Should AI researchers trust AI to vet their work? ZDNet

There is an avalanche of research publishing in the field of machine learning, something Google engineer Cliff Young has likened to a "Moore's Law" of AI publishing, with the number of academic papers on the topic that are posted on the arXiv pre-print server doubling every 18 months. All that creates problems for peer reviewing the work, what with experienced AI researchers too few to possibly read each and every paper carefully. What if machines could do some of the heavy lifting? Should academics trust their own acceptance or rejection to an AI? Also: Google says'exponential' growth of AI is changing nature of compute That's the intriguing question raised by a report posted Thursday, on arXiv, by a machine learning researcher at Virginia Tech, Jia-Bin Huang, titled "Deep Paper Gestalt." Huang used a convolutional neural network, or CNN, the stock tool of machine learning for image recognition, to sift through over 5,000 papers submitted to academic conferences dating back to 2013. Huang writes that purely on the basis of the look of a paper -- its mix of text and images -- his neural net can select a "good" paper, one deserving of inclusion in a conference, with 92% accuracy.