For the majority of web users, generative AI is AI. Large Language Models (LLMs) like GPT and Claude are the de factogateway to artificial intelligence and the infinite possibilities it has to offer. After mastering our syntax and remixing our memes, LLMs have captured the public imagination.

They’re easy to use and fun. And – the odd hallucination aside – they’re smart. But while the public plays around with their favourite flavour of LLM, those who live, breathe, and sleep AI – researchers, tech heads, developers – are focused on bigger things. That’s because the ultimate goal for AI max-ers is artificial general intelligence (AGI). That’s the endgame.

To the professionals, LLMs are a sideshow. Entertaining and eminently useful, but ultimately ‘narrow AI.’ They’re good at what they do because they’ve been trained on specific datasets, but incapable of straying out of their lane and attempting to solve larger problems.

The diminishing returns and inherent limitations of deep learning models is prompting exploration of smarter solutions capable of actual cognition. Models that lie somewhere between the LLM and AGI. One system that falls into this bracket – smarter than an LLM and a foretaste of future AI – is OpenCog Hyperon, an open-source framework developed by SingularityNET.

With its ‘neural-symbolic’ approach, Hyperon is designed to bridge the gap between statistical pattern matching and logical reasoning, offering a roadmap that joins the dots between today’s chatbots and tomorrow’s infinite thinking machines.

Hybrid architecture for AGI

SingularityNET has positioned OpenCog Hyperon as a next-generation AGI research platform that integrates multiple AI models into a unified cognitive architecture. Unlike LLM-centric systems, Hyperon is built around neural-symbolic integration in which AI can learn from data and reason about knowledge.

That’s because withneural-symbolic AI, neural learning components and symbolic reasoning mechanisms are interwoven so that one can inform and enhance the other. This overcomes one of the primary limitations of purely statistical models by incorporating structured, interpretable reasoning processes.

At its core, OpenCog Hyperon combines probabilistic logic and symbolic reasoning with evolutionary programme synthesis and multi-agent learning. That’s a lot of terms to take it, so let’s try and break down how this all works in practice. To understand OpenCog Hyperon – and specifically why neural-symbolic AI is such a big deal – we need to understand how LLMs work and where they come up short.

Read More: OpenCog Hyperon and AGI: Beyond large language models