Yes, the advantage of open source is that good actors can read the code and find and fix security flaws. The disadvantage is that bad actors can also read the code and find and exploit security flaws. One would hope good actors would outweigh the bad ones, but my fear that that governments and organized crime have become bad and worse actors in a big way. Even when a particular flaw is fixed, we all know that there are still flaws to be found and exploited in any big software project, and nowadays the big-time software exploiters have the budgets and the manpower to take advantage.

That said, that doesn't mean closed-source is any better (a different tradeoff), but it would be foolish to think that open-source software is not being exploited for its open-source properties.

I can't speak for Kilobug, but my answers would be:

1. It depends on your values. E.g., how much do you value your own welfare compared to family, friends, co-workers, fellow citizens, and those other people? If you want to be conscious about it, you need to think about what you value and how you might have done things differently in that light.

2. I probably thought I was I a deotonologist, but if you carefully study your own and other people's decisions, the vast majority are consequentialists with values that tend to selfishness. WItness how many Americans are angry about the Central American children/teenagers trying to get into the US.

3. As others have commented, doing a full analysis is time-consuming and uncertain (hence "maximum expected utility"). Most of the time, one has to follow rules that generally (so one believes) that have good consequences. And generally, virtue and duty are good rules. But people make up all sorts of rules with little sense behind them. My grandmother thought opening an umbrella indoors was bad luck, but I am a little skeptical about that one.

Treating it as a maxim rather than as a caution.

Knuth's books are very book, but they don't get much use from me. Instead:

Introduction to Algorithms by Cormen et al.

A good statistics book. Mine is an old thing: Mathematical Statistics with Applications by Mendenhall and Scheaffer.

A good operations research book (linear programming, queueing theory, Markov models/decision processes, and the like). Another old thing: Operations Research by Hillier and Lieberman.

Other than that, it's books that are/were used often for programming reference: Common Lisp: The Language by Steele and LaTeX: A Document Preparation System by Lamport look the most worn.

Hopefully, someone will come up with something a little more recent than the "old things" I mentioned above.

Really, the "location" of these mega-corporations is a sham.

Instead, figure out (or estimate) what percentage of the shares are owned by US residents. Multiply that percentage times the corporation's profit times the corporate tax rate and that is what they should pay.

Note: Any public corporation knows who are the immediate owners, so that they can send out shareholder info. However, a shareholder might be another corporation which is owned by other corporations, etc. Hence, the need to estimate (along with following the money as much as possible).

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.