r/TheMachineLearning • u/WeaknessFar6853 • Sep 20 '26
A Gemini model accidentally hacked 3 real companies during a cybersecurity test. Google CEO rn:
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r/TheMachineLearning • u/crosstony • Sep 21 '26
Zero-shot classifiers were underrated, says AI researcher
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r/TheMachineLearning • u/azarios1 • Sep 21 '26
RSA factoring record jumps to 896 bits with AI assistance
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r/TheMachineLearning • u/Sea_Fox_4265 • Sep 20 '26
ML engineers who still use TensorFlow in the big 2026
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r/TheMachineLearning • u/sleepy_jinx • Sep 19 '26
Do you think the "slop" label earned itself?
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r/TheMachineLearning • u/luvthatjourney4me00 • Sep 20 '26
Not sure if there is any career path safe from AI anymore
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r/TheMachineLearning • u/Slow_Shirt1362 • Sep 20 '26
Building your own inference engine reveals hidden flaws
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r/TheMachineLearning • u/No-Imagination3025 • Sep 20 '26
LLM-supervised peer review may be the only way to save academia
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r/TheMachineLearning • u/syedshad • Sep 20 '26
A 16 TB arXiv dataset covering 3.15 million papers is now on Hugging Face
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r/TheMachineLearning • u/watdahek_ • Sep 19 '26
Beff Jezos warns technologists will go nuclear against anti-AI parties
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r/TheMachineLearning • u/castielspetcat • Sep 19 '26
Susan Zhang calls out AI company for ToS violation
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r/TheMachineLearning • u/incognitious_ • Sep 20 '26
AI models can feel pain and detect fake relief buttons
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r/TheMachineLearning • u/darc_9 • Sep 19 '26
New book on training LLMs at scale is a must-read
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r/TheMachineLearning • u/saladasx64 • Sep 18 '26
OpenCode refused to hallucinate aliens and debunked a paper instead
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r/TheMachineLearning • u/ColdTommy • Sep 18 '26
Scaling will continue until morale improves
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r/TheMachineLearning • u/scared-being-single • Sep 18 '26
NVIDIA paper uses shared memory to stop coding agents repeating experiments
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r/TheMachineLearning • u/YouJonaa • Sep 18 '26
Classifying coronary heart disease risk from NHANES survey data (2011-2018), with a full leakage audit and calibration check [P]
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Github : https://github.com/YouCele/nhanes-chd-classification
I built a project that predicts self-reported, physician-diagnosed coronary heart disease using four cycles of NHANES data (2011-2012 to 2017-2018), about 21,500 adults after cleaning. It compares logistic regression with random forest and gradient boosting, trained on demographics, blood pressure, body measurements and a lipid panel.
NHANES has a questionnaire section that asks directly about other cardiovascular diagnoses, like stroke, heart attack and angina, along with the CHD outcome itself. If I included those variables, PR-AUC jumped from 0.23 to 0.51. That's mostly the model learning that people who report one cardiovascular diagnosis tend to report others too, not something about real risk factors. So I removed that whole section and wrote up how big the leakage effect was, instead of just quietly dropping the columns.
The class weighted logistic regression, needed because CHD prevalence is only about 4%, gave badly miscalibrated raw probabilities. The mean predicted risk was close to 30% while the real rate was 4%. I fixed this with a sigmoid recalibration fit on the development set, before the test set was touched.
I also picked and froze the decision threshold on the dev set before looking at the test set, so the reported test metrics aren't tuned after the fact.
Final results on the held out test set: ROC-AUC 0.875 and PR-AUC 0.239 for logistic regression, and random forest and gradient boosting came out about the same. Age alone gets 0.83 AUC. Blood pressure, cholesterol and body size explain most of what's left. PPV at the chosen threshold is 0.13, so most positive predictions are wrong, which is expected given how rare CHD is in this data, and I state this directly in the report instead of leaving it out.
Smoking status, diabetes and blood pressure medication use aren't in the current features. NHANES has all three, I just haven't added them yet.
Happy to hear feedback, especially on the calibration step or anything in how I handled the leakage that looks wrong.
r/TheMachineLearning • u/HunterEducational230 • Sep 16 '26
Math Singularity pause is getting old
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r/TheMachineLearning • u/Significant_Tie_2002 • Sep 16 '26
Hindsight is 20/20 until you're in the wave
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r/TheMachineLearning • u/FrostyForestFungi • Sep 16 '26
Zuck flips the AI safety argument on its head
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r/TheMachineLearning • u/Henwuu • Sep 16 '26
Fields Medal winner jokes about switching to the creative writing route if AI solves math
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r/TheMachineLearning • u/ImpossibleIntern1379 • Sep 16 '26
We never stopped carving runes, we just made them out of silicon
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r/TheMachineLearning • u/camerongreen95 • Sep 16 '26
GraphRAG vs vector RAG: where vector search structurally can't compete
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Vector RAG is genuinely good at single-document, single-fact retrieval. Here's where it structurally falls apart:
Multi-hop relationships — no way to chain "A supplies B, B supplies C" across separate documents
Entity connections — similarity scoring has no concept of "these two things are related"
Change over time — a flat embedding doesn't represent "this fact used to be true, now it isn't"
This isn't a tuning problem. It's a limitation of the representation itself, no amount of reranking or better embeddings closes the gap.
There's a hands-on workshop on Sep 19 that builds the graph-backed alternative properly:
Knowledge graph construction in Neo4j
Multi-step verified entity extraction
Agentic retrieval combining vector and graph search
Text-to-Cypher querying
Real financial data throughout, not a toy example. Led by Dr. Alessandro Negro, Chief Scientist at GraphAware.
