| Large Language Models (LLMs) like GPT have been an incredible advance, but they hit fundamental computational limits: - Finite computation cannot solve transfinite problems. LLMs are brute-force statistical machines that approximate functions but struggle with generalization, reasoning, and efficiency. - Scaling is hitting a wall. LLMs require quadratic resources as input length increases, leading to massive inefficiencies. - Interpretability is broken. Current models are black boxes that resist explanation, limiting their use in safety-critical domains. We've developed a new AI architecture, based on Infinite Time Turing Machines (ITTMs), that goes beyond finite-state deep learning. We call it the Universal State Machine (USM), and it: - Doesn’t require pre-training at massive scale. Instead of massive static parameter sets, it dynamically adapts via a computationally queryable knowledge graph. - Runs efficiently at inference time, avoiding the exponential costs of LLMs. - Is inherently interpretable. No more black boxes—USM exposes its state transitions and reasoning process. We believe this is the most significant shift in AI since deep learning and presents a real alternative to LLMs. If you're interested in AI beyond Deep Learning, we'd love your thoughts. Read the whitepaper: https://opensource.getren.xyz/ittm
Visit our website: https://getren.xyz
Announcement Tweet: https://x.com/renxyzinc/status/1882204607558148313 (Discussion welcome! I’ll be around to answer technical questions.) |