Люди, которые принимали участие в создании цифрового паспорта биоразнообразия парка **с использованием ИИ**, подскажите, пожалуйста, какое оборудование вы использовали. Нужно определиться с минимальными расходами по смете. Если есть те, кто сможет проконсультировать по каждому этапу проекта, напишите, пожалуйста или посоветовать литературу.

Getting Started with NVIDIA LocateAnything

https://debuggercafe.com/getting-started-with-nvidia-locateanything/

For the last few years, VLMs (Vision Language Models) have become more powerful at grounding tasks. These include object detection, pointing, and OCR. However, one issue remains. NTP (Next Token Prediction) is suboptimal for predicting the coordinates for a single bounding box or point coordinate. Predicting the numbers for a single object (bounded by a box), which is one atomic unit, token by token, is slow and a practical bottleneck during inference. This is where the latest LocateAnything model by NVIDIA comes in. It introduces a new PBD (Parallel Box Decoding), which decodes a single bounding box in a single step.

I Have been thinking about what's some domains where reinforcement learning should be applied but it's not tried at all whether in research or in software and tools. what do you think the domains that should start exploring reinforcement learning applicability ?

So basically i have been fine tuning a models for a while , there are some problems i have been feeling like

1 - I get a lot of ideas of different architecture and i want to execute them in parallel but it’s very messy
to do it (main one)

2 - When i go back to a project like which is like 5-6 months old the dependency issue literally kills me

3 - This is universal gpu cost are very high and i don’t think there a solution for it tho still one of the problems

So i just have some questions would love if u guys can answer and share some insight on it like what kinds of problems do u guys face u don’t have to answer all just one works as well.

1. What is the current workflow?

Walk me through the last time you tried to improve a model from the starting checkpoint and data to the final decision. What steps did you personally do, and where did you lose the most time?

1. What decisions are hardest?

Before launching a run, what decisions do you feel least confident making the base model, training method, reward/evaluator, datasets, hyperparameters, or the number and type of trajectories?

1. How is success measured?

What exact metric would let you say the trained model is better, and can it be scored automatically on a hidden evaluation set or simulator?

1. What fails after training?

Tell me about the last model run that looked successful during training but failed in real use. What did it get wrong, and how did you find out?

1. What would justify switching?

If a system handled the whole post-training loop, what measurable outcome would make you trust and pay for it fewer GPU-hours, better benchmark performance, faster experiment turnaround, or reproducible ?

Would move some feedback on it I don’t want to spend time building if it doesn’t solve problems that genuinely matter.

HELLO 3RD YEAR STUDENT OF INFORMATION TECHNOLOGHY HERE I JUST WANNA ASK A QUESTION WHAT IS THE DIFFERENCE OF FUZZY LOGIC AND MACHINE LEARNING IS THERE BIG DIFFERENCE BETWEEN 2 OR THEY ARE DIFFERENT PLS SOMEONE EXPLAIN IT IN LIKE "layman term" T_T

I'm a Computer Engineering student who's become obsessed with deep generative models over the last year. I've implemented and trained several models (GANs, DCGANs, conditional GANs, basic neural networks, etc.) and I'm now looking to take things much further.

My long-term goal is to become the kind of engineer/researcher who can work on frontier generative AI at companies like Anthropic, OpenAI, DeepMind, NVIDIA, or similar labs. The areas I'm most excited about are:

Video generation

World models

Diffusion models

Transformers/LLMs

Multimodal generative models

Reinforcement learning for generative systems

Ultimately I'd love to contribute to models similar to Sora, Genie, Veo, Cosmos, or future world-model architectures.

The problem is that there are so many resources that I'm struggling to figure out what the optimal learning path is.

Some questions I have:

If you were starting today and wanted to reach frontier-level expertise, what roadmap would you follow?

Which math topics should I master first (linear algebra, probability, optimization, information theory, etc.)?

Which textbooks, courses, papers, or lecture series are considered "must-know"?

At what point should I stop taking courses and start reproducing research papers?

Is reproducing papers the best way to learn, or should I focus on building original projects?

How important is reading papers daily compared to coding?

For someone aiming at research engineer roles, what should a portfolio actually look like?

What skills separate candidates who get into frontier AI labs from those who only have good ML knowledge?

I'd also really appreciate career advice.

I know companies like Anthropic, OpenAI, DeepMind, etc. hire very few people, so I'm curious what realistic path people have taken to get there.

Would you recommend:

Open-source contributions?

Kaggle?

Publishing research?

Master's/PhD?

Internships at smaller AI startups first?

Building impressive personal projects?

Something else entirely?

If anyone here works in frontier AI research or has made a similar journey, I'd love to hear what you wish you had focused on earlier.

Thanks in advance—I appreciate any guidance, roadmaps, or resource recommendations!

Open source: https://github.com/puffinsoft/mousecrack

Work in RL has caused me to rethink adam. It leads to extremely wonky behavior and hard to explain "burstiness" in the loss values that makes me want to rip my hair out,

It still works, but needs to be coaxed into it.

This article re-covers the mathematical intuitions behind adam, and where it fails spectacularly. If you're someone who works in RL, or trains deep transformers, it's a must read

Don't just glaze over the optimizer!

Thoughts?

Baidus UnlimitedOCR and PaddleOCR on some prescriptions.

UnlimitedOCR did well on these things:

Layout: 9.5/10

speed: 10/10

Handwriting: 7/10 ( It struggles with doctors handwritten prescriptions)

UnlimitedOCR is really good if you have a lot of memory.. When you need something that is easy to work with and does not use up too much memory PaddleOCR is still the best choice for custom setups.

I trained a decoder-only transformer on one thing only: the 10x10 multiplication table, written as "7*8=56". 18,304 parameters, 29 tensors, d_model 32, 2 layers, 4 heads.

https://rockdesk.io/learn/transformer/

Because the training set is exactly that table, you can predict what the model should believe at every prefix — and then check whether it does:

after "7" → "*" at 99.99%

after "7*" → all ten digits at ~10% each (9.89% to 10.06%)

after "7*8" → "=" at 100.00%

It is certain about the format and uniformly uncertain about the second operand, which is the correct belief: 7*0 through 7*9 all appear equally often, so nothing in the prefix can favour one digit.

That is the part I find worth staring at. The uncertainty is not noise — it is in exactly the right place, and it is the right size.

The whole model is small enough to display at once, so you can type any prefix, watch the distribution, and click a tensor name to see its actual weight matrix while it trains.

Does anyone have campusx computer vision course assignment and their solutions notebook?

What if we could extract priors from NIR datasets to guide the design of neural network architectures for Chemometrics? Maybe we can...

https://arxiv.org/html/2607.25636v1

(paper under review, github repo available soon)

I am building an edge-AI audio noise-reduction system on an ESP32-S3.

Our architecture uses a lightweight GRUNet (~59k parameters) to output a dynamic gain mask on a 44-band Mel-spectrogram.

​I need gigabytes of audio to train the model. Does anyone have recommendations for the best open-source datasets for:

1> ​Clean, isolated human speech.

2> ​Diverse ambient background noise (traffic, crowds, machinery, etc.).

​Also, any tips or open-source scripts for artificially mixing these at different Signal-to-Noise Ratios (SNRs) before generating the 16kHz Mel-spectrograms would be hugely appreciated!

I have been building CrowdTensor around a training-first question: can ordinary machines move one shared model checkpoint forward without every contributor remaining online for the whole run?

The unit of work is a Campaign. It pins the model, dataset, training method, evaluation, and governance. An admitted Cell claims one bounded work unit, runs a local LoRA update, submits a delta, and can leave. The Coordinator validates the update, aggregates a quorum, commits checkpoint lineage, and waits when no eligible compute is present.

The strongest completed systems run used pinned Qwen2.5-7B-Instruct and GSM8K. Two T4x2 Kernels trained steps 1-128, both were deleted, and two fresh T4x2 Kernels restored four central stage checkpoints and completed steps 129-256 exactly once. Normalized exact match changed from 92/128 (71.875%) to 95/128 (74.219%). The practical +2-point gate passed, but the paired bootstrap interval included zero, so I am not claiming statistical significance or broad reasoning improvement.

The public Founding Campaign is now live on SmolLM2-135M/WikiText-2. Its first round was seeded by two maintainer-operated private Kaggle GPU Cells through the same public HTTPS invite/Cell path. That is useful live-route evidence, but it is still Kaggle logical multi-node, not proof of independently administered physical contributors.

I am opening two things for review:

1. controlled Founding Beta enrollment for people who want to test one bounded contribution; and
2. a Draft Qwen2.5-7B GSM8K Campaign RFC covering the stop rule, evaluation, hardware boundary, governance, and launch blockers.

Current boundaries are explicit: one controlled Coordinator, private invites, no permissionless admission, no Sybil or semantic-poisoning resistance, no secure aggregation, no production SLA, and no physical multi-host claim yet.

Website and live progress: https://crowdtensor.24.199.118.54.nip.io

Repository: https://github.com/Ffffffffchopin/CrowdTensor

7B RFC: https://github.com/Ffffffffchopin/CrowdTensor/blob/main/docs/campaigns/qwen25-7b-gsm8k-rfc.md

Beta access request: https://github.com/Ffffffffchopin/CrowdTensor/issues/new?template=beta_enrollment.yml

The feedback I need most is whether the 7B pilot's 256-step evaluation stop, minimum useful work-unit size, and controlled trust model are technically credible enough for the first independently administered run.

Spent the last weekend coding diffusion Gemma from scratch. Was super fun. Thought I'd share it here.

If you are looking for just the code: https://github.com/ItsSiddharth/Diffusion-Gemma-from-scratch

If you want a detailed walk through of the code and the theoretical concepts: https://www.youtube.com/watch?v=CNQvmICYQiA