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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Over 14 minutes, this 10th episode of the What Makes This Paper Great? video series unpacks, critiques, and celebrates Information as Thing. The video begins by noting its citation record; profiles the author; and explains the paper’s method of conceptual analysis. Information As Thing is then situated alongside other definitions that similarly cast information as having multiple types.
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An old adage, often attributed to French writer Anaïs Nin, suggests we don’t see things as they are we see things as we are. Using deceptively simple words, the saying suggests that seeing is not just a process where wavelengths are converted to electric impulses that are then processed by the brain in mysterious ways to create what we then believe to see but rather that seeing, to some significant extent, is also a socially shaped and conditioned process, which is why it is relevant to information behavior research.
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Our 21st-century global society needs research-informed information professionals of all stripes more than ever. Librarians serve our communities more vitally than ever with valuable resources, and as teachers of life skills and information literacy; archivists and records curators preserve historical documentation against “alternate facts;” information architects and others help shape the tools and terminologies that society uses to make sense of information around us.
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In today’s rapidly evolving field of information science (IS), continuous learning is no longer optional, it’s essential. But keeping up with the latest trends and developments can feel overwhelming if you go it alone. That’s where the power of supportive networks comes in. By cultivating a network of mentors, colleagues, and fellow lifelong learners, you can create a dynamic learning environment that fuels your professional growth.
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What is one of the best ways to find new career opportunities in the information field? In my experience, networking is the key to successful job seeking. Networking is also invaluable for building professional connections, developing a sense of community, and establishing your reputation. As a past president of ASIS&T, I participated in the Second Humans of the Library in June 2024, a series organized by ASIS&T’s SIG-III. In this article, I summarize some of the stories I shared about the ways that professional networks have helped me on my career journey.
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In this episode of InfoFire, I am in conversation with Professor Javed Mostafa, Dean of the Faculty of Information, University of Toronto, Canada, on the topic “Trends in Health Informatics.” Our conversation covered various issues such as Electronic Health Records (EHR); interoperability; EHR and precision medicine; the personalization-privacy paradox; wearable devices and remote health; unstructured health data and machine learning; data analytics; telemedicine; AI and health informatics; and interdisciplinarity in HI.
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A new video series at INFIDEOS, Information Science Workout, invites people to learn major concepts of Information Science through their bodies. This approach resonates with embodied cognition and multimodal pedagogy, two movements that recognize the important role the body plays in making sense of the world. As the Introduction to the Series declares, “Your body can be your teacher.”
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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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The enduring human quest to understand if we are alone in the universe has driven the development of the Search for Extraterrestrial Intelligence (SETI) programs. These programs employ a diverse toolkit of techniques aimed at detecting potential technosignatures, or signals indicative of technological activity beyond Earth. Despite these advancements, traditional methods face inherent limitations, particularly regarding the selection of appropriate models to interpret experimental data. Traditional model selection methods often rely on researcher intuition and experience, introducing subjectivity and potential biases. To address these limitations, the Finite Information Quantity (FIQ) approach is presented as an objective solution for model selection. FIQ acknowledges the inherent constraints in information processing capacity within physical systems, providing objective criteria for evaluating different models. By considering comparative uncertainty, FIQ minimizes bias and promotes a more comprehensive understanding of phenomena.
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