Any switchover of this kind will run into teething problems. We switched over most of our academic admin office (about 30 computers) to Linux/OpenOffice. Despite the support of two experienced Linux sysadmins and backing from On High, there was considerable grousing that still continues some 6 months later. File opening speeds, minor formatting things, print speeds - anything that might be imagined to be a little worse than the good old Win/Office system. I think it is mostly two factors: resistance to any kind of change, and the loss of freedom to mess around with the system. The slight but obvious user interface issues are a good added excuse. On the flip side, though: No viruses. No files lost. No idiot using someone else's machine and wiping out data. Automatic remote backup. The sysadmins are happy! Unfortunately these things do not seem to figure in the tally of the staff, even when one of their colleagues who has yet to switch has had all her files scrambled by one of the latest viruses. In short - it is hard to get people to change. But there are enormous savings, and not just financially.
On the same theme: The same Netcraft survey also says that Apache is up 0.54 percent and Microsoft down 0.21 percent in September. Apache is at its highest ever level. Most of those Apache servers are running Linux.
If market share were the only reason to go with Linux, it would never have gotten off the ground.
Neuromorphic electronic elements have been around a long time. There was an article from the 60's (in the Cold Spring Harbor symposia series, if I remember correctly) describing a vacuum-tube implementation of a neuron. I thought that was hilarious, given the hype that was going into the more recent silicon versions There is a basic design principle: don't do in hardware what you can do in software. Neuronal simulator packages like GENESIS and NEURON can simulate extremely realistic neuronal models in real time or faster. These neuronal models use 'compartments' which are little cylindrical segments of the cell, and they build up the geometry and electrical properties by putting them together. In other words, it is a spatial discretization of the cell. The finer the divisions, the more accurate the model. Last time I benchmarked, a PIII or Athlon class PC could handle something like 100 compartments in real time. That could either be 10 rather coarsely modeled neurons, or 1 very accurately modeled neuron. It is a great deal easier and cheaper to throw several PCs together in parallel to make a big network model, than it is to design an analog VLSI chip from scratch. The models are much more flexible, and can incorporate the latest data. You don't even want to think about what the debugging of VLSI would be like... I think Carver Mead's approach was the most practical: use the principles from real neural circuits, embody the equivalent computations in analog VLSI without being too picky about how neuron-like they were, and really use VLSI on a large scale. That is what he did with the artificial retina.
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Any switchover of this kind will run into teething problems. We switched over most of our academic admin office (about 30 computers) to Linux/OpenOffice. Despite the support of two experienced Linux sysadmins and backing from On High, there was considerable grousing that still continues some 6 months later. File opening speeds, minor formatting things, print speeds - anything that might be imagined to be a little worse than the good old Win/Office system. I think it is mostly two factors: resistance to any kind of change, and the loss of freedom to mess around with the system. The slight but obvious user interface issues are a good added excuse.
On the flip side, though: No viruses. No files lost. No idiot using someone else's machine and wiping out data. Automatic remote backup. The sysadmins are happy! Unfortunately these things do not seem to figure in the tally of the staff, even when one of their colleagues who has yet to switch has had all her files scrambled by one of the latest viruses.
In short - it is hard to get people to change. But there are enormous savings, and not just financially.
If market share were the only reason to go with Linux, it would never have gotten off the ground.
Neuromorphic electronic elements have been around a long time. There was an article from the 60's (in the Cold Spring Harbor symposia series, if I remember correctly) describing a vacuum-tube implementation of a neuron. I thought that was hilarious, given the hype that was going into the more recent silicon versions There is a basic design principle: don't do in hardware what you can do in software. Neuronal simulator packages like GENESIS and NEURON can simulate extremely realistic neuronal models in real time or faster. These neuronal models use 'compartments' which are little cylindrical segments of the cell, and they build up the geometry and electrical properties by putting them together. In other words, it is a spatial discretization of the cell. The finer the divisions, the more accurate the model. Last time I benchmarked, a PIII or Athlon class PC could handle something like 100 compartments in real time. That could either be 10 rather coarsely modeled neurons, or 1 very accurately modeled neuron. It is a great deal easier and cheaper to throw several PCs together in parallel to make a big network model, than it is to design an analog VLSI chip from scratch. The models are much more flexible, and can incorporate the latest data. You don't even want to think about what the debugging of VLSI would be like... I think Carver Mead's approach was the most practical: use the principles from real neural circuits, embody the equivalent computations in analog VLSI without being too picky about how neuron-like they were, and really use VLSI on a large scale. That is what he did with the artificial retina.