AI Based Search Demystified

AI-based search refers to the application of artificial intelligence (AI) techniques and technologies to enhance the process of searching and retrieving information from various sources, such as databases or websites.

This new form of search involves leveraging AI algorithms and AI language models to understand user queries, interpret the context, and deliver more accurate and relevant search results.

Given how important search engines are to the health of eCommerce businesses worldwide, as well as most other web entities and digital business models, it’s important to understand how AI-based search may potentially impact the space.

In this article, I’ll breakdown the basics of AI search engines, and how these innovations may impact the way AI is used digital marketing landscape in the coming years.

AI Based Search vs. Traditional Search Engines

Traditional search engines typically rely on keyword matching and statistical algorithms to provide search results. However, AI-based search goes beyond keyword matching by incorporating advanced techniques like natural language processing (NLP), machine learning (ML), and deep learning (DL) to understand and interpret the meaning behind the search queries.

Here are some key components and aspects of AI-based search:

• Natural Language Processing (NLP): NLP enables the search system to comprehend and analyze human language by processing and understanding the structure, meaning, and context of the search queries. NLP techniques, such as entity recognition, sentiment analysis, and language understanding, help refine search results to align with user intent.
• Machine Learning (ML): ML algorithms are utilized to train search systems on large volumes of data, allowing them to learn from patterns, preferences, and user behavior. By analyzing user interactions, ML models can adapt and improve search results over time, enhancing relevance and personalization.
• Deep Learning (DL): DL, a subset of ML, employs artificial neural networks to process vast amounts of data and extract complex patterns and features. Deep learning models can be used for tasks like image recognition, voice search, and semantic understanding, enabling more accurate and intelligent search capabilities.
• Personalization: AI-based search systems can tailor search results based on user preferences, behavior, and past interactions. By learning from individual user data and patterns, search engines can provide personalized recommendations and suggestions, enhancing the overall search experience.
• Contextual Understanding: AI-based search engines aim to grasp the context and intent behind a search query to generate more relevant results. By considering factors such as user location, browsing history, and other contextual information, the search system can deliver more accurate and contextually appropriate information.
• Semantic Search: AI-based search employs semantic understanding techniques to comprehend the meaning of words, phrases, and relationships between different entities. This allows the search engine to identify and retrieve information that matches the intent of the user, even if the specific keywords are not explicitly present.
• Natural Language Generation (NLG): AI-based search systems can generate human-like responses and summaries to provide more comprehensive and meaningful search results. NLG techniques can be utilized to create concise summaries, contextual explanations, or even full answers to complex queries.

Google’s Generative AI Search Engine Updates

Ai-based search is also referred to as generative search, which specifically refers to a concept in AI search engines where the system generates novel and creative content as part of the search results, rather than simply retrieving and presenting existing information.

It involves the use of generative models, such as generative adversarial networks (GANs) or variational autoencoders (VAEs), to create new data that aligns with the user’s search query or intent.

In generative search, the AI search engine goes beyond traditional keyword matching and information retrieval to produce unique and original content that complements the user’s query. The generative models employed in this context are trained on vast amounts of data and learn to generate new instances based on the patterns and structures present in the training data.

Google is currently working on their own form of generative AI-based search that is above and beyond their in-house AI language model, Bard.

This AI-based search engine is an augmented for of the traditional Google search, that will include eye-catching modules of content pulled by the AI. It will potentially provide search results that are more open-ended, intelligent, and customized for the user.

Here are some key aspects and applications of generative search in AI search engines:

• Creative Content Generation: Generative models can create new text, images, or even multimedia content that is relevant to the user’s search query. For example, if a user searches for “imagine a futuristic city,” the generative search engine can use a trained model to generate unique and imaginative visual representations of such a city. This goes far beyond the original capabilities of search engines into something that’s completely new and potentially more useful for users, which is part of the reason why so many marketers envision this technology taking off.
• Contextual Expansion: Generative search can help expand the user’s search context by generating additional related information that may not be explicitly present in the original search query. By understanding the user’s intent, the search engine can generate relevant content to provide a broader understanding of the topic.
• Personalization and Recommendations: Generative search models can be trained on personalized data, such as the user’s preferences, browsing history, or social media activity. This enables the search engine to generate personalized recommendations or suggestions tailored to the individual user’s interests.
• Creative Problem Solving: Generative search can be employed in problem-solving scenarios where users seek creative solutions or ideas. The AI search engine can generate novel concepts, designs, or strategies to address specific challenges or open-ended queries. This is a major divergence from traditional search results, where the user will only ever receive a list of results that may potentially contain an answer to the question the user is asking, not a specific answer.
• Content Summarization and Synthesis: Generative models can assist in summarizing and synthesizing large volumes of information. By analyzing and understanding the user’s search query, the search engine can generate concise and coherent summaries or synthesize information from multiple sources to provide comprehensive insights. This will potentially reduce the need for hundreds of search results, as the vast bulk of all relevant information will be provided above the fold in the various AI based search modules.

It is worth noting that while generative search has the potential to produce innovative and valuable content, the generated results may not always be accurate or reliable. The generated content should be evaluated and validated by users or domain experts to ensure its quality and usefulness.

Will AI-Based Search Replace Current Search Engines?

It is unlikely that AI-based search will completely replace current search engines in the foreseeable future. While AI-based search brings significant advancements and improvements to the search capabilities we know and love, current search engines have evolved over many years and are deeply embedded in modern digital infrastructure.

AI-based search engines build upon existing search engine technologies and enhance them with AI techniques like natural language processing, machine learning, and deep learning.

These advancements allow for more accurate and personalized search results, better understanding of user intent, and improved contextual understanding. However, there are several factors that contribute to the coexistence of AI-based search engines with current search engines:

Infrastructure and Adoption

Current search engines are widely adopted and integrated into various platforms, websites, and applications. Replacing them with AI-based search engines would require significant changes to existing infrastructure and widespread adoption of the new technology, which can be a slow and complex process.

Reliability and Trust

Current search engines have established a reputation for reliability and trustworthiness over time.

Users have come to rely on them for finding information quickly and accurately. While AI-based search engines have the potential to provide better results, building trust and establishing reliability will take time and consistent performance. It will be a while yet before people trust the reliability of AI search results vs these traditional results.

It is worth noting that AI-based search engines are already making strides in certain areas. Voice-activated assistants, such as Siri, Google Assistant, or Amazon Alexa, heavily rely on AI technologies to deliver search results through natural language interactions. TikTok also uses an effective AI-powered algorithm.

Additionally, AI-powered search is increasingly being adopted in specific domains like eCommerce, customer support, and enterprise search, where the benefits of AI techniques can be more easily realized.

AI-based search engines are evolving and enhancing search capabilities, but the complete replacement of current search engines is unlikely to occur anytime soon.

Instead, we can expect a coexistence where AI-based search engines continue to be integrated into specific applications and domains, complementing the functionalities of existing search engines.

Professional SEO services and other cornerstones of digital marketing will still be needed in order to ensure websites are following best practices, creating backlinks, and publishing optimized and relevant content. Likewise, other forms of digital marketing such as social media marketing will be needed for brand building.

AI search is just one part of a growing shift in the way content is delivered and synthesized across the web.