Many people are already familiar with Apple’s voice search called Siri, or the search engine behind it called Wolfram Alpha. This search engine can use natural language to search vast sets of data and even compute math. However, this is just a tiny fraction of what the language can do, and I don’t even think it’s a good introduction to what’s possible. To understand the raw power of the underlying technology, you really have to understand what it is and a little about how it works.The Wolfram website has wonderful documentation and explanations, but for the uninitiated it can seem bewildering. They have repackaged the language so many different ways, that it can be hard for the beginner to understand exactly what it is. That’s why I want to venture my own introduction.Let’s start with it’s origins. Mathematica was designed as a desktop tool for computational research and exploration. It continued evolving and the breakthrough was realizing those symbols could be anything: images, sounds, algorithms, geometry, data-sets ... anything. So, it became more than just a language.Stephen Wolfram calls this a knowledge-based language because it has smart-objects built in that can be computed.The language doesn’t simply find results, it computes results into actual models, analysis and other symbolic objects. The real power is that the results remain symbolic objects that can be further manipulated symbolically (i.e. embed in another symbolic object, operate on it).In short, anything can be computed. Pretty abstract, I know. Don’t worry, we’ll get to examples soon.The actual syntax is a combination of Objects and Operators which are grouped and ordered by square brackets [ ]. The stuff at the center of the formula gets read first and then it expands out like a Russian doll.Out of many potential examples, I have carefully selected one from their site to illustrate it’s simplicity and power.Let’s say we want our system to determine the difference between poetry and prose. This would be difficult to program directly because there are so many variables and the differences are so subtle. With Wolfram Language, that hard stuff becomes easy. You can train it recognize the difference very quickly. Here’s how it works, let’s use Shakespeare for an example:First, scan all of Hamlet and call that type of stuff prose. Then scan all of Shakespeare’s Sonnets and call that stuff poetry. Easy.Next, train the system with machine learning: Classify and Predict are the two big functions. We want to Classify which is poetry and which is prose. Wolfram looks at our situation and instantly determines that the Markov Method is the best for differentiating among all the subtle differences between prose and poetry.That’s it. Any system using this bit of training will automatically be able to detect the difference between poetry and prose with a high degree of accuracy. The key to this accuracy is the size of the data set. You really need at millions of data points to train it reliably. But with Wolfram, many of those data sets are already built in. Easy.This is just one tiny example to illustrate what the language looks like and how it goes beyond symbols to work with computable objects. We could continue translating poems into interactive maps, and interactions into music, and so on.How does Wolfram compare with other products like Apache Hadoop and others? Well, it’s a totally different thing. In those products everything is manual. The various axis (and all the variables) are manually defined. Instead, Wolfram intelligently applies formulas and makes choices to optimize results based on specific conditions. It makes the hard stuff automatic. Plus, it’s capable of much more than machine learning; that’s just one example of hundreds: sound, 3d-geometry, language, images, etc. — and a mixture of them all.Mathematica is still the most powerful and polished way to access the Wolfram Language. Their new Programming Cloud (and other cloud offerings) signal serious intent to move to the web, but it is still early days. The language is very mature for desktop exploration, and some companies have even made Mathematica applications for small scale internal use, which can be quite useful.Even though the Wolfram website has signaled intent to make it more broadly deployable within commercial services, I don’t think this is the proper way to use the language. Within my own company, we find Wolfram extremely handy for research, but not deployment within a web-based product. In short, it isn’t performant:Commercial products require more than a powerful language, they are made within an ecosystem of services and vendors that all have to work together. Without machine learning built into the native cloud where data is stored, it can’t be deployed in a SaaS product in a way that lives up to expectations.While Stephen Wolfram would love for his language to be used within commercial products, I think he resents having to play nice with lower level languages. His alternative of making API requests across the web isn’t a good way to embed intelligence within products. And I don’t think we will ever see entire SaaS products built entirely with a functional language. Programming is the art of automation.The Wolfram Community is full of very smart people using the language for research and exploration. They represent the cutting edge of computation. Personally, I’m looking forward to when we can see intelligence woven into commercial and consumer products that solve real problems for people on a daily basis.From a quick cheer to a standing ovation, clap to show how much you enjoyed this story.CEO of Learning Machine. www.learningmachine.com