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The last post explained how conversational AI will transform customer experience and the technology will get to the point of facilitating that transformation. This post will expound upon that topic and how Interactions is perfecting that technology.
Natural Language Processing Revisited
The last post used Uxplanet.org’s definition of NLP, “a type of artificial intelligence technology that aims to interpret, to recognize, and understand user request in the form of free language (uxplanet.org).” NLP is basically about developing applications that can understand human languages. A concrete example of NLP would be search engines. For instance, if someone regularly searches for tickets to football games, the searches made by the user provide the search engine with enough textual data where the search engine then provides the user with relevant advertisements for companies selling tickets to football games.
To reiterate, AI is still in the relatively early stages, and the average precision rate is around 60–70% (uxplanet.org), which is not high enough for AI models to be able to grasp context, sarcasm, or complex sentences. AI is a great technology and requires prodigious amounts of data to train models that can recognize and respond to human speech. Data needs to be run through AI models hundreds of thousands to millions of times to make the models better predictors of what is being asked by human customers, which takes time.
Think about a child learning to speak; the child does not learn how to speak overnight or even within a year, it takes several years for the child to grasp their native language fully.
First, they listen to their parents (and or whoever else) speak around the house. Eventually, they speak their first word; then they start to speak in full sentences, then they go to school and learn how to read, write, grasp
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NLP For Conversational AI
Conversational AI is essentially about data input and output. According to Martechseries.com, “the customer inputs data through a user interface (voice, chat, etc.).” The system then needs to understand the user’s intent, provide accurate information or take appropriate action, then capture and analyze data from the interaction. The data is then run through the AI models (using machine learning specifically) to better understand the needs of customers.
Just as a search engine works to ingest data relating to human language, these user interfaces do too. For example, if someone searches for football tickets over a voice or chat-based application, the result will be the same, they will receive relevant offers related to purchasing football tickets. At least this is the goal for voice and chat-based apps.
Conversational AI technology is trained in basically the same way. If a company wants to build a chatbot with human-like comprehension, then they need to leverage machine learning tools that are capable of analyzing vast quantities of unstructured data (the majority of data in the world). Such as search engine data, social media posts, emails, webchats, text messages, call transcripts or messages from other bots, and more. The company would then create ML algorithms that can ingest this data and essentially assign values to phrases that the company wants the chatbot to learn; this goes back to the mention of Neural Networks.
Neural networks are designed to recognize patterns. According to skymind.ai, “the patterns they recognize are numerical, contained in vectors, into which all real-world data, be it images, sound, text or time series, must be translated (Skymind.ai).” Neural networks essentially work as a component of machine learning algorithms. They recognize the patterns, then kick that data back to the machine learning algorithm for clustering or classification. An example of the ability of neural networks is the ability to discern what is spam and what is not spam in your email inbox.
While this explanation makes the technology sound simple, many variables go into creating AI-based applications, and sophisticated algorithms and mathematics are required to make them work at a high level, which is why NLP has a precision rate of 60-70%.
What Does This Mean For Customer Experience?
User experience can only benefit from AI models that continuously learn and improve. As AI becomes more sophisticated, companies are able to adjust their algorithms to listen to their customer’s intent and gauge what their customers care about. For example, Martechseries.com talks about accurately discovering the “right mix of data and user input (martechseries.com).” Ultimately this means listening for context, specific conversations, and taking into account historical information. Advances in AI will make conversations with customers even more accurate in the future.
What Does This Mean For Developers?
Running data through the AI models reduces the burden on developers over time. Unsupervised machine learning models can correct themselves, which makes developers’ jobs easier after the AI models have had time to process and learn from ingested data many times over. According to O’Reilly.com, machine learning is already making coding more efficient.
According to Oreilly.com, Google’s Jeff Dean reported that “500 lines of TensorFlow code has replaced 500,000 lines of code in Google Translate (O’Reilly.com).” The article recognizes that lines of code is a questionable metric, but it also makes the point several times over, that there have been many significant increases in the automation of programming tasks due to machine learning. These advances bode well for conversational AI, as this means that the advancements mentioned earlier may happen sooner.
The Interactions Product
Interactions offers an intelligent virtual assistant that provides human-like customer service capabilities. The virtual assistant also knows when a topic is too complicated for it to understand and when to alert a human that their assistance is needed.
The Technology Behind Interactions’ Product
Interactions has the unique place in the industry where the AI model helps reduce the amount of human customer service interaction required and when the customer asks a question requiring human involvement the correct responses can be reintroduced (by human customer service reps) into the AI model so that it “learns” new situations it has not seen before. So by providing the market with a system that allows this continuous improvement of learning models AI can move into the marketplace rapidly and assist human productivity.
There are some camps (groups of people) who believe that AI will not get where it needs to be for 20 years. Other groups recognize that companies like Interactions with their continuous improvement model through human interaction can accelerate the process of building great models that learn and make people more productive rapidly. So not all industries may achieve “perfect AI” for some years, but ones who make use of technology like that available from Interactions will accelerate the use of these technologies and improve human productivity.
Interactions has the advantage of having been in the industry for a long time, which has allowed them time to collect vast amounts of data, which in turn made their AI models more sophisticated.
Automatic Speech Recognition
Interactions further differentiates itself with its automatic speech recognition (ASR) technology. Their ASR technology acoustic models to predict how words sound in a particular environment, such as on the phone, or in the car. Interactions combines these acoustic models with language models and pronunciation for increased accuracy. Again, AI models sometimes struggle with precision due to the models misunderstanding the subtle nuances of language, such as pronunciation. Interactions differentiates itself this way.
Natural Language Processing
As mentioned before, NLP can only be human-like if it picks up on things like context and understands user intent. Interactions’ NLP can do that by identifying people, locations, topics, and intentions. Another way in which Interactions sets its AI models apart.
Dialog Management
Interactions’ technology works across any channel, text, voice, chat, or social, all on a single platform. Allowing for the best technology for the task to be used. Dialog management enables seamless interaction with the virtual assistant and leverages knowledge-based approaches to the customers’ specific vertical. That is, the platform can use data from past conversations with customers in the same vertical to enhance their experience.
Voice Biometrics
The virtual assistant also uses voice biometrics technology, which allows customers to securely verify their identity using their voice.
Conclusion:
While there is plenty of room for improvement in the field of AI, Interactions is leading the way in intelligent virtual customer care and sets itself apart in many different ways. Check out the Interactions website to learn more about what the company has to offer.