31–40 of 42 posts
These tweet counts are not accurate. I searched for one with 0 tweets and found plenty of tweets mentioning it.
https://twitter.com/ZetaVector/status/1631590029926494211?s=...
What are some of the best tools people are using these days to consumer research papers? (outside of clicking on the PDF)
What are some of the best tools people are using these days to consumer research papers? (outside of clicking on the PDF)
Zeta Alpha, of course ;-) https://search.zeta-alpha.com/
Earlier quoted context omitted.
Disagree, or can you name some recent AI developments outside of DL that are anywhere near as groundbreaking?
To be clear: I think the hype around deep learning is 100% justified (in fact I think it is underplayed). But in terms of groundbreaking I do think "Zero-Knowledge Proofs for Machine Learning"[1] from 2020 has the potential to unlock some really revolutionary applications in the sense of "things that were not really possible without it". [1] https://dl.acm.org/doi/abs/10.1145/3411501.3418608
It looks like it might be more interesting on what industries or problem domains those most cited AI papers were focused.
According to Fig 3, Google was that most-cited problem domain. The most cited papers are for Google problems. You must have Google money and compute to achieve top-cited research.
Earlier quoted context omitted.
As far as I know, the field of robotics sees plenty of innovation with no deep learning involved. Whether you count robotics as AI is up to debate, I suppose
Any real innovation on the mechatronics side? The computer vision and controls are now increasingly dominated by deep learning.
Earlier quoted context omitted.
Semantic Scholar has 1,111. [0] I tend to trust Semantic more than GS. GS tends to overestimate. For example on GS I have 164 citations on one paper and semantic says 150. FWIW Scite says 49.[1] [0] https://www.semanticscholar.org/paper/ColabFold%3A-making-pr... [1] I'll note that this paper is an arxiv paper and has not been accepted at a conference but I'd also argue that conference acceptance means little in ML. I…
Please explain!
First, this peer review via conferences/journals/etc is relatively new in the scientific process. Really only the last 50 years has this paradigm been the main way for publishing. Prior to that scientists have just published in the open and and peer review happened by peers reading and responding. Not too different from what we see with arxiv, twitter, and blogging.
Second, we need to talk about how good the review process actually is. There's been a lot of writing on the NeurIPS experiments [0] is the most famous one. But the Google paper[1] notes that reviewers are "good at identifying bad papers but not good at identifying good papers." I'll go a step further than them and suggest a plausible model that makes this statement true: reviewers are reject happy. We need a confusion matrix to really see this but if you reject every paper you'd have a 100% success rate of rejecting bad papers but a 0% success rate of approving good papers. We have a good demonstration that ML conferences (journals aren't our priority like other academic areas, conferences are. This is an oddity) are an extremely noisy process and not very meaningful.
So how do we capture a signal in this noisy process? Citations are at least some signal. Obviously this isn't a fantastic signal either because big labs and companies are going to be able to popularize their work more and this will get more citations. But this still isn't any worse than we were 100 years ago. I'd argue that the noisy process of conferencing is worse than where we were 100 years ago (democratization of science aside).
Unfortunately, the only way to identify if a paper is good is to have experts evaluate them. I don't think we have a good alternative for this and adding significantly noisy signals aren't helpful.
[0] https://blog.mrtz.org/2014/12/15/the-nips-experiment.html