The future of search using AI

Beyond just analysing keywords, AI-powered search engines understand the context of queries. This involves interpreting the intent behind a search and considering factors such as the user’s location, device used, and even the time of day.

If you search for “pizza places” on your phone at lunchtime, an AI-powered search engine considers your location, the time, and the fact you’re using a mobile device. It then shows nearby pizza restaurants open for lunch, rather than just any pizza-related information.

Conversational search leverages natural language processing (NLP) to allow users to interact with search engines in a more natural, human-like manner. Instead of relying on specific keywords, users can ask questions or make requests in full sentences, just as they would when talking to another person.

Perplexity AI’s search engine uses natural language processing to personalise search results by understanding user queries’ context and summarising information with citations. It updates with newer sources, allows follow-up queries, and offers various search modes for free.

Visual search enables users to use images or videos as their search queries. This is particularly useful in scenarios where describing an item in words is difficult. By analysing visual content, AI can identify objects, text, and even context within the image or video, returning relevant information

Imagine you see a stylish lamp in a magazine but don’t know where to buy it. By taking a photo and using a visual search app, like Google Lens, the app analyzes the image, identifies the lamp, and shows you where you can purchase it online

The technical complexity of developing and maintaining complex AI algorithms demands high-level expertise.

Ethical considerations around data privacy and compliance with stringent regulations are critical.

Integrating AI seamlessly with existing digital infrastructures presents its own set of challenges, that demands a balanced approach to innovation and user-centric design.