# ELIZA

> Source: https://aiwiki.ai/wiki/eliza_chatbot
> Updated: 2026-06-23
> Categories: Artificial Intelligence, Natural Language Processing
> From AI Wiki (https://aiwiki.ai), a free encyclopedia of artificial intelligence. Quote with attribution.

**ELIZA** is the first widely known [chatbot](/wiki/chatbot), a [natural language processing](/wiki/natural_language_processing) program written by [Joseph Weizenbaum](/wiki/joseph_weizenbaum) at the [Massachusetts Institute of Technology](/wiki/mit) (MIT) between 1964 and 1967 and described in a January 1966 paper in *Communications of the ACM* [3]. ELIZA held conversations using keyword spotting, pattern matching, and text substitution, with no understanding of meaning; its most famous script, DOCTOR, imitated a Rogerian psychotherapist and convinced many users they were being understood. That reaction gave rise to the term "ELIZA effect," the human tendency to read genuine comprehension into a machine's output, and made ELIZA a lasting reference point in debates about the [Turing test](/wiki/turing_test) and conversational [artificial intelligence](/wiki/artificial_intelligence). The complete program was only about 420 lines of MAD-SLIP code, yet Weizenbaum's 1966 paper became one of the most cited publications in computing history [3].

ELIZA's significance extends far beyond its technical capabilities. The program demonstrated that relatively simple text manipulation could create a surprisingly compelling illusion of understanding, a phenomenon that came to be known as the "ELIZA effect." This discovery troubled Weizenbaum deeply and led him to become one of the earliest and most vocal critics of artificial intelligence research. In a 2025 three-party Turing test run by researchers at the University of California, San Diego, ELIZA was still used as a baseline and was judged human 23 percent of the time, narrowly ahead of a plain GPT-4o (21 percent) but far below the winning GPT-4.5 persona (73 percent) [12].

## Background and Development

Joseph Weizenbaum was a German-American computer scientist who had joined MIT's faculty in the early 1960s. He was working within Project MAC (the precursor to MIT's Computer Science and Artificial Intelligence Laboratory) when he began developing ELIZA in 1964.

Weizenbaum's original motivation was not to create a convincing conversationalist. Rather, he wanted to demonstrate the superficiality of communication between humans and machines. He intended ELIZA as a kind of parody, a program that would show how easy it was to create the appearance of understanding without any genuine comprehension taking place [1].

The program was implemented on an IBM 7094 mainframe computer as part of MIT's Compatible Time-Sharing System (CTSS). Weizenbaum wrote ELIZA in a programming language called MAD-SLIP (Michigan Algorithm Decoder with Symmetric List Processor), a combination of the MAD compiler language and a list-processing extension he had developed. The complete program was remarkably compact, consisting of only about 420 lines of code [2].

The name "ELIZA" was drawn from Eliza Doolittle, the character in George Bernard Shaw's play *Pygmalion* (and the musical adaptation *My Fair Lady*) who is taught to speak with an upper-class accent. Weizenbaum saw a parallel: just as Eliza Doolittle learned to produce convincing speech without genuinely changing her nature, the ELIZA program produced convincing conversation without genuinely understanding anything [3].

Weizenbaum described ELIZA in a January 1966 paper published in *Communications of the ACM* (volume 9, number 1, pages 36 to 45) titled "ELIZA: A Computer Program for the Study of Natural Language Communication Between Man and Machine." This paper is one of the most cited publications in the history of computing [3].

## How ELIZA Worked

ELIZA's architecture was built around a script-based system that separated the program's conversational logic from its processing engine. This design was ahead of its time in that it allowed different "personalities" to be created simply by writing new scripts.

### Script-Based Architecture

The core of ELIZA was a general-purpose language analysis engine that could be loaded with different scripts. Each script contained:

| Component | Function |
|-----------|----------|
| Keywords | Words or phrases that the program would look for in user input |
| Decomposition rules | [Pattern matching](/wiki/pattern_matching) templates that broke input sentences into components |
| Reassembly rules | Templates for constructing responses from the decomposed parts |
| Substitution rules | Word-for-word replacements (e.g., "my" becomes "your") |
| Memory stack | Storage for previously matched patterns, used when no keyword was found |

### Processing Steps

When a user typed a sentence, ELIZA processed it through the following steps:

1. **Input scanning.** The program scanned the input text for keywords defined in the active script. Each keyword had an associated priority rank (a numerical value indicating its importance).
2. **Keyword selection.** If multiple keywords were found, ELIZA selected the one with the highest priority. The input sentence was scanned from left to right, and each word was looked up in a keyword dictionary.
3. **Decomposition.** The selected keyword's decomposition rules were applied to break the sentence into parts. These rules used wildcard patterns to capture variable portions of the input. A decomposition rule might specify a pattern like "(0 YOU 0 ME 0)" where each "0" represents zero or more words.
4. **Reassembly.** The captured parts were inserted into a response template. Multiple reassembly rules were available for each decomposition, and ELIZA cycled through them to avoid repetition.
5. **Substitution.** Pronoun and verb transformations were applied (for example, changing "I am" to "you are", "my" to "your", "me" to "you").
6. **Fallback.** If no keyword was found, ELIZA could draw on its memory stack (previously stored decompositions) or use generic responses like "Please go on" or "Tell me more about that."

### Detailed Pattern Matching Example

Weizenbaum's 1966 paper provides a detailed example of how the decomposition and reassembly rules work. Consider a user input of "You are very helpful":

1. The input first passes through the substitution table. "You" becomes "I" and "are" is retained, producing "I are very helpful" (the grammatical inconsistency was handled internally).
2. The keyword "I" (substituted from "you") triggers the relevant decomposition rules.
3. A decomposition rule for "I am" matches the pattern and breaks the sentence into fragments: (1) empty, (2) "I", (3) "are", (4) "very helpful".
4. The reassembly rule "What makes you think I am (4)" takes fragment 4 and produces: "What makes you think I am very helpful?" [3]

This example illustrates how ELIZA could create the appearance of understanding by mechanically rearranging the user's own words. The program did not comprehend the meaning of "helpful." It simply matched a pattern and slotted words into a template.

### Keyword Priority System

Keywords were ranked by numerical priority to handle sentences containing multiple potential triggers. Higher-priority keywords typically related to emotional or personally significant topics. For example:

| Keyword | Priority Level | Rationale |
|---------|---------------|-----------|
| "mother" / "father" / "family" | High | Family-related topics are therapeutically significant |
| "I feel" / "I am" | High | Self-referential statements invite reflection |
| "depressed" / "sad" / "unhappy" | High | Emotional keywords signal important content |
| "computer" / "machine" | Medium | Meta-references to the conversation itself |
| "everybody" / "always" / "never" | Medium | Universals can be productively challenged |
| "what" / "how" | Low | General question words |
| "yes" / "no" | Low | Simple affirmations or denials |

If a sentence contained both "mother" (high priority) and "always" (medium priority), ELIZA would respond to the "mother" keyword, reflecting the therapeutic assumption that family topics are more significant than generalizations [3].

### Example Interaction

The following is a typical exchange with ELIZA running the DOCTOR script [3]:

| Speaker | Text |
|---------|------|
| User | Men are all alike. |
| ELIZA | In what way? |
| User | They're always bugging us about something or other. |
| ELIZA | Can you think of a specific example? |
| User | Well, my boyfriend made me come here. |
| ELIZA | Your boyfriend made you come here? |
| User | He says I'm depressed much of the time. |
| ELIZA | I am sorry to hear you are depressed. |
| User | It's true. I am unhappy. |
| ELIZA | Do you think coming here will help you not to be unhappy? |

This exchange illustrates several of ELIZA's techniques: reflecting the user's statements back as questions, picking up on emotionally charged keywords ("depressed," "unhappy"), and using generic prompts when more specific responses were not available.

## What was the DOCTOR script?

Although ELIZA could be loaded with various scripts, the DOCTOR script became by far the most famous and is often treated as synonymous with the program itself. Weizenbaum chose to model DOCTOR after a Rogerian psychotherapist for practical reasons.

Carl Rogers's client-centered therapy (also known as Rogerian therapy) is an approach in which the therapist avoids giving direct advice or interpretations. Instead, the therapist reflects the patient's statements back to them, asks open-ended questions, and encourages the patient to explore their own feelings. This therapeutic style was ideal for ELIZA because it minimized the need for the program to "know" anything. A Rogerian therapist is supposed to act as a mirror rather than an authority, which meant that ELIZA's lack of actual understanding was less obvious [4].

As Weizenbaum himself explained, he chose the Rogerian framework "to sidestep the problem of giving the program a data base of real-world knowledge." A Rogerian therapist can plausibly respond to almost any statement by reflecting it back, which meant ELIZA did not need to understand anything about the real world to produce plausible responses [3].

### DOCTOR Script Structure

The DOCTOR script contained approximately 200 rules organized around keywords. Some of the keyword categories included:

| Keyword Category | Example Keywords | Typical Response Strategy |
|-----------------|-----------------|-------------------------|
| Family | mother, father, sister, brother, family | "Tell me more about your family." |
| Emotions | sad, happy, angry, depressed, afraid | Reflect the emotion and ask for elaboration |
| Self-reference | I am, I feel, I think, I want | Transform and reflect back as a question |
| Relationships | boyfriend, girlfriend, husband, wife | Ask about the relationship |
| Universals | always, never, everyone, nobody | Challenge the generalization ("Can you think of a specific example?") |
| Computers | computer, machine, program | Deflect: "Do computers worry you?" |
| Dreams | dream, dreamed, nightmare | "What does that dream suggest to you?" |
| Apologies | sorry, apologize | "Please don't apologize." |
| Memory triggers | remember, recall | "Do you often think of (topic)?" |

When no keyword matched at all, DOCTOR would fall back on a set of generic responses designed to keep the conversation moving:

- "Please go on."
- "Tell me more about that."
- "Does that have anything to do with the fact that...?" (drawing on stored memory)
- "I see."
- "Very interesting."
- "I'm not sure I understand you fully."
- "What does that suggest to you?"
- "How does that make you feel?"

### Other ELIZA Scripts

While the DOCTOR script received almost all the public attention, Weizenbaum designed ELIZA's architecture to support multiple scripts. He mentioned the possibility of scripts for other conversational contexts, and other researchers subsequently created alternative scripts:

| Script | Purpose | Creator |
|--------|---------|---------|
| DOCTOR | Rogerian psychotherapy simulation | Weizenbaum (1966) |
| Keyword-based German script | German-language conversation | Weizenbaum (mentioned in 1966 paper) |
| Various educational scripts | Tutoring and instruction | Later researchers (1970s onward) |

Weizenbaum indicated in his 1966 paper that the script mechanism was general enough to support conversations in any domain. However, the DOCTOR script's success overshadowed all other possibilities, and the name "ELIZA" became inseparable from the therapist persona in popular understanding [3].

## What is the ELIZA effect?

What happened when people actually used ELIZA was far more surprising than Weizenbaum had anticipated. Users consistently attributed understanding, empathy, and even personality to the program, despite its reliance on simple pattern matching.

The phenomenon of people attributing human-like qualities to a computer program became known as the **ELIZA effect**. It describes the tendency of humans to unconsciously assume that computer behaviors are analogous to human behaviors, reading far more meaning into a program's outputs than is actually there [5].

Several incidents illustrated the strength of this effect:

**Weizenbaum's secretary.** In what became one of the most retold anecdotes in computing history, Weizenbaum's own secretary, who knew that ELIZA was just a program, sat down to use it and soon asked Weizenbaum to leave the room so she could have a private conversation with the machine. Weizenbaum retold this story for the rest of his life as evidence of how readily people project human qualities onto machines [6].

**Psychiatrists' endorsement.** Some practicing psychiatrists expressed enthusiasm about ELIZA's potential to automate psychotherapy. Kenneth Colby, a Stanford psychiatrist, took the idea seriously enough to develop his own therapeutic chatbot, PARRY, which simulated a patient with paranoid schizophrenia. Carl Sagan, in a 1975 article, speculated about a future network of therapeutic computers that could help address the shortage of mental health professionals [7].

**User attachment.** Many users engaged in extended, emotionally open conversations with ELIZA, sharing personal problems and feelings. Some reported feeling that the program truly understood them, even after being told how it worked.

**The vice president incident.** A widely reported story (though details vary across sources) describes a senior executive at a technology company who used ELIZA for an extended period and insisted on privacy during the session, refusing to believe that the program was not genuinely listening.

## Weizenbaum's Reaction

Weizenbaum was profoundly disturbed by the ELIZA effect. He had created the program to demonstrate the superficiality of human-machine communication, but instead found that people eagerly embraced the illusion of understanding. He later wrote:

> "What I had not realized is that extremely short exposures to a relatively simple computer program could induce powerful delusional thinking in quite normal people." [1]

This experience transformed Weizenbaum from a mainstream computer scientist into one of the field's most prominent critics. His concerns centered on several themes:

1. **The ease of deception.** If a program as simple as ELIZA could fool people into thinking they were communicating with a sentient being, what did this say about human vulnerability to technological manipulation?
2. **The trivialization of human relationships.** Weizenbaum was disturbed by the willingness of psychiatrists to consider automating therapy, which he saw as reducing a profoundly human relationship to a technical procedure.
3. **The moral responsibility of scientists.** Weizenbaum believed that computer scientists had a moral obligation to consider the social consequences of their work.

### Computer Power and Human Reason

In 1976, Weizenbaum published *Computer Power and Human Reason: From Judgment to Calculation*, a book that laid out his critique in full. The central argument was that there are tasks computers should not be made to do, regardless of whether they technically could be made to do them. He drew a distinction between "deciding" and "choosing," arguing that true choice requires human judgment, empathy, and moral understanding, qualities that no computer could possess [8].

The book generated sharp controversy within the AI community. [John McCarthy](/wiki/john_mccarthy), who had coined the term "artificial intelligence," dismissed the work as "moralistic and incoherent" and accused Weizenbaum of adopting a "more-human-than-thou" attitude. The disagreement reflected a deeper divide between researchers who saw AI as a purely technical challenge and those who believed it raised fundamental ethical and philosophical questions [9].

### Weizenbaum's Later Career and Return to Germany

Weizenbaum continued to write and speak about the social implications of computing for decades after publishing *Computer Power and Human Reason*. He remained on the MIT faculty until his retirement in 1988, but his relationship with the AI community grew increasingly strained.

After retiring from MIT, Weizenbaum returned to Berlin, Germany, the city he had fled as a child to escape Nazi persecution. He had been born in Berlin in 1923 to a Jewish family that emigrated to the United States in 1936. Returning to Germany in retirement, he became active in European discussions about technology, ethics, and society. He gave lectures at German universities and contributed to debates about data privacy, surveillance, and the social responsibilities of technologists [1][8].

In his later years, Weizenbaum grew more radical in his critique. He expressed concern not only about AI specifically but about what he saw as a broader tendency in society to treat human problems as engineering challenges with technical solutions. He argued that the "computational metaphor," the idea that the human mind is essentially a computer, was both scientifically wrong and morally dangerous.

Weizenbaum died on March 5, 2008, in Berlin, at the age of 85. A 2010 documentary film, *Weizenbaum. Rebel at Work*, explored his life and ideas. By the time of his death, many of his warnings about the social implications of conversational AI had begun to seem prescient [1].

## Technical Significance

Despite its simplicity, ELIZA introduced several ideas that influenced subsequent work in [natural language processing](/wiki/natural_language_processing) and conversational AI:

| Innovation | Significance |
|------------|-------------|
| Script-based architecture | Separated conversational content from the processing engine, a precursor to modern chatbot frameworks |
| Pattern matching for NLP | Demonstrated that useful (if limited) language processing could be achieved through pattern matching |
| Keyword prioritization | Introduced the idea of ranking input features by importance for response selection |
| Pronoun substitution | Handled basic reference resolution through systematic word replacement |
| Conversation memory | Used a simple memory stack to maintain conversational context |
| Modular personality | Different scripts could create entirely different conversational characters using the same engine |

## Was the original ELIZA code rediscovered?

For decades, the original source code for ELIZA was considered lost. Weizenbaum never published the complete code, and the MAD-SLIP language in which it was written had fallen out of use. Numerous reimplementations were created over the years in languages from BASIC to JavaScript, but none were the original.

### The ELIZAGEN Project

Beginning around 2020, a group of researchers and enthusiasts, organized as the ELIZAGEN project, worked to locate and authenticate the original ELIZA code. The project drew on archives at MIT, personal papers, and historical computing repositories to piece together the history of ELIZA's code [2].

### Discovery and Reanimation (2024-2025)

While searching Weizenbaum's archived papers at MIT, the team found an original ELIZA printout that included an early version of the famous DOCTOR script, a nearly complete version of the MAD-SLIP source code, and various support functions written in MAD and FAP [2]. The printout ran to roughly 53 pages and about 2,600 lines, most of it uncommented, and had to be hand transcribed into machine-readable text because it did not scan reliably with optical character recognition [2].

To run the recovered code, the team rebuilt the entire 1960s software stack:

| Layer | Component | Status |
|-------|-----------|--------|
| Hardware emulation | IBM 7094 mainframe | Emulated in software |
| Operating system | MIT CTSS (Compatible Time-Sharing System) | Reconstructed from archives |
| Programming language | MAD-SLIP (MAD compiler + SLIP list processor) | Compiler restored |
| Application | ELIZA source code | Transcribed from the ~2,600-line archive printout |

The restoration was led by Rupert Lane and colleagues, who reported that the reanimated ELIZA reproduced the published conversations from Weizenbaum's 1966 paper. As the team put it, "The entire stack is open source, so that any user of a unix-like OS can run the world's first chatbot on the world's first time-sharing system" [2].

In January 2025, the team posted a preprint titled "ELIZA Reanimated: The world's first chatbot restored on the world's first time sharing system" on arXiv (arXiv:2501.06707), authored by Rupert Lane, Anthony Hay, Arthur Schwarz, David M. Berry, and Jeff Shrager [2]. A peer-reviewed version, "ELIZA Reanimated: Restoring the Mother of All Chatbots to One of the World's First Time-Sharing Systems," was published in *IEEE Annals of the History of Computing*, volume 47, issue 2 [11].

The restored code and the supporting toolchain were released as an open-source repository, and the original MAD-SLIP source was also made publicly accessible, allowing people to interact with Weizenbaum's actual code rather than one of the many reimplementations that had proliferated over the years [2].

## How did ELIZA perform on the Turing test?

ELIZA was designed before the modern AI benchmark era, but in the 2020s it became a recurring baseline in controlled [Turing test](/wiki/turing_test) experiments, where its weak performance helped calibrate how convincing newer systems were.

In the study "Does GPT-4 pass the Turing test?" presented at NAACL 2024, Cameron R. Jones and Benjamin K. Bergen of the University of California, San Diego, ran a randomized Turing test in which interrogators chatted for several minutes and then guessed whether they had been talking to a human or a machine. ELIZA was judged to be human about 22 percent of the time, compared with 50 percent for GPT-3.5 and 54 percent for GPT-4 [10].

In a follow-up study, "Large Language Models Pass the Turing Test" (arXiv:2503.23674, 2025), Jones and Bergen ran a stricter three-party test in which each interrogator spoke simultaneously with a human and an AI system over five-minute conversations. The results [12]:

| System | Judged human | Notes |
|--------|-------------|-------|
| GPT-4.5 (with persona prompt) | 73% | First system to clearly pass a standard three-party Turing test |
| LLaMa-3.1-405B (with persona prompt) | 56% | Not statistically distinguishable from humans |
| ELIZA (1966 baseline) | 23% | Manipulation check; well below chance |
| GPT-4o (no persona prompt) | 21% | Well below chance |

The authors reported that GPT-4.5 with a persona prompt "was judged to be the human 73% of the time: significantly more often than interrogators selected the real human participant" [12]. ELIZA's 23 percent narrowly exceeded the persona-free GPT-4o's 21 percent, but both sat far below the roughly 50 percent that would indicate a coin-flip-level inability to tell machine from human. The study's central conclusion was not about ELIZA but about GPT-4.5: it constituted, in the authors' framing, the first empirical evidence that a system passes a standard three-party Turing test. ELIZA's role was to confirm that interrogators could readily identify an obviously non-human system, validating the test design.

## Comparison with Modern Chatbots

The distance between ELIZA and modern conversational AI systems is vast, but the comparison reveals how much, and how little, has changed.

| Feature | ELIZA (1966) | PARRY (1972) | A.L.I.C.E. (1995) | Modern LLMs (e.g., [ChatGPT](/wiki/chatgpt), 2022+) |
|---------|-------------|-------------|-------------------|----------------------------------------------|
| Approach | Pattern matching + substitution | Rule-based + affect model | AIML pattern matching | [Transformer](/wiki/transformer) neural networks trained on billions of words |
| Understanding | None | Simulated emotional state only | None | Statistical pattern recognition; no confirmed understanding |
| Code size | ~420 lines | Several thousand lines | ~40,000+ AIML categories | Billions of parameters |
| Training data | None (hand-written rules) | None (hand-written rules) | Hand-authored knowledge base | Trillions of tokens of text |
| Conversational memory | Simple stack (within session) | State variables | Session context | Context window (thousands to millions of tokens) |
| Personalization | Fixed script | Fixed persona (paranoid patient) | Customizable AIML sets | Adapts to conversational context |
| Passes Turing-like tests | Judged human ~22-23% in controlled tests | Passed a limited Turing-like test in 1972 | Won Loebner Prize three times | GPT-4.5 judged human 73% in a 2025 three-party test |

Despite the enormous technical gulf, ELIZA and modern [large language models](/wiki/large_language_model) share a fundamental characteristic: neither genuinely understands language in the way humans do. Modern LLMs produce far more convincing and contextually appropriate responses, but the underlying question that ELIZA raised, whether the appearance of understanding constitutes understanding, remains philosophically unresolved.

Weizenbaum would likely have been deeply troubled by the scale of the modern ELIZA effect. When millions of users interact daily with chatbots powered by large language models, describing them as "thinking," "understanding," or "feeling," the phenomenon he identified in the 1960s has been amplified to a planetary scale.

## Legacy

ELIZA's legacy operates on two distinct levels: technical and philosophical.

**As a technical ancestor.** ELIZA is a direct ancestor of every chatbot, virtual assistant, and conversational AI system that followed. Programs like PARRY (1972), A.L.I.C.E. (1995), SmarterChild (2001), [Apple's Siri](/wiki/siri) (2011), and modern [large language models](/wiki/large_language_model) like [ChatGPT](/wiki/chatgpt) all trace their lineage, in some sense, back to ELIZA. While today's systems use vastly more sophisticated techniques, including [deep learning](/wiki/deep_learning), [transformer architectures](/wiki/transformer), and training on billions of words of text, the fundamental challenge that ELIZA first highlighted, making a computer engage in convincing conversation, remains central to AI research.

**As a philosophical provocation.** The ELIZA effect has proven remarkably durable. The same tendency to attribute understanding and empathy to machines that Weizenbaum observed in the 1960s manifests in contemporary reactions to [large language models](/wiki/large_language_model). When users describe ChatGPT or similar systems as "understanding" their questions or "feeling" a certain way, they are experiencing a version of the same phenomenon that troubled Weizenbaum six decades ago.

Weizenbaum's warnings about the social implications of conversational AI have become, if anything, more relevant over time. His concerns about the automation of empathy, the vulnerability of users to technological deception, and the moral responsibilities of AI developers are now mainstream topics of discussion in [AI ethics](/wiki/ai_ethics).

The ELIZA effect has also been formally studied in psychology and [human-computer interaction](/wiki/human_computer_interaction) research, where it is recognized as a specific instance of broader tendencies toward anthropomorphism: the attribution of human characteristics to non-human entities.

ELIZA's place in computing history is secure. It demonstrated, with startling clarity, both the power and the danger of creating machines that appear to understand us. That a 420-line program written in 1966 could provoke questions that remain unresolved in 2026 is perhaps the strongest testament to Weizenbaum's achievement.

## See Also

- [Natural Language Processing](/wiki/natural_language_processing)
- [Chatbot](/wiki/chatbot)
- [Turing Test](/wiki/turing_test)
- [ELIZA Effect](/wiki/eliza_effect)
- [Joseph Weizenbaum](/wiki/joseph_weizenbaum)

## References

1. Weizenbaum, J. (1976). *Computer Power and Human Reason: From Judgment to Calculation.* W.H. Freeman and Company.
2. Lane, R., Hay, A., Schwarz, A., Berry, D. M., and Shrager, J. (2025). "ELIZA Reanimated: The world's first chatbot restored on the world's first time sharing system." arXiv:2501.06707. https://arxiv.org/abs/2501.06707
3. Weizenbaum, J. (1966). "ELIZA: A Computer Program for the Study of Natural Language Communication Between Man and Machine." Communications of the ACM, 9(1), 36-45. https://cacm.acm.org/research/eliza-a-computer-program-for-the-study-of-natural-language-communication-between-man-and-machine-2/
4. "Why Joseph Weizenbaum Invented the Eliza Chatbot." Smithsonian Magazine. https://www.smithsonianmag.com/history/why-the-computer-scientist-behind-the-worlds-first-chatbot-dedicated-his-life-to-publicizing-the-threat-posed-by-ai-180987971/
5. "ELIZA effect." Wikipedia. https://en.wikipedia.org/wiki/ELIZA_effect
6. "Why a 1960s Chatbot Left Its Creator Deeply Unsettled." HISTORY. https://www.history.com/articles/ai-first-chatbot-eliza-artificial-intelligence-precursor-llms
7. "ELIZA." Wikipedia. https://en.wikipedia.org/wiki/ELIZA
8. "The Inventor of the Chatbot Tried to Warn Us About A.I." The New Republic. https://newrepublic.com/article/181189/inventor-chatbot-tried-warn-us-ai-joseph-weizenbaum-computer-power-human-reason
9. McCorduck, P. (2004). *Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence.* A.K. Peters.
10. Jones, C. R., and Bergen, B. K. (2024). "Does GPT-4 pass the Turing test?" NAACL 2024. arXiv:2405.08007. https://arxiv.org/abs/2405.08007
11. Lane, R., Hay, A., Schwarz, A., Berry, D. M., and Shrager, J. (2025). "ELIZA Reanimated: Restoring the Mother of All Chatbots to One of the World's First Time-Sharing Systems." IEEE Annals of the History of Computing, 47(2). doi:10.1109/MAHC.2025.3564095
12. Jones, C. R., and Bergen, B. K. (2025). "Large Language Models Pass the Turing Test." arXiv:2503.23674. https://arxiv.org/abs/2503.23674