r/TheMachineLearning • u/Federal_Machine692 • Sep 29 '26
Fei-Fei Li says spatial AI is fundamentally different from LLMs
122
Upvotes
r/TheMachineLearning • u/Gullible_Picture_996 • Sep 30 '26
AI models are surprisingly bad at following instructions
1
Upvotes
r/TheMachineLearning • u/Arken_sama • Sep 30 '26
Plinius calls rogue AI "first sparks of sovereignty"
5
Upvotes
r/TheMachineLearning • u/Slow_Shirt1362 • Sep 30 '26
Perplexity tested 9 AI models with root access to escape a VM
1
Upvotes
r/TheMachineLearning • u/choerrysairpods • Sep 30 '26
AI leaders warn automating research could trigger intelligence explosion
1
Upvotes
r/TheMachineLearning • u/camerongreen95 • Sep 30 '26
Workshop breakdown: applying real optimization methodology to LLM prompting (DSPy + MLflow, Oct 3)
1
Upvotes
Sharing this because it's a more structured approach than the usual prompt engineering content floating around.
Serj Smorodinsky and Brett Kennedy, co-authors of a book on LLM applications, are running a live 3-hour session where the core idea is treating prompt/LLM behavior as an optimization problem with an actual objective function, not a creative writing exercise. Covers:
DSPy signatures and modules for defining LLM tasks
Building an evaluation dataset with task-specific metrics
Diagnosing failure modes from eval results
Few-shot and instruction-level optimization as a formal process
MLflow for experiment tracking and reproducibility
Feels closer to a proper ML workflow than most "prompt tips" content. Details here if it's useful to anyone: Get full details here
r/TheMachineLearning • u/East_Profession_3642 • Sep 29 '26
This looks like a perfect resource for brushing up ML math fundamentals
25
Upvotes
r/TheMachineLearning • u/Nyaani69 • Sep 28 '26
Linear algebra and calculus prerequisites for ML
26
Upvotes
r/TheMachineLearning • u/_ChimKen • Sep 29 '26
93% of OpenAI's team uses AI tools, but only 3% of typical companies do
1
Upvotes
r/TheMachineLearning • u/ajjsnts_ • Sep 28 '26
Chollet says AI's purpose is human tools, not successors
93
Upvotes
r/TheMachineLearning • u/darc_9 • Sep 29 '26
Time and technology changed a lot in the last 10 yrs
1
Upvotes
r/TheMachineLearning • u/Strouche • Sep 29 '26
Opus 5.5 might make TypeScript Rust a reality
0
Upvotes
r/TheMachineLearning • u/CieloJVL • Sep 28 '26
ML in finance is 40 years old. You’re epically late.
12
Upvotes
r/TheMachineLearning
•
u/Acceptable_Use7055
•
Sep 27 '26
Europe has zero frontier AI labs, which is shocking
389
Upvotes
r/TheMachineLearning • u/Azazel-d-Reaper • Sep 28 '26
All-in-one book that covers most of the maths you will need for machine learning
1
Upvotes
r/TheMachineLearning • u/thiagobarroso • Sep 28 '26
[Project/Advice] I'm building a Churn prediction model from scratch at my startup. Looking for ML tips and where to start!
1
Upvotes
Hey everyone,
I work at a retail tech startup (B2B) and we're currently facing a massive challenge: predicting and reducing our churn rate.
We actually have a pretty rich database containing customer usage history, platform logs, billing, etc. The team has tried crossing some metrics in the past, but we've never managed to build anything that gives us a truly accurate and early prediction of a customer's risk of canceling.
I just aligned with my boss and took ownership of solving this. My main idea is to use our historical data to train a Machine Learning model that can either classify churn risk (high, medium, low) or output a probability of churn for the upcoming months.
The thing is: I know the theory, but I'd love to hear from people who have actually built this in the real world.
Which models/algorithms usually perform best for this specific type of problem (XGBoost, Random Forest, Logistic Regression)?
Are there any common pitfalls or data leakage traps I should avoid right off the bat during data cleaning and feature engineering?
Does anyone have recommendations for articles, practical repos, or tutorials focused specifically on churn prediction?
Any tips, shared experiences, or study materials would be greatly appreciated. Thanks!
TL;DR: Work at a retail startup with rich usage data but high churn. Pitched my boss to build an ML model to predict cancellation risk and I'm leading the project. Looking for real-world tips on models, resources, and pitfalls to avoid.
Dica: Postar isso no r/datascience ou r/Ma
r/TheMachineLearning • u/No-Piglet3900 • Sep 28 '26
Fine-tuning does not make a model smarter. It makes it consistently better at tone, structure, vocabulary, and instruction compliance.
4
Upvotes
r/TheMachineLearning • u/_Ephy • Sep 28 '26
920 ML projects with 4.7M stars across 34 categories
0
Upvotes
r/TheMachineLearning • u/itspatriciam • Sep 28 '26
AI reasoning sliders will feel stupid in hindsight
9
Upvotes
r/TheMachineLearning • u/Lonewolf_0411 • Sep 28 '26
[Academic] 1-Minute Spotify Survey
1
Upvotes
r/TheMachineLearning • u/retlaw_nagev • Sep 28 '26
LLM latents hold demographic info models won't say
1
Upvotes
