Search (7 results, page 1 of 1)

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Abstract

In a new study, computer scientists found that artificial intelligence systems fail a vision test a child could accomplish with ease. "It's a clever and important study that reminds us that 'deep learning' isn't really that deep," said Gary Marcus , a neuroscientist at New York University who was not affiliated with the work. The result takes place in the field of computer vision, where artificial intelligence systems attempt to detect and categorize objects. They might try to find all the pedestrians in a street scene, or just distinguish a bird from a bicycle (which is a notoriously difficult task). The stakes are high: As computers take over critical tasks like automated surveillance and autonomous driving, we'll want their visual processing to be at least as good as the human eyes they're replacing. It won't be easy. The new work accentuates the sophistication of human vision - and the challenge of building systems that mimic it. In the study, the researchers presented a computer vision system with a living room scene. The system processed it well. It correctly identified a chair, a person, books on a shelf. Then the researchers introduced an anomalous object into the scene - an image of elephant. The elephant's mere presence caused the system to forget itself: Suddenly it started calling a chair a couch and the elephant a chair, while turning completely blind to other objects it had previously seen. Researchers are still trying to understand exactly why computer vision systems get tripped up so easily, but they have a good guess. It has to do with an ability humans have that AI lacks: the ability to understand when a scene is confusing and thus go back for a second glance.

Type

a

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Abstract

We provide a model for the reception of knowledge from externalized information sources. The model is based on a cognitive understanding of information processing and draws up ideas of an exchange of information in communication processes. Karl Popper's three-world theory with its orientation on falsifiable scientific knowledge is extended by John Searle's concept of collective intentionality. This allows a consistent description of externalization and reception of knowledge including scientific knowledge as well as everyday knowledge.

Type

a

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Abstract

Do you fret about staying on top of a deluge of information? Don't worry, says Tom Standage, Leibniz felt the same

Content

"Overflowing inboxes, endlessly topped up by incoming emails. Constant alerts, notifications and text messages on your smartphone and computer. Infinitely scrolling streams of social-media posts. Access to all the music ever recorded, whenever you want it. And a deluge of high-quality television, with new series released every day on Netflix, Amazon Prime and elsewhere. The bounty of the internet is a marvellous thing, but the ever-expanding array of material can leave you feeling overwhelmed, constantly interrupted, unable to concentrate or worried that you are missing out or falling behind. No wonder some people are quitting social media, observing "digital sabbaths" when they unplug from the internet for a day, or buying old-fashioned mobile phones in an effort to avoid being swamped. This phenomenon may seem quintessentially modern, but it dates back centuries, as Ann Blair of Harvard University observes in "Too Much to Know", a history of information overload. Half a millennium ago, the printing press was to blame. "Is there anywhere on Earth exempt from these swarms of new books?" moaned Erasmus in 1525. New titles were appearing in such abundance, thousands every year. How could anyone figure out which ones were worth reading? Overwhelmed scholars across Europe worried that good ideas were being lost amid the deluge. Francisco Sanchez, a Spanish philosopher, complained in 1581 that 10m years was not long enough to read all the books in existence. The German polymath Gottfried Wilhelm Leibniz grumbled in 1680 of "that horrible mass of books which keeps on growing"."

Type

a

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Abstract

It is suggested that, in addition to data, information and knowledge, the information sciences should focus on understanding, understood as a higher-order knowledge, with coherent and explanatory potential. The limited ways in which understanding has been addressed in the design of information systems, in studies of information behaviour, in formulations of information literacy and in impact studies are briefly reviewed, and future prospects considered. The paper is an extended version of a keynote presentation given at the i3 conference in June 2015.

Type

a

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Abstract

In April 2010, Bill Gates gave a talk at MIT in which he asked: 'are the brightest minds working on the most important problems?' Gates meant improving the lives of the poorest; improving education, health, and nutrition. We could easily add improving peaceful interactions, human rights, environmental conditions, living standards and so on. Philosophy of Information (PI) proponents think that Gates has a point - but this doesn't mean we should all give up philosophy. Philosophy can be part of this project, because philosophy understood as conceptual design forges and refines the new ideas, theories, and perspectives that we need to understand and address these important problems that press us so urgently. Of course, this naturally invites us to wonder which ideas, theories, and perspectives philosophers should be designing now. In our global information society, many crucial challenges are linked to information and communication technologies: the constant search for novel solutions and improvements demands, in turn, changing conceptual resources to understand and cope with them. Rapid technological development now pervades communication, education, work, entertainment, industrial production and business, healthcare, social relations and armed conflicts. There is a rich mine of philosophical work to do on the new concepts created right here, right now.
Philosophy "done informationally" has been around a long time, but PI as a discipline is quite new. PI takes age-old philosophical debates and engages them with up-to-the minute conceptual issues generated by our ever-changing, information-laden world. This alters the philosophical debates, and makes them interesting to many more people - including many philosophically-minded people who aren't subscribing philosophers. We, the authors, are young researchers who think of our work as part of PI, taking this engaged approach. We're excited by it and want to teach it. Students are excited by it and want to study it. Writing a traditional textbook takes a while, and PI is moving quickly. A traditional textbook doesn't seem like the right approach for the philosophy of the information age. So we got together to take a new approach, team-writing this electronic text to make it available more rapidly and openly.

Content

Vgl. auch unter: http://www.socphilinfo.org/teaching/book-pi-intro: "This book serves as the main reference for an undergraduate course on Philosophy of Information. The book is written to be accessible to the typical undergraduate student of Philosophy and does not require propaedeutic courses in Logic, Epistemology or Ethics. Each chapter includes a rich collection of references for the student interested in furthering her understanding of the topics reviewed in the book. The book covers all the main topics of the Philosophy of Information and it should be considered an overview and not a comprehensive, in-depth analysis of a philosophical area. As a consequence, 'The Philosophy of Information: a Simple Introduction' does not contain research material as it is not aimed at graduate students or researchers. The book is available for free in multiple formats and it is updated every twelve months by the team of the p Research Network: Patrick Allo, Bert Baumgaertner, Anthony Beavers, Simon D'Alfonso, Penny Driscoll, Luciano Floridi, Nir Fresco, Carson Grubaugh, Phyllis Illari, Eric Kerr, Giuseppe Primiero, Federica Russo, Christoph Schulz, Mariarosaria Taddeo, Matteo Turilli, Orlin Vakarelov. (*) The version for 2013 is now available as a pdf. The content of this version will soon be integrated in the redesign of the teaching-section. The beta-version from last year will provisionally remain accessible through the Table of Content on this page."

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Abstract

In this article we advance the conjecture that conscious awareness is equivalent to data compression. Algorithmic information theory supports the assertion that all forms of understanding are contingent on compression (Chaitin, 2007). Here, we argue that the experience people refer to as consciousness is the particular form of understanding that the brain provides. We therefore propose that the degree of consciousness of a system can be measured in terms of the amount of data compression it carries out.

Type

a

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