Photo by Dhilip Ananthakrishnan on Pexels

A University of Arizona doctoral candidate is pushing the boundaries of artificial intelligence with a novel ‘proto-matrix’ system built within the Unity game engine. This innovative approach aims to cultivate a primordial form of sentience by prioritizing continuous, in-situ self-modification over traditional backpropagation techniques.

Rejecting the limitations of backpropagation, which the researcher argues is primarily suited for offline function fitting, the ‘proto-matrix’ consists of 24 independent neural networks, or ‘agents,’ inhabiting a dynamically simulated environment. These agents undergo continuous evolution, adapting both their physical forms and neural networks through an evolutionary loop favoring survival and reproduction.

Unlike conventional AI models, the agents learn through localized weight adjustments guided by Hebbian/eligibility-trace rules, fostering continuous adaptation without reliance on backpropagation. The simulated world features scarce resources and environmental hazards, introducing pressures that demand rapid adaptation and long-term strategic planning.

The project’s success hinges on the agents’ ability to autonomously adapt to environmental changes and exhibit stable social behaviors, such as coordinated actions and resource sharing, all without external gradient updates. The researcher’s goal is for at least two agents to demonstrate these complex behaviors, serving as an indicator of primordial consciousness. Remarkably, this ambitious project aims to achieve this level of AI using a single high-end gaming PC, suggesting that advanced sentience research doesn’t necessarily require vast computational resources. The original research was shared on Reddit’s r/artificial subreddit.