Introduction to AI
Artificial Intelligence (AI) is the field of computer science dedicated to solving cognitive problems commonly associated with human intelligence, such as learning, problem solving, pattern recognition, and language understanding. AI is used in many of our day-to-day applications like voice assistants, recommendation systems, and autonomous vehicles.
Over the last year, the introduction of Large Language Models (LLMs) from companies like OpenAI through services like ChatGPT have popularized consumer use of AI through chat bot interfaces.
What is an AI Model?
An AI model is a mathematical representation of a real-world process. For instance, language models are AI models trained to understand and generate human-like text based on the data they are fed.
Understanding Large Language Models (LLMs)
Large Language Models (LLMs) like GPT-4 by OpenAI, are a type of AI model that utilizes machine learning to write human-like text. They're trained on a diverse range of internet text, but they don't know specifics about which documents were in their training set or have access to any personal or confidential information unless explicitly provided in the conversation.
How do LLMs work?
LLMs predict the probability of a word (or a "token", as we'll learn later) based on the previous words used in a sentence. This way, they generate coherent and contextually appropriate sentences. However, it's important to note that LLMs don't understand text the way humans do and are not capable of independent thought or consciousness.
Foundational Models in AI
A foundational model is a pre-trained model that serves as a starting point for specific AI applications. These models are trained on vast amounts of data and can be fine-tuned or adapted for specific tasks, like translation, text generation, or even creating art. GPT-4 is an example of a foundational language model.
Why are foundational models important?
Foundational models have several advantages:
Versatility: These models can be fine-tuned for a wide array of tasks.
Efficiency: It is more resource-efficient to fine-tune these models than to train a new model from scratch for each new task.
Accessibility: Foundational models make high-quality AI available to a wider range of users, not just those with huge computational resources.
