How neural networks drive smarter search results

To optimize your search user experience and help your subscribers or ecommerce customers easily find what they need — whether that’s products, services, snippets of information, or a combination — what kind and caliber of search tools are you providing?

In this era of Big Data, search is a continually evolving technology, and with each new generation of search features, companies are implementing groundbreaking capabilities.

That’s especially true with neural search, the equivalent of a quantum leap forward in a world of data science already brightly lit by web search technology.

The first step in assessing the various search methods is knowing the differences between all the latest search system options for your website or app.

The old guard: keyword search

Many of today’s search engine operations are based on matching keywords, the method that Google search brought into the mainstream. In the workflow for this traditional indexing-based model, the search engine tries to identify the best content match for the user’s input data: their keywords. 

However, the keyword-centered model is starting to be teamed with AI-based search methods that allow for “understanding” of what queries mean.

These promising new approaches include vector search and neural search.

Even if you’re not a data scientist, you’re probably familiar with artificial intelligence (AI), so let’s start there.

AI search: intelligent options

AI, machine learning, machine intelligence. Regardless of what people call it, this branch of computer science is focused on the ability of machines to perform tasks — including related to search, language understanding, visual recognition, and robotics — that normally require human intelligence. Or, more accurately, to do as much as “machinely possible”? Which could mean doing things more effectively than people can. AI encompasses multiple applications and types of algorithms, with amazing new applications emerging all the time. 

The same notable progress with AI is going on specifically in the domain of search. Intelligent search (also known as AI search and cognitive search) capabilities are what you get from a search engine that has the ability to not just read, process, and match entered keywords with content but begin to “understand” people’s underlying intent as they’re using search applications, and reliably respond with the information they’re after.

AI-powered search utilizes a range of modern technologies, including machine learning and natural language processing (NLP), to better understand which specific information each user is trying to track down. Once that’s accurately identified, with the right fine-tuning, in a highly timely fashion, the best search query results can be optimally ranked and provided to the user.

How widespread is AI search? Many companies offer it. Others just layer AI capabilities on top of an existing keyword search solution (which can slow down result retrieval and make training the model difficult). Still other companies are not actually delivering AI search as they say they are. But regardless of varying implementation levels, AI-powered search is not going away.

Vector search

Vector search uses machine learning to determine the meaning and context of data (both for text search and image search), and translate it into numeric representations. 

A traditional keyword-based search engine doesn’t know that certain words might be related. Because vector-based search engines understand relationships between words, they can provide better search results than keyword search.

A significant number of companies are using vector search. A key reason: with vector search, as opposed to keyword search, you get more-relevant search results, and faster. Besides determining intent and meaning, vectors are good for use on tasks such as ranking search results, automating synonyms, and clustering documents.

Neural search

Neural search uses AI to determine relationships between data points. In a nutshell, this up-and-coming technology converts data to vectors, which facilitates speed and flexibility, plus is more powerful than traditional keyword search. 

The mechanics of neural search: neural networks 

The power behind neural search is neural networks (NN), or, more accurately, artificial neural networks (ANNs).

Neural networks are algorithms (computational models) that are meant to mimic the human brain and emulate the human thought process. A neural network is a series of nodes (computational units) that are connected through inputs and outputs. 

Trained neural networks can identify patterns in words, plotting words near or far from each other, based on their meanings.

Neural networks are a growing positive phenomenon for companies: according to Gartner, in the past few years, the use of artificial neural networks in business has grown 270%.

When did (artificial) neural networks originate?

Since the ’60s and ’70s, machine learning and artificial intelligence have relied on symbolic AI learning through straightforward rule-based algorithms. Researchers and scientists have used logical if-then structures, expecting that the learning mechanisms underlying AI would map directly to their representations in code.

But as human understanding of how the human brain works has evolved, computer scientists have been rethinking their approaches to ML, too. They’ve been moving away from code-based machine learning to systems that mirror human understanding of neurons. That’s led to the rise of neural network technology: systems featuring connections between artificial neurons.

How neural networks function

Machines use statistical models and neural networks to understand language. Humans’ goal: have them be able to come up with effective problem-solving strategies, such as those needed in manufacturing facilities or other work environments, ideally in ways that are superior to the ways people can envision.

The first step is to train the model. Words are converted into numbers, called vectors, which are then fed into a complex software process called a neural network (NN). The NN model calculates and recalculates the numbers in the vector until the word is “classified” by the machine. Words with similar meanings are plotted close together in a “vector space” (a multidimensional graph). 

As the baseline model receives training data — as each new word comes in — it “learns,” calibrating itself and undergoing mini transformations of its numbers, or “weights.” The finished model has all of its weights set up to accurately recognize nearly every word it receives.

Models used in machine learning are also algorithms that are continually being optimized: The more data they take in, the better their algorithmic performance becomes. Machine-learning algorithms survey what’s new, make educated guesses about the data, and improve their “understanding.” This leads to vast improvements in processes such as the optimization of navigation skills for self-driving cars.

Machine-learning methods

The types of machine learning include:

Supervised learning

Just as a student might do when a teacher is in charge, a machine-learning system is given expected inputs and outputs along with its datasets. In theory, if it understands what output is desired, it can respond by mapping strategies to data. Supervised learning can help ensure that data is classified accurately and facilitate the creation of the best learning strategies.

Unsupervised learning

This is the computerized version of a problem-solving, pattern-finding “independent study” in a dataset without labels (unstructured data). Supervision and prep work by humans is minimal. Used often for solving statistics and probability problems, unsupervised learning means that requested outputs are not communicated, and that insights are likely to have less bias than with other types of artificial intelligence.

Reinforcement learning

In this type of machine learning, the computer program — an intelligent agent — learns by interacting with what’s in the environment. Typical uses include online games that feature AI-powered players and the process of training a robot on how to move correctly.

Neural networks

Neural networks are a type of multilayered machine-learning model used for making sense of large amounts of data and recognizing complex patterns. Various types of neural networks are used for different applications. For example, convolutional neural networks (CNNs) are great for analyzing images.

In a neural network, complex decision-making tasks are broken into parts. Contrary to what seems intuitive, the easier tasks live deeper in the network. As input to the system dictates these simpler tasks’ behavior, output rises up through the layers and supports the decision making.

Deep learning

The terms machine learning and deep learning (a subset of machine learning that has come to be thought of as deep neural networks, DNN), are often used interchangeably. They aren’t all that different: the “deep” part of the deep learning model is that a deep neural network uses more layers of processing than a simple network.

Regardless of the machine learning vs. deep learning network nuances, any type of learning process is still learning, of course. But deep learning is a more scalable algorithm, says industry pioneer Andrew Ng (one of the co-founders of Google Brain (Wikipedia): performance continues to improve as deep-learning algorithms receive and process more data.

One real-world application of deep-learning use cases is facial-recognition software. Fairly straightforward functions, such as recognizing boundaries and mapping patterns, serve as a foundation for taking on more-complex tasks, such as recognizing color and doing predictive mapping.

Keyword search vs. neural-network-powered search

Don’t dump your keyword search. Traditional keyword-based search is likely to be a mainstay in search functionality for perhaps decades.

Just keep in mind that the addition of self-learning AI is a pretty major game changer. Developers are cranking out machine-learning-centered approaches that complement the strengths of keyword search and thereby give users better search experiences.

AI search is fast and accurate, whereas keyword search can get bogged down in complex dependencies and fail to scale to encompass multiple languages. Accurately matching keywords with available content doesn’t always prove reliable . And the quality of keyword-based search results may not improve much over time.

Neural networks: increased accuracy

Neural-network or vector-based semantic search is based on natural language understanding (NLU), a subset of natural language processing (NLP); it recognizes words that are semantically similar. That makes it smarter than any traditional keyword system. And not just smarter, but more brilliant: with its more linguistically “global” view, it can see complex patterns that humans might easily overlook.

For instance, with neural network search, someone can pull up relevant results that gauge their underlying user intent, even if their query doesn’t quite match the terms used in the destination doc or on the desired web page.

For example, they could enter a clothing brand name and item type in a keyword search engine box. They’d be shown only items from that brand. By contrast, the default behavior of a vector engine would be to “think bigger” than just the brand, so the searcher would  see similar items from other brands, too.

This enhanced level of usability also translates to more-natural interactions, such as when AI chatbots are involved.

However, neural search engines can be slow, plus their price tags are likely to break many companies’ budgets.

The upshot? Improving the search engine experience for users has meant a trade-off between search quality and search speed.

The solution: hybrid search

The best option would obviously be covering all the bases in terms of accuracy and speed.

And now, thanks to recent advancements, that coverage is entirely possible.

Algolia’s search is the industry’s first true hybrid offering of neural-network-based search and traditional keyword search in a single search engine. This state-of-the-art search technology is both high quality and fast (goodbye, trade-offs). It:

• Covers all search and discovery requirements while minimizing costs and infrastructure needs
• Delivers single-digit millisecond query times, regardless of the scale or query throughput 
• Lets you teach it relevance (if needed) using a one-click improvement UI 

Ready to start leveraging neural information retrieval to work for your search capabilities with our powerful API? Transform your enterprise search platform, impress your users, and pump up your conversion rate with our vector-based semantic search algorithm. Get in touch today.