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.
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_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.
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.
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_...
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) The opposite approach is to use data from a single already-interlinked source through an unified API, exemplified by Freebase (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.
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.
In the academic world, the semantic web is pretty much taken for granted.
This is absolutely not the case. Among the Semantic Web community, the SW might be taken for granted (to some extent), but among the wider CS community, there is a lot of scepticism about whether the SW is feasible. Most of the people I know in the academic database research community dismiss the Semantic Web, for example.
I'm a skeptic. I don't believe in the idea that everyone will do their little piece and sometime this magical thing will emerge. Deriving benefit depends on the use of logic, but the web is an illogical mess. The semantic web doesn't scratch any itch that I have. Sure I would like something better than Google to bring my info to me. Right now I am finding the collective smartness to be more relevant than the semantic web. It will have some local success like Linda or Corba, but in the end something else will be the next great answer.
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?
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...
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.
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.
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.
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.
The Blue Waters project (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.
[+] [-] antiform|17 years ago|reply
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_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_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|17 years ago|reply
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.
[+] [-] randomwalker|17 years ago|reply
Look at this diagram: 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) The opposite approach is to use data from a single already-interlinked source through an unified API, exemplified by Freebase (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|17 years ago|reply
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://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.
[+] [-] neilc|17 years ago|reply
This is absolutely not the case. Among the Semantic Web community, the SW might be taken for granted (to some extent), but among the wider CS community, there is a lot of scepticism about whether the SW is feasible. Most of the people I know in the academic database research community dismiss the Semantic Web, for example.
[+] [-] russell|17 years ago|reply
[+] [-] paraschopra|17 years ago|reply
[+] [-] sqs|17 years ago|reply
[+] [-] lkozma|17 years ago|reply
[+] [-] sundeep|17 years ago|reply
[+] [-] tphyahoo|17 years ago|reply
[+] [-] brent|17 years ago|reply
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.
[+] [-] ken|17 years ago|reply
[+] [-] rplevy|17 years ago|reply
[+] [-] russell|17 years ago|reply
[+] [-] markessien|17 years ago|reply
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|17 years ago|reply
[+] [-] jmtame|17 years ago|reply
[+] [-] siong1987|17 years ago|reply
Anyway, the BlueWaters project will be done by 2011. It will be the fastest computer then.
[+] [-] jaydub|17 years ago|reply
Admittedly, the concepts involved are dated since PRAM theory (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
[+] [-] tsally|17 years ago|reply