT-4 is widely estimated to be in that range.
Why it matters
Neural networks are the universal substrate for modern AI. The same basic building block is reused — with different topologies — to power vision, speech, language, recommendation, and robotic control. For executives, this means a single technical capability investment compounds across many use cases.
Example
A convolutional neural network trained on chest X-rays classifies images as normal, pneumonia, or COVID-19 with diagnostic accuracy comparable to board-certified radiologists.
Related terms
- Deep LearningDeep Learning is a class of machine learning that uses neural networks with many layers ("deep" architectures) to learn hierarchical representations directly from raw data such as images, audio, or text.
- TransformerThe transformer is a neural-network architecture built around the self-attention mechanism that has become the dominant model design for language, vision, audio, and multimodal AI since 2017.
- Machine Learning (ML)Machine Learning (ML) is the subfield of AI in which algorithms improve their performance on a task by learning statistical patterns from data, rather than following rules a human wrote by hand.
- Training DataTraining data is the curated dataset used to teach a machine-learning model — every pattern the model can recognize, every bias it inherits, and every limit to its accuracy ultimately traces back to this data.
- AlgorithmAn algorithm is a finite, well-defined sequence of computational steps that takes an input and produces an output, designed to solve a class of problems — for example, sorting a list, ranking search results, or training a neural network.
Source & further reading
Primary source: Goodfellow, Bengio, Courville — "Deep Learning" (MIT Press) (2016).
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