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Ask HN: What's your best resource for keeping up-to-date on AI developments?
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.
- ulrikhansen54 4y agohttps://paperswithcode.com/ https://paperswithcode.com/ 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.
- deleted 4y ago[deleted]
- auggierose 4y agoDo they just pick papers by themselves, selecting from already published papers?
- ulrikhansen54 4y agoThey pull everything from various e-print archives (arXiv, ResearchGate, etc.) AFAIK Edit: Everything tagged with AI/ML
- Jaxan 4y agoI am not sure reading 25 papers per day is a good way of staying up to date.
- polynomial 4y agoOh it's a great way, if you have the time. Therein lies the problem.
- danesparza 4y agoC'mon. That's only 4 papers an hour! :-)
- brudgers 4y agoThe average academic paper is about 12-14 pages including citations. So on average probably 50 pages an hour. With practice, that's entirely reasonable. Without practice it's a lot. Reading technical literature is a skill that develops over time. At first it goes slow. Then it gets normal. At first it might take an hour to read a 12 page paper. After a few months, fifteen minutes becomes enough to read a paper and make a cup of coffee.
- pixl97 4y agoReminds me of a reddit meme I saw a few weeks ago of a picture of a boxer trying to keep up with the rate of change and getting tired out, then saying "damn, singularity got hands"
- ReDeiPirati 4y ago+1 to this, plus how many of those papers are pure bullsh*t? In the last years, I saw lot of crap just for the sake of publishing in AI.
- reaperman 4y agoA lot of the latest high-performing models aren't making it to HN. I use paperswithcode by scanning major tasks for new models that come out #1 across multiple benchmarks and then reading those papers. BeIT-3 for example.
- Veen 4y agoThat'd be great if it had an RSS feed.
- zoenolan 4y agoSome unofficial feeds exist https://github.com/ml-feeds/pwc-feeds https://github.com/ml-feeds/pwc-feeds
- agomez314 4y agoI subscribe to The Neuron which keeps me reasonably informed in a short time amount of time: https://www.theneurondaily.com https://www.theneurondaily.com
- poulpy123 4y ago"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
- Zealotux 4y agoYou might be interested in Andrej Karpathy's intro to GPT https://www.youtube.com/watch?v=kCc8FmEb1nY https://www.youtube.com/watch?v=kCc8FmEb1nY and course https://karpathy.ai/zero-to-hero.html https://karpathy.ai/zero-to-hero.html
- auggierose 4y agohttps://karpathy.ai/zero-to-hero.html https://karpathy.ai/zero-to-hero.html https://course.fast.ai https://course.fast.ai
- tomduncalf 4y agoThe usual recommendations I think are: Andrew Ng’s Coursera for the fundamentals, Andrej Kaparthy’s videos (https://karpathy.ai/zero-to-hero.html https://karpathy.ai/zero-to-hero.html) for more practical and LLM focussed, and also Fast.ai’s courses. I’ve done some of the first two and they seem great.
- Al0neStar 4y agoI recommend the books by Joe Suzuki https://bayesnet.org/books/ https://bayesnet.org/books/ it teaches all the math and no libray/framework is used. edit: the readers page is not up to date the last books are available.
- ngc248 4y agoPedro Domingo's CSEP 546 course for the fundamentals https://www.youtube.com/playlist?list=PLTPQEx-31JXgtDaC6-3HxWcp7fq4N8YGr https://www.youtube.com/playlist?list=PLTPQEx-31JXgtDaC6-3Hx...
- ksplicer 4y agoI check out https://papers.labml.ai/ https://papers.labml.ai/ semi-frequently to see what research twitter is talking about.
- aldarisbm 4y agohttps://www.emergentmind.com/ https://www.emergentmind.com/
- ubj 4y agoTLDR has an AI-specific newsletter you can sign up for: https://tldr.tech/ai https://tldr.tech/ai
- deleted 4y ago[deleted]
- ReDeiPirati 4y agoFor 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...
- gronky_ 4y agoCan you share a few of your best twitter follows for AI?
- ReDeiPirati 4y agoSure here are a couple: - Andrej Karpathy: https://twitter.com/karpathy https://twitter.com/karpathy - David Ha: https://twitter.com/hardmaru https://twitter.com/hardmaru - Yann LeCun: https://twitter.com/ylecun https://twitter.com/ylecun - Jeremy Howard: https://twitter.com/jeremyphoward https://twitter.com/jeremyphoward - Riley Goodside: https://twitter.com/goodside https://twitter.com/goodside
- AdrienBrault 4y agoNot "HN-like", but I have found Simon Willison's blog/newsletter very helpful: - https://simonwillison.net https://simonwillison.net - https://simonw.substack.com https://simonw.substack.com
- A_D_E_P_T 4y agoZvi Mowshowitz's blog. He has recently started posting incredibly detailed weekly AI roundups. Here's one from yesterday: https://thezvi.wordpress.com/2023/04/06/ai-6-agents-of-change/ https://thezvi.wordpress.com/2023/04/06/ai-6-agents-of-chang...
- quaintdev 4y agoMan I wish bloggers enabled RSS feeds because once I read this it's really hard to find them again or any of their future updates.
- input_sh 4y agoIt's wordpress, just go to example.com/rss or example.com/feed and voila!
- fock 4y agoa blog which in the past speculated about Covid, Bird flu and now tells stories about tracts around generative methods. I would not classify this as keeping up with AI in the tech sense.
- A_D_E_P_T 4y agoZvi's updates are very comprehensive and detailed -- and his commentary is excellent, because he combines intellectual curiosity with a careful scrupulousness for factual accuracy. And his mind has some interesting corners. I find that I always come away learning a thing or two from his updates -- and feel as though I'm keeping up with at least those developments which relate to commercially-available AI. His blog is not a repository of scientific work like aRxiv, but more like a curated summary of AI news. It is, after all, a blog.
- artemonster 4y agoWhile 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"
- ryanwaggoner 4y agoThis is a good question for ChatGPT
- PeterStuer 4y agoHere you go. Sure, I can provide a brief overview of the key breakthroughs and advancements that have contributed to the current state of AI, particularly in the domain of deep learning. 1. Availability of data: The explosion of digital data, especially from the internet, has provided a massive amount of training data for AI models. This has allowed AI systems to learn patterns, features, and representations from various data sources more effectively than before. 2. Hardware improvements: The introduction of GPUs (Graphics Processing Units) and specialized hardware, like TPUs (Tensor Processing Units), has significantly accelerated the training of large neural networks. These advancements enable researchers to experiment with larger and more complex models, leading to improved performance. 3. Algorithmic innovations: Key algorithmic advancements have been made to train deep neural networks more efficiently. Some notable examples include: a. Backpropagation: This algorithm is used to train neural networks by minimizing the loss function through gradient descent. Although it was introduced in the 1980s, it became more widely used and optimized in recent years. b. Activation functions: Non-linear activation functions like ReLU (Rectified Linear Unit) have been crucial in addressing the vanishing gradient problem and improving training efficiency in deep networks. c. Dropout: This regularization technique helps prevent overfitting by randomly dropping out neurons during training, encouraging the network to learn more robust features. 4. Architectural advancements: The development of various neural network architectures has led to improved performance in specific tasks. Some prominent architectures include: a. Convolutional Neural Networks (CNNs): These networks are especially effective at image recognition tasks due to their ability to capture spatial patterns and hierarchical features. b. Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM): These architectures excel at handling sequence data, such as time series or natural language processing tasks. c. Transformers: Introduced in 2017, the transformer architecture has become a key component in state-of-the-art natural language processing models like BERT and GPT, due to its self-attention mechanism and ability to handle long-range dependencies. 5. Transfer learning and pre-training: Instead of training models from scratch, researchers have found it effective to pre-train models on large datasets, followed by fine-tuning them on specific tasks. This approach reduces training time, requires less labeled data, and often leads to better performance. These breakthroughs and advancements, combined with a growing research community and increased investment in AI, have led to the current state of AI, where deep learning models can achieve human-level or near-human-level performance on a variety of tasks.
- d4rkp4ttern 4y agoI 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. 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.
- Veen 4y agoI used to deal with the "avoiding going on Twitter" problem by subscribing to interesting AI Twitter feeds in Feedbin, an RSS aggregator. Unfortunately that doesn't work any more because the "genius" in charge revoked Feedbin's Twitter API access a week or so ago. So now I don't check Twitter at all. https://feedbin.com/blog/2023/03/30/twitter-access-revoked/ https://feedbin.com/blog/2023/03/30/twitter-access-revoked/
- ipaddr 4y agoThe genius didn't lose anything here. If you never go on twitter you are a feedbin user. You make twitter no money. No one likes a mooch.
- 411111111111111 4y agoYou gotta be trolling, nobody is that dumb... The people posting on Twitter do it to be heard by others. As the audience decreases, the significance of the platform decreases. Thus, people stop posting on said platform and use other avenues to get their voices heard. As users drop off, advertisers leave, removing a large part of their revenue. Your claim is so completely out of touch with reality...
- ipaddr 4y ago
- zulban 4y agoLots 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.
- edouard-harris 4y agohttps://www.aitracker.org/ https://www.aitracker.org/ is good for a general audience, but doesn't go into as much details as some of the more research-oriented roundups.
- anonzzzies 4y agoHN. It’s curated by the smartest minds. And Arvix but that’s very work intensive.
- gronky_ 4y agoHN with tags and filters would be great
- muzani 4y agoHN lies on the Early Majority border of the innovation curve. It's also highly resistant to new tech of all sorts and tries to bury them. HN is okay for things you don't watch so you don't miss out on anything cool. It used to be 1-2 years behind the curve in the GPT-3 era, but now that things are moving faster, it's only around 3 months or so behind.
- benrapscallion 4y agoSynced [1] https://syncedreview.com/ https://syncedreview.com/
- adt 4y agoThe Memo: https://lifearchitect.ai/memo/ https://lifearchitect.ai/memo/
- pmoriarty 4y agoUpdates like these[1] posted regularly to the ChatGPT subreddit are pretty informative. The real challenge is finding the time to read them all. [1] - https://www.reddit.com/r/ChatGPT/comments/12diapw/gpt4_week_3_chatbots_are_yesterdays_news_ai/ https://www.reddit.com/r/ChatGPT/comments/12diapw/gpt4_week_...
- counttheforks 4y agoIs it just me, or is this post complete nonsense? This AI hype seems to be proliferated by people who have never programmed more than 1kloc in their lives. But maybe that's the point? For example: > “babyagi” is a program that given a task, creates a task list and executes the tasks over and over again. It’s now been open sourced and is the top trending repos on Github atm [Link]. Helpful tip on running it locally [Link] The babyagi project is is an extremely simple 180 line python script. The tips for running it is just rephrasing the readme to set some environment variables. Is this what everyone is getting so hyped about?
- gremlinsinc 4y agoI think Autogpt might be a bit better, it's definitely more than 180 lines of code but it's not nearly as bloated as lang chain.
- bachmitre 4y agoThis weekly newsletter is excellent: https://www.deeplearning.ai/the-batch/ https://www.deeplearning.ai/the-batch/
- mooreds 4y agoThis is a bit more product focused, but I've found it useful: https://www.latent.space/ https://www.latent.space/ It's a newsletter/podcast.
- tikkun 4y agoMy 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.
- 0x008 4y agoyoutube
- fswd 4y agovarious 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.
- jasondigitized 4y agowhat channels?
- HugoDz 4y agoMade this :) https://www.haickernews.com/ https://www.haickernews.com/
- pabl0rg 4y agoMn bc b n bn bn Ng bn v
- lekashman 4y agoI use https://nextomoro.com https://nextomoro.com
- ackatz 4y agoI recently started an AI news aggregator here: https://ainewsfeed.io https://ainewsfeed.io I am planning on adding more feeds very soon to increase the amount of content I also have an aggregator for Cybersecurity: https://cyberfeed.io https://cyberfeed.io
- eon01 4y agoKala - AI/ML weekly (https://faun.dev/newsletter/kala https://faun.dev/newsletter/kala) You'll find both curated news, stories, tutorials, tools, and in-depth content. Disclaimer: I'm the curator of this newsletter.