How Can AI/ML Improve the Search Process?

What Is AI/ML-Powered Search?

Search engines powered by AI and ML learn to generate the most relevant and accurate results for the specific user or query. AI is the technology that teaches a computer or system to think, much like humans do. As a branch of AI, ML has to do with using complex algorithms and data to train machines to learn, and then use that learning to solve the problem it trained for in real-time. In what ways can search engines use these technologies?

• Detecting patterns. AI and ML can look for patterns in behavior, text, speech, images, and search parameters. Then they learn how to draw conclusions, make suggestions, rank results, and filter out unwanted or duplicate information.
• Identifying user intent. Rather than getting stuck in a rut of pattern-driven results, AI and ML can also look for and identify input parameters that signal user intent. The discerned from the results might change predictions, modify the results, or rearrange rankings.
• Analyzing text through natural language processing (NLP). Going beyond actual words and looking for relationships in word combinations or determining intent is possible through NLP. ML makes it possible for machines to adapt as language itself evolves and transforms.
• Analyzing text in photos or videos. ML can analyze pictures, videos, and other file formats that lack “readability”. For example, text contents that appear as images in some PDFs, in order to identify and reformat text into readable formats. Images and videos can also be analyzed for content to be indexed in formats that are searchable. For instance, audio in a video can be converted to text and indexed for search.

These are just some ways AI and ML affect search engine technologies. In real-world applications, these and other features can be used in several ways.

Applications of AI/ML-Powered Search

The applications of AI/ML-powered search go beyond simple ranking of results. It steps into the realm of anticipating search queries and developing dynamic search solutions for a number of user requirements.

Document Search

When searching through large documents or data sets, traditional search processes work as follows: When users search for keywords or ask a question, the search engine will instantly respond with results obtained from indexed data. It will rank the results by predetermined criteria, such as the closest match or the most common occurrence. This is referred to as Basic Conventional Search Processes.

However, efforts were made to improve this by marrying conventional processes with enhanced algorithms to produce better results. AI/ML-powered search processes take this an additional step, making efforts to discern the actual intent of the search and adjust the results and rankings accordingly. This allows users to go beyond keywords or specifically worded questions and search documents or data for specific types of information, such as searching a document for any personal or private data. Additionally, results can include findings based on synonyms and alternate spellings. AI and ML technologies exist that can examine and detect any personally identifiable information (PII), with the option to remove it to avoid exposing it publicly.

OCR Capabilities

Text doesn’t always occur in traditionally readable and editable formats, such as Word, so search engines need to be able to recognize and “read” text in other formats, such as text formatted as images in PDFs, JPEGs, PNGs, TIFFs, video files, and so on. AI and ML equip search engines to identify text in images or handwritten text and convert it into a format that is machine-readable, allowing it now to become indexed, analyzed, and searched.

Fuzzy Search Logic

Fuzzy logic is a way to determine what might an imperfect search input means. For example, what if the user spells a word incorrectly, or what if they use a combination of words that clash collocationally? So when a search engine receives unrecognizable input, fuzzy logic determines possibilities based on the input. It may attempt to solve it by auto-correcting the text based on a dictionary of words, comparing it to frequent similar searches, or factoring in recent trends for popular searches.

Search Suggestions

AI/ML can also prompt a user with suggestions for search text. An example is auto-fill. When a user begins to type a search query, AI/ML search engines will finish the sentence or phrase with suggestions based on dictionaries, trending topics, frequent search parameters, and other criteria.

Neural Search

A gigantic step forward in AI/ML-powered search capabilities is neural search. Neural search leverages pre-trained neural networks to expand the learning and response abilities of AI/ML search engines. Referred as semantic search, because it has the ability to learn to identify semantic connections in order to determine a user’s search intent. Additionally, it has the ability to analyze and “think,” meaning that it can “read,” interpret, and index unstructured data, and because it is always learning, the search results are more flexible, adaptable, and intuitive.