Neural Information Processing Systems http://nips.cc/

An explosion of high-throughput DNA sequencing in the past decade has led to a surge of interest in population-scale inference with whole-genome data.

It has been shown that deep neural network (DNN) based classifiers are vulnerable to human-imperceptive adversarial perturbations which can cause DNN classifiers to output wrong predictions with high confidence. We propose an unsupervised learning approach to detect adversarial inputs without any knowledge of attackers. Our approach tries to capture the intrinsic properties of a DNN classifier and uses them to detect adversarial inputs. The intrinsic properties used in this study are the output distributions of the hidden neurons in a DNN classifier presented with natural images. Our approach can be easily applied to any DNN classifiers or combined with other defense strategy to improve robustness. Experimental results show that our approach demonstrates state-of-the-art robustness in defending black-box and gray-box attacks.

Voters hold divided opinions on artificial intelligence impact, with college-educated and higher-income Americans more optimistic than others, Fox News survey finds.

Detecting test samples drawn sufficiently far away from the training distribution statistically or adversarially is a fundamental requirement for deploying a good classifier in many real-world machine learning applications. However, deep neural networks with the softmax classifier are known to produce highly overconfident posterior distributions even for such abnormal samples. In this paper, we propose a simple yet effective method for detecting any abnormal samples, which is applicable to any pre-trained softmax neural classifier. We obtain the class conditional Gaussian distributions with respect to (low-and upper-level) features of the deep models under Gaussian discriminant analysis, which result in a confidence score based on the Mahalanobis distance. While most prior methods have been evaluated for detecting either out-of-distribution or adversarial samples, but not both, the proposed method achieves the state-of-the-art performances for both cases in our experiments. Moreover, we found that our proposed method is more robust in harsh cases, e.g., when the training dataset has noisy labels or small number of samples. Finally, we show that the proposed method enjoys broader usage by applying it to class-incremental learning: whenever out-of-distribution samples are detected, our classification rule can incorporate new classes well without further training deep models.

'People have got to feel their kids are safe': Liz Kendall has urged the UK's internet regulator to fully use its powers.utomatically'People

Scammers target online car sellers with fake vehicle report requests on Craigslist and Facebook Marketplace, using warning signs like unknown report names and offers above asking price.

A federal prosecutor alleged that one defendant boasted that his father "had engaged in similar business for the Chinese Communist Party." US authorities allege four people based in Florida, Alabama, and California conspired to illegally ship supercomputers and hundreds of Nvidia GPUs to China as recently as July. The charges, which were unsealed in federal court on Wednesday, are part of a wider government effort to crack down on the smuggling of advanced AI chips to China. Over the past few years, the US has introduced a series of export control rules designed to prevent Chinese organizations from acquiring computer chips that have become popular for developing AI chatbots . The restrictions aim to slow China in what US officials have described as a race to develop powerful AI systems, including surveillance tools and autonomous weapons .

CDC updates website language on vaccines and autism link, stating studies haven't ruled out vaccines causing autism spectrum disorder in susceptible children.

This happens in noisy settings when voters have limited information.