Interested in a HN-like source of information and discussions on AI news. Ideally it would include slightly more in-depth and in the weeds discussions on AI research and developments, while staying away from basic news stories and applications.
<a href="https://paperswithcode.com/" rel="nofollow">https://paperswithcode.com/</a> is arguably the best source and overview of all the research. Its also (somewhat) unbiased (owned by Meta), not being an SEO-optimised company blog.
Not "HN-like", but I have found Simon Willison's blog/newsletter very helpful:
- <a href="https://simonwillison.net" rel="nofollow">https://simonwillison.net</a>
- <a href="https://simonw.substack.com" rel="nofollow">https://simonw.substack.com</a>
My take goes against most of the other comments here – don't keep up. It's not practical, the amount of new information and development is too much to process.
I have a daily workflow of scanning r/ML and HN and I subscribe to a few newsletters that I came across. I save bookmarks of tools and repos to raindrop.io and articles to readwise/reader. One good trick is to use the readwise feed email when subscribing to newsletters, so the newsletters go to Readwise instead of your personal email.<p>My big unsolved problem is Twitter — how do I avoid going on twitter more than a half hour a day, by using some type of twitter based filter/aggregator?
Labml daily is a relatively good trend aggregator informed by Twitter. But I still keep discovering interesting things on Twitter not covered by any of the above. And BTW I bookmark twitter threads to Readwise/reader as well.
Zvi Mowshowitz's blog.<p>He has recently started posting incredibly detailed weekly AI roundups. Here's one from yesterday:<p><a href="https://thezvi.wordpress.com/2023/04/06/ai-6-agents-of-change/" rel="nofollow">https://thezvi.wordpress.com/2023/04/06/ai-6-agents-of-chang...</a>
I subscribe to The Neuron which keeps me reasonably informed in a short time amount of time: <a href="https://www.theneurondaily.com" rel="nofollow">https://www.theneurondaily.com</a>
"hijacking" the post to ask where I can find a good introduction to machine learning and AI. Not how to use this or this library but the fundamentals and principles behind. Preferably something explaining clearly the principles first then explaining the maths (from the beginning, my maths are quite far now) then showing practical usage/development (in any high level language like python or julia). I do not need to jump straight to the latest algorithms, I prefer starting with building bricks first
Updates like these[1] posted regularly to the ChatGPT subreddit are pretty informative.<p>The real challenge is finding the time to read them all.<p>[1] - <a href="https://www.reddit.com/r/ChatGPT/comments/12diapw/gpt4_week_3_chatbots_are_yesterdays_news_ai/" rel="nofollow">https://www.reddit.com/r/ChatGPT/comments/12diapw/gpt4_week_...</a>
I recently started an AI news aggregator here: <a href="https://ainewsfeed.io" rel="nofollow">https://ainewsfeed.io</a><p>I am planning on adding more feeds very soon to increase the amount of content<p>I also have an aggregator for
Cybersecurity: <a href="https://cyberfeed.io" rel="nofollow">https://cyberfeed.io</a>
Kala - AI/ML weekly (<a href="https://faun.dev/newsletter/kala" rel="nofollow">https://faun.dev/newsletter/kala</a>)<p>You'll find both curated news, stories, tutorials, tools, and in-depth content.<p>Disclaimer: I'm the curator of this newsletter.
Lots of sources. However, Last Week in AI has been a great podcast since I started listening a couple months ago. Like covid, beware of resources that only started covering AI because it's trendy lately. They quickly summarize and discuss papers and news.
For years I have followed top researchers on Twitter and helped quite a bit to stay up to date on the topic. Today I think it's still quite good for that purpose, although the countless way that Musk is trying to make it worse...
<a href="https://www.aitracker.org/" rel="nofollow">https://www.aitracker.org/</a> is good for a general audience, but doesn't go into as much details as some of the more research-oriented roundups.
This is a bit more product focused, but I've found it useful: <a href="https://www.latent.space/" rel="nofollow">https://www.latent.space/</a><p>It's a newsletter/podcast.
This weekly newsletter is excellent:
<a href="https://www.deeplearning.ai/the-batch/" rel="nofollow">https://www.deeplearning.ai/the-batch/</a>
I check out <a href="https://papers.labml.ai/" rel="nofollow">https://papers.labml.ai/</a> semi-frequently to see what research twitter is talking about.
While we are on the topic, can somebody give a TLDR what breakthroughs made current AI advancements? From what I understand the "foundation" is exactly the same as it was 40 years ago - same neural networks, same activation functions, same architectures, same gradient descent. If I ask some "skeptical" crowd they say: "nothing is new, we just started using GPUs". Some say there were breakthroughs in learning algorithms to facilitate deep learning (i.e. that features are trained and learned by deeper layers automatically). Can someone elaborate on this, please? I tried googling and I only get crap articles that just "wave hands"
TLDR has an AI-specific newsletter you can sign up for:<p><a href="https://tldr.tech/ai" rel="nofollow">https://tldr.tech/ai</a>
various discord channels if you want the latest. As much as I hate discord's UI and ecosystem, it's value in up to date information about AI can't be matched.