Search engines 10 years from now will be a lot better than the ones we have now. We know this because Google itself gets a little better each day. We’re constantly writing and revising new notions of search relevance, and we release improvements almost daily. Those improvements add up for us and for other search engines, so it follows that search engines 10 years from now will be markedly better. Therefore, the real question is not will search be better, but rather how will it be better?

Location

Social

Language

Conclusion

Your location is one potentially useful facet of personalized information. Looking at my questions, the answers to a number of them (What time does J.C. Penney open? How much power does that hydroelectric dam generate? What time doesplay?) require the search engine to know that I was in Yankton, South Dakota and Crofton, Nebraska when I asked. Since location is relevant to a lot of searches, incorporating user location and context will be pivotal in increasing the relevance and ease of search in the future.Another element of personalization is social context. Who am I friends with, and how do I relate to them? How can I harness their knowledge more efficiently? For example, I have a friend who works at a store called LF in Los Angeles (hence, the question about LF in San Francisco). By itself, “LF” is a very ambiguous acronym. According to the first page of search results on Google, it could refer to my friend’s trendy fashion store, but it could also refer to Leapfrog Enterprises, low frequency, Lebhar-Friedman, Li & Fung Investment Group, LF Driscoll Construction Management, large format, or a future concept car design from Lexus. Today, the person typing “LF” has to figure out which is the right result – to “disambiguate” the ambiguous term – but this is something that the search engine needs to get better at. Perhaps we’ll understand the semantics of the question about where LF in San Francisco is, and infer that LF is a store. Or maybe, search could analyze my social graph and realize that one of my friends works at LF, that I saw that friend this weekend, and that in that context “LF” refers to her place of employment. Algorithmic analysis of the user’s social graph to further refine a query or disambiguate it could prove very useful in the future.In addition, there are searches where actually asking a friend helps. I was having a hard time finding out the answer to the question about aspirin versus Coumadin because I was spelling it ‘cumitin’ and Google wasn’t correcting me. A quick email to a doctor friend, and I was back on the right track - equipped with the right spelling and his explanation of the difference, so I could search and learn even more about how these two drugs are used to thin blood. There’s a lot of expertise, knowledge, and context in users’ social graphs, so putting tools in place to make “friend-augmented" search easy could make search more efficient and more relevant.The above examples show how modes, media, and various forms of personalization have the potential to vastly improve search – but what about language? We know there are cases where an answer exists on the web, but not in a language you read. This is why Google is investing in machine translation. We want to be able to unlock the power of web search for anyone speaking any language. The basic concept is – if the answer exists online anywhere in any language, we’ll go get it for you, translate it and bring it back in your native tongue. This is an incredibly empowering idea that could really change the way that users experience the web and communicate with each other, particularly in languages where not a lot of native content is available. You can see our early explorations in this space here, by visiting our cross-language information retrieval tool We’re all familiar with 80-20 problems, where the last 20% of the solution is 80% of the work. Search is a 90-10 problem. Today, we have a 90% solution: I could answer all of my unanswered Saturday questions, not ideally or easily, but I could get it done with today’s search tool. (If you’re curious, the answers are below.) However, that remaining 10% of the problem really represents 90% (in fact, more than 90%) of the work. Coming up with elegant, fitting and relevant solutions to meet the challenges of mobility, modes, media, personalization, location, socialization, and language will take decades. Search is a science that will develop and advance over hundreds of years. Think of it like biology and physics in the 1500s or 1600s: it’s a new science where we make big and exciting breakthroughs all the time. However, it could be a hundred years or more before we have microscopes and an understanding of the proverbial molecules and atoms of search. Just like biology and physics several hundred years ago, the biggest advances are yet to come. That’s what makes the field of Internet search so exciting.So what's our straightforward definition of the ideal search engine? Your best friend with instant access to all the world’s facts and a photographic memory of everything you’ve seen and know. That search engine could tailor answers to you based on your preferences, your existing knowledge and the best available information; it could ask for clarification and present the answers in whatever setting or media worked best. That ideal search engine could have easily and elegantly quenched my withdrawal and fueled my addiction on Saturday. I’m very proud that Google in its first 10 years has changed expectations around information and how quickly and easily it should be able to be retrieved. But I’m even more excited about what Google search can achieve in the future.And here, in order, are the answers to my Saturday questions.Are fab, goy, and eely words? Yes, yes, and yes, according to Merriam-Webster:Search: [fab site:m-w.com ]Result: http://dev.m-w.com/dictionary/fab Search: [goy site:m-w.com]Result: