What is Neural Search?

Neural search, also known as neural information retrieval (neural IR), is a technology that leverages deep neural networks to understand the meaning and context behind natural language queries. 

These can be voice or text-based queries or other types of searches like image search and video search which are comprised largely of unstructured data (e.g., text like questions, statements, and sentence fragments). It is typically used in an enterprise setting so employees can search across a company’s knowledge base to find the information they need to do their jobs. 

The technology that powers neural search engines is also being used for consumer-facing search tools like Monetate’s Personalized Auto Suggest, which displays instant visual results, based on behavioral outcomes as visitors type partial or full search terms.

Neural search engines are built on neural networks, a form of machine learning (ML) that combines data and algorithms in a framework that mimics how the human brain works. This, of course, is an oversimplification. We’ll get into the how, what, and why of neural search now.

Neural Search Examples

The common thread with neural search, regardless of the specific use case, is that it allows users to search for information with natural language – language they’re comfortable speaking or typing – across any type of data source. The AI understands the context of a given search without needing the exact keyword or phrase to find a relevant match. 

Here are a few tangible examples of neural search in action:

• Ecommerce Customer Support – You’re a big retail company with an ecommerce site. Your customer support team often gets questions about product availability, sizing, returns, and sales. With neural search, a support agent or customer can ask a question like “What’s the return window for shoes purchased online?” The engine understands the context and intent behind that question and surfaces the relevant return policy information. It may even suggest related content about initiating a return.
• Healthcare Support – You’re a doctor researching treatment options for a patient with a rare medical condition. You can use neural search to comb through past clinical records, medical journals, and audio transcripts from colleagues. The neural search engine can analyze all that multimodal data and retrieve the most pertinent cases, research papers, voice memos, etc. It understands the search query semantically, which allows it to go beyond simple keyword matching. It also understands that you’re a doctor versus a patient and can return results appropriate for HCPs versus patients.
• Marketing Research & Support – You run marketing for an outdoor apparel brand and want to promote your new line of cold-weather gear on social media. With a neural search engine, you could search for “cozy winter style” and it would analyze user behavior patterns, image data, product info, and other data sources across your company knowledge base. It can, for example, retrieve examples of how customers styled your products last winter which you can use to inform this season’s campaigns. It does all of this without you having to use exact keywords.

Neural Search vs Vector Search vs Keyword Search

To understand the powerful capabilities of neural search, it’s helpful to compare it with other popular forms of search. Here’s a high-level breakdown of three search approaches you’re likely already familiar with. 

Keyword Search 

• Keyword search relies solely on matching the exact words in a user’s query to an indexed database of documents or information. It has limited ability to understand context or semantic meaning. This means that if a user searches for “Sorel Boots” on your ecommerce website and you don’t have that brand, they may get no results or irrelevant results.

Vector Search 

• Vector search uses ML models to detect semantic relationships between words and concepts. Data is represented as dense vector embeddings or mathematical encodings in high-dimensional space. Similar words or ideas get encoded as vectors that are closer together in this space. This allows vector search to make intuitive connections beyond just simplistic keyword matching. For example, it could relate that search for “Sorel Boots” could return “Columbia Boots”, a similar type of boot, based on their semantic similarity.

Neural Search

• Neural search takes vector search a step further by leveraging deep neural networks to build every component of the search engine (the indexer, query processor, and ranking algorithm). As noted above, it can comprehend natural language queries, learn from data continuously, and retrieve the most contextually relevant results across any data modality like text, images, and audio. Neural search models continuously learn and improve by fine-tuning on new data and adapting to changing real-world information.

What is Neural Architecture Search?

Neural architecture search (NAS) is sometimes conflated with neural search, but they’re separate concepts. It applies deep learning models to the process of information retrieval and understanding queries. NAS focuses specifically on automating the design of the neural network architectures that could potentially power systems like search engines.

NAS algorithms explore a vast space of possible network configurations – things like layer types, connections between layers, kernel sizes, etc. NAS uses optimization techniques to discover high-performing neural architectures tailored for specific tasks. This automation means that humans don’t need to manually design these complex systems through trial-and-error.

A NAS system, for example, could explore millions of architectural options and identify one that’s good at understanding natural language queries for products. That discovered architecture could then get deployed to power the search capabilities for an ecommerce support chatbot.

While extremely powerful, NAS requires massive computational resources. But it has revolutionized neural network design by automating what was previously a highly manual, expert-driven process.

What is a Neural Network Search Engine?

A neural network search engine uses deep learning models as the core architecture for the indexer, query processor, and ranking algorithm components. Let’s break down each component for clarity.

• The indexer converts data into a searchable format for fast retrieval.
• The query processor converts a user’s query into a format that can be compared against the indexed data.
• Ranking algorithm components determine how well the indexed data matches the user’s query and ranks the results accordingly.

Data points like words or images are encoded into vector representations – dense numerical vectors in high-dimensional space. Similar items are then positioned closer together based on semantic relationships. This helps neural search engines understand the context of your queries.

Neural network search engines use these vector representations as well as learned neural networks – deep learning models trained on massive datasets – to better understand the semantics and context of user queries. This enables them to retrieve incredibly relevant results that go beyond simplistic keyword matching.

Neural Networks and AI

Neural networks are a fundamental concept in AI, inspired by the biological neural networks in the human brain. They’re computers made from interconnected nodes that can recognize patterns in data, learn from those patterns, and make decisions or predictions.

Neural networks use ML to learn from data and improve their performance on a specific task like information retrieval or image generation without being explicitly programmed. 

Before they can learn and make predictions on their own, the neural networks must go through a training process. In this process, the neural network itself adjusts and tunes the connections or weights between its internal nodes based on the training data. It does this automatically through optimization algorithms like gradient descent.

Simple neural nets have been around for decades. But the explosion of big data and enhanced computing power has allowed much larger and more sophisticated deep neural networks to be effectively trained. These deep learning models now power technologies like neural search by extracting high-level features and patterns that allow for advanced capabilities like natural language understanding.

Why is Neural Search Important?

It is increasingly important as organizations grapple with rapidly growing volumes of fragmented data across different systems like ecommerce websites, CRM systems, and inventory management and ERP tools. Simple keyword search solutions can’t adequately handle this scale and complexity.

With its ability to understand context and semantics, neural search allows employees to find the information they need efficiently using language they’re comfortable using. Natural language queries enhance productivity by reducing the time spent trying to find the right phrase to search for. It makes it easier to find information by connecting it to one centralized location – no more digging through siloed data sources to find an elusive asset or piece of information. 

Neural search engines retrieve relevant results that rigid keyword matching would miss – and the technology gets better on its own, over time. The continuous learning capability of neural search means that quality doesn’t degrade as real-world data evolves. It grows with your company, your specific needs, and as the size of your product catalog (or knowledge base) expands. The models can incrementally adapt to changes in data, queries, products, and business conditions through techniques like fine-tuning on new information.