Raw data need to be cleaned up and organized to feed into the ML algorithm.

The results of the ML algorithm need to be cleaned up and organized so that they can be used by the rest of the system.

No one (currently) can tell you which ML algorithm will work best on your problem and how its parameters should be chosen without a lot of study. Preconceived bias (e.g., that it should be biologically based, blah, blah) can be a killer here.

The best results typically come from combinations of ML algorithms through some kind of ensemble learning, so now your have the problem of choosing a good combination and choosing a lot more parameters.

All of the above need to work together in concert.

Certainly, it's not a bad idea to try to make this process better, but I wouldn't be expecting miracles too soon.

Yes, plus the fact that this kind of decision policy is already evolving with collision avoidance systems in some cars (and experimental self-driving cars). It's not going to be a sudden mystery to be solved 30 years from now.

One of the biggest issues for current MOOCs is the large attrition rate (in the 90% range). Assuming that people signing up are at least average intelligence (on average of course), this suggests that average students are unable, for whatever reasons, to complete these courses. Part of it is that the instructors come from elite universities, are used to teaching elite students, and approach the MOOC in the same way, leaving the average student in the dust. Another part is that average students lack the motivation, discipline, as well as the smarts to learn complex concepts without a real-life instruction.

Why doesn't North Korea just use some version of Apple's walled garden? It sounds perfect for them.

The local paper will typically have recommendations of how to vote for as well. It's not ideal, but you might trust them enough to weed out the crazies.

After taking a few courses from Coursera, a high dropout rate is not surprising. The CS courses are mainly math courses in disguise, which works when you are teaching CS students at the high end of the intelligence spectrum, like at Stanford and other top-tier colleges, but simply loses most students otherwise. Even the NLP course was very focused on the mathematical models, much less so on the linguistics.

I suppose many might say it's not computer science without the math, but you can still teach much about computer technology and software design while being gentler with the math.

Personally, I've enjoyed the courses because I like math (except the quantum computation course, which was dreadful), but I know most of our CS students would be buried by the math. For the record, I'm at a state univ with some good research, but nowhere near a flagship. We do want to graduate some students, and the students we do graduate are in demand in our area.

Redhat, then Fedora

It's not only the extra cost, but it's also a loss of control over private information of the students.

As a CS instructor, I use Blackboard for homework and program submission, for posting solutions and for recording grades. Nothing else. Making a full-fledged web site out of Blackboard is too terrible to think about.

I'm not sure why this would be surprising. ML algorithms work best if the future behaves like the past, if it has the same probability distribution as the training data. Some algorithms can handle slow changes if they can continually get new training data, but large changes is a problem.

This has always been the case. Science is not a uniform march to the Truth. There is a difference between well-verified and understood results (think engineering) and working at the margins with not much data and the usual human failings (the vast majority of publications). Scientists are humans, not gods. It takes a lot of effort and error to get to the well-verified and understood part.

This is the key point. There is an enormous amount of evidence that birth control improves public health. From a scientific point of view, it is a no-brainer.

This quote comes from http://www.rhrealitycheck.org/article/2011/11/03/testimony-of-douglas-laube-md, which also includes citations for the above points. I would also include reduction of teenage pregnancy as an additional plus.

That is going to be the result of this kind of policy. I know in Computer Science, there are lots of bad conferences and journals that are easy to publish in.

I suppose privacy is impractical to those who want to sell our personal information.

In theory, theory and practice are the same. In practice, they are not.

- Albert Einstein -