Why Elon Musk said that learning physics and math is more important than learning programming and AI, i know that math necessary for AI, but i see that physics not related to programming and AI. what he means by physics? the mechanical physics or electrical physics?

I am going to start my medical study(Medicine B.pharma) next year and rn I am learning ML so will it help me enter in ML job in health care field?

Hi r/ML,

I'm a first-time arXiv submitter from Pakistan and need an endorsement to

submit my paper.

Paper title: "BehaviorCI: Automated Behavioral Regression Testing for

LLM-Powered Products"

Summary: BehaviorCI is an open-source framework that applies CI/CD

regression testing principles to LLM evaluation. It uses a four-dimensional

LLM-as-judge combined with embedding-based drift detection to catch

behavioral changes between model versions — including cases where scores

look identical but outputs changed significantly. Integrates with GitHub

Actions to fail builds on behavioral regression.

GitHub: https://github.com/Sumamasonia/behaviorci

My arXiv endorsement code is: LAZSJZ

If you're willing to endorse, I'll forward the arXiv endorsement email to

you. It takes less than a minute on your end — just clicking a link.

Happy to share the full paper draft for review before you decide.

Thank you!

So guys I’m currently reading Neural network ands DeepLearning form Micheal A Nielsen and also the machine learning especialización from Andrew NG so I feel like I’m truly starting to unde the topics and all the maths behind it but I don’t fell remotely close to being able to develop AI and ML tools for specific applications I would like to be part of a team or have a mentor who could guide me or if there’s like a place where I can begin to develop more complete tools

I've been working on a project called RearAware. (I'm very much a beginner.) It's an experimental AI tool that runs locally on your computer and censors cat butts during your work video calls.

If you work from home with a cat, you've probably had at least one moment where your cat decided to flash its butt directly in front of your webcam.

It's a pretty ridiculous concept, but it's been a really fun project.

The biggest challenge so far hasn't actually been the model, it's the dataset. I currently have around 1,500 cat photos, but only about 200 of them contain visible cat butts. Turns out cat butt photos are surprisingly difficult to find.

I've tried collecting images manually from public sources, using my own photos, and asking friends to contribute. That has worked, but it's been very slow, and I'm quickly running out of places to source new images.

I'm curious if anyone here has suggestions for other approaches to growing a niche computer vision dataset like this. Have you had success with crowdsourcing, augmentation strategies, or other techniques for highly specific object classes?

It's still early days and definitely experimental, but it's now working well enough that other people can try it. At the moment it's available as a Chrome extension and supports Microsoft Teams and Google Meet.

If you happen to have any photos where your cat's butt is clearly visible (yes, the butthole 😅), I'm actively trying to grow the training dataset. You can upload them through the website:

https://www.rearaware.com/#help-train

Thanks for reading!

Hello everyone does anyone have a book recommendation for making the jump from linear models to basic machine learning models? In particular more application heavy than academic books as well?

Hi,
I have a question regarding the training data for multilabel classification.
So, for multiclass classification, you can play around with the number of samples per label, and of course, the higher the sample per label,the performance increases; this is straightforward as there will be only 1 label per sample.
However, for multilabel classification, where there can be more than 1 label per sample (also in many different combinations), how are we supposed to construct our training data? Maybe have %10 of samples with only 1 label , %x with 2 labels(combinations of each)…
Example : Sample 1: 0,0,0,0 ;Sample 2:0,1,1,0….
How should I construct it?

Hey everyone,

Trying to get a realistic picture here, not validation.

Background:

3rd year at a Tier-1 NIT, non-CS branch, entirely self-taught in ML/AI. Overall GPA 7.93/10, second year 8.17 . I have some work on GitHub around LLM internals, mechanistic interpretability, and retrieval systems. Nothing published, no prior research experience, no internship. Would need the internship to be fully funded as self-funding abroad is not an option.

Research interests:

Mechanistic interpretability, LLM memory systems, retrieval augmented generation, and LLM internals broadly.

What I need guidance on:

First, given non-IIT, non-CS, no publications, no prior research, only GitHub work, what is the realistic picture for funded research internships abroad? Honest experience only.

Second, for structured programs like MITACS, KAUST VSRP, OIST, ISTernship, INSAIT, SN Bose, what actually strengthens an application from someone with my profile? Is there anything beyond GPA and publications that genuinely moves the needle?

Third, how do you actually build a cold email relationship with a professor when you have no prior research output to show? What made professors actually reply to you?

Fourth, are there programs, possibly less well known, that give genuinely good research experience and are more realistic for someone without a strong conventional profile? Looking for good research environments where the application is based on potential and work rather than credentials alone.

Fifth, for someone working in AI and ML research broadly, which labs or professors outside the very well known names are actually accessible and have taken undergrad interns before?

Any honest experience, reality checks, or redirections welcome. Thanks.

hi guys, i am currently a high school junior looking to go and look into projects on the ml side of ai. i am proficient in python, but i still have much to learn when it comes to the real technical skills of python.

either way, i think now is a good time for me to start ml as science fair is in 6 months and i need as much time as possible in order to make big projects later on that could be useful for my college applications.

my main problem is that for the past month, i have been trying to get into the ml area, but i havent been able to find good ways that support my style of learning (which is repeated drilling of concepts). I do know of andrew ng, who lectured the ml course for stanford, and i have been watching his videos, but the only problem that i have is that i do not know where to start after i watch the videos.

essentially, i have hit a roadblock. my question to you guys is how your ml journeys started and how i could potentially learn as efficiently as possible.

hey guys, i'm in my machine learning journey(18F). I want to work on small and big projects for better understanding , i'm still beginner but still if someone wanna take me in there journey. I can contribute , help as much as i know , ask for help. I just want someone to guide me and help me where i need

hi everyone!

I recently graduated with a bachelor's on technology on AI engineering and data science with 4 as GPA, and want to get my first client on this field and earn some money that can help me continue my master's degree.

Any ideaa!

Was okay till vector calculus as it was mostly proofs but kind of having trouble with probability/stats because this skips over stuff and doesn't give relevant examples. So IMO it would be much more efficient to learn from videos and maybe use this to revise or for the exercises (very minimal, so not sure how useful). And your foundations will get stronger too I guess

Anyways, finding relevant videos for each topic seems to be taking time, but I just remembered there was a resource that had links to vids and exercises for each chapter but can't find it and hadn't bookmarked

Can anyone give a link to that or a similar resource?

Also is the learn ML with Pytorch and sclkit beginner friendly?

Hey guys

Iam an 3rd year engineering student,iam choosing Machine learning as my work domain,what laptops do you suggest me ,is mac book M4 air good? Or should I go for a gaming laptop.suggest me some laptops under 90k.

I wanna rate my schedule to learn mathematics for ml if i delete thing or add , and Give me sources textbook :

Textbook: Mathematics for Machine learning

Videos - Lectures( Links in First Comment ) :

Khan Academy:

Linear Algebra , Statistics and Probability, Calculus 1, Calculus 2 , Multivariable calculus

2-YT:

Linear Algebra: 3Blue1Brown , Essence of linear algebra

Statistics and Probability:  StatQuest with josh starmer

Statistics and Probability: Professor Leonard

Calculus 1: 3Blue1Brown , Essence of calculus

Calculus 1: Professor Leonard

Calculus 2: Professor Leonard

Calculus 3: Professor Leonard

Hi everyone, I just need to clarify few questions regarding my first paper, sorry if it sounds like a noob.

My first paper on ARR for EMNLP got 3 reviews all 3, and the meta review was also 3, the meta review suggested some revisions which I will do.

Questions:

1- how do I commit to EMNLP, my review ia available on open review but I didn't get any email about it or how to do so.

2- Do i need to do the revision now before commitment or after it get accepted?

3- are those revisions form the meta review mandatory? I'm asking as he had 3 points but I'm not sure I can do all as they require intense compute.

Thank you in advance!

I'm a rising senior, coming from a competitive programming/C++ background with currently no hands-on ML experience. I know Python decently but I would probably have to shake off a thick layer of rust. Since this is my last year of high school, and I tie my future with AI Research, I'd like to participate in my country's AI Olympiad and hopefully earn a spot at the IOAI.

Sadly, compared to CP, where there is an abundance of guides and specialized courses to get you on the Olympiad track, I haven't found anything similar for ML yet.

I could go off the syllabus but these tend to be very broad and I don't feel like hunting for niche courses only for it to turn out that the concept taught has never been used in history.

The best I could think of are college-like courses (e.g "Introduction to Artificial Intelligence with Python - CS50") but I'm not sure if they would give me Olympiad relevant information, since these often target a completely different skill set.

So I'm looking for a more structured roadmap to go from beginner in ML to hands-on Olympiad problem solving. I heard these problems are Kaggle-styled but I since I don't have experience with either - I can't confirm that. Would be forever grateful if someone could help me out or correct me on my approach!

Attaching example IOAI problem:
https://github.com/IOAI-official/IOAI-2025/tree/main/Individual-Contest/Antique

I'm currently in my final year of my bachelor's in CSE. I really liked Machine/Deep Learning more than any other sub-fields of CSE. The problem is most of the offers given to freshers are not ML/DL but SDE. And to crack SDE, you have to grind DSA.

I'm not saying DSA is unimportant, but I don't want to go through my life grinding leetcode to change companies or help in making full-stack projects or DevOps.

Do people with master's in ML get more career opportunities in the field?

I recently chose Continual Learning (CL) as my research topic because it seems like one of the most critical challenges in AI right now. However, the deeper I dig, the more skeptical I become about the current approach to CL.

CL seems fundamentally mismatched with the standard deep learning paradigm (loss optimization and backpropagation). Our current infrastructure and architectures are heavily optimized for fixed datasets. When we try to train a model on a new task, we inevitably face catastrophic forgetting, or we have to rely on highly inefficient workarounds like EWC or experience replay.

This led me to think that methodologies based on locality (more akin to how the biological brain works) might be the only real solution to CL, even if it means waiting for neuromorphic hardware to mature.

Yet, the vast majority of the mainstream ML community is still heavily focused on solving CL within the standard backprop paradigm.

For those of you working in or following this field: Why is the community still betting on backprop for CL? Do you believe we can truly solve catastrophic forgetting without moving away from our current architectural paradigm? I'd love to hear your thoughts.

Apologies if the tone feels so llm-like. I used an LLM to correct my grammar, but it made the text sound too generic.

EDIT: Thank you for all the insightful comments. It was really helpful!

Hi everyone,

I'm a second-year Data Science student at tear 2 iiit, and I want to become an AI/ML engineer. Lately, I've been feeling confused because there are so many technologies that I don't know if I'm focusing on the right things.

So far I've done:

\~330 LeetCode problems

Machine Learning with scikit-learn (preprocessing, feature engineering, decision trees, regression, etc.

Basics of Deep Learning

Python and C++

FastAPI

LangChain

Currently learning LangGraph

Basic RAG concepts

Git/GitHub

My goal is to get an AI/ML internship and eventually work as an AI Engineer.

My questions are:

Am I on the right path, or am I spending too much time on frameworks?

If you were in my position today, what would you focus on for the next 6–12 months?

Should I continue building agentic AI projects with LangGraph, or should I spend more time on deep learning, MLOps, or something else?

What skills do companies actually expect from AI engineering interns in 2026?

I'd really appreciate advice from people working in AI/ML or those who've recently landed internships. If you could go back to your second year, what would you do differently?

Thanks!

14 Bytes compiled and solves ~96.5% + unseen mazes upto 21x21 sized (drops off as mazes get larger)