In a recent article by Wenqi Glantz on Towards Data Science, the challenges and solutions in developing Retrieval-Augmented Generation (RAG) systems are thoroughly examined.
This week in AI, OpenAI announced a significant update to GPT-4, promising to process much larger inputs – up to a staggering 128k tokens. However, the excitement was met with a dose of reality: the model still struggles with long context windows. Let’s dive into the recent findings and what this means for the future of Large Language Models (LLMs) like GPT-4 and Llama.
We’re thrilled to bring to your attention an exciting development – the launch of Meta’s Llama 2. This new iteration of their groundbreaking open-source large language model is free to use for research and commercial purposes, marking a pivotal moment in AI technology.
Review of the Meta’s article about the first AI model based on Yann LeCun’s vision for more human-like AI
The Promise and Potential of Large Language Models: Read a Recap of OpenAI Founding Member Andrej Karpathy’s Talk
The experiment aimed to evaluate the performance and efficiency of running GPT-4, a large language model, on CPUs compared to GPUs. Preliminary results showed that the model achieved comparable accuracy on CPUs, with an acceptable increase in training time and inference latency.
In this post, we’ll delve into the limitations of LLMs, providing a balanced perspective on their potential and the challenges they present.
As a robust language model, GPT has been instrumental in driving innovations across multiple industries. So, what exactly makes GPT a game changer for enterprises, and why do organizations need their own version?