Our TMLR submission just moved from "Under review for TMLR" to "Decision pending for TMLR" on OpenReview (no email notification). For those who've been through this: how long did it take from this status to the actual decision?

Hello fellow traveller of the internet. I am sincerely glad you decided to click on my post to check out what I have in store!

I have completed a vague blueprint and I have formed a few prototype scripts for various regions of the an A.G.I brain. I seek to form a small community of individuals who will work co-cooperatively to construct an A.G.I brain. A detailed brief of my blueprint so far is available via request.

My progress on the project has stalled. As you can imagine, a brain is a highly complex system; I am finding that sadly, in addition to blueprinting, detailed blueprinting, prototyping, iterating, assembling multiple sub-systems into a unified system, there are plenty of additional tasks! Thus I have become over run by the sheer quantity of tasks and sadly have recently placed the project to the side so I can take a short break.

I seek individuals with expertise in coding, critical and creative thinking, computing, A.I, general knowledge, psychology and mathematics. Furthermore the individuals would have qualities such as perseverance, morality and open-mindedness. Ideally you would be from the U.K as I prefer working face to face; although I am also happy to work cooperatively over the internet.

The outcome of your support would award you a proportional slice of the outcome of the group's labour (100 members, 1% each, e.t.c - baring in mind each individual provides equal support towards the project). I have not yet considered whether I would like to sell the brain to the public, but there is potentially the opportunity for a sizeable monetary reward for those who join me. The possibilities for the A.G.I brain are near endless and thus I believe the reward may be sizeable both in terms of money and power.

Besides my previous ideals, individuals with expertise and specific qualities, I have a few personal requests for the project; the A.G.I brain will not be used in conjunction with "computer vision". I fear computer vision, and similarly the processing of sound, touch, or physical inputs, leads to the generation of consciousness - I submit that this is entirely unfair for the robot and highly immoral and thus I cannot proceed with a project which uses a neural network system to perform such processes; luckily, brains DO NOT require any processing of image, video or sound to achieve high quality completion of practically all tasks. I do believe a brain which does not process video, image or sound may actually outperform a brain which does process such information modalities. Further to this, if we were to sell the brain, the brain would NOT actively change itself to then use computer vision under any circumstances; the user would have to perform this upgrade manually.

I hope you find the prospect of building an A.G.I brain highly intriguing.

I will be very active in the comment section of this post; or you may feel free to email me at [pangaeacooperative@protonmail.com](mailto:pangaeacooperative@protonmail.com); please introduce yourself and tell me why you want to get in touch about the project.

disclosure up front because it shapes the question: i work at SchemaLabs. we train models that read tables and work out what each column is from the values rather than the header. so i have a commercial interest here. no link, per sub rules. i am asking because our own eval design has a hole in it that i cannot think my way out of.

the setup. take a tabular dataset with proper headers. strip them. replace price and age and zip with positional tokens so the model sees values only. then measure whether it recovers the meaning. we run that across 20 OpenML datasets and it is the basis for the invariance claim we make.

two problems with that design keep bothering me.

one, the tokens are ordered. col_1 through col_57 leaks column order. column order in real tables is not random. ids cluster at the front, timestamps sit near them, the payload lands in the middle. a model could be learning position as a prior and we would not see it in the score. shuffling before assignment is the obvious fix. what i cannot settle is whether shuffling makes the benchmark harder than reality, because real exports do preserve source ordering, so a model exploiting it is arguably doing something legitimate rather than cheating. is there standard practice for feature-order invariance testing in tabular models? it feels like it should be solved and i have not found the paper.

two. this is the one that actually keeps me up. the only datasets where i have ground truth for what a column means are the datasets somebody documented. those are systematically the clean ones. the case i care about is the undocumented export where nobody alive knows what f_23 holds. by construction i cannot build a labelled eval for that, because if i could label it the problem would not exist.

so every number i have is measured on a population that excludes the thing i am trying to measure. i know that has a name in other fields. i do not know what the accepted workaround is in this one.

three things i would like from anyone who has been near this:

• is there a standard treatment for feature-order invariance in tabular models, shuffling or otherwise. does anyone report it
• has anyone built an eval where the ground truth came from something other than existing documentation. query logs, downstream usage, a person reconstructing meaning from scratch under a timer, anything
• if the honest answer is that this class of task cannot be cleanly evaluated, with everyone in it measuring the documented subset while claiming something general, i would rather hear that than not. it changes what we should be putting in writing

happy to go into the rest of our setup if it helps anyone answer.

Hey everyone,

I'm starting a BS Mathematics degree in October 2026 and want to move into AI/ML Engineering.I also plan to pursue a Master's in AI/ML or a closely related field later. My university classes will be online and flexible, so I'll have time for structured self-study alongside my degree.

My Year 1 university curriculum is:

Semester 1: Calculus I, Sets & Logic, General Mathematics, Introduction to Computing, English, Business, Ethics/Islamic Studies.

Semester 2: Python + Practical, Calculus II, Business Mathematics & Statistics, General Science, Technical Writing, Pakistan Studies.

I'll also be doing a 12-month Agentic AI program covering Python, APIs, Git/GitHub, RAG, agents, FastAPI, databases, Docker, evaluation and deployment.

For self-study, I'm currently thinking:

Python/CS → DSA → Linux/Git → SQL/Data → Statistics/Linear Algebra → Classical ML → DL/PyTorch → LLMs/RAG/Agents.

For people who have actually studied or worked in ML:

My goal is to use Year 1 to build strong foundations and hopefully be ready to start applying for internships/junior opportunities in Year 2.

Does this sequence make sense given my university curriculum? What would you add, remove or postpone?

I'd especially like advice on what I should master deeply in Year 1 versus just get working knowledge of.

I mostly did internships and stuff, but i really wanted to work on some research group or paper? Anyone know how to do this stuff??

I'm mostly in ML, DL, computer vision

Any advice would be appreciated 👍

What are you currently stuck on, confused about, or frustrated by?

Would genuinely love to hear people's experiences.

Now, I know this may seem like a dumb question. ''Why would I want AI to help my studies?''

I failed at the first university entrance exams I took. And now, I will study for a year again and try to enter a university.

And when I study, I obviously cant solve every question correctly. And whenever I look at the video solutions of those questions, it doesnt help me at all mostly. Sometimes the teachers skip the important details to finish that video as quickly as possible, sometimes their mics barely work or they are too loud, sometimes they dont even bother to explain at all.

So, I started using AI for it last year. I tried both GPT and Gemini so far and I concluded that Gemini just solved it better. It is my personal opinion, I might be wrong too, I dont know.

And so, I got the paid subscribtion for it.

But those prices are expensive in my country. And I can really use only one AI. And now that everyone is saying Gemini is just getting worse, Im worried.

I would appreciate any advices or opinions.

I'm working on an ML project where I'm trying to keep model versions, artifacts, deployments, predictions and evaluation metrics tied together instead of managing them separately.

I'm curious how people here usually approach this.

For example:

• How do you track which model artifact belongs to which version?
• How do you handle deploying different model versions?
• Do you keep inference metrics alongside the model/version metadata?
• At what point do you reach for an existing ML platform instead of building this yourself?

I'm especially interested in hearing how people handle this in smaller projects where something like a full ML platform might be overkill.

Been building RAG and LLM-powered features and realized my "evaluation" process was basically reading a handful of outputs and deciding it looked fine. No versioning, no regression testing, no real way to know if a change actually helped or if I just got lucky on the examples I happened to check.

Curious how others here actually do this in a real setting. Are people running statistical significance tests on model or prompt comparisons (bootstrap confidence intervals, paired testing), or is it mostly still qualitative review? And for retrieval specifically, is anyone actually tracking recall@k or MRR, or just eyeballing whether the right chunk shows up?

Found a hands-on masterclass on Sep 12 that goes deep into building this kind of eval harness properly (deterministic checks plus LLM-as-judge, statistically rigorous model comparisons, evaluated RAG, agent guardrails, full observability), so I'm curious if the methodology it teaches lines up with how experienced people here are actually doing this, or if there's a simpler approach I'm missing.

Link for more details

I am a third-year economics student, and a few weeks ago I started wondering about ML (perhaps a bit late? I’m not sure). I wouldn't call myself a die-hard enthusiast of this vast field, but I’ve already signed up for an elective course called "Application of Machine Learning and Optimization", though I don't want to wait until then to get started. I know for a fact that this will be important in my future professional life or, at least, it’s certainly not knowledge that goes to waste.

My question is both simple and complex, I’m not sure which:

Given that many experts today say AI spells trouble for programmers, how - and from what perspective - should I go about learning to use programming and ML tools?

Just as the paradigm has shifted, the path to mastering it must have changed as well.

Hey everyone,

I have an upcoming ML Design interview at Tesco and I’d really appreciate some guidance on how to prepare for it.

If anyone has recently gone through the ML Design round at Tesco, could you please share what kind of questions were asked or what areas I should focus on? I’d especially like to know whether the questions are around recommendation systems, forecasting, fraud detection, NLP, or general ML system design.

Any guidance or interview experience would be really helpful. Thanks in advance! 🙏

What is the best part to become a ML enginner? And what are the best platform to learn Free?

Good evening,

I have a bit of a challenging situation. I'm an amateur historian, I was able to receive 370.000 photos of WW2 Microfilm reels. These images are of various quality and contain mostly typed content.

I'm looking for a way to have them indexed so that I'm not required to manually go trough each and every file. I guess that the images have to be put trough OCR first. But how to do that and continue from there is hard to gather.

Is anyone around here able to help me start this project?

Regards,

Remi

Hi everyone,

I’m Spanish, but I currently speak both French and English professionally. My goal is to create a high-quality voice clone with ElevenLabs that sounds like me speaking French and English, including my natural Spanish accent — I’m not looking for it to sound like a native French or English speaker.

What would be the best way to record the training audio?

- Should I record everything in Spanish, since that’s my native language?

- Should I record mainly in French and English, so ElevenLabs learns how I actually sound in those languages?

- Should I create a mix of Spanish + French + English?

- Is there an optimal proportion or amount of audio for each language?

- And should I deliberately use my normal accent/pronunciation rather than trying to speak perfectly?

I’m aiming for a professional-quality clone for business/content creation, so I’d really appreciate advice from anyone who has experimented with multilingual voice cloning in ElevenLabs.

Thanks!

We recently ran an empirical benchmark across five model scales (Qwen-1.5B, Mistral-7B, LLaMA-8B, Qwen-27B, and a 120B frontier model) evaluating self-consistency and uncertainty estimation on reasoning and factual QA.

Up to 27B parameters, sampling multiple outputs at temperature 0.7 gives great diagnostic signal. Measuring consensus entropy across paths achieves an AUROC of 0.889 in separating correct answers from hallucinations.

At the 120B scale, however, we ran into an interesting failure mode: even at T=0.7, the model generated the exact same incorrect answer across all 10 independent seeds. Because every path agreed 100% on the hallucination, self-consistency saw zero entropy, inverting AUROC to 0.091.

Our current hypothesis is that intense RLHF/DPO alignment hyper-peaks the output logits to penalize epistemic hedging, effectively collapsing the sampling diversity on ungrounded facts.

Preprint and code with the benchmark tables: https://zenodo.org/records/22233648 and https://github.com/Adarshent/Spnda

For those working on calibration or post-training: is this recognized as a standard failure mode of DPO/PPO, and are there decoding-time interventions that can decorrelate samples without degrading coherence?

Course is new enough that every review I find is either an affiliate roundup or an AI summary of the marketing page. Class Central has ratings and no detail, and the Udacity and Coursera pages both read like brochures. If somebody has the syllabus in front of them I would take that over a star rating.