AI unmasked: How is nmodes different

While Chatbots have been around for a little while now, their presence is more noticeable thanks to Facebook and Microsoft’s recent advancements.

Initially customers complained about the robot-like experience and the limited functionality of first generation bots and rarely found them useful. The customers were skeptical about how valuable in practice chatbots actually are, which has left recent AI vendors like nmodes with the task to combat the leftover stigma from the poor customer experiences and shortcomings of these initial offerings.

Chatbots, like an IVR?

We’re all used to calling into a contact center and punching numbers into a menu to be routed to the correct agent or service to address our needs. Interactive Voice Response solutions (IVRs) drive this interaction and are basically If/then routing trees that “listen” to the digit entered and “transfer” the user to the appropriate next step. While tremendous advancements in technology have brought voice recognition capabilities, those first generation IVRs were all about automated actions based on prompts.  Enter your account number, press 1 to speak to an agent, etc…

The first generation Chatbots are just like an IVR. They can respond to prompts to progress through a predetermined process or display some canned information like pricing, a contact number, route to an agent, etc., but that was about the extent of it. Still 1^stgeneration Chatbots came with 4^thgeneration expectations. While these basic functions have tremendous value to a business, the customer expectation is very different when dealing with a phone call vs. a chat session. Consumers have experienced IVR routing for decades whereas chat is still relatively new and is perceived as a conversation with a person, rather than interacting with a machine. Add on the fact that many vendors and consumers mislabeled Chatbots as Artificial Intelligence in the beginning and the expectation of a dynamic, responsive customer experience is even greater.

So it’s no surprise that customers were less than impressed with “Artificial Intelligence” that could only display simple answers and basic information. We were expecting Hal from 2001: A Space Odyssey or KIT from Knight Rider, and we got a pixelated PONG instead.

Let’s talk…

Now, Artificial Intelligence has evolved to be integrated into Chatbots to deliver a more powerful user experience.  While these new versions of Chatbots coming out are powered by Artificial Intelligence, AI powered chat also exists independent of bots in some instances. Confusing? Yeah, I was too.

The beauty behind true Artificial Intelligence is its ability to recognize the context of a conversation and respond with relevant, contextual information dynamically. A customer can now “speak” to technology the same way they would hold a conversation and the AI has the ability to “read” the customer’s intent to provide information quickly and efficiently. No more are you limited to a set of canned responses. The AI can reach in to a wider array of relevant information to craft unique responses based on any number of criteria. While in most cases AI is still limited to a few topics per use case, the technology is growing quickly, making almost daily improvements in functionality and customer experience.

What is even cooler is that the longer the AI is deployed, the more it “learns” and improves the speed and quality of responses. So while the scope of AI interactions is limited at first, the maturity curve is quick, delivering an ever-improving customer experience without having to invest in additional people, processes, or technology. It really is like a “growing up” of technology, right before your eyes. 

AI training is a critical part of conversational AI solutions, a part that makes AI software different from any kind of software previously created.
AI training is not coding.
Unlike all other existing software which is fully coded.

Let us consider a simple example:
We create chatbots for two companies, one company is selling shoes, another is selling cars. From the software standpoint it is one chatbot solution running as an online service accessed remotely or a program available locally. In both cases they are two identical instances of the same software (one instance for the shoes company, another for the cars company).
Yet, for the first company the chatbot is supposed to talk about flip-flops, summer shoes, high heels and so on. For the second company, however, the chatbot is not expected to know any of that. Instead, the chatbot should be able to support conversations about car brands, car models, should know how to tell Toyota Camry from Toyota Corolla, etc. This shoes and cars knowledge is not programmable. It is trainable. It is not coded, instead it is a part of language processing capability that AI solutions like chatbots have. And herein lies the major differentiation and advantage of the AI solutions compared to traditional software.

How to train AI?
There are several ways to do it. Sometimes AI system can train itself, improve its linguistic ability over time. It also can be trained by professional linguists. And in some cases, by the users. The latter is the desirable scenario because businesses know better than anybody else what they want their chatbot to talk about.
It is not easy, given the existing state of AI technology, and usually requires a high level of technical knowledge. You may have heard mentions of intents and entities in chatbot discussions. These are examples of linguistic elements AI training is currently based on.
Without proper understanding of what these linguistic elements are and how language acquisition process works in existing AI systems it is better to leave AI training to professional linguists.