Artificial Intelligence (AI) is rapidly transforming the way that we live and work. From Large Language Models to Machine Learning to Neural Networks, there can be a lot to understand about what is becoming an everyday technology. For many, the language of AI can be hard to pin down and intimidating leading to missed opportunities and many being put of adopting tools that would be productive for them.
This short explainer aims to break down some of the most common AI terms and provide a point-of-reference whether you are a student, professional, business owner or just curious about how AI works in the background.
Firstly, we'll look at some core AI Concepts:
✔️ Artificial Intelligence: the simulation of human intelligence in computers such that they can perform complicated tasks, such as learning, reasoning, problem-solving and perception.
✔️ Machine Learning: a subset of AI where systems learn from large datasets to improve, without being explicitly programmed to do that task.
✔️ Neural Network: a computational model, inspired by the human brain. A Neural Network consists of interconnected nodes that process input data and can be used to make complicated functions from inputs to outputs.
✔️ Deep Learning: a branch of machine learning that uses large, deep, neural networks to find and process complex patterns in large datasets.
✔️ Generative AI: systems capable of creating new content, inspired by large training datasets, such as written word and art.
That is the basics of AI, these are normally applied by a range of algorithms and tools:
✔️ Large Language Models (LLMs): LLMs, such as ChatGPT, Claude and Llama are generative AI systems that have learned on large datasets of human language. They have a very wide range of uses, including text generation, programming, summarisation, translation and customer support.
✔️ Retrieval Augmented Generation (RAG): RAG systems combine LLMs with a specific dataset, and give it explicit access to that dataset during generation. This is designed to overcome common problems with LLMs including out-of-date information, hallucinations (confidently stating incorrect facts). They are really useful for looking over enterprise specific datasets where accuracy is key.
✔️ Code Assistants: AI-Assistants for helping to write code, debug and document more efficiently. These are particularly useful for documentation, refactoring and code completion.
Zero Point AI's key product is a RAG LLM, which can quickly scan through 100s of documents and find the ones that are relevant to your task. With this RAG LLM, you can be sure of the accuracy with useful references to the source material, embedded in the AI's answer. Zero Point AI's RAG LLM is also built to run on local hardware – not expensive and potentially insecure cloud based systems – so you can be sure that your sensitive data is safe and being processed by efficient and sustainable models. Find out more by contacting Zero Point AI.