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"https://cdn.jsdelivr.net/npm/glightbox/dist/css/glightbox.min.css", "/tags/#viajante", "https://dev.to/fernand0", "/assets/image/favicon.ico", "/assets/image/favicon-114x114.png", "https://instagram.com/ftricas", "/tags/#tsp" ], "httpBodyMetaTags": { "description": "De vez en cuando nos salimos un poco más del tema y traemos alguna curiosidad. En este caso tiene que ver con amebas y problemas difíciles, de la mano de An Amoeba-Based Computer Calculated Approximate Solutions to a Very Hard Math Problem.\n\nEl problema difícil es el viejo conocido del viajante de comercio (Travelling Salesman problem TSP) y podemos hablar de computación biológica (y no la más habitual, que es la bioinspirada).\nNo se si vale la pena recordar que el problema consiste en determinar un recorrido óptimo para visitar una serie de ciudades volviendo a la de origen y pasando una vez por cada una. A pesar de lo sencillo que es describirlo, es un problema que es costoso de resolver de forma exacta cuando el número de ciudades crece.\n\nPor lo visto las amebas son capaces de generar soluciones aproximadas al problema (recordamos aquí que cuando los problemas son difíciles -desde el punto de vista del coste, aclaro- nos conformamos con tener soluciones aproximadas suficientemente buenas).\n\n\n A team of Japanese researchers from Keio University in Tokyo have demonstrated that an amoeba is capable of generating approximate solutions to a remarkably difficult math problem known as the “traveling salesman problem.”\n\n\nPor lo visto, lo resuelven hasta 8 ciudades con una calidad de la solución bastante buena, en un tiempo que crece de manera lineal:\n\n\n Yet as these Japanese researchers demonstrated, a certain type of amoeba can be used to calculate nearly optimal solutions to the traveling salesman problem for up to eight cities. Even more remarkably, the amount of time it takes the amoeba to reach these nearly optimal solutions grows linearly, even though the number of possible solutions increases exponentially.\n\n\nPor lo visto, este tipo de amebas son capaces de expandir su cuerpo en varias direcciones en busca de alimento, lo que las hace particularmente interesantes:\n\n\n The reason this amoeba is considered especially useful in biological computing is because it can extend various regions of its body to find the most efficient way to a food source and hates light.\n\n\n¿Cuándo podremos usar esto? De momento son solo experimentos de laboratorio que podrían ser la base para construir computadores biológicos de bajo consumo energético.\nEsto es, de momento, no.\n\n\n For now, however, the Japanese researchers’ experiment remains in the lab, but it provides the foundation for low-energy biological computers that harness the natural mechanisms of amoebas and other microorganisms to compute.", "og:description": "De vez en cuando nos salimos un poco más del tema y traemos alguna curiosidad. En este caso tiene que ver con amebas y problemas difíciles, de la mano de An Amoeba-Based Computer Calculated Approximate Solutions to a Very Hard Math Problem.\n\nEl problema difícil es el viejo conocido del viajante de comercio (Travelling Salesman problem TSP) y podemos hablar de computación biológica (y no la más habitual, que es la bioinspirada).\nNo se si vale la pena recordar que el problema consiste en determinar un recorrido óptimo para visitar una serie de ciudades volviendo a la de origen y pasando una vez por cada una. A pesar de lo sencillo que es describirlo, es un problema que es costoso de resolver de forma exacta cuando el número de ciudades crece.\n\nPor lo visto las amebas son capaces de generar soluciones aproximadas al problema (recordamos aquí que cuando los problemas son difíciles -desde el punto de vista del coste, aclaro- nos conformamos con tener soluciones aproximadas suficientemente buenas).\n\n\n A team of Japanese researchers from Keio University in Tokyo have demonstrated that an amoeba is capable of generating approximate solutions to a remarkably difficult math problem known as the “traveling salesman problem.”\n\n\nPor lo visto, lo resuelven hasta 8 ciudades con una calidad de la solución bastante buena, en un tiempo que crece de manera lineal:\n\n\n Yet as these Japanese researchers demonstrated, a certain type of amoeba can be used to calculate nearly optimal solutions to the traveling salesman problem for up to eight cities. Even more remarkably, the amount of time it takes the amoeba to reach these nearly optimal solutions grows linearly, even though the number of possible solutions increases exponentially.\n\n\nPor lo visto, este tipo de amebas son capaces de expandir su cuerpo en varias direcciones en busca de alimento, lo que las hace particularmente interesantes:\n\n\n The reason this amoeba is considered especially useful in biological computing is because it can extend various regions of its body to find the most efficient way to a food source and hates light.\n\n\n¿Cuándo podremos usar esto? De momento son solo experimentos de laboratorio que podrían ser la base para construir computadores biológicos de bajo consumo energético.\nEsto es, de momento, no.\n\n\n For now, however, the Japanese researchers’ experiment remains in the lab, but it provides the foundation for low-energy biological computers that harness the natural mechanisms of amoebas and other microorganisms to compute." }, "httpBodyByteSize": 48213, "httpBodyPageTitle": "Computación biológica y el problema del viajante de comercio – fernand0 @ GitHub.io – Programación, seguridad, software libre ..." }, "/Restricciones-Claves-Seguridad/": { "date": "2026-02-07T16:36:23Z", "httpProtocol": "h2, tls/1.3", "httpStatus": 200, "ipAddress": "185.199.110.153", "ipASN": "Fastly, Inc.", "ipCountry": "United States", "httpHeaderHash": "NWFOF7X5CHB4RD2YU3557NUOAKSLBLTB", "httpHeaderTechnologies": [ "Fastly", "Varnish", "GitHub Pages" ], "httpHeaderUrls": [ "a6cc:1ACE62:D005C8:E87146:698767AE" ], "httpHeaderByteSize": 769, 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peores\u0026source=https://fernand0.github.io//Restricciones-Claves-Seguridad/" ], "httpBodyMetaTags": { "description": "Algunos administradores que pueden hacerlo (seguramente, los mismos que pueden obligarnos a cambiar de contraseña, como contábamos en Los cambios frecuentes de contraseña son contraproducentes creen que añadir una restricción a nuestra contraseña indicando que contenga una cifra y un caracter especial (o variaciones similares) mejora las contraseñas.\nLo cierto es que, si permitimos estos caracteres en las claves puede que mejoren (siempre que no se hagan substituciones obvias: l por 1, e por 3, …). Pero siobligamos a que estén, le estamos dando pistas a los ‘malos’: pueden hacer búsquedas en el espacio de claves, poniendo menos alternativas en algunas posiciones (el caso más delirante, que he visto en un par de sitios es ‘forzar’ a que esos caracteres estén en determinadas posiciones). \nSin olvidarnos de que cuando eso contraviene nuestras propias costumbres en la generación de contraseñas, tenemos la receta segura para que no la recordemos, o la hagamos peor para estar seguros de recordarla.\n\nDe eso hablaban en How Password Constraints Give You a False Sense of Security y yo lo traigo aquí para animar a tantos y tantos administradores a no ser creativos en estas cosas.\n\nHace un poco de numerología con lo que ahorraría un hipotético atacante sabiendo que tiene que haber, digamos, un dígito:\n\n\n Assume they can test about 31 billion passwords per second. Cracking their way through your reasonably complicated eight-character password could take, at most, 212,903 seconds. That’s 3,548 minutes, or roughly two and a half days.\n\n Now, let’s talk about constraints for a minute. Assume that the service you’re using requires you to have an eight-character password. Abrams notes that takes 70.6 trillion passwords out of the mix, since every password from a single character long to seven character long is now invalid. That saves the cracking tool a whopping 2,277 seconds, or nearly 38 minutes. That’s not too bad.\n\n\nVale la pena leer el resto de argumentos numéricos pero, al final, la conclusión es que lo mejor es que la contraseña sea más larga, incluso en sistemas con restricciones.\n\n\n Instead of worrying about the best way to make your shorter password harder to guess or brute-force, Abrams advises that it’s a lot better to pick a longer password, because even if a service has password constraints, they’ll have much less of an impact: […]", "og:description": "Algunos administradores que pueden hacerlo (seguramente, los mismos que pueden obligarnos a cambiar de contraseña, como contábamos en Los cambios frecuentes de contraseña son contraproducentes creen que añadir una restricción a nuestra contraseña indicando que contenga una cifra y un caracter especial (o variaciones similares) mejora las contraseñas.\nLo cierto es que, si permitimos estos caracteres en las claves puede que mejoren (siempre que no se hagan substituciones obvias: l por 1, e por 3, …). Pero siobligamos a que estén, le estamos dando pistas a los ‘malos’: pueden hacer búsquedas en el espacio de claves, poniendo menos alternativas en algunas posiciones (el caso más delirante, que he visto en un par de sitios es ‘forzar’ a que esos caracteres estén en determinadas posiciones). \nSin olvidarnos de que cuando eso contraviene nuestras propias costumbres en la generación de contraseñas, tenemos la receta segura para que no la recordemos, o la hagamos peor para estar seguros de recordarla.\n\nDe eso hablaban en How Password Constraints Give You a False Sense of Security y yo lo traigo aquí para animar a tantos y tantos administradores a no ser creativos en estas cosas.\n\nHace un poco de numerología con lo que ahorraría un hipotético atacante sabiendo que tiene que haber, digamos, un dígito:\n\n\n Assume they can test about 31 billion passwords per second. Cracking their way through your reasonably complicated eight-character password could take, at most, 212,903 seconds. That’s 3,548 minutes, or roughly two and a half days.\n\n Now, let’s talk about constraints for a minute. Assume that the service you’re using requires you to have an eight-character password. Abrams notes that takes 70.6 trillion passwords out of the mix, since every password from a single character long to seven character long is now invalid. That saves the cracking tool a whopping 2,277 seconds, or nearly 38 minutes. That’s not too bad.\n\n\nVale la pena leer el resto de argumentos numéricos pero, al final, la conclusión es que lo mejor es que la contraseña sea más larga, incluso en sistemas con restricciones.\n\n\n Instead of worrying about the best way to make your shorter password harder to guess or brute-force, Abrams advises that it’s a lot better to pick a longer password, because even if a service has password constraints, they’ll have much less of an impact: […]" }, "httpBodyByteSize": 47981, "httpBodyPageTitle": "Las restricciones sobre contraseñas generan contraseñas peores – fernand0 @ GitHub.io – Programación, seguridad, software libre ..." }, "/Transplantes-De-Software/": { "date": "2026-02-07T17:10:13Z", "httpProtocol": "h2, tls/1.3", "httpStatus": 200, "ipAddress": "185.199.109.153", "ipASN": "Fastly, Inc.", "ipCountry": "United States", "httpHeaderHash": "WYWCLOLSQQO74FORNNIE6TE7N7CWPXRU", "httpHeaderTechnologies": [ "Fastly", "Varnish", "GitHub Pages" ], "httpHeaderUrls": [], 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The Internet is awash with open source software that could provide parts for donor programs, though there’s no particular reason the donor code needs to be open source.\n\n\nSi leemos algunos detalles más en System fixes bugs by importing functionality from other programs—without access to source code podemos ver que se trata de algo que deberíamos denominar, más bien, ‘microtransplantes’, porque se trataría de pequeños bloques de código.\n\nEn el artículo (seguramente no accesible para todos), Automatic error elimination by horizontal code transfer across multiple applications se puede ver, por ejemplo un fallo en el programa gif2tiff, corregido mediante la ‘donación’ desde ImageMagick:\n\n#define MaximumLZWBits 12\nif (data_size \u003e MaximumLZWBits)\n ThrowBinaryException(CorruptImageError," }, "httpBodyByteSize": 48011, "httpBodyPageTitle": "Transplantes de código para arreglar fallos – fernand0 @ GitHub.io – Programación, seguridad, software libre ..." } }, "forfutureuse.org": { 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I see many in my environment answering with a definitive “No”. I argue that, yes it is. Keep reading.\n\n\n in the following I use “ChatGPT” and “LLM” interchangeably because ChatGPT has become a household name for Large Language Models (LLMs)\n\n\nWhy do we care whether ChatGPT is AGI or not?\nIt is not mere semantics or hair splitting.\nMy argument is that IF ChatGPT is NOT an AGI, then we have still some years before we have to deal with the consequences.\n\nIf ChatGPT is indeed an AGI, it implies that:\n\n\n as we co-exist with an AGI (it is pleonasmic), we should not wait for an hypothetical day when it would arise.\nThe “when is the singularity happening” would be a misplaced question because it would have happened already, without a bang.\nThe emergence of an AGI would have taken place in a series of incremental steps over a few years.\nIf so, we should acknowledge the fact that some early forms of AGI have emerged, and act accordinly: how do we humans, as individuals, organizations and societies, coexist with an artificial intelligence which is “human like”?\n\n\nSo the question is indeed not trivial.\n\nIs ChatGPT an AGI? The counter arguments\nThe consensus is that AGI will arise in years, decades, or never. “Today” is not an option:\n\n\n\nsource: emerji.com\n\nConsidering that AGI would already exist is considered naive, several arguments come to mind:\n\n\n large language models (LLMs) are “merely spitting text from powerful statistical associations”.\n LLMs are not autonomous, they merely answer to a prompt entered by a human\n they are not self-conscious\n proofs of their non AGI character are the mistakes they make: hallucinations, hands with 8 fingers, non factuality, etc.\n\n\nIs ChatGPT an AGI? The arguments in favor\nWhat is an AGI? The definition is muddy. Reading from many different sources, I’d list these criteria:\n\n\n self conscious\n autonomous\n able to accomplish general tasks\n able to perfectly mimic human intelligence\n able to perfectly mimic various aspects of human cognition (intelligence, emotion, creativity, subjective viewpoint, etc)\n causing a direct existential threat to humanity if misaligned\n\n\nI would argue that:\n\n\n Large Language Models meet critera 3, 4 and 5.\n\n\n\n Large Language Models don’t meet criteria 2, but the current state of technology allows for it\n\n\n\n Criteria 1 is not relevant\n\n\n\n Criteria 6 is a possible consequence not a part of the definition of an AGI\n\n\nLet’s review these claims in turn:\n\n1. Is self-consciousness an important criteria for AGI?\nShould we wait for a clear sign that LLMs are self-conscious before we declare an AI to be AGI?\nI would say, absolutely not.\nThere are many reasons, the one I prefer is that since Freud we have accepted that humans are themselves far from being fully self-conscious.\nCognitive psychology has added piles and piles of more evidence that we humans are conscious of ourselves in some capacity, not in an absolute sense.\nIn my view, it follows that requiring for an AI to be self-conscious to be declared “AGI” is non sensical - we don’t have a yardstick to measure self-consciousness against.\nIt merely needs to mimic self consciousness as a human would experience it. And as we see fom current LLMs, they are pretty impressive at mimicking a human’s thought process.\n\n2. Autonomy\nGranted, the current versions of LLMs are not autonomous.\nThey merely function when prompted by a human request, and they stop functioning once this request has been answered.\nBut… automating an infinite loop of prompts and answers is pretty trivial to do.\nActually, dozens of thousands of software developers are currently working at creating autonomous forms of LLMs at the moment, in public.\n\nSo “autonomy” is probably not a feature of an AGI which would happen at the switch of a button (Skynet style), but more like the produce an entire community pushing with their classic tools to achieve it.\n\nOh and they are well aware that there are risks attached to it. Not existential risks (see bullet point #6, below) but risks nonetheless:\n\n\n\nsource\n\n3. Ability to accomplish general tasks\nI’ll pass quickly on this one. LLMs are good at accomplishing general tasks, there is a consensus on that, and every day brings more developments on the topic.\n\nThe latest objection I heard is “general tasks, but just on text”.\nThis not a solid objection as LLMs can do image, and multimode too since Sept 26, 2023:\n\nChatGPT can now see, hear, and speak. Rolling out over next two weeks, Plus users will be able to have voice conversations with ChatGPT (iOS \u0026 Android) and to include images in conversations (all platforms). https://t.co/uNZjgbR5Bm pic.twitter.com/paG0hMshXb— OpenAI (@OpenAI) September 25, 2023\n\n(follow this link to the video if it does not display)\n\nIt is still software and screens, not actual operations and actions in the “real world”.\nTrue, the development of non digital types of operations is always slower (robots…), but I would count it as a limitation in “reach”, not a sign of a lack of intelligence.\n\n4. Ability to perfectly mimic human intelligence\nThis one can also be adressed quickly.\nDo you remember the Turing test?\nThis would consist for a human interrogator to discuss with a human and a computer, but the two would be hidden from the interrogator by a curtain.\nAfter a long conversation, if the human interrogator cannot guess which of the two dialogues he had was with the computer, then the computer passed the Turing test.\n\nThis test was, as far as I could remember, presented as the true and super hard criteria for an AI to (ever) meet.\n\nGuess what: ChatGPT broke the Turing test (and anyone who used ChatGPT is not really surprised I guess).\nThe funny thing is that the Wikipedia page for the definition of AGI still lists the Turing test as the kind of criteria for an AGI to pass - all while maintaining that an AGI is still an hypothetical, non existing scenario.\n\n5. Ability to perfectly mimic various aspects human cognition (intelligence but also emotion, creativity, subjective viewpoint, etc)\n\n Humans have ChatGPT powered lovers.\n AI-powered dialogue with patients is rated as being more empathetic than “real” human responders.\n Images generated by AI win photography contests (and photographies by humans can get disqualified because they were mistaken for an AI-generated product! 🤦).\n ChatGPT launches boom in AI-written e-books on Amazon\n\n\n6. Causing a direct existential threat to humanity if misaligned\nThe discussion of this point is important.\nIn my view, points 2 to 5 are the most valid to define what is an AGI and what’s not. I hope I convinced you that current LLMs (as of 2023) meet these criteria.\n\nPoint 6, about the existential threat, is one of the possible consequences of an AGI, yet it tends to be taken as part of its definition.\nI would be tempted to blame the Terminator movies for that: they imprinted in us the idea of a “switch” that would “awake” an AGI, and putting immediately the issue of the existenial threat to humanity at the forefront.\n\nInstead, what points 2 to 5 above indicate is that AGI has already emerged, by all reasonable definitions. LLMs are generalists, mimic human intelligence, mimic human subjectivity, can be made autonomous, and their capacity to be self aware or not is a moot issue. And what comes to fore is not an existential threat to humanity, but more pressing issues such as:\n\nWhat does it look like to:\n\n\n work with an AGI\n learning and teaching with an AGI\n finding emotional suppport with an AGI\n love and friendship with AGIs\n develop policies for society with an AGI\n sports, warfare, literature, leisure, … with AGI\n\n\nThese questions should not be addressed when and if AGIs emerge. They should be opened now.\n\nAbout me\nI am a professor at emlyon business school where I conduct research in Natural Language Processing and network analysis applied to social sciences and the humanities. I teach about the impact of digital technologies on business and society. I build nocode functions 🔎, a click and point web app to explore texts and networks. It is fully open source. Try it and give some feedback, I would appreciate it!\n\n\n my email: analysis@exploreyourdata.com 📧\n or on Twitter: @seinecle 📱\n you can also read the other articles of this blog 👓, where I write about the process of developing the app.", "og:description": "Is ChatGPT a form of “Artificial General Intelligence” (AGI)? I see many in my environment answering with a definitive “No”. I argue that, yes it is. Keep reading.\n\n\n in the following I use “ChatGPT” and “LLM” interchangeably because ChatGPT has become a household name for Large Language Models (LLMs)\n\n\nWhy do we care whether ChatGPT is AGI or not?\nIt is not mere semantics or hair splitting.\nMy argument is that IF ChatGPT is NOT an AGI, then we have still some years before we have to deal with the consequences.\n\nIf ChatGPT is indeed an AGI, it implies that:\n\n\n as we co-exist with an AGI (it is pleonasmic), we should not wait for an hypothetical day when it would arise.\nThe “when is the singularity happening” would be a misplaced question because it would have happened already, without a bang.\nThe emergence of an AGI would have taken place in a series of incremental steps over a few years.\nIf so, we should acknowledge the fact that some early forms of AGI have emerged, and act accordinly: how do we humans, as individuals, organizations and societies, coexist with an artificial intelligence which is “human like”?\n\n\nSo the question is indeed not trivial.\n\nIs ChatGPT an AGI? The counter arguments\nThe consensus is that AGI will arise in years, decades, or never. “Today” is not an option:\n\n\n\nsource: emerji.com\n\nConsidering that AGI would already exist is considered naive, several arguments come to mind:\n\n\n large language models (LLMs) are “merely spitting text from powerful statistical associations”.\n LLMs are not autonomous, they merely answer to a prompt entered by a human\n they are not self-conscious\n proofs of their non AGI character are the mistakes they make: hallucinations, hands with 8 fingers, non factuality, etc.\n\n\nIs ChatGPT an AGI? The arguments in favor\nWhat is an AGI? The definition is muddy. Reading from many different sources, I’d list these criteria:\n\n\n self conscious\n autonomous\n able to accomplish general tasks\n able to perfectly mimic human intelligence\n able to perfectly mimic various aspects of human cognition (intelligence, emotion, creativity, subjective viewpoint, etc)\n causing a direct existential threat to humanity if misaligned\n\n\nI would argue that:\n\n\n Large Language Models meet critera 3, 4 and 5.\n\n\n\n Large Language Models don’t meet criteria 2, but the current state of technology allows for it\n\n\n\n Criteria 1 is not relevant\n\n\n\n Criteria 6 is a possible consequence not a part of the definition of an AGI\n\n\nLet’s review these claims in turn:\n\n1. Is self-consciousness an important criteria for AGI?\nShould we wait for a clear sign that LLMs are self-conscious before we declare an AI to be AGI?\nI would say, absolutely not.\nThere are many reasons, the one I prefer is that since Freud we have accepted that humans are themselves far from being fully self-conscious.\nCognitive psychology has added piles and piles of more evidence that we humans are conscious of ourselves in some capacity, not in an absolute sense.\nIn my view, it follows that requiring for an AI to be self-conscious to be declared “AGI” is non sensical - we don’t have a yardstick to measure self-consciousness against.\nIt merely needs to mimic self consciousness as a human would experience it. And as we see fom current LLMs, they are pretty impressive at mimicking a human’s thought process.\n\n2. Autonomy\nGranted, the current versions of LLMs are not autonomous.\nThey merely function when prompted by a human request, and they stop functioning once this request has been answered.\nBut… automating an infinite loop of prompts and answers is pretty trivial to do.\nActually, dozens of thousands of software developers are currently working at creating autonomous forms of LLMs at the moment, in public.\n\nSo “autonomy” is probably not a feature of an AGI which would happen at the switch of a button (Skynet style), but more like the produce an entire community pushing with their classic tools to achieve it.\n\nOh and they are well aware that there are risks attached to it. Not existential risks (see bullet point #6, below) but risks nonetheless:\n\n\n\nsource\n\n3. Ability to accomplish general tasks\nI’ll pass quickly on this one. LLMs are good at accomplishing general tasks, there is a consensus on that, and every day brings more developments on the topic.\n\nThe latest objection I heard is “general tasks, but just on text”.\nThis not a solid objection as LLMs can do image, and multimode too since Sept 26, 2023:\n\nChatGPT can now see, hear, and speak. Rolling out over next two weeks, Plus users will be able to have voice conversations with ChatGPT (iOS \u0026 Android) and to include images in conversations (all platforms). https://t.co/uNZjgbR5Bm pic.twitter.com/paG0hMshXb— OpenAI (@OpenAI) September 25, 2023\n\n(follow this link to the video if it does not display)\n\nIt is still software and screens, not actual operations and actions in the “real world”.\nTrue, the development of non digital types of operations is always slower (robots…), but I would count it as a limitation in “reach”, not a sign of a lack of intelligence.\n\n4. Ability to perfectly mimic human intelligence\nThis one can also be adressed quickly.\nDo you remember the Turing test?\nThis would consist for a human interrogator to discuss with a human and a computer, but the two would be hidden from the interrogator by a curtain.\nAfter a long conversation, if the human interrogator cannot guess which of the two dialogues he had was with the computer, then the computer passed the Turing test.\n\nThis test was, as far as I could remember, presented as the true and super hard criteria for an AI to (ever) meet.\n\nGuess what: ChatGPT broke the Turing test (and anyone who used ChatGPT is not really surprised I guess).\nThe funny thing is that the Wikipedia page for the definition of AGI still lists the Turing test as the kind of criteria for an AGI to pass - all while maintaining that an AGI is still an hypothetical, non existing scenario.\n\n5. Ability to perfectly mimic various aspects human cognition (intelligence but also emotion, creativity, subjective viewpoint, etc)\n\n Humans have ChatGPT powered lovers.\n AI-powered dialogue with patients is rated as being more empathetic than “real” human responders.\n Images generated by AI win photography contests (and photographies by humans can get disqualified because they were mistaken for an AI-generated product! 🤦).\n ChatGPT launches boom in AI-written e-books on Amazon\n\n\n6. Causing a direct existential threat to humanity if misaligned\nThe discussion of this point is important.\nIn my view, points 2 to 5 are the most valid to define what is an AGI and what’s not. I hope I convinced you that current LLMs (as of 2023) meet these criteria.\n\nPoint 6, about the existential threat, is one of the possible consequences of an AGI, yet it tends to be taken as part of its definition.\nI would be tempted to blame the Terminator movies for that: they imprinted in us the idea of a “switch” that would “awake” an AGI, and putting immediately the issue of the existenial threat to humanity at the forefront.\n\nInstead, what points 2 to 5 above indicate is that AGI has already emerged, by all reasonable definitions. LLMs are generalists, mimic human intelligence, mimic human subjectivity, can be made autonomous, and their capacity to be self aware or not is a moot issue. And what comes to fore is not an existential threat to humanity, but more pressing issues such as:\n\nWhat does it look like to:\n\n\n work with an AGI\n learning and teaching with an AGI\n finding emotional suppport with an AGI\n love and friendship with AGIs\n develop policies for society with an AGI\n sports, warfare, literature, leisure, … with AGI\n\n\nThese questions should not be addressed when and if AGIs emerge. They should be opened now.\n\nAbout me\nI am a professor at emlyon business school where I conduct research in Natural Language Processing and network analysis applied to social sciences and the humanities. I teach about the impact of digital technologies on business and society. I build nocode functions 🔎, a click and point web app to explore texts and networks. It is fully open source. Try it and give some feedback, I would appreciate it!\n\n\n my email: analysis@exploreyourdata.com 📧\n or on Twitter: @seinecle 📱\n you can also read the other articles of this blog 👓, where I write about the process of developing the app." }, "httpBodyByteSize": 47703, "httpBodyPageTitle": "Is ChatGPT Artificial General Intelligence? – Nocode functions - blog – The journey of an academic and app developer" }, "/blog/easy-bibliographic-coupling-bibliometric-scientometric-network-with-excel-file/": { "date": "2026-02-08T19:27:33Z", "httpProtocol": "http/1.1, tls/1.3", "httpStatus": 200, "ipAddress": "176.9.5.74", "ipASN": "Hetzner Online GmbH", "ipCountry": "Germany", "httpHeaderHash": "LXRFJ54RYJHKWV5KOV4LSNT7OBI4HTNW", "httpHeaderTechnologies": [ "Nginx:1.22.1", "Fastly", "Varnish", "GitHub Pages" ], "httpHeaderUrls": [], "httpHeaderByteSize": 807, "httpBodyHash": "JUIM5D7A4XCYHQG5JZ3OL5QUMLYEGJEZ", "httpBodyTechnologies": [ "jsDelivr", "Plyr:3.6.5", "Darkmode.js:1.5.7", "KaTeX:@", "Mermaid", "Twitter", "Lightbox" ], "httpBodyUrls": [ "https://unpkg.com/ress/dist/ress.min.css", "/assets/image/favicon-144x144.png", "/assets/image/favicon.ico", "https://app.vosviewer.com/", "https://github.com/seinecle", "/assets/image/favicon-32x32.png", "/blog/search", "https://gephi.org", "https://openalex.org/", "https://www.linkedin.com/in/levallois/", "https://github.com/seinecle/blog/assets/1244100/71735d1a-2b5a-445f-a08c-e86900747925", "whatsapp://send?text=New function - 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easy bibliographic coupling from an Excel file\u0026summary=New function - easy bibliographic coupling from an Excel file\u0026source=https://nocodefunctions.com/blog/easy-bibliographic-coupling-bibliometric-scientometric-network-with-excel-file/", "/blog/light-stripe-flow/", "/blog/nocodefunctions-5-improvements/", "/blog/style.css", "/assets/image/favicon-60x60.png", "https://github.com/seinecle/nocodefunctions-web-app", "https://fr.wikipedia.org/wiki/Digital_Object_Identifier", "https://cdn.jsdelivr.net/npm/glightbox/dist/css/glightbox.min.css", "https://twitter.com/seinecle", "/assets/image/favicon-16x16.png", "https://en.wikipedia.org/wiki/Bibliographic_coupling", "https://facebook.com/sharer/sharer.php?u=https://nocodefunctions.com/blog/easy-bibliographic-coupling-bibliometric-scientometric-network-with-excel-file/", "https://reddit.com/submit/?url=https://nocodefunctions.com/blog/easy-bibliographic-coupling-bibliometric-scientometric-network-with-excel-file/\u0026resubmit=true\u0026title=New function - easy bibliographic coupling from an Excel file", "/blog/assets/image/avatar.jpg", "https://nocodefunctions.com", "/assets/image/favicon-96x96.png", "https://cdn.jsdelivr.net/gh/mcstudios/glightbox/dist/js/glightbox.min.js", "https://blog.ourresearch.org/new-study-shows-openalex-is-a-good-alternative-to-scopus-for-demographic-research/", "https://github.com/seinecle/Gaze", "https://cdn.plyr.io/3.6.5/plyr.js" ], "httpBodyMetaTags": { "description": "A new function is available: send an Excel file with titles or DOIs, receive a network based on the common references they share (“bibliographic coupling”)\n\n\n\nA free, easy and transparent method to generate bibliometric networks from Excel or .txt data\nThe general idea behind bibliometric networks is that you want to find relations and similarities (or lack of) among the authors, publications, and organizations active in science and research.\n\nThe function presented here makes a focus on one of these networks, called “bibliographic coupling“:\n\n\n 2 publications will be connected if they cite one or several references in common.\n\n\nThe function is free, click and point, and fully transparent: the code is open sourced with a Creative Commons license and the data used for the calculations can be retrieved, reused and shared.\n\nExisting tools: the case of VOSviewer\nThere are many tools which provide such a service already. Let’s mention VOSviewer, which is the leading desktop software specializing in exactly this.\nTo create such a bibliographic coupling network using VOSviewer, you:\n\n\n Click on create\n Choose “bibliographic data / bibliographic coupling”\n\n\n\n\nThen, choose a type of bibliometric data which will be loaded either from an API source or from a bibliometric file you have.\n\n\n\nComparison: what’s new with this function?\nThe function just published has a few interesting features which can complement what VOSviewer already offers:\n\n1. it accepts publication titles and DOIs as ways to identify the publications that you want to analyze.\nMy use case was that my bibliometric data is sitting in an Excel file, and it does not follow one specific bibliometric format.\nIt is a collage of several sources: extraction from Web of Science, my own refs stored in Zotero, and the references extracted from a publication formatted as a pdf.\nHow to create a bibliograpic coupling network from the 200+ refs I had in my table? I figured that using the titles of the publications, or their DOIs, was the best route.\n\nPublication titles: super handy but not super accurate\nA slight difference in the way an apostroph is written in a title can prevent it from being recognized: ‘ is not ʼ !\nSo don’t expect to retrieve a network that will cover 100% of your publications. Titles remain a very convenient way to have a rough idea of the network that your dataset will create.\n\nDOIs\nDigital Object Identifiers are always attached to a publication these days, so you can expect that most if not all of your publications will be identified. The function accepts DOIs formatted in many forms, from the shortest to the very long:\n\n-\u003e 10.1016/j.ecolecon.2010.06.020\n\n-\u003e doi.org/10.1016/j.ecolecon.2010.06.020,\n\n-\u003e https://doi.org/10.1016/j.ecolecon.2010.06.020,\n\n-\u003e https://doi-org.em-lyon.idm.oclc.org/10.1016/j.ecolecon.2010.06.020\n\n2. you can use data from an Excel or .txt file.\nThis is convenient if the data you handle is not formatted in a classic bibliometric format (bibtex, RIS, Zotero…).\nIn the case of an Excel file, just upload it, select the column where your titles or DOIs are, and you are all set.\nFor txt files that’s even simpler: upload a txt file where there is a title or a DOI on each line (careful: for one analysis your file should contain titles or DOIs, not a mix of both.)\n\n3. The output is an online visualization with the VOSviewer app and a gexf file for Gephi\nAs usual for nocodefunctions.com, the output can be directly visualized and explored online (thanks to VOSviewer online), or downloaded as a gexf file for further analysis in Gephi, NodeXL and more.\n\n4. The data used for the computations can be shared\nThis current version of the function outputs the bibliographic coupling network.\nThe underlying data for creating the network is simple and could be interesting to retrieve as well: this is the list of references of each publication, which has been used to assess the similarity between any 2 publications and hence create the network.\nThis list of references is not shared with the user at the moment, but it could be done without too much work, as an Excel file that the user would download at the end of the analysis.\nGet in touch at analysis@exploreyourdata.com if that would be something you need.\n\nHow does it work? Thank you OpenAlex!\nThe function proceeds in two steps:\n\na. it searches for your titles or DOIs on OpenAlex, and retrieves the references\nThis step happens in this part of the code. What is OpenAlex?\nIt is this relatively new, free data source for bibliometrics.\nAs of August 2023 it is not perfect in terms of coverage or data quality but they keep improving and that is what you should bet on for the future.\n\nb. each pair of journals is compared for the references they share\nThis part happens in this part of the code.\nIt was already written for this other function (check option 2), because similarity computations is actually a very common operation.\n\nNext steps\nI developed this function for my research needs, so I don’t have precise plans to expand it further.\nUnless you provide feedback and requests!\n\nAbout me\nI am a professor at emlyon business school where I conduct research in Natural Language Processing and network analysis applied to social sciences and the humanities. I teach about the impact of digital technologies on business and society. I build nocode functions 🔎, a click and point web app to explore texts and networks. It is fully open source. Try it and give some feedback, I would appreciate it!\n\n\n my email: analysis@exploreyourdata.com 📧\n or on Twitter: @seinecle 📱\n you can also read the other articles of this blog 👓, where I write about the process of developing the app.", "og:description": "A new function is available: send an Excel file with titles or DOIs, receive a network based on the common references they share (“bibliographic coupling”)\n\n\n\nA free, easy and transparent method to generate bibliometric networks from Excel or .txt data\nThe general idea behind bibliometric networks is that you want to find relations and similarities (or lack of) among the authors, publications, and organizations active in science and research.\n\nThe function presented here makes a focus on one of these networks, called “bibliographic coupling“:\n\n\n 2 publications will be connected if they cite one or several references in common.\n\n\nThe function is free, click and point, and fully transparent: the code is open sourced with a Creative Commons license and the data used for the calculations can be retrieved, reused and shared.\n\nExisting tools: the case of VOSviewer\nThere are many tools which provide such a service already. Let’s mention VOSviewer, which is the leading desktop software specializing in exactly this.\nTo create such a bibliographic coupling network using VOSviewer, you:\n\n\n Click on create\n Choose “bibliographic data / bibliographic coupling”\n\n\n\n\nThen, choose a type of bibliometric data which will be loaded either from an API source or from a bibliometric file you have.\n\n\n\nComparison: what’s new with this function?\nThe function just published has a few interesting features which can complement what VOSviewer already offers:\n\n1. it accepts publication titles and DOIs as ways to identify the publications that you want to analyze.\nMy use case was that my bibliometric data is sitting in an Excel file, and it does not follow one specific bibliometric format.\nIt is a collage of several sources: extraction from Web of Science, my own refs stored in Zotero, and the references extracted from a publication formatted as a pdf.\nHow to create a bibliograpic coupling network from the 200+ refs I had in my table? I figured that using the titles of the publications, or their DOIs, was the best route.\n\nPublication titles: super handy but not super accurate\nA slight difference in the way an apostroph is written in a title can prevent it from being recognized: ‘ is not ʼ !\nSo don’t expect to retrieve a network that will cover 100% of your publications. Titles remain a very convenient way to have a rough idea of the network that your dataset will create.\n\nDOIs\nDigital Object Identifiers are always attached to a publication these days, so you can expect that most if not all of your publications will be identified. The function accepts DOIs formatted in many forms, from the shortest to the very long:\n\n-\u003e 10.1016/j.ecolecon.2010.06.020\n\n-\u003e doi.org/10.1016/j.ecolecon.2010.06.020,\n\n-\u003e https://doi.org/10.1016/j.ecolecon.2010.06.020,\n\n-\u003e https://doi-org.em-lyon.idm.oclc.org/10.1016/j.ecolecon.2010.06.020\n\n2. you can use data from an Excel or .txt file.\nThis is convenient if the data you handle is not formatted in a classic bibliometric format (bibtex, RIS, Zotero…).\nIn the case of an Excel file, just upload it, select the column where your titles or DOIs are, and you are all set.\nFor txt files that’s even simpler: upload a txt file where there is a title or a DOI on each line (careful: for one analysis your file should contain titles or DOIs, not a mix of both.)\n\n3. The output is an online visualization with the VOSviewer app and a gexf file for Gephi\nAs usual for nocodefunctions.com, the output can be directly visualized and explored online (thanks to VOSviewer online), or downloaded as a gexf file for further analysis in Gephi, NodeXL and more.\n\n4. The data used for the computations can be shared\nThis current version of the function outputs the bibliographic coupling network.\nThe underlying data for creating the network is simple and could be interesting to retrieve as well: this is the list of references of each publication, which has been used to assess the similarity between any 2 publications and hence create the network.\nThis list of references is not shared with the user at the moment, but it could be done without too much work, as an Excel file that the user would download at the end of the analysis.\nGet in touch at analysis@exploreyourdata.com if that would be something you need.\n\nHow does it work? Thank you OpenAlex!\nThe function proceeds in two steps:\n\na. it searches for your titles or DOIs on OpenAlex, and retrieves the references\nThis step happens in this part of the code. What is OpenAlex?\nIt is this relatively new, free data source for bibliometrics.\nAs of August 2023 it is not perfect in terms of coverage or data quality but they keep improving and that is what you should bet on for the future.\n\nb. each pair of journals is compared for the references they share\nThis part happens in this part of the code.\nIt was already written for this other function (check option 2), because similarity computations is actually a very common operation.\n\nNext steps\nI developed this function for my research needs, so I don’t have precise plans to expand it further.\nUnless you provide feedback and requests!\n\nAbout me\nI am a professor at emlyon business school where I conduct research in Natural Language Processing and network analysis applied to social sciences and the humanities. I teach about the impact of digital technologies on business and society. I build nocode functions 🔎, a click and point web app to explore texts and networks. It is fully open source. Try it and give some feedback, I would appreciate it!\n\n\n my email: analysis@exploreyourdata.com 📧\n or on Twitter: @seinecle 📱\n you can also read the other articles of this blog 👓, where I write about the process of developing the app." }, "httpBodyByteSize": 40009, "httpBodyPageTitle": "New function - easy bibliographic coupling from an Excel file – Nocode functions - blog – The journey of an academic and app developer" } }, "protokolleditionen.eu": { "/": { "date": "2026-02-08T14:07:05Z", "httpProtocol": "h2, tls/1.3", "httpStatus": 200, "ipAddress": "185.199.108.153", "ipASN": "Fastly, Inc.", "ipCountry": "United States", "httpHeaderHash": "QOTSROMK7AFTKB4VKVBMOCRWJRQAK3HM", "httpHeaderTechnologies": [ "Fastly", "Varnish", "GitHub Pages" ], "httpHeaderUrls": [ "ad8e:86683:1F39E0F:23A9ED6:69889889" ], "httpHeaderByteSize": 771, "httpBodyHash": "EUOACOJFOJEGRYFKZFRNX4QBVNZWJ57N", "httpBodyTechnologies": [ "jsDelivr", "Plyr:3.6.5", "Darkmode.js:1.5.7", "KaTeX:@", "Mermaid", "Lightbox" ], "httpBodyUrls": [ "/search", "https://kgparl.de", "/assets/image/favicon-16x16.png", "/assets/image/favicon-152x152.png", "/aktuelles/umfrage/2025/04/13/Umfrage/", "https://cdn.jsdelivr.net/npm/fork-awesome@1.2.0/css/fork-awesome.min.css", "/assets/image/favicon-76x76.png", "https://cdn.plyr.io/3.6.5/plyr.css", "https://cdn.jsdelivr.net/npm/glightbox/dist/css/glightbox.min.css", "/page/2/", "/assets/image/favicon-180x180.png", "https://cdn.jsdelivr.net/npm/mermaid/dist/mermaid.min.js", "https://cdn.jsdelivr.net/gh/mcstudios/glightbox/dist/js/glightbox.min.js", "/", "/assets/image/favicon-120x120.png", "https://cdn.plyr.io/3.6.5/plyr.js", "https://unpkg.com/ress/dist/ress.min.css", "https://cdn.jsdelivr.net/npm/katex@0.13.18/dist/katex.min.js", "/feed.xml", "/impressum", "/assets/image/avatar.png", "/assets/image/favicon.ico", "/assets/image/favicon-96x96.png", "https://github.com/Fraktionsprotokolle-de/protokolleditionen.github.io", "https://cdn.jsdelivr.net/npm/katex@latest/dist/contrib/auto-render.min.js", "/style.css", "/ueber-uns", "https://fraktionsprotokolle.de", "/assets/image/favicon-60x60.png", "/assets/image/favicon-114x114.png", "/assets/image/favicon-192x192.png", "/aktuelles/2025/11/05/CfP-Auf-Achse/", "/aktuelles/2025/11/03/Termin-Digitale-Editionen/", "/page/2", "/page/3", "https://tei-c.org", "/assets/image/favicon-57x57.png", "/assets/image/favicon-144x144.png", "https://cdn.jsdelivr.net/npm/katex@0.13.18/dist/katex.min.css", "/aktuelles/workshop/2025/02/12/Call-for-Contributions-DigiEdBerlin/", "https://cdn.jsdelivr.net/npm/darkmode-js@1.5.7/lib/darkmode-js.min.js", "http://www.correspsearch.net", "/assets/image/favicon-32x32.png", "/assets/image/favicon-72x72.png", "http://www.protokolleditionen.eu", "/aktuelles/2025/11/06/Treffen-des-Arbeitskreises/" ], "httpBodyMetaTags": { "description": "Arbeitskreis Digitale Protokolleditionen", "og:description": "Arbeitskreis Digitale Protokolleditionen" }, "httpBodyByteSize": 12436, "httpBodyPageTitle": "protokolleditionen.eu – Arbeitskreis Digitale Protokolleditionen" } } }