AI milestones in 2015
7 documented milestones from 2015.
Deep residual networks make training at 100+ layers practical with identity shortcuts
Kaiming He and colleagues introduced residual learning, letting networks train at depths of up to 152 layers. Their ResNet won first place on five tracks at the ILSVRC and COCO 2015 competitions, including ImageNet classification, detection, localisation, and COCO detection and segmentation.
OpenAI Founded
In December 2015, a group of technology investors and researchers (including Greg Brockman, Ilya Sutskever, Wojciech Zaremba, John Schulman, Elon Musk, and Sam Altman) announced the founding of OpenAI, a non-profit artificial intelligence research laboratory in San Francisco, with approximately one billion US dollars in pledged funding.
Andrew M. Dai and Quoc V. Le Introduced Semi-Supervised Sequence Learning
In November 2015, Andrew M. Dai and Quoc V. Le at Google Brain published 'Semi-Supervised Sequence Learning', showing that pre-training recurrent neural networks with unsupervised objectives, language modelling or sequence autoencoding, before supervised fine-tuning improved text classification accuracy and training stability, anticipating the pre-train-then-fine-tune paradigm later adopted widely in NLP.
Blue Brain Project Digital Reconstruction of the Rat Somatosensory Cortex Microcircuitry
On 8 October 2015, Henry Markram and colleagues at the Blue Brain Project published a detailed computational reconstruction of 31,000 neurons and 37 million synapses in a 0.29 mm³ column of juvenile rat somatosensory cortex, creating the first large-scale digital model of a mammalian cortical microcircuit.
Moley Robotics Demonstrates Robotic Kitchen System at Hannover Messe
In April 2015, London-based Moley Robotics unveiled a prototype robotic kitchen system at Hannover Messe, comprising a pair of dexterous robotic arms capable of replicating recorded human cooking movements, integrated with an oven, hob, and dishwasher in a fitted kitchen unit.
DeepMind's DQN learns to play Atari games from raw pixels
DeepMind's Deep Q-Networks algorithm learned to play Atari 2600 games directly from raw pixels, matching or exceeding the score of a human tester on roughly half of the games tested, without any prior knowledge of the rules.
Neurorobotics Platform, Human Brain Project
In 2015, the Human Brain Project, a European Commission Flagship Initiative, released its Neurorobotics Platform, a simulation environment allowing researchers to connect large-scale brain models to virtual robot bodies and run closed-loop cognitive experiments without physical hardware.