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This is very inspirational but not sure if this is done in the best way. The core value of Mendeleev's approach is to find gaps and predict things that should e

This is very inspirational but not sure if this is done in the best way. The core value of Mendeleev's approach is to find gaps and predict things that should exist but we haven't found yet. However for this to happen, one must isolate the "atoms" that can't be further broken down and identify the pattern in these atoms. The periodic table created in Figure 4 of this paper doesn't exactly sounds like isolation of "atoms" of data structures.

esturk 8y ago
You're right based on the modern day thinking of what the periodic table is but you're wrong to suggest that people knew what they were doing from the very start. The formulation of the periodic table is quite like modern day data science. Chemists even before Mendeleev observed patterns and tried to build a model to predict missing ones. The beauty is that this was done without understanding the underlying mechanism of how it all worked, ie what makes an element (protons), what makes their periodicity (electrons). That came 50+ years later when we discovered protons and electron orbitals. So going back to the article, their approach is fine because they're just formulating some model from observed patterns. Their reasoning for the underlying mechanism could be completely wrong but that's fine too until a better model comes along.
- whatshisface 8y ago
  >The formulation of the periodic table is quite like modern day data science. That's because data science is just science. The only distinction is that you're studying somebody's sales pipeline instead of the secrets of the universe.
Sirupsen 8y ago
I am fascinated with unifying theories such as the periodic table (Mendeleev's) and the Copernican view of planets orbiting the sun. The previous, Earth-centric model had more deviations than predictive power. The periodic table is also an example of a structure with tremendous predictive power. I think it's also interesting to distinguish between systems that reveal intrinsic order (e.g. periodic table), and systems that superimpose external order (e.g. a performance review framework). Behavioural economics is an example of a field that desperately needs a modern-day equivalent of Mendeleev to come around and structure. I am craving more examples of this, please, anyone, share!
- kopo 8y ago
  They removed the image from the main article for some reason but I thought this was cool - https://en.wikipedia.org/wiki/File:The_Cognitive_Bias_Codex_-_180%2B_biases,_designed_by_John_Manoogian_III_(jm3).png https://en.wikipedia.org/wiki/File:The_Cognitive_Bias_Codex_...
- frutiger 8y ago
  Physics is full of such examples. Newton’s theory of gravitation unified the motion of apples falling from a tree, cannonballs thrown into walls, the motion of planet earth around the sun, and even motion of all the known planetary bodies. Electromagnetic waves gives us one understanding of X-rays, gamma radiation, visible light, infrared, radio waves, etc. Quantum field theory unifies every microscopic theory of matter and energy we have ever known. This list could be much longer.
- TuringTest 8y ago
  If you know programming, you may enjoy Peter Van Roy's components of programming paradigms. https://en.wikipedia.org/wiki/Programming_paradigm#Overview https://en.wikipedia.org/wiki/Programming_paradigm#Overview
- r-bryan 8y ago
  I can't find it now but I recently read of one or two guys who systematically tabulated juggling tricks. Gaps in the table led to new tricks which astonished experienced jugglers -- the way gaps in the periodic table led Mendeleev to predict properties of undiscovered elements. One guy gives talks on juggling at math conferences and on math at juggling conferences.
  - gwilliams 8y ago
    This reminds me of the effort to enumerate all ways of tying a necktie: http://www.tcm.phy.cam.ac.uk/~tmf20/tieknots.shtml http://www.tcm.phy.cam.ac.uk/~tmf20/tieknots.shtml
  - theoh 8y ago
    https://en.wikipedia.org/wiki/Siteswap https://en.wikipedia.org/wiki/Siteswap
fsavard 8y ago
I confess I haven't read the whole thing but that figure 4 looks more like a set of approaches, rather than a continuum as in the periodic table. Their figure 1, which appears to describe fundamental design tradeoffs, makes me think of CAP theorem "maps", like this: https://www.researchgate.net/figure/CAP-Theorem_fig4_221462089 https://www.researchgate.net/figure/CAP-Theorem_fig4_2214620... But the whole idea of figuring out first principles and "fundamental" design choices is quite appealing. They have a section saying that a big goal here would be automatic design of optimal data structures. They talk of machine learning techniques (they mention Bayesian optimization and reinforcement learning). That seems a very interesting research direction. In the same vein there's this talk by Jeff Dean about how they use ML at Google to replace all sorts of heuristics in data structure design (bloom filters etc.) with machine learning to optimize performance, though from what I recall it doesn't automatically change the algorithm itself: http://learningsys.org/nips17/assets/slides/dean-nips17.pdf http://learningsys.org/nips17/assets/slides/dean-nips17.pdf (discussed on HN previously https://news.ycombinator.com/item?id=15892956 https://news.ycombinator.com/item?id=15892956) EDIT: I think they cite a paper by Dean and others which was part of that talk
- eternalban 8y ago
  > I confess I haven't read the whole thing This is a sort of functional bug in forums such HN. Commenting on paper like this requires time to digest and reflect on the content. For a conceptual paper such as OP I would need at least a day (to figuratively sleep on it) but returning a couple of days later to make (hopefully) informed comment is no longer interactive. There is archival and future reference value, of course, but then rebuttals to possible misconceptions will not accompany the comment.
  - fsavard 8y ago
    It's quite funny I made a very similar lament a while ago: https://news.ycombinator.com/item?id=16230668 https://news.ycombinator.com/item?id=16230668 I only post when I think I can somehow add value to the discussion, at least value for some people who might read the comment. Obviously some people with in-depth knowledge of the paper and field in general will probably already be aware of the link I posted (for example), but others might find it an interesting avenue to explore. I know I often come to HN comments for this reason: a topic seems interesting, I want to learn of related stuff and/or get opinions from people with deeper expertise. So even if it's only for "future reference", it might actually bring value _for some_. At least that's what I tell myself :)
carapace 8y ago
FWIW I was able to kinda sorta do that for basic recursion combinators: http://joypy.osdn.io/notebooks/Recursion_Combinators.html#conclusion-patterns-of-recursion http://joypy.osdn.io/notebooks/Recursion_Combinators.html#co... In "Functional Programming with Bananas, Lenses, & Barbed Wire" http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.125 http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.1... they talk about morphisms: Hylomorphism, Anamorphism, Catamorphism, Paramorphism. If you break them down using Joy (programming language) in terms of the genrec general recursion combinator, they form simple patterns, which then suggest at least two other unnamed combinators: Hylo-, Ana-, Cata- H == [P ] [pop c ] [G ] [dip F ] genrec A == [P ] [pop []] [G ] [dip swap cons] genrec C == [not] [pop c ] [uncons swap] [dip F ] genrec Para-, ?-, ?- P == c swap [P ] [pop] [[F ] dupdip G ] primrec ? == [] swap [P ] [pop] [[swap cons] dupdip G ] primrec ? == c swap [not] [pop] [[F ] dupdip uncons swap] primrec Etc.

esturk 8y ago

You're right based on the modern day thinking of what the periodic table is but you're wrong to suggest that people knew what they were doing from the very start. The formulation of the periodic table is quite like modern day data science. Chemists even before Mendeleev observed patterns and tried to build a model to predict missing ones. The beauty is that this was done without understanding the underlying mechanism of how it all worked, ie what makes an element (protons), what makes their periodicity (electrons). That came 50+ years later when we discovered protons and electron orbitals. So going back to the article, their approach is fine because they're just formulating some model from observed patterns. Their reasoning for the underlying mechanism could be completely wrong but that's fine too until a better model comes along.

whatshisface 8y ago
>The formulation of the periodic table is quite like modern day data science. That's because data science is just science. The only distinction is that you're studying somebody's sales pipeline instead of the secrets of the universe.

Sirupsen 8y ago

I am fascinated with unifying theories such as the periodic table (Mendeleev's) and the Copernican view of planets orbiting the sun. The previous, Earth-centric model had more deviations than predictive power. The periodic table is also an example of a structure with tremendous predictive power. I think it's also interesting to distinguish between systems that reveal intrinsic order (e.g. periodic table), and systems that superimpose external order (e.g. a performance review framework). Behavioural economics is an example of a field that desperately needs a modern-day equivalent of Mendeleev to come around and structure. I am craving more examples of this, please, anyone, share!

kopo 8y ago
They removed the image from the main article for some reason but I thought this was cool - https://en.wikipedia.org/wiki/File:The_Cognitive_Bias_Codex_-_180%2B_biases,_designed_by_John_Manoogian_III_(jm3).png https://en.wikipedia.org/wiki/File:The_Cognitive_Bias_Codex_...
frutiger 8y ago
Physics is full of such examples. Newton’s theory of gravitation unified the motion of apples falling from a tree, cannonballs thrown into walls, the motion of planet earth around the sun, and even motion of all the known planetary bodies. Electromagnetic waves gives us one understanding of X-rays, gamma radiation, visible light, infrared, radio waves, etc. Quantum field theory unifies every microscopic theory of matter and energy we have ever known. This list could be much longer.
TuringTest 8y ago
If you know programming, you may enjoy Peter Van Roy's components of programming paradigms. https://en.wikipedia.org/wiki/Programming_paradigm#Overview https://en.wikipedia.org/wiki/Programming_paradigm#Overview
r-bryan 8y ago
I can't find it now but I recently read of one or two guys who systematically tabulated juggling tricks. Gaps in the table led to new tricks which astonished experienced jugglers -- the way gaps in the periodic table led Mendeleev to predict properties of undiscovered elements. One guy gives talks on juggling at math conferences and on math at juggling conferences.
- gwilliams 8y ago
  This reminds me of the effort to enumerate all ways of tying a necktie: http://www.tcm.phy.cam.ac.uk/~tmf20/tieknots.shtml http://www.tcm.phy.cam.ac.uk/~tmf20/tieknots.shtml
- theoh 8y ago
  https://en.wikipedia.org/wiki/Siteswap https://en.wikipedia.org/wiki/Siteswap

fsavard 8y ago

I confess I haven't read the whole thing but that figure 4 looks more like a set of approaches, rather than a continuum as in the periodic table. Their figure 1, which appears to describe fundamental design tradeoffs, makes me think of CAP theorem "maps", like this: https://www.researchgate.net/figure/CAP-Theorem_fig4_221462089 https://www.researchgate.net/figure/CAP-Theorem_fig4_2214620... But the whole idea of figuring out first principles and "fundamental" design choices is quite appealing. They have a section saying that a big goal here would be automatic design of optimal data structures. They talk of machine learning techniques (they mention Bayesian optimization and reinforcement learning). That seems a very interesting research direction. In the same vein there's this talk by Jeff Dean about how they use ML at Google to replace all sorts of heuristics in data structure design (bloom filters etc.) with machine learning to optimize performance, though from what I recall it doesn't automatically change the algorithm itself: http://learningsys.org/nips17/assets/slides/dean-nips17.pdf http://learningsys.org/nips17/assets/slides/dean-nips17.pdf (discussed on HN previously https://news.ycombinator.com/item?id=15892956 https://news.ycombinator.com/item?id=15892956) EDIT: I think they cite a paper by Dean and others which was part of that talk

eternalban 8y ago
> I confess I haven't read the whole thing This is a sort of functional bug in forums such HN. Commenting on paper like this requires time to digest and reflect on the content. For a conceptual paper such as OP I would need at least a day (to figuratively sleep on it) but returning a couple of days later to make (hopefully) informed comment is no longer interactive. There is archival and future reference value, of course, but then rebuttals to possible misconceptions will not accompany the comment.
- fsavard 8y ago
  It's quite funny I made a very similar lament a while ago: https://news.ycombinator.com/item?id=16230668 https://news.ycombinator.com/item?id=16230668 I only post when I think I can somehow add value to the discussion, at least value for some people who might read the comment. Obviously some people with in-depth knowledge of the paper and field in general will probably already be aware of the link I posted (for example), but others might find it an interesting avenue to explore. I know I often come to HN comments for this reason: a topic seems interesting, I want to learn of related stuff and/or get opinions from people with deeper expertise. So even if it's only for "future reference", it might actually bring value _for some_. At least that's what I tell myself :)

carapace 8y ago

FWIW I was able to kinda sorta do that for basic recursion combinators: http://joypy.osdn.io/notebooks/Recursion_Combinators.html#conclusion-patterns-of-recursion http://joypy.osdn.io/notebooks/Recursion_Combinators.html#co... In "Functional Programming with Bananas, Lenses, & Barbed Wire" http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.125 http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.1... they talk about morphisms: Hylomorphism, Anamorphism, Catamorphism, Paramorphism. If you break them down using Joy (programming language) in terms of the genrec general recursion combinator, they form simple patterns, which then suggest at least two other unnamed combinators: Hylo-, Ana-, Cata- H == [P ] [pop c ] [G ] [dip F ] genrec A == [P ] [pop []] [G ] [dip swap cons] genrec C == [not] [pop c ] [uncons swap] [dip F ] genrec Para-, ?-, ?- P == c swap [P ] [pop] [[F ] dupdip G ] primrec ? == [] swap [P ] [pop] [[swap cons] dupdip G ] primrec ? == c swap [not] [pop] [[F ] dupdip uncons swap] primrec Etc.