AI milestones in 2014
7 documented milestones from 2014.
Kingma and Ba present the Adam optimiser
Diederik P. Kingma and Jimmy Ba presented Adam, an optimisation algorithm combining and extending existing adaptive gradient methods, adapting learning rates using bias-corrected estimates of the first and second moments of gradients, making it efficient and broadly practical, particularly where heavy hyperparameter tuning is not feasible.
Amazon Introduces Alexa and the Echo Smart Speaker
In November 2014, Amazon introduced Alexa, a cloud-based voice assistant, alongside the Amazon Echo smart speaker. Alexa accepted natural-language voice commands for tasks including music playback, information retrieval, smart-home control, and calendar management, bringing always-on voice interaction into consumer homes at scale.
Neural Turing Machine Introduced by Alex Graves, Greg Wayne, and Ivo Danihelka
In October 2014, Alex Graves, Greg Wayne, and Ivo Danihelka at Google DeepMind published 'Neural Turing Machines', a preprint proposing a neural network architecture augmented with an external memory matrix and differentiable read/write operations, enabling the system to learn algorithms such as sorting and copying from examples alone.
Bahdanau, Cho and Bengio introduce soft attention for neural machine translation
Dzmitry Bahdanau, Kyunghyun Cho and Yoshua Bengio proposed an attention mechanism letting neural translation models search source sentences dynamically, rather than compressing everything into a single fixed-length vector, achieving performance comparable to phrase-based systems on English-to-French translation.
IBM TrueNorth: a neuromorphic integrated circuit with one million programmable neurons
In August 2014, researchers at IBM Research published a description of TrueNorth, a neuromorphic chip containing one million programmable spiking neurons and 256 million synapses on a 4096-core CMOS integrated circuit, built under the DARPA SyNAPSE programme.
Goodfellow and colleagues propose generative adversarial networks
Ian Goodfellow and seven co-authors proposed training two neural networks against each other: one generating samples, one judging them. The setup, requiring only backpropagation and no Markov chains, could recover the training data distribution under idealised theoretical assumptions.
SoftBank Robotics and Aldebaran Unveil Pepper, a Humanoid Robot with Emotion Recognition
In June 2014, SoftBank Robotics and its subsidiary Aldebaran Robotics unveiled Pepper, a 1.2-metre humanoid robot equipped with an emotion-recognition system capable of detecting human facial expressions, voice tone, and body language, intended for retail and customer-service deployment.