Warning: The future of search is going to get very personal. Hold on to your tin foil hats.
Let’s begin with a brief history of the search and knowledge discovery field.
Search + Analytics
In 1997, Google launched the Google.com search engine. 4.3 seconds later, Google started looking at everyone’s search queries. Just kidding. Sort of. Search and analytics have been tied at the hip since the very beginning. Companies learned that what people are searching for is useful and valuable information. Analytics can be used to evaluate the performance and effectiveness of a search solution, and they can be used to improve it going forward. By looking at what people are searching for and how they are interacting with the search results, the experience can be continuously modified to yield the best results.
Machine Learning and Artificial Intelligence
Good UX Means Good Business
In a world where technology is rapidly advancing and user expectations are rising, it’s no longer enough to have an average user experience; to delight your users and surpass your competition you must strive for the exceptional.
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Search and analytics are great, but they can produce an enormous amount of data very quickly — too much for human review. Machine learning and artificial intelligence (AI) techniques now allow for the automated processing of search analytics, and even automated reactions and responses. Google has introduced AI techniques for everything from adjusting their search-ranking algorithm to checking for mobile UI compliance and spam prevention. Think about self-driving cars, or playing games like Chess or Go. Artificial intelligence is reaching a point where it can perform certain tasks with greater speed and accuracy than humans, and it is definitely the hot topic in search and knowledge discovery. Everything from image search to curating your Facebook News Feed has started using machine learning algorithms to get the best results with minimal or no human intervention. For reference, I wrote a few articles on the basics of machine learning last year (one, two, three).
So what’s next? What will we be talking about as the cutting edge of search and knowledge discovery a year from now?
Prediction #1: Personal Intelligent Search Agents
Most of us search for and view content all day long. My first prediction is that we will each have personal AI agents that watch what we search for and what we read throughout the day, and start to recommend content to us proactively based on our behavior or interests or needs. I might search for content 10 times a day (that’s probably low) but in between or over night, my agent could be scouring the web or my corporate intranet for content that I want or need to see. Why should I have to search for something manually if a machine learning solution can figure out that I need to see it before I even realize that I do? Imagine signing in to your computer in the morning and getting an alert: “While you were away: 3 new documents that you should review.”
Prediction #2: Life Stream Search
Each of us produces and consumes an enormous amount of data in a single day. Think about all the conversations we have, all the things we look at with our eyes (people, pictures, documents, signs) and all the fragments of information we juggle in our heads. My second prediction is that we will have devices that record all of those conversations, capture pictures of everything we are looking at during the day, and give each of us a private search engine of our daily life. No more forgetting someone you met or where you left your keys or what your kids asked you to pick up at the grocery store — just search for it in your life stream. Speech and image recognition technology, as well as the size of personal audio and video devices, is improving to the point that this will soon be practical.
Prediction #3: Graph Overload (in a good way)
The relationships between various pieces of data and documents are becoming increasingly important in providing high quality search relevancy. People are not just interested in find a certain document in a vacuum. They want to find documents related to certain people or to a certain project or to a certain topic. They want to find documents that their coworkers recently edited, or restaurants that their friends enjoyed. My third prediction is an explosion of convergence and interconnecting of knowledge graphs from countless sources. Your Facebook graph will connect (if it doesn’t already) with your restaurant reviews on Yelp. Your client list (past and present) will relate to documents in your content management system. Your flight schedule will coordinate with your availability in Outlook. This graph war will escalate in fascinating and complex ways. Search engines like IBM Watson and WolframAlpha will exhaustively mine the facts and relationships in these knowledge graphs to yield search results with relevancy we could only dream of years ago.
The future seems very bright for search and knowledge discovery. Increasing computational power and creative solutions will continue to revolutionize they way we search for information. Continue to follow our thoughts on the subject by signing up for our Digital Tech digest with the form below.