Powered by natural language processing, AI search engines can comprehend the context and meaning of search queries. This enables them to deliver more relevant and personalized search results.
Imagine searching for a historical figure and being presented with a concise biography, notable achievements, and related works. This is just one example of the emerging visually dynamic and interactive search results.
Visual Search
The visual search task is a common everyday activity: picking out food from the supermarket shelf, looking for your phone in a crowded train station, or playing games like “Where’s Wally?” The human brain can only process a limited amount of information at one time, so mechanisms such as attention are used to control the selection of a subset of the visual scene. Research in this area aims to understand the forces that govern these attentional deployments, how they relate to search engine innovation, and what can be done to enhance visual search performance.
Using the power of computer vision, artificial intelligence can analyze images and understand what they are showing. This allows computers to perform searches that are much more sophisticated than surface-level keywords, delivering results that are better aligned with user intent. For example, when searching for a restaurant recommendation, Google’s AI search engine considers your location, past dining preferences, and other personal data to deliver the most relevant results.
Marketers can leverage the advanced capabilities of AI search engines to elevate their strategies and drive deeper engagement with consumers. By understanding the underlying intent behind search queries, AI search can unlock new avenues for marketers to tap into consumer pain points and deliver meaningful experiences. This can include contextualizing content for local audiences and prioritizing the inclusion of culturally appropriate and locally-relevant information.
Natural Language Processing
Artificial intelligence transforms people’s work by automating repetitive, time-consuming tasks and freeing human workers to focus on more rewarding work. This productivity boost will likely slash unemployment rates and create entirely new industries that wouldn’t exist without AI advancements. But what will happen to those who lose their jobs due to AI technology?
Natural language processing is an area of AI that enables computers to understand, interpret, and generate human language. It analyzes text and audio to extract meaningful information using machine learning techniques. Some notable NLP algorithms include tokenization, which breaks texts into their parts; part-of-speech tagging, which labels each word in a sentence with its grammatical part of speech; and named entity recognition, which recognizes words and phrases that refer to people or places.
The most advanced NLP models use deep learning to make sense of the text, generate coherent responses, and engage in conversation with humans. Several companies have developed these technologies to provide customer service, build bots, and serve as virtual assistants. However, NLP has challenges, including the fact that human languages can change over time. As a result, the hard computational rules NLP uses may become obsolete as the characteristics of real-world language change. Moreover, large NLP models consume a significant amount of energy during both training and inference. This may restrict their use to larger corporations that can afford these high costs.
Voice Search
A natural language search engine allows users to speak their queries, which can help them find what they’re looking for without using traditional keyword searches. Technology transforms how people interact with search engines and how marketers connect with their audience.
Using AI, voice search can discern the user’s intent and provide results that align with their interests. This is accomplished by analyzing the context of the query, interpreting user intent, and utilizing other data to make informed decisions about which results to display.
As search engines evolve, they’ll likely continue to be more personalized to individual users. User data, such as demographic information or search history, can be integrated. It can also be done by introducing additional features like visualization of search results or adding new options such as the ability to sort by time, location, or price.
Google has been making significant progress in this area, introducing its generative AI search experiment Search Generative Experience last year. The feature aims to reduce the information required to answer a search query by displaying multimedia content, such as images and video, within a colorful summary box at the top of the results. It also tries to be responsible, addressing known limitations of generative AI and applying additional guardrails.
Data-Driven Insights
To make AI applications intelligent, they must be fed a vast amount of relevant data. Search engines leverage this information to understand user input better and provide results that are personalized to each individual.
For example, e-commerce companies use intelligent product recommendations to help customers find items that align with their preferences based on their previous purchases, research, and consumption habits. This allows them to offer a more tailored shopping experience and enhance customer satisfaction.
But the impact of AI goes beyond search. It is embedded in many areas of our lives, from smart thermostats that optimize energy usage to GPS software that directs us along the fastest route. Even the medical field benefits from AI, with a program that analyses heart rhythms during emergency calls to recognize cardiac arrest faster than human operators.
Machine learning is transforming search, elevating relevance with semantic context. But implementing the technology requires time and resources to select, train and maintain models. Moreover, many developers need help integrating machine learning into their search applications.
