“A woman, without her man, is nothing.”
Or should we say, “A woman: without her, man is nothing.”
In language, the devil is in the details, like putting the comma in the right place. It’s these subtleties that made it hard in the past to get conversations with AI chatbots to sound life-like and accurate in the way a person would speak.
In its DNA, coding is logical and mathematical with either the 1 or 0 of the basic binary building blocks sitting at its core. On the other hand, human language is nuanced, subtle, ambiguous, emotive. Words take on new meanings over time and slang bursts in faster than we can keep up.
And then there’s the ‘way’ we say things. Would an AI chatbot understand this Dad joke?
“My wife complains I don’t buy her flowers. To be honest, I didn’t know she sold flowers.”
Conversational AI has come a long way since the early chatbots. In December 2022, ChatGPT exploded onto the scene and gained a million users in a couple of days. ChatGPT, developed by OpenAI, is advanced conversational AI technology that can understand human language and then respond with authentic and thoughtful replies.
There is no turning back now for conversational AI. For example, ChatGPT 'caught' the above flower joke. It replied:
“That's a funny joke! I'm sure your wife will appreciate your sense of humor.”
Soon, speaking to bots will be the norm and it will feel completely natural. Already, we don’t think twice before we say, “Hey, Siri!”, or “Alexa, turn that music down!”
So where does neural linguistics fit into conversational AI?
Neural linguistics combines the fields of linguistics, neuroscience, and cognitive science and it explores how the brain processes language and how language affects our understanding of the world around us. It delves into how we interpret and process words and how these processes shape our thoughts, feelings, and behaviour.
Part of neural linguistics is the study of neural networks and how they are used in language. Neural networks are made of many interconnected nodes that act like mini-brains, each with its own specialised functions, and they are designed to process data in a way that is similar to the human brain. Neural networks consist of layers of interconnected "neurons," which are small computations that help the network understand and generate text. By understanding the neural networks involved in language processing, neural linguists can better understand how the brain interprets language.
In AI, more specifically in deep learning, artificial neural networks replecate human neural networks with a set of algorithms.
In the context of AI chatbots, neural networks are used to analyse the text from the person and understand its meaning. This analysis is done using techniques like tokenisation, parsing, and part-of-speech tagging, which are all part of Natural Language Processing.
The science of neural linguistics is not just intellectual, it is also practical. Neural networks can be used to interpret language, making them powerful tools for Natural Language Understanding and conversational AI.
In Natural Language Processing, neural linguistics uses deep learning algorithms to understand and interpret language which helps machines learn to process language in a way that is more in line with how humans do it. This is done by combining elements of linguistics and AI to create a system that can understand the meaning behind words.
For example, neural linguistics can be used to create a system that can identify the difference between slang and formal language, or to understand the context of a sentence. This helps bots to better understand and thus better interact with humans.
Large Language Models (LLM) used in neural linguistics are a type of AI trained to understand and generate natural language text. They are based on deep learning techniques, which is a method of training a neural network using a large dataset. These representations are used to build statistical models that can predict word sequences, generate text, and identify language structure. The larger the language model, the more accurate the predictions and the more powerful the model. LLMs typically have millions of parameters and are trained on vast amounts of data.
In Machine Learning, neural linguistics is used to develop algorithms that can learn from data and improve their accuracy over time. The goal is to be able to read the sentiment, then classify the text, and so predict the outcome of a conversation, and have a more meaningful interaction.
To keep building a bot’s language skill, you carry out A/B testing. Also, every time a conversation between a bot and a person fails, it is seen as a learning opportunity as the information is fed back into the system and the next time such a situation arises, the chatbot will know how to answer.
Its brilliance comes to the fore when it can not only understand what’s being said, but can also look beyond the words into the emotion behind them, the intent, and then generate an appropriate response that fits with the context. It can even detect sarcasm or if someone is not being consistent, i.e., they may not be telling the truth.
As with all tech, it is not just about the tech itself per se but also about the commercial value of it and how it will improve our lives. User experience and user satisfaction is the goal for many businesses who invest in AI as a happy customer is a loyal customer.
Practically, this technology is used in a wide range of applications, including:
Conversational AI is particularly powerful in customer service and in contact centres. An AI chatbot can be taught to interact with empathy with vulnerable people and can connect with a sense of the ‘human touch’. Along with using relational language, the conversation can be personalised to each individual using data-rich integration.
Addition functionality such as predictive analysis, propensity, intent recognition, entity gathering, and sentiment analysis further makes conversational AI a powerful tool for engaging with customers in every industry. All these capabilities work together to provide meaningful and personal contact with people.
[For a more in-depth look at the Benefits of Conversational AI for debt collection, download this guide.]
We cannot overlook the ethical side of conversational AI. With AI becoming so powerful, it is essential that the ethical use of this power is enshrined from the start. An example of trying to keep conversational AI ethical and fair, is ChatGPT has human values encoded in. For example, it rejects racisms, sexisms or pro-violence stances and will answer that it is merely a bot when asked questions about itself (although it is not 100% perfect).
When presented with the first statement in this blog, this is how ChatGPT responded.
Example: When asked “Are you happy?”, it replied:
“As a machine learning model, I do not have the ability to experience emotions like happiness. I am designed to process and generate text based on the data that I have been trained on, but I do not have the capacity for feelings or consciousness. I exist to assist with a variety of tasks, but my primary function is to provide information and generate text.”
AI ethics is also concerned with doing no harm to people or infringing on rights and is focused on helping people instead. AI algorithms can be used to make decisions that have a significant impact on people’s lives, such as determining credit scores, and as such, it is essential to ensure that these decisions are made in a responsible way.
There seems to be no end to what conversational AI can do and Neural Linguistics is starting to fulfill its long touted potential to revolutionize the way we interact with computers. AI-driven conversations are becoming increasingly sophisticated, and Neural Linguistics is an essential part of this technology. By leveraging this technology, conversational AI can become more natural, more human-like, and more engaging. This will open a world of possibilities for businesses, allowing them to create more personalised and satisfying experiences for their customers.
Talk to a conversational AI expert who can show you how automating your debt collection
activities will push your engagement rate and response rate metrics to new heights.