What is conversational AI?
Conversational AI is a type of artificial intelligence that enables consumers to interact with computer applications the way they would with other humans.
Conversational AI has primarily taken the form of advanced chatbots, or AI chatbots that contrast with conventional chatbots. The technology can also enhance traditional voice assistants and virtual agents. The technologies behind conversational AI are nascent, yet rapidly improving and expanding.
A conversational AI chatbot can answer frequently asked questions, troubleshoot issues and even make small talk -- contrary to the more limited capabilities that exist when a person converses with a conventional chatbot. Additionally, while a static chatbot is typically featured on a company website and limited to textual interactions, conversational AI interactions are meant to be accessed and conducted via various mediums, including audio, video and text.
Processes and components in conversational AI models
Conversational AI typically entails a combination of natural language processing (NLP) and machine learning (ML) processes with conventional, static forms of interactive technology, such as chatbots. This combination is used to respond to users through humanlike interactions. Static chatbots are rules-based and only provide a set of predefined answers to the user. A conversational AI model, on the other hand, uses NLP to analyze and interpret human speech for meaning and ML to learn new information for future interactions.
NLP processes large amounts of unstructured human language data and creates a structured data format so machines can understand the information to make decisions and produce responses. To further understand NLP, consider its two subtopics that play a crucial role in conversational AI: natural language understanding (NLU) and natural language generation (NLG).
- NLU is what enables a machine or application to understand the language data in terms of context, intent, syntax and semantics, and ultimately determine the intended meaning.
- NLG is the process by which the machine generates text in human-readable languages, also called natural languages, based on all the input it was given. The goal is to explain the structured data for humans to understand.
Real-world benefits and challenges of conversational AI
Conversational AI is expanding and offering benefits to many industries, including but not limited to the following:
- Healthcare. Conversational AI can help patients describe their conditions online through a series of questions meant to circumvent wait times.
- Retail. When traditional customer service representatives aren't available, AI-powered chatbots are able to meet customers' demands on a 24/7 basis, even during holidays. Historically, call centers and in-person visits were the only way to conduct customer interactions. Now, customer support is no longer limited to office hours, because AI chatbots are available through various mediums and channels, including email and websites.
- Banking. Bank personnel can alleviate the pressure put on them by having AI chatbots handle complex requests in a manner that conventional chatbots would struggle with.
- IoT. Common household devices, such as Amazon Echo and Apple's Siri, have conversational AI capabilities. Conversational AI agents can even be interacted with through smart home devices.
- Human resources. Conversational AI can automate the time-consuming process of sifting through candidate credentials manually. As is the case in banking, conversational AI alleviates much of the burden human workers face.
There are some clear challenges with conversational AI development. The first is that conversational AI models have thus far been trained primarily in English and have yet to fully accommodate global users by interacting with them in their native languages. Secondly, companies that conduct customer interactions via AI chatbots must have security measures in place to process and store the data that is transmitted. Finally, conversational AI can be thrown off by slang, jargon and regional dialects, for instance, and developers must train the technology to properly address such challenges in the future.