[D] Keeping track of ML advancements

Submitted by Anis_Mekacher t3_121mvp5 in MachineLearning

General ML question, how do you guys keep track of all the advancements made in AI and the flood of papers coming out?

I'm pretty new to AI, and although I've been following the developments since 2016, I only started taking it seriously and doing development last year. I just started my master's in ML and want to keep up with the developments made in the field. But it feels like a new paper, blog post, or conference gets released with astonishing improvements every second day. With 20 hours of work a week and my studies, I don't seem to catch up with everything going on. So I'm wondering how others are dealing with it.

Questions:

  • Do you read all of the papers/blog posts that get released?
  • The ones you read, do you read them in detail or just skim over them or look for a TLDR?
  • Do you filter only the papers in the topics you're interested in?
  • Is there any website with a clear overview and development of models? I know about paperswithcode[.]com, but I'm looking more for a website with a chronological timeline of the models released and their previous versions and related developments, etc...
  • Is it important that I stay up-to-date with everything going on in the field ?

Many thanks to anyone who responds !!

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Comments

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These-Assignment-936 t1_jdmpulh wrote

We have a book/paper club going at work where the engineers present a recent publication

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Anis_Mekacher OP t1_jdmrjpc wrote

Replying to These-Assignment-936 (#2,354,132)

That's a great idea. Is it something like a bi/weekly meeting where you get to explain the main concepts and ideas behind a paper in a short amount of time?

It won't work in my case, because my current job is more or less in the cybersecurity field and not a lot of people in my company are interested in AI or its developments.

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These-Assignment-936 t1_jdmrqcp wrote

Replying to Anis_Mekacher (#2,354,325)

Yes. Weekly. But we have a huge team.

There’s always people interested. Maybe not always AI engineers.

You could do a series where people summarize accessible papers. A lot of literature review papers are readable even by a layperson

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MrFlufypants t1_jdpt0vm wrote

We do a journal series at work. Rule is every engineer has to do one before we get to do another one. Gives presenting skills and forces us to hear new stuff since we all have different preferences.

Big issue is that recently many of the coolest advancements have been by Facebook, openai, and google and they are increasingly releasing “reports” instead of “papers”. We are getting a lot more “And then they did this incredibly revolutionary thing but only said they used a ‘model’”. They aren’t giving details because they want to keep their work private. Big bummer.

I also read any papers that make the top of this sub, and I’ll usually read a couple of the best performing papers from the big conferences

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Anis_Mekacher OP t1_jdq4r88 wrote

Replying to MrFlufypants (#2,365,885)

>>> We do a journal series at work. Rule is every engineer has to do one before we get to do another one. Gives presenting skills and forces us to hear new stuff since we all have different preferences.

Is it "open source" for anyone to access? I think such blogs are an excellent advertisements for the companies

>>> Big issue is that recently many of the coolest advancements have been by Facebook, openai...

I've noticed that trend too, it's disappointing, especially when considering that most of these companies' AI teams were built on published papers and open-source stuff

>>> I also read any papers that make the top of this sub, and I’ll usually read a couple of the best performing papers from the big conferences

I've been doing that too and tbh. I like to take my time and read papers thoroughly doing that for each paper that reaches the top of this sub is pretty time-consuming, but overall this sub is an amazing resource to start.

thanks !!

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Necessary_Ad_9800 t1_jdq69kz wrote

Is there website that brings out newsletters or YouTube channels talking about weekly news?

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NovelspaceOnly t1_jdqx9t2 wrote

This might sound a bit corny. I try to have a sparse BFS understanding of the field at any given time and a DFS on topics I'm interested in like interpretability, NLP, and GNNs.

Four things that I think are important are - contributing to open source, joining discord communities, at the very min "skimming" papers(reading abstracts, conclusions, and charts), and I also topic model researchers' Github repos i find on paperswithcode. As a 5th - ML Twitter if you can maintain your sanity.

The sixth sense and the most important one is to have a strong math background, IMO it is the most important aspect that helps generalize new research. grok linear algebra, probability, and calc. Mostly linear algebra though because the tensor notation really helps with probability and functional analysis. A lot of physics can be understood through the lens of tensor analysis and probability.

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theogognf t1_jdqy28l wrote

I stay up-to-date mainly by browsing https://paperswithcode.com/ in the morning and once a week at work. There have definitely been a good number of times that I stumble across some new method or repo to play around with for my main area of interest that ends up having some immediate return. I occasionally browse by all topics there, but I usually only filter by my main interests. I can't imagine staying current without some other similar site

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aozorahime t1_jdv9sd2 wrote

only get updated from Twitter since I follow some prominent AI people there. If I got some interesting topic related to my research interest, I read the papers, mostly skimming, and starred their repo also. Whenever I have free time (mostly weekends), I will focus to study their findings.

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