Experts often present two differing views here for how to make AI safe and secure: give it away for free and distribute it widely like open-source software, or keep it contained and regulated like nuclear energy. I believe there is a third option – something that stems from parenting.
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En las últimas dos décadas, la IA ha avanzado a pasos agigantados hasta el punto de que estamos hablando seriamente de que es tan inteligente como los humanos o incluso que los supera. Pero antes de dejarnos llevar demasiado por esa emoción o ese miedo, es importante que miremos hacia dentro y nos preguntemos: ¿qué nos hace inteligentes, conscientes y humanos?
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There is little doubt that GenAI can provide valuable services in certain areas e.g., by aiding and arguably empowering people who lack experience in say, writing business English or formatting CVs in specific ways, especially when applying for jobs that require submitting job applications online as is very common nowadays. However, it also creates uncanny language with a sensation of unease. Examples include employing idioms, phrases, or expressions that are used slightly incorrectly, or any other “slight but noticeable deviations from natural human writing can create a sense of disconnection and discomfort in the reader.” (ibid) Or, as Robertson (2024) puts it, “AI used to be weird. Now ‘sounds like a bot’ is just shorthand for boring.”
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Robots asesinos. Inteligencia artificial distópica. Rebeliones de máquinas. ¿Por qué nos fascina tanto la idea de que nuestros aparatos se vuelvan contra nosotros? En este artículo, desentraño nuestra obsesión con el ascenso de las máquinas y demuestro que gran parte de su atractivo es en realidad la proyección de nuestros propios miedos y dilemas.
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One of the concerns is that Generative AI’s output in response to user prompts often draws from copyrighted works, eliciting a question nowadays: Can the content created by Generative AI qualify for copyright protection itself? Here, we strive to extend our insights into the question.
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Google’s recent announcement about “AI Overviews” has created divisive reception. On one hand, it’s helping us by giving a shortcut to an answer and saving us effort to sift through a bunch of results. On the other hand, this shortcut robs us of our agency and ability to verify, question, and learn. Is this the future we want for accessing and interacting with information?
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Mnemohistory, a term coined by German Egyptologist Jan Assmann, refers to the study of how societies remember and construct their historical narratives. Unlike traditional historiography, which focuses on the objective recording and analysis of past events, mnemohistory emphasises the subjective processes through which memories are formed, preserved, and transmitted across generations. It explores the ways in which collective memory shapes, and is shaped by, cultural, social, and political contexts. Mnemohistory investigates the symbols, rituals, and narratives that communities use to create a sense of shared identity and continuity with the past. Originating from the Greek word “mnemos” meaning memory, and “historia,” meaning enquiry or knowledge, mnemohistory looks into the interaction between memory and history, recognising that our understanding of the past is not static but constantly reinterpreted through the lens of present concerns and future aspirations.
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The study aims to comprehensively explore the impact of artificial intelligence (AI) on information retrieval systems, analysing the evolution, challenges, and future directions. It explores the role of AI in enhancing search relevance, user experience, and ethical considerations in information retrieval contexts. The findings highlight AI’s transformative capabilities in enhancing relevance, personalisation, and semantic understanding within information retrieval systems. Ethical considerations, such as bias mitigation and data privacy, are also addressed.
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If artificial intelligence is so smart, why can’t it explain itself? This somewhat flippant question has preoccupied AI developers and researchers from the earliest days. Over 50 years later, the question is still relevant and increasingly urgent. When generative AI can hallucinate with impunity, there is a problem. Explainability is part of the answer.
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Killer robots. Dystopian AI. Machine uprisings. Why are we utterly fascinated with the idea of our gadgets turning against us? Here I unravel our obsession with the rise of the machines and show that a big part of the appeal is really the projection of our own fears and dilemmas.
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