Search means a lot of things, but even if we limit to mean web-search, as most people understand it, there is a lot more to it than the actual technology that m

Search means a lot of things, but even if we limit to mean web-search, as most people understand it, there is a lot more to it than the actual technology that matches queries to documents. IR is for all intents and purposes a solved problem -- in fact it was solved a long time ago, and I highly recommend the seminal book “Managing Gigabytes”. I also recommend https://github.com/phaistos-networks/Trinity/wiki/IR-Search-Links https://github.com/phaistos-networks/Trinity/wiki/IR-Search-... this page(disclaimer: I am maintaining it) for some interesting/important links to IRC technologies, developments, etc. While some novel ideas come out from time to time, the fundamentals haven’t changed -- progress there is incremental and mostly specific to different encoding schemes or ways to execute queries faster by using JIT or more cache-aware datastructures, etc. Managing and queries documents based on keywords and boolean operators is one thing, and Lucene/Solr, and Trinity (https://github.com/phaistos-networks/Trinity https://github.com/phaistos-networks/Trinity) among other technologies can be used to take care of those challenges. But that’s the easy part (assuming you can do this fast enough, because you almost always can’t afford long-running queries): - User Interfaces: Not just how results are presented, but also how users can construct or input queries. What options can be come available for filtering matches? - Ranking: Precision is key, and rather simple formulas (tf/idf, BM25, etc) generally don’t work well for many/most domains. Furthermore, ranking is almost always not just about relevancy. It factors in static context scores (e.g document “popularity”), personalisation biases(how likely is it for user to mean Soccer or American Football for [football]),and other signals, fused together somehow to determine the final ranking of matched documents. - Scale: Getting everything right is one thing, getting everything right at massive scale is whole different game. What may work on small scale(algorithms, technologies, services) may not work at all when you scale out. - Everything else not directly related to search but either important or fundamental to a good experience/business: from matching queries to ads, to analytics, to autosuggestions, to training ML models to power all that, etc. Web search is not a zero sum game. Bing makes over 3nb / year and while it may not have a chance to catch up with Google anytime soon, that’s a great business right there. Ditto for DDG. There are also companies that offer a different or better experience and access to datasets google doesn’t yet. So, all told, search may be solved only in terms of the basic IR technology that makes it all work, and arguably a lot better than it used to be in terms of user interfaces, ranking, etc, but it will take a lot longer until those other aspects of web search may be considered ‘solved’.