Body: <p>With over 100 papers published in the area of artificial intelligence, machine learning and their subfields every day (<a href="http://data-mining.philippe-fournier-viger.com/too-many-machine-learning-papers/" rel="nofollow noreferrer">source</a>), accounting for ~3% of all publications world wide per year (<a href="https://hai.stanford.edu/sites/default/files/ai_index_2019_report.pdf" rel="nofollow noreferrer">source</a>) and dozens of annual conferences like NeurIPS, ICML, ICLR, ACL, ... I wonder how you keep up with the current state of the art and latest developments? The field is progressing very fast, models that were considered SOTA not even a decade ago are now (almost) outdated (<a href="https://arxiv.org/pdf/1706.03762.pdf" rel="nofollow noreferrer">Attention Is All You Need</a>). A lot of this progress is driven by big tech companies (<a href="https://hai.stanford.edu/sites/default/files/ai_index_2019_report.pdf" rel="nofollow noreferrer">source</a>), e.g. 12% of all papers accepted at NeurIPS 2019 have at least one author from Google and DeepMind (<a href="https://huyenchip.com/2019/12/18/key-trends-neurips-2019.html" rel="nofollow noreferrer">source</a>).</p>
  <p>My strategy is to read blogs and articles to maintain a general overview and not to miss any important breakthroughs. To be up to date in subfields of my own interest, I read specific papers once in a while. What are your personal strategies? Continuous education is a big keyword here. It's not about understanding every detail and being able to reproduce results, but rather maintaining a bird's eye view, having an idea about the direction of research and knowing whats already possible.</p>
  <p>To name a few of my preferred sources there are the research blogs of the big players: <a href="https://openai.com/blog/" rel="nofollow noreferrer">OpenAI</a>, <a href="https://deepmind.com/blog" rel="nofollow noreferrer">DeepMind</a>, <a href="https://ai.googleblog.com/" rel="nofollow noreferrer">Google AI</a>, <a href="https://ai.facebook.com/blog/?page=1" rel="nofollow noreferrer">FAIR</a>. Further there are very good personal blogs with a more educational character, like the well known one of <a href="https://colah.github.io/" rel="nofollow noreferrer">Christopher Olah</a>, the recently started one of <a href="https://yoshuabengio.org/" rel="nofollow noreferrer">Yoshua Bengio</a> and the one from <a href="http://jalammar.github.io/" rel="nofollow noreferrer">Jay Alammar</a>. Unfortunately finding personal blogs is hard, it often depends on luck and referrals, also the update frequency is generally lower since these people have (understandably) other important things to do in life as well.</p>
  <p>Therefore I'm always looking for new sources, which I can bookmark and read later, if I like to avoid doing other stuff.</p>
  <p><strong>Can you name any other personal / corporate research blogs or news websites that publish latest advances in ML &amp; AI?</strong></p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-07-21T10:53:39.770Z
Comments.Text: It's an interesting subject, but any question here that asks "What are your favorite..." is going to be off-topic sadly. If there's any way you could [edit] the question to make it more objective and suitable for the site, that would be better.
Comments.UserDisplayName: Neil Slater
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-07-21T11:42:02.350Z
Comments.Text: I edited my question and hopefully removed some of it's former subjectivity.
Comments.UserDisplayName: Tinu
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-07-21T12:20:14.293Z
Comments.Text: Well it is less subjective, but still hard to fit in the format. I guess a community wiki answer might work, if there is interest to put together a list for an answer.
Comments.UserDisplayName: Neil Slater
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-07-21T13:01:57.847Z
Comments.Text: This seems to be a duplicate of https://ai.stackexchange.com/q/13418/2444. Can you confirm that?
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-07-21T15:21:53.267Z
Comments.Text: The content of the question is similar, but the answer focuses less on blogs and more on pages gathering the latest papers.
Comments.UserDisplayName: Tinu
Tags: <machine-learning><research><papers><state-of-the-art><ai-development>
Title: Ways to keep up with the latest developments in Machine Learning and AI?
UserDisplayName: Tinu
ThreadText: # Ways to keep up with the latest developments in Machine Learning and AI?
  
  (Asked by: username_0 on 2020-07-21)
  
  With over 100 papers published in the area of artificial intelligence, machine learning and their subfields every day ([source](http://data-mining.philippe-fournier-viger.com/too-many-machine-learning-papers/)), accounting for ~3% of all publications world wide per year ([source](https://hai.stanford.edu/sites/default/files/ai_index_2019_report.pdf)) and dozens of annual conferences like NeurIPS, ICML, ICLR, ACL, ... I wonder how you keep up with the current state of the art and latest developments? The field is progressing very fast, models that were considered SOTA not even a decade ago are now (almost) outdated ([Attention Is All You Need](https://arxiv.org/pdf/1706.03762.pdf)). A lot of this progress is driven by big tech companies ([source](https://hai.stanford.edu/sites/default/files/ai_index_2019_report.pdf)), e.g. 12% of all papers accepted at NeurIPS 2019 have at least one author from Google and DeepMind ([source](https://huyenchip.com/2019/12/18/key-trends-neurips-2019.html)).
  
  My strategy is to read blogs and articles to maintain a general overview and not to miss any important breakthroughs. To be up to date in subfields of my own interest, I read specific papers once in a while. What are your personal strategies? Continuous education is a big keyword here. It's not about understanding every detail and being able to reproduce results, but rather maintaining a bird's eye view, having an idea about the direction of research and knowing whats already possible.
  
  To name a few of my preferred sources there are the research blogs of the big players: [OpenAI](https://openai.com/blog/), [DeepMind](https://deepmind.com/blog), [Google AI](https://ai.googleblog.com/), [FAIR](https://ai.facebook.com/blog/?page=1). Further there are very good personal blogs with a more educational character, like the well known one of [Christopher Olah](https://colah.github.io/), the recently started one of [Yoshua Bengio](https://yoshuabengio.org/) and the one from [Jay Alammar](http://jalammar.github.io/). Unfortunately finding personal blogs is hard, it often depends on luck and referrals, also the update frequency is generally lower since these people have (understandably) other important things to do in life as well.
  
  Therefore I'm always looking for new sources, which I can bookmark and read later, if I like to avoid doing other stuff.
  
  **Can you name any other personal / corporate research blogs or news websites that publish latest advances in ML & AI?**
  
  **Tags:** machine-learning, research, papers, state-of-the-art, ai-development
  > **Comments:**
  > **username_1**: It's an interesting subject, but any question here that asks "What are your favorite..." is going to be off-topic sadly. If there's any way you could [edit] the question to make it more objective and suitable for the site, that would be better.  
  > **username_0**: I edited my question and hopefully removed some of it's former subjectivity.  
  > **username_1**: Well it is less subjective, but still hard to fit in the format. I guess a community wiki answer might work, if there is interest to put together a list for an answer.  
  > **username_2**: This seems to be a duplicate of https://ai.stackexchange.com/q/13418/2444. Can you confirm that?  
  > **username_0**: The content of the question is similar, but the answer focuses less on blogs and more on pages gathering the latest papers.  
  
  ---
  
  ## 0 answers
  
  