How does this work?

For decades, search engines have relied on a type of technology known as a reverse-index lookup. This type of technology is similar to looking up words in the back of a book, finding the page numbers and locations of those words, then turning to the page where the desired content is located. However, because the process of using a search engine requires the user to curate their own content, by selecting from a list of search results and then choosing whichever is most useful, users tend to waste significant amounts of time jumping from search result pages in a search engine, to content, and back again in search of useful content.

At iAsk.Ai, we believe a search engine should evolve from simple keyword matching systems to an advanced AI that can understand what you’re looking for, and return relevant information to help you answer simple or complex questions easily. We use complex algorithms that can understand and respond to natural language queries, including the state-of-the art in deep learning, artificial intelligence known as transformer neural networks. To understand how these work, we first need to know what a transformer neural network is.

A transformer neural network is an artificial intelligence model specifically designed to manage sequential data, such as natural language. It’s primarily used for tasks like translation and text summarization. Unlike other deep learning models, transformers don’t necessitate processing sequential data in a specific order. This feature enables them to handle long-range dependencies where the comprehension of a particular word in a sentence may rely on another word appearing much later in the same sentence. The transformer model, which revolutionized the field of natural language processing, was first introduced in a paper titled “Attention is All You Need” by Vaswani et al.

The core innovation of the transformer model lies in its self-attention mechanism. Unlike traditional models that process each word in a sentence independently within a fixed context window, the self-attention mechanism allows each word to consider every other word in the sentence to better comprehend its context. This is achieved by assigning varying weights or “attention” to different words. For instance, in the sentence “The cat sat on the mat”, while processing the word “sat”, more attention would be allocated to “cat” and “mat” than “the” or “on”. This enables the model to capture both local and global context.

Now, let’s explore how search engines utilize transformer neural networks. When you input a query into a search engine, it must comprehend your question to deliver an accurate result. Traditionally, search engines have employed strategies such as keyword matching and link analysis to ascertain relevance. However, these techniques may falter with intricate queries or when a single word possesses multiple meanings.

Using transformer neural networks, search engines can more accurately comprehend the context of your search query. They are capable of interpreting your intent even if the query is lengthy, complex or contains ambiguous terms. For instance, if you input “Apple” into a search engine, it could relate to either the fruit or the technology company. A transformer network leverages context clues from your query and its inherent language understanding to determine your probable meaning.

After a search engine comprehends your query through its transformer network, it proceeds to locate pertinent results. This is achieved by comparing your query with its index of web pages. Each web page is depicted by a vector, essentially a numerical list that encapsulates its content and significance. The search engine utilizes these vectors to identify pages that bear semantic similarity to your query.

Neural networks have substantially enhanced our capacity to process natural language queries and extract pertinent information from extensive databases, such as those utilized by search engines. These models allow each word in a sentence to interact uniquely with every other word based on their respective weights or ‘attention’, effectively capturing both local and global context. New technology has revolutionized the way search engines comprehend and respond to our searches, making them more precise and efficient than ever before.