How much of the AI technology is built on glued together Python code?

Vector databases have been on the rise as a long term storage option for large language models. Beyond the common use cases, there are some interesting use cases for vector based representations and vector databases as a their search engine.

If you work in bursts of high efficiency followed by breaks, you could be a momentum worker. Outside of nine to five schedules, I propose the idea of momentum based working and how it fits in with many jobs.

Use this LaTex template for an efficient and up-to-date thesis. It comes with best practices, modern packages with little custom tricks to make it easy to extend.

The rise of large language models have created demand for a different type of databases. Instead of data, we are now interested in storing and searching vectors.

There has been a lot of buzz surrounding neural networks in recent years with achievements made throughout various domains such as computer vision and natural language processing. But what on Earth are they? In these lecture notes and corresponding lectures we will explore, investigate and dissect some of the ideas behind neural networks.

Large language models are getting more accessible every day with reduced resource requirements and easy to use wrapper libraries. Langchain is one of the leading ones to get started with Python and language models.

Throwing huge amounts of data into a very large neural network and then trying to remove information is like finding a needle in a black whole. We need a simple and effective way to address training data collection.

OpenAI has released its latest large language model, GPT 4. This is its explanation of what it is.