Conversational AI
Conversational AI platforms are, in some ways, the ultimate link between people and artificial intelligence. When you use conversational AI solutions, there is no need to understand code or even type in a prompt. Instead, you simply ask questions as you would with an actual person and receive answers in the same way. Conversational AI technology is more than a hands-free method of computer use; it represents how people will get the most out of groundbreaking artificial intelligence capabilities.
Use Cases of Conversational AI
There are many ways to describe how conversational AI technology is being applied today in the real world. To name just a few, you will find conversational AI platforms being used in industries including retail, education, office administration, corporate training, and insurance.
Then there are the media through which AI-enabled services are delivered. Examples are:
Text Chatbots
If you’ve ever gone to a website page and noticed a pop-up with the phrase “Can I help you?”, then you’ve seen a text chatbot. They tend to involve the user typing in a simple request and the chatbot asking questions to refine the request until it provides a link to a relevant webpage or an actual answer.
Voice Response Chatbots
One step up in user-friendliness from text is the voice response chatbot. Instead of typing in a query and getting a text answer in return, this technology allows you to ask questions or give commands verbally. Examples include Amazon Alexa and Apple’s Siri.
Interactive Agents
The technology behind interactive agents adds another layer of sophistication to conversational AI solutions, namely, a lifelike avatar that has the appearance and physical movements of a real person. This technology provides an even more natural context to the whole goal of conversational AI, which is to feel like you are communicating with another human being. In fact, the latest generation of interactive agents has the ability to analyze (and express) non-verbal communication cues as a way of gaining a deeper understanding of what the user needs.
Key Features of Conversational AI
There isn’t exactly one strict definition that covers all types of artificial intelligence technologies. Some sources argue that there are three kinds of AI, others seven. In reality, AI is a continuum that is always developing in terms of abilities and terminology, while various layers of artificial intelligence work together to produce ever-innovative results.
In the case of conversational AI, we can define it as the function that enables communication between user and computer through natural language processing (NLP; see below). The communication can be either voice or text.
This helps to differentiate it from rule-based AI chatbots, where you need to use certain keywords and can only receive limited responses. In comparison, conversational AI platforms allow you to ask anything. As their underlying abilities “learn” more (i.e. through machine learning), their answers get better and minimize their “I’m having trouble understanding you” responses.
How Does Conversational AI Work?
NLP is the core technology powering conversational AI. For the average user, you’ll recognize NLP as the technology that allows you to talk instead of type. But there is a lot more to it than that. Firstly, NLP includes text, which is a capability of its own. Then, for voice communication, NLP must understand what you want regardless of accent, individual speech patterns, or the language you speak and then give you a relevant and understandable answer. Behind this ability are a range of functions:
- Input processing is where you ask questions and give commands. The system records your query and filters out background noise if you are speaking.
- Natural Language Understanding (NLU) converts your voice query into text, parses it to define your query, and then sends it to other AI components for processing. For text, a similar parsing step occurs because text queries are made in natural language, as opposed to a programming language that the computer understands without NLU.
- The dialogue management function receives the output after other AI components process it, and then compares it to the query to determine how to phrase the answer.
- Natural Language Generation (NLG) is essentially the opposite of NLU in that it takes the textual answer from the previous step and converts it to something understandable by a person; this includes setting a limit on the length of the answer.
- Output processing sends the answer to you; when an audio response is used, voice synthesis technology puts the output in audio form while accounting for language type, accent, etc.
What Is the Future Value of Conversational AI?
At the most basic level, conversational AI is a form of computer input and output that uses natural language processing. In the past, using a computer meant typing in simple queries and reading a textual output. For advanced applications, the user would also require knowledge of a programming language. Ironically, the complexity of programming skills needed for developing AI is one of the greatest barriers to its advancement.
Conversational AI has the potential to change this. Of course, we are already using it for relatively elementary situations. For instance, if you use ChatGPT, then you’ll know that you just type in something that seems to explain what you want – no programming skills required. If the output isn’t quite what you are looking for, then you revise parts of your original query.
But your query can consist of many details and dozens of words, meaning that we are no longer at the “simple” stage of query input. It’s the same story with programming AI in the first place. Conversational AI has the potential to open up the use of artificial intelligence to unskilled users, who only need to describe what they want, and AI takes care of the rest.
Conversational AI vs. Generative AI
The technology that enables this computer-assisted programming of artificial intelligence is generative AI. Boiled down to its essential abilities, generative AI processes a query by looking at information stored in local databases and the internet; uses statistical modeling to choose what seems like a good answer, and generates a response. Conversational AI is the medium through which we can communicate with generative AI functions.
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