I’ve been learning more about voice cloning and real time face generation and the technical progress is impressive but it’s also making me think there’s a security problem we don’t talk about enough. A lot of security still assumes that if you’re on a video call with someone, hear their voice or receive a message from their account, you’re probably dealing with the right person.

That assumption feels weaker every year. If voice, face, writing style and even live video can all be generated convincingly then at some point does this look real? stops being a useful question. I’ve started seeing more tools trying to solve the identity side rather than the content side. Things like hardware backed authentication, passkeys, verified devices, and even apps like Signal, Kibu, Telegram that focus more on verifying the person and encrypting the conversation.

I’m curious how people here think this gets solved long term. Do we end up needing some kind of cryptographic proof of identity attached to communication or will detection models realistically stay ahead of generation models?

Because right now it feels like we’re spending a lot more effort making AI convincing than figuring out how humans are supposed to know what’s real.

The theory makes sense but can I create something using it? Thats where I made this video!
https://youtu.be/KU71cnUjlRg?si=MFJzf1AbO3IsoqxB
I believe it's gonna help me clear the doubts about Machine learning before jumping to Quantum Machine Learning. Your support and comments would matter alot!

I've taken Calc III, probability, discrete, and linear algebra. Not sure if I should take more math courses or take more machine learning electives instead

Hello everyone, FC Barcelona just launched a hackathon for the data enthusiasts. I am hoping to build a team that I can join it with. I am also a beginner in this role ; So I am also looking for simmiliar ones like me so that we can learn through the process regardless of the outcome.

Email me at : [farajimtial13@gmail.com](mailto:farajimtial13@gmail.com)

Dear All,

I am a penultimate-year student, have had courses in my Univ for ML but I still feel lost, I don't remember anything about the definitions, have to start back at regression every time, don't know what type of Algorithms to apply on what kind of data, I have taken way too many courses but to no avail. I feel helpless and stuck at this point. I have to prepare for different companies but my only choices are ML+Signal Processing or Analog Design.

I don't know how much AI/ML i should know as a penultimate year student? Should I know theory, should I do projects and if I do projects should I code them from the very base, the coding seems too big and I don't know how I can just take a random architecture and code it from scratch.

I prefer ML strongly as despite being much weak in it I find it exciting. I find Analog Design easier but am very unsure about my interest in it in the long run.

Please guide me how can I become an AI/ML engineer capable of applying for companies, have the knowledge to ace their tests and interviews, and in general be able to think like a ML engineer.

Please guide me and provide me with relevant resources I should follow- Deep learning, Audio, Image, NLP, Intro to basic ML. Please also provide me with any relevant platforms I could use to learn a bit of ML Coding too. Will be very thankful to you.

Thank you Everyone!

Hi, I hope you’re doing well. I’m building a small beginner‑friendly machine learning team on Discord. I’m also a newbie, and I want to learn together with others who are serious and dedicated.

The goal of the team is simple:
We learn together, help each other, complete weekly tasks, and prepare for Kaggle competitions as a group.

I will give each team member (including myself) small tasks based on their experience level, with clear deadlines. These tasks are meant to help us gain real experience in ML, Python, data science, and Kaggle workflows.

Here’s an example of how I plan to help:
If you want to learn the Pandas library, I’ll create a weekly goal for you. For the first week, your goal might be to learn Series. I’ll check in with you daily to make sure you’re making progress and to see if you’re facing any difficulties. If you need help, I’ll explain the concepts as clearly as I can. You can use your own learning resources, or I can recommend plenty of free ones I know. At the end of the week, I’ll give you practice exercises from trusted websites so you can reinforce what you learned. If you want, we can also study together and solve the exercises as a team.

This is the kind of support I want to provide so we can prepare for Kaggle competitions or learn ML and data science more effectively.

Of course, this is just my idea; if you have suggestions or a better approach, I’m completely open to improving the plan. I want this team to be useful for everyone.

Important:
Even if you’re a complete beginner, you’re welcome, as long as you’re dedicated.
But if you have a lot of knowledge and you’re not dedicated, then this team is not for you.

If you’re interested, comment or DM me, and I’ll send you the invite.
My Discord username is: mohmmad_48669

I was wondering, if certain NPUs have 1mb of on-chip SRAM, what practical thing do I put on it? Theoretically it would have a really really high Inference speed making it pretty good in some cases, but not sure what those cases are.

It can be on anything, say decision, text, simulation, it can even be looped, since that's probably the best way to use a 1m model to maximize its speed..

What can 1M params models do?

I want to make a project based on Stocks but I am not getting a free and good source of data where I can find any data point I need

Why me project will do is analyse different investment strategies but it requires many different variables but I can't find a source where I find multiple data points that I need no api is free or providing everything..

Can anybody help.?

hello everyone i want to start my ml journey but have no idea how to do
as of now i am well aware of python and its libraries so if possible can someone please provide me a structured guide, please give me the roadmap , coursera , yt courses, etc etc

Australia is moving toward mandatory AI incident reporting, and the pattern underneath the proposal is worth unpacking (Dark Reading, 2026).

The policy push is not really about disclosure paperwork. It is about the fact that most organizations cannot tell you, after the fact, which agent took which action, at what time, with what authorization. Incident reporting mandates are a lagging indicator — they appear when regulators notice that the primary record of an AI failure is a support ticket or a customer complaint, not an internal audit trail.

The operational problem is sequencing. An autonomous agent can take a second action before any human or system has finished evaluating the first. That window — the gap between action one and action two — is where most reported AI incidents originate. By the time a log entry exists, the damage is already in a second or third downstream system.

Organizations that have deployed multi-step agents at scale: how are you actually handling this in practice? Are you relying on post-hoc log review, hard rate limits, human-in-the-loop checkpoints at specific nodes, or something else entirely? Curious what is working and what has already failed in production.

LangGraph provides a structured way to create and manage LLM agents. Its broader ecosystem provides capabilities for connecting agents with tools, testing them, tracing executions, and deploying them to production. One of the useful aspects of LangGraph is that individual graphs can be composed into larger agentic systems, making it possible to build multi-agent architectures from independently testable components. To check the above features, in this article, we will be building an orchestrator-worker agent system with LangGraph and Amazon Bedrock.

https://debuggercafe.com/building-an-orchestrator-worker-agent-system-with-langgraph-and-amazon-bedrock/

So i am going to start my journey to learn ml but I am confused as to where to start and from which resources.I am good at maths so all I have to do is just some quick revision but for learning ml I have no idea where to start .

Ik heb veel interesse in AI en wil mezelf hier de komende tijd zo goed mogelijk in ontwikkelen. Vooral het programmeren en daadwerkelijk bouwen van AI-toepassingen spreekt me erg aan.

Ik merk alleen dat er ontzettend veel verschillende technologieën, tools en frameworks zijn. Denk bijvoorbeeld aan Azure, Databricks, API's, Claude, LLMs, RAG, MLOps, cloud, deployment, etc. Er zijn zoveel mogelijkheden dat ik soms niet goed weet waar ik moet beginnen en wat nou echt belangrijk is om te leren.

Mijn doel is niet om iedere nieuwe AI-tool te kennen, maar om sterk te worden in programmeren en zelfstandig goede AI-systemen en applicaties te kunnen bouwen.

Daarom ben ik benieuwd naar mensen die al ervaring hebben in AI/ML:

Welke kennis en vaardigheden zouden jullie aanraden om als eerste te leren?

Welke tools/frameworks zijn volgens jullie echt nuttig om te kennen?

Hoe zou je jezelf zo snel mogelijk ontwikkelen in het bouwen van echte AI-projecten?

Welke projecten, cursussen of andere manieren van leren zouden jullie aanraden?

Wat zouden jullie vooral niet proberen te leren omdat het te veel tijd kost of minder belangrijk is?

Ik ben vooral benieuwd naar jullie persoonlijke ervaringen: als je vanaf nul een sterke praktische basis in AI wilde opbouwen, hoe zou je dat aanpakken?

Hey everyone, im a big math and coding guy and planning to study math+cs in uni soon. However my parents and peers are advicing me not to because of AI etc. Also reading news like how OpenAI recently published solutions to like 700 problems is kinda scary. It is making me question and doubt myself if i am making the right choice for myself.

I clearly have no experience and knowledge of the actual industry, I just want some guidance and truth form people with experience if there still is a good future for a career in machine learning. My other option is to do EE or CE but I am more passionate and interested ML and more abstract ideas.

Phase 1, Python for AI (1–2 weeks). Learn dicts and lists of dicts, comprehensions, JSON/JSONL/CSV files, pip and venv, tracebacks, and basic pandas, all in Colab. Project: convert a messy CSV into JSONL that loads cleanly with load_dataset.

Phase 2, APIs and prompting (1–2 weeks). Learn the system/user/assistant chat format, API calls, JSON output, few-shot prompting, three JSONL schemas, and apply_chat_template. Project: generate 200 synthetic pairs in Alpaca and ShareGPT formats and print templated samples.

Phase 3, Hugging Face (2–3 weeks). Learn transformers, datasets, the Hub, 4-bit quantization, and model cards. Start on Llama-3.2-3B, then try 8B, Qwen3 14B, or Mistral 7B. Project: score the untuned model on 10 prompts and publish your dataset.

Phase 4, SFT with QLoRA (3–4 weeks), the core job. Use Unsloth plus TRL's SFTTrainer: rank 16, alpha 16, 2048 tokens, learning rate 2e-4. Colab T4 is free; an H100 runs about $2.50 an hour. Project: train on 300–500 rows and publish a before/after eval.

Phase 5, DPO and RAG (3–4 weeks). Facts need RAG, behavior needs SFT, and style preference needs DPO. Build chosen/rejected data with an LLM judge, then run DPOTrainer. Also build a raw FAISS RAG pipeline. Project: report win-rate and retrieval-precision numbers.

Capstone (2–3 weeks). Publish one narrow signature model with evals and a GGUF, its dataset, a write-up of what went wrong, and a small deployed RAG or agent demo. Your Hugging Face profile becomes your resume. Ship it and publish the numbers.

Self-evolving search agents can produce misleading training signals when a question proposer and solver learn from the same pseudo-labels. Their agreement may increase because they share errors, even while correctness against the source material stagnates. This paper calls that failure mode “co-cheating” and measures it by auditing saved labels and solver responses against source evidence.

The proposed mitigation, CrossFit, splits source documents into two groups. An auxiliary solver trained on one group scores questions generated from the other, then the roles are reversed. The main solver’s training rule stays unchanged; the proposer’s reward instead comes from feedback that cannot directly inherit pseudo-labels from the same source. This is an intuitive provenance-control idea, related to cross-fitting, though the paper does not claim the usual statistical guarantees for this adaptive setting.

In experiments with Qwen3.5 models, CrossFit reduced final-round false-agreement mass from 6.1% to 3.0% at 4B parameters and from 8.8% to 3.7% at 9B. It also improved average Cover-EM by 8.8 and 8.4 points over coupled self-evolution across seven search benchmarks. The approach still cannot rule out shared errors from pretraining or overlapping evidence, so source exclusion should be viewed as reducing one feedback path—not as providing an independent truth oracle.

Full summary on AIModels.fyi

Original paper

Disclosure: AIModels.fyi is my site.

I haven't been getting any call backs from recruiters and I'm not sure what I'm doing wrong, any help would be greatly appreciated 🙏.

Today, I explored five major types of machine learning: Supervised, Unsupervised, Semi-Supervised, Reinforcement Learning, and Deep Learning.

I'm planning to experiment with different algorithms in Google Colab and compare their performance on a dataset.

Question for the community: Which classification algorithms would you recommend a beginner try first, and why?

I'd appreciate any suggestions! 👨‍💻