The Future of Search Engines: Voice Search, Lens and More

Search has become widespread in everyone’s daily lives, with 93 percent of online engagements beginning with a search engine and accounting for 64 percent of all website traffic.

It’s remarkable to see how search engines have evolved over the last two decades since search engines became a thing, from simple navigation engines that take you to the correct website to the platforms we see today, with everyday queries addressed immediately on search engine results pages (SERPs).

When you type a word or phrase into the search field, you can get the right answer to any question, find the specific product you’re looking for, or learn more about a topic.

However, today’s degree of search precision did not start this way. As a result, let us briefly go back in time to observe how search engines have evolved. The history of search engines can help us better understand and appreciate what the future has in store for us, so we can better understand and appreciate it.

The Evolution of Search Engines

Vannevar Bush gave an unsettling premonition to the search engine in 1945 when he wrote a column in The Atlantic Monthly asking scientists to contribute to the creation of a corpus of information for the entire world. 

He presented a practically infinite, quick, dependable, and associative memory narrative and retrieval mechanism. He referred to it as a “Memex,” which sounds familiar, right?

Gerald Salton later provided the groundwork for modern search engine technology. His teams at Harvard and Cornell created Salton’s Magic Automatic Retriever of Text (the SMART system). 

The strategy made use of key search engine operations and ideas such as Inverse Document Frequency (IDF), Term Frequency (TF), term discrimination values, andrelevance feedback mechanisms.

Alan Emtage invented the first search engine, Archie (short for archives), in 1990. Archie solved the scattered data issue by integrating a script-based data gatherer with a sequence matcher to retrieve file names that match a search term.

Archie was so popular that the University of Nevada System Computing Services group created Veronica, a service that performed the same function as Archie but operated with plain text files. Another search engine, Jughead, quickly followed.

In the years that followed, several other types of search engines were invented. Excite, which was made by six Stanford students, was followed by World Wide Web Wanderer, which used bots to count active web servers and measure the growth of the Internet.

For the first time, Alta Vista provided the globe with limitless bandwidth and was also the first to support natural language searches. Yahoo launched in 1994, but it first outsourced its search services.

As we all know, the true apex of search engine history occurred with the creation of Google in 1996. Google, however, is more of a ranking system than a search engine. 

Originally known as Backrub, it was created to use backlinks for search purposes. It used citation notation to rank pages, so each mention of a website on another site counted toward the mentioned site.

Many other search engines emerged and fell (some thrived) throughout Google’s battle for supremacy, including MSN, AskJeeves, and Bing, but there is little doubt that Google today rules as the monolithic deity of the search engines. 

The desire to get more people to see your business through Google’s ranking system has led to a huge industry, including search engine optimization, digital marketing, and website design firms.

The Modern Search Engine

Google is the undisputed leader in the field of search engines. It quickly established itself as the major online search competition, accounting for over two-thirds of all online searches. Its brand has become associated with online search, and it continues to set the bar forhow other search engines work.

After more than two decades of Google supremacy, the essential concepts of search have stayed consistent, but practically everything else about technology and information availability has changed.

Due to developments in machine learning, we can now access high-quality information with increased precision. Technologies like “neural embeddings” help us figure out what people are looking for without having to know specific search terms.

Due to natural embeddings, Google Search is able to match a question like “What is my shoe size?” to particular articles regarding the “shoe size measurement for different regions.”

Other upgrades, such as highlighted snippets (the descriptive box at the top of a search result page) help users locate what they’re looking for more quickly, and the Jobs in Search feature has given millions of individuals the chance to uncover new possibilities by streamlining the job search process.

These two trends—more conversational queries and visual searches—will have a huge influence on consumer behavior as search enters its next phase of evolution.

Conversational Queries: Google Voice Search

Remember when you had to call a phone number from your mobile device by saying the name of your contact? That’s how voice search appeared in its early days (to be more precise, in 2010). And, to no one’s surprise, only a few people used it.

Voice search has come a long way since then. Google stated in June 2011 that it would begin to roll out voice search on Google.com. Initially, the functionality could only be accessed in English. Google Voice Search now supports around 60 languages.

The way people think about written and spoken searches changed a lot when the Hummingbird search engine came out in 2013.

The new algorithm prioritizes natural language processing and was designed to take into account the user’s intent as well as the context of the query. Since then, search queries organized into sentences haveyielded more relevant results.

When using voice search, people generally ask a long query in the same way they would normally talk. For example, “where should I go for my next family trip?” “Where’s the best place to stay in Tbilisi?” “What side attractions are available for adults and kids in Tbilisi?” As a result, the Hummingbird release provided a significant boost to voice search optimization.

Visual Search: Google Lens

The use of visual search, like voice search, is rapidly increasing. Another step toward better user experienceis visual search. To keep and expand their user base, platforms such as Google, Instagram, Pinterest, and Amazon rely on offering excellent experiences.

Visual search uses pictures as the user uploads them to a search engine to display related content. Artificial Intelligence (AI) is used by search engines to analyze picture uploads far better than in the past.

AI is used to detect the content and context of a picture, allowing search engines to swiftly display related content and images. Using a visual search tool makes it easier for people to find information on the web without having to type in a search term.

For example, you can use a picture in Google search or Google Lens to get results that include related photos, products, websites that utilize similar images, and results with information relevant to the image.

Google Lens assists users with real-time text translation, plant and animal identification, product discovery, and other tasks. This opens up new potential for businesses to generate imagery and connect physical and online experiences. For instance, you can use Google Lens to read a restaurant’s menu to obtain reviews of a certain meal.

Everywhere there is search functionality, visual search options are being added. Visual search tools have previously been deployed by Google, Pinterest, Instagram, Amazon, and Microsoft. It is already being used in e-commerce, social media, websites, apps, and even real-world situations.

Google MUM: The Future of Search

According to the search engine giant, the new algorithm update, MUM, is a large-scale AI. It has the potential to transform internet search into a significantly more sophisticated service, working as a virtual research assistant as it sifts the web for answers to complex queries.

MUM, which stands for multitask unified model, is the latest in a series of behind-the-scenes improvements to the Google search engine that the organization claims have resulted in significant improvements in the quality of its results.

These include the establishment of a “knowledge graph” a decade ago, which specified the link between different ideas, providing a level of semantic understanding to search.

Final Verdict

For the foreseeable future, we are unlikely to witness another significant revolution in search engines as we know it. However, we will continue to see the gradual introduction of new features and updates to the search engines we’ve grown accustomed to.

Get the latest search news, trends, & tips in your inbox.Sign Up to our Newsletter

Leave a Reply

Your email address will not be published. Required fields are marked *