What follows is my attempt to explain AI through the medium of a glossary, with a focus on LLMs (Large Language Models). These aren’t Wikipedia definitions, but my take on what these terms mean. The purpose is to understand how LLMs can be used and the surrounding context. I’ll start with an AI hierarchy diagram and a slightly tongue-in-cheek definition of AI.

AI

AI is a buzzword, a marketing term, a label used to sell products and remain relevant in today’s world (at least in 2024). A rather cynical definition to start with, but one I feel should be pointed out before diving into a more technical definition. I say this because I often get asked why everything is labeled as “AI”, and my first response is, it’s the new “cloud”. Many people didn’t really understand what the cloud meant and still don’t. The same can be said of AI.

AI isn’t just a buzzword and there is real meaning behind it. There are many places where you can look this up, so I’ll just concentrate on one particular AI technique, and that is the Transformer Architecture. This technique has come to dominate in the last few years, mostly due to its most successful and notorious implementation, GPT.

ChatGPT uses GPT, which is a Large Language Model (LLM) but is also a Transformer Model. This is a type of deep learning, which is a branch of machine learning, which in turn is a subset of AI.

So, some healthy scepticism. When someone claims, “my product is AI enabled” or “… uses AI”, I usually assume one of three things:

  • They are bluffing and don’t really know what AI means.
  • It uses an AI technique that wouldn’t have been called AI two years ago.
  • It uses an LLM such as GPT.

This assumption can be wrong, but if the label AI is applied to something without further explanation, it gives you a starting point to ask what is meant by “AI” when presented with a claim of its usage.

Large Language Model (LLM)

LLMs are pre-trained neural nets (an artificial brain) that are expensive to run and very expensive to build. They contain a lot of data and are created from vast amounts of human-created text. They can be used to write poetry, write computer code, pretend to be a dead relative, etc. In short, their utility is endless and the results look disturbingly clever.

GPT-4 is an example of an LLM. It is rumored to have cost £100 million to build and is “trained” on 1.76 trillion parameters. One example of the training data or the text that goes into GPT-4 is called Common Crawl. Common Crawl is a collection of data from 250 billion web pages, collected over the last 17 years.

Despite the huge cost to build and its almost human-like capability, most LLMs can be accessed via a chatbot for free or at a low monthly fee (typically $20 or less per month). All that endless power is democratically available to everyone and has been for the last two years. I feel like this is fundamentally why AI has been hyped up so much recently.

Relevant examples listed below are grouped by whether they are proprietary or open (hosted on Hugging Face). Each model is generally a family of models, and in each instance, I am referring to the latest or biggest unless specified otherwise.

All the models listed below are examples of foundation models. Foundation models are generally fine-tuned (turned into a fine-tuned model) before they can be used for applications like chatting or language translation. Without fine-tuning, models can be less useful and potentially dangerous.

Proprietary Models

Model Description Size (in parameters)
GPT Models from “Open” AI (yes, the quotes are deliberate). Forms the backend to ChatGPT and Copilot (tools that I use). 1.76 trillion (GPT-4)
Gemini Models from Google that power the Gemini assistant. Competes with GPT. Unknown but rumored to be 1.56 trillion (Gemini Ultra).
Claude From Anthropic. Known for being safe and ethical. Unknown but rumored to be 2 trillion (Claude 3 Opus).

All three of these models are comparable and have various strengths and weaknesses. Different sources will claim that one is better than the others; however, Claude appears to beat the others in IQ tests and is often regarded as generally outperforming GPT and Gemini.

Open Models

These models are generally smaller than proprietary models, so they may be less capable but are still good enough for many applications. Because they are smaller, they don’t require as large an investment to build and therefore can be given away for free and may be easier to include in a commercial product.

See Hugging Face Models for a canonical listing.

Model Description Size (in parameters)
BLOOM From Hugging Face. 176 billion
Llama Models from Meta. 70 billion
Mistral Models from Mistral. 141 billion (Mixtral 8x22B)
Gemma Smaller models from Google built similarly to Gemini. Notable for being small enough to run at home. 7 Billion

Hugging Face

Hugging Face is a website, a bit like GitHub, that hosts LLM models, tools to build LLM models, and tools to make use of LLM models. Everything is free and community maintained.

LLM Apps

ChatGPT (again with the ChatGPT) is an example of an LLM app. It hosts a user interface that allows a human to interact with a fine-tuned LLM model by asking questions of the LLM and responding with relevant answers.

You can also interact with LLMs via an API. This allows you to build whatever you like on top of the LLM and potentially build an AI agent. This is an application that performs intelligent tasks on your behalf, such as looking up train times or today’s weather.

Langchain

Langchain is a toolkit for creating LLM apps and AI agents. It allows you to chain together various components, such as an LLM with an API. This way, you could ask the LLM, “When is the next train to Winchester from London?” Langchain would get the LLM to turn this question into an API request. The request would be sent to the relevant API endpoint (rail inquiries API). The API would respond with some XML or JSON, which would be fed back into the LLM. The LLM would then turn this into a conversational output such as “The next train is at 10:00 on platform 9 (and three-quarters).”

Chroma DB

If you’ve ever noticed how slow ChatGPT takes to respond to a question, you may be thinking that it will be a bottleneck for an app. That is where Chroma DB comes in. It stores something called “Vector Embeddings.”

These are somewhat like a cache of LLM responses. So if I asked, “What are the prices of rucksacks?” I could store the response in Chroma DB. Then if someone asked a similar question in the future, like “What are the costs of rucksacks?” then it would respond more quickly and thus make it viable for a performant app.