Artificial intelligence (AI) is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.

A large language model (LLM) is a deep learning algorithm that can recognize, summarize, translate, predict and generate text and other forms of content based on knowledge gained from massive datasets.

Natural language processing (NLP) refers to the branch of computer science—and more specifically, the branch of artificial intelligence or AI—concerned with giving computers the ability to understand text and spoken words in much the same way human beings can.

Generative Pre-trained Transformers, commonly known as GPT, are a family of neural network models that uses the transformer architecture and is a key advancement in artificial intelligence (AI) powering generative AI applications such as ChatGPT. GPT models give applications the ability to create human-like text and content (images, music, and more), and answer questions in a conversational manner. Organizations across industries are using GPT models and generative AI for Q&A bots, text summarization, content generation, and search.

Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another.

Artificial neural networks (ANNs) are comprised of a node layers, containing an input layer, one or more hidden layers, and an output layer. Each node, or artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network.

Neural networks rely on training data to learn and improve their accuracy over time. However, once these learning algorithms are fine-tuned for accuracy, they are powerful tools in computer science and artificial intelligence, allowing us to classify and cluster data at a high velocity. Tasks in speech recognition or image recognition can take minutes versus hours when compared to the manual identification by human experts. One of the most well-known neural networks is Google’s search algorithm.

Reinforcement learning from human feedback (RLHF) is a subfield of reinforcement learning that focuses on how artificial intelligence (AI) agents can learn from human feedback.

In traditional reinforcement learning, an agent learns to take action in an environment in order to maximize a reward signal. In RLHF, the agent receives feedback from a human teacher in the form of positive or negative reinforcement, in addition to any traditional reward signal. This allows the agent to learn from a broader range of experiences and to learn more efficiently, as it can learn from the expertise and perspective of a human.

RLHF involves training multiple models at different stages, which typically include pre-training a language model, training a reward model, and fine-tuning the language model with reinforcement learning.

Tokens are the basic units of text or code that an LLM AI uses to process and generate language. Tokens can be characters, words, subwords, or other segments of text or code, depending on the chosen tokenization method or scheme. Tokens are assigned numerical values or identifiers, and are arranged in sequences or vectors, and are fed into or outputted from the model. Tokens are the building blocks of language for the model.