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.
- 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.)