Resolving the Human-subjects Status of Machine Learning s Crowdworkers acm.org - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from acm.org Daily Mail and Mail on Sunday newspapers.
The power of human language and thought arises from systematic compositionality—the algebraic ability to understand and produce novel combinations from known components. Fodor and Pylyshyn1 famously argued that artificial neural networks lack this capacity and are therefore not viable models of the mind. Neural networks have advanced considerably in the years since, yet the systematicity challenge persists. Here we successfully address Fodor and Pylyshyn’s challenge by providing evidence that neural networks can achieve human-like systematicity when optimized for their compositional skills. To do so, we introduce the meta-learning for compositionality (MLC) approach for guiding training through a dynamic stream of compositional tasks. To compare humans and machines, we conducted human behavioural experiments using an instruction learning paradigm. After considering seven different models, we found that, in contrast to perfectly systematic but rigid probabilistic sy
Python & command-line tool to gather text on the Web: web crawling/scraping, extraction of text, metadata, comments - GitHub - adbar/trafilatura: Python & command-line tool to gather text on the Web: web crawling/scraping, extraction of text, metadata, comments
Honeypot security technique can also stop attacks in natural language processing
Borrowing a technique commonly used in cybersecurity to defend against these universal trigger-based attacks, researchers at the Penn State College of Information Sciences and Technology have developed a machine learning framework that can proactively defend against the same types of attacks in natural language processing applications 99% of the time.
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Honeypot security technique can also stop attacks in natural language processing
Jessica Hallman
July 28, 2021
UNIVERSITY PARK, Pa. As online fake news detectors and spam filters become more sophisticated, so do attackers’ methods to trick them including attacks through the “universal trigger.” In this learning-based method, an attacker uses a phrase or set of words to fool an indefinite number of inputs. A successful attack could mean more fake news appearing in your social media feed or spam reaching your email inbox.