Exploring 12 RAG Pain Points and Their Solutions
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.
GPT
Navigating the Limits of Long Context Windows in GPT-4
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.
I-JEPA: human-like AI model
Review of the Meta’s article about the first AI model based on Yann LeCun’s vision for more human-like AI
State of GPT: Andrej Karpathy’s Talk
The Promise and Potential of Large Language Models: Read a Recap of OpenAI Founding Member Andrej Karpathy’s Talk
theMind Experiment: running LLM on CPUs
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.
8 Reasons Why Enterprises Need Their Own AI Language Model
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?