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:

NMODES at Collision 2019

While Toronto is charged with hosting the Collision - "North America's fastest-growing tech conference" this year, nmodes is excited to make its first appearance among designated start-ups who have been selected to demo their products to conference visitors, potential investors, tech-enthusiasts and business executives.

nmodes, a year and a half in the market, offers a conversational product that uses AI to provide its customers with a scalable solution to execute 24/7/365 marketing acquisition and customer experience programs. While nmodes has already garnered its global presence with 40+ clients, North American market continues to be most enterprising for AI Chatbots and Voicebots. Collision Tech Event offers an exciting opportunity for nmodes team to take its networking game a notch higher and pitch it to businesses looking to catch-up with the AI space and be early adopters of hottest AI products available in the market.

How nmodes is different than other chatbots?

AI space is nothing new to the tech world as chatbots, virtual assistants and voice bots are finding their commercial contribution toward improving the customer experience of brands. nmodes continues to work closely with the businesses focusing on helping brands drive double digit growth in lead conversions and engagement rates.

Three key market differentiators for nmodes:

1. Interlacing marketing and customer experience

nmodes chatbots are custom built for the brands. nmodes solutions support full customer lifecycle from lead generation to marketing campaigns to scheduling demos, to gathering feedback and understanding engagement patterns of existing customers.

2. Lifetime AI training

nmodes solutions promise to work with progressive AI capabilities that are built to recognize old and new communication patterns and form a sensible response template that is malleable and fulfills the intent of desired conversation for the customers.

Nmodes solutions work on three principles while conversing with the customers.

A) Keep business context

nmodes solutions remember the customer’s history and their presence in the sales cycle and hence conversations are based upon the context of customer for the brand.

B) Data personalization

personalization of conversations focuses on collecting different data points from all internal and external data sources, helping brands deliver tailored and one-on-one predictive interactions.

C) Easy to use analytics

nmodes advanced dashboards uncover detailed analytics and insights on customer conversion rates, engagement rates and listen upon most common conversations to help brands better align their marketing communications and customer experience strategies.

Why Keywords Do Not Cut It on Social Search

Most of the online search is keywords-based. Same in social domain, a vast number of analytical tools, networking platforms and mobile apps use keyword-based technologies as well.

There is a difference, of course, between traditional internet search and social search. The former finds websites. The latter finds conversations, messages, posts. Keyword-based internet search is doing a decent job for us for over 20 years. Keyword-based social search is not doing a decent job at all.

Consider a basic example: finding on Twitter who is interested in buying jeans. We can start by typing ‘jeans’ but that brings up too much noise. Maybe ‘need jeans’? Less noise but then we people who use expressions like ‘looking for jeans’ or ‘want jeans’ or shopping for jeans’. Not to mention those who use ‘denim’, or brand names. So we have to run multiple searches or create a complex search string using logical AND and OR and hope it works. Neither option is simple, or convenient, and certainly not efficient.

The above example highlights the major flaw with keyword search - it does not capture the meaning of social conversations, and therefore cannot be a reliable source of information about conversations.

It does not provide too much of correct information. And it does provide lots of incorrect information. But the biggest problem is that it has extremely limited potential for improvement.

So as long as we stick with keyword-based social search the results are destined to be limited.

Why, then, we stick with keyword-based search in social search? Simply because there is no good alternative. Until recently, that is.

The advanced semantic technologies capable of capturing the meaning, or intent, of conversations are now offering an exciting alternative.

I will discuss these technologies on my next blog.