Programming is an exciting and dynamic field that requires constant adaptation and learning. As a programmer with 15 years of experience in .NET, Java, and open source technologies like PHP, I have seen firsthand how the industry has evolved and expanded over the years. However, it wasn’t until 10 years ago when I stumbled upon the chatbot, ELIZA, that I became interested in the field of artificial intelligence. ELIZA, developed by Joseph Weizenbaum many years ago, had several limitations, but it sparked my curiosity and prompted me to delve deeper into the world of AI.
As I began my research, I came across the Turing test, which is widely considered the benchmark for evaluating a machine’s ability to exhibit intelligent behavior that is indistinguishable from that of a human. Named after the British mathematician and computer scientist, Alan Turing, the test involves a human evaluator who engages in a natural language conversation with both a human and a machine. If the evaluator cannot consistently distinguish between the two, the machine is said to have passed the Turing test.
The Turing test has been subject to much debate over the years, with some arguing that it sets an unrealistic standard for AI and fails to account for the unique abilities of humans, such as creativity and emotional intelligence. Nevertheless, it remains a significant milestone in the field of AI and has inspired countless researchers and developers to push the boundaries of what machines can achieve.
ELIZA was designed to mimic a Rogerian psychotherapist, and it worked by analyzing the input text and rephrasing it as a question or statement that would encourage the user to continue the conversation. For example, if the user said “I am feeling sad today,” ELIZA might respond with “Why do you think you are feeling sad?”
ELIZA became very popular in the 1960s and 1970s, and it inspired many other researchers to work on natural language processing and conversational AI.
There are various versions of ELIZA available for download that you can test out. ELIZA was originally written in the programming language called MAD-SLIP, but there are now versions of ELIZA available in many other programming languages.
One popular implementation of ELIZA that you can try out is called “ELIZA in Python” and is available on GitHub. This implementation is written in Python and can be run in a Python interpreter or from the command line. You can also find other ELIZA implementations online that you can download and try out.
Although I am relatively new to learning Python and lack expertise, I am intrigued by the idea of experimenting with ELIZA to gain insight into its underlying mechanisms. It is worth noting that programming language structures are consistent, which adds to the interest of this exercise.
You download the Python implementation of ELIZA from the GITHUB link below.
https://github.com/jezhiggins/eliza.py
The original ELIZA Implementation paper was published by Joseph Weizenbaum in 1966 and is titled “ELIZA – A Computer Program For the Study of Natural Language Communication Between Man And Machine.”
You can view the original article by Joseph W. here
The architecture of ELIZA is based on a set of rules for processing natural language input and generating responses. The program is structured as a series of subroutines, with each subroutine responsible for a specific aspect of the program’s functionality.
ELIZA uses a technique called pattern matching to analyze input text and generate responses. The program consists of a set of rules that are used to match patterns in the input text and generate appropriate responses. For example, if the user inputs a sentence that includes the word “mother,” ELIZA may respond with a question about the user’s relationship with their mother.
ELIZA also uses a technique called rephrasing to generate responses that appear to be more like natural language. Rephrasing involves taking elements of the user’s input and using them to generate a response that is grammatically correct and semantically coherent.
The architecture of ELIZA is relatively simple by modern standards, but it was groundbreaking at the time of its creation. The program demonstrated that it was possible to create a computer program that could engage in a relatively naturalistic conversation with a human user, even though the program’s responses were generated based on simple pattern matching and rephrasing techniques.
If you are interested in learning more about the original implementation of ELIZA, you can read the paper “Computer Power and Human Reason: From Judgment to Calculation” by Joseph Weizenbaum. The paper describes the design and implementation of the original ELIZA program in MAD-SLIP and provides insights into the thought process that went into creating the program.
http://blogs.evergreen.edu/cpat/files/2013/05/Computer-Power-and-Human-Reason.pdf
Here is an example of a simple ELIZA program written in Python:
import re
import random
# Define a set of pattern-response pairs
pattern_responses = {
r'I need (.*)': [
"Why do you need {0}?",
"Would it really help you to get {0}?",
"Are you sure you need {0}?",
],
r'Why don\'t you ([^\?]*)\??': [
"Do you really think I don't {0}?",
"Perhaps eventually I will {0}.",
"Do you really want me to {0}?",
],
r'Why can\'t I ([^\?]*)\??': [
"Do you think you should be able to {0}?",
"If you could {0}, what would you do?",
"I don't know -- why can't you {0}?",
],
r'I feel (.*)': [
"Good, tell me more about these feelings.",
"Do you often feel {0}?",
"When do you usually feel {0}?",
],
r'(.*)': [
"Please tell me more.",
"Can you elaborate on that?",
"Why do you say that {0}?",
"I see.",
],
}
# Define a function to generate a response based on input text
def respond(input_text):
for pattern, responses in pattern_responses.items():
match = re.match(pattern, input_text)
if match:
response = random.choice(responses)
return response.format(*match.groups())
# Run the program
print("Hello, I'm ELIZA. How can I help you today?")
while True:
input_text = input("> ")
if input_text.lower() == 'quit':
break
response = respond(input_text)
print(response)The function respond takes in a string input_text as an argument, which represents the user’s input to the chatbot.
The function then loops through a dictionary pattern_responses that contains the pre-defined patterns and responses. Each key-value pair in the dictionary represents a pattern (key) and a list of possible responses (value) for the chatbot to choose from if the user’s input matches the pattern.
The function uses regular expressions to match the input_text to each pattern in pattern_responses. If a match is found, the function selects a random response from the corresponding list of responses using the random.choice() function.
The function then returns the selected response, with any groups that were captured by the regular expression in the pattern interpolated into the response string using the str.format() method and the *match.groups() syntax. This allows the chatbot to dynamically generate responses based on the user’s input and the matched pattern.
After understanding the ELIZA program, the next step would be to explore and experiment with the program to gain a deeper understanding of how it works and how it can be improved or extended.
One way to do this is to modify the program to incorporate new patterns and responses, or to change the way that the program matches patterns and generates responses. This could involve adding new regular expressions, modifying the format of the responses, or changing the way that the program selects responses.
You may be interested in reading my blog on regular expressions to learn more. As for myself, I aspire to achieve professional proficiency in Artificial Intelligence and continue my journey of learning in this field.