Conversational AI Theory
1 Overview of Conversational AI
What is Conversational AI?
Conversational artificial intelligence can be defined is the application of computing technology to facilitate a natural conversation between computers and people. Conversational AI can be achieved using several different modes of communication including voice, text or chat. Voice is when the user speaks directly to the system and the AI agent can respond using a synthesized voice. Text is when the AI agent responds to a query and produces a textual reply. A chat is similar to text except it generally represents a number of short message exchanges between the user and the computer in near real-time. Some of the most well-known examples of conversational AI systems in use today are the digital assistants such as Amazon Alexa, Apple Siri, Google Assistant and IBM Watson.
Benefits of Conversational AI
Conversational AI systems can achieve numerous benefits over traditional computer systems including;
- Voice can be a more efficient and convenient way to interface with machines.
- A more intelligent system that can extract user intents then evaluate and determine correct responses without have to be pre-programmed for every condition or user utterance.
- There are numerous business advantages to using a conversational AI system including achieving a consistent (and hopefully better) customer experience, a contact less customer experience, increases efficiency, reduction in operational costs and consistent up-selling opportunities.
The Turning Test
1950 Allen Turning proposed a method of inquiry to determine whether a computer is capable of thinking liking a human being. His original work hypothesized that a computer could mimic human responses under specific conditions. This idea eventually evolved into what is now called the Turing test. The Turning Test is benchmark for Conversational AI system to evaluate how well it responds and mimics the responses of a human, such that it become indistinguishable from a real person to fulfill a specific task. If the user can not tell if the responses are coming from a real person or a computer. The system has “passed” the Turning Test.
Notable Dates in Conversational AI History
When PARRY met ELIZA
In 1966 Joseph Weizenbaum developed Eliza. Eliza was programmed to respond to a number of keywords and produce responses that mimicked a psychotherapist. In 1972 Kenneth Colby developed Parry. Parry was program to model the behavior of a paranoid schizophrenic. In January 1973, as part of a international computer conference ELIZA and PARRY were connected together using the ARPNET. ELIZA was located at MIT and PARRY was at Stanford. Below is a transcript of their conversation.
Transcript of ELIZA and PARRY conversation
ELIZA: Good Evening. Tell me your problems.
Please terminate input with a period or a question mark.
PARRY: People get on my nerves sometimes.
ELIZA: I am not sure I understand you fully.
PARRY: You should pay more attention.
ELIZA: Suppose you should pay more attention.
PARRY: You’re entitled to your own opinion.
ELIZA: What makes you think
I am entitled to my own opinion?
PARRY: It bothers me just to be around people in general.
ELIZA: Please go on.
PARRY: I went to the track at Bay Meadows a while back.
ELIZA: What does that suggest to you?
PARRY: I went to the races.
ELIZA: What else would you like to discuss?
PARRY: Horse racing interests me.
ELIZA: Why do you say that just now?
PARRY: There’s a lot of crooked gambling going on there.
ELIZA: I am not sure I understand you fully,
PARRY: It’s clear to me if not to you.
ELIZA: Do you think it’s likely that not to I?
PARRY: I went to the races.
ELIZA: Please go on.
PARRY: I gamble on horses.
ELIZA: What does that suggest to you?
PARRY: I went to the races.
ELIZA: What else would you like to discuss?
PARRY: Gambling has been nothing but trouble to me.
ELIZA: Why do you say that just now?
PARRY: I’ve had experience with crooks gambling at the track.
ELIZA: I am not sure I understand you fully.
PARRY: I’ve already told you enough about that.
ELIZA: Oh? I enough about that?
PARRY: I went to the races.
ELIZA: Please go on.
PARRY: Do you know anything about bookies?
ELIZA: What makes you think I know
anything about bookies?
PARRY: A bookie didn’t pay me off once.
ELIZA: What does that suggest to you?
PARRY: I beat up the bookie who did not pay off, and I thought
he might use his friends in the underworld to get even with me.
ELIZA: What else would you like to discuss?
PARRY: What do you know about racketeers?
ELIZA: Why do you ask?
PARRY: What experience have you had gambling?
ELIZA: Why does that question interest you?
PARRY: I like horse races.
ELIZA: What else do you like?
PARRY: I know the mob controls the big rackets.
ELIZA: Who are you really talking about?
PARRY: Have you ever really thought about the MAFIA?
ELIZA: Suppose you ever really
thought about the MAFIA.
PARRY: Let’s talk about something else.
ELIZA: I am not sure I understand you fully.
PARRY: You keep asking me the same things,
why not change the topic?
ELIZA: In what way?
PARRY: What do you mean?
ELIZA: Why are such questions on your mind?
PARRY: I thought I told you, people get on my nerves.
ELIZA: Your nerves?
PARRY: Well, I don’t know.
ELIZA: Don’t you really know?
PARRY: I would say so.
ELIZA: Please go on.
PARRY: I would rather not discuss that anymore.
You are a real nag.
ELISA: Does it please you to believe I am a real nag?
PARRY: I have had enough of this.
ELISA: What does that suggest to you?
PARRY: Goodbye.
ELISA: It’s been my pleasure, that’s $399.29 please.
What’s interesting about the conversational tone and responses from the transcript appears to somewhat accurately portray those of a psychiatrist and paranoid schizophrenic. Although there is a definite gap in appearing to understand the deeper meaning behind the conversation this is one of the first times that two chatbots were connected together, and demonstrated that chatbots could be given distinct personalities.
Evolution of Conversational AI
Conversational AI systems in the 1960’s relied on having hard-coded static responses and per-determined conversational pathways based on specific user input or keywords. The the 1980’s this was updated to using statistical inference data models to determine the best pre-programmed response to send. Modern Conversational solutions now rely on pre-trained neural networks to create a natural language understanding platform which is capable of responding to a wide-rand of of user utterances without having to be explicitly programmed for each utterance.
Key Technological Advances
It is much easier to program boolean and logical operations. Such as responding yes or no to predefined prompts or selecting a menu option one through nine on a phone voice menu. These primitive systems really do not leverage for the Artificial Intelligence principles as they operate by using pre-programmed conversational pathways. Improvements in voice to text transcription, combined with increased computing resources as allowed for larger trading sets which are able to interpret and process more diverse natural language conversations as well as languages and dialects from around the world.