Search engines, unlike popular beliefs, have been around for over three decades now. Ever since the conceptualization and creation of the first one in 1990, search engines have come a long way in simplifying referencing and association tasks on the internet. However, with the rapid progress and proliferation of the world wide web and subsequent rise in the number of web pages, the sheer volume of data began to overpower traditional algorithms. As this problem grew, AI was brought in to address key issues and mitigate the concerns that came with growing amounts of information and an ever-expanding user base. The advent of natural language processing was especially important as this allowed computers to make precise estimations of human requests, allowing them to accurately reference searches with existing content and web pages on the internet. However, despite the presence of artificial intelligence and machine learning in search engine algorithms, the existing paradigm is set to be further changed by the arrival of advanced language models.
Ever since the release of ChatGPT and other iterations of interactive artificial intelligence, companies have been quick to identify and tap into the potential they hold to streamline searches. Microsoft’s Bing was the first to notice this potential and partnered up with OpenAI to provide customers with a search engine that does more than mere sequencing of search results. In an industry where Google has enjoyed a monopoly, the arrival of AI search engines might just end up changing extant realities. Despite AI search’s existence for a fairly considerable period, the new iterations of generative artificial intelligence might end up altering the future of search engines as a whole, since modern users expect more information at greater ease. The article explores what AI search means for the industry alongside its potential effects on students and academic users that might have more pointed intent in their queries.
Bing, ChatGPT, and More: What Search Engine AI Means for Future Web Users
Search engines form the backbone of knowledge sharing on the internet.
Nearly all search engines rely on artificial intelligence technologies to help them index and display relevant information to users entering a search query. What started with a primarily keyword-based search protocol progressively evolved to include more complex guidelines to ensure quality content gets the most attention as opposed to keyword density. Carrying out these analyses in the timeframes of a simple search on the internet necessitated search engine artificial intelligence. Soon enough, this was also followed up with more pointed opportunities for marketing and sponsored search results as the algorithm adapted to what a user would find relevant. While all of this including the core analytics aspect of search engine mechanics remains applicable to today’s search platforms, the arrival of AI search engines and chatbots that provide more than search results might be signaling another stage in their evolution.
Partnerships such as those between Bing and ChatGPT introduce a shift in thought and approach. Users can now not only look for web-based answers to their queries but also have artificial intelligence respond with detailed replies. Big data and key applications in machine learning have made it possible to sequence and bring about structured responses to user queries in the shortest of times. This has also been improved by the consistent increase in the number of parameters involved in the training data sets of language models and the duration they’re allowed to train for autonomously. Following in the footsteps of OpenAI’s ChatGPT, Google was not too far behind and launched their language model AI named Bard in the latter half of March 2023. Though the trajectory of Bard has so far been focused on development and training the model to achieve increasing levels of accuracy, it has been touted as distinct from Google’s search engine. However, Bard’s capabilities might be integrated into Google’s massive search engine architecture in the future. This signals a shift from a traditional approach to search and referencing alongside the financial implications it bears for the company.
Along with these major names, several other search engine firms are also working feverishly to integrate generative capabilities within their respective search tools. These platforms offer several unique features to users, and nuanced AI search is set to gain massive popularity in the coming years. Search engines like Andi use logical reasoning to populate search results relevant to the user’s query. The algorithm emulates human-like thought to provide useful data and information. Similarly, YouChat’s search engine You.com allows users to filter out advertorial content and promotions by using user-centric preferences and language model AIs to provide a personalized experience. Other platforms based on similar tenets include NeevaAI, which protects user information from advertisers and other bots that crawl search history data to provide promotional search results. DuckDuckGo’s DuckAssist is another privacy-centric search engine that prioritizes relevance and immediate access to information over disparate search results. Lastly, customizable search is another key nuance that AI companies are exploring, and firms like Waldo enhance the degree of control a user has over their search results page. Such advances are bound to expand market potential and will define the trajectory of search engines in the near future.
New AI Search Engines and the Quality of Search Results
The effectiveness of a search engine is dictated by the quality of its results.
AI in search engines has streamlined results while directing users to information, alongside being crucial to digital marketing techniques. However, this has not been without concerns surrounding quality. Search engine artificial intelligence has features that allow users to automatically complete their search queries. This is not good for nuanced search protocols and often restricts people to a very limited number of generic queries that might be inherently biased to include only those questions that are deemed useful by a section of users. As individuals continue to use these techniques, the algorithm is further bolstered and produces these results more frequently. While not completely disadvantageous, it does embody a degree of AI bias. Moreover, AI search engines also make it difficult for users to carry out in-depth searches, necessitating a separate advanced search feature in most search engines.
The arrival of generative AI-supported search might also have to address the same issues as it begins dealing with large volumes of queries from prospective users. Though the in-depth explanations by an AI chatbot might be helpful for users looking into more obscure topics, the true potential of these AI search engines will determine the amount of control users will have over augmenting their search parameters. Another dynamic to watch out for will be the usage of keywords and how search engines react to them over time. Lastly, the drawbacks of language model generators such as hallucinatory results might be misleading and must be addressed effectively. Though these tools are still in their experimental phases, they might have a far-reaching effect on users across the board.
Why AI Search Will Matter to Students
AI search engines will have a major impact on students and on the academic community at large.
The arrival of language models galvanized the academic community into addressing questions about the future of education in an increasingly mechanized world. Teaching in the age of AI is bound to witness changes, and student participation in education is also influenced by digital tools such as search engines. Areas of academics like research rely heavily on search engines alongside other referencing methods to support students’ academic undertakings. Facilitating transparent AI search engines that allow users complete control over what they look for is integral to education and the future of knowledge sharing as a whole. Future search engines will have to be modeled on the tenets of responsible AI as a good chunk of learning now emanates from complex interactions of humans with the internet. Taking these concerns into account, developers and technology advocacy groups must ensure that larger interests are aligned with AI ethics to ensure the streamlined and productive development of modern search engines.
FAQs
1. What are some examples of AI search engines?
Here are a few examples of some popular AI search engines rated favorably by several users:
- Microsoft Bing
- Google Bard
- NeevaAI
- YouChat
- Perplexity AI
- Andi
2. How do AI search engines work?
AI search engines combine information from their data set as well as real time web search results to summarize information in response to a user query. Using generative AI, they’re capable of putting together important highlights of key info along with presenting available web results either as references or citations.
3. Do search engines use AI?
While popular search engines like Google already deploy AI to sift through gargantuan databases and search results, the arrival of generative AI in the search engine space is more recent. Several companies are actively experimenting with AI search engines to produce distinct tools for a simpler web search experience.