This is good advice. After 26 years of working in this field under stress that makes an air traffic controller look like a librarian, it has finally taken a big toll on me.
Sure, we'll use the embargo clause to explain this. The real reason is that, when you have machines capable of making new machine parts, you want to know when they move around and start building Skynet.
These boxes have internal slots for 45 blades. The current generation blades are Atoms and run a variety of Linux OS offerings. Future blades will be geared towards memcache, GPU, and other types of clusters. I got a couple of these at work for eval a little while back, and it's a pretty interesting package.
Only good reason I can think of is where multiple shots are required to weaken / overcome some kind of hardened defense. Of course, these things will be firing some kind of armor piercing / explosive / incendiary round, so that may not be a requirement. Or unless the psychological effect of a spray of highly-accurate bullets warrants the barrage.
It actually is, since a machine is more likely to keep an automatic weapon on-target than a human being is, just because of the shear strength required. If a machine is firing at me from over 600 yards away with a large-bore rifle, there's not a lot I can do about it as a human unless I have a rocket launcher of some kind. And then only if I'm very, very lucky.
Do you think that there will be a computer in the near future that optimizes frequent workloads onto FPGA circuitry? Kind of like the storage optimization that occurs in certain SAN hardware now? This would require an OS component capable of identifying candidates from the workload and dynamically porting that to a hardware layer on the system.
In addition to GPU, Cray has recently added the Intel Phi coprocessor to its XC30 Cascade supercomputers (http://investors.cray.com/phoenix.zhtml?c=98390&p=irol-newsA...). I think that this supports the argument that certain problems are better handled on traditional processors than on GPGPU platforms.
I agree. It only makes sense for certain types of workloads and for systems that are constantly in use (like supercomputers). This has been borne out through the movement of Bitcoin miner rigs from GPUs to FPGAs to ASICs. The power consumption and comparative performance of the GPU cannot win out over these devices.
As a counterpoint to this article, there are the dozens of recruiters that have examined my resume with my extensive embedded systems and database experience and have used their analytical skill to determine that I am a perfect fit for a marketing person for a car dealership. Or, which happens more often than not, that I will leave my current senior position to take a contract position for half the pay and four positions down the ladders at the company at which I already work. Sorry, but not impressed by most.
Depending upon the individual, I agree with the sentiment behind this article. Even if you go to work for a company - and I currently work for a huge company - it is not a requirement to have a bachelor's degree to get a very good-paying job in technology. Some people are perfectly capable of teaching themselves the skills that they need for the job at hand; these individuals are likely wasting their time in college. There are many, however, that cannot do this; these people really need the college environment to gain the necessary skills to do their job well.
I've always wondered about this. It seems from my experience that substituting a letter like z for s in a name or taking out the vowels causes a little bit of unresolved tension in a person's mental process. Perhaps this causes them to consciously or subconsciously pay more attention or the name, giving the marketer a few moments of opening to set the hook?
I think that Clarke's three laws are very applicable to the physics of a startup, especially the second law. We never know what we are capable of achieving until diving off the deep end of a project using a technology that we have never used before. Some of my favorite experiences were achieved by throwing caution to the wind and jumping into the deep water.