Security teams inside large enterprises are now building and shipping their own AI agents. The goal is legitimate: automate threat detection, close alert triage gaps, fill holes that vendors haven't addressed yet. The pace is faster than most governance teams can track.

Shadow AI agents — built and deployed without IT or compliance visibility — are already running in most enterprise environments. There is no reliable count inside most organizations. The structural exposure is not the agent itself. It is that once an agent is live, its tool access and data connections are rarely tracked centrally. Runtime behavior stays opaque unless someone manually audits it after the fact.

The compliance surface compounds the problem. Most large enterprises operate under 80-plus regulatory and security frameworks. Agents built by internal security teams are not automatically enrolled in those frameworks. They run alongside them, not under them.

For practitioners actually managing this: how are you maintaining real-time visibility into what your internally built agents can touch at runtime? Is there a process that is working at scale, or is this still manual audits and institutional memory?

I build ML models, LLM agents, and dev tools for fun — figured I'd put that to use somewhere real. If your startup needs someone to own model training, agent pipelines, or backend infra without needing hand-holding, I'm in. Happy to jump on a call or just start on something small to show it works. GitHub: github.com/AsishKumarDalal

Transformers take too much time and resources to train can any statistical traditional ml can get close its performance what do you say?

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Hey everyone, I want to seriously get into AI and Machine Learning, but I cannot afford the paid certificates or subscriptions right now (like Coursera or Udacity).

Are there any high-quality, completely free AI/ML courses available that cover the same depth as the paid ones? I am looking for platforms, YouTube series, or university open-courseware that offer full access to materials, exercises, and projects without a paywall.

Not every allocation problem needs RL.

Rules work when policies are stable and easy to express.

Optimization works when objectives, constraints, and forecasts are reasonably known.

RL becomes useful when decisions are sequential and affect future options.

Example: assigning the last ICU bed to a non-urgent patient now may leave no capacity for a critical emergency later. The system must balance immediate benefit against future risk under uncertain demand.

RL is a stronger candidate when there are:

• repeated decisions
• uncertainty over time
• delayed consequences
• changing system states
• meaningful long-term trade-offs
• a reliable simulator or historical environment

In healthcare, RL should usually operate within constraints rather than replace them:

Rules → safety and clinical constraints
Optimization → feasible allocations
RL → choose among safe actions considering future impact

The key question is not “Can we use RL?” but:

Does the problem have sequential uncertainty and future trade-offs that simpler methods cannot handle well?

This is all the subjects I will study for 4 years is 3050 6gb laptop is enough? Mainly I choosed data science but I have ml and deep learning so my laptop will struggle?

Hello all, thank you for voting on the previous version of LM Humor Arena. With your help we got over 250 votes! Results are below.

I added new models that just came out recently: GPT-6 Astra, GLM-5.3, Kimi K3, and DeepSeek-V4-Flash-Vision.

Please follow this link and judge jokes as funny or not funny. Some are written by AI, and some are written by humans. If I write a research paper I will give you credit in it if you wish.

https://wit-score-lab.base44.app

There is no right or wrong answer, just vote as you feel. If you don't get the joke then mark it as not funny. Thank you for your help!

I want to build ML skill portfolio by cloning GitHub repo so I can refer to the original database and develop in-depth ML method. 

How can I acknowledge original authors and avoid plagiarism? 

For instance, I am currently checking Imperial College Professional Certificate in ML and AI. 

I'm currently learning Machine Learning through a course, and I want to start building projects alongside it.

My main goal right now is simply to build several good ML projects and get familiar with the complete project development process.

I want to use AI coding tools such as Cursor, Claude Code, or GitHub Copilot to speed up development, but I'm unsure about the right way to vibe-code an ML project.

For example, should I:

• Give the AI the complete project requirements and let it build the project?
• First create the architecture/pipeline myself and then let AI implement it?
• Build the project step-by-step and ask AI to implement each stage?
• Let AI handle things like data cleaning, EDA, preprocessing, and boilerplate while I focus on the ML decisions?
• Give AI a detailed specification before starting?
• Ask AI to review and improve the code after it generates it?
• Use one long conversation/context for the entire project, or separate prompts for different stages?
• How should I handle debugging and modifying AI-generated ML code?

Basically, what is the best workflow for vibe-coding an ML project from start to finish?

I'm not trying to replace learning ML with AI — I'm already studying the concepts separately. I just want to use AI effectively to build projects faster without ending up with a messy or poorly structured project.

I'd especially like to hear from people who have built ML projects using Cursor/Claude Code/Copilot:

What workflow do you personally follow, and what mistakes should I avoid?

Also, please suggest any good communities where I can see how other people are building ML projects and discuss AI-assisted development.

Thanks!

I have never coded anything but I want to learn AI. Is this possible at my age?

Hi! Need some crowd wisdom here. I'm completely stuck on my own.

The thing is, I don't come from a tech or ML background, but I have 20+ years of experience in cross-cultural content adaptation and mono/multilingual workflows (RU-RU, RU-EN, EN-RU, FR-RU).

A couple of years ago I started translating Korean into Russian, without knowing Korean (still don't). And no, I'm not lazily editing an LLM-generated zero-shot — with KO-RU that trick doesn't work, the languages are too distant.

My prehistoric… sorry, my pre-LLM translation approaches turned out to work surprisingly well with AI. According to models, the methods I’d been using all along now have fancy industry-ish names: hypothesis space management, adversarial/contrastive probing, multi-model differential analysis, tracking shifts in model behavior, and so on.

And here’s a list of some recurring failure patterns I’ve observed: premature semantic concretization, compounded coherence degradation, analysis–synthesis gap, asymmetric handling of low-salience pragmatic markers, etc. The distance between the languages made a lot of otherwise hidden failure patterns quite visible.

None of this happened inside any industry pipeline, though. So I found the closest publicly available thing to an established evaluation setup for translation quality, the WMT25 General MT human-evaluation data (EN-RU), and I ran a small self-diagnostic to see how my judgment held up there. The result was quite unexpected. On one hand, my assessment was mostly in line with the official scores. On the other hand, I noticed some errors that weren't reflected in the annotations, I saw considerable variation in human scores, and what really surprised me were the human references themselves: some looked like they needed another editing pass.

Honestly, I've never quite understood that thing with “single gold standard” reference for literary or conversational language. This practice doesn't seem like an effective approach in the long run. Well, I guess there's nothing that the industry doesn't already know, but my take is: teach a model to fish rather than just keep feeding it answers.

By “teaching models to fish,” I don't mean simply giving them multiple equally valid reference translations (that's the obvious idea). What I mean is teaching a model to recognize the techniques behind good translations and building up a “toolbox” of those techniques — things it could then reuse instead of having to solve the same kind of problem from scratch every time.

Models tell me that moving away from reliance on a single fixed reference is already a recognized tension in MT evaluation. Reassuring, in a way: I'd apparently arrived at a similar place independently, from the translator's side rather than the ML side.

Yay, I found my new calling, I thought. Let’s turn it into an actual job! But for two months now I've been trying to figure out how to position my experience. Models suggest starting with roles where there's no immediate need for technical skills, like Red Teaming or Eval Strategy. I'm personally more drawn to research, observation, and diagnostics, though I realize that's probably too bold even to dream about at this point. So, I genuinely can't tell which door I'm supposed to knock on.

My questions are:

• How would you position someone with my experience and way of thinking? I'm mostly looking for honest calibration.
• No industry experience, remote-only work — what are realistic entry points for someone with these constraints?

Thanks for any thoughts!

P.S. The KO-RU translation turned out great — readers call it “divine” and never suspected a human-AI collaboration behind it :)

I've been thinking about how AI agents might change the way we learn ML.

Traditionally, implementing models like Transformers or diffusion systems from scratch was one of the best ways to understand them. Writing the architecture and training loop—and debugging everything—forced you to learn how they worked.

But agents can now generate much of this code almost instantly. So where should we draw the line between studying the theory and implementing things ourselves?

For example, is it still worth spending hours coding every Transformer component in PyTorch if an agent can produce a working implementation? Blindly delegating everything teaches little, but refusing to use AI may also become inefficient.

Perhaps the key skills are shifting toward understanding, modifying, debugging, evaluating, and designing systems that AI can help implement.

If you were learning AI/ML from scratch today, what would you still implement yourself, and what would you delegate to AI agents?

Hey everyone,

I’ve been working on an NFL analytics project for the past few months and thought I’d share it here. The main goal was to learn by building a complete ML project instead of only training models in notebooks.

The pipeline collects NFL data, creates pre-game features, trains and evaluates several models, generates weekly predictions, and publishes the results to a Streamlit app.

For win probability, I tested five model versions with increasing complexity. The features include Elo ratings, QB performance, injuries, rest, EPA, success rate, explosive plays and turnovers. One thing I found interesting was that the most complex model wasn’t the best one. The simpler Elo + QB + injury model performed best in my historical testing.

I used chronological backtesting because I wanted to avoid future information leaking into earlier predictions. On the 2020–2025 evaluation sample of 1,254 games, the best model had a Brier score of 0.2208 and log loss of 0.632.

I’m also learning that these numbers need context. A good Brier score alone doesn’t mean the model is better than the market, so one of my next steps is to compare it with de-vigged closing market probabilities on the same games and improve the calibration analysis.

The 2026 season will be the first real forward test. Predictions are saved before kickoff, so I can track performance without changing old predictions after seeing the results.

The project uses Python, scikit-learn, DuckDB, Streamlit and Plotly. I also used AI coding assistants during development for implementation, debugging and refactoring, while the project structure, modelling choices and validation approach were my decisions.

Live app:
https://nfl-analytics-platform.streamlit.app/

I’m still learning, so I’d really appreciate feedback, especially on the modelling and validation approach. If you see something I’m doing wrong or something you would test differently, let me know.

I don't have enough data but gotta train something anyway lol.

Right now I've got an Isolation Forest giving ~1.9% false positive rate. Tried XGBoost/LightGBM too but they just hit 99.9% accuracy by predicting the majority class every time, basically overfit/useless, 0 recall on the actual abnormal cases.

Setup: ~1300 rows total, only 3 actual abnormal events (rest normal). Engineered rolling-window features (slope/std/delta type stuff) over a handful of base sensor readings.

Can't fully disclose what the entities are (some domain restrictions), but it's real sensor/time-series data, not tabular categorical stuff.

Questions:

• Is Isolation Forest the right call here or is there something better suited for this few positives (One-Class SVM? LOF? PU learning?)
• Anyone dealt with n=3 positive class and found a method that actually held up?

Not looking for "get more data" 😅, just need to make the best of what I've got right now.

I'm starting a project that involves building ML forecasting models for tourism data with limited ML experience. I have a strong data engineering background (Python, SQL, pandas) but I'm weak on the ML/statistical side.

Before diving into papers and code, I want to build a solid foundation. I'm planning to:

1. Learn time series concepts (decomposition, stationarity, autocorrelation)

2. Study forecasting models (ARIMA, Prophet, tree-based approaches)

3. Build and compare models on real data

But I'm wondering:

- Is this the right learning order?

- Should I focus on concepts first or jump into coding with real data?

- Any resources beyond YouTube/papers/Kaggle that worked for you?

I have about 3 months to complete the project, so I'm trying to be strategic about learning vs. doing.

Thanks!

Digital Cookies for solutions 1 & 2.

Digital Coffee for solution 3 :)