Generative AI has played a key role in elevating chatbots and voice assistants to new levels of personalisation. In this article, we’ll explore the integration of large language models (LLMs) into Conversational AI technology and the promising opportunities it brings to the field.
According to Juniper Research’s report, by the end of 2023, LLM-based chatbots will handle up to 70% of customer interactions.
What’s wrong with Conversational AI, and why does it need neural networks?
People have always aimed to create the perfect conversational partner who understands everything, responds to any questions, displays empathy, and has a rich vocabulary.
However, until recently, dialogue interfaces had limited functionality. They did not take into account the conversation history, provided pre-written responses, and mostly performed only simple tasks like “tell me the news,” “play a lullaby,” “turn on the lights,” or “clarify delivery terms.”
Today, with the advancement of AI technologies, we are getting closer to solving these challenges. The emergence of LLM models could significantly improve the quality of dialogue systems – providing more natural speech and personalised responses based on message history, acting as a whisper agent for customer centre operators, and much more.
Let’s dive into the future that awaits voice assistants and chatbots based on neural networks and how they will change our user experience.
Dialogue systems based on LLMs have enormous potential for development. Machine learning technologies allow the creation of voice or text assistants with higher speech recognition and understanding accuracy. The improvements in this area significantly expand the application field for conversational AI.
Let’s consider some use cases:
One of the advantages of ChatGPT is the ability to engage in full-fledged dialogues with users. Until recently, virtual assistants like Alexa or Siri provided information on request, and if they interacted with users, they did so in a limited format and according to specific rules.
LLM-based chatbots and virtual assistants can maintain the conversation context throughout the entire dialogue. Furthermore, they can generate detailed and personalised responses. For example, such a chatbot can explain the concept of nuclear physics to an adult and a child, considering their backgrounds.
This natural dialogue is possible because neural networks were trained on extensive datasets. Leveraging this experience, AI models can emulate real conversations, adapting to users based on speech characteristics, delivery style, and dialogue context.
2. Better user intent understanding
Another valuable feature is the ability to predict user actions or requests based on past interactions. For instance, you can use information from the customer’s profile or consider their search history on the company’s website.
Customer service is already one of the most promising neural network use areas. AI can create call scripts for voice assistants, compose emails, or draft messages for chatbots, all tailored to the specific individual.
IBM Watson Assistant – a platform for creating customer support chatbots. This service allows the creation of virtual assistants that understand the context of the conversation and simulate full-fledged communication with the user, achieving response accuracy up to 95%.
ChatGPT can already provide companies with valuable information about customer preferences and behaviour, enabling them better to tailor their services and products to the target audience.
How many times have each of us encountered boring lessons or lectures? It seems that ChatGPT can significantly change approaches to education. And it’s not just about using neural networks to help write essays or complete assignments.
LLM-based chatbots will help create personalised learning scenarios. For example, for school students, a common course can be transformed into an interactive quiz conducted by a voice assistant. For employees, it can provide onboarding or coaching in the form of an exciting quest.
Variations are possible even in the traditional setting where students use ChatGPT as a cheat sheet. Perhaps in future exams, teachers will assign improving AI bot responses as one of the tasks.
In modern companies, teams may be physically and linguistically distributed. Both customers and employees speak different languages. ChatGPT can translate documents and data quickly, which is crucial for multilingual organisations.
Moreover, AI-based chatbots can transform documents into other formats, summarise meetings, and transcribe video and audio recordings. This significantly saves time and reduces the risk of human error.
For example, a Generative AI chatbot can become an indispensable assistant in healthcare. In just one minute, it can analyse the availability of doctors in the internal database and schedule a patient at a convenient time without human involvement.
Ultimately, using Generative AI for business will enhance employee personal efficiency. Gen AI-based assistants can help plan meetings, remind of upcoming events, advise schedule planning and much more.
Voice assistants are already actively used to control smart homes, but with AI, they can do even more. Understanding natural language, bots based on ChatGPT can quickly determine user intentions, even if the command is not given explicitly.
For example, for phrases like “Get ready for a party” or “I’m tired and want to sleep,” the chatbot will adjust the climate control settings, lighting scenarios, and music differently.
Voice assistants can learn from conversations with users. This allows them to understand user intentions better and provide accurate responses. Users can set reminders for tasks such as watering plants or turning off lights using a voice-controlled speaker.
Eventually, there have been discussions that smart devices will become full-fledged conversational partners for elderly individuals living alone, not just home assistants. It seems this is becoming a reality with the era of neural networks.
But is the integration of LLMs into Conversational AI entirely smooth? Many companies are already embedding ChatGPT into their conversational solutions via APIs. LLM models offer significant personalisation possibilities, but even the most advanced ones can make mistakes and inaccuracies.
Often, this is because ChatGPT does not pull information in real time. For example, the GPT 3.5 model trained on data from 2021 will not be aware of the latest developments. However, OpenAI has recently announced that the 4.0 version now has Internet access.
ChatGPT becomes less intelligent after interacting with people. At this point, the reason for this is unknown. Researchers are inclined towards two approaches. The first is that AI interacts with humans, learns from them, and performs worse. The second reason is that OpenAI may have made the model less capable.
The bot uses all available information to generate responses, some of which may contain errors and inaccuracies. Such responses can mislead or lead to false conclusions. For example, this was one of the reasons why the New York State Education Department blocked the use of ChatGPT in schools.
Many countries are discussing the need for legal regulation of Generative AI to prevent misinformation and fraud cases. Efforts to create standards are ongoing, but technology is spreading too rapidly.
Progress in AI enables the development of helpful and easy-to-use AI assistants that can handle more and more complex tasks. They improve their understanding of natural language, can deduce our intentions, and personalise responses.
Unfortunately, even large language models cannot always correctly determine the context of a conversation and give a relevant response. Using LLMs in a specific business will require fine-tuning with the company’s data, such as product catalogues, articles, websites, and customer databases. This process should be accompanied by ongoing testing and monitoring.
However, it seems that these challenges can be overcome. Some companies are already integrating ChatGPT into their chatbots and voice assistants. It’s only a matter of time before LLMs become crucial to Conversational AI.