On Thursday, Google and Computer History Museum (CHM) It was jointly released Source code for AlexenetCNN, which is a lot of credit for transforming the field of artificial intelligence in 2012 by proving that “deep learning” can achieve things that traditional artificial intelligence techniques cannot.
Deep learning, which uses multi -layer nervous networks that can learn from data without explicit programming, is a great departure from traditional artificial intelligence methods that depend on handmade rules and features.
Python Code, now available on Chm’s Jaytap page As an open source program, fans of artificial intelligence and researchers offer a glimpse of a major moment in the history of computing. AlexNet was the moment of water gatherings in artificial intelligence because it can accurately define things in unprecedented accuracy photos – the classification of images is corrected into one of 1000 categories such as “Strawberry”, “School Bus” or “Golden Retriever” with much less errors than previous systems.
Like the original Eniac circles or the Babbage District Plans, the Alexnet Code for Future Historians Code may provide an insight into how the relatively simple implementation of technology has reshaped our world. Although deep learning has enabled health care, scientific research and access to tools, it has also been easy for developments such as Deepfakes, mechanical monitoring and the possibility of widespread functional displacement.
But in 2012, these negative consequences still feel like a long scientific dream for many. Instead, experts simply surprised that the computer can finally recognize the images accurately.
Teaching computers to see
CHM also explains in my detail Blog postAlexenet originated from the work of graduate students at the University of Toronto Alex Crespsky and Elijah SutsfarAlong with their advisor Jeffrey Henton. The project has proven that deep learning can outperform traditional computer vision methods.
The nervous network won the 2012 Imagenet competition by identifying things in the pictures much better than any previous way. According to what was reported that the veteran warrior in the vision of the computer Yan Lacun, who attended the presentation in Florence, Italy, has immediately realized its importance in this field, and it is said after the presentation and Alexnette described as “an unambiguous turning point in the history of the computer’s vision.” As ARS was also detailed in November, Alexnet represented the approximately three critical techniques that would define modern artificial intelligence.