There is a Natural Language Processing (NLP) technique called Question Answering (QA), where users ask a question in natural language to a system, and receive an answer. For example we might ask, “what is the maximum take-off weight of the Airbus A380?” and receive the answer “575,000 kg”. This is an improvement over plain search engines, which only take keywords as a query and answer with entire pages or documents of information, from which the user has to extract the desired answer.
I have written about Search Agents before (1, 2). Make sure to read those posts to know what I’m talking about here. This post delves a bit deeper into business models for Search Agents. The key business issue is how to monetize search users with this novel approach to search, while keeping costs from use of third party search services low. So how to make money with a Search Agent? A straightforward way to earn a commission would be to run banner ads or text ads from an ad network on the results pages.
Messaging apps are normally location-independent by design. If your device is connected to the Internet, sending a greeting or a snapshot to any other online device is normal. But what if the spatial dimensions were re-introduced to a chat application? Send a message to anyone listening in a 100 meter radius or to anyone in the same metropolitan area. What if the app guaranteed that you really have to be where you are to send and receive such messages?
If you use leading search engines a lot, you have probably noticed that they haven’t improved much over the years. They really seem to be stuck in the 2000s, just with more spam than back then. One way in which they could improve, is by using Machine Learning (ML) for processing search results. Some of the companies involved have good ML and AI teams, so why can’t they use that know-how to improve their search engines?
We all know of things online which we consider to be the best of a category. The best YouTube channel on mathematics, the best blog about marketing, the best Twitter about painting, the best website of essays, etc. This idea is for a simple website where users submit and vote on suggestions for the best resource in various categories. While the site focuses on the best and places it prominently, the runner ups would be displayed too.
This is a follow-up post about Search Agents. Here I want to talk a bit more about a possible architecture. The diagram above shows a Search Agent with its peers. To the left, in red, is the browser through which the user submits search queries to the Agent, whose components are shown in green. On the basis of these queries, the web client backend contacts sources on the Internet, such as various search engines, search boxes on social sites and important sources such as Wikipedia.