Effective Altruism Forum
EA Forum

Hide table of contents
by Matt Putz
1 min read5 answers 2

13

AI safetyArtificial intelligence
Frontpage

I'm looking for a way to make sure I reliably learn about the biggest developments such as Transformers, AlphaFold, or the grokking paper. I don't currently want to spend too much time on this, so optimal frequency would probably be monthly, though weekly is fine as well.

If you have other mechanisms of staying up to date with machine learning, I'd be curious to hear about those as well.

13

0
0

Reactions

0
0
New Answer
New Comment


6 Answers sorted by
Top

gwern

18
0
0

I subscribe to Import AI, Rohin Shah's Alignment newsletter (mostly via the LW/AF), ChinAI (weekly), Ruder's NLP (probably dead), Creative AI (annual), State of AI (annual), Larks (annual), miscellaneous blogs & subreddits (/r/machinelearning/, /r/mlscaling, /r/reinforcementlearning, /r/thisisthewayitwillbe/, being the main ones), and the 2 AKs on Twitter (Arxiv ~daily). If you need even more ML than that, well, you'd better set up an Arxiv RSS feed and drink from the firehose.

Matt Putz1
0
0

Super helpful, thanks for your answer!

Reply

nmulani

5
0
0

Curious to hear people's answers to this - it's a tough space to keep up to date in. For high-level summaries of technical developments, I like Import AI (https://jack-clark.net/) and Last Week in AI (https://lastweekin.ai/). But both can be weighted towards a policy/public affairs focus. There are likely newsletters that are better suited for purely keeping tabs on new research papers.

For an annual view of developments, I also like State of AI (https://www.stateof.ai/)

Matt Putz2
0
0

This is great, thanks!

Reply

electroswing

3
0
0

Yannic Kilcher's youtube channel profiles fairly recent papers / "ML news" events. The videos on papers are 30-60mins, so more in depth than reading an abstract, and less time consuming than reading the paper yourself. The "ML news" videos are less technical but still a good way to keep up to date on what DeepMind, Meta, NVIDIA, etc. are up to. 

anonymous6

2
0
0

The Transformers paper (Attention is All You Need) was only a poster at NIPS 2017 (not even a spotlight let alone an oral presentation). I don’t know if anyone at the time predicted the impact it would have.

It’s hard to imagine a newsletter that could have picked out that paper at the time as among the most important of the hundreds included. For comparison, I think probably that at the time, there was much more hype and discussion of Hinton and students’ capsule nets (also had a NIPS 2017 paper).

I think this is generally true of ML research. It’s usually very hard to predict impact in advance. You could probably do pretty well with 6 months to a year lag though.

I will recommend the TWIML podcast which interviews a range of good researchers, but not only on the biggest stuff.

gwern12
0
0

It’s hard to imagine a newsletter that could have picked out that paper at the time as among the most important of the hundreds included. For comparison, I think probably that at the time, there was much more hype and discussion of Hinton and students’ capsule nets (also had a NIPS 2017 paper).

People at the time thought it was a big deal: https://twitter.com/Miles_Brundage/status/1356083229183201281 Even the ones who were not saying it would be "radically new" or "spicy" or "this is going to be a big deal" or a "paradigm shift" were still at least asking if it might be (out of all the hundreds of things they could have been asking about but weren't).

Incidentally, I don't know if I count, but "Attention Is All You Need" was in my June 2017 newsletter & end-of-year best-of list (and capsule nets were not - I didn't like them, and still don't, it struck me as overly-hardwired and inflexible compared to existing attention methods even prior to Transformers, hardware-unfriendly, weak on toy problems, and essentially something only of interest because Hinton had been hinting at or talking about it for years; my opinion of CapsuleNets has not improved since*). So, I don't find it h... (read more)

Reply
2
anonymous6
Wow, that certainly is more “attention” than I remember at the time. I think filtering on that level of hype alone would still leave you reading way too many papers. But I can see that it might be more plausible for someone with good judgment + finger on the pulse to do a decent job predicting what will matter (although then maybe that person should be doing research themselves).

Arthur Conmy

1
0
0

For capabilities things, https://dblalock.substack.com/ is pretty good (though some things the author is very excited about I find underwhelming).

EDIT: weekly quick summaries of papers

Comments3
Sorted by
New & upvoted
Click to highlight new comments since:
Matt Putz7
0
0

(I accidentally asked multiple versions of this question at once.

This was because I got the following error message when submitting:

"Cannot read properties of undefined (reading 'currentUser')"

So I wrongly assumed the submission didn't work.

@moderators)

Reply
JP Addison🔸3
0
0

We've gotten multiple reports of this, and you're the first person to get the exact error message, thank you so much.

Reply
Matt Putz1
0
0

Very glad to have helped!

Reply
More from Matt Putz
44
[April fool's post] Proposal to assign careers by birthdate
Matt Putz
· · 2m read
7
7
View more
Curated and popular this week
LintzA
 ·  · 15m read
 · 
Cross-posted to Lesswrong Introduction Several developments over the past few months should cause you to re-evaluate what you are doing. These include: 1. Updates toward short timelines 2. The Trump presidency 3. The o1 (inference-time compute scaling) paradigm 4. Deepseek 5. Stargate/AI datacenter spending 6. Increased internal deployment 7. Absence of AI x-risk/safety considerations in mainstream AI discourse Taken together, these are enough to render many existing AI governance strategies obsolete (and probably some technical safety strategies too). There's a good chance we're entering crunch time and that should absolutely affect your theory of change and what you plan to work on. In this piece I try to give a quick summary of these developments and think through the broader implications these have for AI safety. At the end of the piece I give some quick initial thoughts on how these developments affect what safety-concerned folks should be prioritizing. These are early days and I expect many of my takes will shift, look forward to discussing in the comments!  Implications of recent developments Updates toward short timelines There’s general agreement that timelines are likely to be far shorter than most expected. Both Sam Altman and Dario Amodei have recently said they expect AGI within the next 3 years. Anecdotally, nearly everyone I know or have heard of who was expecting longer timelines has updated significantly toward short timelines (<5 years). E.g. Ajeya’s median estimate is that 99% of fully-remote jobs will be automatable in roughly 6-8 years, 5+ years earlier than her 2023 estimate. On a quick look, prediction markets seem to have shifted to short timelines (e.g. Metaculus[1] & Manifold appear to have roughly 2030 median timelines to AGI, though haven’t moved dramatically in recent months). We’ve consistently seen performance on benchmarks far exceed what most predicted. Most recently, Epoch was surprised to see OpenAI’s o3 model achi
Dr Kassim
 ·  · 4m read
 · 
Hey everyone, I’ve been going through the EA Introductory Program, and I have to admit some of these ideas make sense, but others leave me with more questions than answers. I’m trying to wrap my head around certain core EA principles, and the more I think about them, the more I wonder: Am I misunderstanding, or are there blind spots in EA’s approach? I’d really love to hear what others think. Maybe you can help me clarify some of my doubts. Or maybe you share the same reservations? Let’s talk. Cause Prioritization. Does It Ignore Political and Social Reality? EA focuses on doing the most good per dollar, which makes sense in theory. But does it hold up when you apply it to real world contexts especially in countries like Uganda? Take malaria prevention. It’s a top EA cause because it’s highly cost effective $5,000 can save a life through bed nets (GiveWell, 2023). But what happens when government corruption or instability disrupts these programs? The Global Fund scandal in Uganda saw $1.6 million in malaria aid mismanaged (Global Fund Audit Report, 2016). If money isn’t reaching the people it’s meant to help, is it really the best use of resources? And what about leadership changes? Policies shift unpredictably here. A national animal welfare initiative I supported lost momentum when political priorities changed. How does EA factor in these uncertainties when prioritizing causes? It feels like EA assumes a stable world where money always achieves the intended impact. But what if that’s not the world we live in? Long termism. A Luxury When the Present Is in Crisis? I get why long termists argue that future people matter. But should we really prioritize them over people suffering today? Long termism tells us that existential risks like AI could wipe out trillions of future lives. But in Uganda, we’re losing lives now—1,500+ die from rabies annually (WHO, 2021), and 41% of children suffer from stunting due to malnutrition (UNICEF, 2022). These are preventable d
Rory Fenton
 ·  · 6m read
 · 
Cross-posted from my blog. Contrary to my carefully crafted brand as a weak nerd, I go to a local CrossFit gym a few times a week. Every year, the gym raises funds for a scholarship for teens from lower-income families to attend their summer camp program. I don’t know how many Crossfit-interested low-income teens there are in my small town, but I’ll guess there are perhaps 2 of them who would benefit from the scholarship. After all, CrossFit is pretty niche, and the town is small. Helping youngsters get swole in the Pacific Northwest is not exactly as cost-effective as preventing malaria in Malawi. But I notice I feel drawn to supporting the scholarship anyway. Every time it pops in my head I think, “My money could fully solve this problem”. The camp only costs a few hundred dollars per kid and if there are just 2 kids who need support, I could give $500 and there would no longer be teenagers in my town who want to go to a CrossFit summer camp but can’t. Thanks to me, the hero, this problem would be entirely solved. 100%. That is not how most nonprofit work feels to me. You are only ever making small dents in important problems I want to work on big problems. Global poverty. Malaria. Everyone not suddenly dying. But if I’m honest, what I really want is to solve those problems. Me, personally, solve them. This is a continued source of frustration and sadness because I absolutely cannot solve those problems. Consider what else my $500 CrossFit scholarship might do: * I want to save lives, and USAID suddenly stops giving $7 billion a year to PEPFAR. So I give $500 to the Rapid Response Fund. My donation solves 0.000001% of the problem and I feel like I have failed. * I want to solve climate change, and getting to net zero will require stopping or removing emissions of 1,500 billion tons of carbon dioxide. I give $500 to a policy nonprofit that reduces emissions, in expectation, by 50 tons. My donation solves 0.000000003% of the problem and I feel like I have f
Recent opportunities in AI safety
3
Apply to become a Futurekind AI Facilitator or Mentor (deadline: April 10)
Jay Luong
· · 2m read
0
0
30
Apply to MATS 8.0!
Ryan Kidd
·
0
0
35
AIS Netherlands is looking for a Founding Executive Director (EOI form)
7 authors
· · 5m read
4
4
View more