What Is Conversational AI?

Conversational AI is a form of artificial intelligence that enables a dialogue between people and computers. Thanks to its rapid development, a world in which you can talk to your computer as if it were a real person is becoming something of a reality.

For years, many businesses have relied on conversational AI in the form of chatbots to support their customer support teams and build stronger relationships with clients. But the technology is quickly developing beyond this use case and is set to take on an even greater presence in people’s everyday lives.

“We’ve wanted to have human-like conversations with these systems for a long time now, and we’ve thought we might be nearing it for the last 10 to 12 years,” said Joe Bradley, the chief scientist and a senior VP of data science and machine learning at LivePerson. “We really have finally turned that corner.”

 

What Is Conversational AI?

Conversational AI is a form of artificial intelligence that enables people to engage in a dialogue with their computers. This is achieved with large volumes of data, machine learning and natural language processing — all of which are used to imitate human communication.

“It’s about having a system that’s able to carry on a conversation with a human user, usually to solve a task or answer the user’s question, in a way that imitates a human having the same conversation,” said Yves Normandin, a VP of AI technologies and products at customer experience tech company Waterfield Tech.

People may be most familiar with virtual assistants like Siri or Alexa, but conversational AI has taken on other forms as well, including speech-to-text tools like Descript and Otter.ai and sophisticated chatbots like OpenAI’s ChatGPT.

Despite conversational AI’s expansion, technologies like chatbots have traditionally been viewed by consumers as “kind of lousy,” according to Bradley. “And people weren’t exactly wrong. You had to do a lot of painstaking work and thoughtful optimization in order to make them good.”

This perception has shifted, with consumers turning to AI like fashion chatbots and mental health chatbots for support. But conversational AI is still limited to performing specific tasks and hasn’t come close to rivaling human intelligence. That’s because these systems continue to be trained on information only, which is a “very two-dimensional way to learn about the universe,” Bradley said. “Once we teach these things in different modes I think [it] will create a much more robust kind of intelligence.”

A Look at the Bigger Picture The Future of AI: How Artificial Intelligence Will Change the World

 

How Does Conversational AI Work?

Replicating human communication with AI is an immensely complicated thing to do. After all, a simple conversation between two people involves much more than the logical processing of words. It’s an intricate balancing act involving the context of the conversation, the people’s understanding of each other and their backgrounds, as well as their verbal and physical cues.

Mimicking this kind of interaction with artificial intelligence requires a combination of both machine learning and natural language processing. 

 

Machine Learning in Conversational AI

Machine learning is a process made up of a set of algorithms, features (individual variables) and data sets that teaches a computer how to do certain tasks. As the input grows, the machine gets better at recognizing patterns and making predictions, thus improving its ability to perform that certain task. 

 

Natural Language Processing in Conversational AI

Meanwhile, natural language processing teaches computers how to understand language, conversations and speech. Natural language processing consists of four steps: input generation, input analysis, output generation and reinforcement learning. 

Input Generation

During input generation, users provide a prompt (either voice or text) through a conversational AI website or app. 

Input Analysis

If the prompt is text-based, the AI will use natural language understanding, a subset of natural language processing, to analyze the meaning of the prompt and derive its intention. If the prompt is speech-based, it will use a combination of automated speech recognition and natural language understanding to analyze the input. 

Output Generation

Then comes dialogue management, which is when natural language generation (a component of natural language processing) formulates a response to the prompt. 

Reinforcement Learning

Finally, through machine learning, the conversational AI will be able to refine and improve its response and performance over time, which is known as reinforcement learning.

 

Deep Learning in Conversational AI

About a decade ago, the industry saw more advancements in deep learning, a more sophisticated type of machine learning that trains computers to discern information from complex data sources. This further extended the mathematization of words, allowing conversational AI models to learn those mathematical representations much more naturally by way of user intent and slots needed to fulfill that intent. 

Bradley said every conversational AI system today relies on things like intent, as well as concepts like entity recognition and dialogue management, which essentially turns what an AI system wants to do into natural language. And in the future, deep learning will advance the natural language processing abilities of conversational AI even further.

Normandin attributes conversational AI’s recent meteoric rise in the public conversation to a number of recent “technological breakthroughs” on various fronts, beginning with deep learning. Everything related to deep neural networks and related aspects of deep learning have led to major improvements on speech recognition accuracy, text-to-speech accuracy and natural language understanding accuracy.

Learn the Basics Artificial Intelligence vs. Machine Learning vs. Deep Learning: What’s the Difference?

 

How Is Conversational AI Used?

While conversational AI is still most common in customer service — with over 54 percent of organizations using chatbots or some other type of conversational AI for customer-facing duties — it has branched out into other applications and is now widespread across a variety of industries, from HR to the Internet of Things.

 

Customer Support Chatbots

A familiar use case is virtual call center agents for customer support. Just as some companies have web designers or UX designers, Normandin’s company Waterfield Tech employs a team of conversation designers who are able to craft a dialogue according to a specific task. Usually, this involves automating customer support-related calls, crafting a conversational AI system that can accomplish the same task that a human call agent can.

Eventually, as this technology continues to evolve and grow more sophisticated, Normandin anticipates that virtual call agents will be treated similarly to their human counterparts in terms of their training and oversight. Rather than handcrafting automated conversations like they do right now, these bots will already know what to do. And they’ll have to be continuously supervised in order to catch mistakes, and coached so they don’t make those mistakes again. However, this requires that companies get comfortable with some loss of control.

 

Brand Ambassadors 

Before it was acquired by Hootsuite in 2021, Heyday focused on creating conversational AI products in retail, which would handle customer service questions regarding things like store locations and item returns. Now that it operates under Hootsuite, the Heyday product also focuses on facilitating automated interactions between brands and customers on social media specifically. Incidentally, the more public-facing arena of social media has set a higher bar for Heyday.

“We’re moving away from private conversations to being able to do that in public,” Christine Dupuis, Heyday’s senior director of product and AI, told Built In. “If a chatbot is answering publicly there’s a lot more scrutiny. It’s a higher bar to be in the social media space.”

 

Administrative Assistants

Some companies use conversational AI to streamline their HR processes, automating everything from onboarding to employee training. The healthcare industry has also adopted the use of chatbots in order to handle administrative tasks, giving human employees more time to actually handle the care of patients. Some even go so far as to make medical diagnoses themselves. 

 

Accessibility Tools 

Many companies look to chatbots as a way to offer more accessible online experiences to people, particularly those who use assistive technology. Commonly used features of conversational AI are text-to-speech dictation and language translation.

 

Examples of Conversational AI

OpenAI — ChatGPT

ChatGPT is an AI chatbot that responds to written prompts and questions, going so far as to write full-length essays. Developed by OpenAI, the chatbot was trained with data collected from human-driven conversations. There have been other iterations of ChatGPT in the past, including GPT-3 — all of which made waves when they were first announced. But ChatGPT is a true trailblazer.

 

Google — Gemini

Google’s Gemini is a suite of generative AI tools designed by Google DeepMind and meant to be an upgrade to the company’s Bard chatbot. To compete with ChatGPT, Gemini goes beyond text and processes images, audio, video and code. This allows it to respond to prompts and questions using a broader range of formats than Bard, which was limited to text. 

The initial version of Gemini comes in three options, from least to most advanced — Gemini Nano, Gemini Pro and Gemini Ultra. Google is also planning to release Gemini 1.5, which is grounded in the company’s Transformer architecture. As a result, Gemini 1.5 promises greater context, more complex reasoning and the ability to process larger volumes of data. 

 

Anthropic — Claude AI

Anthropic’s Claude AI serves as a viable alternative to ChatGPT, placing a greater emphasis on responsible AI. Like ChatGPT, Claude can generate text in response to prompts and questions, holding conversations with users. However, Claude takes greater care to avoid harmful responses due to a method called “constitutional AI.” This training method ingrains principles into the chatbot, encouraging it to practice certain behaviors and improve when it makes mistakes.      

 

IBM — Watson Assistant

IBM’s Watson computer first made headlines when it played a game of Jeopardy! in 2011. Running software called DeepQA, Watson had been fed an immense amount of data from encyclopedias and open-source projects for a few years before the match — and then managed to win against two top competitors.

Today, Watson has many offerings, including Watson Assistant, a cloud-based customer care chatbot. The bot relies on natural language understanding, natural language processing and machine learning in order to better understand questions, automate the search for the best answers and adequately complete a user’s intended action. It can also be integrated with a company’s CRM and back-end systems, enabling them to easily track a user’s journey and share insights for future improvement.

 

Google — Dialogflow

Dialogflow helps companies build their own enterprise chatbots for web, social media and voice assistants. The platform’s machine learning system implements natural language understanding in order to recognize a user’s intent and extract important information such as times, dates and numbers. And its state-based data models are advanced enough to reuse intents, intuitively define transitions and data conditions and even handle supplemental questions, allowing customers to deviate from the main topic and return to it again without any confusion.

Once they are built, these chatbots and voice assistants can be implemented anywhere, from contact centers to websites.

 

Amazon — Alexa

When it comes to virtual assistants, Alexa is a household name. With Alexa smart home devices, users can play games, turn off the lights, find out the weather, shop for groceries and more — all with nothing more than their voice. What’s more, Alexa talks back. It knows your name, can tell jokes and will answer personal questions if you ask it all thanks to its natural language understanding and speech recognition capabilities.

 

Microsoft — Bing Chat

Bing Chat is Microsoft’s version of an AI chatbot. When responding to a question, it cites its sources, so users can see how it develops its responses and explore other sites for more context. Bing Chat is compatible with Microsoft Edge, but it can be accessed on other browsers as an extension with a Microsoft account. 

 

Apple — Siri 

One of the original digital assistants, Siri is able to process voice commands and reply with the appropriate verbal response or action. Since its introduction on the iPhone, Siri has become available on other Apple devices, including the iPad, Apple Watch, AirPods, Mac and AppleTV. Users can also command Siri to regulate home devices with HomePod and have it complete tasks while on the go with Apple CarPlay. 

 

Google — Google Assistant  

Google’s Google Assistant operates similarly to voice assistants like Alexa and Siri while placing a special emphasis on the smart home. The digital assistant pairs with Google’s Nest suite, connecting to devices like TV displays, cameras, door locks, thermostats, smoke alarms and even Wi-Fi. This way, homeowners can monitor their personal spaces and regulate their environments with simple voice commands. 

 

Samsung — Bixby

Bixby is a digital assistant that takes advantage of the benefits of IoT-connected devices, enabling users to access smart devices quickly and do things like dim the lights, turn on the AC and change the channel. For even more convenience, Bixby offers a Quick Commands feature that allows users to tie a single phrase to a predetermined set of actions that Bixby performs upon hearing the phrase. 

 

Otter.ai  

Otter.ai offers speech-to-text AI services, and teams can use it to record meetings in real time over popular video conferencing platforms. As the technology translates verbal statements into written texts, it also produces brief summaries to help readers find their place in a conversation. Teams can also edit the written text in real time for easier collaboration.

 

Benefits of Conversational AI

Now that conversational AI has gotten more sophisticated, its many benefits have become clear to businesses.
 

Greater Cost Efficiency

The primary pro to implementing this technology is its cost efficiency. For instance, when it comes to customer service and call centers, human agents can cost quite a bit of money to employ. Automating some or all of their work can improve a business’s bottom line. It can also help in labor shortages.

“Finding people and training them and keeping them has become a real nightmare for many companies,” Normandin said. “We’re actually addressing a very serious staffing issue with these automated agents. And once you’ve trained an automated agent, you can replicate them as many times as you want.”

 

Enhanced Customer Service

Conversational AI also stands to improve customer engagement in general, particularly in customer service and other consumer-facing industries. With chatbots, questions can be answered virtually instantaneously, no matter the time of day or language spoken.

“As consumers, people expect really fast answers. Nobody wants to wait on the phone line for one hour or two hours to speak to somebody. The chat is really instant,” Heyday’s Dupuis said. This is “really important” for brands, both in terms of customer relations and business efficiency. With chatbots, companies can be available all over the world all at once, speaking any language necessary. “It makes the brand feel more accessible,” she continued. “It’s a scalable way of providing really good customer service.”

 

Increased Scalability  

The conversational AI space has come a long way in making its bots and assistants sound more natural and human-like, which can greatly improve a person’s interaction with it. 

“If I’m interacting more naturally with a machine, I’ll get more out of it,” Dupuis said. “Getting closer to full sentences and speaking the way I speak, I think that has value.”

These advances in conversational AI have made the technology more capable of filling a wider variety of positions, including those that require in-depth human interaction. Combined with AI’s lower costs compared to hiring more employees, this makes conversational AI much more scalable and encourages businesses to make AI a key part of their growth strategy.   

 

Challenges of Conversational AI

It’s important to remember that a lot of challenges are yet to be overcome in this space.
 

Human Language Nuances

Conversational AI still doesn’t understand everything, with language input being one of the bigger pain points. With voice inputs, dialects, accents and background noise can all affect an AI’s understanding and output. And with text, slang and unscripted language can do the same. Humans have a certain way of talking that is immensely hard to teach a non-sentient computer. Emotions, tone and sarcasm all make it difficult for conversational AI to interpret intended user meaning and respond appropriately and accurately.

 

Accuracy Issues

Even if it does manage to understand what a person is trying to ask it, that doesn’t always mean the machine will produce the correct answer — “it’s not 100 percent accurate 100 percent of the time,” as Dupuis put it. And when a chatbot or voice assistant gets something wrong, that inevitably has a bad impact on people’s trust in this technology.

“If I’m talking to a chatbot and that experience doesn’t meet my very high standard for my expectations about communication with a human, then it’s very jarring to us,” Bradley said. “You can have all these things set up, but as soon as that bot makes a stupid mistake and reveals itself as this kind of relatively dumb computing thing, your feet fall out from under you.”

 

Limited Understanding of Context

For conversational AI to really be improved and widely adopted in the future, Dupuis thinks it will need to become more “context-based and proactive,” where a system will be able to anticipate a user’s needs and future questions before they’re even asked.

“Once you can detect and codify a little bit of what the conversation looks like from a machine learning perspective, you can learn on top of that the overall pattern of what people are asking and what people will ask next,” she said. “Understanding the broader context and offering more proactive suggestions, I think we’re going towards that more and more.”

 

Frequently Asked Questions