PredictorY

The Pyramids of Giza are near Pizza Hut. What's the problem? Just remember that, thousands of years from now, when empires have risen and fallen, the Pizza Hut will still be here.

I don't know about software engineers specifically (and I know that this title is abused by applying it to other positions), but I can tell you that both general pay-scales and the cost of living vary quite a bit in the United States, as well as with the type of organization one works for (finance and pharmaceuticals pay above average, nonprofits unsurprisingly pay below average).

Also, the general pay level and cost of living tend to be highly correlated. Those people you've heard about who make truckloads of money in San Francisco? They also pay twice (or more) what people in other American locales do for housing!

The hot item today, both popular and used on a variety of platforms, is Java. A lot can happen in 50 years, so it's conceivable to me than Java does not make it to 50, but I'm guessing that some version of the JVM does (whatever language it's ultimately programmed in).

It's too bad the author doesn't once explain what "KPI" means.

I used my VISA a few hours ago in the northeastern US without a problem.

I suggest beginning with "Computer Systems That Learn" by Weiss and Kulikowski.

Most often, "machine learning" refers specifically to supervised learning. Generally, such technology requires an historic set of examples of sufficient volume and quality (what this means varies greatly from problem to problem) from which to learn. Some problems do not come with a reasonable set of training data, and none is forthcoming: these situations are a poor choice for machine learning. Each of these factors makes example data less desirable: candidate explanatory variables which are few in number or highly redundant, missing values and poor measurement. All of the challenges one faces in traditional statistical data collection and sampling apply.

Some problems are not learning problems at all. Retail companies with poor management and consequent poor service, for instance, need machine learning less than they need competent management.