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Man, this is an example of how difficult it is to know what is BS or not if you're not an expert on the subject. On one hand, this article was published in Natu

Man, this is an example of how difficult it is to know what is BS or not if you're not an expert on the subject. On one hand, this article was published in Nature, which I thought was trustworthy. On the other, there's this comment on a social media platform that links to a blog that also seems legit. No wonder misinformation spreads so fast. Even after reading both, I don't know what to make of it. The reaction and comments here just confuse me more.

TeMPOraL 6y ago
This phenomenon has a name: epistemic learned helplessness. https://slatestarcodex.com/2019/06/03/repost-epistemic-learned-helplessness/ https://slatestarcodex.com/2019/06/03/repost-epistemic-learn...
QuesnayJr 6y ago
Nature in particular seems vulnerable to the academic equivalent of click-bait articles. I think the top journals within a specific field are more reliable.
PartiallyTyped 6y ago
Nature has published some very questionable papers in AI/ML that are filled with malpractices. Another bogus paper that comes to mind was predicting earthquakes with a deep(read huge) neural network that appears to have information leakage and was fuelled with the hype of DL when a simple logistic regression (i.e. single neuron) could perform just as well [1,2,3]. [1] https://www.reddit.com/r/MachineLearning/comments/c4ylga/d_misuse_of_deep_learning_in_nature_journals/ https://www.reddit.com/r/MachineLearning/comments/c4ylga/d_m... [2] https://www.reddit.com/r/MachineLearning/comments/c8zf14/d_was_this_quake_ai_a_little_too_artificial/ https://www.reddit.com/r/MachineLearning/comments/c8zf14/d_w... [3] https://www.nature.com/articles/s41586-019-1582-8 https://www.nature.com/articles/s41586-019-1582-8 / https://arxiv.org/pdf/1904.01983.pdf https://arxiv.org/pdf/1904.01983.pdf
- lumost 6y ago
  This is a frighteningly common practice in DL research. Baselines are rarely taken with resect to alternate techniques, largely due to publication bias. On one hand papers about DL applications are of interest to the DL community, and useful to see if there is promise in the technique. On the other hand, they may not be particularly useful to industry, or to forwarding broader research goals.
  - throwawaygh 6y ago
    A good rule of thumb is to be slightly more suspicious of "DL for X" unless X was part of the AI/ML umbrella in the 2000s. If no one was publishing about X in AAAI/NIPS/ICML before 2013 or so then there's a pretty good chance that "DL for X" is ignoring 30+ years of work on X. This is becoming less true if one of the paper's senior author comes from the field where "X" is traditionally studied. Another good rule of thumb is that physicists writing DL papers about "DL for X" where X is not physics are especially terrible about arrogantly ignoring 30+ years of deeply related research. I don't quite understand why, but there's an epidemic of physicists dabbling in CS/AI and hyping it way the hell up.
    - lumost 6y ago
      Anecdotally, having come from a physics background myself - DL is more similar to the math that physicists are used to than traditional ML techniques or even standard comp-sci approaches are. In combination with the universal approximation proofs of DL, it's easy to get carried away and think that DL should be the supervised ML technique. Curiously, having also spent heavy time on traditional data-structures and algorithms gave me an appreciation for how stupendously inefficient a neural net is and part of me cringes whenever I see a one-hot encoding starting point...
      - throwawaygh 6y ago
        Re: similar to the math they know, this makes sense. I don't understand why over-hyping and over-selling is so common with AI/ML/DL work (to be fair, over-hyping is more related to AI than physicists in particular. But people from non-CS fields get themselves into extra trouble perhaps because they don't realize there are old-ish subfields dedicated to very similar problems to the ones they're working on.)
bawolff 6y ago
The articles don't contradict each other when it comes to cited facts - you can believe both! I suppose its all in the implications though, which are contradicting as the nature article implies it is a big deal. The nature article doesn't give any examples of interesting conjectures, or examples of interesting consequences if any of the conjectures should be true. They talk a lot about alternate formulae to calculate things we already know how to calculate. Why would we care? Do they have a smaller big-oh? Nature references the theory of links between other areas of math, if true that's great, but if its true surely they would have mentioned an example of such a link? Anyways I lean towards this not being that interesting, even if you base that just on what the nature article said.
- sanxiyn 6y ago
  Re why would we care: this is a search algorithm for numerical coincidences. Most numerical coincidences are trivial, for example can be derived from hypergeometric function relation which was known to Gauss. In fact it would be interesting to automatically filter formulae which can be derived from hypergeometric function relation... On the other hand, numerical coincidences can lead to deep theory, monstrous moonshine is a prime example. Hope is that by searching for numerical coincidences, we can discover one leading to deep theory without already knowing that deep theory. This seems reasonable.
  - yharris 6y ago
    That's a very good point - and it really motivates these kinds of computer searches. The Nature paper has quite a lot of detail in its supplementary https://static-content.springer.com/esm/art%3A10.1038%2Fs41586-021-03229-4/MediaObjects/41586_2021_3229_MOESM1_ESM.pdf https://static-content.springer.com/esm/art%3A10.1038%2Fs415... Table 3 inside also shows new conjectures for constants such as Catalan's and zeta(3). These results do not seem to trivially arise from known knowledge.
zests 6y ago
Sometimes popular science is itself the misinformation. The authors stretch the findings to land in prestigious journals. The news stretches the findings further to sell clicks (c.f. Gell-Mann Amnesia effect). The people on the internet selectively quote articles and selectively ignore others. The algorithm tries to only show you content that you like. The truth doesn't have a chance.
omginternets 6y ago
Science/Nature are prestigious, but the quality of their articles are often questionable. Part of the problem is the short format, which makes it difficult to include a lot of context and sanity-checking. Another issue is that they prioritize the “sexiness” of the research over pretty much everything else.
- throwawaygh 6y ago
  I'll never understand why Science/Nature carry any currency in CS and Math. TBH I consider them negative signals in these fields, and I encourage others to do the same when hiring -- the same way that a prestigious newspaper or magazine would treat someone with a bunch of (non-news) Buzzfeed bylines. There are some exceptions. E.g., a Science/Nature paper summarizing several years worth of papers published in "real" venues. Truly novel work that's reported on for the first time in Nature/Science is almost universally garbage. At least in CS/Math.
  - kevinventullo 6y ago
    In my experience, at least in pure math, publishing in Nature/Science doesn’t carry any weight. The most prestigious journals for a given subfield usually specialize in that subfield (with a name like Journal of Geometric Topology), with a few exceptions like Annals and JAMS. Even those are still focused heavily on pure math; I can’t think of any which are cross-disciplinary outside of math.
testfoobar 6y ago
This is why I have empathy for conspiracy believers. From their perspective, their understanding of the world is accurate. This is also why I see the inevitable failure of social media platforms in regulating truth-vs-non-truth.
kevinventullo 6y ago
FWIW that blog is written by one of the top leading number theorists in the US today. Of course, his opinions are his and you’re free to form your own, but just wanted to clarify that the blog is very much legit.
- David147 6y ago
  It seems like Calegari is chasing after PR and may be angry at computer scientists getting into his field. His criticism was discussed and found incorrect by the peer review process: https://static-content.springer.com/esm/art%3A10.1038%2Fs41586-021-03229-4/MediaObjects/41586_2021_3229_MOESM2_ESM.pdf https://static-content.springer.com/esm/art%3A10.1038%2Fs415...
  - spekcular 6y ago
    This is an incredibly strange slate of reviewers. Only the first seems to really understand the mathematical context. It's odd to appeal to the peer review process when the "peers" are not suited to complete the review. I assure you that Calegari knows more about number theory than any of those referees, and the reasons why the paper is bad are well-explained on his blog (cf. the two links above) and by referee #1. Speaking of "peer review," look at how all the excellent mathematicians commenting on that blog agree with him!
    - scihive 6y ago
      Calegari gets to cherry-pick comments he approves or rejects on his blog, so calling it "peer review" is taking the concept out of context =).
      - Daniel51 6y ago
        True! I tried several times to comment on his blog - but Calegari didn't confirm my comments It's hypocritical to criticize but to avoid criticism ...
    - timkam 6y ago
perl4ever 6y ago
As a thorough non-expert, I don't take headlines in the style of The Register seriously, even if the article is in Nature. Although, if it was really from The Register it probably would have said "boffins" rather than "humans".

TeMPOraL 6y ago

This phenomenon has a name: epistemic learned helplessness. https://slatestarcodex.com/2019/06/03/repost-epistemic-learned-helplessness/ https://slatestarcodex.com/2019/06/03/repost-epistemic-learn...

QuesnayJr 6y ago

Nature in particular seems vulnerable to the academic equivalent of click-bait articles. I think the top journals within a specific field are more reliable.

PartiallyTyped 6y ago

Nature has published some very questionable papers in AI/ML that are filled with malpractices. Another bogus paper that comes to mind was predicting earthquakes with a deep(read huge) neural network that appears to have information leakage and was fuelled with the hype of DL when a simple logistic regression (i.e. single neuron) could perform just as well [1,2,3]. [1] https://www.reddit.com/r/MachineLearning/comments/c4ylga/d_misuse_of_deep_learning_in_nature_journals/ https://www.reddit.com/r/MachineLearning/comments/c4ylga/d_m... [2] https://www.reddit.com/r/MachineLearning/comments/c8zf14/d_was_this_quake_ai_a_little_too_artificial/ https://www.reddit.com/r/MachineLearning/comments/c8zf14/d_w... [3] https://www.nature.com/articles/s41586-019-1582-8 https://www.nature.com/articles/s41586-019-1582-8 / https://arxiv.org/pdf/1904.01983.pdf https://arxiv.org/pdf/1904.01983.pdf

lumost 6y ago
This is a frighteningly common practice in DL research. Baselines are rarely taken with resect to alternate techniques, largely due to publication bias. On one hand papers about DL applications are of interest to the DL community, and useful to see if there is promise in the technique. On the other hand, they may not be particularly useful to industry, or to forwarding broader research goals.
- throwawaygh 6y ago
  A good rule of thumb is to be slightly more suspicious of "DL for X" unless X was part of the AI/ML umbrella in the 2000s. If no one was publishing about X in AAAI/NIPS/ICML before 2013 or so then there's a pretty good chance that "DL for X" is ignoring 30+ years of work on X. This is becoming less true if one of the paper's senior author comes from the field where "X" is traditionally studied. Another good rule of thumb is that physicists writing DL papers about "DL for X" where X is not physics are especially terrible about arrogantly ignoring 30+ years of deeply related research. I don't quite understand why, but there's an epidemic of physicists dabbling in CS/AI and hyping it way the hell up.
  - lumost 6y ago
    Anecdotally, having come from a physics background myself - DL is more similar to the math that physicists are used to than traditional ML techniques or even standard comp-sci approaches are. In combination with the universal approximation proofs of DL, it's easy to get carried away and think that DL should be the supervised ML technique. Curiously, having also spent heavy time on traditional data-structures and algorithms gave me an appreciation for how stupendously inefficient a neural net is and part of me cringes whenever I see a one-hot encoding starting point...
    - throwawaygh 6y ago
      Re: similar to the math they know, this makes sense. I don't understand why over-hyping and over-selling is so common with AI/ML/DL work (to be fair, over-hyping is more related to AI than physicists in particular. But people from non-CS fields get themselves into extra trouble perhaps because they don't realize there are old-ish subfields dedicated to very similar problems to the ones they're working on.)

bawolff 6y ago

The articles don't contradict each other when it comes to cited facts - you can believe both! I suppose its all in the implications though, which are contradicting as the nature article implies it is a big deal. The nature article doesn't give any examples of interesting conjectures, or examples of interesting consequences if any of the conjectures should be true. They talk a lot about alternate formulae to calculate things we already know how to calculate. Why would we care? Do they have a smaller big-oh? Nature references the theory of links between other areas of math, if true that's great, but if its true surely they would have mentioned an example of such a link? Anyways I lean towards this not being that interesting, even if you base that just on what the nature article said.

sanxiyn 6y ago
Re why would we care: this is a search algorithm for numerical coincidences. Most numerical coincidences are trivial, for example can be derived from hypergeometric function relation which was known to Gauss. In fact it would be interesting to automatically filter formulae which can be derived from hypergeometric function relation... On the other hand, numerical coincidences can lead to deep theory, monstrous moonshine is a prime example. Hope is that by searching for numerical coincidences, we can discover one leading to deep theory without already knowing that deep theory. This seems reasonable.
- yharris 6y ago
  That's a very good point - and it really motivates these kinds of computer searches. The Nature paper has quite a lot of detail in its supplementary https://static-content.springer.com/esm/art%3A10.1038%2Fs41586-021-03229-4/MediaObjects/41586_2021_3229_MOESM1_ESM.pdf https://static-content.springer.com/esm/art%3A10.1038%2Fs415... Table 3 inside also shows new conjectures for constants such as Catalan's and zeta(3). These results do not seem to trivially arise from known knowledge.

zests 6y ago

Sometimes popular science is itself the misinformation. The authors stretch the findings to land in prestigious journals. The news stretches the findings further to sell clicks (c.f. Gell-Mann Amnesia effect). The people on the internet selectively quote articles and selectively ignore others. The algorithm tries to only show you content that you like. The truth doesn't have a chance.

omginternets 6y ago

Science/Nature are prestigious, but the quality of their articles are often questionable. Part of the problem is the short format, which makes it difficult to include a lot of context and sanity-checking. Another issue is that they prioritize the “sexiness” of the research over pretty much everything else.

throwawaygh 6y ago
I'll never understand why Science/Nature carry any currency in CS and Math. TBH I consider them negative signals in these fields, and I encourage others to do the same when hiring -- the same way that a prestigious newspaper or magazine would treat someone with a bunch of (non-news) Buzzfeed bylines. There are some exceptions. E.g., a Science/Nature paper summarizing several years worth of papers published in "real" venues. Truly novel work that's reported on for the first time in Nature/Science is almost universally garbage. At least in CS/Math.
- kevinventullo 6y ago
  In my experience, at least in pure math, publishing in Nature/Science doesn’t carry any weight. The most prestigious journals for a given subfield usually specialize in that subfield (with a name like Journal of Geometric Topology), with a few exceptions like Annals and JAMS. Even those are still focused heavily on pure math; I can’t think of any which are cross-disciplinary outside of math.

testfoobar 6y ago

This is why I have empathy for conspiracy believers. From their perspective, their understanding of the world is accurate. This is also why I see the inevitable failure of social media platforms in regulating truth-vs-non-truth.

kevinventullo 6y ago

FWIW that blog is written by one of the top leading number theorists in the US today. Of course, his opinions are his and you’re free to form your own, but just wanted to clarify that the blog is very much legit.

David147 6y ago
It seems like Calegari is chasing after PR and may be angry at computer scientists getting into his field. His criticism was discussed and found incorrect by the peer review process: https://static-content.springer.com/esm/art%3A10.1038%2Fs41586-021-03229-4/MediaObjects/41586_2021_3229_MOESM2_ESM.pdf https://static-content.springer.com/esm/art%3A10.1038%2Fs415...
- spekcular 6y ago
  This is an incredibly strange slate of reviewers. Only the first seems to really understand the mathematical context. It's odd to appeal to the peer review process when the "peers" are not suited to complete the review. I assure you that Calegari knows more about number theory than any of those referees, and the reasons why the paper is bad are well-explained on his blog (cf. the two links above) and by referee #1. Speaking of "peer review," look at how all the excellent mathematicians commenting on that blog agree with him!
  - scihive 6y ago
    Calegari gets to cherry-pick comments he approves or rejects on his blog, so calling it "peer review" is taking the concept out of context =).
    - Daniel51 6y ago
      True! I tried several times to comment on his blog - but Calegari didn't confirm my comments It's hypocritical to criticize but to avoid criticism ...
  - timkam 6y ago

perl4ever 6y ago

As a thorough non-expert, I don't take headlines in the style of The Register seriously, even if the article is in Nature. Although, if it was really from The Register it probably would have said "boffins" rather than "humans".