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Volume 1 Issue 7, July 2019

Feature activation maps for digital pathology

Machine learning is often described as a tool that sacrifices transparency and simplicity in return for higher predictive power. Our cover image features progress by Faust et al. towards increased transparency of machine-learned models for digital pathology, by demonstrating how learned features in histopathologic images correlate to human-understandable patterns and groupings.

See Faust et al.

Image: Kevin Faust, Ugljesa Djuric and Phedias Diamandis, University Health Network. Cover design: Karen Moore


  • Civil liberty groups are raising the alarm over the ubiquitous use of automated facial recognition. As a society, we need to decide on the acceptable use of this technology and how to build in safeguards to protect human rights.



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Comment & Opinion

  • Artists have always been at the forefront of experimenting with digital tools. The AI: More than Human exhibition at the Barbican Centre, London (until August 26th), features some intriguing AI-inspired installations. We spoke to four artists about their work and influences.

    • Yann Sweeney
    • Jacob Huth
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Books & Arts

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  • Classical statistical analysis in many empirical sciences has lagged behind modern trends in analytics for large-scale datasets. The authors discuss the influence of more variables, larger sample sizes, open data sources for analysis and assessment, and ‘black box’ prediction methods on the empirical sciences, and provide examples from imaging neuroscience.

    • Danilo Bzdok
    • Thomas E. Nichols
    • Stephen M. Smith
    Review Article
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Challenge Accepted

  • Could this be the year that AI is going to surpass human performance in playing the popular video game Angry Birds? The organizers of the annual AIBIRDS competition discuss the challenges involved.

    • Jochen Renz
    • XiaoYu Ge
    • Peng Zhang
    Challenge Accepted
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