In recent days, Large Language Models (LLMs) like GPT-4 and Claude-2 have taken the tech world by storm, offering unprecedented capabilities in natural language understanding and generation. However, as these models evolve, an intriguing trend is emerging: they are becoming more like computing systems. This article delves into this evolution, focusing on the phi-1.5 model’s structured training data and MemGPT’s computer-like architecture.
The Rise of Structured Training Data: phi-1.5
The “Textbooks Are All You Need” approach has given birth to the phi-1.5 model, a smaller yet highly capable language model. Unlike traditional models trained on a broad range of internet text, phi-1.5 utilises structured, high-quality “textbook-like” data. This focused approach not only minimises the model’s size but also produces more reliable and less biased outputs, setting a new standard in the field.
MemGPT: The Operating System Analogy
MemGPT is another fascinating development in the landscape of language models. Its architecture features a multi-level memory system akin to the hierarchical memory found in operating systems. This innovative design allows for better context management and even enables self-directed learning, as the model can autonomously update and search through its memory based on the current context.
Stripping Away Biases
One of the most significant advantages of using structured training data, as seen in phi-1.5, is the potential reduction of biases. By focusing on high-quality, textbook-like data, the model is less likely to inherit the biases often found in broader internet text. This not only improves the model’s reliability but also has ethical implications, contributing to the ongoing efforts to make AI more responsible and fair.
The Computer-Like Architecture of MemGPT
MemGPT’s architecture goes beyond multi-level memory systems. It incorporates function calls and events, drawing further parallels with computer systems. These features allow MemGPT to manage data movement and perform specific tasks, making it more than just a text generator — it’s evolving into a computational agent.
The Human Aspect: What’s Missing?
While these advancements are undoubtedly exciting, they also raise questions about what’s missing. These computer-like models excel in computational efficiency and scalability but often lack the nuances of human interaction and emotion. This gap presents an opportunity for biologically-inspired models, which could offer a more natural and effective way to manage context, understand language, and make decisions.
The evolution of language models like phi-1.5 and MemGPT towards becoming more like computing systems is both intriguing and impactful. This shift has implications for the future of machine learning and natural language processing, particularly concerning the reduction of biases and the ethical responsibilities of AI. As these models continue to evolve, the line between language models and computing systems may become increasingly blurred, opening up new possibilities and challenges for the field.