We've all heard about intelligent voice assistants or virtual assistants.
We actively use many of them in our daily lives: they help us to measure time in the kitchen, to remember things to do at certain times or places, or to play our favorite music without pressing a single button.
But... even among voice assistants there are categories.
Not everyone is equally intelligent.
In the following article, we are going to take a look at the main topics we need to know in order to truly understand how the universe of intelligent voice assistants works.
We'll start by defining what conversational artificial intelligence is and how it works (spoiler: it's not magic, even if it seems like it) before delving into the world of artificial neural networks, which mimic the human brain to learn. Then we'll look at the different types of conversational AI in the market today: chatbots, voice assistants and IVR.
Finally, let's open the discussion to the million-dollar question: What's the difference between an interactive virtual assistant and a traditional chatbot? To find out, we'll dive into specific benefits and specific use cases, both in everyday life and in business operations.
Conversational Artificial Intelligence
Conversational Artificial Intelligence is the application of Machine Learning. It is used to develop speech and language-based applications that allow humans to naturally interact with devices, machines, and computers that use audio.
Think of Conversational AI as the “brain” that powers a virtual assistant. It encompasses a variety of technologies that work together to enable automated communication through voice.
This allows him to understand the customer's intent, decipher language, context, and respond in a human-like manner.
Conversational AI is used when you ask your virtual assistant to wake you up in the morning or when you ask for the direction to get to your trip. You speak with your normal voice, the device understands, finds the best answer and responds with a natural tone.
How does Conversational AI work?
To understand how artificial intelligence can translate human language into something that a machine can understand to respond in a similar way to a human one, it is necessary to visualize the process.
At first glance, it's simple: a person interacts with a virtual agent and receives an appropriate response. But there are actually a number of different technologies working behind the scenes to make this happen smoothly.
The first step is natural language processing (NLP): Natural Language Processing).
The job of the NLP is to correct spelling, identify synonyms, interpret grammar, recognize sentiment, and divide a request into words and sentences that make it easier for the virtual agent to understand.
Once the request has been prepared using NLP, a series of models of Machine Learning and Deep Learning.
Collectively known as Natural Language Understanding (NLU), or Natural Language Understanding), these models allow conversational AI to identify the correct intention or topic of a request and extract important information that can be used to take action.
Now that the request has been properly understood, it's time to formulate a response to the user.
By combining information collected through NLU with a structured hierarchy of conversational flows, a virtual agent can respond appropriately, either responding with a simple question or completing a complex request.
Over time, as the virtual agent answers more questions and AI trainers improve their knowledge, Conversational AI is becoming increasingly intelligent: learn new variations for each attempt and how to improve your answers.
