In the ever-evolving landscape of artificial intelligence, the boundaries of what’s possible are constantly being pushed. Here are some of the next steps in AI.
One of the most exciting frontiers is the fusion of traditional deterministic programming with the adaptive capabilities of neural networks. Here’s a glimpse into the next big leaps that could be made in machine learning and Large Language Model (LLM) architectures.
1. Neural Networks That “Understand” Code
The first step in this ambitious journey is to train neural networks to internalize and execute the logic of deterministic programs. Instead of merely recognizing patterns probabilistically or generating text, these advanced networks would be capable of understanding and not just simulating but directly executing the behaviour of specific pieces of software.
2. Crafting a Universal Neural “Codebase”
Building on this idea, the next goal is to create a universal “codebase” within a neural model. This codebase would aggregate the logic of a vast array of software, potentially drawing from rich platforms like GitHub. Such a model would be a melting pot of algorithms, functions, and software logic, all accessible within its neural architecture. They would use the most efficient form of each algorithm in every application.
3. Adapting to the Ever-Evolving World of Software
Software is not static. New algorithms are developed, bugs are fixed, and optimizations are discovered. To remain relevant and effective, our neural “codebase” would need the ability to continuously update, be retrained, and incorporate new software logic. For example there could be a new “codebase” model released each month or year with all the new features incorporated across the open source scene. It doesn’t have to be a neural network based model but it could be a fully compiled version of all the programs the model has access to. It could provide an interpreted version so people can still learn from it. Imagine a version of google but for software functionality.
4. The Synergy of Codebases and Language Models
The true power of this approach is realised when we integrate our “codebase” neural model with existing Large Language Models (LLMs). Such a fusion would allow the LLM to generate responses that not only draw from its vast knowledge of language but also leverage the embedded software logic from the neural “codebase.” The result? An LLM that can answer direct queries with the precision of a seasoned programmer and the adaptability of a neural network.
5. Delivering Fully Processed, Comprehensive Responses
The ultimate goal is to ensure that our enhanced LLM provides fully processed information in its responses. If there’s a calculation that would make an answer clearer, the LLM would automatically perform it. If there’s a piece of logic that could provide deeper insight, the LLM would apply it. The aim is to ensure that every response is as comprehensive, useful, and insightful as possible. The difficulty here is distinguishing between high levels of knowledge that would be required and “processed” where the answers are at the level of asker but use all the abilities of all humans’ knowledge and code in the models.
In Conclusion
The fusion of deterministic programming with neural networks represents a bold step forward in the world of artificial intelligence. By integrating the precision of software with the adaptability of neural networks, we’re on the brink of creating AI models that are more versatile, intelligent, and useful than ever before. The future of machine learning is not just about understanding or generating text; it’s about understanding, generating, and executing. The next chapter in AI promises to be an exciting one!