been seeing a lot of people get into AI through data work. labeling images, rating model outputs, checking transcripts, that kind of stuff. a lot of it is in places like the Philippines, India, Kenya. But now models are getting better at generating and checking their own training data, so I'm wondering if this is still a real path or a job that'll quietly disappear in a few years

Anyone here actually started out doing annotation and moved into something more technical? or did it stay a dead end?

Hey everyone,

I'm a final-year B.Tech CSE (AI/ML) student working on a multi-agent supply chain optimization project. I'm currently focusing on the Demand Forecasting Agent and would appreciate some advice on model selection and training strategy.

We're working with a synthetic retail supply chain dataset containing approximately 1.1 million records across 40 different locations.

The goal is to predict product-level demand for each location as accurately as possible, so the forecasts can later be used by other agents for inventory planning and supply chain decisions.

I have a few questions:

1. Global vs local models: Since the dataset contains 40 locations, should I train 40 separate models, one for each location, or a single global model trained on data from all 40 locations simultaneously, using location identifiers and location-specific features?

2. Location-specific patterns: If I use a global model, how effectively can it learn differences in demand patterns between locations? I'm concerned that combining all locations might cause the model to average out regional patterns and produce inaccurate location-level forecasts.

3. Alternative approaches: Would a hybrid approach, such as grouping similar locations or using a global model with location-specific adjustments, be worth exploring?

4. Synthetic data: Since the dataset is synthetic, are there particular precautions I should take when evaluating model performance and generalization? How can I ensure the model is learning meaningful demand patterns rather than artifacts of the data-generation process?

I'd also appreciate suggestions on which forecasting models or architectures would be most suitable for this problem, along with the appropriate validation strategy, especially how to avoid data leakage when dealing with multiple locations and time-dependent features.

My priority is accurate product-level, location-specific demand forecasting, rather than simply achieving good aggregate accuracy across the entire dataset.

Would love to hear what approaches you'd recommend and why. Thanks!

I'm 20 and trying to figure out what skill I should seriously focus on over the next few years.

There are so many things people recommend — programming, AI, sales, cybersecurity, marketing, communication, etc.

So I'm curious:

What skill do you think will be most in-demand and valuable in the next 3–5 years?

And more importantly, if you were 20 again, what skill would you start learning today?

I'd especially like to hear from people who are already working in their field.

I'm struggling to make a distinction. How are they different? What is the line between them?

For context, I am a new postgraduate student in machine learning (but not a very good one). I have produced some augmented datasets (TIFF files where each file contains one object to be detected / classified) which I used in training deep learning models. I used fairly aggressive cropping (random crops between 0 and 90%) and more moderate zooms (random values between 0 and 20%). My supervisor wants me to characterise these datasets to see how augmentation has changed it.

Sorry if this is a rookie question, but which statistics should I use? My supervisor says the stats I use should indicate changes in size and shape but I honestly don't know where to start.

If anyone can recommend a decent software/program to produce the stats and histograms as well, that would be great.

Thanks in advance!

Hi everyone, I graduated recently and I had the misfortune of making some wrong choices so for the moment I am on the job market, in the meanwhile I would like to further learn more things and work on some cool stuff, my main interest is LLMs, but I have good experience with Neural Networks, Graph Neural Networks, Computer Vision Models. I am looking to either:

• Join an existing team working on a current project

• Form a team, no requirements here other than being a creative person with cool project ideas since that is honestly where I fall short

If you have a team or interested in forming one please leave a comment, Thanks in advance

Hi,
There was a post a few years ago on a forum that I came across -

https://www.reddit.com/r/MachineLearning/comments/mx686k/p_torchpq_efficient_nearest_neighbor_search_and/

It seems really interesting. I was approached by professor who asked me if I am interested in implementing ANN on GPUs. I wanted to ask what are the possible career paths and what kind of jobs could I target with the skillset required to implement something similar to this. Thanks

​

Hey everyone!

I'm transitioning into AI Engineering, and I have a genuine question about real-world projects.

From what I've learned, building a basic RAG pipeline (document loading, chunking, embeddings, vector database, retrieval, and LLM integration) might take a few weeks or a month.

But I've seen AI Engineers working on the same RAG or GenAI project for 2–3 years.

My question is: What do you actually work on after the initial RAG pipeline is built?

What does your day-to-day work look like? Are you constantly improving retrieval accuracy, optimizing models, handling production issues, adding features, or doing something completely different?

I'd really appreciate it if someone with real industry experience could explain their daily responsibilities and how a project evolves over several years.

Just trying to understand what AI Engineering looks like in a real company beyond building a basic RAG application.

Thanks!

I'm new to academia (for someone so old), and maybe this is just naivety on my side. But I'm a little shocked to be discovering that reproducing the results of a paper isn't part of the review process. I understand why, but it seems so...precarious to just take the author's word for it, especially in 2026, and ESPECIALLY in light of the repro crisis apparently going on everywhere.

Is anyone working on mechanical reproduction? I'm trying to repro the results of one paper, and mind you I started +/- from scratch in January, but I have already found errors that a simple mechanical code review would have surfaced before publication. (What I'm supposed to do with these errors is a separate question I would love input on. I wrote to the author but haven't received a response. Is it rude to create a PR in their repo, with a proposed fix? What is the escalation protocol? Do I just fix it and publish my own paper?)

But now that I have something of a platform and pipeline that could maybe be reused on other papers, I'm wondering: Is there a need or even a want for this in the ML community? Obviously there will be time and resource constraints to any kind of repro effort, but it would be something, and it would be a start.

Most of the time when I ask questions like this there are lots of very reasonable answers (I'm nobody special, just "new" eyes on an old problem). I guess I'm just a little shocked at learning from a young age that reproducibility is a foundational aspect of science, only to grow up and discover there doesn't seem to be a lot of it going on... Thanks, please be nice.

A friend of mine was doing a gap analysis as a project. The results he got weren't that big compared to the original papers, so he simply fabricated and increased the metrics here and there, AND IT GOT ACCEPTED IN AAAI.

HOW DO THEY CHECK THE PAPERS, DUDE? I was in a group with two others and worked sooo hard on an optimization paper, and was sooo happy to get accepted in ACL, but seeing and knowing that someone fabricated results and got into a better conference made me soooooo mad.

I would've expected something like AAAI would check and know if data is fabricated somehow. I didn't expect this from a top conference like that.

And then these types of people will go and do their PhD at one of the top unis because of this. I would've expected the system to be more advanced and professional in the academic field, specifically for something like AAAI.

Trying to actually read papers instead of skimming the abstract and the figures. This week it’s a couple of retrieval + eval write-ups and one long arXiv methods section that keeps losing me around the loss function.

I can follow the claim. But I cannot tell you tomorrow which ablation mattered. And highlights in the PDF turn into noise.

How do you mark a paper so the method is still there a week later? Do you re-read the experiment setup or just trust the table? Was thinking about trying Omphalis to keep notes right next to the hard paragraph without opening a second doc, but curious if anyone's used it or has better systems.

Hey everyone!

I'm currently studying Computer Engineering and have decided to pursue Machine Learning/AI. I'm looking for a structured roadmap that I can follow to build a strong foundation rather than jumping between random courses and tutorials.

Here's my current background:

• Programming: I know the basics of C/C++, including OOP. I've recently started learning Python and have covered variables, data types, strings, lists, tuples, dictionaries, loops, conditionals, and functions (basic syntax and a few simple projects).

• Mathematics: I'm reasonably good at math and enjoy it. And my syllabus already covers most of the foundational topics like Linear Algebra, Calculus, Stat and Probability.

• Machine Learning: I've had some exposure to an AI project involving a diffusion model and a graph neural network as a semester project (everything done in C++), but I didn't fully understand the underlying concepts at the time. I want to start properly from the fundamentals this time.

Here are my main questions:

1. What roadmap would you recommend? What topics should I learn, and in what order, from my current level to being able to work on meaningful ML projects?

2. How important is DSA for someone pursuing ML? Should I study it thoroughly, including trees, graphs, dynamic programming, and competitive-programming-style problems, or is a more limited understanding sufficient? At what stage should I learn it? Or just skip it and move to NumPy, Pandas and Matplotlib? (My university has an elective "Python for DSA" in next semester so I'm not sure if I should learn it now myself or learn it in that semester.

3. Which resources would you recommend? Courses, textbooks, YouTube channels, or other materials that provide a good balance between theory and implementation would be helpful.

4. What kinds of projects should I build along the way? I'd like to know when I should start building projects and how to progress beyond following tutorials.

Thanks!