Scheduled Commit

data/clustering_individual-f36c9f97-e795-4522-8eb7-7dee254b42c7.jsonl

@@ -34,3 +34,5 @@
 {"tstamp": 1723685573.7619, "task_type": "clustering", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1723685573.529, "finish": 1723685573.7619, "ip": "", "conv_id": "c1d7fffe9b4f4b97b6da4b6d4763e320", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": ["開心", "愉快", "高興", "飲料"], "ncluster": 1, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1723685600.2419, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1723685599.9872, "finish": 1723685600.2419, "ip": "", "conv_id": "cb294b2c2e2a47939380d2f9bfd75e78", "model_name": "text-embedding-004", "prompt": ["開心", "愉快", "高興", "飲料", "Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "ncluster": 4, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1723685600.2419, "task_type": "clustering", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1723685599.9872, "finish": 1723685600.2419, "ip": "", "conv_id": "c1d7fffe9b4f4b97b6da4b6d4763e320", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": ["開心", "愉快", "高興", "飲料", "Pikachu", "Darth Vader", "Yoda", "Squirtle", "Gandalf", "Legolas", "Mickey Mouse", "Donald Duck", "Charizard"], "ncluster": 4, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1723694291.0234, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1723694290.6859, "finish": 1723694291.0234, "ip": "", "conv_id": "dce48122f7484abc9eeedf7985e34307", "model_name": "embed-english-v3.0", "prompt": ["rose", "lily", "sunflower", "tulip", "athletics", "swimming", "rowing", "cycling"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1723694291.0234, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1723694290.6859, "finish": 1723694291.0234, "ip": "", "conv_id": "73a1dd22a41a4a5eadf6cd11eabab9b4", "model_name": "text-embedding-004", "prompt": ["rose", "lily", "sunflower", "tulip", "athletics", "swimming", "rowing", "cycling"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/retrieval_battle-f36c9f97-e795-4522-8eb7-7dee254b42c7.jsonl

@@ -75,3 +75,4 @@
 {"tstamp": 1723685201.3728, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "99c7605b2b3443e2af5d43d698bcb00e", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "automatic quest generation via LLM", "0_output": [["automatic quest generation via LLM", "Title: AutoManual: Generating Instruction Manuals by LLM Agents via Interactive Environmental Learning\n\nAbstract: Large Language Models (LLM) based agents have shown promise in autonomously completing tasks across various domains, e.g., robotics, games, and web navigation. However, these agents typically require elaborate design and expert prompts to solve tasks in specific domains, which limits their adaptability. We introduce AutoManual, a framework enabling LLM agents to autonomously build their understanding through interaction and adapt to new environments. AutoManual categorizes environmental knowledge into diverse rules and optimizes them in an online fashion by two agents: 1) The Planner codes actionable plans based on current rules for interacting with the environment. 2) The Builder updates the rules through a well-structured rule system that facilitates online rule management and essential detail retention. To mitigate hallucinations in managing rules, we introduce \\textit{case-conditioned prompting} strategy for the Builder. Finally, the Formulator agent compiles these rules into a comprehensive manual. The self-generated manual can not only improve the adaptability but also guide the planning of smaller LLMs while being human-readable. Given only one simple demonstration, AutoManual significantly improves task success rates, achieving 97.4\\% with GPT-4-turbo and 86.2\\% with GPT-3.5-turbo on ALFWorld benchmark tasks. The source code will be available soon."]], "0_corpus": "arxiv", "1_conv_id": "bef5e273ea5b4f33a0cc0d0a09b0933a", "1_model_name": "BM25", "1_prompt": "automatic quest generation via LLM", "1_output": [["automatic quest generation via LLM", "Title: EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms\n\nAbstract: The rise of powerful large language models (LLMs) has spurred a new trend in building LLM-based autonomous agents for solving complex tasks, especially multi-agent systems. Despite the remarkable progress, we notice that existing works are heavily dependent on human-designed frameworks, which greatly limits the functional scope and scalability of agent systems. How to automatically extend the specialized agent to multi-agent systems to improve task-solving capability still remains a significant challenge. In this paper, we introduce EvoAgent, a generic method to automatically extend expert agents to multi-agent systems via the evolutionary algorithm, thereby improving the effectiveness of LLM-based agents in solving tasks. Specifically, we consider the existing agent frameworks as the initial individual and then apply a series of evolutionary operators (e.g., mutation, crossover, selection, etc.) to generate multiple agents with diverse agent settings. EvoAgent can be generalized to any LLM-based agent framework, and can automatically extend the existing agent framework to multi-agent systems without any extra human designs. Experimental results across various tasks have shown that EvoAgent can automatically generate multiple expert agents and significantly enhance the task-solving capabilities of LLM-based agents."]], "1_corpus": "arxiv"}
 {"tstamp": 1723685249.8148, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "57ba620d45ff4bc39ff0987f4d32c3ea", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "automatic game quest generation with LLM", "0_output": [["automatic game quest generation with LLM", "Title: Automatic Bug Detection in LLM-Powered Text-Based Games Using LLMs\n\nAbstract: Advancements in large language models (LLMs) are revolutionizing interactive game design, enabling dynamic plotlines and interactions between players and non-player characters (NPCs). However, LLMs may exhibit flaws such as hallucinations, forgetfulness, or misinterpretations of prompts, causing logical inconsistencies and unexpected deviations from intended designs. Automated techniques for detecting such game bugs are still lacking. To address this, we propose a systematic LLM-based method for automatically identifying such bugs from player game logs, eliminating the need for collecting additional data such as post-play surveys. Applied to a text-based game DejaBoom!, our approach effectively identifies bugs inherent in LLM-powered interactive games, surpassing unstructured LLM-powered bug-catching methods and filling the gap in automated detection of logical and design flaws."]], "0_corpus": "arxiv", "1_conv_id": "82985ff41a6c430db9049ecac2edbe89", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": "automatic game quest generation with LLM", "1_output": [["automatic game quest generation with LLM", "Title: Game Generation via Large Language Models\n\nAbstract: Recently, the emergence of large language models (LLMs) has unlocked new opportunities for procedural content generation. However, recent attempts mainly focus on level generation for specific games with defined game rules such as Super Mario Bros. and Zelda. This paper investigates the game generation via LLMs. Based on video game description language, this paper proposes an LLM-based framework to generate game rules and levels simultaneously. Experiments demonstrate how the framework works with prompts considering different combinations of context. Our findings extend the current applications of LLMs and offer new insights for generating new games in the area of procedural content generation."]], "1_corpus": "arxiv"}
 {"tstamp": 1723687504.7765, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "c7a53620a2b448448040265261c4aa84", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "Abandoned cart retargerting", "0_output": [["Abandoned cart retargerting", "Cart Life\n\nCart Life is Hofmeier's first game, drawing upon his experience as an illustrator. He developed the game in his spare time using Adventure Game Studio while working what he describes as a \"bunch of bad jobs\". He initially planned to finish the development in 30 days but he worked on development for three years. He wanted to make a game which had no high scores, points, or action, and originally envisaged it as a comedy.\nOf the game's pixel art design, Hofmeier said that he did not choose it to be nostalgic but rather because he wanted players to fill in the extra details with their own thoughts and experiences, saying that it took more time and effort than other possible styles. During development Hofmeier spoke to a number of street vendors to research their work who were enthusiastic about the creation of the game.\nDuring development Richard Hofmeier experimented with many elements which were eventually removed, including a fourth character and a number of extra stores and locations. At the 2013 Independent Games Festival, Hofmeier spray-painted his own booth to instead display Porpentine's game Howling Dogs, saying that he thought \"Cart Life had already overstayed its welcome... I wanted people to see this game.\""]], "0_corpus": "wikipedia", "1_conv_id": "893229cb3ec146f08f260faf97d3f551", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "Abandoned cart retargerting", "1_output": [["Abandoned cart retargerting", "Shopping cart\n\nElectronic and magnetic Electronic systems are sometimes used by retailers. Each shopping cart is fitted with an electronic locking wheel clamp, or \"boot\".  A transmitter with a thin wire is placed around the perimeter of the parking lot, and the boot locks when the cart leaves the designated area. Store personnel must then deactivate the lock with a handheld remote control to return the cart to stock. Often, a line is painted in front of the broadcast range to warn customers that their cart will stop when rolled past the line. However, these systems are very expensive to install and although helpful, are not foolproof. The wheels can be lifted over the electronic barrier and/or pushed hard enough that the locks break. There are also safety concerns if the person pushing the trolley is running, and also if the trolley doesn't lock and is taken onto a road, locking due to magnetic materials under the road. Some cities have required retailers to install locking wheel systems on their shopping carts. In some cases, electronic systems companies have encouraged passage of such laws to create a captive audience of potential customers.\nPhysical\nA low-tech form of theft prevention utilizes a physical impediment, such as vertical posts at the store entrance to keep carts from being taken into the parking lot. This method also impedes physically disabled customers, which may be illegal in many jurisdictions. For example, in the United States it would be a violation of the Americans with Disabilities Act of 1990."]], "1_corpus": "wikipedia"}
+{"tstamp": 1723694153.0811, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "8aa957e161b94dc284b62b0a8e69387c", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "who played the colorado kid in rio bravo", "0_output": [["who played the colorado kid in rio bravo", "Ricky Nelson\n\nEric Hilliard Nelson (May 8, 1940 – December 31, 1985) was an American musician and actor. From age eight he starred alongside his family in the radio and television series The Adventures of Ozzie and Harriet. In 1957, he began a long and successful career as a popular recording artist.\nHis fame as both a recording artist and television star also led to a motion picture role co-starring alongside John Wayne, Dean Martin, Walter Brennan, and Angie Dickinson in Howard Hawks's western feature film Rio Bravo (1959). He placed 54 songs on the Billboard Hot 100 and its predecessors between 1957 and 1973, including \"Poor Little Fool\" in 1958, which was the first number one song on Billboard magazine's then-newly created Hot 100 chart. He recorded 19 additional top ten hits and was inducted into the Rock and Roll Hall of Fame on January 21, 1987. In 1996 Nelson was ranked No. 49 on TV Guide's 50 Greatest TV Stars of All Time."]], "0_corpus": "wikipedia", "1_conv_id": "dfeae7fbdd61453f9b4c8f9daa2e11ef", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "who played the colorado kid in rio bravo", "1_output": [["who played the colorado kid in rio bravo", "Rio Bravo (film)\n\nDude and Chance return to the hotel so Dude can take a bath, but Burdette's men capture Carlos' wife Consuelo and use her to lure Chance into a trap. Dude tells Chance to take the men to the jail, under pretext that Stumpy would let Joe out. However, Stumpy opens fire, as Dude secretly predicted. In the chaos, some men drag Dude off to Nathan, who demands a tradeDude for Joe. Chance agrees, but brings Colorado as backup. Dude and Joe brawl during the trade, and a firefight ensues. Stumpy throws some sticks of dynamite from the wagons into the warehouse where Burdette and his men are holed up; Chance and Dude detonate them with their guns, abruptly ending the fight.\nWith both Burdettes and their few surviving gunmen in jail, Chance is able to finally spend some time with Feathers and admit his feelings for her. Colorado volunteers to guard the jail, allowing Stumpy and Dude to enjoy a night out in the town.\nCast\nJohn Wayne as John T. Chance\nDean Martin as Dude\nRicky Nelson as Colorado Ryan\nAngie Dickinson as Feathers\nWalter Brennan as Stumpy\nWard Bond as Pat Wheeler\nJohn Russell as Nathan Burdette\nPedro Gonzalez Gonzalez as Carlos Robante\nEstelita Rodriguez as Consuela Robante\nClaude Akins as Joe Burdette\nBing Russell as the cowboy killed by Joe Burdette at the start of the movie. (Uncredited)\nNesdon Booth as Clark\nWalter Barnes as Charlie the Bartender (uncredited)\nJames B. Leong as Burt (uncredited)\nMalcolm Atterbury and Harry Carey Jr. also receive screen credits in the film's opening, but their scenes were deleted from the final film."]], "1_corpus": "wikipedia"}

data/retrieval_individual-f36c9f97-e795-4522-8eb7-7dee254b42c7.jsonl

@@ -365,3 +365,7 @@
 {"tstamp": 1723687547.1365, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1723687546.9485, "finish": 1723687547.1365, "ip": "", "conv_id": "645dfee15ed047849cca81f10a31f707", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "Automatic customer follow up", "output": [["Automatic customer follow up", "Title: Intent Matching based Customer Services Chatbot with Natural Language Understanding\n\nAbstract: Customer service is the lifeblood of any business. Excellent customer service not only generates return business but also creates new customers. Looking at the demanding market to provide a 24/7 service to customers, many organisations are increasingly engaged in popular social media and text messaging platforms such as WhatsApp and Facebook Messenger in providing a 24/7 service to customers in the current demanding market. In this paper, we present an intent matching based customer services chatbot (IMCSC), which is capable of replacing the customer service work of sales personnel, whilst interacting in a more natural and human-like manner through the employment of Natural Language Understanding (NLU). The bot is able to answer the most common frequently asked questions and we have also integrated features for the processing and exporting of customer orders to a Google Sheet."]], "corpus": "arxiv"}
 {"tstamp": 1723691457.0512, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1723691456.5476, "finish": 1723691457.0512, "ip": "", "conv_id": "751d5e8350fd4bf1b26190e1d0fbf381", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "who is the writer of 50 shades of grey", "output": [["who is the writer of 50 shades of grey", "Fifty Shades of Grey (film)\n\nFifty Shades of Grey is a 2015 American erotic romantic drama film directed by Sam Taylor-Johnson from a screenplay by Kelly Marcel. Produced by Focus Features, Michael De Luca Productions, and Trigger Street Productions, and distributed by Universal Pictures, it is based on E. L. James' 2011 novel of the same name, and serves as the first installment in the Fifty Shades film series. Starring Dakota Johnson, Jamie Dornan,  Jennifer Ehle and Marcia Gay Harden, the film's story follows Anastasia \"Ana\" Steele (Johnson), a college graduate, who begins a sadomasochistic relationship with young business magnate Christian Grey (Dornan).\nDevelopment of the film commenced in March 2012, when Universal and Focus Features secured the rights to the trilogy following a bidding war. After Marcel was hired to write the film's screenplay, multiple directors, including Joe Wright, Patty Jenkins, Bill Condon, Bennett Miller, and Steven Soderbergh, were attached to the film. Taylor-Johnson was confirmed as the director in June 2013. She cited 9½ Weeks (1986), Last Tango in Paris (1972), and Blue Is the Warmest Colour (2013) as her inspirations for the film. The main cast joined the project between October and December 2013. Principal photography commenced in December 2013 and concluded in February 2014. Reshoots involving scenes between Dornan and Johnson took place in Vancouver during the week of October 2014. Danny Elfman was hired to compose the film's score."]], "corpus": "wikipedia"}
 {"tstamp": 1723691457.0512, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1723691456.5476, "finish": 1723691457.0512, "ip": "", "conv_id": "15863f028cfd4e2c9d54540758fd4246", "model_name": "text-embedding-3-large", "prompt": "who is the writer of 50 shades of grey", "output": [["who is the writer of 50 shades of grey", "E. L. James\n\nErika Mitchell (born 7 March 1963), known by her pen name E. L. James, is a British author. She wrote the best-selling Fifty Shades series of erotic romance novels, which spawned a multimedia franchise including a film trilogy of the same name. Prior to this, she wrote the Twilight fan fiction \"Master of the Universe\" that served as the basis for the Fifty Shades series under the web name Snowqueens Icedragon. In 2019 she published her first book unconnected with the fictional world of Fifty Shades, The Mister, to negative critical reaction.\nThe Fifty Shades novels have sold over 150 million copies worldwide, over 35 million copies in the United States and set the record in the United Kingdom as the fastest selling paperback of all time. In 2012, Time magazine named her one of \"The World's 100 Most Influential People\"."]], "corpus": "wikipedia"}
+{"tstamp": 1723694044.5667, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1723694042.3065, "finish": 1723694044.5667, "ip": "", "conv_id": "8aa957e161b94dc284b62b0a8e69387c", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "who played the colorado kid in rio bravo", "output": [["who played the colorado kid in rio bravo", "Ricky Nelson\n\nEric Hilliard Nelson (May 8, 1940 – December 31, 1985) was an American musician and actor. From age eight he starred alongside his family in the radio and television series The Adventures of Ozzie and Harriet. In 1957, he began a long and successful career as a popular recording artist.\nHis fame as both a recording artist and television star also led to a motion picture role co-starring alongside John Wayne, Dean Martin, Walter Brennan, and Angie Dickinson in Howard Hawks's western feature film Rio Bravo (1959). He placed 54 songs on the Billboard Hot 100 and its predecessors between 1957 and 1973, including \"Poor Little Fool\" in 1958, which was the first number one song on Billboard magazine's then-newly created Hot 100 chart. He recorded 19 additional top ten hits and was inducted into the Rock and Roll Hall of Fame on January 21, 1987. In 1996 Nelson was ranked No. 49 on TV Guide's 50 Greatest TV Stars of All Time."]], "corpus": "wikipedia"}
+{"tstamp": 1723694044.5667, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1723694042.3065, "finish": 1723694044.5667, "ip": "", "conv_id": "dfeae7fbdd61453f9b4c8f9daa2e11ef", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "who played the colorado kid in rio bravo", "output": [["who played the colorado kid in rio bravo", "Rio Bravo (film)\n\nDude and Chance return to the hotel so Dude can take a bath, but Burdette's men capture Carlos' wife Consuelo and use her to lure Chance into a trap. Dude tells Chance to take the men to the jail, under pretext that Stumpy would let Joe out. However, Stumpy opens fire, as Dude secretly predicted. In the chaos, some men drag Dude off to Nathan, who demands a tradeDude for Joe. Chance agrees, but brings Colorado as backup. Dude and Joe brawl during the trade, and a firefight ensues. Stumpy throws some sticks of dynamite from the wagons into the warehouse where Burdette and his men are holed up; Chance and Dude detonate them with their guns, abruptly ending the fight.\nWith both Burdettes and their few surviving gunmen in jail, Chance is able to finally spend some time with Feathers and admit his feelings for her. Colorado volunteers to guard the jail, allowing Stumpy and Dude to enjoy a night out in the town.\nCast\nJohn Wayne as John T. Chance\nDean Martin as Dude\nRicky Nelson as Colorado Ryan\nAngie Dickinson as Feathers\nWalter Brennan as Stumpy\nWard Bond as Pat Wheeler\nJohn Russell as Nathan Burdette\nPedro Gonzalez Gonzalez as Carlos Robante\nEstelita Rodriguez as Consuela Robante\nClaude Akins as Joe Burdette\nBing Russell as the cowboy killed by Joe Burdette at the start of the movie. (Uncredited)\nNesdon Booth as Clark\nWalter Barnes as Charlie the Bartender (uncredited)\nJames B. Leong as Burt (uncredited)\nMalcolm Atterbury and Harry Carey Jr. also receive screen credits in the film's opening, but their scenes were deleted from the final film."]], "corpus": "wikipedia"}
+{"tstamp": 1723694200.8365, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1723694200.2798, "finish": 1723694200.8365, "ip": "", "conv_id": "271182448a064d668966336cd4d672ca", "model_name": "text-embedding-004", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "corpus": "wikipedia"}
+{"tstamp": 1723694200.8365, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1723694200.2798, "finish": 1723694200.8365, "ip": "", "conv_id": "d7088532d8114732ac92ccd4da8d0a56", "model_name": "text-embedding-3-large", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe test was introduced by Turing in his 1950 paper \"Computing Machinery and Intelligence\" while working at the University of Manchester. It opens with the words: \"I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \"Are there imaginable digital computers which would do well in the imitation game?\" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \"machines can think\".\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \"mind\", \"understanding\", or \"consciousness\", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "corpus": "wikipedia"}