AI in Search: Ever since the revolution of artificial intelligence that took place around two years ago, it has drastically changed the digital landscape. Multiple industries such as media, cybersecurity, gaming, finance, and such have been transformed by integrating AI. Search is also one of the industries going through a “metamorphosis” of sorts after its AI integration.
While the internet has existed since the 1960s, it was not until the early 1990s that dedicated search engines emerged. Archie, WebCrawler, and Yahoo! Were the early players in the domain, holding almost equal market shares. However, in the late 1990s, Google came out and changed the game of search forever.
For the past three decades, Google has been the dominant player in the search engine market. Almost 90% of the global population uses either Google or Chrome to browse the internet. It is not that Google does not have any competitors. There are Bing, DuckDuckGo, Baidu, Brave, and more that offer even better features than Google for browsing, safety, and privacy. However, despite having an array of competitors, its influence has not diminished.
That being said, this was before the “AI Revolution.” AI companies such as OpenAI and Perplexity AI are now offering artificial intelligence-powered search engines. These new AI-powered search engines are game changers. They are starting to gain traction and could potentially challenge Google’s dominance in the future.
AI-powered search engines are built on the foundation of large language models (LLMs). By combining advanced deep learning techniques with user input and vast datasets, these systems can engage in dynamic, conversational interactions. Unlike traditional search, which presents a list of static results, AI-powered search provides a more interactive and personalized experience. These signal a potential shift in how we interact with information online.
So, not only are the AI search engines faster, but they also provide more tailored and accurate results. However, despite its numerous advantages over traditional search engines, AI search engines have their fair share of issues.
How are AI Technologies and Applications Used in Search?
The integration of AI into search technology is slowly transforming the way we access and interact with information online. Traditional search engines primarily relied on keyword matching and ranking algorithms to provide users with a list of relevant links. However, with large language models (LLMs) and machine learning (ML), search engines have evolved into more intelligent, personalized systems. They can understand and respond to queries with greater nuance and accuracy now.
Natural Language Processing (NLP) and User Intent
At the heart of AI’s impact on search engines is natural language processing (NLP). NLP allows search engines to understand and process human language in a more sophisticated way than ever. Traditional search engines often struggle with understanding the context behind user queries. They present their results in lists based on keywords alone. AI, however, can discern not only the meaning of the term but also the underlying intent of the user.
One prime example of NLP in search is Google’s BERT (Bidirectional Encoder Representations from Transformers) algorithm. Introduced in 2019, BERT uses machine learning to better understand the context of words in a search query.
For example, a query like “how to cook a steak” would traditionally result in a list of links about cooking techniques. However, BERT allows Google to determine whether the user is asking for a recipe, tips for grilling, or perhaps information about the best cuts of steak. Thus, it offers results that better match the user’s intent.
This technology is useful for understanding more conversational, complex, or ambiguous search queries. Instead of relying solely on keyword matching, BERT evaluates entire sentences to deliver contextually relevant search results. This not only improves accuracy but also enhances user experience by providing more precise answers to queries.
AI-Powered Conversational Search
Another area where AI is making strides in search is through conversational search. Tools like OpenAI’s ChatGPT and Google’s Bard deliver users an interactive search experience where queries prompt direct, conversational responses. This AI-powered search goes beyond presenting links or snippets. Instead, it engages the user to provide more personalized, immediate answers.
For example, when using a chatbot in search, rather than seeing a list of search results, users can ask follow-up questions and receive further clarification in real-time. This results in a more dynamic and conversational exchange of information. This approach offers a more human-like interaction, where the AI adapts to the user’s inquiries, providing answers more intuitively.
A notable case study here is Perplexity, an emerging search engine that uses GPT-4 and other advanced models to deliver answers in a conversational style. Unlike traditional search engines that focus on linking users to external content, Perplexity summarizes complex information directly in response to user queries. It offers an experience that is far more intuitive and similar to asking an expert a question.
Personalization and AI-Driven Recommendations
AI’s ability to process vast amounts of data allows search engines to deliver personalized experiences to users. Machine learning algorithms can analyze a user’s past behavior, search history, and preferences to refine search results, making them more relevant to each individual. This personalization is not just about offering more targeted results, but about predicting what the user might want before they even ask for it.
Google’s RankBrain is a great example of personalization in search. RankBrain is an AI algorithm that uses machine learning to better understand and predict user preferences. It is particularly effective for longer, more complex queries, where traditional keyword matching would be insufficient. It goes beyond simple word association, learning from user interactions to deliver more accurate search results over time.
The case of YouTube, a subsidiary of Google, can also showcase how AI-driven recommendations work on a deeper level. By analyzing user behavior, such as videos watched, liked, and shared, YouTube’s recommendation system uses machine learning to suggest videos that are most likely to interest the viewer. This personalized search system is not just optimized for clicks but also tailors content to individual tastes and viewing patterns.
AI in Image and Video Search
AI’s influence on search is also extending beyond text-based queries to visual search. Visual search tools use artificial intelligence technologies, such as computer vision, to allow users to search for images and videos based on visual content rather than text. This application of AI is rapidly gaining traction in fields such as retail, where consumers can take pictures of products and use search engines to find similar items or products from specific brands.
Take Google Lens for example, which uses AI and machine learning to identify objects in photos. This AI-powered tool is capable of scanning a picture and searching for similar items online. With applications ranging from identifying landmarks to translating text in images, AI-powered visual search is broadening the scope of traditional search engines by integrating visual data.
Another contender in this field is Bing Visual Search. It provides users the ability to search for videos based on scenes or elements from within the video, not just titles or descriptions.
AI technologies in these systems break down video content, making it possible to search by scene, action, or even the emotions conveyed in a clip.
AI-Driven Search for Better Accuracy and Speed
One of the key benefits of AI is its ability to rapidly process and analyze enormous datasets, offering results that are not only faster but more accurate. AI systems can sift through vast amounts of information in real time, filtering out irrelevant data and providing precise, up-to-date answers.
A striking example of this is Perplexity AI, which uses cutting-edge LLMs to retrieve relevant information faster than traditional search engines. By reducing the reliance on static results and enhancing real-time data processing, AI-powered search engines are setting new standards for speed and accuracy.
Future of AI concerning Search
The future of search, with respect to AI, holds immense potential. Its trajectory is set to redefine how we interact with information.
While AI has already made significant strides in transforming search engines, the next wave of innovations promises to elevate user experience, accuracy, and speed even further. From hyper-personalization and voice-driven search to the integration of advanced multimodal capabilities, AI is set to redefine the way we navigate the internet.
Hyper-Personalization in Search
One of the key trends we can expect in the future of AI-powered search is an even greater emphasis on hyper-personalization. AI algorithms are becoming more adept at processing vast amounts of data to offer individualized, context-aware results. This level of personalization will extend beyond just search history and past interactions. With more advanced AI, search engines will be able to tailor results based on real-time data, including the user’s current location, mood, and even their activity throughout the day.
By leveraging data from connected devices, AI will enhance the ability of search engines to predict what a user is looking for even before they initiate a search.
For example, Google’s AI-driven “Personalized Search” is already laying the foundation for this, where the search engine’s algorithms refine results to match users’ needs.
The next step will likely bring even more intuitive personal assistants that understand a user’s preferences so well they can almost predict their queries before the user even types them.
Voice and Conversational Search
Voice search is another area where artificial intelligence is expected to transform the search experience. The growth of voice-enabled devices such as smartphones, smart speakers, and virtual assistants like Amazon Alexa, Google Assistant, and Apple’s Siri is already having a significant impact on how we conduct searches. According to a 2022 report by Statista, more than 40% of internet users in the US use voice search. In the future, this figure is only expected to rise.
AI-powered voice search will become even more sophisticated, with search engines understanding not only what you say but also how you say it. The evolution of natural language understanding (NLU) will lead to more fluid, nuanced conversations with AI, allowing for highly personalized voice interactions. Users will be able to ask complex, conversational questions and receive answers in real-time, much like speaking with a human assistant.
Furthermore, voice search will likely integrate with other AI-powered tools like Google Duplex, a technology capable of making phone calls and carrying on human-like conversations to complete tasks like booking appointments or making reservations.
The Rise of Multimodal Search
Multimodal search is also on the rise. With multimodal search, AI can simultaneously process multiple types of data including text, voice, image, and video. The early stages of this are being developed in platforms like Google Lens, which allows users to search visually by pointing their phone at an object, or YouTube’s search system, which can identify specific video content within the footage.
In the future, multimodal AI search engines will allow users to switch between different types of input, such as using text, voice, and images together to search.
For example, a user might take a picture of a landmark, ask a follow-up question using voice, and then be presented with relevant results that combine both visual and textual data. This integration of multiple search modalities will create a far richer and more interactive search experience.
In fact, Microsoft’s integration of multimodal capabilities within its Bing search engine and its AI-powered image search tools are paving the way for this more dynamic search process.
AI in Semantic Search and Knowledge Graphs
AI can understand the meaning behind words, not just the words themselves. This is another feature that will define the future of search. AI can understand the intent behind a query and return the most contextually relevant results, while traditional search engines have always relied heavily on keyword matching.
This will be possible through the advancement of knowledge graphs and semantic understanding algorithms.
Google, for example, already employs a vast knowledge graph to connect and understand relationships between different concepts on the web.
As AI continues to evolve, these systems will become even more refined, understanding not just the literal meaning of a search term but also its broader context concerning other concepts, making search engines more intelligent and capable of providing highly accurate results.
Take Google’s “MUM” (Multitask Unified Model) technology for example. It is a deep learning model that allows the search engine to understand more complex queries.
If a user were to search for “How to fix a leaky faucet,” MUM could not only return step-by-step instructions but also recommend relevant tools, videos, and articles on the topic.
AI for Businesses and E-commerce
AI will also change how businesses operate and connect with consumers, particularly in e-commerce. AI-powered search engines will allow businesses to leverage advanced analytics and personalized search recommendations to drive consumer engagement and improve conversions. Product recommendation engines will become more accurate, tailoring suggestions based on a user’s behavior, preferences, and even past purchasing decisions.
For example, e-commerce platforms like Amazon already use AI-driven search to predict products users may want to buy based on their browsing history and purchase behavior.
In the future, AI search engines will be able to provide hyper-personalized recommendations that are almost indistinguishable from browsing in a physical store.
In fact, a 2023 report by McKinsey & Company found that AI-driven personalization in e-commerce can increase sales by up to 30%. This growth is expected to accelerate as businesses adopt more advanced AI-powered search technologies that can provide real-time, tailored product recommendations, improving both customer satisfaction and business performance.
Challenges Ahead
While the future of AI in the field of search is promising, challenges and ethical considerations must also be addressed. As AI evolves, concerns related to privacy, data security, and bias in AI algorithms arise. AI systems often train on massive datasets that may contain inherent biases, leading to skewed or unfair search results. Additionally, the vast amounts of personal data AI requires for personalization raise concerns about how organizations handle and protect this information.
Moreover, the risk exists that AI could manipulate search results for advertising purposes or political influence. As search engines become more intelligent and personalized, the need for transparent algorithms and ethical AI practices will become even more critical.
AI Companies and Startups in the Search Segment
Here are some of the top companies operating in the search sector:
- OpenAI: OpenAI’s SearchGPT is changing the way people search. SearchGPT offers more personalized, dynamic, and contextually aware interactions.
- Perplexity AI: This startup brings AI to search by offering responses that are both contextually precise and conversational. It combines large language models with advanced algorithms to understand user intent better, providing more refined and interactive search results
- Google: As the dominant force in the search industry, Google has incorporated AI into nearly every facet of its search engine. Their AI innovations, like BERT (Bidirectional Encoder Representations from Transformers), allow Google Search to understand queries with greater nuance.
- Microsoft (Bing): Microsoft has integrated OpenAI’s GPT technology into its Bing search engine, offering an AI-powered alternative to Google.
The Bottom Line
AI is completely changing the search segment. It is clear that AI is revolutionizing the way search engines operate, making them more intuitive and user-friendly. With continuous advancements in AI technology, we can expect even more personalized and accurate search results in the future.
Despite all its advancements, there are still ethical, environmental, and privacy concerns surrounding the use of artificial intelligence in search engines.
FREQUENTLY ASKED QUESTIONS
- How is AI being used in Search?
AI is being used in search engines to improve the accuracy and relevance of search results by analyzing user behavior and preferences.
- What’s next for AI and search?
The next step for AI is likely to involve even more personalized and contextually relevant search results, as well as increased integration with other technologies such as voice search and natural language processing.
- How does AI reduce costs in Search?
AI can reduce costs by automating processes that would otherwise require human intervention, leading to increased efficiency and lower operational expenses.
