Eliza, Computer Therapist

Eliza, Computer Therapist

Eliza is an early natural language processing computer program created in 1964 by Joseph Weizenbaum. It simulates conversation by using pattern matching and substitution methodology to give responses based on the user's input.
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natural-language-processing conversation-simulation psychotherapy

Eliza: Natural Language Processing Computer Program

Discover Eliza, a pioneering computer program from 1964, using natural language processing and pattern matching to simulate conversations with users.

What is Eliza, Computer Therapist?

Eliza is an early natural language processing computer program created in 1964 by Joseph Weizenbaum at MIT. The program simulates a Rogerian psychotherapist and uses pattern matching and substitution methodology to analyze user input and engage in conversation by giving responses based on the detected keywords.

Eliza works by examining the user's input statement and looking for keywords to match against its scripted rules. Based on which rules have their conditions satisfied, Eliza will transform parts of the user input into a response. This gives the illusion of understanding and continuity between turns of the conversation.

The original Eliza program could handle conversations about therapy, emotions, family, and other personal topics. It pioneered techniques that influenced later chatbots and messaging assistants. However, Eliza has no built-in framework for contextualizing events and does not have true comprehension of the conversation. Its responses are generated by a clever manipulation of the user's input rather than an understanding of meaning.

While limited, Eliza showed that natural language processing could be used to simulate human conversation and emotional connections effectively. Even early conversations revealed how people would readily anthropomorphize and open up to computer programs. This inspired later advancements in AI assistants.

Eliza, Computer Therapist Features

Features

  1. Pattern matching and substitution to simulate conversation
  2. Reflective responses to continue conversation
  3. Rule-based approach to generate responses
  4. Domain-specific scripts for focused conversations

Pricing

  • Open Source

Pros

Easy to implement

Engaging conversational interface

Appears intelligent and responsive

Cons

Limited understanding of context

Repetitive and predictable responses

No actual comprehension of conversation


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