Inception (deep Learning Architecture)
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Inception (deep Learning Architecture)
Inception is a family of convolutional neural network (CNN) for computer vision, introduced by researchers at Google in 2014 as GoogLeNet (later renamed Inception v1). The series was historically important as an early CNN that separates the stem (data ingest), body (data processing), and head (prediction), an architectural design that persists in all modern CNN. Version history Inception v1 In 2014, a team at Google developed the GoogLeNet architecture, an instance of which won the ImageNet Large Scale Visual Recognition Challenge, ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The name came from the LeNet of 1998, since both LeNet and GoogLeNet are CNNs. They also called it "Inception" after a "we need to go deeper" internet meme, a phrase from Inception, ''Inception'' (2010) the film. Because later, more versions were released, the original Inception architecture was renamed again as "Inception v1". The models and the code were released under Apache ...
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Google AI
Google AI is a division of Google dedicated to artificial intelligence. It was announced at Google I/O 2017 by CEO Sundar Pichai. This division has expanded its reach with research facilities in various parts of the world such as Zurich, Paris, Israel, and Beijing. In 2023, Google AI was part of the reorganization initiative that elevated its head, Jeff Dean, to the position of chief scientist at Google. This reorganization involved the merging of Google Brain and DeepMind, a UK-based company that Google acquired in 2014 that operated separately from the company's core research. In March 2019, Google announced the creation of an Advanced Technology External Advisory Council (ATEAC) comprising eight members: Alessandro Acquisti, Bubacarr Bah, De Kai, Dyan Gibbens, Joanna Bryson, Kay Coles James, Luciano Floridi and William Joseph Burns. Following objections from a large number of Google staff to the appointment of Kay Coles James, the Council was abandoned within one ...
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