In the context of convolutional neural networks (ConvNets), LBH mention pooling, but not its pioneer (Weng, 1992), who replaced Fukushima's (1979) spatial averaging by max-pooling, today widely used by many, including LBH, who write: "ConvNets were largely forsaken by the mainstream computer-vision and machine-learning communities until the ImageNet competition in 2012," citing Hinton's 2012 paper (Krizhevsky et al., 2012). Earlier, committees of max-pooling ConvNets were accelerated on GPU (Ciresan et al., 2011a), and used to achieve the first superhuman visual pattern recognition in a controlled machine learning competition, namely, the highly visible IJCNN 2011 traffic sign recognition contest in Silicon Valley (relevant for self-driving cars). The system was twice better than humans, and three times better than the nearest non-human competitor (co-authored by LeCun of LBH). It also broke several other machine learning records, and surely was not "forsaken" by the machine-learning community. In fact, the later system (Krizhevsky et al. 2012) was very similar to the earlier 2011 system. Here one must also mention that the first official international contests won with the help of ConvNets actually date back to 2009 (three TRECVID competitions) - compare Ji et al. (2013).

Cybercriminals are collaborating to refine their attacks and businesses must do the same by leveraging a growing body of open-source security tools, a security expert has advised as open-source machine learning puts the technology into the mainstream. Mainstream adoption of machine-learning techniques has become crucial for businesses that are being inundated with security-related data and are well past the hope of having humans – or security information and management (SIEM) platforms – keep up with the flood, Cloudera chief security architect Eddie Garcia recently told CSO Australia. "The machine learning part makes a huge difference," he said. "Whereas before SIEM technology searched for known patterns like DDoS or brute-force attacks, machine learning recognises a baseline of what normal activity is, and uses this to recognise anomalies." Machine-learning techniques exploded into the mainstream during 2016, with the launch of the Intel-Cloudera based Apache Spot platform http://spot.incubator.apache.org a turning point in the adoption of machine-learning techniques to security analytics.