Tables of data (LLMs) and other layers work to predict the highest probability responses

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A large language model (LLMs) works by predicting the most likely next word or sequence of words given a prompt, which is then used for autocomplete.

This is achieved by using a neural network that has been trained on vast amounts of text data, allowing it to learn the patterns and relationships between words.

The model takes in the prompt as input, processes it through several layers of mathematical operations, and outputs a probability distribution over all possible next words.

The word with the highest probability is then selected as the autocomplete suggestion.

The process is repeated as the user continues to type, and the model updates its prediction based on the new context.

Examples of LLMs have a variety of motivations behind their creation, and not all are as functional as others:

OpenAI’s GPT-4
OpenAI’s GPT-3
Meta’s LLaMA (Large Language Model Meta AI)

What is a neural network, and does a neural network actually have anything to do with neurons?

Neural Nets Were Inspired by Neurons

A neural network is a type of artificial intelligence model that is inspired by the structure and function of the human brain.

Neural Nets Work Differently than Actual Neurons

Although the term “neural” suggests a connection to neurons, the mathematical operations performed by a neural network are not a direct model of the biophysics of neurons.

Neural Nets Are Organized by Nodes, then Layers

A neural network consists of interconnected nodes, which are organized into layers.

Each node performs a simple calculation on the inputs it receives and passes the result to the next layer of nodes.

The inputs are transformed through multiple layers of computation, leading to a prediction or decision.

The weights of the connections between nodes are learned from the data, allowing the network to capture complex patterns and relationships.

In summary, while the name “neural network” was inspired by the structure of the brain, the mathematical operations performed by a neural network are not a direct model of the biophysics, nor even roughly function analogously to biological neurons, at least this year (2023).

How many layers do typical neural networks have, and is there any such thing as a typical neural network?

There is no such thing as a “typical” neural network, as the architecture of a neural network can vary greatly depending on the problem it is being used to solve and the specific design choices made by the practitioner. However, most neural networks used in practice have several layers, with a few being the most common.

Feedforward | Feed Forward | Feed-Forward

Feedforward neural networks, which are the most basic type of neural network, typically have an input layer, one or multiple hidden layers, and an output layer. The number of hidden layers and the number of nodes in each layer can vary greatly, and can range from just a few to hundreds or even thousands.

Convolutional

Convolutional neural networks, which are used for image and video analysis, typically have a smaller number of hidden layers, but each layer can contain many more nodes than in a feedforward network.

Recurrent

Recurrent neural networks, which are used for processing sequential data like text or speech, typically have a smaller number of hidden layers as well, but also have connections that form a loop, allowing information to be passed from one step of the sequence to the next.

Numbers of Layers Vary Depending on Context

In summary, the number of layers in a neural network can vary greatly depending on the specific problem and design choices, but most neural networks have several hidden layers, ranging from a few to hundreds or even thousands.

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