The difficulty with self-driven learning is that there is simply too much to be learned, and too little time. The function of a teacher is to help us focus on things that are worth spending the time to learn, that is, to ensure we start with the precursors to as many branches of knowledge as possible - leaving us as many options for specialization as possible.
And that's only best case - their measured rates were between 75 and 90% for the picture recall method. On the other hand, I imagine with frequent reinforcement there would be no problem getting recall accuracy higher.
In most environments, the human factor is the weakest link, not the false positive probability. It doesn't matter if the probability of guessing the password is 1/100,000 or as they'd probably get with a bit better training algorithm and a bigger database 1/10,000,000 --- the point is that the user can't write their password down on a sticky note on their monitor.
Think of it as sacrificing limited security against one unlikely technique (brute force attack) for perfect security against a more common one (human fallibility).
See IEEE computer society 4th annual international design competition for the winning team's project report. They (from National Taiwan University) had a system which did this, but allowed two-way interaction with the lecturer and facilitated collaboration. I think something like this could actually be useful.
Slashdot Mirror
Nature recently started a weekly podcast. http://www.nature.com/nature/podcast/index.html
The difficulty with self-driven learning is that there is simply too much to be learned, and too little time. The function of a teacher is to help us focus on things that are worth spending the time to learn, that is, to ensure we start with the precursors to as many branches of knowledge as possible - leaving us as many options for specialization as possible.
Try
Approximate round trip times in milli-seconds:
Minimum = 9000ms, Maximum=10000ms, Average=9100ms
And that's only best case - their measured rates were between 75 and 90% for the picture recall method. On the other hand, I imagine with frequent reinforcement there would be no problem getting recall accuracy higher.
In most environments, the human factor is the weakest link, not the false positive probability. It doesn't matter if the probability of guessing the password is 1/100,000 or as they'd probably get with a bit better training algorithm and a bigger database 1/10,000,000 --- the point is that the user can't write their password down on a sticky note on their monitor.
Think of it as sacrificing limited security against one unlikely technique (brute force attack) for perfect security against a more common one (human fallibility).
Aren't the modern chips RISC underneath anyway? The underlying architecture hasn't stayed the same, it's just a compatibility interface. Yes?
See IEEE computer society 4th annual international design competition for the winning team's project report. They (from National Taiwan University) had a system which did this, but allowed two-way interaction with the lecturer and facilitated collaboration. I think something like this could actually be useful.