nmodes Introduces Arabic language support for Advanced AI Solutions

Meet Eliza, the Mother of AI..

Today, Artificial Intelligence seems to be the buzz of every major enterprise. Salesforce is formally announcing Einstein this fall, IBM has worked on Watson for years now, and after 20 years of working with AI, Microsoft has made a few attempts to bring the technology to the market. With all this activity, you may be asking yourself what kind of impact AI will have on you and your business, and where you might want to look to investigate the possibilities Artificial Intelligence represents.

Before we discuss how AI will impact customer support and consumer experience, and how you may leverage it in your contact center, I thought it would be fun to take a look where AI got its start.

The term AI was coined by computer scientist John McCarthyin 1956 who subsequently went on to create the Dartmouth Conference to advance the ideas and technologies associated with machine intelligence. While this collective of thought leaders and scientists made huge advancements through programs at MIT and others, most of their work was only circulated in academic fields.

Not many people were aware of Artificial Intelligence, how it worked or its potential uses, until around 1964 when MIT computer Scientist Joseph Weizenbaumwrote Eliza, a program based on Natural Language Processingthat was able to successfully question and respond to human interactions in such a way as to almost sound like a real human being. Eliza, with almost no information about human responses was able to use scripts and pattern  matching to simulate responses that might occur between two people.

The most famous of these simulations, highlighting  AI ability to intersect with modern needs and technology, was DOCTOR. DOCTOR was able to question and respond to a human in such a way so as to almost sound like an actual psychotherapist. As the human subject made statements, DOCTOR asked questions and made statements relevant to the conversation as if it were a present and conscious being… almost.

Over the years  computer scientists, whether academics or industry professionals,  have worked tirelessly to improve upon these developments with the hope of delivering a computer program capable not only to ask and respond, but to understand the context of a conversation. A program that can relate relevant data to responses, thus providing value to the human it’s conversing with, while helping to chart the course of the conversation, just as if you and I were talking over a cup of coffee or across a conference room table.

Why is this important, you may ask? With the introduction of Chatbots, we began to see some of the potential in Artificial Intelligence. Companies could now front-end customer chat interactions that allowed the company to be more responsive to its customers while shortening wait times and deflecting inquiries from the call center, which as we all know are hugely expensive.

The one problem with Chatbots? Customers hated dealing with limited technology that was cold, often incorrect, and frustrating. People are accustomed to dealing with the cold, sterile nature of technology when they type numbers in a phone to be routed but expected a human to be chatting with them. These negative experiences have made a number of companies a little gun shy about implementing true Artificial Intelligence. The last thing a business wants is a customer complaining, especially on Social Media, about a poor customer experience due to a bad interaction with technology.

There is a significant difference between Chatbot technology and true AI, consequently the outcomes and customer experience are proving to be very different. Where a Chatbot is more like an IVR, answering simple questions and routing customers to the correct agent, Artificial Intelligence is aware of the conversation and able to present relevant responses, thereby providing a faster response and shorter customer interaction times and better customer service. I mean, if Eliza’s DOCTOR could simulate a psychotherapist in 1964, what can AI do for your contact center in 2016?

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.