Yes, Linux has plenty of problems. Unfortunately, the alternatives (*BSD, Windows XP, Mac OS X, etc.) suffer from pretty much the same problems: big, monolithic kernels, use of a cumbersome and unsafe systems programming language, server code rife with security problems and buffer overflows, decades of legacy code everywhere, etc.
So, to me, all the UNIX-like systems (Linux, *BSD, etc.) are pretty much interchangeable. But because Linux+GNU+X11 has the most widespread adoption, the most software available for it, the best distributions, and the most drivers, I stick with it.
If Mr. De Raadt wants to do something about it, he should start a new operating system project from scratch, rather than tinkering with BSD.
ironically, much of this material was collected and edited by BountyQuest, which reportedly received $1+ million from Amazon CEO Jeff Bezos in the name of patent reform
You have to ask yourself: why do you tell a company about prior art, and why do they ask about it?
Presumably, the primary goal you have telling a company about prior art is to convince them not to file the patent, or at least make them look bad among the tech savvy. But companies don't give a damn about that sort of thing, so don't waste your breath.
On the other hand, companies have a strong interest in hearing about prior art before filing the patent because when they include your prior art reference into their patent application, the patent becomes stronger. Rather than causing them to rethink their application, your reports of prior art to them are valuable background reseach that save companies lots of money in legal fees.
So, just about the worst thing you can do is to tell a company about prior art for a patent application, because doing so will make the patent even harder to fight.
What's a better strategy? Make sure as many people as possible know about the prior art, but only well after the patent has issued. Never contact the company filing the patent directly with prior art, even after it has issued--they have their lawyers, they should pay for their own prior art searches. And generally, it is probably also a good idea to wait with prior art discussions in public forums (like Slashdot) until after the patent has issued.
As for Bezos and Bounty Quest, it is sleazy at best for Bezos to use Bounty Quest as a repository for prior art for his own patent filings even if he decided to do so after the fact. If Bounty Quest was set up with such uses in mind, it is deceptive and may be fraudulent. In any case, it seems to be just another example of big companies deceiving and abusing people who are interested in doing volunteer work for the common good.
Overall, how good has research image software AI become in recent years? Have there been any key software or mathematical breakthroughs that have substantially increased the 'intelligence' of software? How far along is this technology?
The problem is not a lack of intelligence, it's a lack of documentation, reproducibility, calibration, and statistical validity.
Based on your knowledge of software, what are some things researchers can do to help the software better do its job? For example, using a high quality scanner at higher resolutions generally helps results. What other things can be done to promote better results?
While higher resolution scans are generally a good thing, the don't necessarily increase the accuracy or validity of the results (and could even decrease it, depending on what the software does).
Until you get better software, you simply can't trust the measurements blindly: you have to go over spots that are important to you manually and possibly carry out measurements by hand. Other conceptually simple things you can do is compare the results from using multiple image analysis packages, multiple scans at slightly different settings and resolutions, and repeating the experiment itself multiple times; results that are consistent across those conditions are more likely to be "real" than results you get from a single analysis.
Finally, all applications that I know of in this area are expensive commercial solutions. As the companies that produce the applications are for-profit, the algorithms and technology used are completely closed and proprietary. Thus it is hard to understand what the software is really doing. Does anybody know of any open source (or at least 'open algorithm') solutions? Even if they are inferior at this point in time, being able to clearly understand what the AI is doing makes us better off in several ways.
Well, there are quite a few published algorithms for this problem, and many of them have been implemented in open source form. Many of them work well at identifying and quantifying visually obvious, isolated spots, which is what they were designed for, but there is little reason to believe that they give meaningful results when spots are fuzzy and/or overlapping. There are some methods that potentially can quantify overlapping spots, but validating such methods is difficult and I doubt that the commercial packages have done this.
I work in an academic research group working on finding and precisely quantifying fuzzy spots in another domain (and we are planning on releasing our software fully documented and in open source form); quantitative analysis of gels would be another possible application. If you like, let me know your contact information and I'll get in touch with you.
Slashdot Mirror
Yes, Linux has plenty of problems. Unfortunately, the alternatives (*BSD, Windows XP, Mac OS X, etc.) suffer from pretty much the same problems: big, monolithic kernels, use of a cumbersome and unsafe systems programming language, server code rife with security problems and buffer overflows, decades of legacy code everywhere, etc.
So, to me, all the UNIX-like systems (Linux, *BSD, etc.) are pretty much interchangeable. But because Linux+GNU+X11 has the most widespread adoption, the most software available for it, the best distributions, and the most drivers, I stick with it.
If Mr. De Raadt wants to do something about it, he should start a new operating system project from scratch, rather than tinkering with BSD.
ironically, much of this material was collected and edited by BountyQuest, which reportedly received $1+ million from Amazon CEO Jeff Bezos in the name of patent reform
You have to ask yourself: why do you tell a company about prior art, and why do they ask about it?
Presumably, the primary goal you have telling a company about prior art is to convince them not to file the patent, or at least make them look bad among the tech savvy. But companies don't give a damn about that sort of thing, so don't waste your breath.
On the other hand, companies have a strong interest in hearing about prior art before filing the patent because when they include your prior art reference into their patent application, the patent becomes stronger. Rather than causing them to rethink their application, your reports of prior art to them are valuable background reseach that save companies lots of money in legal fees.
So, just about the worst thing you can do is to tell a company about prior art for a patent application, because doing so will make the patent even harder to fight.
What's a better strategy? Make sure as many people as possible know about the prior art, but only well after the patent has issued. Never contact the company filing the patent directly with prior art, even after it has issued--they have their lawyers, they should pay for their own prior art searches. And generally, it is probably also a good idea to wait with prior art discussions in public forums (like Slashdot) until after the patent has issued.
As for Bezos and Bounty Quest, it is sleazy at best for Bezos to use Bounty Quest as a repository for prior art for his own patent filings even if he decided to do so after the fact. If Bounty Quest was set up with such uses in mind, it is deceptive and may be fraudulent. In any case, it seems to be just another example of big companies deceiving and abusing people who are interested in doing volunteer work for the common good.
Overall, how good has research image software AI become in recent years? Have there been any key software or mathematical breakthroughs that have substantially increased the 'intelligence' of software? How far along is this technology?
The problem is not a lack of intelligence, it's a lack of documentation, reproducibility, calibration, and statistical validity.
Based on your knowledge of software, what are some things researchers can do to help the software better do its job? For example, using a high quality scanner at higher resolutions generally helps results. What other things can be done to promote better results?
While higher resolution scans are generally a good thing, the don't necessarily increase the accuracy or validity of the results (and could even decrease it, depending on what the software does).
Until you get better software, you simply can't trust the measurements blindly: you have to go over spots that are important to you manually and possibly carry out measurements by hand. Other conceptually simple things you can do is compare the results from using multiple image analysis packages, multiple scans at slightly different settings and resolutions, and repeating the experiment itself multiple times; results that are consistent across those conditions are more likely to be "real" than results you get from a single analysis.
Finally, all applications that I know of in this area are expensive commercial solutions. As the companies that produce the applications are for-profit, the algorithms and technology used are completely closed and proprietary. Thus it is hard to understand what the software is really doing. Does anybody know of any open source (or at least 'open algorithm') solutions? Even if they are inferior at this point in time, being able to clearly understand what the AI is doing makes us better off in several ways.
Well, there are quite a few published algorithms for this problem, and many of them have been implemented in open source form. Many of them work well at identifying and quantifying visually obvious, isolated spots, which is what they were designed for, but there is little reason to believe that they give meaningful results when spots are fuzzy and/or overlapping. There are some methods that potentially can quantify overlapping spots, but validating such methods is difficult and I doubt that the commercial packages have done this.
I work in an academic research group working on finding and precisely quantifying fuzzy spots in another domain (and we are planning on releasing our software fully documented and in open source form); quantitative analysis of gels would be another possible application. If you like, let me know your contact information and I'll get in touch with you.