What Is AI Engine and Do I Need It?

sasha uritsky

Mar 29, 2018

Chatbots and assistant programs designed to support conversations with human users rely on natural language processing (NLP). This is a field of scientific research that aims at making computers understand the meaning of sentences in natural language. The algorithms developed by NLP researchers helped power first generation of virtual assistants such as Siri or Cortana. Now the same algorithms are made available to the developer community to help companies build their own specialized virtual assistants. Industry products that offer NLP capabilities based on these algorithms are often called AI engines.

The most powerful and advanced AI engines currently available on the market are (in no particular order): IBM Watson, Google DialogFlow, Microsoft LUIS, Amazon Lex.

All these engines use intents and entities as primary pnguistic identifies to convey the meaning of incoming sentences. All of them offer conversation flow capability. In other words, intents and entities help to understand what the incoming sentence is about. Once the incoming sentence is correctly identified you can use the engine to provide a reply. You can repeat these two steps a large number of times, thus creating a conversation, or dialog.

In terms of language processing ability and simplicity of user experience IBM Watson and Google DialogFlow are currently above the pack. Microsoft LUIS is okay too; still, keeping in mind that Microsoft are aggressively territorial and like when users stay within their ecosystem, it is most efficient to use LUIS together with other Microsoft products such as MS Bot Framework.

Using AI engine conversation flow to create dialogs makes building conversations a simple, almost intuitive, task, with no coding involved. On the flip side, using AI engine conversation flow limits your natural tendency to make conversations natural. The alternative, delegating the conversation flow to the business layer of your chatbot, adds richness and flexibility to your dialog but makes the process more comppcated as it now requires coding. Cannot sell a cow and drink the milk at the same time, can you?

Amazon Lex lacks the semantic sophistication of their competitors. One can say (somewhat metaphorically) that IBM Watson was created by linguists and computer scientists while Amazon Lex was created by sales people. As a product it is well packaged and initially looks pleasing on the eye, but once you start digging deeper you notice the limitations. Also, Amazon traditionally excelled in voice recognition component (Amazon Alexa) and not necessarily in actual language processing.

The space of conversational AI is fluid and changes happen rapidly. The existing products are evolving continuously and a new generation of AI engines is in the process of being developed.

Interested in reading more? Check out our other blogs:

OUR AWESOME CHATBOT FEATURES

NMODES chatbots and conversational AI solutions come with a unique set of features.

Our goal is to make it easy for businesses to create and manage chatbots. The features we offer are important for successful implementation of a chatbot not only because they significantly improve its quality, but they also allow you to edit your chatbot in natural language, without any need to have technical knowledge or AI specialist in the team.

1. Free AI Training

Our chatbot solutions come with free AI training for life.

WHAT IS AI TRAINING? LEARN ABOUT BASIC CONCEPTS nmodes.com

We will train your chatbot and continue to enhance it indefinitely.
It is our responsibility to ensure that your chatbot has the updated natural language processing capabilities. It is also our responsibility to guarantee that it understands not only common language but also language that is specific to your business, such as names of the products, terminology used in your industry, inventory list, and more.

Your chatbot will be interacting with the customers all the time. We will enable it to learn from these interactions continuously and improve its language understanding and responses as a result of this learning.

2. Editing in natural language

We realize that AI is a complex body of knowledge and one of your biggest concerns is that you are not familiar with it well enough. We made sure that you don’t need to be technically savvy to successfully manage a chatbot. Using our simple and friendly online interface you can control your chatbot in real time using common natural language. No technical knowledge is required.

At NMODES we continuously improve our AI capabilities. We use our AI not only to make the experience of your customers, conversing with your chatbot, better, but also to make your own experience, conversing with our platform, better.

Eventually the platform will be able to interact with you fully in natural language. We are not entirely there yet (it is an immense task). Still, we hold true to our promise that there is no need in being technically savvy to operate our platform even today. When the platform does not understand natural language our highly trained specialists are always ready to take over and provide support.

3. Real time connectivity

Often there is a need for chatbot to access structured data (such as inventory database) to answer customer’s question. We made it easy for your chatbot to create external queries in real time and modify the responses accordingly. Your chatbot is able to decide in the middle of the conversation, based on the information it received from your database, how to respond and how to proceed with the conversation.

These are the most exciting among the features we created so that our customers have easy and enjoyable chatbot experiences. But there are other features available: conversational templates, dynamic AI Engines clustering, multiple widget skins and more! Let us know if want to see the full list of features.

To learn about the core technologies required to build a chatbot check out this post:

CHATBOT PLATFORMS. HOW TO CHOOSE THE RIGHT ONE?nmodes.com

Artificial Intelligence Chat Is Evolving Faster Than IVR

Although it doesn’t feel like all that long ago, way back in the 90s one of the most important factors to a call center’s success was the ability to route a customer to the right support agent with the IVR (Interactive Voice Response). Countless hours were spent identifying the most efficient call routing patterns and expert agent capabilities to ensure that your request reached the right person quickly. This technology is still widely used today and there are still teams in the largest companies programming IVR systems to accomplish pretty much the same goal.

As the standard for customer support evolved there have been many attempts to improve the function and the customer experience associated with IVRs to reduce hold times and provide more relevant support faster. Even today some companies will use their IVR system as a way to keep a customer on hold, rather than provide a solution, when agents are inundated with calls.

For those of us who’ve worked in the voice industry for some time, we’ve seen first-hand the attempts to accomplish a customer’s need before reaching an agent. First there was expert agent routing that delivered your call to the agent most qualified to help you. Then came advances in voice recognition, which today has evolved to be a very effective tool to increase containment rates and deflect calls from reaching a live agent. My two favorite examples of the power of voice recognition are Cox Communications and Capital One, two examples of great voice recognition and routing.

Our memory, however, is short. It wasn’t so long ago that we were all pulling our hair out punching digits into the phone or constantly repeating “agent”, “Agent”, “AGENT”, AGENT!!!!!”.

Whether it was a limit of computational power or the sheer cost of developing and implementing advanced call center technology, it took decades for phone systems to be able to front end the customer support process as efficiently as they do today. Thankfully we all survived to see it without boiling over from the hypertension usually associated with calling with a customer service department.

Bad customer experience is definitely not the case with Chat Artificial Intelligence (Chat AI). While we seem to hear about the shortcomings of Chat AI like the disconnected conversations and the robotic like responses, these experiences are usually the product of Chatbots with limited AI functionality or early stage deployments. The increases in both computational power and the massive advancements in machine learning are driving excellent customer experiences that improve over time.

When was the last time you heard of technology actually performing better, on its own, without a ton of additional development work or continuous updates? Well, that’s the case with Artificial Intelligence. Like a person, the more experience it has interacting with customers and information, the better it performs with little need to be manually improved or fine-tuned.

Today, AI Chat can be used to answer a large majority of customer requests and because Artificial Intelligence learns as it is used, customers prefer to interact through AI chat to avoid all of the frustrations commonly associated with calling a contact center agent.