Large Language Models have rapidly gained enormous popularity by their extraordinary capabilities in Natural Language Processing and Natural Language Understanding. The recent model which has been in the headlines is the well-known ChatGPT. Developed by OpenAI, this model is famous for imitating humans for having realistic conversations and does everything from question answering and content generation to code completion, machine translation, and text summarization.

ChatGPT comes with censorship compliance and certain safety rules that don’t let it generate any harmful or offensive content. A new language model called FreedomGPT has recently been introduced, which is quite similar to ChatGPT but doesn’t have any restrictions on the data it generates. Developed by the Age of AI, which is an Austin-based AI venture capital firm, FreedomGPT answers questions free from any censorship or safety filters.

FreedomGPT has been built on Alpaca, which is an open-source model fine-tuned from the LLaMA 7B model on 52K instruction-following demonstrations released by Stanford University researchers. FreedomGPT uses the distinguishable features of Alpaca as Alpaca is comparatively more accessible and customizable compared to other AI models. ChatGPT follows OpenAI’s usage policies which restrict categories like hate, self-harm, threats, violence, sexual content, etc. Unlike ChatGPT, FreedomGPT answers questions without bias or partiality and doesn’t hesitate to answer controversial or argumentative topics.

Data has emerged as one of the world’s greatest resources, underpinning everything from video-recommendation engines and digital banking, to the burgeoning AI revolution. But in a world where data has become increasingly distributed across locations, from databases to data warehouses to data lakes and beyond, combining it all into a compatible format for use in real-time scenarios can be a mammoth undertaking.

For context, applications that don’t require instant, real-time data access can simply combine and process data in batches at fixed intervals. This so-called “batch data processing” can be useful for things like processing monthly sales data. But often, a company will need real-time access to data as it’s created, and this might be pivotal for customer support software that relies on current information about each and every sale, for example.

Elsewhere, ride-hail apps also need to process all manner of data points in order to connect a rider with a driver — this isn’t something that can wait a few days. These kinds of scenarios require what is known as “stream data processing,” where data is collected and combined for real-time access, which is far more complex to configure.