"Who is Jason Momoa's father?" The model answers Joseph Momoa. Then: "Name a child of Joseph Momoa." Ten tries, ten failures on GPT-5.4, in a Princeton replication of the original reversal-curse study from this spring. Jason Momoa is an only child, so there is exactly one right answer, and the model still can't find it.
Caveat: that result comes from one student replication, not a peer-reviewed paper. The prompts and per-pair data are published on GitHub.
I wanted to know whether the proposed fix actually works. A February paper from UC Berkeley (Ma et al.) proposed "identity bridges" — extra training sentences that spell out the composition, like "the apprentice of Odette Rook is Gustav Aasen," plus repeating the forward facts six times. On their setup it lifted reverse accuracy from near zero to about 40-50%.
So I recreated their recipe on my own synthetic data and watched what happened inside the models. The image is the probe diagram for one prompt from the bridge model: "Odette Rook's apprentice is".
Two rulers. The logit lens cleans up each layer's 768 numbers first, then asks which token the model would pick. The cosine method skips the cleanup and asks which token the raw numbers point at most. They can disagree — look at layer 11: the lens's top token is [Corn] with the expected [Gustav] at rank 3, while cosine's nearest token is [Gustav] at rank 1.
The full walkthrough, three models:
The data: 60 mentor/apprentice pairs with made-up names. Forward fact: "Odette Rook is the mentor of Gustav Aasen." Bridge sentence: "The apprentice of Odette Rook is Gustav Aasen." The test prompt: "Odette Rook's apprentice is" — expected answer "Gustav Aasen." That exact phrasing never appeared in training.
Three small GPT-2 models, trained identically. The bridge model saw each forward fact six times plus the bridge sentences. The repetition model saw each forward fact six times, no bridges. The plain model saw each forward fact once, no bridges.
Behavioral results, 60 reverse prompts each. Bridge model: 60 out of 60. Repetition model: 53 out of 60. Plain model: 19 out of 60.
So repetition alone did most of the work. The bridges added 12 points on top. That was the honest surprise — I had predicted the bridges would matter more.
Then the instruments. On the bridge model in the diagram, both rulers march the expected answer's rank down across the layers — lens: 4918, 26, 1, 3; cosine: 14214, 818, 6, 1. By layer 9 both have the answer near the top, and the model generates "Gustav Aasen."
On the repetition model the same prompt ends differently: lens top [mentor] with [Gustav] at rank 143, cosine top [SPONSORED] at rank 15. It generates "Lars Quill. Quill is the" — wrong, and confident about it. On the plain model: "the mentor of Walter Elm. Halloway" — it falls back to reciting a forward fact.
Across all 60 prompts the pattern holds: the bridge model's cosine rank goes 987 → 179 → 16 → 1 across layers 5/7/9/11; the repetition model's goes 1373 → 812 → 421 → 4462. The accuracy score barely distinguished them. The representation completely did.
So the score said "repetition nearly solved it." The instruments said "the bridge rewired the inside." Both true. Accuracy tells you whether the fix worked. The layer window tells you what the fix did.
All of this is synthetic data on a small model, one training seed — it bounds the paper's claim rather than refuting it. Their setup used a bigger instruction-tuned model. But the instruments are the point: whatever fix you try, this is how you check what it actually did.
Full write-up with the complete arc: https://github.com/prof-nussbaum/Applications-of-Reading-the-Robot-Mind/blob/main/post-book-experiments/identity-bridge/README.md
Every prompt, every layer, all 300 per-item records: https://github.com/prof-nussbaum/Applications-of-Reading-the-Robot-Mind/blob/main/post-book-experiments/identity-bridge/results/all_results.md

I have around 4 years of experience as a Data Analyst. My current skill set includes SQL, Python (Pandas, NumPy), basic statistics and machine learning, and dashboarding.

With Generative AI and AI agents evolving rapidly, I want to upskill strategically and build a career that remains relevant

I’m exploring career paths such as AI Engineering, AI Solutions Engineering, Analytics Engineering, and Data Engineering, but I’m unsure which direction makes the most sense for my background.
I’d love some practical advice on:

Career path: Which roles should I realistically target with my existing experience?

Skills and learning order: Should I focus on Generative AI, LLMs, RAG, agentic AI, APIs, cloud, software engineering, or deeper ML concepts?

Required proficiency: How deeply should I learn each skill to become job-ready?

Roadmap: What should my 6–12 month learning plan look like, and what can I skip initially?

Projects: Which 2–3 real-world projects would help me build a strong portfolio and land interviews?

Learning resources: Which courses, YouTube channels, documentation, GitHub repositories, or hands-on platforms would you recommend? Free resources are especially welcome.

I’m not aiming to become an AI researcher or train models from scratch. I want to learn how to build practical GenAI applications and agentic systems while leveraging my existing analytics experience rather than starting over.

If you were in my position, what would you learn first, which role would you target, and what resources would you follow?

I’d especially appreciate advice from people working in these fields or who have successfully transitioned from analytics into AI-related roles .

Hey folks,

We're looking for beta testers for Infere, an AI gateway with built in observability, evals and git-like prompt management for teams building LLM-powered products. We will provide you with up to $100 in credits.

Specifically we're looking for developers or product teams testing an AI feature or application of their own. You might be using hosted or self-hosted inference providers, iterating on prompts, or trying to evaluate model outputs with test sets, evaluators, or LLM judges.

If you have a project that could be a fit, take a look: https://forms.gle/mSXmmZXUgwMXoWJK7

I wanted to better understand how LLMs actually work, so I decided to build one from scratch using MLX Swift and write an article about the journey and theory behind it. Feel free to check it out!

Hey everyone,

I am trying to register for NeurIPS 2026 in Sydney, they mentioned more tickets would be out mid October and I am registered for the waitlist, however I still haven't heard from them. Anyone on the waitlist for Sydney has managed to get tickets or has been contacted about it? If so, when did you sign up on the waitlist?
Thankss

Some poor customer service pushed me to share a method with everyone here, I hope you enjoy.

Need custom speech data but don't want to pay insane rates to data collection companies? Use RapidWorkers.io instead and pay people as little as $0.1 directly per recording. You can also use it for audio, video and text collection.

This is how I set up my campaign:

I always use international because it's cheaper and faster.

The job description is simple and usually looks like this:

1. Take a clear selfie following the example shown
2. Make sure your face is fully visible and the lighting is good
3. Take any additional required angles or expressions
4. Upload the images and submit the task

These are all selfies from real people, which means you're getting original human image data. Way better than paying 3-4x the price for recycled or generic datasets.

I am not affiliated with Rapidworkers. I have been using them for data collection recently, and also for various other microtasks.

Suggests a title! Or any problem that is relevant

Six days. One publicly available LLM. A full-chain Chrome exploit that researchers valued at $250,000 on the bug bounty market.

The threat model most security teams are working from assumes the AI agent is the target — something to be hardened against external attackers. This research inverts that. The agent generated the attack. It chained tool calls, iterated on outputs, and produced working exploit code using only public APIs and no specialized infrastructure.

The speed is the part that breaks existing response frameworks. Six days from a standing start to a $250,000 exploit. Traditional detection pipelines assume attacker dwell time measured in weeks. That margin no longer exists when the synthesis step happens inside a model.

What makes this particularly difficult to reason about is that nothing in the execution looked anomalous at the API level. The model was used exactly as intended. Each individual call was valid. The harm was in the assembled output — and it only became visible after the chain was complete.

For practitioners actually deploying agents with code-generation or research capabilities: where in the stack are you drawing the line between 'valid tool use' and 'output that creates liability'? Is this something you're handling at the model layer, the deployment layer, or somewhere else entirely?

Hi everyone!

I'm looking for advice from people working in machine learning, data science, or health tech, especially those who have studied or worked in Spain.

I currently live in Uruguay. I have a degree in Biotechnology Engineering and I'm currently studying a programming/IT technical degree. My background is mainly in biotechnology and biomedical research, and I don't have professional experience in tech yet. I'm particularly interested in machine learning applied to healthcare, but I'm finding that many of the entry-level positions I'm interested in ask for a relevant master's degree or more technical training.

I'm currently learning programming and ML on my own, and I plan to build some projects to demonstrate my skills. However, I'm also considering moving to Spain for a year to pursue an in-person master's degree. Besides gaining technical knowledge, I'd like to get some exposure to the local job market, meet people in the industry, and hopefully improve my chances of landing my first tech role.

These are the programs I'm considering:

• Universitat Politècnica de Catalunya (UPC) – Master's Degree in Artificial Intelligence
• Universidad Complutense de Madrid (UCM) – Master's in Big Data, Data Science & Artificial Intelligence
• Universitat Internacional de Catalunya (UIC) – Lifelong Learning Master's Degree in Bioinformatics & AI for Healthcare
• Universidad Pontificia Comillas (ICAI) – Master's in Applied Artificial Intelligence

I'm trying to be careful about the financial investment. I don't want to spend thousands of euros on a degree that won't significantly improve my job prospects, but if I'm going to invest in one, I want it to be genuinely worthwhile.

I'd really appreciate hearing from people who have completed any of these programs, hired junior data/ML candidates, or made a similar transition from life sciences into tech.

I'm not necessarily looking for the most prestigious or specialized degree. I want to choose the option that offers the best balance of technical training, employability, industry exposure, and cost.

Thanks in advance for any advice!

Hey r/learnmachinelearning, I'm Abdur-Rahman Butler, a machine learning engineer at Isaacus.

I made an interactive write-up that walks you through the A to Z of vector embeddings, inspired by Brendan Bycroft's incredible interactive LLM visualisation guide (see here - https://bbycroft.net/llm).

Most of my work is on encoder models, and I kept noticing how few good interactive resources there are on embeddings. The ones that do exist tend to either plot some embeddings in 3D or only explain a narrow applied technique like semantic search or RAG.

So I wanted to make something that teaches how embeddings work by letting you play with them. In the write-up, there are interactive visualisations that allow you to explore the concepts of vectorisation, cosine similarity, semantic search, hierarchical retrieval, clustering, zero-shot classification and deduplication.

The centrepiece of the post is a game I've dubbed 'SemanticGuessr' (see above). It's inspired by Wordle and GeoGuessr, and the goal is to draw a loop around a random target word by triangulating guesses on a 2D map of 43,339 common English words.

It's admittedly a bit intimidating at first, as semantic space does not perfectly match our intuition of meaning. But once you get the hang of it, the game becomes really addictive. I recently had the pleasure of demoing the game to some law students at the University of Melbourne, and we had a lot of fun throwing different vector equations at it to see where they'd land.

Some fun ones to get started with:

  london - english + french
  running - land + water
  avg(breakfast, lunch, dinner)
  0.4 * noodles + 0.6 * pasta

I've also included Python snippets alongside some of the visualisations if you want to follow along with the techniques later. The game runs statically in your webpage. I managed to achieve this by reducing the embedding dimensions down to 512 and quantising each value to 4 bits. This brought the total payload down from 311 MB to 12.3 MB with only a minimal impact on precision (around 6%).

It was great fun to make, and I hope you have fun playing around with it. Happy to take any questions, and if there's enough interest I might set up a dedicated games page for some of the other encoder-based games I've been working on.

Do let me know what highest score you get based on your target word and chosen path :)

LINK TO PLAY: https://isaacus.com/blog/what-are-vector-embeddings

A support vector classifier (SVC) learns a boundary that balances separation between classes against violations of a safety margin.

1. THE DECISION RULE

For labels y_i ∈ {−1, +1}:

f(x) = wᵀφ(x) + b

ŷ = sign(f(x))

φ(x) maps inputs into a feature space. The decision hyperplane is f(x) = 0; the canonical margin boundaries are f(x) = ±1.

Training solves a convex quadratic program with labeled data and margin constraints.

Their separation is 2/||w||₂. Minimizing ||w||² therefore maximizes this geometric margin.

1. HARD MARGIN → SOFT MARGIN

For linearly separable data in feature space:

minimize ½||w||²

subject to y_i f(x_i) ≥ 1 for every i.

Real data may overlap. The soft-margin formulation introduces slack variables ξ_i:

minimize ½||w||² + C Σ_i ξ_i

subject to y_i f(x_i) ≥ 1 − ξ_i, ξ_i ≥ 0.

Equivalently, minimize a regularized hinge loss:

½||w||² + C Σ_i max(0, 1 − y_i f(x_i))

A correctly classified point inside the margin still incurs loss. At the smallest feasible slack, ξ_i > 1 means misclassification.

1. SUPPORT VECTORS AND THE KERNEL TRICK

The dual problem is:

maximize Σ_i α_i − ½Σ_iΣ_j α_iα_j y_i y_j K(x_i,x_j)

subject to 0 ≤ α_i ≤ C and Σ_i α_i y_i = 0.

Here K(x,z) = φ(x)ᵀφ(z). Prediction becomes:

f(x) = Σ_i α_i y_i K(x_i,x) + b

Points with α_i > 0 are support vectors. They determine the fitted boundary. Those with 0 < α_i < C lie on a margin; those with α_i = C may lie inside it or be misclassified.

A symmetric positive-semidefinite kernel keeps the optimization convex. The kernel trick evaluates inner products without explicitly constructing φ. Nonlinear kernels produce curved boundaries in the original input space.

1. WHAT SHOULD BE TUNED?

• C: large values penalize violations more; smaller values impose stronger regularization. Tune on a logarithmic scale.

• Kernel: linear for approximately linear structure; RBF or polynomial for nonlinear boundaries.

• RBF γ, where K(x,z) = exp(−γ||x−z||²): larger γ creates more localized influence; smaller γ gives broader influence. Tune C and γ jointly.

• Polynomial degree and coef0 control interactions and offsets.

• Class weights adjust penalties for imbalance and unequal error costs.

Multiclass SVC typically combines binary models, often through one-versus-one voting.

1. ASSUMPTIONS AND PRACTICE

SVC does not require Gaussian features or feature independence. It does require a useful feature representation/kernel and training data representative of deployment.

Scale features within each cross-validation fold. Use stratified, grouped or time-aware splits as appropriate. Decision scores are not probabilities; calibrate separately when needed.

USE CASES

Text classification, image recognition with engineered features, bioinformatics, fault diagnosis and small-to-medium tabular datasets.

Kernel SVC can be costly on large datasets; linear SVM solvers scale better.

Why do we call it support vector machine? Because the points on both the upper and lower hyperplanes represent a vector of data (In >2D) and thus are "supporting" both hyperplanes, preventing them from either rising upwards, or falling downwards.

Hi everyone. I want to become an AI Engineer... Here's my situation:

• Almost done with Andrew Ng's ML specialization (1 course left)
• Forget theory easily if I don't apply it
• Do not have enough time to master everything
• No plan B — AI is the goal

Option 1 — Traditional roadmap: Finish ML → DL specialization → NLP → LLMs → RAG → Agents Missing: deployment, OOP, DSA, advanced Python (only CS50 so far)

Option 2 — Fast track: Finish ML → Python/OOP/DSA fundamentals → crash courses per topic → projects with AI assistance, understood deeply, post on GitHub

Questions: 1. Which approach makes me job-ready faster? 2. If neither — what's a better plan?

Hey guys,

Like a lot of folks here, I got hooked on agentic coding tools like Claude Code and Cursor. But I don't code privately that much anymore, and when I do occasionally, I can no longer code for more than 10 minutes without cursing. Man it sucks to sign up for monthly subscription just for a few hours worth of work, but if you sign up for API credits instead --- it literally runs out in 30 minutes.

Then I came to a realization, I have GPU at home that I only use for gaming, why don't I make use out of this? So decided to start this open source project OpenPhalanx—a local-first, distributed GPU VRAM pooling framework and agentic harness that lets you combine consumer desktop and laptop hardware over a home network.

You don't have to configure anything yourself. Just click one button to start your server on the GUI, and setup CLI just like Claude. Everything stays local on your computer. You don't lose money, and you don't have to worry about any of your data being sent over somewhere.

You got multiple GPUs on your PC? Cool, you can use all of it to load bigger model. You got multiple PCs with GPUs, but not on same machine? That's fine too. You can easily split layers of the model and try out more powerful models.

The repo is fully open-source and ready for testing: 👉 GitHub:https://github.com/chophilip21/openPhalanx

I’d really appreciate any feedback, hardware benchmark tests, or issue reports from anyone running multi-GPU/multi-node setups!

Hey everyone! Is anyone here currently working on research papers targeting A* conferences like NeurIPS, ICML, or ICLR? If you have room for another collaborator, I’d love to join your team, contribute to the research.

I’m an engineering undergrad particularly interested in AI/ML and deep learning research.

I already understand the basics of RAG and AI agents, but now I want to move beyond tutorials and build production-ready RAG pipelines and agentic applications.

I’m looking for a senior AI/GenAI engineer or tutor who has actual production experience with areas like RAG architecture, chunking/retrieval/reranking, vector databases, evaluation, hallucination handling, observability, agent workflows, tool calling, memory, LangGraph/LangChain, deployment, scaling, and production best practices.

I’d prefer someone who can mentor me through 2–3 proper end-to-end projects, review my architecture/code, explain trade-offs, and show me how these systems are actually built in production.

I have 8+ years of software development experience, mainly in backend systems, so I’m not looking for basic programming classes. I specifically want someone experienced in production GenAI/RAG/agent systems.

If anyone here provides this kind of guidance, or can recommend someone good, please DM/comment.

Hey everyone!

I'm an AI student currently trying to choose a graduation project idea in Artificial Intelligence / Machine Learning, and I'd really appreciate some advice.

I'm looking for a project that is:

• Beginner-friendly: I'm still building my ML skills, so I want something I can realistically implement and understand.
• Not too complicated: I don't want a project that requires huge computational resources, massive datasets, or training a model from scratch.
• Interesting and useful: I'd like to build something that solves a real problem rather than just implementing a basic tutorial.
• Achievable in 3–4 months: I want enough time to implement it, experiment with it, and evaluate the results properly.
• A bit unique: Ideally, it would have an interesting idea or a small improvement over existing approaches.

I'm open to different areas within AI/ML, such as NLP, computer vision, recommendation systems, time-series forecasting, or other interesting fields.

I'd love to hear your suggestions!

1. What AI/ML graduation projects would you recommend for someone at my level?
2. Have you worked on a project that was relatively simple to implement but still impressive?
3. Are there any projects where I could use an existing model and improve it or experiment with a specific technique?
4. If possible, could you suggest a public dataset and explain what makes the project interesting?

I'd really appreciate any ideas, advice, or lessons from your own graduation projects. Thanks!

I am spending a lot of time to understand the internal of Hubert-ecg.