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The things that really broke Desktop linux' back was that there was never a cross-distro way of installing things (rpm vs apt, package managers changing all the time). If Fedora, Ubuntu and openSuse had worked together when the world was watching Vista fail, linux could have grabbed a significant market share. It's sad to say this, but I think it's too late now. Linux might succeed on severs, smartphones and cloud os devices, but it has lost the war for the desktop to Windows and Mac Os X.

I agree that we do live in an amazing age we live in, even though I wouldn't put too much emphasis on being able to fly (as this has been around for about 60 years now). I think technologically speaking, a major breakthrough was definitely the internet and mobile communication.

However, I would like to say something about the current generation. I believe the reason they are whiners is maybe because many youngsters in the west are facing the perspective of not being better off financially than the generation of their parents, putting lots of pressure upon them.

I think many people (at least here in Europe) are skeptical of technology because they perceive globalization, outsourcing, technological advancement and increasing competition as interlinked factors, which do not necessarily improve their quality of life, but rather present them with additional unknowns.

Again speaking for Europe, technology would be more widely accepted and hopefully appreciated, if there was a way to give the current generation more certainty that there will be enough water to flow all boats.

I'm getting my PhD in Machine Learning right now, so I'll try to give you some ideas.

First of all Machine Learning will be useful to you, because you'll understand much about statistics, data mining, optimization and efficient programming. If you don't get to work on an ML project in industry directly, some of the enabling techniques might come in handy.

Now, ML is also interesting for industry. Google is essentially a Machine Learning company. They use statistics to evaluate and display data, be it website ranking, recommender systems, or whatnot. Some goes to some extend for MS, Facebook and Yahoo.

Now, there is also increasing demand in the computational biology community for ML methods. Pharma companies are producing terrabytes of data that will need to be analysed with intelligent algorithms, so that might be an interesting direction to consider.

In times of unlimited data access, studying how to teach computers to learn from data seems like the right thing to do.