What is artificial intelligence and how does it work

Artificial Intelligence (AI) refers to the capability of machines to exhibit human-like intelligence, performing tasks such as pattern recognition, learning, and language comprehension. While traditionally these skills have been exclusive to humans, technological strides since the 1950s have empowered machines to emulate these cognitive functions. Today, AI finds applications in diverse fields including robotics, automotive manufacturing, healthcare, and finance. This article delves into the evolution of large language models (LLMs), with a particular emphasis on Generative Pre-trained Transformers (GPT), which have seen remarkable progress since the launch of GPT-1 by OpenAI in 2018.

Generative Pre-trained Transformers, or GPTs, are sophisticated software models rooted in neural network architecture, reminiscent of how the human brain processes information. At their core, GPTs function by ingesting textual information and generating responses that closely mimic human language. As a prominent type of LLM, they leverage deep learning methodologies to excel in natural language processing tasks, forming the foundation of platforms like ChatGPT.

The development of large language models begins with the training phase, where models are exposed to extensive datasets composed of text. This text is broken down into smaller units known as 'tokens', which are then embedded into a multidimensional space to capture relationships and contextual nuances between them. The model's proficiency in detecting intricate patterns within text is largely contingent on the quality and scale of the training data. During training, the model uses this data to assign numeric values to input tokens and iteratively adjusts parameters—known as weights and biases—through a process called backpropagation, a method designed to minimize error between actual and predicted outputs. Once the model's performance reaches a satisfactory level, it transitions to the inference stage.

Inference represents the operational stage where users interact with the model by providing prompts, to which the model responds. At this juncture, the model's parameters are fixed and unalterable by users. User inputs are tokenized, and their respective values are calibrated based on inter-token relationships and their positional context within the input. This process enables the model to prioritize which tokens require more contextual focus. The calibrated token values are processed through the network, with each pass determining the likelihood of subsequent words in the output sequence. This iterative process continues—evaluating and generating each next token—until an end-of-sequence token is reached.

While the inner workings of large language models are deeply mathematical, this overview aims to equip users with a fundamental understanding of how these tools operate, without delving into the complexities of mathematical equations. Through this knowledge, users can engage with LLMs effectively, appreciating both their potential and limitations.