Insight. Applied.
I tend to listen more to opinions when people have placed money on their espoused outcome—having “skin in the game”, as Nicholas Taleb calls it in his latest book.
However, this robotics developer seems to have gone one better. He literally has skin in the game.
Good scientists aren’t supposed to experiment on themselves, but I will feel better if the first people to use autonomous cars are those who built the guidance systems.
At least it’s more impressive than Test-Driven Development.
I thought the CIA’s “best practices” for coding malware were interesting.
It’s basically a list of things you have to consider when dealing with a sophisticated attacker. Basic audit features aren’t enough if your data and systems present an attractive target.
The Des Moines Register reports that an information security officer used malicious code to manipulate lottery results in Wisconsin, Colorado and Oklahoma.
Apparently
Examiners found out-of-place programs known as dynamic link libraries, or DLLs, that had been written onto the Wisconsin computer. The programs were designed to “redirect” a drawing if certain conditions were met…
This is a problem with applications that use common libraries (such as DLLs). It’s difficult to be sure what code is being executed.
A 2012 study of National Health Service data in the UK found that there were
Before jumping to the conclusion that the UK is at the forefront of an exciting/disturbing evolutionary trend we should probably look for a simpler explanation—and it’s data coding errors.
Each procedure has an associated code and data entry errors resulted in men being assigned to female-only procedures. Obviously there are going to be all sorts of other errors, but it’s more difficult (and considerably less hilarious) to determine whether a patient actually had an ear infection when recorded as having a back problem.
This illustrates one of the biggest challenges for data science—garbage-in results in garbage-out. You can have the most sophisticated analysis algorithms available, but, if you are analyzing the wrong thing, you’ll draw the wrong conclusions.
Clearly it’s possible to perform statistical checks on the data—as the 2012 study illustrates. However, knowing that the data is wrong does little more than render it worthless. It could have, and should have, been checked at the point of entry. Simple logic in the data entry software could have checked the gender of the patient against a list of gender-specific codes and prevented the incorrect data from entering the system in the first place.
It’s always more efficient to fix data upstream.
Of course, gender checks would only catch some of the errors. Other techniques would be required for gender-neutral procedures. As I don’t know the data well enough, it’s difficult to come up with specific recommendations. However, some ideas might include the following.
One of the cheapest things we can do to improve the quality of data analysis is to improve the quality of data entry. Making basic checks in data entry systems is very, very far from rocket science.
The Microsoft Data Science Virtual Machine (DSVM) now comes pre-configured with Microsoft R Server Developer Edition.
As you can scale the DSVM according to your needs, this is an easy way to get going with some heavy duty R computations.