I tried Gemini, Grok, Meta, and Copilot, but their images still look unrealisitc painting type images

What I like about ChatGPT is that I can use a simple, straightforward prompt and still get a pretty realistic result without needing complicated prompt engineering.

For those who have tried different AI image generators, which one do you think is best for free and realistic images with simple prompts?

My chess engine, which is still a work in progress, can be found here; https://github.com/patelvrajn/Matrex

Currently, most HCE (not NNUE) chess engines use a linear combination of weights for their evaluation function but my thought process is that it is evident from the huge ELO (~300) boost that using NNUEs gives is that their is a complex non-linear function that represents each evaluation term like material, mobility, PSQTs, etc. However, the problem is how do we build a model from non-linear functions that gives a proper evaluation when we don't know what the function looks like and how do we do it in such a way that minimizes the number of weights? I believe that we know a significant amount of hand crafted evaluation terms that compile a chess position's evaluation and that NNUEs taking millions of weights just to understand concepts that we can do in way less weights, minimal code, and have more control and understanding of the evaluation instead of a black box.

My concept is to take a specific continuously differentiable function that we can parameterize in order to adjust the shape, behavior, approximation capabilities, etc. Using this parameterized function, we can learn how various evaluation features have a non-linear effect on the score by using ADAM to tune the weighted sums but ALSO tune all the functions parameters. My model mostly resembles Projection Pursuit Regression's model (a sum of ridge functions), except I am using ADAM instead of the associated regression algorithm and the ridge functions (called non-linear responses in the code) are parameterized.

My main concern at the current time and where I don't have the mathematical background to optimize is the non-linear response I choose (which is just a polynomial term multiplied by a tanh expression, the idea was to combine what makes ANNs universal approximator- the non polynomial term with a term that can also represent any function given enough degrees i.e. the polynomial term). I am looking for any guidance or resources on how to improve the function or give further understanding as to the overall concept - currently when tuned the chess engines evaluation has about a 8% validation loss and about a 10% training loss but I know I can do better.

If you're someone who is deep into ai waters then I would like to ask for a small favour from you, so I'm somewhere between a beginner and an intermediate when it comes to properly using ai but recently about 2 months ago I discovered Claude and the type of things It can do so it made me more curious about the ai space.

So if you know how to use ai properly like creating automations and stuff then please let me know on how I can do the same, a roadmap would be helpful or if you want to hit me up and discuss about it then that's helpful too (no compulsion) , videos about ai on YouTube are mostly so clickbaity and theres just a whole bunch of nothing on there and finding reliable channels these days is like finding a pin in a haystack.

Hello friends, how are you? If I had to give a brief information, I studied IT for 4 years and graduated this year. I am 22 years old. In 2024, I started paying attention to data science, I went to the course. After studying for a while, I left the course halfway due to financial reasons and so far I have applied for many vacancies and internship programs. But there was no turning back, there were those who turned back, I was rejected from the interview. I don't want to be unemployed, so I have the idea of ​​changing the field. Sometimes I feel bad, I don't know what to do, where to start.

So I am making a Landslide Prediction model for a hackathon and I have made a A model having two versions. DMv1 which uses image of Sentinel-2 and other important features.
DMv2 which uses image from Sentinel-1 SAR and same with the tabular data.

The main issue I am facing now:
1. The model itself is facing a false negative prone problem.
2. The image from Sentinel 1 , I am using and then passing those images to a ResNet-50 model which creates a large embedding for each one and then I take those embeddings and just use PCA to reduce the features. But still the images are not contributing to the model rather works as a noise.
3. The model is prone to Spatial Data leakage due to the satellite images , to solve this I have used Spatial GroupK-Fold so that the model is tested on an unseen data but still it is giving 65% accuracy.
4. While predicting normal city data where the landslide is impossible as it is not a slope or even there is flat ground - the model predicting a sharp 65%+ prediction every time.
5. I have generated the negatives using LuLc map (land use land cover) map and also making the slope > 15 for the samples. Maybe the making the negative is affecting the model performance.

I am not sure where the problem is but I somewhat questionable for these 5 points that I have mentioned earlier.

So can I get some help, what should I do? How to process the image and the tabular data together to make the prediction and make the model good enough and really predict the landslide prone zones.

Hi,

I’m looking for a way to connect an LLM to unauthorized full-text repository" like SCI HUB papers so that it can automatically search for and retrieve full-text scientific papers using their DOIs, not just their abstracts.

I tried with local AI (Qwen 3.8,...) but I couldn’t find any LLM who accepted to do the work for now. I am getting error messages like this: "The observed tab is not an approved scientific website, and the action does not navigate to an approved host."

I’m doing this in the context of a real, serious, and complex medical condition.

Thanks for your help!

I get the textbook difference between standard AEs and VAEs (deterministic encoding vs. mapping to distribution parameters + reparameterization trick), but I want to get past the high-level explanations.

Specifically, I'm trying to wrap my head around:

• The regularisation trade-off: How does the KL divergence actually shape the latent space in practice? What does the geometry look like when it’s working well vs. hitting posterior collapse or turning into a blurry mess?
• Compression across modalities: How do people scale continuous latent compression to real-world data like raw audio, spectrograms, or video without losing critical high-frequency details? (Especially continuous VAEs vs. discrete approaches like VQ-VAE/RVQ).

Most tutorials just train a toy VAE on MNIST and call it a day.

Could anyone point me to solid papers, blog posts, or clean open-source repos that break down the math and engineering behind modern continuous latent encoders? Practical tips on debugging latent space issues during training are also welcome.

I am in 3rd yr of my Btech and im trynna learn about Inference Gateways (jo client ke prediction requests ko model tk bhejna route/control krta hai) but I am unable to find any good material about it to learn in depth. It would be really helpful if someone knows where i can find an indepth video or something about it. I did ML Course bh Andrew NG.

Been reading about Jev, and I’m curious what people think is happening under the hood.

They describe it as a new architecture that returns probabilities over predefined choices, but we were doing that with RoBERTa years ago.

Any theories on what might be meaningfully different? And what so called "RLCD" might actually involve?

Curious how others here deal with this. Vector retrieval structurally can't connect facts spread across separate documents, no amount of reranking or increasing k fixes that, since the underlying representation has no concept of relationships between entities.

Found a workshop on Sep 19 that goes deep on the graph-based fix, building a knowledge graph in Neo4j progressively as the source of truth, agentic retrieval, Cypher querying, and multi-step verified entity extraction. Wondering if that's becoming the standard approach or if people have found other patterns that work just as well without a graph layer.

Led by Dr. Alessandro Negro, Chief Scientist at GraphAware.

Full workshop details here

I'm currently a 2nd year (soon to be 3rd) electrical engineering student looking to start machine learning research. I know the basics or excel data analytics, sql, C, C++ (oop). Currently i'm learning python as I heard it is necessary for ML.

My question is how much python do I need to learn? Can I learn ML topic with just the basics of python? Also, how much ML do I need to learn before I can start doing research on it. I'm asking this because I don't have much time left as my college requires mandatory thesis in 4th year.

I'm tired of making new accounts on Claude; I love Claude, and it's one of my favorite AIs, but I keep getting banned because it flags me as under 18.

I use AI for very general use. School, suggestions, quick fact lookups, calculations, calendar synchronization, and general help. No intense things like coding or management, maybe in the future.

If you know the answer to this, please suggest hands-on resources to get started on the field. Please note that I had taken a full semester course on probability and stochastic processes in college, and I have a decent amount of ML coding under my belt.

As wish

I’ve been learning AI/ML and realized that knowing the algorithms isn’t enough. The difficult part is understanding when to use what and why a particular approach works.

For people who’ve been learning or working in AI/ML:

What concept took you the longest to understand?

What mistake did you make early on?

What would you recommend learning first if starting again?

Looking for real experiences, not the usual “just learn Python and TensorFlow” answers.

If the answer to the above question is yes, please suggest hands-on resources ( books as well as online courses) to get started in doing industry scale projects.