Timeline The history of computer vision

TIMELINE

THE HIST RY OF COMPUTER VISION

Computer Vision is the science behind computers identifying and understanding information from visual sources, via a process of replication. Implemented in applications to make sense of the pixels in a certain image, The AI algorithm automatically analyses digitised images and videos, producing metadata based on it, such as: date, time, camera type, geographical location, and even objects – such as people, animals, cars or buildings.

2003

1960s

According to motionmetrics. com, Computer Vision began in earnest during the 1960s at universities that viewed the project as a stepping stone to artificial intelligence. Early researchers and academics – including Larry Roberts and Marvin Minsky – were extremely optimistic about the future of these related fields and promoted artificial intelligence as a technology that could transform the world.

AlexNet breakthrough

Although Google and US government bodies had successfully utilised forms of Computer Vision up until this point, 2012 was a pivotal year. A team from the University of Toronto entered a deep neural network called AlexNet that changed the game for artificial intelligence and computer vision projects. Deep neural networks revolutionised the field of artificial intelligence: AlexNet achieved an error rate of 16.4% and, in years following, error rates at the ILSRVC fell to just a few percent; now, deep neural networks are the gold standard for image recognition tasks. These achievements paved the way for artificial intelligence to infiltrate Silicon Valley.

2016 2017 2022

Deep(er) Learning

A global effort from IBM Research, including engineers and scientists from teams in Tokyo, India, and the Thomas J Watson Research Center, has given rise to PowerAI, with new deep learning framework algorithms and performance tuning to help highlight the features of the S822LC server. Advantages of highbandwidth NVLinks between the GPUs and to the CPUs, combined with the IBM Caffe deep learning framework optimisations, results in lower deep learning training time.

Apple’s facial recognition blunder

Craig Federighi, a Senior Vice President of Software Engineering at Apple, tried to test-drive the new phone's facial recognition software in front of a live audience. iPhone X's Face ID – where the user looks at their iPhone X front-screen for the phone to recognise them and allow access to the phone – suffered a technical hitch, which came just after the 10th anniversary edition of the iPhone had been revealed, causing a temporary crash in Apple’s market value.

New generation of Computer Vision

Berlin-based Mobius Labs’ Computer Vision technology is used by the European press agency, ANP and stock photography community, EyeEm. Mobius’ CEO and Chief Scientist, Appu Shaji, claims vendors are now looking to democratise the technology: “Such technologies employ a technique called ‘few shot learning’, allowing the training of very specific concepts using small amounts of data sets. The training is no-code, which allows everyone to easily navigate through the machine learning process. These solutions are lightweight and easy to install on-premises in mobile phones, laptops and even satellites.”