A deep neural network known as AlexNet, built by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton at the University of Toronto, won the 2012 ImageNet Large Scale Visual Recognition Challenge. Its error rate was far lower than that of any competitor.
What happened
- AlexNet achieved a top-5 error rate of 15.3%, compared with 26.2% for the second-placed entry.
- It was trained on around 1.2 million labelled images using two NVIDIA graphics processors, showing that gaming hardware could make training large networks practical.
- The team described the approach in a paper presented at the NIPS conference in December 2012.
- Google acquired the researchers’ start-up, DNNresearch, in 2013.
Why it mattered
AlexNet is widely seen as the moment deep learning overtook other approaches to computer vision, starting the wave of investment and research that led to today’s AI systems.
Lessons for organisations
Rapid advances in AI mean organisations should keep their AI risk assessments under review as capabilities change. Standards such as ISO/IEC 42001 can help structure governance of how AI is selected, used, and monitored, including checks on data quality.
Source: Turing Post
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