RSS (Really Simple Syndication) is a format for syndicating news and the content of news-like sites, including major news sites like Wired, news-oriented community sites like Slashdot, and personal weblogs. But it's not just for news. Pretty much anything that can be broken down into discrete items can be syndicated via RSS: the "recent changes" page of a wiki, a changelog of CVS checkins, even the revision history of a book. Once information about each item is in RSS format, an RSS-aware program can check the feed for changes and react to the changes in an appropriate way.
RSS-aware programs called news aggregators are popular in the weblogging community. Many weblogs make content available in RSS. A news aggregator can help you keep up with all your favorite weblogs by checking their RSS feeds and displaying new items from each of them.
My take is that computers are a liberation from mindless computation and a tool for exploration. Only the most severe of Luddites wouldn't use a calculator, a computer is just an extension of that. For example, factorising polynomials, solving equations symbolically, and so forth. These are the sort of things that computers can save us an enormous amount of grunt work doing. Why not use them?
For exploration, they are also useful. New series for p have been conjectured (and proved by hand) based on computer programs which search for certain sorts of integer relations. This has led to the ability to find the nth hexadecimal digit of p without finding the previous n-1 digits, something that could have been exceedingly difficult without this computer program.
With regard to things like the proof of the 4 colour theorem using computers, there is a more interesting debate here. I'm going to make an analogy with a similar debate in neuroscience which is quite interesting. David Marr, who was a researcher into visual systems in the brain, made a distinction between Type 1 and Type 2 theories. A Type 1 theory is, broadly, one in which there are nice formulas like you get in physics or maths. A Type 2 theory is essentially computational. For example, many models of the way the brain work use PDP (parallel distributed processing, a sort of computer model of neurons in the brain). Marr's point was that in general we should prefer Type 1 theories, because they give us a general and intuitive understanding of how the thing being studied works. For example, there are certain cells in the visual system which detect edges in the visual field. So, if you have a theory of vision which involves certain cells finding the edges in the visual field then that gives you a better understanding than some sort of computation model which performs the function of the visual system but which you don't really understand. However, he also pointed out that since animals evolved and were not designed, there may not always be a Type 1 theory explaining why, in an abstract general way, the brain (or any other bit of the body) worked, it just does.
Returning to maths, I think the analogy is that a Type 1 theory is one which can be completely understood by a single individual by working through it line by line, whereas a Type 2 theory might involve a computer proof like in the 4 colour theorem. In general, we prefer to get proofs which don't use computers, because they might give us an intuitive insight into why the theorem is true, or it might lead to an interesting new branch of maths (like the Taniyama-Shimura conjecture that Wiles proved). However, there might be some theorems which just do not have proofs which a single person could read and fully understand. The 4 colour theorem might be just such an example. It would be foolish to discount any such theorems a priori, and so I would advocate a cautious use of computers in mathematical proof. The danger in overusing computers is that if they become too widely used, people will search for computer proofs without finding the insightful way of solving the problem which doesn't use a computer. The danger in not using them enough is that huge amounts of effort could be wasted trying to find neat analytical solutions to problems which are not really amenable to traditional methods (not using a computer) of attack.
Professor Roukes' homepage has a link to his earlier published paper on attogram mass detection (2004). The abstract mentions that mass sensing of individual molecules will be realizable with optimized NEMS devices. Also there is link to paper which discusses the ultimate limit to mass sensing based on NEMS. Needless to say that so far it is not the physics of these nanostructures but the extrinsic amplifier noise which limited the measurement.
Old Slashdot story about Samsung's zero dead pixel policy. I assume that Samsung has matured their display technology far better than Sony and offering zero dead pixel is not impossible.
Considering the time it takes to start the program (from clicking the icon to scanning plugins to open the window with picture), I think 5 days starting time for the next version isn't that bad.
Testing ICs is an exponentially hard problem these days. One-third of the cost is devoted to it. Thus it may be a good idea to test the chip for only the applications it is needed (in some restrictive environments) and if it passes, it can still be deployed. It will ease some of the economic hammer on the manufacturing these days.
Xilinx offer EasyPath option by testing for a customer-specific application. Customers use EasyPath customer specific FPGAs to achieve lower unit costs for volume production once they know their design is fixed and no longer requires the full programmability of an FPGA.
Very possible. But I wonder how does the bandwidth between the processors will compare for the two cases (and will determine what kind of supercomputing applications can be run on them) ? Blue gene is custom designed ( each chip = two processors, four accompanying mathematical engines, 4MB of memory and communication systems for five separate networks). On the other hand google uses commercially available servers and hence may be able to offer the service lot cheaper.
Actually if you think about it, it is no less than a tower
A typical carbon nanotube is ~1-5 nm in diameter. A 2 micrometer long nanotube means an aspect ratio (length/height to diameter) of almost 1000. Even the tallest skyscraper don't go beyond 7:1. WTC had height to width ratios of 6.49 to 1. Bank of American plaza has the highest with 7.24 to 1.
Can someone tell details about how they do it ? The sighted news articles doesn't give any details. Can they grow individual carbon nanotubes vertically ? There had been earlier work on controlled alignment of carbon nanofibers from ORNL folks. Their technique could grow the nanotubes in different directions using electric field. There is also an option of controlling the direction of growth using polarized light.
If precise formation as well as placement can be achieved, it will get over the biggest hurdle in getting into the electronics. There are still other issues (eg. contacts, surface adsorbtion etc) to be addressed though.
I don't want to flame you, but I would take a scientific/engineering approach rather than accepting opinion from a wall-street magazine. It would be worthwhile to follow the bubble burst of the MEMS technology in the recent 4-5 years. Even after 10 years of work, MEMS elements have serious issues in packaging. Intel withdrew their MEMS program as it doesn't have enough yield. So just making prototype is not the end of the story.
As an engineer you have to take things with a pinch of salt. Every scientific idea may not be technologically feasible. In the end economics determine if the product will even hit the market or not. Nanotechnology is not cheap, so it is worthwhile consideration to see if it is even possible to tackle the important issues rather than hoping someone else will do it.
(a) Reliability: No words about how reliable the system and elements are. It is one thing to make a 1M by 1M array and another to make bigger. Silicon semiconductor industry is lot more mature in transferring electronic processes. MEMS process still have low yield and haven't found commercial success yet (except the accelerometers used in air bags etc).
(b) Testing: How are they going to test this trillion element chip ? Testing complexity grows exponential with number of elements and it will require serious consideration. It may be worthwhile to make smaller components which can be tested easily (modern chips has one-third cost devoted to testing)
(c) Redundancy: Is this process going to give more yield than conventional electronic processes ? If no, common technique of redundancy has to be utilized. This brings in the cost in terms of power, speed and delay. For example if the yield is only 90%, that means you will need ~110% resources. Not only you have to make up for the defective components, you will have to provide lot more redundancy for testing. At some point it becomes worthless as the performance will drop to floor.
But still it is a good work and perhaps will generate some new ideas.
Quantum computing relies on processing information within a quantum system with many continuous degrees of freedom. The practical implementation of this idea requires complete control over all of the 2^n independent amplitudes of a many-particle wavefunction, where n>1000. The principles of quantum computing are discussed from the practical point of view with the conclusion that no working device will be built in the forseeable fu
Laser Interferometer Gravitational Wave Observatory from Caltech is working on same subject. LIGO will search for gravitational waves created in supernova collapses of stellar cores (which form neutron stars and black holes), collisions and coalescences of neutron stars or black holes, rotations of neutron stars with deformed crusts and the remnants of gravitational radiation created by the birth of the universe. LIGO is a joint project between scientists at the California Institute of Technology (Caltech) and the Massachusetts Institute of Technology (MIT), sponsored by the National Science Foundation (NSF).
Nano is a big hooplaa around. If you ask a semiconductor engineer or a traditional chemist, they had been practicing nanotechnology for ages. Chemists have for centuries played around with molecules. Semiconductor industry has for long time employed materials which were only few nanometers thick.
From the nature article: Rong's chip produces laser light when it is 'pumped' with another laser.
This is old stuff (see bottom note on the article, result was published in Oct 2004). Intel showed they can lase silicon with another laser. So how am I going to find another laser to pump this one ?
Silicon is indirect bandgap semiconductor. There is no easy way to make lasers out of it unless you introduce some traps to facilitate optical transistions. Can anyone explain how does it work ?
-a
Slashdot Mirror
RSS-aware programs called news aggregators are popular in the weblogging community. Many weblogs make content available in RSS. A news aggregator can help you keep up with all your favorite weblogs by checking their RSS feeds and displaying new items from each of them.
For exploration, they are also useful. New series for p have been conjectured (and proved by hand) based on computer programs which search for certain sorts of integer relations. This has led to the ability to find the nth hexadecimal digit of p without finding the previous n-1 digits, something that could have been exceedingly difficult without this computer program.
With regard to things like the proof of the 4 colour theorem using computers, there is a more interesting debate here. I'm going to make an analogy with a similar debate in neuroscience which is quite interesting. David Marr, who was a researcher into visual systems in the brain, made a distinction between Type 1 and Type 2 theories. A Type 1 theory is, broadly, one in which there are nice formulas like you get in physics or maths. A Type 2 theory is essentially computational. For example, many models of the way the brain work use PDP (parallel distributed processing, a sort of computer model of neurons in the brain). Marr's point was that in general we should prefer Type 1 theories, because they give us a general and intuitive understanding of how the thing being studied works. For example, there are certain cells in the visual system which detect edges in the visual field. So, if you have a theory of vision which involves certain cells finding the edges in the visual field then that gives you a better understanding than some sort of computation model which performs the function of the visual system but which you don't really understand. However, he also pointed out that since animals evolved and were not designed, there may not always be a Type 1 theory explaining why, in an abstract general way, the brain (or any other bit of the body) worked, it just does.
Returning to maths, I think the analogy is that a Type 1 theory is one which can be completely understood by a single individual by working through it line by line, whereas a Type 2 theory might involve a computer proof like in the 4 colour theorem. In general, we prefer to get proofs which don't use computers, because they might give us an intuitive insight into why the theorem is true, or it might lead to an interesting new branch of maths (like the Taniyama-Shimura conjecture that Wiles proved). However, there might be some theorems which just do not have proofs which a single person could read and fully understand. The 4 colour theorem might be just such an example. It would be foolish to discount any such theorems a priori, and so I would advocate a cautious use of computers in mathematical proof. The danger in overusing computers is that if they become too widely used, people will search for computer proofs without finding the insightful way of solving the problem which doesn't use a computer. The danger in not using them enough is that huge amounts of effort could be wasted trying to find neat analytical solutions to problems which are not really amenable to traditional methods (not using a computer) of attack.
Didn't we cover this before ?
Professor Roukes' homepage has a link to his earlier published paper on attogram mass detection (2004). The abstract mentions that mass sensing of individual molecules will be realizable with optimized NEMS devices. Also there is link to paper which discusses the ultimate limit to mass sensing based on NEMS. Needless to say that so far it is not the physics of these nanostructures but the extrinsic amplifier noise which limited the measurement.
Here. The page has more details and link to movies.
Earlier slashdot story regarding NIST study about potential lifespan of CD-Rs and DVD-Rs.
Old Slashdot story about Samsung's zero dead pixel policy. I assume that Samsung has matured their display technology far better than Sony and offering zero dead pixel is not impossible.
Other groups working on optical interconnects: (incomplete list)
Heriot Watt
Cornell University
IBM Zurich
Delft
UIUC
Intel
Stanford
Considering the time it takes to start the program (from clicking the icon to scanning plugins to open the window with picture), I think 5 days starting time for the next version isn't that bad.
1-foot-long, 11-pound satellite called Nanosputnik
1 foot = 0.304 x 10^9 nanometer
11 pound = 4 989.5 x 10^9 nanogram
Quite a big nano I would say..
-Flamebit-
I would include them as well in the list.
As long as they are not replacing the cute nurse ...
Xilinx offer EasyPath option by testing for a customer-specific application. Customers use EasyPath customer specific FPGAs to achieve lower unit costs for volume production once they know their design is fixed and no longer requires the full programmability of an FPGA.
Very possible. But I wonder how does the bandwidth between the processors will compare for the two cases (and will determine what kind of supercomputing applications can be run on them) ? Blue gene is custom designed ( each chip = two processors, four accompanying mathematical engines, 4MB of memory and communication systems for five separate networks). On the other hand google uses commercially available servers and hence may be able to offer the service lot cheaper.
Are we out of names now. They names this little baby : OGLE-TR-122b. May be I should change my address to 127.0.0.1 too. -a
A typical carbon nanotube is ~1-5 nm in diameter. A 2 micrometer long nanotube means an aspect ratio (length/height to diameter) of almost 1000. Even the tallest skyscraper don't go beyond 7:1. WTC had height to width ratios of 6.49 to 1. Bank of American plaza has the highest with 7.24 to 1.
If precise formation as well as placement can be achieved, it will get over the biggest hurdle in getting into the electronics. There are still other issues (eg. contacts, surface adsorbtion etc) to be addressed though.
I don't want to flame you, but I would take a scientific/engineering approach rather than accepting opinion from a wall-street magazine. It would be worthwhile to follow the bubble burst of the MEMS technology in the recent 4-5 years. Even after 10 years of work, MEMS elements have serious issues in packaging. Intel withdrew their MEMS program as it doesn't have enough yield. So just making prototype is not the end of the story.
As an engineer you have to take things with a pinch of salt. Every scientific idea may not be technologically feasible. In the end economics determine if the product will even hit the market or not. Nanotechnology is not cheap, so it is worthwhile consideration to see if it is even possible to tackle the important issues rather than hoping someone else will do it.
(b) Testing: How are they going to test this trillion element chip ? Testing complexity grows exponential with number of elements and it will require serious consideration. It may be worthwhile to make smaller components which can be tested easily (modern chips has one-third cost devoted to testing)
(c) Redundancy: Is this process going to give more yield than conventional electronic processes ? If no, common technique of redundancy has to be utilized. This brings in the cost in terms of power, speed and delay. For example if the yield is only 90%, that means you will need ~110% resources. Not only you have to make up for the defective components, you will have to provide lot more redundancy for testing. At some point it becomes worthless as the performance will drop to floor.
But still it is a good work and perhaps will generate some new ideas.
Recognition always helps. An earlier Slashdot story
Quantum computing: a view from the enemy camp
Quantum computing relies on processing information within a quantum system with many continuous degrees of freedom. The practical implementation of this idea requires complete control over all of the 2^n independent amplitudes of a many-particle wavefunction, where n>1000. The principles of quantum computing are discussed from the practical point of view with the conclusion that no working device will be built in the forseeable fu
So this is the last part of the sequel .. Frodo will destroy the precisious pop-ups !!! Keep faith ... really.
Laser Interferometer Gravitational Wave Observatory from Caltech is working on same subject. LIGO will search for gravitational waves created in supernova collapses of stellar cores (which form neutron stars and black holes), collisions and coalescences of neutron stars or black holes, rotations of neutron stars with deformed crusts and the remnants of gravitational radiation created by the birth of the universe. LIGO is a joint project between scientists at the California Institute of Technology (Caltech) and the Massachusetts Institute of Technology (MIT), sponsored by the National Science Foundation (NSF).
So what is the big buzz around?
This is old stuff (see bottom note on the article, result was published in Oct 2004). Intel showed they can lase silicon with another laser. So how am I going to find another laser to pump this one ?
Silicon is indirect bandgap semiconductor. There is no easy way to make lasers out of it unless you introduce some traps to facilitate optical transistions. Can anyone explain how does it work ? -a