A few days ago, Stephen Wolfram announced his Alpha project. The system is supposed to be something like the next level of search engines. A few commentators that were actually able to get a demonstration say it might be even bigger than google – ok, we have heard this several times before but anyways…
Rather than looking up information it calculates the answer to a question by using a vast amount of data and ‘hand-engineered scripts’:
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 is the location of Timbuktu?” or “How many protons are in a hydrogen atom?,” “What was the average rainfall in Boston last year?,” “What is the 307th digit of Pi?,” or “what would 80/20 vision look like?”
Think about that for a minute. It computes the answers. Wolfram Alpha doesn’t simply contain huge amounts of manually entered pairs of questions and answers, nor does it search for answers in a database of facts. Instead, it understands and then computes answers to certain kinds of questions.
A extensive review can be found here and there is also a comment from Doug Lenat, the (main) developer of Cyc, the “artificial intelligence project that attempts to assemble a comprehensive ontology and knowledge base of everyday common sense knowledge, with the goal of enabling AI applications to perform human-like reasoning.”
Having just discussed this with a few of my colleagues doing finance and risk management, one of the first questions that arose was if this knowledge computation can be turned into a trading strategy. Something like a knowledge hedge fund. I guess this would be indeed an interesting question, i.e., if these algorithms can be turned into something like a predictor for all kinds of things. Imagine a question “How much will the houseprice in my neighborhood XYZ suffer from the downturn?”, or “What is the average price for a ticket from say New York to Hong Kong?”.