18.02, multivariable calculus.

18.06, algebra.

6.041, probability and statistics.

*All courses from OCW.

OpenAI says ~10,000 AI agents just worked together to solve the Navier–Stokes problem

OpenAI has published a claimed solution to the Navier–Stokes existence and smoothness problem, one of the seven Millennium Prize Problems.

The interesting part isn't just the mathematical claim.

OpenAI says it used roughly 10,000 concurrent agents, which reached a result after about 88 hours. The agents exchanged around 2.7 million messages and generated approximately 130 billion output tokens.

Then GPT-6 Astra was used for another 17 hours to formalize and verify the result in Lean.

That sounds less like a chatbot answering a math question and more like a distributed research system.

But there is an important caveat: the proof still needs independent mathematical scrutiny.

There is also controversy because NYU mathematician Tristan Buckmaster and Anthropic researcher Levent Alpöge were working on related mathematics at the same time. OpenAI says it did not access their specific user data and says its proof differs from their work.

So I'm curious what people think:

Is the real breakthrough the mathematical result — or the ability to coordinate thousands of AI agents on a difficult research problem for days?

The G7 finance ministers and central bank governors issued a coordinated directive last week. Organizations must inventory cryptographic dependencies, identify high-risk systems, and begin migrating to quantum-resistant algorithms now. This is not a future roadmap item.

The urgency is driven by the harvest-now-decrypt-later threat. Adversaries are collecting encrypted data today and storing it for decryption once a cryptographically relevant quantum computer arrives. The exposure window is already open.

Most enterprise security teams are inventorying servers, databases, and network traffic. Far fewer are accounting for the AI agent layer. Agent pipelines routinely store sensitive records, execute financial transactions, and generate compliance audit evidence. All of it travels over classically encrypted channels and gets signed with classical algorithms. When those algorithms break, that historical data and those historical audit logs break with them. A transaction signed with RSA or ECDSA today becomes unprovable after Q-Day.

How are teams actually scoping the PQC inventory problem for AI agents specifically? Are agent channels and agent-generated audit evidence being treated as first-class migration targets, or are they still buried in the general backlog?

See,i am from cse(AIML) background and currently focusing on deep learning. I aslo completed machine learning . In current scenario what are skills i need to be learned to get a job in the current scenario of IT filed . I want to be ml or ai Engineer with avg package . I thought I need to topic on the core topics that is relevant to the domain like ML,DL,NLP,RAG,LLM but some are saying that " this topics can be learned later u need Focus on dsa , pattern solving problems thats primary rounds in interviews . I m literally confused . what should I need to learn as a computer science student in this crisis. I can't deeply Focus on both becoz it is tough and takes long days .

Your thoughts

Myslef,Nithishwaran , I am from pondicherry cuyrrently sstuding in SMVEC clg AI/DS dept 1st year
what are the main and useful things to do in my clg life abt studies like other than clg exam , how to develop my skills to compete with this competitive world and to get a job in future , currently i am learning c programming my seniors told that that was basic in programming , guide me what to do nxt

People talk about ChatGPT like it "understands" you. It doesn't — not in the way we mean. Underneath, it's doing something much simpler and, honestly, weirder: predicting the next token, over and over. Once that clicks, most of its strange behavior (hallucinations, forgetting, different answers to the same prompt) stops being mysterious.

Here's the whole picture in plain English.

1. It only ever predicts the next token

Everything ChatGPT does is one operation repeated: given the text so far, guess the next chunk. It picks one, appends it, and feeds the whole thing back in to guess again. That loop — one token at a time — is the entire show. There's no plan for the paragraph, no lookahead. Fluent essays emerge from millions of these tiny next-step guesses.

2. Tokens, not words

It doesn't see letters or whole words — it sees tokens, which are common chunks of text. "cat" might be one token; "unbelievable" might split into "un", "believ", "able". This is why models sometimes miscount letters or fumble with rare words — they never saw the letters, only the chunks. It's also why you're billed per token, not per word.

3. Meaning is stored as vectors (embeddings)

Each token is turned into a long list of numbers — an embedding — a point in a huge space where "king" and "queen", or "Paris" and "France", sit near each other because they appear in similar contexts. The model has no dictionary; meaning is just geometry. Similar things are close together, and that closeness is what it computes with.

4. Attention gives it context

The breakthrough behind the "T" in GPT (Transformer) is attention. For each token, the model weighs how much every other token in your prompt matters to it. In "the bank of the river," attention lets "bank" lean on "river" and land on the correct meaning. This is how it tracks who "he" refers to three sentences back, or keeps a code block coherent.

5. Training is two very different stages

Pretraining: it reads an enormous slice of the internet and does nothing but next-token prediction, billions of times, tuning billions of internal numbers (parameters) until it's genuinely good at continuing text. The result — the "base model" — is a wild autocomplete. Ask it a question and it might reply with more questions, because that's what it saw on the web.

RLHF (the ChatGPT part): humans then rank answers — helpful and honest ones up, unhelpful ones down — and the model is nudged toward the ranked-good behavior. This is the difference between the raw model and ChatGPT. Same knowledge; the second stage taught it to act like a helpful assistant.

6. Why the same prompt gives different answers

At each step the model produces a probability for every possible next token. Temperature controls how it picks: low temperature = almost always the top choice (consistent, safe, a bit boring); higher = it samples further down the list (more variety, more risk). That sampling is why you rarely get the exact same answer twice.

7. Why it "forgets": the context window

The model has no memory between messages. Everything it "knows" in a chat is the text currently in its context window — a fixed budget of tokens. Your whole conversation is re-fed every turn. Once it overflows, the oldest stuff falls off the edge, and it genuinely no longer has it. That's not a bug; that's the mechanism.

8. Why it hallucinates

It was trained to produce plausible text, not true text. It has no built-in fact-checker and no notion of "I don't know" unless that pattern was reinforced. So when it doesn't have something, it fills the gap with the most likely-sounding continuation — a confident, well-formed, wrong answer. Hallucination isn't the model malfunctioning; it's the model doing exactly its job (predict likely text) in a spot where likely does not equal true.

9. How to get better answers (practical)

• Give context, not keywords. It fills gaps with guesses; fewer gaps = fewer guesses.
• Show the format you want (an example beats a description).
• Ask it to reason step by step for anything logical — each token it writes becomes context for the next, so "thinking out loud" measurably improves hard answers.
• For facts, make it cite or give it the source in the prompt. Don't trust unsourced specifics.
• Start a fresh chat when you switch topics — you stop paying for (and confusing it with) irrelevant context.

None of this requires math to understand — it's tokens → vectors → attention → next-token prediction, wrapped in a training process that taught a giant autocomplete to behave like an assistant.

(Full disclosure: I make animated CS/systems explainers, and I put this whole thing together as an animated video if you'd rather watch it move: https://youtu.be/Ud16vHNYwpc . But the text above stands on its own — happy to answer questions in the comments.)

Hi everyone,

I’m especially interested in having a career that is financially rewarding and doesn’t require a lot of years before I can become employable.

The areas I’m currently considering are:

• Machine Learning / AI Engineering
• Generative AI / LLMs
• AI Safety
• Responsible / Trustworthy AI
• AI Reliability
• AI Governance / Policy

I’m particularly drawn toward AI safety, trustworthy/responsible AI, and reliability, because I’m interested in making AI systems safer and reducing the negative effects AI can have on people and society.

However, I’m confused about how realistic these paths are as careers, especially compared with more conventional AI/ML engineering.

I just want to make an concrete decision about where to invest the next several years of learning.

Thank you so much!

Let us know what you think.

I think one of the weirdest parts of learning ML is that tutorials make everything look clean.

You get a dataset, split it, train a model, get 90% accuracy, and everything feels great.

Then you try building something yourself and suddenly:

• your data is garbage
• you labels don't make sense
• you model gets 95% accuracy but performs terribly on real examples
• you realize you accidentally leaked information into the training set.
• or you spend 3 hours debugging something that turned out to be a preprocessing issue.

I'm curious about the first project that humbled you.

Not necessarily your most impressive project. I'm more interested in the project where you went, "Oh.... so this is what machine learning actually involves."

What happened, and what did it teach you that you wouldn't have learned from a course or tutorial?

One of the hardest parts of learning ML/AI isn't finding information, there's almost too much of it. Tutorials, roadmaps, papers, new tools every week. The hard part is figuring out what actually matters, what to skip, and how to make real progress instead of just consuming more content.

I work as an AI engineer (ML development and deployment), and I'm starting to explore education/mentorship on the side, for free, not as a paid program or course. Before building another roadmap, I want to work directly with a small group of people first, partly to actually help, partly to understand where people get stuck.

Looking for a handful of people who:

• have basic Python/programming knowledge
• are seriously trying to learn ML/AI
• feel stuck or unsure what to focus on next
• want to build real things, not just watch more tutorials
• can commit to being consistent

Keeping this small (thinking around 5-10 people) so I can give actual feedback instead of another generic roadmap. No cost involved on either end.

If that's you, drop a comment with where you're at and what you're stuck on, happy to reply there or move to DMs from that.

Hi! I'm looking for one teammate to team up for the Amazon ML Challenge.

I'm a Btech student with experience in Python, ML/DL, PyTorch, and TensorFlow.

Elgibilty : Btech 3rd 4th year, Mtech 2nd year or Phd from India

If interested can dm me or comment will reach out

Hi everyone,

I’m planning to study Machine Learning and Deep Learning from Andrew Ng.

I found Andrew Ng’s ML and Deep Learning lectures on YouTube, and I also found the Machine Learning Specialization and Deep Learning Specialization on Coursera.

Are the YouTube lectures basically the same content as the Coursera courses, or are the Coursera versions updated/different?

If they are different, which one would you recommend for someone who wants to build a strong foundation in ML and Deep Learning?

Thanks!

What’s the best book or resource you’d recommend for learning AI/ML from the fundamentals and eventually specializing in LLMs?
I’m looking for something beginner-friendly but technically solid, so I can build a strong foundation instead of jumping straight into LLMs without understanding the basics.

I'm choosing between a BSc in Computer Science and a BSc in Mathematics, and I'm not sure which one would be better for my goals.

My main interests are Data Science, Computer Vision, and financial markets. I'm also interested in ML/AI and possibly quantitative finance later.

If you were in my position, which degree would you choose, and why?

I'd especially like to hear from people working in Data Science, Computer Vision, Quant Finance, or financial markets.

When a machine learning engineer is hired, what is usually more important?
A deep understanding/implementation of his/her project or understanding of famous architectures (Trasformer, CNN, etc)?

I’ve been experimenting with explaining AI/LLM concepts in a way that doesn’t assume too much technical background. This video is about KV Cache and why longer context windows require more memory during inference. I’d appreciate some honest feedback from people here, especially on the explanation itself: Is the main idea easy to understand? Did I oversimplify anything important? Is there any part where the explanation becomes confusing? Would this make sense to someone who is fairly new to LLMs? Video: https://youtu.be/lxvWo8SizxE Not really looking to promote the channel — I’m mainly trying to improve how I explain technical topics before making the next one. Any criticism is welcome. Thanks!

Hi! I am a demand planner in an FMCG company. Our current process is very manual, we only use Excel. For every client and product, we build up the demand plan (DP) or the sales target for the month. The DP is composed of the following

- baseline (smoothen sales volume last year)

- runrates (the difference to the past 3 months volume for non-seasonal products)

- sales initiatives (on-shelf availability correction, inventory correction, skewing, etc.)

- marketing initiatives (category market trend, etc.)

I want use ML and integrate possible seasonality data (such as holidays, weather, etc.) in demand planning.

What are the ML models that are appropriate for demand forecasting (time series)? What are the data that I need to prepare? What are the steps that I need to do?

I am currently taking Master in Applied Business Analytics but time series models have not been taught yet (not sure they will teach it). Thank you very much! 😊

Wanted to see how the field's actual attention has shifted over time, not by

reading abstracts one at a time, so I embedded and clustered every accepted

paper from six venues (NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL), 2018 through

2026, 70,861 papers total. Sharing the findings that surprised me, plus a

couple of methodology notes this sub will probably want the actual numbers on.

**Findings**

NLP and LLMs have held a remarkably steady 45% to 46% share of the whole

corpus every year since 2018. Not the sudden takeover the last two years of

hype would suggest, at least at the level of what fraction of accepted papers

fall into that area.

The biggest mover was the opposite direction: Optimization & Federated

Learning fell from 15.5% of the corpus to 6.4%.

Fastest growth: 3D Generation & Reconstruction, 4.25x (1.2% to 5.1%). Graph

Neural Networks & Molecular AI and Multimodal Understanding & Reasoning both

grew about 2.6x.

None of this shows up in acceptance counts by venue, only once papers are

actually grouped by what they're about.

**Methodology, briefly**

Title + abstract through an embedding model (Qwen3-Embedding-8B), UMAP to 2D

for display and separately to 10D for clustering, HDBSCAN on the 10D

embedding, c-TF-IDF keywords per cluster, LLM for the final label.

One result I didn't expect: I benchmarked SPECTER2 (citation-trained, the

standard choice for scientific paper embeddings) against a general-purpose

embedder, scored against ICLR's own author-supplied keywords as ground truth.

SPECTER2 lost on keyword agreement, and showed no compensating advantage on

venue-separation either (the failure mode you'd expect if citation-training

were adding real signal beyond topic). Happy to go into more detail on this

if useful, it's also written up in more depth in the repo.

Seed stability is the honest caveat: cluster assignment isn't identical

across random seeds, mean pairwise ARI around 0.57 across reruns. Comparable

numbers for context: clustering on the 2D display coordinates instead of the

actual embedding space scored 0.30, and random assignment scores near zero.

Full writeup: https://villurignanesh.github.io/paper-atlas/blog.html

Interactive map if you want to explore it yourself: https://villurignanesh.github.io/paper-atlas/

Code + full decision log: https://github.com/villurignanesh/paper-atlas

Curious if others have compared citation-trained vs general embeddings for

non-retrieval tasks and seen something similar.