rukmal-w
Joined 12 years ago
Karma
16
Submissions
18
Member since
Mar 13
Undergraduate Sophomore - Technology Evangelist - Washington Husky
Seatte WA, USA · http://rukmal.me
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Comment · 1 year ago
Hey HN,
We’re launching something we’ve been quietly building: a new computational architecture we call the Universal State Machine (USM). It doesn’t use gradient descent, it has no learned parameters, and it doesn’t require expensive training.
Announcement X Post: https://x.com/renxyzinc/status/1899539629411270758
Yet, it performs on par with GPT-1 on language tasks, including zero-shot reasoning and basic dialogue – and it does this using a symbolic, graph-based runtime that evolves in real time.
What’s the trick? Instead of training a dense neural net, USM constructs a dynamic knowledge graph of symbolic states. Each “learning episode” adds structure to the graph, and querying it is a traversal problem, not a matrix multiplication. Inference is logarithmic in graph size.
Key ideas: - No backprop, no SGD - Stateless components and graph-local routing - Online, modular, interpretable learning - Super-efficient inference (GHz speeds vs tokens/sec) - Not a transformer, but equivalent in expressivity
Why we think this matters: We believe the future of AI needs to move past brute-force scaling. The USM offers a new path: efficient, interpretable, dynamic intelligence without GPUs or billion-dollar training runs.
In the video, we demo the USM performing real-time sequence generation, using symbolic reasoning – without expensive training. It’s early, but we’re excited to share this new paradigm and open the conversation.
You can learn more about the underlying theoretical concepts behind the USM in the whitepaper: https://opensource.getren.xyz
Would love your thoughts – technical feedback, skepticism, wild ideas welcome.
– The Ren Team https://getren.xyz
Comment · 1 year ago
Our first release will be in a few weeks, along with the accompanying technical paper.
Comment · 1 year ago
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.)
Comment · 12 years ago
Oh, awesome. I didn't know that. My team will definitely take a look at that before designing the metrics we collect. Thanks for your input.
Comment · 12 years ago
That's a good idea jgeorge. Do you have any idea as to what kind of metrics we would have to collect about songs to generate this kind of data?
Comment · 12 years ago
I'm aware of Echonest. It's lacking when it comes to song relationship data, which is why the music genome project is considered to be better.
Actual people listening to and classifying songs are why it's better than the alternatives - I was just thinking of crowdsourcing it as the Music Genome project is proprietary.
Comment · 12 years ago
Didn't know that. I'll add it now. Thanks!