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Abstract

Computer können längst nicht alles. Online-Übersetzer zum Beispiel stoßen schnell an ihre Grenzen. Für den Kognitionswissenschaftler Douglas R. Hofstadter herrscht in vielen Maschinen effiziente Scheinintelligenz.

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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.

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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"."

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Content

Doch obwohl das in der Science-Fiction schon tausendmal durchgespielt wurde, gibt es noch immer keinen Computer, der Menschen imitieren kann. Die Rechnerleistungen werden zwar immer größer, aber »dennoch sind die Computer so dumm wie zuvor (.) Sie prozessieren Informationen anhand einer bestimmten und von Menschen eingegebenen Syntax. Mit der Bedeutung, der Semantik, den Ergebnissen können sie nichts anfangen«, schreibt Feustel. Das klassische Beispiel ist der Android Data in der Serie »Star Trek«, der keine Witze versteht, so sehr er sich auch müht - so setzte die Kulturindustrie vor 30 Jahren diesen Vorbehalt in Szene. Heute überwiegen hingegen Plots wie im Film »Lucy« von Luc Besson, in dem Mensch und Maschine als zwei Arten von Informationsflüssen prinzipiell kompatibel sind. Angesichts von Big-Data-Strömen und den »Deep Learning«-Prozessen der viel beschworenen Algorithmen wird allenthalben die Hoffnung - oder Befürchtung - artikuliert, es könne plötzlich eine selbstständige Intelligenz im Netz entstehen, die eben nicht mehr nur syntaktisch verarbeitet, sondern »semantisches« Bewusstsein entwickelt. Die Information könne quasi lebendig werden und als Geist aus der Flasche steigen.

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Abstract

Here, we introduce PI now. We cover core ideas, explaining how they relate both to traditional philosophy, and to the conceptual issues arising all over the place - such as in computer science, AI, natural and social sciences, as well as in popular culture. This is the first version, for 2013. Next year we'll tell you about PI 2014. We hope you love PI as much as we do! If so, let us have your feedback, and come back in 2014. Maybe some of you will ultimately join us as researchers. Either way, enjoy it. Yours, Patrick, Bert, Simon, Nir, Federico, Carson, Phyllis, Andrew, Eric, Giuseppe, Federica, Christoph, Mariarosaria, Matteo, Orlin, and Hector.

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