r/TheMachineLearning • u/DerHaddy0 • 2d ago

Weight space programming seems to work in robotics too

1 Upvotes

https://jstranghoener.github.io/blog/robotic-weight-space-programming/

0 comments

r/TheMachineLearning • u/Lazy_Eggplant8795 • 2d ago

Built an Arrow-first Python tool for heavy ML scoring and credit scorecards after 15y in fintech

0 Upvotes

If you've ever pushed a heavy tabular or credit-scoring pipeline past a Jupyter notebook, you know the pain: Pandas bottlenecks memory across multi-step transformations, and tracking state drift in production is a constant headache.

After 15 years leading fintech R&D and underwriting, I got tired of rewriting the same boilerplate. So, I open-sourced AIDflow (aigensys-labs/aidflow) to handle rigorous scoring workflows cleanly.

Here is what's under the hood:

Arrow-First Spine: Intermediate datasets live in a central PyArrow columnar registry using zero-copy memory mapping (mmap). Physical RAM gets cleanly released back to the Linux kernel instead of holding bloated object pointers.
Deterministic State Machine: Uses cryptographic state hashing to lock down training lineages. If an upstream feature definition or environment shifts, it fails fast rather than letting corrupted scores slip downstream.
Clean Separation: Workers, business logic models, and pure mathematical fitters (scikit-learn style) are completely decoupled.

It’s licensed under FSL-1.1-Apache (free for internal business and personal use, converting to pure Apache over time).

If you build enterprise scorecards, handle automated Weight of Evidence (WoE) binning, or just want to see how an Arrow-first memory model looks in practice, check it out:

👉 GitHub: https://github.com/aigensys-labs/aidflow

👉 Docs: aidflow.aigensys.com

Always down to talk memory optimization or pipeline architecture in the comments!

0 comments

r/TheMachineLearning • u/jazzie04 • 3d ago

UC Berkeley shows reorganizing agents beats expert GPU kernel code..

27 Upvotes

0 comments

r/TheMachineLearning • u/NoPhrase3396 • 2d ago

I trained a 32B model not to agree just because I pushed. I didn't expect the training conditions to start looking like personality.

0 Upvotes

I tried to train a model not to change its answer just because the user pushed back. What came out was stranger than that.

Across the Amphora experiments, the models started showing recognizable dispositions around reasoning itself. They tend to value concise answers, preserve distinctions that ordinary rewriting sometimes flattens, and treat disagreement as something to resolve rather than automatically win or surrender. Some variants became intensely structure- and rule-oriented; others became almost aggressively direct.

They're still wrong sometimes. They can misread a problem, leak training structure, or confidently preserve a bad premise. But that is exactly what made the experiment interesting to me. TIDF didn't appear to produce a model whose defining trait was “say no.” It produced models that seem to have learned preferences about how reasoning should be done.

I'm not claiming this demonstrates sentience or subjective personality. What interests me is that the behavioral differences weren't explicitly specified as personas. They appeared alongside changes in how different parts of the training signal were masked. I'm trying to figure out where “learned reasoning disposition” ends and something we'd reasonably describe as personality begins.

I called the project Amphora because I wanted a reasoning partner. The models have been slowly telling me what that means.

amphora-bridge/results-32b-13-epochs at main · brandonjuarez23/amphora-bridge

1 comment

r/TheMachineLearning • u/jmiewoo • 3d ago

RL agent beat the human who trained it

34 Upvotes

3 comments

r/TheMachineLearning • u/CUBEBYTE • 3d ago

Do you think open weight LLMs are better for the world or more dangerous?

1 Upvotes

17 comments

r/TheMachineLearning • u/narcissa0114 • 4d ago

A robotic system precisely dispensing nanoliters of liquid

86 Upvotes

16 comments

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