ChatGPT is a Neural Network, here’s how it works

Everything you need to know about ChatGPT's neural network

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In the expanding world of AI, neural network is a commonly used term. That’s due to it being a major part of machine learning, a process essential to developing AI algorithms. A lot of the most well-known artificial intelligence software out there uses this kind of machine learning, including OpenAI’s ChatGPT. OpenAI’s leading chatbot uses impressive technology to keep up as one of the dominant transformer-based Natural Language Processing (NLP) AI programs. This article will discuss the neural network technology used in a platform like ChatGPT.

Quick Answer

ChatGPT is a natural language processing model that uses a type of neural network trained on a vast selection of text. This provides the chatbot with the ability to understand and create conversational responses to user prompts.

What is a Neural Network?

A neural network is a type of machine learning algorithm modeled loosely on the human brain’s neural networks. It is a parameterized machine learning model composed of layered interconnected nodes. These nodes can learn to make accurate predictions for complex data like video and image recognition, text generation, and sound. The power comes from stacking many layers to build deep neural networks.

If you’d like to understand a little more about what exactly a neural network is, take a look at our handy neural network article. We discuss how this type of machine learning works and where it is utilized.

Here are some key characteristics of a neural network:

  • Consists of an interconnected group of nodes, like neurons in the brain. These are known as artificial neurons or processing units.
  • Nodes are arranged in layers, including an input layer, one or more hidden layers, and an output layer.
  • Each node in one layer is connected to nodes in the next layer with varying connection strengths or weights.
  • Data enters the input layer, passes through the hidden layers where computations occur, and generates results from the output layer.
  • Nodes apply an activation function to the weighted sum of received inputs and pass them to the next layer of nodes.
  • The network generates predictions or classifications by passing input data through the architecture and performing computations at each node.
  • The weights between nodes get adjusted through backpropagation during the training process to improve prediction accuracy.
  • Well-suited for complex pattern recognition tasks involving image, text, speech, and video data.

ChatGPT is a Neural Network

Essentially, a neural network is an interconnected group of nodes that enables computers to learn by example and recognize patterns in data. ChatGPT is a natural language processing model that uses a neural network to power its understanding and conversational responses to users’ text prompts. Specifically, ChatGPT is powered by a large language model called GPT-3.5 (or GPT-4 for users on a paid subscription), which was developed by OpenAI. The LLM used in ChatGPT is based on a neural network that consists of 176 billion neurons, exceeding the 100 billion found in the human brain.

ChatGPT uses a feed-forward neural network as well as a normalization layer to generate its human-like responses. The feed-forward neural network applies a non-linear transformation to the input sequence, and this makes sure the model can learn complex patterns in a given set of data. The normalization layer helps to stabilize this training process by making sure that the input values to each layer are of a relative scale size. A machine learning technique called fine-tuning ensures that it responds to prompts accurately.

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How is a Neural Network trained?

The neural network that is utilized in ChatGPT’s platform plays a major part in the efficiency and inner workings of the chatbot. But how is it trained? The training process of a neural network is based on an iterative process, through each iteration of the system the model improves and produces more accurate outputs.

With each iteration in the process, there is a pass forward through the model’s layers which computes the output for each training example. Next, is to determine the effect of each parameter on the final output by completing another pass backward through the layers. A gradient is produced in respect of each parameter, this paired with some pre-parameter optimization which is passed to an optimization algorithm will compute the next iteration’s parameters and a new pre-parameter state. Parallelism techniques are used to share this training process across different dimensions, including data parallelism, pipeline parallelism, and Tensor parallelism.

Finding out how your favorite AI chatbot is trained is a great way to understand just how you’re communicating with this helpful AI tool. This is why, we’ve dedicated a whole article to explore the training process of ChatGPT.

Other AI tools that use a Neural Network

The neural network architecture seen in the GPT model certainly makes the chatbot’s process more efficient. Using a neural network within an AI tool involves a robust training process that supplies the chatbot with the ability to create conversational text responses to prompts. ChatGPT isn’t the only AI tool out there that uses this kind of machine learning, plenty of other AI tools on the market also utilize this power. The list below shows some other popular AI tools that are neural networks.

Wrapping up

So there you have it. You can now recognize that ChatGPT is a neural network, and understand how it works. It uses state-of-the-art transformer architecture like GPT-3 and GPT-4 trained on vast amounts of text data to enable the conversational abilities it presents to users through an accessible interface and application layer. Using such machine learning supplies a range of AI tools with the ability to respond to user prompts in a human-like way and understand conversational aspects of language.

If you’d like to check out some AI chatbots that provide an alternative to ChatGPT and use the same type of machine learning, check out our ChatGPT alternatives buying guide.

Marla writes across a wide range of topics across PC Guide, including AI, PC hardware, and news on the latest tech releases. She's a passionate writer that's interested in the future of technology.