The future of search using AI - Deepstash
The future of search using AI

The future of search using AI

Curated from: pytechacademy.medium.com

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6% of e-commerce visits that include engagement with AI-powered recommendations drive 37% of revenue — Salesforce

One day per working week (19.8% of work time) is wasted by employees searching for information to do their job effectively — Interact

DID YOU KNOW

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AI Algorithms presents results based on user preferences

AI Algorithms presents results based on user preferences

AI algorithms are at the heart of personalised search experiences. These algorithms analyse a user’s past behavior, including search queries, clicks, and even time spent on various pages. By understanding what each user finds relevant and engaging, AI can tailor search results to match personal preferences. 

Netflix uses sophisticated AI algorithms to analyse a vast array of user data, including the genres you watch most frequently, the titles you’ve rated highly, how much of a show or movie you watch before stopping, and even the time of day you prefer to watch certain types of content.

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Contextual understanding gives accurate results for queries

Contextual understanding gives accurate results for queries

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.

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Conversational search enables natural interactions

Conversational search enables natural interactions

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.

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Visual search allows image based queries

Visual search allows image based queries

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

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Challenges

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.

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IDEAS CURATED BY

rajaathota72

Serial Entrepreneur | AI ML and Blockchain

CURATOR'S NOTE

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