Let’s Talk About Conversational AI

Billions of people use instant messaging apps daily to exchange bite-sized chunks of information on platforms including WhatsApp, iMessage, WeChat, Signal, Slack, Facebook Messenger, and Snapchat. In fact, as of this year, the amount of time we spend on messaging apps may surpass our use of social networks.

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Read “We Talk, It Acts: Conversational AI.”

These messaging platforms have become increasingly sophisticated, with capabilities far beyond simply enabling users to send and receive text messages, photos, and videos. Many of them allow users to exchange documents and files, voice memos, location information, and sometimes even cash. And intriguingly, they’re creating new opportunities for us to interact not just with each other but, thanks to chatbots, also with machines.

Rise of the chatbots

A chatbot is a service that, in its most basic form, responds through pre-programmed rules to queries it receives through a messaging interface. Despite the name, a chatbot doesn’t necessarily do any chatting. Rather, you ask it a question or tell it to do something (such as order a product or make a reservation), and it responds accordingly.

Chatbots have been around for decades, and they are becoming more sophisticated. Eliza, a program that simulates conversation by asking a handful of questions and repeating parts of the answers, dates back to 1966. Many of today’s chatbots are Eliza’s direct descendants; they use predefined message templates to deliver programmed answers. More advanced chatbots that use machine learning and artificial intelligence (AI) to develop increasingly better answers and interact in a more natural way are emerging.

Chatbots are ideally suited for delivering services from within a messaging app in a frictionless, personal way. Instead of having to install and launch a separate application, the user can text a chatbot from within the messaging app just as they would a human contact to hail a cab, buy a t-shirt, order a pizza, reserve a conference room, approve a workflow, or submit a vacation request. Some experts even believe that chatbots will replace standalone mobile applications to a certain extent since a chatbot with limited or no graphical components that operates within a messaging platform is cheaper to build and run than a full-featured app.

The Machines Talk Back

As machines have become more sophisticated at understanding and responding in natural language, we’ve seen a massive growth in another type of conversational application: digital assistants, like Apple’s Siri, Amazon’s Alexa, Google Assistant, and Samsung’s Bixby. These dedicated apps, included in smartphones, smart speakers, and a rapidly growing variety of other devices, are enabled with natural language processing that helps them understand casual speech input by text or voice.

Digital assistants can interact with other applications and parse open-ended questions, like “How do I get to the nearest subway station?”, “What’s the score for the Giants game?”, and “What are the top three deals I still need to close this month?”, through one interface. General Motors is fully integrating Alexa into some of its vehicles, enabling drivers to use their voice to navigate, make phone calls, and to operate the automobile entertainment system.

Enterprise use cases are also beginning to materialize. Gartner has predicted that by 2023, one-fourth of employee interactions with applications will use voice, compared to 3% in 2019.

Conversational artificial intelligence

Based on the recent advances in machine learning and AI, we’re seeing the text-based, rule-based conversational applications typical of the first chatbots give way to intelligent solutions. We’re getting closer to being able to talk to these applications as if they’re people and having them learn from our transactions and behavior to refine their responses. Using them to access content, request customer service, and make transactions may soon become seamless.

In other scenarios for conversational AI, a technician might send a photo of a broken part to a parts and maintenance bot, which would use deep learning-based image processing to identify the part, automatically submit a replacement order, and send the technician the predicted delivery date and installation instructions via the same messaging channel. An employee can use Slack’s AttendenceBot to submit a leave request by sending the message, “I’ll be taking the first week of August off.” Bots can also get in touch proactively with users based on certain dynamic criteria or even take action autonomously within pre-determined constraints, enabling users to focus on more important tasks.

The conversation continues

Eventually, we may be able to simply talk to a device and have it do our bidding without interacting with a single app. We’ll say what we need and the smart systems behind the scenes will apply machine learning to determine what we want, ask questions to clarify and add context, and then deliver on the request, whether it involves running reports, providing customer support, or changing business travel plans on the fly. A consumer might tell their phone to find the next available slot for grocery delivery or to locate the nearest place stocking paper towels. In a business context, a shift manager might ask a tiny black box in their warehouse, “What are the three most important orders we need to fulfill this week, and what’s the best way to make them happen?” and get the optimal response an instant later.

The more we talk to these systems, the smarter they’ll get. Instead of forcing us to learn how they work, they’ll learn how we work and adapt themselves to suit. This isn’t simply the emergence of a new interface. It’s an entirely new paradigm for computing, and it will change how we use technology at home and in the enterprise.