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**LONDON, UK, October 17th, 2024/Chainwire/--**A dynamic shift is unfolding in the world of crypto-powered competitions as MetaWin, known for its thrilling Web3 contests, opens its doors to a new community. In a bold new move, MetaWin is embracing the $dydx token, bringing together the excitement of decentralized trading with high-stakes entertainment. MetaWin’s $DYDX integration offers a compelling opportunity for $DYDX holders to participate in a unique prize draw as part of the launch. The platform is offering a 5,000 DYDX token prize pool, valued at approximately $4,600, which will be awarded to eligible participants through a free-entry draw. To qualify, participants must hold a minimum of 100 DYDX tokens in their wallets. Launch Prize Draw Details What: A chance to win from a 5,000 DYDX token pool (~$4,600) How to Enter: Hold a minimum of 100 DYDX tokens in a compatible wallet Cost: Free entry Purpose: To celebrate DYDX’s acceptance on MetaWin Users can click here to enter the 5,000 DYDX draw for free. Rebecca Hanwell, Operations Manager of MetaWin, shares her excitement: “Decentralized platforms are the future, and by integrating $DYDX, we’re aligning our product with that vision. It’s a perfect fit. We’re thrilled to offer traders new ways to win, all within an ecosystem they know and trust.” Beyond the prize draw, MetaWin offers features designed with Web3 users in mind, including Web3 wallet connectivity, instant withdrawals, L2 support, and seamless swaps in and out. These features aim to enhance the user experience, making it easy for participants to engage with contests, participate in prize draws, and seamlessly manage their tokens. MetaWin has hinted at upcoming competitions, giveaways, and new prize pools, signaling further engagement opportunities for traders and Web3 enthusiasts. As the platform expands, MetaWin remains focused on delivering decentralized entertainment options that resonate with the evolving needs of the crypto community. About MetaWin MetaWin stands as the premier platform for on-chain prize competitions and instant win games, offering a diverse array of entertaining challenges for users. Through the utilisation of cutting-edge blockchain technology, MetaWin ensures a transparent, fair, and secure gaming environment, making it the preferred destination for blockchain enthusiasts and gamers alike. At the forefront of Web3 innovation, MetaWin is supported by a robust community of 250,000 connected wallets. Renowned for its ability to craft impactful digital experiences, MetaWin remains dedicated to pushing the boundaries of the cryptocurrency landscape. For more information, users can visit MetaWin.com. Users can follow MetaWin on social media and join MetaWin’s community: X| Instagram| Telegram | Discord Contact Metawin Metawin pr@metawin.inc This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. **LONDON, UK, October 17th, 2024/Chainwire/--**A dynamic shift is unfolding in the world of crypto-powered competitions as MetaWin, known for its thrilling Web3 contests, opens its doors to a new community. In a bold new move, MetaWin is embracing the $dydx token, bringing together the excitement of decentralized trading with high-stakes entertainment. MetaWin, MetaWin, MetaWin’s $DYDX integration offers a compelling opportunity for $DYDX holders to participate in a unique prize draw as part of the launch. The platform is offering a 5,000 DYDX token prize pool, valued at approximately $4,600, which will be awarded to eligible participants through a free-entry draw. To qualify, participants must hold a minimum of 100 DYDX tokens in their wallets. 5,000 DYDX 5,000 DYDX Launch Prize Draw Details What: A chance to win from a 5,000 DYDX token pool (~$4,600) How to Enter: Hold a minimum of 100 DYDX tokens in a compatible wallet Cost: Free entry Purpose: To celebrate DYDX’s acceptance on MetaWin Users can click here to enter the 5,000 DYDX draw for free. Users can click here to enter the 5,000 DYDX draw for free. Users can click here to enter the 5,000 DYDX draw for free. Rebecca Hanwell, Operations Manager of MetaWin, shares her excitement: “Decentralized platforms are the future, and by integrating $DYDX, we’re aligning our product with that vision. It’s a perfect fit. We’re thrilled to offer traders new ways to win, all within an ecosystem they know and trust.” Rebecca Hanwell, Operations Manager of MetaWin, shares her excitement: “Decentralized platforms are the future, and by integrating $DYDX, we’re aligning our product with that vision. It’s a perfect fit. We’re thrilled to offer traders new ways to win, all within an ecosystem they know and trust.” Beyond the prize draw, MetaWin offers features designed with Web3 users in mind, including Web3 wallet connectivity, instant withdrawals, L2 support, and seamless swaps in and out. These features aim to enhance the user experience, making it easy for participants to engage with contests, participate in prize draws, and seamlessly manage their tokens. MetaWin has hinted at upcoming competitions, giveaways, and new prize pools, signaling further engagement opportunities for traders and Web3 enthusiasts. As the platform expands, MetaWin remains focused on delivering decentralized entertainment options that resonate with the evolving needs of the crypto community. About MetaWin MetaWin stands as the premier platform for on-chain prize competitions and instant win games, offering a diverse array of entertaining challenges for users. Through the utilisation of cutting-edge blockchain technology, MetaWin ensures a transparent, fair, and secure gaming environment, making it the preferred destination for blockchain enthusiasts and gamers alike. At the forefront of Web3 innovation, MetaWin is supported by a robust community of 250,000 connected wallets. Renowned for its ability to craft impactful digital experiences, MetaWin remains dedicated to pushing the boundaries of the cryptocurrency landscape. For more information, users can visit MetaWin.com . Users can follow MetaWin on social media and join MetaWin’s community: MetaWin.com MetaWin.com X | Instagram | Telegram | Discord X X Instagram Instagram Telegram Telegram Discord Discord Contact Metawin Metawin pr@metawin.inc This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

MetaWin Integrates $DYDX, Launches New Prize Draw with 5,000 DYDX Tokens For Eligible Participants

MetaWin integra $DYDX y lanza un nuevo sorteo de premios con 5000 tokens DYDX para participantes elegibles

MetaWin-ը միավորում է $DYDX-ը և մեկնարկում է նոր մրցանակային խաղարկություն 5000 DYDX նշաններով իրավասու մասնակիցների համար

MetaWin Integrasikan $DYDX, Luncurkan Undian Berhadiah Baru dengan 5.000 Token DYDX untuk Peserta yang Memenuhi Syarat

MetaWin משלבת $DYDX, משיקה הגרלת פרס חדשה עם 5,000 אסימוני DYDX למשתתפים מתאימים

MetaWin が $DYDX を統合し、対象参加者に 5,000 DYDX トークンが当たる新しい抽選を開始

MetaWin integrē $DYDX, uzsāk jaunu balvu izlozi ar 5000 DYDX žetoniem atbilstošajiem dalībniekiem

MetaWin $DYDX एकीकृत गर्दछ, योग्य सहभागीहरूका लागि 5,000 DYDX टोकनहरूको साथ नयाँ पुरस्कार ड्र लन्च गर्दछ

MetaWin интегрирует $DYDX и запускает новый розыгрыш призов с 5000 токенов DYDX для участников, соответствующих требованиям

MetaWin waxay isku daraysaa $DYDX, waxay bilawday barbardhac cusub oo ay la gashay 5,000 DYDX Token ee ka qaybqaatayaasha xaqa u leh

MetaWin ผสานรวม $DYDX เปิดตัวการจับรางวัลใหม่ด้วยโทเค็น DYDX จำนวน 5,000 โทเค็นสำหรับผู้เข้าร่วมที่มีสิทธิ์

MetaWin Yi Hlanganisa $DYDX, Yi Sungula Ntlangu Lowuntshwa wa Masagwadi hi 5,000 wa Tithokini ta DYDX Eka Vatekaxiave lava Fanelekaka

MetaWin 集成 $DYDX，推出新抽奖活动，符合条件的参与者可赢取 5,000 个 DYDX 代币

Table of Links Abstract and 1. Introduction Why ELIZA?
The Intelligence Engineers
Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs
From IPL to SLIP and Lisp
A Critical Tangent into Gomoku
Interpretation is the Core of Intelligence
The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion
Finally ELIZA: A Platform, Not a Chat Bot!
A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community
Another Wave: A BASIC ELIZA turns the PC Generation on to AI
Conclusion: A certain danger lurks there
Acknowledgements and References 13 Acknowledgements This paper developed through extensive discussions over several years with “Team ELIZA”, including Anthony Hay, Art Schwarz, Sarah Ciston, Peggy Weil, Peter Millican, David Berry, and Mark Marino. I am especially indebted to Anthony Hay and Art Schwarz for detailed discussion about technical aspects of ELIZA and SLIP, such as how symbol manipulation worked in SLIP. Terry Winograd and John Markoff participated in helpful background discussions, and Anne Rawls and Clemens Eisenmann provided access to the Garfinkel archives, and Andrei Korbut introduced us to them. Many anonymous commenters in the Hacker News community helped clarify concepts, and provided additional pointers to information that I could not have easily discovered on my own. I am especially indebted to the MIT Archivists, Myles Crowley, Mikki Macdonald, and Allison Schmitt. References [1] https://grammarist.com/new-words/chatbot/. Accessed 2024-04-18. [2] https://sites.google.com/view/elizagen-org/news#h.ykbzq5nuiccs. Accessed 2024-04-18. [3] https://plato.stanford.edu/entries/church-turing/. Accessed 2024-04-18. [4] https://en.wikipedia.org/wiki/Von_Neumann_architecture. Accessed 2024-04-18. [5] https://en.wikipedia.org/wiki/Information_Processing_Language. Accessed 2024-04-18. [6] https://en.wikipedia.org/wiki/Dartmouth_workshop. Accessed 2024- 04-18. [7] https://en.wikipedia.org/wiki/John_McCarthy_(computer_scientist). Accessed 2024-04-18. [8] https://plato.stanford.edu/entries/meaning/. Accessed 2024-04-18. [9] https://en.wikipedia.org/wiki/Conceptual_blending. Accessed 2024- 04-18. [10] https://sites.google.com/view/elizagen-org/news#h.74lv9le8r29u. Accessed 2024-04-18. [11] https://en.wikipedia.org/wiki/ARPANET. Accessed 2024-04-18. [12] https://en.wikipedia.org/wiki/History_of_personal_computers. Accessed 2024-04-18. [13] https://en.wikipedia.org/wiki/HAL_9000. Accessed 2024-04-18. [14] https://sites.google.com/view/elizagen-org/. Accessed 2024-04-18. [15] https://en.wikipedia.org/wiki/HyperCard. Accessed 2024-04-18. [16] https://sites.google.com/view/elizaarchaeology/. Accessed 2024- 04-18. [17] D. G. Bobrow. “Natural language input for a computer problem solving system”. PhD Dissertation. Cambridge, MA: Massachusetts Institute of Technology, May 1964. [18] L. Cameron. What to know about the scientist who invented the term ”software engineering”. https://www.computer.org/publications/tech-news/events/what-to-know-aboAccessed: 2024-04-18. Oct. 2018. [19] D. Chisnall. Influential programming languages, Part 4: Lisp. https://www.informit.com/articles/artAccessed 2024-04-18. 2011. [20] Noam Chomsky and George A. Miller. “Introduction to the formal analysis of natural languages”. In: Handbook of Mathematical Psychology. Ed. by R. Duncan Luce, Robert R. Bush, and Eugene Galanter. Vol. 2. Hoboken, NJ: John Wiley and Sons, 1963, pp. 269–321. [21] K. M. Colby. “Modeling a paranoid mind”. In: The Behavioral and Brain Sciences 4 (1981), pp. 515–560. [22] K. M. Colby and J. P. Gilbert. “Programming a computer model of neurosis”. In: J. Math Psych 1 (1964), pp. 405–416. [23] Ada Augusta Countess of Lovelace. Notes on L. F. Menabrea’s “Sketch of the analytical engine invented by Charles Babbage”. http://imaginaryinstruments.org/lovelace-anaAccessed 2024-04-18. Sept. 1843. [24] G. Dyson. Turing’s cathedral. Vintage Books, 2012. [25] C. Eisenmann et al. ““Machine Down”: making sense of human–computer interaction—Garfinkel’s research on ELIZA and LYRIC from 1967 to 1969 and its contemporary relevance”. In: AI & Soc (2023). doi: 10.1007/s00146-023-01793-z. [26] E. Feigenbaum. An information processing theory of verbal learning. Paper P-1817. The RAND Corporation, 1959. [27] H. Gelernter, J. R. Hansen, and L. L. Gerberich. “A Fortran-compiled list-processing language”. In: JACM 7.2 (Apr. 1960), pp. 87–101. [28] D. Gentner and A. L. Stevens. Mental models. Hillsdale, NJ: Lawrence Erlbaum Associates, 1983. [29] B. F. Green et al. “Baseball: An automatic question-answerer”. In: Name of Journal or Conference Volume Number.Issue Number (1961). Additional Information, Page Numbers. [30] F. J. Gruenberger. The history of the JOHNNIAC. RAND memorandum RM-5654-PR. 1968. [31] P. Hayward. Flexible discussion under student control in the ELIZA computer program. Educational Research Center, Massachusetts Institute of Technology, Cambridge. 1967. [32] D. R. Hofstadter. G¨odel, Escher, Bach: An eternal golden braid. New York: Basic Books, 1979. [33] P. McCorduck. Machines who think. W.H. Freeman and Co., 1979. [34] M. T. McGuire, S. Lorch, and G. C. Quarton. “Man-machine natural language exchanges based on selected features of unrestricted input—II. The use of the time-shared computer as a research tool in studying dyadic communication”. In: J. Psychiat. Res. 5.2 (1967). [35] A. Newell and H. A. Simon. “Computer science as empirical inquiry: Symbols and search”. In: Communications of the ACM 19.3 (1976), pp. 113– 126. [36] S. North. “ELIZA”. In: Creative Computing (1977), pp. 100–103. [37] C. Petzold. The annotated turing. Wiley, 2008. [38] G. C. Quarton, M. T. McGuire, and S. Lorch. “Man-machine natural language exchanges based on selected features of unrestricted input—I. The development of the time-shared computer as a research tool in studying dyadic communication”. In: J. Psychiat. Res. 5.2 (1967). [39] R. C. Schank and R. P. Abelson. Scripts, plans, goals, and understanding: An inquiry into human knowledge structures. Hillsdale, NJ: Lawrence Erlbaum Associates, 1977. [40] J. R. Searle. “Minds, brains, and programs”. In: Behavioral and Brain Sciences 3.3 (1980), pp. 417–457. [41] P. Seibel. Coders at Work. APress, 2009. [42] SLIP: University of Michigan executive system for the IBM 7090 computer. Vol. 2. University of Michigan Computing Center, 1965. [43] E. F. Taylor. ELIZA: A skimmable report on the ELIZA conversational tutoring system. Educational Research Center, Massachusetts Institute of Technology, Cambridge. 1968. [44] A. Turing. “Computing machinery and intelligence”. In: Mind 49 (1950), pp. 433–460. [45] D. Walden and R. Nickerson, eds. A culture of innovation: Insider accounts of computing and life at BBN. A Sixty Year Report 18 October 1948 to 1 July 2010. Waterside Publishing, 201. [46] J. Weizenbaum. Computer power and human reason. W.H. Freeman and Co., 1976. [47] J. Weizenbaum. “ELIZA—a computer program for the study of natural language communication between man and machine”. In: Communications of the ACM 9.1 (1966), pp. 36–45. doi: 10.1145/365153.365168. [48] J. Weizenbaum. “How to make a computer appear intelligent”. In: Datamation (Feb. 1962), pp. 24–26. [49] J. Weizenbaum. “Symmetric list processor”. In: Comm. ACM 6.9 (1963), pp. 524–536. Author:
(1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). This paper is available on arxiv under CC BY 4.0 license. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Why ELIZA? The Intelligence Engineers Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs From IPL to SLIP and Lisp A Critical Tangent into Gomoku Interpretation is the Core of Intelligence The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion Finally ELIZA: A Platform, Not a Chat Bot! A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community Another Wave: A BASIC ELIZA turns the PC Generation on to AI Conclusion: A certain danger lurks there Acknowledgements and References Why ELIZA? Why ELIZA? The Intelligence Engineers The Intelligence Engineers Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs From IPL to SLIP and Lisp From IPL to SLIP and Lisp A Critical Tangent into Gomoku A Critical Tangent into Gomoku Interpretation is the Core of Intelligence Interpretation is the Core of Intelligence The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion Finally ELIZA: A Platform, Not a Chat Bot! Finally ELIZA: A Platform, Not a Chat Bot! A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community Another Wave: A BASIC ELIZA turns the PC Generation on to AI Another Wave: A BASIC ELIZA turns the PC Generation on to AI Conclusion: A certain danger lurks there Conclusion: A certain danger lurks there Acknowledgements and References Acknowledgements and References 13 Acknowledgements This paper developed through extensive discussions over several years with “Team ELIZA”, including Anthony Hay, Art Schwarz, Sarah Ciston, Peggy Weil, Peter Millican, David Berry, and Mark Marino. I am especially indebted to Anthony Hay and Art Schwarz for detailed discussion about technical aspects of ELIZA and SLIP, such as how symbol manipulation worked in SLIP. Terry Winograd and John Markoff participated in helpful background discussions, and Anne Rawls and Clemens Eisenmann provided access to the Garfinkel archives, and Andrei Korbut introduced us to them. Many anonymous commenters in the Hacker News community helped clarify concepts, and provided additional pointers to information that I could not have easily discovered on my own. I am especially indebted to the MIT Archivists, Myles Crowley, Mikki Macdonald, and Allison Schmitt. References [1] https://grammarist.com/new-words/chatbot/. Accessed 2024-04-18. [2] https://sites.google.com/view/elizagen-org/news#h.ykbzq5nuiccs. Accessed 2024-04-18. [3] https://plato.stanford.edu/entries/church-turing/. Accessed 2024-04-18. [4] https://en.wikipedia.org/wiki/Von_Neumann_architecture. Accessed 2024-04-18. [5] https://en.wikipedia.org/wiki/Information_Processing_Language. Accessed 2024-04-18. [6] https://en.wikipedia.org/wiki/Dartmouth_workshop. Accessed 2024- 04-18. [7] https://en.wikipedia.org/wiki/John_McCarthy_(computer_scientist). Accessed 2024-04-18. [8] https://plato.stanford.edu/entries/meaning/. Accessed 2024-04-18. [9] https://en.wikipedia.org/wiki/Conceptual_blending. Accessed 2024- 04-18. [10] https://sites.google.com/view/elizagen-org/news#h.74lv9le8r29u. Accessed 2024-04-18. [11] https://en.wikipedia.org/wiki/ARPANET. Accessed 2024-04-18. [12] https://en.wikipedia.org/wiki/History_of_personal_computers. Accessed 2024-04-18. [13] https://en.wikipedia.org/wiki/HAL_9000. Accessed 2024-04-18. [14] https://sites.google.com/view/elizagen-org/. Accessed 2024-04-18. [15] https://en.wikipedia.org/wiki/HyperCard. Accessed 2024-04-18. [16] https://sites.google.com/view/elizaarchaeology/. Accessed 2024- 04-18. [17] D. G. Bobrow. “Natural language input for a computer problem solving system”. PhD Dissertation. Cambridge, MA: Massachusetts Institute of Technology, May 1964. [18] L. Cameron. What to know about the scientist who invented the term ”software engineering”. https://www.computer.org/publications/tech-news/events/what-to-know-aboAccessed: 2024-04-18. Oct. 2018. [19] D. Chisnall. Influential programming languages, Part 4: Lisp. https://www.informit.com/articles/artAccessed 2024-04-18. 2011. [20] Noam Chomsky and George A. Miller. “Introduction to the formal analysis of natural languages”. In: Handbook of Mathematical Psychology. Ed. by R. Duncan Luce, Robert R. Bush, and Eugene Galanter. Vol. 2. Hoboken, NJ: John Wiley and Sons, 1963, pp. 269–321. [21] K. M. Colby. “Modeling a paranoid mind”. In: The Behavioral and Brain Sciences 4 (1981), pp. 515–560. [22] K. M. Colby and J. P. Gilbert. “Programming a computer model of neurosis”. In: J. Math Psych 1 (1964), pp. 405–416. [23] Ada Augusta Countess of Lovelace. Notes on L. F. Menabrea’s “Sketch of the analytical engine invented by Charles Babbage”. http://imaginaryinstruments.org/lovelace-anaAccessed 2024-04-18. Sept. 1843. [24] G. Dyson. Turing’s cathedral. Vintage Books, 2012. [25] C. Eisenmann et al. ““Machine Down”: making sense of human–computer interaction—Garfinkel’s research on ELIZA and LYRIC from 1967 to 1969 and its contemporary relevance”. In: AI & Soc (2023). doi: 10.1007/s00146-023-01793-z. [26] E. Feigenbaum. An information processing theory of verbal learning. Paper P-1817. The RAND Corporation, 1959. [27] H. Gelernter, J. R. Hansen, and L. L. Gerberich. “A Fortran-compiled list-processing language”. In: JACM 7.2 (Apr. 1960), pp. 87–101. [28] D. Gentner and A. L. Stevens. Mental models. Hillsdale, NJ: Lawrence Erlbaum Associates, 1983. [29] B. F. Green et al. “Baseball: An automatic question-answerer”. In: Name of Journal or Conference Volume Number.Issue Number (1961). Additional Information, Page Numbers. [30] F. J. Gruenberger. The history of the JOHNNIAC. RAND memorandum RM-5654-PR. 1968. [31] P. Hayward. Flexible discussion under student control in the ELIZA computer program. Educational Research Center, Massachusetts Institute of Technology, Cambridge. 1967. [32] D. R. Hofstadter. G¨odel, Escher, Bach: An eternal golden braid. New York: Basic Books, 1979. [33] P. McCorduck. Machines who think. W.H. Freeman and Co., 1979. [34] M. T. McGuire, S. Lorch, and G. C. Quarton. “Man-machine natural language exchanges based on selected features of unrestricted input—II. The use of the time-shared computer as a research tool in studying dyadic communication”. In: J. Psychiat. Res. 5.2 (1967). [35] A. Newell and H. A. Simon. “Computer science as empirical inquiry: Symbols and search”. In: Communications of the ACM 19.3 (1976), pp. 113– 126. [36] S. North. “ELIZA”. In: Creative Computing (1977), pp. 100–103. [37] C. Petzold. The annotated turing. Wiley, 2008. [38] G. C. Quarton, M. T. McGuire, and S. Lorch. “Man-machine natural language exchanges based on selected features of unrestricted input—I. The development of the time-shared computer as a research tool in studying dyadic communication”. In: J. Psychiat. Res. 5.2 (1967). [39] R. C. Schank and R. P. Abelson. Scripts, plans, goals, and understanding: An inquiry into human knowledge structures. Hillsdale, NJ: Lawrence Erlbaum Associates, 1977. [40] J. R. Searle. “Minds, brains, and programs”. In: Behavioral and Brain Sciences 3.3 (1980), pp. 417–457. [41] P. Seibel. Coders at Work. APress, 2009. [42] SLIP: University of Michigan executive system for the IBM 7090 computer. Vol. 2. University of Michigan Computing Center, 1965. [43] E. F. Taylor. ELIZA: A skimmable report on the ELIZA conversational tutoring system. Educational Research Center, Massachusetts Institute of Technology, Cambridge. 1968. [44] A. Turing. “Computing machinery and intelligence”. In: Mind 49 (1950), pp. 433–460. [45] D. Walden and R. Nickerson, eds. A culture of innovation: Insider accounts of computing and life at BBN. A Sixty Year Report 18 October 1948 to 1 July 2010. Waterside Publishing, 201. [46] J. Weizenbaum. Computer power and human reason. W.H. Freeman and Co., 1976. [47] J. Weizenbaum. “ELIZA—a computer program for the study of natural language communication between man and machine”. In: Communications of the ACM 9.1 (1966), pp. 36–45. doi: 10.1145/365153.365168. [48] J. Weizenbaum. “How to make a computer appear intelligent”. In: Datamation (Feb. 1962), pp. 24–26. [49] J. Weizenbaum. “Symmetric list processor”. In: Comm. ACM 6.9 (1963), pp. 524–536. Author: (1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). Author: Author: (1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). This paper is available on arxiv under CC BY 4.0 license. This paper is available on arxiv under CC BY 4.0 license. available on arxiv available on arxiv

Who Made ELIZA Possible? 

Table of Links Abstract and 1. Introduction Why ELIZA?
The Intelligence Engineers
Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs
From IPL to SLIP and Lisp
A Critical Tangent into Gomoku
Interpretation is the Core of Intelligence
The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion
Finally ELIZA: A Platform, Not a Chat Bot!
A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community
Another Wave: A BASIC ELIZA turns the PC Generation on to AI
Conclusion: A certain danger lurks there
Acknowledgements and References 12 Conclusion: A certain danger lurks there Weizenbaum’s goal to use ELIZA as a platform for the study of the human process of interpretation was thwarted by exactly what he did not want to see; The “kludge” [in nearly the original sense of the term] supplanted the reality. Instead of using ELIZA as a tool to study interpretation and interaction with AI, it became a cause celebre in-and-of-itself, the DOCTOR script being the only one ever seen, because it was so good for such a simple program – exactly the opposite of Weizenbaum’s point! Moreover, exactly what Weizenbaum did not want to happen with regard to Lisp vs. SLIP came to pass; instead of people using Fortran to build complex AI programs. Nearly everyone in AI, or involved in symbolic and/or list processing turned to Lisp, and SLIP eventually faded away. In 1950, Alan Turing wrote: “I believe that in about fifty years’ time it will be possible to programme computers, with a storage capacity of about 109, to make them play the imitation game so well that an average interrogator will not have more than 70 per cent, chance of making the right identification after five minutes of questioning. The original question, ‘Can machines think!’ I believe to be too meaningless to deserve discussion. Nevertheless I believe that at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted. I believe further that no useful purpose is served by concealing these beliefs. The popular view that scientists proceed inexorably from well-established fact to well-established fact, never being influenced by any unproved conjecture, is quite mistaken. Provided it is made clear which are proved facts and which are conjectures, no harm can result. Conjectures are of great importance since they suggest useful lines of research.”[44, p. 442] A mere decade and a half later, Joseph Weizenbaum wrote: “With ELIZA as the basic vehicle, experiments may be set up in which the subjects find it credible to believe that the responses which appear on his[17] typewriter are generated by a human sitting at a similar instrument in another room.”[47, p. 42] Regardless of how close this description may seem to Turing’s test, Weizenbaum did not build ELIZA to pass that test. Rather he built it to run experiments, including ones akin to the Turing Test, that could be used to study human interpretive processes (especially in the case of artificial intelligence). He believed that this research was critically important, but not merely for the academic reasons that motivated Turing and most AI researchers: “[The] whole issue of the credibility (to humans) of machine output demands investigation [...] [Important] decisions increasingly tend to be made in response to computer output. The ultimately responsible human interpreter of ’What the machine says’ is, not unlike the correspondent with ELIZA, constantly faced with the need to make credibility judgments. ELIZA shows, if nothing else, how easy it is to create and maintain the illusion of understanding, hence perhaps of judgment deserving of credibility. A certain danger lurks there.”[47, pp. 42–43] The problem of how humans impute agency, correctness, and intelligence to machines is not only still present, but has become exponentially more important in recent years, with the widespread diffusion of internet bots and large language models. Our modern computational lives might have been better had Weizenbaum pursued his goal of using ELIZA to study people’s interpretive interaction with computers, and especially with AIs. Unfortunately, his fear that “[t]here is a danger [...] that the example will run away with what it is supposed to illustrate”[47, p. 43] was too prescient. Author:
(1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). This paper is available on arxiv under CC BY 4.0 license. [17] Even for the time, it is striking that Weizenbaum constantly refers to ELIZA’s interlocutors as male, given that the only example he provides of a conversation with ELIZA is (putatively) with a woman! Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Why ELIZA? The Intelligence Engineers Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs From IPL to SLIP and Lisp A Critical Tangent into Gomoku Interpretation is the Core of Intelligence The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion Finally ELIZA: A Platform, Not a Chat Bot! A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community Another Wave: A BASIC ELIZA turns the PC Generation on to AI Conclusion: A certain danger lurks there Acknowledgements and References Why ELIZA? Why ELIZA? The Intelligence Engineers The Intelligence Engineers Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs From IPL to SLIP and Lisp From IPL to SLIP and Lisp A Critical Tangent into Gomoku A Critical Tangent into Gomoku Interpretation is the Core of Intelligence Interpretation is the Core of Intelligence The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion Finally ELIZA: A Platform, Not a Chat Bot! Finally ELIZA: A Platform, Not a Chat Bot! A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community Another Wave: A BASIC ELIZA turns the PC Generation on to AI Another Wave: A BASIC ELIZA turns the PC Generation on to AI Conclusion: A certain danger lurks there Conclusion: A certain danger lurks there Acknowledgements and References Acknowledgements and References 12 Conclusion: A certain danger lurks there Weizenbaum’s goal to use ELIZA as a platform for the study of the human process of interpretation was thwarted by exactly what he did not want to see; The “kludge” [in nearly the original sense of the term] supplanted the reality. Instead of using ELIZA as a tool to study interpretation and interaction with AI, it became a cause celebre in-and-of-itself, the DOCTOR script being the only one ever seen, because it was so good for such a simple program – exactly the opposite of Weizenbaum’s point! Moreover, exactly what Weizenbaum did not want to happen with regard to Lisp vs. SLIP came to pass; instead of people using Fortran to build complex AI programs. Nearly everyone in AI, or involved in symbolic and/or list processing turned to Lisp, and SLIP eventually faded away. In 1950, Alan Turing wrote: “I believe that in about fifty years’ time it will be possible to programme computers, with a storage capacity of about 109, to make them play the imitation game so well that an average interrogator will not have more than 70 per cent, chance of making the right identification after five minutes of questioning. The original question, ‘Can machines think!’ I believe to be too meaningless to deserve discussion. Nevertheless I believe that at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted. I believe further that no useful purpose is served by concealing these beliefs. The popular view that scientists proceed inexorably from well-established fact to well-established fact, never being influenced by any unproved conjecture, is quite mistaken. Provided it is made clear which are proved facts and which are conjectures, no harm can result. Conjectures are of great importance since they suggest useful lines of research.”[44, p. 442] A mere decade and a half later, Joseph Weizenbaum wrote: “With ELIZA as the basic vehicle, experiments may be set up in which the subjects find it credible to believe that the responses which appear on his[17] typewriter are generated by a human sitting at a similar instrument in another room.”[47, p. 42] Regardless of how close this description may seem to Turing’s test, Weizenbaum did not build ELIZA to pass that test. Rather he built it to run experiments, including ones akin to the Turing Test, that could be used to study human interpretive processes (especially in the case of artificial intelligence). He believed that this research was critically important, but not merely for the academic reasons that motivated Turing and most AI researchers: “[The] whole issue of the credibility (to humans) of machine output demands investigation [...] [Important] decisions increasingly tend to be made in response to computer output. The ultimately responsible human interpreter of ’What the machine says’ is, not unlike the correspondent with ELIZA, constantly faced with the need to make credibility judgments. ELIZA shows, if nothing else, how easy it is to create and maintain the illusion of understanding, hence perhaps of judgment deserving of credibility. A certain danger lurks there.”[47, pp. 42–43] The problem of how humans impute agency, correctness, and intelligence to machines is not only still present, but has become exponentially more important in recent years, with the widespread diffusion of internet bots and large language models. Our modern computational lives might have been better had Weizenbaum pursued his goal of using ELIZA to study people’s interpretive interaction with computers, and especially with AIs. Unfortunately, his fear that “[t]here is a danger [...] that the example will run away with what it is supposed to illustrate”[47, p. 43] was too prescient. Author: (1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). Author: Author: (1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). This paper is available on arxiv under CC BY 4.0 license. This paper is available on arxiv under CC BY 4.0 license. available on arxiv available on arxiv [17] Even for the time, it is striking that Weizenbaum constantly refers to ELIZA’s interlocutors as male, given that the only example he provides of a conversation with ELIZA is (putatively) with a woman!

How ELIZA’s Success Revealed the Pitfalls of Machine Credibility

The Noonification: ELIZA: The Accidental Chatbot That Shaped AI History (9/10/2024)

Table of Links Abstract and 1. Introduction Why ELIZA?
The Intelligence Engineers
Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs
From IPL to SLIP and Lisp
A Critical Tangent into Gomoku
Interpretation is the Core of Intelligence
The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion
Finally ELIZA: A Platform, Not a Chat Bot!
A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community
Another Wave: A BASIC ELIZA turns the PC Generation on to AI
Conclusion: A certain danger lurks there
Acknowledgements and References 11 Another Wave: A BASIC ELIZA turns the PC Generation on to AI Another defining event in ELIZA’s descendancy occurred almost exactly a decade later, in 1977 Creative Computing, one of the magazines that, along with BYTE, was the “Hacker News” and “GitHub” of the mid-70s personal computer explosion, published an ELIZA knock-off written in BASIC.[36] This was coincidentally well-timed as it coincided nearly exactly with the so-called “1977 trinity”: The year that the Commodore Pet, the Apple II, and the TRS-80 all appeared.[12] Within a few years, millions of computer hobbyists – neither academics nor otherwise professionally involved with computers – had personal computers of all sorts, mostly with BASIC as their primary user-level programming language. Not a small number of those hobbyists were interested enough by the possibility of AI – perhaps with HAL9000, the murderous computer from 2001[13], still in recent memory – to type in this BASIC ELIZA (which was only a couple pages of code), and experiment with it themselves. Because of its simplicity, and the personal computer explosion, this ELIZA begat hundreds of knock-offs through the decades, in every conceivable programming language, making it perhaps the most copied and knocked-off program in history.[16] Just as Cosell’s Lisp ELIZA was spread by the ARPANet, this BASIC ELIZA, was spread by the wide-spread availability of personal computers. As a result of these coincidences, and an inherent interest in AI (or at least in talking with computers) the version of ELIZA that was known in the academic community was Cosell’s Lisp version, and the version known to the public was the BASIC version. But until it was rediscovered in 2021, the original MADSLIP ELIZA, was forgotten, and had not been seen by anyone for at least 50 years.[16, 14] Author:
(1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). This paper is available on arxiv under CC BY 4.0 license. [16] Indeed, I curate a web site, ELIZAGen.org[14], dedicated to the history of ELIZA and ELIZA-like programs. In that capacity I am regularly sent new, or newly-discovered knockoffs of one or another of the ELIZA threads, usually my own BASIC ELIZA. Just last week, as I write this in April of 2024, I received an email pointing out a version of my ELIZA that ended up, through channels unknown, in Apple’s HyperCard programmable notecard system for the original Apple Macintosh computers.[15] Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Why ELIZA? The Intelligence Engineers Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs From IPL to SLIP and Lisp A Critical Tangent into Gomoku Interpretation is the Core of Intelligence The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion Finally ELIZA: A Platform, Not a Chat Bot! A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community Another Wave: A BASIC ELIZA turns the PC Generation on to AI Conclusion: A certain danger lurks there Acknowledgements and References Why ELIZA? Why ELIZA? The Intelligence Engineers The Intelligence Engineers Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs From IPL to SLIP and Lisp From IPL to SLIP and Lisp A Critical Tangent into Gomoku A Critical Tangent into Gomoku Interpretation is the Core of Intelligence Interpretation is the Core of Intelligence The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion Finally ELIZA: A Platform, Not a Chat Bot! Finally ELIZA: A Platform, Not a Chat Bot! A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community Another Wave: A BASIC ELIZA turns the PC Generation on to AI Another Wave: A BASIC ELIZA turns the PC Generation on to AI Conclusion: A certain danger lurks there Conclusion: A certain danger lurks there Acknowledgements and References Acknowledgements and References 11 Another Wave: A BASIC ELIZA turns the PC Generation on to AI Another defining event in ELIZA’s descendancy occurred almost exactly a decade later, in 1977 Creative Computing, one of the magazines that, along with BYTE, was the “Hacker News” and “GitHub” of the mid-70s personal computer explosion, published an ELIZA knock-off written in BASIC.[36] This was coincidentally well-timed as it coincided nearly exactly with the so-called “1977 trinity”: The year that the Commodore Pet, the Apple II, and the TRS-80 all appeared.[12] Within a few years, millions of computer hobbyists – neither academics nor otherwise professionally involved with computers – had personal computers of all sorts, mostly with BASIC as their primary user-level programming language. Not a small number of those hobbyists were interested enough by the possibility of AI – perhaps with HAL9000, the murderous computer from 2001[13], still in recent memory – to type in this BASIC ELIZA (which was only a couple pages of code), and experiment with it themselves. Because of its simplicity, and the personal computer explosion, this ELIZA begat hundreds of knock-offs through the decades, in every conceivable programming language, making it perhaps the most copied and knocked-off program in history.[16] Just as Cosell’s Lisp ELIZA was spread by the ARPANet, this BASIC ELIZA, was spread by the wide-spread availability of personal computers. As a result of these coincidences, and an inherent interest in AI (or at least in talking with computers) the version of ELIZA that was known in the academic community was Cosell’s Lisp version, and the version known to the public was the BASIC version. But until it was rediscovered in 2021, the original MADSLIP ELIZA, was forgotten, and had not been seen by anyone for at least 50 years.[16, 14] Author: (1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). Author: Author: (1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). This paper is available on arxiv under CC BY 4.0 license. This paper is available on arxiv under CC BY 4.0 license. available on arxiv available on arxiv [16] Indeed, I curate a web site, ELIZAGen.org[14], dedicated to the history of ELIZA and ELIZA-like programs. In that capacity I am regularly sent new, or newly-discovered knockoffs of one or another of the ELIZA threads, usually my own BASIC ELIZA. Just last week, as I write this in April of 2024, I received an email pointing out a version of my ELIZA that ended up, through channels unknown, in Apple’s HyperCard programmable notecard system for the original Apple Macintosh computers.[15]

Another Wave: A BASIC ELIZA Turns the PC Generation On to AI

Table of Links Abstract and 1. Introduction Why ELIZA?
The Intelligence Engineers
Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs
From IPL to SLIP and Lisp
A Critical Tangent into Gomoku
Interpretation is the Core of Intelligence
The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion
Finally ELIZA: A Platform, Not a Chat Bot!
A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community
Another Wave: A BASIC ELIZA turns the PC Generation on to AI
Conclusion: A certain danger lurks there
Acknowledgements and References 10 A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community Our thesis is that Joseph Weizenbaum, afraid of people misinterpreting computers and AI, developed ELIZA not as an AI, but as a platform to support research about people’s interpretive process, perhaps more specifically their interaction with AIs. Weizenbaum, of course, had to provide an example – the DOCTOR script – to demonstrate the platform, but, as above, he had no illusion that it was actually intelligent, nor even very easily interpreted as intelligent; Much more “intelligent” programs already existing in various game playing programs (even his own, from the Datamation paper!), in Colby’s Parry, and in other programs that solved math word problems[17], did database lookups from natural language queries[29], and others. Although some of the sort of research envisioned by Weizenbaum was carried out by the MGH team, a specific event thwarted this use case for ELIZA, and, in almost the deepest possible irony, led to exactly the conflation of ELIZA with AI that Weizenbaum set out to study, not to bring into being. Weizenbaum built his ELIZA in MAD-SLIP on the IBM 7090, which was the primary machine at MIT’s Project MAC, on the 5th-through-9th floors of Tech Square. Almost immediately after ELIZA’s publication in 1966, Bernie Cosell created a Lisp knock-off of ELIZA, based on the algorithm and DOCTOR script in Weizenbaum’s paper. Cosell worked at Bolt Beranek and Newman (BBN), a large RAND-like consulting company situated nearby MIT, that commonly hired MIT graduates. Coincidentally, at almost the same time, BBN was a founding site of the ARPAnet[14], and Cosell’s Lisp ELIZA diffused rapidly through that network and across the soon-to-be Lisp-centered world of academic AI. As a result, Cosell’s Lisp ELIZA rapidly became the dominant strain, and Weizenbaum’s MAD-SLIP version, inaccessible on the ARPAnet, was almost instantly forgotten. Through interesting and complex criss-crossing histories, described in detail in [45], Danny Bobrow, a recent MIT AI graduate who headed up BBN’s AI program, brought McCarthy, and thus Lisp, to BBN, where Cosell was exposed to it, and built an ELIZA knock-off in Lisp as a side-project.[15] Cosell reports: “When I was working on the PDP-1 time-sharing system [...] I thought I would learn Lisp. That spring, Joe Weizenbaum had written an article for Communications of the ACM on ELIZA. I thought that was way cool. [...] He described how ELIZA works and I said, “I bet I could write something to do that.” And so I started writing a Lisp program on [the] PDP-1 system at BBN.”[41, p. 540] He continues, “I wrote that program and got it up and working. Playing with it was an all-BBN project. [...] It was written, at first, in the PDP-1 Lisp. But they were building a Lisp on the PDP-6 at that point—or maybe the PDP-10. But it was the Lisp that had spread across the ARPANet. So [ELIZA] went along with it [...].”[41, p. 541] Once Cosell’s Lisp ELIZA hit the academic world via the rapid spread of the ARPANet, Weizenbaum’s MAD-SLIP version was no longer relevant, and the name “ELIZA” (and the “DOCTOR” script), as well as the concept, was, from that point forward, associated with Cosell’s Lisp version, although its origin was still correctly attributed to Weizenbaum via the CACM paper, leading to a 50- year-long, community-wide misapprehension that ELIZA had been written in Lisp. In addition to being promulgated by Cosell’s knock-off being the one that was most easily available, via the ARPANet, it was a natural confusion because Lisp was rapidly becoming the go-to language of AI. Once Lisp came onto the scene no one thought much again about SLIP, or, for that matter, IPL-V. Author:
(1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). This paper is available on arxiv under CC BY 4.0 license. [14] At the time what became the internet, and then the web, was called the ARPAnet, and BBN built its core hardware and developed the programs that ran it.[11]) [15] Coincidentally, Bobrow was also the author of STUDENT, the program mentioned above that solved math word problems.[17] Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Why ELIZA? The Intelligence Engineers Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs From IPL to SLIP and Lisp A Critical Tangent into Gomoku Interpretation is the Core of Intelligence The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion Finally ELIZA: A Platform, Not a Chat Bot! A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community Another Wave: A BASIC ELIZA turns the PC Generation on to AI Conclusion: A certain danger lurks there Acknowledgements and References Why ELIZA? Why ELIZA? The Intelligence Engineers The Intelligence Engineers Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs From IPL to SLIP and Lisp From IPL to SLIP and Lisp A Critical Tangent into Gomoku A Critical Tangent into Gomoku Interpretation is the Core of Intelligence Interpretation is the Core of Intelligence The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion Finally ELIZA: A Platform, Not a Chat Bot! Finally ELIZA: A Platform, Not a Chat Bot! A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community Another Wave: A BASIC ELIZA turns the PC Generation on to AI Another Wave: A BASIC ELIZA turns the PC Generation on to AI Conclusion: A certain danger lurks there Conclusion: A certain danger lurks there Acknowledgements and References Acknowledgements and References 10 A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community Our thesis is that Joseph Weizenbaum, afraid of people misinterpreting computers and AI, developed ELIZA not as an AI, but as a platform to support research about people’s interpretive process, perhaps more specifically their interaction with AIs. Weizenbaum, of course, had to provide an example – the DOCTOR script – to demonstrate the platform, but, as above, he had no illusion that it was actually intelligent, nor even very easily interpreted as intelligent; Much more “intelligent” programs already existing in various game playing programs (even his own, from the Datamation paper!), in Colby’s Parry, and in other programs that solved math word problems[17], did database lookups from natural language queries[29], and others. Although some of the sort of research envisioned by Weizenbaum was carried out by the MGH team, a specific event thwarted this use case for ELIZA, and, in almost the deepest possible irony, led to exactly the conflation of ELIZA with AI that Weizenbaum set out to study, not to bring into being. Weizenbaum built his ELIZA in MAD-SLIP on the IBM 7090, which was the primary machine at MIT’s Project MAC, on the 5th-through-9th floors of Tech Square. Almost immediately after ELIZA’s publication in 1966, Bernie Cosell created a Lisp knock-off of ELIZA, based on the algorithm and DOCTOR script in Weizenbaum’s paper. Cosell worked at Bolt Beranek and Newman (BBN), a large RAND-like consulting company situated nearby MIT, that commonly hired MIT graduates. Coincidentally, at almost the same time, BBN was a founding site of the ARPAnet[14], and Cosell’s Lisp ELIZA diffused rapidly through that network and across the soon-to-be Lisp-centered world of academic AI. As a result, Cosell’s Lisp ELIZA rapidly became the dominant strain, and Weizenbaum’s MAD-SLIP version, inaccessible on the ARPAnet, was almost instantly forgotten. Through interesting and complex criss-crossing histories, described in detail in [45], Danny Bobrow, a recent MIT AI graduate who headed up BBN’s AI program, brought McCarthy, and thus Lisp, to BBN, where Cosell was exposed to it, and built an ELIZA knock-off in Lisp as a side-project.[15] Cosell reports: “When I was working on the PDP-1 time-sharing system [...] I thought I would learn Lisp. That spring, Joe Weizenbaum had written an article for Communications of the ACM on ELIZA. I thought that was way cool. [...] He described how ELIZA works and I said, “I bet I could write something to do that.” And so I started writing a Lisp program on [the] PDP-1 system at BBN.”[41, p. 540] He continues, “I wrote that program and got it up and working. Playing with it was an all-BBN project. [...] It was written, at first, in the PDP-1 Lisp. But they were building a Lisp on the PDP-6 at that point—or maybe the PDP-10. But it was the Lisp that had spread across the ARPANet. So [ELIZA] went along with it [...].”[41, p. 541] Once Cosell’s Lisp ELIZA hit the academic world via the rapid spread of the ARPANet, Weizenbaum’s MAD-SLIP version was no longer relevant, and the name “ELIZA” (and the “DOCTOR” script), as well as the concept, was, from that point forward, associated with Cosell’s Lisp version, although its origin was still correctly attributed to Weizenbaum via the CACM paper, leading to a 50- year-long, community-wide misapprehension that ELIZA had been written in Lisp. In addition to being promulgated by Cosell’s knock-off being the one that was most easily available, via the ARPANet, it was a natural confusion because Lisp was rapidly becoming the go-to language of AI. Once Lisp came onto the scene no one thought much again about SLIP, or, for that matter, IPL-V. Author: (1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). Author: Author: (1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). This paper is available on arxiv under CC BY 4.0 license. This paper is available on arxiv under CC BY 4.0 license. available on arxiv available on arxiv [14] At the time what became the internet, and then the web, was called the ARPAnet, and BBN built its core hardware and developed the programs that ran it.[11]) [15] Coincidentally, Bobrow was also the author of STUDENT, the program mentioned above that solved math word problems.[17]

The Accidental AI: How ELIZA's Lisp Adaptation Derailed Its Original Research Intent

Finally, ELIZA: A Platform, Not a ChatBot!

Table of Links Abstract and 1. Introduction Why ELIZA?
The Intelligence Engineers
Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs
From IPL to SLIP and Lisp
A Critical Tangent into Gomoku
Interpretation is the Core of Intelligence
The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion
Finally ELIZA: A Platform, Not a Chat Bot!
A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community
Another Wave: A BASIC ELIZA turns the PC Generation on to AI
Conclusion: A certain danger lurks there
Acknowledgements and References 2 Why ELIZA? Why Joseph Weizenbaum built ELIZA does not appear to be much of a mystery; Isn’t the answer just as simple as Pamela McCorduck put it in Machines Who Think[33]: “[...] Weizenbaum got interested in language. Ed Feigenbaum introduced him to [...] Kenneth Colby, a psychiatrist who had [...] turned to computers as a possible way of gaining new insights into neurotic behavior. [...] In 1963, Weizenbaum went to MIT, [where] he designed a program that would answer [simple questions]. It was a short, tricky program, based on sleight of hand, and it led Weizenbaum to ask himself some very serious questions about mystification and the computer [...]. [If] you could do a simple question-answering machine, why not a complicated one? How different would complexity make such a machine? Could you seem to have complex responses based on simple rules? [...] Weizenbaum drove into work many a morning with his neighbor Victor Yngve, who had developed the COMIT language, for pattern matching. If you were going to play around with matching patterns, why not the patterns in English words and sentences? ELIZA was the result. ELIZA was intended to simulate—or caricature, as Weizenbaum himself suggests—the conversation between a Rogerian psychoanalyst and a patient, with the machine in the role of analyst.” The reason that Weizenbaum gives McCorduck for choosing the Rogerian setting was the simplicity of creating an “illusion of mutual understanding”: “What I mean here is the cocktail party conversation. Someone says something to you that you really don’t fully understand, but because of the context and lots of other things, you are in fact able to give a response which appears appropriate, and in fact the conversation continues for quite a long time. We do it all the time, not only at cocktail parties. Indeed, I think it’s a very necessary mechanism, because we can’t, even in serious discussion, probe to the limit of possible understanding. [...] That’s necessary. It’s not cheating.”[33, pp. 251–253] When Weizenbaum was looking for a context where he could carry on that sort of illusion, he needed one where ignorance would not destroy the illusion of understanding: “For example [he goes on], in the psychiatric interview the psychiatrist says, tell me about the fishing fleet in San Francisco. One doesn’t say, “Look, he’s a smart man—how come he doesn’t know about the fishing fleet in San Francisco?” What he really wants to hear is what the patient has to say about it. [...]”[33, pp. 251–253] [italics as in the original] However, as mentioned above, the title of Weizenbaum’s 1966 paper belies – or at least complexifies – the post hoc account he gave McCorduck. The title of the paper is not “ELIZA – A Computer Program that Simulates or Caricatures the conversation between a Rogerian psychoanalyst and a patient.” but rather: “ELIZA – A Computer Program For the Study of Natural Language Communication Between Man And Machine”.[47] This, and other features of that paper that I go into below, as well as a recently discovered manuscript signed in Weizenbaum’s own hand[2], suggest that ELIZA was not, as quoted by McCorduck “intended to simulate—or caricature [...] the conversation between a Rogerian psychoanalyst and a patient”, but rather that it was intended, just as the title of the paper says, as a platform for research in [human]-machine communication. To understand the details of this misinterpretation, we need to understand the context in which ELIZA appeared, and how ELIZA came it into the public eye. Author:
(1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). This paper is available on arxiv under CC BY 4.0 license. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Why ELIZA? The Intelligence Engineers Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs From IPL to SLIP and Lisp A Critical Tangent into Gomoku Interpretation is the Core of Intelligence The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion Finally ELIZA: A Platform, Not a Chat Bot! A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community Another Wave: A BASIC ELIZA turns the PC Generation on to AI Conclusion: A certain danger lurks there Acknowledgements and References Why ELIZA? Why ELIZA? The Intelligence Engineers The Intelligence Engineers Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs From IPL to SLIP and Lisp From IPL to SLIP and Lisp A Critical Tangent into Gomoku A Critical Tangent into Gomoku Interpretation is the Core of Intelligence Interpretation is the Core of Intelligence The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion Finally ELIZA: A Platform, Not a Chat Bot! Finally ELIZA: A Platform, Not a Chat Bot! A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community Another Wave: A BASIC ELIZA turns the PC Generation on to AI Another Wave: A BASIC ELIZA turns the PC Generation on to AI Conclusion: A certain danger lurks there Conclusion: A certain danger lurks there Acknowledgements and References Acknowledgements and References 2 Why ELIZA? Why Joseph Weizenbaum built ELIZA does not appear to be much of a mystery; Isn’t the answer just as simple as Pamela McCorduck put it in Machines Who Think[33]: “[...] Weizenbaum got interested in language. Ed Feigenbaum introduced him to [...] Kenneth Colby, a psychiatrist who had [...] turned to computers as a possible way of gaining new insights into neurotic behavior. [...] In 1963, Weizenbaum went to MIT, [where] he designed a program that would answer [simple questions]. It was a short, tricky program, based on sleight of hand, and it led Weizenbaum to ask himself some very serious questions about mystification and the computer [...]. [If] you could do a simple question-answering machine, why not a complicated one? How different would complexity make such a machine? Could you seem to have complex responses based on simple rules? [...] Weizenbaum drove into work many a morning with his neighbor Victor Yngve, who had developed the COMIT language, for pattern matching. If you were going to play around with matching patterns, why not the patterns in English words and sentences? ELIZA was the result. ELIZA was intended to simulate—or caricature, as Weizenbaum himself suggests—the conversation between a Rogerian psychoanalyst and a patient, with the machine in the role of analyst.” The reason that Weizenbaum gives McCorduck for choosing the Rogerian setting was the simplicity of creating an “illusion of mutual understanding”: “What I mean here is the cocktail party conversation. Someone says something to you that you really don’t fully understand, but because of the context and lots of other things, you are in fact able to give a response which appears appropriate, and in fact the conversation continues for quite a long time. We do it all the time, not only at cocktail parties. Indeed, I think it’s a very necessary mechanism, because we can’t, even in serious discussion, probe to the limit of possible understanding. [...] That’s necessary. It’s not cheating.”[33, pp. 251–253] When Weizenbaum was looking for a context where he could carry on that sort of illusion, he needed one where ignorance would not destroy the illusion of understanding: “For example [he goes on], in the psychiatric interview the psychiatrist says, tell me about the fishing fleet in San Francisco. One doesn’t say, “Look, he’s a smart man—how come he doesn’t know about the fishing fleet in San Francisco?” What he really wants to hear is what the patient has to say about it. [...]”[33, pp. 251–253] [italics as in the original] However, as mentioned above, the title of Weizenbaum’s 1966 paper belies – or at least complexifies – the post hoc account he gave McCorduck. The title of the paper is not “ELIZA – A Computer Program that Simulates or Caricatures the conversation between a Rogerian psychoanalyst and a patient.” but rather: “ELIZA – A Computer Program For the Study of Natural Language Communication Between Man And Machine”.[47] This, and other features of that paper that I go into below, as well as a recently discovered manuscript signed in Weizenbaum’s own hand[2], suggest that ELIZA was not, as quoted by McCorduck “intended to simulate—or caricature [...] the conversation between a Rogerian psychoanalyst and a patient”, but rather that it was intended, just as the title of the paper says, as a platform for research in [human]-machine communication. To understand the details of this misinterpretation, we need to understand the context in which ELIZA appeared, and how ELIZA came it into the public eye. Author: (1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). Author: Author: (1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). This paper is available on arxiv under CC BY 4.0 license. This paper is available on arxiv under CC BY 4.0 license. available on arxiv available on arxiv

ELIZA Reinterpreted: The World’s First Chatbot Was Not Intended as a Chatbot at All

Author:
(1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). Table of Links Abstract and 1. Introduction Why ELIZA?
The Intelligence Engineers
Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs
From IPL to SLIP and Lisp
A Critical Tangent into Gomoku
Interpretation is the Core of Intelligence
The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion
Finally ELIZA: A Platform, Not a Chat Bot!
A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community
Another Wave: A BASIC ELIZA turns the PC Generation on to AI
Conclusion: A certain danger lurks there
Acknowledgements and References Abstract ELIZA, often considered the world’s first chatbot, was written by Joseph Weizenbaum in the early 1960s. Weizenbaum did not intend to invent the chatbot, but rather to build a platform for research into humanmachine conversation and the important cognitive processes of interpretation and misinterpretation. His purpose was obscured by ELIZA’s fame resulting, in large part, from the fortuitous timing of it’s creation and it’s escape into the wild. In this paper I provide a rich historical context for ELIZA’s creation, demonstrating that ELIZA arose from the intersection of some of the central threads in the technical history of AI. I also briefly discuss how ELIZA escaped into the world, and how its accidental escape, along with several coincidental turns of the programming language screws, led both to the misapprehension that ELIZA was intended as a chatbot, and to the loss of the original ELIZA to history for over 50 years. “We can only see a short distance ahead, but we can see plenty there that needs to be done.” (The last line of Turing’s 1950 MIND paper [44]) 1 Introduction ELIZA, often considered the world’s first chatbot, was written by Joseph Weizenbaum at MIT in the early 1960s.[47] In building ELIZA, Weizenbaum did not intend to invent the chatbot.[1] Instead, he intended to build a platform for research into human-machine conversation. This may seem obvious – after all, the title of Weizenbaum’s 1966 CACM paper is “ELIZA– A Computer Program For the Study of Natural Language Communication Between Man And Machine.”, not, for example, “ELIZA - A Computer Program that Engages in Conversation with a Human User”. But Weizenbaum’s purpose for ELIZA was obscured by the circumstances of its creation, and by its own fame resulting, in large part, from the fortuitous timing of it’s creation and it’s escape into the wild. In this paper I try to provide a rich historical context for ELIZA’s creation. ELIZA arose from the intersection of some of the central threads in the technical history of AI. In addition to explaining how these intersect in Weizenbaum’s creation of ELIZA, I also briefly discuss how ELIZA escaped into the world through no action or intention of Weizenbaum’s, and how its accidental escape, along with a coincidental turn of the programming language screw, led to the misapprehension that ELIZA was intended as a chatbot. This paper is available on arxiv under CC BY 4.0 license. [1] Of course, it would not have been called a “chatbot” at the that time anyway, as that term was not invented until the mid 1990’s[1], but we’ll use that term here as it is the currently appropriate term. Author: (1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). Author: Author: (1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Why ELIZA? The Intelligence Engineers Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs From IPL to SLIP and Lisp A Critical Tangent into Gomoku Interpretation is the Core of Intelligence The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion Finally ELIZA: A Platform, Not a Chat Bot! A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community Another Wave: A BASIC ELIZA turns the PC Generation on to AI Conclusion: A certain danger lurks there Acknowledgements and References Why ELIZA? Why ELIZA? The Intelligence Engineers The Intelligence Engineers Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs From IPL to SLIP and Lisp From IPL to SLIP and Lisp A Critical Tangent into Gomoku A Critical Tangent into Gomoku Interpretation is the Core of Intelligence Interpretation is the Core of Intelligence The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion Finally ELIZA: A Platform, Not a Chat Bot! Finally ELIZA: A Platform, Not a Chat Bot! A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community Another Wave: A BASIC ELIZA turns the PC Generation on to AI Another Wave: A BASIC ELIZA turns the PC Generation on to AI Conclusion: A certain danger lurks there Conclusion: A certain danger lurks there Acknowledgements and References Acknowledgements and References Abstract ELIZA, often considered the world’s first chatbot, was written by Joseph Weizenbaum in the early 1960s. Weizenbaum did not intend to invent the chatbot, but rather to build a platform for research into humanmachine conversation and the important cognitive processes of interpretation and misinterpretation. His purpose was obscured by ELIZA’s fame resulting, in large part, from the fortuitous timing of it’s creation and it’s escape into the wild. In this paper I provide a rich historical context for ELIZA’s creation, demonstrating that ELIZA arose from the intersection of some of the central threads in the technical history of AI. I also briefly discuss how ELIZA escaped into the world, and how its accidental escape, along with several coincidental turns of the programming language screws, led both to the misapprehension that ELIZA was intended as a chatbot, and to the loss of the original ELIZA to history for over 50 years. “We can only see a short distance ahead, but we can see plenty there that needs to be done.” (The last line of Turing’s 1950 MIND paper [44]) 1 Introduction ELIZA, often considered the world’s first chatbot, was written by Joseph Weizenbaum at MIT in the early 1960s.[47] In building ELIZA, Weizenbaum did not intend to invent the chatbot.[1] Instead, he intended to build a platform for research into human-machine conversation. This may seem obvious – after all, the title of Weizenbaum’s 1966 CACM paper is “ELIZA– A Computer Program For the Study of Natural Language Communication Between Man And Machine.”, not, for example, “ELIZA - A Computer Program that Engages in Conversation with a Human User”. But Weizenbaum’s purpose for ELIZA was obscured by the circumstances of its creation, and by its own fame resulting, in large part, from the fortuitous timing of it’s creation and it’s escape into the wild. In this paper I try to provide a rich historical context for ELIZA’s creation. ELIZA arose from the intersection of some of the central threads in the technical history of AI. In addition to explaining how these intersect in Weizenbaum’s creation of ELIZA, I also briefly discuss how ELIZA escaped into the world through no action or intention of Weizenbaum’s, and how its accidental escape, along with a coincidental turn of the programming language screw, led to the misapprehension that ELIZA was intended as a chatbot. This paper is available on arxiv under CC BY 4.0 license. This paper is available on arxiv under CC BY 4.0 license. available on arxiv available on arxiv [1] Of course, it would not have been called a “chatbot” at the that time anyway, as that term was not invented until the mid 1990’s[1], but we’ll use that term here as it is the currently appropriate term.

ELIZA: The Accidental Chatbot That Shaped AI History

ELIZA: Slučajni čet-bot koji je oblikovao istoriju veštačke inteligencije

ELIZA: El chatbot accidental que dio forma a la historia de la IA

ELIZA: il chatbot accidentale che ha plasmato la storia dell'intelligenza artificiale

ELIZA: AIの歴史を形作った偶然のチャットボット

ELIZA: atsitiktinis pokalbių robotas, sukūręs AI istoriją

ELIZA: Chay Chatbot Accidental nisqa AI Historiata Ruwasqan

ELIZA: Случайный чат-бот, который сформировал историю ИИ

ELIZA: Sheekada shilka ah ee qaabaysay Taariikhda AI

ELIZA: Chatbot aksidental që i dha formë historisë së AI

ELIZA: Ang Aksidenteng Chatbot na Hugis sa Kasaysayan ng AI

ایلیزا: حادثاتی چیٹ بوٹ جس نے اے آئی کی تاریخ کو شکل دی۔

ELIZA：改变人工智能历史的意外聊天机器人

The digital age has transformed how we choose where to eat, stay, and play, with online reviews playing a critical role in our decisions. Yet, this reliance on online recommendations has led to a growing problem: fake reviews. In 2023, research found that up to 30% of online reviews were inauthentic, leading to widespread distrust among consumers and eroding confidence in businesses that once seemed reliable. The Problem of Fake Reviews The prevalence of fake reviews is a symptom of deeper issues. Intense competition among businesses, combined with the ease of manipulating online reviews, has created an environment where unethical practices thrive. Companies can easily purchase positive reviews—or negative ones for their competitors—distorting the true picture for consumers. This not only harms businesses that rely on genuine customer feedback but also misleads consumers, leading to disappointing experiences based on fabricated information. Recent surveys reveal that online reviews influence the purchasing decisions of 93% of users. However, trust in these reviews is steadily declining, with 67% of consumers now acknowledging fake reviews as a significant problem. The impact is substantial: consumers waste time and money on inferior products and services, legitimate businesses are overshadowed by those willing to engage in dishonest tactics, and overall confidence in online reviews continues to erode. Current Solutions Overview Several approaches have been tried to combat fake reviews, each with its own limitations: Human Moderation: Traditional moderation systems rely on human reviewers to detect and remove fraudulent content. While effective to some extent, this method is time-consuming, expensive, and often fails to keep pace with the sheer volume of reviews being posted. Automated Systems: Recently, review platforms have started deploying machine learning-based systems to identify suspicious patterns in reviews. These systems can catch many fake reviews but are not foolproof, often struggling with nuanced or sophisticated fake reviews. Additionally, automated systems risk flagging legitimate feedback as fraudulent, which can frustrate honest users and damage the platform’s credibility. Verified Purchase Badges: Some platforms have introduced verified purchase badges or similar markers to indicate that a reviewer actually used the product or service. While helpful, this approach is not universally adopted and doesn’t fully address the problem—especially in industries like hospitality, where experiences are subjective and not always tied to a simple transaction. Moreover, the verification process can sometimes be circumvented, allowing fake reviews to slip through. Reputation-Based Systems: By assigning reputation scores to users based on the quality and consistency of their reviews, this approach aims to encourage honest feedback. The idea is that users with a history of trustworthy reviews will be more reliable, helping to filter out fake content. However, building a strong reputation takes time, during which fake reviews can still be posted. Additionally, these systems are not immune to manipulation; coordinated groups can work together to inflate or deflate reputation scores, undermining their overall effectiveness Blockchain and AI: Innovative Approaches to Combat Fake Reviews Blockchain and AI provide powerful tools in the battle against fake reviews, offering complementary methods to enhance trust and authenticity. Blockchain’s decentralized and immutable ledger ensures that once reviews and recommendations are recorded, they cannot be altered or deleted without network consensus, safeguarding the integrity of the content and preventing tampering. This technology also enables identity verification, tying reviews to verified users and making it more difficult for fake accounts to flood platforms with misleading feedback. Additionally, blockchain tech can support token-based incentive systems, rewarding users with cryptocurrency or tokens for posting honest reviews, which further discourages dishonest submissions. AI complements blockchain by adding an extra layer of protection through content analysis and filtering before it reaches the public. AI algorithms can detect patterns, language, and behavior that indicate fake reviews, flagging suspicious content in real-time. Together, these technologies help ensure that the information consumers rely on is both genuine and trustworthy. However, despite the significant advancements in the crypto, Web3, and DeFi sectors, the full potential of blockchain and AI in combating fake reviews has yet to be fully realized. Case Study: daGama’s Multi Level Anti Fake System(MLAFS) daGama is pioneering the use of blockchain and AI to tackle fake recommendations. Its Multi-Level Anti-Fake System (MLAFS) effectively integrates blockchain’s secure ledger with AI's detailed analysis, creating a robust barrier against fraudulent content. This dual-layered approach ensures that only genuine recommendations make it through. In addition, it introduced the “Review and Earn” concept to tackles a critical issue in current systems: the lack of incentives for users to provide honest feedback, ensuring that users are rewarded with tokens for valuable recommendations that are upvoted and tipped by other community members. The platform’s RWL-Map (Real World Locations Map) is a dynamic, user-driven ecosystem that helps users discover the best locations, restaurants, and venues based on real, verified recommendations. With a global launch slated for 2024, daGama is set to transform the review industry by restoring trust and simplifying the process of discovering new places to visit. Conclusion As the online review industry grapples with the persistent challenge of fake reviews, traditional methods—such as human moderation, automated systems, verified purchase badges, and reputation-based systems—have proven to be only partially effective. Each approach has its limitations, often addressing only part of the problem while leaving significant gaps that can be exploited by those intent on manipulating recommendations. However, innovative solutions leveraging blockchain and AI present a promising way forward. By combining the immutability and transparency of blockchain with the analytical power of AI, these technologies offer a more robust defense against fraudulent content. The potential impact of blockchain and AI on the review industry goes beyond just preventing fake recommendations; it also fosters a culture of trust and transparency that benefits consumers and businesses alike. By restoring confidence in the accuracy of online reviews, these technologies make it easier for consumers to make informed decisions and for businesses to build genuine reputations based on authentic feedback. Looking ahead, as blockchain and AI technologies continue to mature, we can expect even more sophisticated systems to emerge, potentially incorporating other technologies like natural language processing, sentiment analysis, and decentralized identity verification. These advancements will make it increasingly difficult for fraudulent actors to manipulate review systems and will help build a more reliable, user-centric digital landscape. The digital age has transformed how we choose where to eat, stay, and play, with online reviews playing a critical role in our decisions. Yet, this reliance on online recommendations has led to a growing problem: fake reviews. In 2023, research found that up to 30% of online reviews were inauthentic, leading to widespread distrust among consumers and eroding confidence in businesses that once seemed reliable. research found research found The Problem of Fake Reviews The prevalence of fake reviews is a symptom of deeper issues. Intense competition among businesses, combined with the ease of manipulating online reviews, has created an environment where unethical practices thrive. Companies can easily purchase positive reviews—or negative ones for their competitors—distorting the true picture for consumers. This not only harms businesses that rely on genuine customer feedback but also misleads consumers, leading to disappointing experiences based on fabricated information. Recent surveys reveal that online reviews influence the purchasing decisions of 93% of users. However, trust in these reviews is steadily declining, with 67% of consumers now acknowledging fake reviews as a significant problem. The impact is substantial: consumers waste time and money on inferior products and services, legitimate businesses are overshadowed by those willing to engage in dishonest tactics, and overall confidence in online reviews continues to erode. Recent surveys Recent surveys Current Solutions Overview Several approaches have been tried to combat fake reviews, each with its own limitations: Human Moderation: Traditional moderation systems rely on human reviewers to detect and remove fraudulent content. While effective to some extent, this method is time-consuming, expensive, and often fails to keep pace with the sheer volume of reviews being posted. Automated Systems: Recently, review platforms have started deploying machine learning-based systems to identify suspicious patterns in reviews. These systems can catch many fake reviews but are not foolproof, often struggling with nuanced or sophisticated fake reviews. Additionally, automated systems risk flagging legitimate feedback as fraudulent, which can frustrate honest users and damage the platform’s credibility. Verified Purchase Badges: Some platforms have introduced verified purchase badges or similar markers to indicate that a reviewer actually used the product or service. While helpful, this approach is not universally adopted and doesn’t fully address the problem—especially in industries like hospitality, where experiences are subjective and not always tied to a simple transaction. Moreover, the verification process can sometimes be circumvented, allowing fake reviews to slip through. Reputation-Based Systems: By assigning reputation scores to users based on the quality and consistency of their reviews, this approach aims to encourage honest feedback. The idea is that users with a history of trustworthy reviews will be more reliable, helping to filter out fake content. However, building a strong reputation takes time, during which fake reviews can still be posted. Additionally, these systems are not immune to manipulation; coordinated groups can work together to inflate or deflate reputation scores, undermining their overall effectiveness Blockchain and AI: Innovative Approaches to Combat Fake Reviews Blockchain and AI provide powerful tools in the battle against fake reviews, offering complementary methods to enhance trust and authenticity. Blockchain’s decentralized and immutable ledger ensures that once reviews and recommendations are recorded, they cannot be altered or deleted without network consensus, safeguarding the integrity of the content and preventing tampering. This technology also enables identity verification, tying reviews to verified users and making it more difficult for fake accounts to flood platforms with misleading feedback. Additionally, blockchain tech can support token-based incentive systems, rewarding users with cryptocurrency or tokens for posting honest reviews, which further discourages dishonest submissions. AI complements blockchain by adding an extra layer of protection through content analysis and filtering before it reaches the public. AI algorithms can detect patterns, language, and behavior that indicate fake reviews, flagging suspicious content in real-time. Together, these technologies help ensure that the information consumers rely on is both genuine and trustworthy. However, despite the significant advancements in the crypto, Web3, and DeFi sectors, the full potential of blockchain and AI in combating fake reviews has yet to be fully realized. Case Study: daGama’s Multi Level Anti Fake System(MLAFS) daGama is pioneering the use of blockchain and AI to tackle fake recommendations. Its Multi-Level Anti-Fake System (MLAFS) effectively integrates blockchain’s secure ledger with AI's detailed analysis, creating a robust barrier against fraudulent content. This dual-layered approach ensures that only genuine recommendations make it through. In addition, it introduced the “Review and Earn” concept to tackles a critical issue in current systems: the lack of incentives for users to provide honest feedback, ensuring that users are rewarded with tokens for valuable recommendations that are upvoted and tipped by other community members. The platform’s RWL-Map (Real World Locations Map) is a dynamic, user-driven ecosystem that helps users discover the best locations, restaurants, and venues based on real, verified recommendations. With a global launch slated for 2024, daGama is set to transform the review industry by restoring trust and simplifying the process of discovering new places to visit. Conclusion As the online review industry grapples with the persistent challenge of fake reviews, traditional methods—such as human moderation, automated systems, verified purchase badges, and reputation-based systems—have proven to be only partially effective. Each approach has its limitations, often addressing only part of the problem while leaving significant gaps that can be exploited by those intent on manipulating recommendations. However, innovative solutions leveraging blockchain and AI present a promising way forward. By combining the immutability and transparency of blockchain with the analytical power of AI, these technologies offer a more robust defense against fraudulent content. The potential impact of blockchain and AI on the review industry goes beyond just preventing fake recommendations; it also fosters a culture of trust and transparency that benefits consumers and businesses alike. By restoring confidence in the accuracy of online reviews, these technologies make it easier for consumers to make informed decisions and for businesses to build genuine reputations based on authentic feedback. Looking ahead, as blockchain and AI technologies continue to mature, we can expect even more sophisticated systems to emerge, potentially incorporating other technologies like natural language processing, sentiment analysis, and decentralized identity verification. These advancements will make it increasingly difficult for fraudulent actors to manipulate review systems and will help build a more reliable, user-centric digital landscape.

Leveraging Blockchain And AI To Eliminate Fake Reviews

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