Picking random points on the surface of a sphere so that the points are uniformly distributed is not as straightforward as you might think. Naively picking random spherical coordinates ϕ and θ will not give a uniform distribution of points. The problem is important enough to warrant a dedicated article in encyclopedias, such as Wolfram MathWorld (see Sphere Point Picking). Uniform sampling from Sphere[] is now available in the Wolfram Language with the RandomPoint function: In fact, RandomPoint can be used to uniformly sample from any bounded geometric region, in any dimension. In 2D:

The Wolfram Language has had extensive support for string manipulation since Mathematica 5, and in Version 10 it provided uniform symbolic access to a huge repository of computable data via the Wolfram Knowledgebase. Taking advantage of both of these fundamental capabilities, along with new machine learning functionality with Classify and Predict, we're excited to be making further inroads into the rich domains of natural language processing and text analytics with TextCases, new in Version 10.2. TextCases, like its sister functions Cases and StringCases, finds instances of patterns in a given input. Whereas Cases operates on Wolfram Language expressions and StringCases on strings, TextCases assumes that the input is human understandable text, from which one can extract known syntactic and semantic entities. These include basic textual types such as words, sentences, and paragraphs, but also more sophisticated semantic types such as countries, cities, and numbers. As a simple example, let's use TextCases to find instances of countries in a sentence:

Despite the ever-growing list of tools I have for communication, email remains one of the most important. I depend on email to find out about all sorts of things: my ultimate Frisbee game is rained out, flights to Denver are only $80, my Dropbox account is almost full, my neighbor's cat is missing (again). While filters are able to hide the pure junk and sort everything else into reasonable categories, reading and responding to email still requires a lot of manual interaction. The new mail receivers in the Wolfram Language finally let me automatically interact with email. MailReceiverFunction is a Wolfram Language function that I deploy to the cloud to operate on incoming emails. When I deploy a function, I get an email address. Emails sent to that address will be processed by the function.

A classic problem in numerical date notation is that various countries list year, month, and day in different orders, which was one of the motivations for the introduction of the ISO-8601 date element and interchange formats (Randall Monroe has a nice summary in this xkcd comic). In the upcoming release of the Wolfram Language, we've added built-in support for these ISO date formats: The ISO specification also provides some alternative date representations, such as week dates (year, week of year, and day of week) and ordinal dates (year and day of year):

Cryptography has existed for thousands of years, but before serious computers came around, only specific kinds of messages were worth encrypting. Now that computers routinely manage a huge amount of communication, there is little downside to invisibly applying cryptography to almost everything, from verifying where information comes from to exchanging information securely. Because of cryptography’s widespread use, we added the basic building blocks of modern cryptography to the Wolfram Language with functions using OpenSSL for key generation, symmetric encryption/decryption, and asymmetric encryption/decryption. The notion of a key in cryptography is similar to the way we use keys in everyday life, in that only someone with a certain key can perform a certain action. One very simple way of arranging this is to have a single key that is used to encrypt as well as decrypt, much like the locking and unlocking of a door: