7 ms·

Ask HN: Interesting recent developments in academic computer science.

37 points by rplevy 18y ago

Curious to learn about any concepts or techniques developed recently in academia that you think should be used more widely in applied settings.

paraschopra 18y ago
Yep, on a related note I was wondering recently if there is any website which lists currently hot and buzz-worthy research papers in CS or other fields. I know faculty of 1000 does that for biology but is there any other website?
- lkozma 18y ago
  I don't know about buzz-worthy, but here are a few buzzword-y ones : semantic web, ubiquitous computing, context-awareness, study of social networks, feel free to add more...
- tphyahoo 18y ago
  lambda the ultimate, no?
- sqs 18y ago
  I like http://hnr.dnsalias.net/wordpress/ http://hnr.dnsalias.net/wordpress/ . And just reading the papers that won awards at VLDB and SOSP.
- sundeep 18y ago
  i like www.hunch.net
jmtame 18y ago
The Blue Waters project (http://www.ncsa.uiuc.edu/BlueWaters/ http://www.ncsa.uiuc.edu/BlueWaters/) is being done in the building across the street from me, I can see it right out the window from this CS room. It's one of those "off limits" things, although you really need to have a use for it first.
- siong1987 18y ago
  Distributed computing will be the next huge thing. I can see it right through the window too. Anyway, the BlueWaters project will be done by 2011. It will be the fastest computer then.
  - markessien 18y ago
    No, it won't be. There was a while when we had 200mhz computers. Then we moved to 2Ghz computers. Looking at all the innovations that happened within that time period, very few were due to the super computers getting faster. Distributed computing just means computers getting faster. There is no killer app for this. Yes, you may say cancer research or flight simulations or so on, but there are not the next big things - not the way the web was.
    - jdunck 18y ago
      1000 core 2GHz machines for the desktop are 5 years out. Parallel is the way forward.
markessien 18y ago
In my opinion, it's obvious what the next big thing is going to be. Image recognition, accelerometer integration, multi-touch and so on. Basically, we're looking at the death of the mouse and keyboard a few years down the line. It's starting now, and it's starting the same way the web started - working poorly, very fragmented, cool but not yet practical. This will change soon.
- likpok 18y ago
  Given that multitouch has been around since the 80's or so (at least the technology), I don't think we're going to see the death of the keyboard. It's just too good of a method of input.
  - swombat 18y ago
    I don't think it's that great... Most importantly, keyboards require a flat surface to be really good. When on the move, keyboards really suck. There's plenty of room for improvement in portable input devices. Even something with a lot less expressiveness, but that, for instance, can be used with your hand in your pocket (perhaps while wearing a HUD of some sort) would be a huge improvement.
    - likpok 18y ago
      It depends on what you're trying to do. Most phones I've used can be operated with one hand, blindly, to do common tasks (raise/lower vol. silence call). The biggest problem, though, is that modern portable devices /need/ the expressiveness of a keyboard. Which is why devices like the Blackberry, the sidekick, etc. took off. The touchscreen changes some of that, but, from what I hear, it doesn't quite work as well.
      - markessien 18y ago
        Why do they 'need' it? If voice recognition were completely perfect, they would not need it.
randomwalker 18y ago
In the academic world, the semantic web is pretty much taken for granted. Curiously, it appears that people in the real world have been saying for so long that the semantic web will never happen that they have failed to notice that has already happened! Look at this diagram: http://en.wikipedia.org/wiki/File:Linking-Open-Data-diagram_2008-03-31.png http://en.wikipedia.org/wiki/File:Linking-Open-Data-diagram_... All these datasets have already been interlinked and are available for you to use. This is the linked open data approach (http://en.wikipedia.org/wiki/Linked_Data http://en.wikipedia.org/wiki/Linked_Data) The opposite approach is to use data from a single already-interlinked source through an unified API, exemplified by Freebase (http://freebase.com http://freebase.com), which is more straightforward but perhaps offers less control. I've found these resources invaluable in more than one project that I'm working on, and every hacker should at least keep abreast of what is available so that you can use it if you need to.
- aristus 18y ago
  You've linked to a PNG file and said "see! semantic web!" I tried to dig into it, looking for some data and to see what you are talking about, and I finally find a piece of RDF, real semantic web stuff: http://dbtune.org:3030/sparql/?query=describe%20%3Chttp://dbtune.org/bbc/peel/artist/1036%3E http://dbtune.org:3030/sparql/?query=describe%20%3Chttp://db... Um, ok. Now what? This is short XML file containing links, half of which are dead. The biggest problems with SW is that no one agreed on the labels, inputs and outputs, and that there are no mechanisms for data preservation or trust. How have those been solved now? (edit) I'm not hating on the idea, btw. It just doesn't seem to be a technological problem. It's a social one. The second you find a way to get people to structure their data for fun & profit, the SW will blossom. And then it will be spammed. And then someone will find a way to index it and filter out the spam, and by then it will be something good, but quite different from what was intended. I am genuinely curious to know what has changed in the last few years that academics now take SW for granted.
  - MaysonL 18y ago
    Fluidinfo seems to have an interesting handle on the problem.
antiform 18y ago
There are a slew of fascinating recent advances in CS and I discover more with every passing semester, but for brevity I will pick three things that have been occupying my mindspace as of late. 1. It seems that lazy functional programming languages (like Haskell) may provide a basis for a serious improvement in more robust natural language processing. A survey paper: [http://cs.uwindsor.ca/~richard/PUBLICATIONS/NLI_LFP_SURVEY_DRAFT.pdf http://cs.uwindsor.ca/~richard/PUBLICATIONS/NLI_LFP_SURVEY_D...] 2. Semi-Human Instinctive AI, a new dynamic, nondeterministic decision-making process, seems to be the new hotness in robotics/learning algorithms. In it, a given agent is given a set of basic behaviors ("instincts") that it hones with both open and closed learning methods in a problem space. [http://en.wikipedia.org/wiki/Semi_Human_Instinctive_Artificial_Intelligence http://en.wikipedia.org/wiki/Semi_Human_Instinctive_Artifici...] 3. Anatoly Shalyto's Automata-based programming, using finite state machines to describe program behavior, seems to have a lot of potential. It attempts to view programs from the context of engineering control theory, which opens the door to the use of powerful techniques from dynamical systems in mathematics.
- likpok 18y ago
  Number 3 seems interesting... If it catches on, architecture classes might get higher precedence in curricula. Moreover, it might unify (to some extent) the theoretical background around hardware and software.
jaydub 18y ago
I'm a part of the XMT project @ UMD http://www.umiacs.umd.edu/~vishkin/XMT/index.shtml http://www.umiacs.umd.edu/~vishkin/XMT/index.shtml Admittedly, the concepts involved are dated since PRAM theory (http://en.wikipedia.org/wiki/Parallel_Random_Access_Machine http://en.wikipedia.org/wiki/Parallel_Random_Access_Machine) dates to the 70's. However, this project marks the first successful commitment of PRAM theory to silicon
brent 18y ago
Well, if you're interested in machine learning NIPS was last week. http://books.nips.cc/nips21.html http://books.nips.cc/nips21.html There were several papers near applied areas like text classification, breaking audio captchas, and even brain machine interfacing. However, even the theoretical papers usually come with examples (e.g. image classification) that show optimistic results. If you were doing any learning task that is definitely the place to find the state of the art.
tsally 18y ago
You should certainly look into research related to Google's Map Reduce and Big Table.
ken 18y ago
I'm more interested in using interesting concepts in academic computer science from 50 years ago. I'm not against new good ideas, but it's not as if we've run out of old good ideas already.
- russell 18y ago
  You mean AI. :-)
- rplevy 18y ago
  I'm interested in both. I'm a fan of Common Lisp, but also of Clojure.

37 points by rplevy 18y ago

Curious to learn about any concepts or techniques developed recently in academia that you think should be used more widely in applied settings.

paraschopra 18y ago

Yep, on a related note I was wondering recently if there is any website which lists currently hot and buzz-worthy research papers in CS or other fields. I know faculty of 1000 does that for biology but is there any other website?

lkozma 18y ago
I don't know about buzz-worthy, but here are a few buzzword-y ones : semantic web, ubiquitous computing, context-awareness, study of social networks, feel free to add more...
tphyahoo 18y ago
lambda the ultimate, no?
sqs 18y ago
I like http://hnr.dnsalias.net/wordpress/ http://hnr.dnsalias.net/wordpress/ . And just reading the papers that won awards at VLDB and SOSP.
sundeep 18y ago
i like www.hunch.net

jmtame 18y ago

The Blue Waters project (http://www.ncsa.uiuc.edu/BlueWaters/ http://www.ncsa.uiuc.edu/BlueWaters/) is being done in the building across the street from me, I can see it right out the window from this CS room. It's one of those "off limits" things, although you really need to have a use for it first.

siong1987 18y ago
Distributed computing will be the next huge thing. I can see it right through the window too. Anyway, the BlueWaters project will be done by 2011. It will be the fastest computer then.
- markessien 18y ago
  No, it won't be. There was a while when we had 200mhz computers. Then we moved to 2Ghz computers. Looking at all the innovations that happened within that time period, very few were due to the super computers getting faster. Distributed computing just means computers getting faster. There is no killer app for this. Yes, you may say cancer research or flight simulations or so on, but there are not the next big things - not the way the web was.
  - jdunck 18y ago
    1000 core 2GHz machines for the desktop are 5 years out. Parallel is the way forward.

markessien 18y ago

In my opinion, it's obvious what the next big thing is going to be. Image recognition, accelerometer integration, multi-touch and so on. Basically, we're looking at the death of the mouse and keyboard a few years down the line. It's starting now, and it's starting the same way the web started - working poorly, very fragmented, cool but not yet practical. This will change soon.

likpok 18y ago
Given that multitouch has been around since the 80's or so (at least the technology), I don't think we're going to see the death of the keyboard. It's just too good of a method of input.
- swombat 18y ago
  I don't think it's that great... Most importantly, keyboards require a flat surface to be really good. When on the move, keyboards really suck. There's plenty of room for improvement in portable input devices. Even something with a lot less expressiveness, but that, for instance, can be used with your hand in your pocket (perhaps while wearing a HUD of some sort) would be a huge improvement.
  - likpok 18y ago
    It depends on what you're trying to do. Most phones I've used can be operated with one hand, blindly, to do common tasks (raise/lower vol. silence call). The biggest problem, though, is that modern portable devices /need/ the expressiveness of a keyboard. Which is why devices like the Blackberry, the sidekick, etc. took off. The touchscreen changes some of that, but, from what I hear, it doesn't quite work as well.
    - markessien 18y ago
      Why do they 'need' it? If voice recognition were completely perfect, they would not need it.

randomwalker 18y ago

In the academic world, the semantic web is pretty much taken for granted. Curiously, it appears that people in the real world have been saying for so long that the semantic web will never happen that they have failed to notice that has already happened! Look at this diagram: http://en.wikipedia.org/wiki/File:Linking-Open-Data-diagram_2008-03-31.png http://en.wikipedia.org/wiki/File:Linking-Open-Data-diagram_... All these datasets have already been interlinked and are available for you to use. This is the linked open data approach (http://en.wikipedia.org/wiki/Linked_Data http://en.wikipedia.org/wiki/Linked_Data) The opposite approach is to use data from a single already-interlinked source through an unified API, exemplified by Freebase (http://freebase.com http://freebase.com), which is more straightforward but perhaps offers less control. I've found these resources invaluable in more than one project that I'm working on, and every hacker should at least keep abreast of what is available so that you can use it if you need to.

aristus 18y ago
You've linked to a PNG file and said "see! semantic web!" I tried to dig into it, looking for some data and to see what you are talking about, and I finally find a piece of RDF, real semantic web stuff: http://dbtune.org:3030/sparql/?query=describe%20%3Chttp://dbtune.org/bbc/peel/artist/1036%3E http://dbtune.org:3030/sparql/?query=describe%20%3Chttp://db... Um, ok. Now what? This is short XML file containing links, half of which are dead. The biggest problems with SW is that no one agreed on the labels, inputs and outputs, and that there are no mechanisms for data preservation or trust. How have those been solved now? (edit) I'm not hating on the idea, btw. It just doesn't seem to be a technological problem. It's a social one. The second you find a way to get people to structure their data for fun & profit, the SW will blossom. And then it will be spammed. And then someone will find a way to index it and filter out the spam, and by then it will be something good, but quite different from what was intended. I am genuinely curious to know what has changed in the last few years that academics now take SW for granted.
- MaysonL 18y ago
  Fluidinfo seems to have an interesting handle on the problem.

antiform 18y ago

There are a slew of fascinating recent advances in CS and I discover more with every passing semester, but for brevity I will pick three things that have been occupying my mindspace as of late. 1. It seems that lazy functional programming languages (like Haskell) may provide a basis for a serious improvement in more robust natural language processing. A survey paper: [http://cs.uwindsor.ca/~richard/PUBLICATIONS/NLI_LFP_SURVEY_DRAFT.pdf http://cs.uwindsor.ca/~richard/PUBLICATIONS/NLI_LFP_SURVEY_D...] 2. Semi-Human Instinctive AI, a new dynamic, nondeterministic decision-making process, seems to be the new hotness in robotics/learning algorithms. In it, a given agent is given a set of basic behaviors ("instincts") that it hones with both open and closed learning methods in a problem space. [http://en.wikipedia.org/wiki/Semi_Human_Instinctive_Artificial_Intelligence http://en.wikipedia.org/wiki/Semi_Human_Instinctive_Artifici...] 3. Anatoly Shalyto's Automata-based programming, using finite state machines to describe program behavior, seems to have a lot of potential. It attempts to view programs from the context of engineering control theory, which opens the door to the use of powerful techniques from dynamical systems in mathematics.

likpok 18y ago
Number 3 seems interesting... If it catches on, architecture classes might get higher precedence in curricula. Moreover, it might unify (to some extent) the theoretical background around hardware and software.

jaydub 18y ago

I'm a part of the XMT project @ UMD http://www.umiacs.umd.edu/~vishkin/XMT/index.shtml http://www.umiacs.umd.edu/~vishkin/XMT/index.shtml Admittedly, the concepts involved are dated since PRAM theory (http://en.wikipedia.org/wiki/Parallel_Random_Access_Machine http://en.wikipedia.org/wiki/Parallel_Random_Access_Machine) dates to the 70's. However, this project marks the first successful commitment of PRAM theory to silicon

brent 18y ago

Well, if you're interested in machine learning NIPS was last week. http://books.nips.cc/nips21.html http://books.nips.cc/nips21.html There were several papers near applied areas like text classification, breaking audio captchas, and even brain machine interfacing. However, even the theoretical papers usually come with examples (e.g. image classification) that show optimistic results. If you were doing any learning task that is definitely the place to find the state of the art.

tsally 18y ago

You should certainly look into research related to Google's Map Reduce and Big Table.

ken 18y ago

I'm more interested in using interesting concepts in academic computer science from 50 years ago. I'm not against new good ideas, but it's not as if we've run out of old good ideas already.

russell 18y ago
You mean AI. :-)
rplevy 18y ago
I'm interested in both. I'm a fan of Common Lisp, but also of Clojure.