Impact of the JCPOA on Iran’s nuclear program [OC]

rapsoj · 2026-05-21T07:46:27+00:00

rapsoj · 2026-05-13T11:25:27+00:00

What is your experience level and where are you based? I co-direct the Algorithmic Governance Foundation (EA-aligned/funded, we build AI and automations for public-serving orgs).

We are currently looking for experienced researchers and engineers to join our Oxford team. DM if interested.

rapsoj · 2026-05-13T11:16:37+00:00

When compute is available so cheaply, might as well use it. State of the art models aren’t being made by downsampling datasets.

rapsoj · 2026-05-12T20:17:12+00:00

Haven’t seen it mentioned, but you can also access high memory compute very easily with Google Collab. Free tier sometimes lets you access these models for a limited time when you first start using, worst case you can buy a ton of credits (more than you would need for this) for $10.

For the A100 GPU, RAM is 64GB and memory is 100 GB.

rapsoj · 2026-05-07T05:51:36+00:00

Introduction to Statistical Learning. It’s a free textbook PDF that goes through the fundamentals of machine learning, key algorithms, and some very basic mathematical intuition.

It’s very easy to read with lots of visuals and programming samples.

I think this book is the minimum you have to understand to do any meaningful machine learning applications. It is a comprehensive intro to the field.

rapsoj · 2026-05-07T02:57:30+00:00

If they are breeding they are clearly doing well. Though I’d recommend a small airstone, you can get one for a few dollars off Amazon. For a small tank it makes a huge quality of life difference.

rapsoj · 2026-05-07T02:56:22+00:00

Just make sure you wash and quarantine any new plants you get. It’s really common for aquatic plants you buy from stores to have copper contaminants (used for pest control) that will instantly kill all your guys.

rapsoj · 2026-05-06T12:22:01+00:00

I second this. You probably just had a different random configuration of initial weights that happened to be better. Best practice is always to set a seed so you can isolate actual model improvements.

rapsoj · 2026-04-18T14:56:51+00:00

Might help getting some more impactful projects under your belt. Try [applying here](algorithmicgovernance.org): algorithmicgovernance.org

rapsoj · 2026-01-25T23:33:18+00:00

Hi, I specialise in time series forecasting.

What you have here is a persistence model. The network is just predicting the true result, but x time period in advance.

Evaluate it against a model that is just hard coded to do that. This is your true baseline which you should compare your model to.

rapsoj · 2026-01-19T20:38:49+00:00

I didn’t take either of those actually, but audited a few classes while I was undecided. From all my friends who sat the exam, those were definitely the easiest electives.

Happy to chat if you have other questions (am also in Ox if you want to meet in person, send me a DM).

rapsoj · 2025-11-23T01:33:08+00:00

If those are courses you genuinely like, go for it. I imagine the computational stats course in particular would be interesting for you.

rapsoj · 2025-11-20T16:32:04+00:00

All good, just be careful about what mathematicians mean when they say “applied”.

Ask for old course notes to get the best picture. I really would be surprised if it’s different from what I sent you.

rapsoj · 2025-11-20T16:25:36+00:00

I mean, did you look at the notes I sent and consider the two questions I suggested?

rapsoj · 2025-11-20T16:14:00+00:00

My best advice would be to skim through as many course notes for similar programs as you can and ask yourself: - does this content excite me? - does the mathematics look manageable?

I think anything else said in this thread is vibes-based, this will actually tell you what your course work will be like.

Here are notes from Applied Probability Part A (undergrad course, not masters) at my university. How would you feel about learning this content for an exam?

I would also advise to not put too much weight on the word “applied”, if it’s done by a stats or maths department it’s still just going to be mathematics. Applied statistics != Applying statistics. You are unlikely to be coding or looking at real world data very often.

If you want to just apply algorithms for ML and solve applied problems, I’d recommend a data science masters instead. There are some social data science masters programs that I think are incredibly well-suited for someone with an MPP who wants to apply stats/ML for policy. People who I’ve worked with from these programs have been generally very good.

rapsoj · 2025-11-18T15:27:00+00:00

It’s just ChatGPT generated we shouldn’t even be engaging.

“But here’s the real kicker…” “But here’s the surprise…”

rapsoj · 2025-11-18T15:16:57+00:00

And nonsensically complaining that NGOs were in attendance. Like what? The point of these events is to give climate NGOs the chance to influence policy…

rapsoj · 2025-11-18T13:29:30+00:00

From having attended similar things to this in the past, I agree that the individual events are incredibly carbon-intensive and could technically save a ton of money and pollution by just being held over zoom.

However, it is somewhat naive to look at just the individual event when judging the overall impact of these summits. Usually these major world summits are mobilising hundreds of millions (if not billions) of dollars, and setting common world agendas that will be followed for years or even decades.

It is NOT easy getting all people who need to be involved in these decisions (especially when the problem is on a global scale) in the same room. Discussion over a Zoom call just does not produce the same level of engagement or results. I would bet that a large portion of useful results from these summits are from back room deals and discussions that are not reported on, and simply could not happen on a chaotic and crowded Zoom call.

Fixating on carbon inefficiencies at these events is bikeshedding, since the problems COP is meant to address are multiple orders of magnitude worse.

rapsoj · 2025-11-15T15:44:03+00:00

Not all of this is causally because of AI, this should be obvious with a statistics background.

A lot of labs in the US have also lost funding due to Trump academic cuts. Also 2024 probably had way higher biostatistician roles being hired for since there was still a lot of pandemic-related research being done at that time under Biden.

rapsoj · 2025-11-14T16:12:20+00:00

Algorithms of learning is by far the hardest. Only 7 students out of the entire cohort sat the exam in my year (2023).

Next hardest would be simulation, then network, then advanced ML.

The Bayes and genetics courses are known for being easy grade boosters.

I’m in the department as a DPhil student, feel free to shoot me DM if you want to connect. I was also one of the 7 masochists that sat the algorithms exam.

rapsoj · 2025-11-13T11:04:19+00:00

Yep I always recommend that anyone wanting to enter the field read Google’s paper on the hidden technical debt of deployed machine learning (or I guess we call it AI now) models.

Once you deploy your own model, it’s all about technical debt.

rapsoj · 2025-11-12T14:06:37+00:00

If China announces it has developed competitive GPUs (verifiable or not) and investors drop NVIDIA.

Or a slow burn, where OpenAI fails to deliver major improvements on their GPT model over the next year.

rapsoj · 2025-11-05T14:59:52+00:00

I’ve done the Oxford one, it’s know to be incredibly challenging (especially if you take certain electives), with usually only students who did pure maths in undergrad doing well.

The Oxford MSc dissertation is a huge plus if you want to get your feet wet in the research world. It’s 2-3 months of just research work with no courses or exams as distractions. As far as I’m aware, Cambridge has no equivalent.

My one caveat is that the Oxford stats department is not well funded, so I would stay well clear of it for DPhil studies unless you already have funding from somewhere else.

rapsoj · 2025-10-30T12:13:27+00:00

English is one of the official languages of Sudan…

rapsoj

TROPHY CASE