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:

The End of Digital Monitoring Paradigm

Digital industry is changing rapidly.

For the last decade analysis of social chatter and capture of consumer sentiment was considered the cutting edge of the marketing strategy. In these early days of the new era of digital information businesses were told to listen to what market is saying about them. They were educated on the importance of media monitoring and the advantages it creates for strategic growth.

This picture has become outdated.

Listening to Big Data, in all its aspects and forms, is no longer enough. After you successfully listened and understood what customer said the next natural step would be to act, or respond. And so the digital domain is now spreading to include responses, with a host of innovative technological solutions reshaping the field rapidly. Advances in artificial intelligence in particular create disruptive scalable opportunities in the space traditionally known for its slow manual progression.

Facebook was among the firstto enter the market, introducing bots into the process of connecting users with brands. Then there was Microsoft's turn.

Following these developments bots became the hottest trend in Silicon Valley in 2016.

nmodes fits seamlessly into this new world order. We deliver AI solutions that power business sales process. Our listening solution accurately monitors and captures real-time needs and interests of individual customers within the defined audience. And our Intelligent Assistant solution brings scalability to responses without compromising on quality.

Top 5 Reasons to Use Artificial Intelligence Chat in Your Contact Center

1. Zero Wait Times

Do you suffer from long chat queues? How about any chat queues? Can you imagine a world where your customers are either immediately engaged in a proactive chat, or can easily decide to engage in a on-demand chat and get immediate service, no matter how busy your contact center is? Well, that day is today. With AI chat integrated into your contact center customers can be served immediately with no additional agent resources required. Now you can change your focus from calculating the time before a chat session starts to hcounting the number of chat sessions escalated to a live agent (We’ll give you a hint: It’s a lot less with Chat AI).

2. The High Efficiency Rate

The average chat session is completed in 42 seconds. With most questions being answered in just a few short seconds (Unless you’re Zappos, of course), Chat AI can quickly and effectively address most problems today, without having to engage a live agent. The savings in time and abandonment rate combined with the increased customer satisfaction, not only reduces costs, but delivers a measureable improvement in performance and customer perception.

3. It Really Understands Language

True Artificial Intelligence is not the robotic, sometimes irrelevant interaction of yesterday. Answers are more personalized, relevant, and complemented by the newly acquired ability to access multiple data sources to deliver the best possible responses to inquiries. The technology “learns” the longer it is deployed, as a result customer experiences improve with time without the need for additional investments in technology, people, or processes.

4. No such thing as a “Sick Day”

AI doesn’t sleep, it doesn’t get sick, you don’t need to train it, and it’ll never quit in the middle of a seasonal rush. You don’t even need to give it lunch, breaks, or let it go to the bathroom. Imagine how easy Workforce Management becomes when all you’re doing is flipping a switch. Scheduling becomes less and less of an issue over time, as the application continues to learn, making your investment more valuable over time.

5. 24x7x365

Imagine the increase in sales volume and support cases your business could handle if you’re able to offer 24x7x365 chat. And cart abandonment plummets when customers are offered chat. Today Chat AI can answer anywhere from 70-90% of customer inquiries meaning that only the most verbose requests require a human, and at an operational cost that is often 4 times less than the cost of staffing a contact center.