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.
WHAT IS AI TRAINING
Social Marketing is Simple
In its very essence social marketing is based on one simple foundation - give first, take later.
This concept of giving to the community is hardly possible to overestimate. It defines the way social networks operate and goes even deeper, to the basic principles of social interactions among humans.
In fact it is a much healthier foundation for business than traditional one, based on advertising.
Yet it runs contrary to what many entrepreneurs and business people perceive as a proper marketing approach.
Traditional marketing, such as billboards, radio ads, posters, banners, emails blasts, etc is based on two principles, a) the statistical law of big numbers, aiming to reach out to as large audience as possible while knowing that only a small percent would become interested, b) message of self-promotion and self-advertisment.
Social marketing negates both of these principles.
Social marketing is personal, it operates individually, and in a personalised way. Which makes perfect sense from a common perspective. Would you rather be bombarded by the generic ads that in most cases have nothing to do with your interests and desires, or approached on a one-on-one basis with a chance to discuss your specific needs?
Social marketing is directed towards promoting the interests of others, not yours (or your business). Again it makes sense as we are a social species, we live in societies and rely on communication. The most successful communication strategy is the one that takes care of the needs of your communication partner.
And so, opposing the traditional marketing approach, social marketing is based on the idea of giving to the community. Which makes it more efficient than traditional marketing, if measured against the effort applied. In other words, taken 100 random prospects, we are more likely to convert them into customers if using social marketing than traditional marketing.
But is it scalable?
(to be continued)
Meet Eliza, the Mother of AI
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?