can I get some insights on ai and robotics some papers to implement and get my hands dirty
Ask HN: AI/ML papers to catch up with current state of AI?
11–20 of 52 posts
Re: Ask HN: AI/ML papers to catch up with current state of AI?
#12you don’t need papers, Arxiv are self aggrandizement from some meme in East Asia
just join communities on discord or locallama on reddit
Re: Ask HN: AI/ML papers to catch up with current state of AI?
#13At the Twitter section at the bottom there is usually good papers
https://news.mioses.com
Re: Ask HN: AI/ML papers to catch up with current state of AI?
#14https://trendingpapers.com/
This tool can help you find what's new & relevant to read. It's updated every day (based on ArXiv).
You can filter by category (Computer Vision, Machine Learning, NLP, etc), by release date, but most importantly, you can rank by PageRank (proxy of influence/readership), PageRank growth (to see the fastest growing papers in terms of influence), total # of citations, etc...
Re: Ask HN: AI/ML papers to catch up with current state of AI?
#15I kind of despair of keeping up to date with ML, at least to the extent that I might ever get current enough to be paid to work with it. I did Andrew Ng's Coursera specialisation a few years back - and I've worked through some of the developer-oriented courses, implemented some stuff. read more than a few books, read papers (the ones I might have a hope of understanding), and tried to get a former employer to take it seriously. But its seeming like unless you have a PhD or big-co experience then its very difficult to keep up to date by working in the field.
Notwithstanding the above, I'd agree with others here who suggest learning by doing/implementing, not reading papers.
Re: Ask HN: AI/ML papers to catch up with current state of AI?
#16The book that just came out, "Understanding Deep Learning", is an excellent overview of the current state of AI: https://udlbook.github.io/udlbook/
Read that first, then to keep up to date you can follow up with any papers that seem interesting to you. A good way to be aware of the interesting papers that come out is to follow @_akhaliq on X: https://twitter.com/_akhaliq
Re: Ask HN: AI/ML papers to catch up with current state of AI?
#17Build something of personal interest to you. Start by looking for similar open-source projects online. Look at the online posts of the authors. Then look for the papers that you think will be useful for your project. Before you know it, you'll become an expert in your area of interest.
Above all, be wary of programmatic lists that claim to track the most important recent papers. There's a ridiculous amount of hype/propaganda and citation hacking surrounding new AI research, making it hard to discern what will truly stand the test of time. Tomas Mikolov just posted about this.[a]
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Re: Ask HN: AI/ML papers to catch up with current state of AI?
#18Hey, imho best overall technical intro to LLMs (I guess that´s your main interest as you mentioned qlora + llama) is by Simon Willis [1]. Additionally or if you prefer videos, the recent 1h "busy persons intro" by Andrei Karpathy is great + dense as well [2]. [1] https://simonwillison.net/2023/Aug/3/weird-world-of-llms/ [2] https://youtu.be/zjkBMFhNj_g?si=M6pRX66NrRyPM8x- EDIT: Maybe I misunderstood as you asked abou…
Is he referencing a particular “niche discord server?”
Re: Ask HN: AI/ML papers to catch up with current state of AI?
#19Posted in another thread, but sadly I got no replies...
Related question: how can I learn how to read the mathematical notation used in AI/ML papers? Is there a definitive work that describes the basics? I am a post-grad Engineer, so I know the fundamentals, but I'm really struggling with a lot of the Arxiv papers. Any pointers hugely appreciated.
Re: Ask HN: AI/ML papers to catch up with current state of AI?
#20Posted in another thread, but sadly I got no replies... Related question: how can I learn how to read the mathematical notation used in AI/ML papers? Is there a definitive work that describes the basics? I am a post-grad Engineer, so I know the fundamentals, but I'm really struggling with a lot of the Arxiv papers. Any pointers hugely appreciated.
I particularly enjoyed Kevin Murphy's book [0] for being just rigorous enough to satisfy but not too dry, but also not trying to add humor unnecessarily. It's not the best introduction text but it's great for someone with a little familiarity in the field who wants to broaden their understanding. There are proofs to rationalize some approaches, but not to the degree that would satisfy a hardcore mathematicians maybe, but tbh I think that's a good thing for a book of this scope.
If you find a sample, it may include the index of symbols in the beginning which is pretty comprehensive and may satisfy your question on its own.
https://www.goodreads.com/book/show/15857489-machine-learnin...