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Pushing the boundaries of possibility, natural language understanding (NLU) is a revolutionary field of machine learning that is transforming the way we communicate and interact with computers.
NLU is the technology that enables computers to understand and interpret human language. It has been shown to increase productivity by 20% in contact centers and reduce call duration by 50%. Beyond contact centers, NLU is being used in sales and marketing automation, virtual assistants, and more.
In this post, we’ll explore what NLU is, its applications, why it matters for your business, and how you can implement it.
Natural language is something we're all familiar with. It’s the language we use to communicate with each other every day. NLU is a computer technology that enables computers to understand and interpret natural language. It is a subfield of artificial intelligence that focuses on the ability of computers to understand and interpret human language.
NLU uses natural language processing (NLP) to analyze and interpret human language. NLP is a set of algorithms and techniques used to make sense of natural language. This includes basic tasks like identifying the parts of speech in a sentence, as well as more complex tasks like understanding the meaning of a sentence or the context of a conversation.
It's often used in conversational interfaces, such as chatbots, virtual assistants, and customer service platforms. NLU can be used to automate tasks and improve customer service, as well as to gain insights from customer conversations.
NLU provides many benefits for businesses, including improved customer experience, better marketing, improved product development, and time savings.
Customers are the beating heart of any successful business, and their experience should always be a top priority.
NLU can be used to personalize at scale, offering a more human-like experience to customers. For instance, instead of sending out a mass email, NLU can be used to tailor each email to each customer. Or, if you’re using a chatbot, NLU can be used to understand the customer’s intent and provide a more accurate response, instead of a generic one.
Even your website's search can be improved with NLU, as it can understand customer queries and provide more accurate search results.
A marketer's job becomes harder every year. Competition keeps growing, digital mediums become increasingly saturated, consumers have less and less time, and the cost of customer acquisition rises.
NLU can help marketers personalize their campaigns to pierce through the noise. For example, NLU can be used to segment customers into different groups based on their interests and preferences. This allows marketers to target their campaigns more precisely and make sure their messages get to the right people.
Whether you're dealing with an Intercom bot, a web search interface, or a lead-generation form, NLU can be used to understand customer intent and provide personalized responses.
If customers are the beating heart of a business, product development is the brain. NLU can be used to gain insights from customer conversations to inform product development decisions.
For example, NLU can be used to identify and analyze mentions of your brand, products, and services. This can help you identify customer pain points, what they like and dislike about your product, and what features they would like to see in the future.
This kind of customer feedback can be extremely valuable to product teams, as it helps them to identify areas that need improvement and develop better products for their customers.
As digital mediums become increasingly saturated, it's becoming more and more difficult to stay on top of customer conversations.
NLU can help you save time by automating customer service tasks like answering FAQs, routing customer requests, and identifying customer problems. This can free up your team to focus on more pressing matters and improve your team's efficiency.
A lot of acronyms get tossed around when discussing artificial intelligence, and NLU is no exception. NLU, a subset of AI, is an umbrella term that covers NLP and natural language generation (NLG).
These terms are often used interchangeably, but they should not be confused. Let’s take a closer look.
Today's Natural Language Understanding (NLG), Natural Language Processing (NLP), and Natural Language Generation (NLG) technologies are implementations of various machine learning algorithms, but that wasn't always the case. Early attempts at natural language processing were largely rule-based and aimed at the task of translating between two languages.
As machine learning techniques were developed, the ability to parse language and extract meaning from it has moved from deterministic, rule-based approaches to more data-driven, statistical approaches.
Natural language understanding is an umbrella term that encompasses NLP and NLG, similar to how "artificial intelligence" encompasses machine learning, deep learning, and other subfields.
NLU is the process of understanding a natural language and extracting meaning from it. NLU can be used to extract entities, relationships, and intent from a natural language input.
NLP is the process of analyzing and manipulating natural language to better understand it. NLP tasks include text classification, sentiment analysis, part-of-speech tagging, and more. You may, for instance, use NLP to classify an email as spam, predict whether a lead is likely to convert from a text-form entry or detect the sentiment of a customer comment.
NLG is the process of generating natural language from structured data. ChatGPT made NLG go viral by generating human-like responses to text inputs. NLG can be used to generate natural language summaries of data or to generate natural language instructions for a task such as how to set up a printer.
NLU, NLP, and NLG are crucial components of modern language processing systems and each of these components has its own unique challenges and opportunities.
NLU is the broadest of the three, as it generally relates to understanding and reasoning about language. NLP is more focused on analyzing and manipulating natural language inputs, and NLG is focused on generating natural language, sometimes from scratch.
When implementing an NLU system, the different approaches include rule-based systems, statistical models, and hybrid models.
Rule-based systems use a set of predefined rules to interpret and process natural language. These rules can be hand-crafted by linguists and domain experts, or they can be generated automatically by algorithms.
Statistical models use machine learning algorithms such as deep learning to learn the structure of natural language from data. Hybrid models combine the two approaches, using machine learning algorithms to generate rules and then applying those rules to the input data.
When selecting the right tools to implement an NLU system, it is important to consider the complexity of the task and the level of accuracy and performance you need.
Akkio is an easy-to-use machine learning platform that provides a suite of tools to develop and deploy NLU systems, with a focus on accuracy and performance.
Akkio uses its proprietary Neural Architecture Search (NAS) algorithm to automatically generate the most efficient architectures for NLU models. This algorithm optimizes the model based on the data it is trained on, which enables Akkio to provide superior results compared to traditional NLU systems.
In a head-to-head comparison with other AutoML platforms, Akkio was found to be (by far) the fastest and most cost-effective solution, while maintaining similar or superior accuracy.
We've explored the value of NLU and the potential it holds for businesses, both large and small. NLU technology is already being used by the biggest players in the industry, such as with Microsoft's Cortana, Amazon’s Alexa, Apple's Siri, and IBM's Watson.
Akkio’s no-code AI for NLU is a comprehensive solution for understanding human language and extracting meaningful information from unstructured data. Akkio’s NLU technology handles the heavy lifting of computer science work, including text parsing, semantic analysis, entity recognition, and more.
Akkio is used to build NLU models for computational linguistics tasks like machine translation, question answering, and social media analysis. With Akkio, you can develop NLU models and deploy them into production for real-time predictions.
We also offer an extensive library of use cases, with templates showing different AI workflows. Akkio also offers integrations with a wide range of dataset formats and sources, such as Salesforce, Hubspot, and Big Query.
To demonstrate the power of Akkio's easy AI platform, we'll now provide a concrete example of how it can be used to build and deploy a natural language model.
First, users simply connect their data source to the Akkio platform.
Next, users can select the data they want to use to train their model. Akkio offers an intuitive interface that allows users to quickly select the data they need.
Once the data is selected, users can simply hit "Train" and the model will be trained on their data in minutes.
Finally, users can deploy the model to any environment they choose. Akkio offers a wide range of deployment options, including cloud and on-premise, allowing users to quickly deploy their model and start using it in their applications.
Language is how we all communicate and interact, but machines have long lacked the ability to understand human language.
Now, businesses can easily integrate AI into their operations with Akkio's no-code AI for NLU. With Akkio, you can effortlessly build models capable of understanding English and any other language, by learning the ontology of the language and its syntax. Even speech recognition models can be built by simply converting audio files into text and training the AI.
With Akkio's intuitive interface and built-in training models, even beginners can create powerful AI solutions. Beyond NLU, Akkio is used for data science tasks like lead scoring, fraud detection, churn prediction, or even informing healthcare decisions. Try Akkio today and see the power of AI for yourself.