The world of search engines has come a long way since the early days of the internet. Gone are the days when users had to sift through pages and pages of search results to find the information they were looking for.
Today, with the help of artificial intelligence (AI), search engines have become smarter, more efficient, and more user-friendly than ever before. In this article, OERLive explores how incorporating AI into search engines adds a lot of layers of features for users.
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Personalisation
One of the most significant benefits of incorporating AI into search engines is the ability to offer personalised search results. By analysing a user’s search history and behaviour, AI-powered search engines can provide more relevant search results. For example, if a user frequently searches for vegan recipes, an AI-powered search engine (such as the ones proposed by Google and Microsoft’s Bing) can prioritise vegan recipes in search results.
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Natural Language Processing
Natural Language Processing (NLP) is an AI-powered technology that helps computers understand human language. By incorporating NLP into search engines, users can enter natural language queries rather than just keywords.
This means that search engines can better understand the user’s intent and provide more relevant search results. For example, a user can ask “What is the best restaurant in Muscat?” rather than just searching for “best restaurant Muscat.”
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Visual Search
Visual search is a relatively new feature in search engines, enabled by AI. Visual search allows users to search for information using images or videos. AI-powered search engines can recognise objects in images and provide relevant search results.
For example, if a user takes a picture of a product they want to buy, an AI-powered search engine can identify the product and provide links to buy it. Google, Yandex, Bing and a host of other search engines currently offer visual search options to its users, and to good and surprising effects.
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Conversational Search
Conversational search is another feature that AI enables. Conversational search allows users to ask questions and get answers in a conversational style. AI-powered chatbots can understand natural language queries and provide relevant search results.
For example, a user can ask a chatbot, “What’s the weather like today?” and the chatbot will provide the current weather conditions.
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Advanced Filters
AI-powered search engines also offer advanced filters that allow users to refine their search results. Users can filter their search results by date, location, language, and more. AI-powered filters also allow users to search for specific types of content, such as videos, images, or news articles.
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Semantic Search
Semantic search is an AI-powered technique that goes beyond just matching keywords to provide relevant search results. Semantic search takes into account the context of the query and provides search results based on the intended meaning. For example, if a user searches for “apple,” a semantic search engine can determine whether the user is looking for information about the fruit or the technology company.
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Predictive Analytics
AI-powered search engines can use predictive analytics to anticipate the user’s needs and provide relevant search results before the user even asks for them. By analysing a user’s search history and behaviour, AI-powered search engines can predict what the user is looking for and provide suggestions accordingly.
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Improved Accuracy
AI-powered search engines can also improve the accuracy of search results by filtering out irrelevant results and providing more relevant ones. By using machine learning algorithms, search engines can learn from user feedback and improve their accuracy over time.
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Multi-Lingual Support
AI-powered search engines can also support multiple languages, making it easier for users to search for information in their native language. By using NLP technology, search engines can understand queries in multiple languages and provide search results in the user’s preferred language.
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Faster Search Results
AI-powered search engines can also provide faster search results by using predictive analytics to anticipate the user’s needs and provide relevant results faster. By analysing the user’s search history and behaviour, search engines can prioritise search results and provide them faster.
