Natural Language Processing is a branch of Artificial Intelligence that teaches computers to use language the way people do. When we’re speaking our native language, we don’t recognize the vast amount of rules that we’re implementing on the fly. We speak instinctively, using complex grammatical structures, diverse vocabulary, and phonetics. Layered on top of the structure of language itself, we apply rules of turn taking, respond to social cues, and use emotional intuition to navigate the social context in which we’re speaking.
Natural Language Processing creates a computational structure that represents the linguistic and contextual aspects of communication and implements it in a myriad of contexts. This process of feedback and repetition simulates the natural processes by which we learn language as humans, arming the computational system with the ability to implement language appropriately based on context and various other cues.
Before AI – or the processes by which computers teach themselves – there was no way to represent the complexity of language in a computational system. The Turing Test, theorized by Alan Turing, the Godfather of modern computing, measures computational intelligence by the inability to distinguish human and machine when asked the same question. Eliza was MIT’s famous language processing machine that almost passed the Turing Test in the 60s. When asked a question that could have multiple answers, Eliza would mimic the behavior of a psychotherapist, responding with a question or inquiring about her examiner’s feelings.
Although Eliza tricked plenty of scientists at MIT and nearly passed the Turing Test, her answers were bound by the limits of machine learning. Both the inputs and outputs had to be programmed in fixed relation beforehand. A modern example of this process is when we ‘speak’ with an artificial customer service agent on the phone. When we give an answer the machine doesn’t know, it repeats the pre-programmed menu or refers us to a live human agent. We can detect that our conversational partner is a machine–not a human–based on the use of canned answers, and the inability to deviate from a script.
Deep learning, on the other hand, lets computers respond to unanticipated questions. By harnessing large amounts of processing power, deep learning generates an array of possible solutions within a variable set of parameters. A machine implementing natural language processing can respond to a how or why question according to the context in which the question is asked and the characteristics of its interlocutor. If I asked MIT’s Eliza computer of the 1960s, “Do I look good in red?” She would have responded, “I don’t know, how do you feel in red?” If Eliza were still around today, she may respond based on my vocal intonation, perceived gender, the time of day, and the environment. Similar to the way a human responds, Eliza may tell me, “Red might not be appropriate for an evening dinner engagement.”
Applied in professional contexts, Deep Learning and Natural Language Processing let computers factor in a variety of contextual data to inform the decisions of a medical professional, lawyer, or chat sales agent. Today’s economy is based on services that require us to execute a vast amount of language-based tasks. Many of these tasks have the potential to be automated. Consider the number of times throughout her career a doctor diagnoses strep throat, a lawyer writes a memo regarding an arbitration decision, or a salesperson recommends the best means of utilizing a service? An intelligent machine can execute each of these language-based tasks with a human agent evaluating the proposed solutions, and making the final call.
Perhaps more than the medical and legal professions, which imply decision-making based on moral imperatives, sales guidelines, and e-commerce organizations are well-positioned to benefit from AI technology in its current stage of development. A recent report by McKinsey states that 29% of customer service positions and 36% of sales representative positions could be automated by AI and other technology. Rather than replace human agents, machines and humans can work together to accomplish the objective better. AI can be used to optimize customer service interactions, ensure a high level of customer care, and increase conversion through the intelligent support of the online sales process.
If we think back to the ‘what if’ scenario of Eliza as a deep learning computer, we start to generate ideas of how AI can be helpful to live chat agents. The answer to the question, “Do I look good in red?” could take into account the demographic of the customer and their purchase history or their current mood and plans for the evening. Just like the turn-taking strategies and receptivity we develop when learning natural language, an AI chat sales solution can analyze a customer’s chat behavior in real time, suggest a response with a statistically positive outcome, and provide live feedback based on how to match a customer’s personality to increase conversion.
A conversation between two people is a garden of forking paths with thousands of possible scripts. The best chat sales solution helps live chat agents gracefully navigate language while optimizing the possibility of a positive outcome. Natural Language Processing and Deep Learning anticipate the myriad possibilities of chat agent and customer interactions making the perfect synthesis of human + machine.
Learn more about the chat sales solutions from the team at RapportBoost.