Wolfram Alpha is almost ready to rock the world. Being the next big thing in the search arena, comparison to Google was inevitable, but as of now it is not being considered as "Google Killer". This all new "Answers Engine" is one of the most awaited launches scheduled in May.
Here is a snippet of an interesting article published on Twine by Nova Spivack which answers the 5W and H of Wolfram Alpha.
Stephen Wolfram is building something new — and it is really impressive and significant. In fact it may be as important for the Web (and the world) as Google, but for a different purpose.
A Computational Knowledge Engine for the Web
In a nutshell, Wolfram and his team have built what he calls a “computational knowledge engine” for the Web. OK, so what does that really mean? Basically it means that you can ask it factual questions and it computes answers for you.
It doesn’t simply return documents that (might) contain the answers, like Google does, and it isn’t just a giant database of knowledge, like the Wikipedia. It doesn’t simply parse natural language and then use that to retrieve documents, like Powerset, for example. Instead, Wolfram Alpha actually computes the answers to a wide range of questions — like questions that have factual answers such as “What country is Timbuktu in?” or “How many protons are in a hydrogen atom?” or “What is the average rainfall in Seattle?”
How Does it Work?
Wolfram Alpha is a system for computing the answers to questions. To accomplish this it uses built-in models of fields of knowledge, complete with data and algorithms, that represent real-world knowledge. For example, it contains formal models of much of what we know about science — massive amounts of data about various physical laws and properties, as well as data about the physical world.
Building Blocks for Knowledge Computing
Wolfram Alpha is almost more of an engineering accomplishment than a scientific one — Wolfram has broken down the set of factual questions we might ask, and the computational models and data necessary for answering them, into basic building blocks — a kind of basic language for knowledge computing if you will. Then, with these building blocks in hand his system is able to compute with them — to break down questions into the basic building blocks and computations necessary to answer them, and then to actually build up computations and compute the answers on the fly.
Wolfram’s team manually entered, and in some cases automatically pulled in, masses of raw factual data about various fields of knowledge, plus models and algorithms for doing computations with the data. By building all of this in a modular fashion on top of the Mathematica engine, they have built a system that is able to actually do computations over vast data sets representing real-world knowledge. More importantly, it enables anyone to easily construct their own computations — simply by asking questions.
How Smart is it and Will it Take Over the World?
Wolfram Alpha is like plugging into a vast electronic brain. It provides extremely impressive and thorough answers to a wide range of questions asked in many different ways, and it computes answers, it doesn’t merely look them up in a big database.
In this respect it is vastly smarter than (and different from) Google. Google simply retrieves documents based on keyword searches. Google doesn’t understand the question or the answer, and doesn’t compute answers based on models of various fields of human knowledge.
But as intelligent as it seems, Wolfram Alpha is not HAL 9000, and it wasn’t intended to be. It doesn’t have a sense of self or opinions or feelings. It’s not artificial intelligence in the sense of being a simulation of a human mind. Instead, it is a system that has been engineered to provide really rich knowledge about human knowledge — it’s a very powerful calculator that doesn’t just work for math problems — it works for many other kinds of questions that have unambiguous (computable) answers.
There is no risk of Wolfram Alpha becoming too smart, or taking over the world. It’s good at answering factual questions; it’s a computing machine, a tool — not a mind.
Relationship to the Semantic Web
There is no reason that one MUST use the Semantic Web stack to build something like Wolfram Alpha. In fact, in my opinion it would be far too difficult to try to explicitly represent everything Wolfram Alpha knows and can compute using OWL ontologies. It is too wide a range of human knowledge and giant OWL ontologies are just too difficult to build and curate.
It would of course at some point be beneficial to integrate with the Semantic Web so that the knowledge in Wolfram Alpha could be accessed, linked with, and reasoned with, by other semantic applications on the Web, and perhaps to make it easier to pull knowledge in from outside as well. In this area, the standards of the Semantic Web could be quite useful to the project.
However for the internal knowledge representation and reasoning that takes places in the system, it appears Wolfram has found a pragmatic and efficient representation of his own, and I don’t think he needs the Semantic Web at that level. It seems to be doing just fine without it.
Wolfram Alpha, at its heart is quite different from a brute force statistical search engine like Google. And it is not going to replace Google — it is not a general search engine: You would probably not use Wolfram Alpha to shop for a new car, find blog posts about a topic, or to choose a resort for your honeymoon. It is not a system that will understand the nuances of what you consider to be the perfect romantic getaway, for example — there is still no substitute for manual human-guided search for that. Where it appears to excel is when you want facts about something, or when you need to compute a factual answer to some set of questions about factual data.
Maybe Wolfram Alpha could even do a better job of retrieving documents than Google, for certain kinds of questions — by first understanding what you really want, then computing the answer, and then giving you links to documents that related to the answer. But even if it is never applied to document retrieval, I think it has the potential to play a leading role in all our daily lives — it could function like a kind of expert assistant, with all the facts and computational power in the world at our fingertips.