By ItsNotMagicItsScience
Image Source: http://www.everystockphoto.com/photo.php?imageId=5737268&searchId=1036ba3c4ed2c021e8f5e2641c8a23e3&npos=3

When your boss, your professor or your teacher asks you for information on a subject, where do you usually head? For most of us, it’s Google, the search engine that indexes billions of pages of online content, whose robotic code crawlers mine it for keywords and context and whose ever evolving algorithms rank it according to relevance in the simple page that fills our web browsers.

Whether we use it as a jumping off point or an end point for our research, modern life is almost unthinkable without these mechanized search engines and assistants that help us with our daily tasks. From grocery shopping to looking for pet friendly pesticides, we access dozens of pages of user-generated and official or brand-endorsed product reviews and information on a daily basis.

In the ‘old days’ of contextual search, anyone typing in an identical search term, say, “President Obama” would receive the same ranked pages of results. But contextual search is a mathematical ‘old hat’: today many of the search engines and apps we use are considered ‘smart’ they analyze and store our own behavior and shape the results they yield around our tastes.

For example, that “President Obama” search. Someone whose browsing profile – from the online news they read to the people they follow on Twitter – puts them broadly in the Democrat camp will probably pull up a set of results largely supportive of the US President. Someone’s whose online history is more closely associated with a Republican profile might see links to the Birther movement or other sites skeptical or critical of the American leader’s policies.

It’s part of a growing trend of adaptive data that delivers personalized – or individualized – results. iPhone users will be well acquainted with Siri – Apple’s data assistant that can understand natural voice commands and process them accordingly. Currently the application can do simple tasks such as scheduling tasks and appointments as well as searching the Net for restaurants and local landmarks.

But the next step will be adding in a predictive layer. It’s likely that when you ask future iterations of Siri or similar applications for other computer and phone systems for restaurants they will offer you a list based on your own preferences – perhaps mined from your Facebook or social media comments or even you’re recent purchasing history (for example, if you’ve used Near Field Communications or e-wallets to pay for those services in the recent past).

That’s an enormous jump. Imagine travelling to a city like Tokyo for the first time. Instead of doing your own research, you’ll just have to ask Google to recommend places and activities that will interest you. It might be the Museum of Contemporary Art or the trendy boutiques of Cat Street, as well as giving you a handy list of bubble tea serving cafes to stop in at along the way.

Sites like the on-demand video website Netflix are already exploring the boundaries of recommendation algorithms. Faced with a catalogue containing thousands of titles, the recommendation engine is designed to stop customers feeling overwhelmed by choice. By offering a menu of items based around one’s previously seen movies, the company hopes to make it easier and faster for customers to select their next based movie.

In an information-based age, these selective filters are designed to help us navigate the terabytes of data that are created daily. Content aggregators will help to select the news we see, the television we watch, even the memes we follow. Of course, the worry is that these filters could eventually narrow the information we receive. Instead of leading us to new information and ideas – one of the founding principles of the Internet – instead it feeds us with an unvarying menu of content that simply reinforces our beliefs. So, perhaps the Siri of the future will need a special ‘Open Mind’ tab to remind us that there’s life beyond ourselves.