The field of machine learning is constantly evolving, sometimes slowly, and at other times we experience the tech equivalent of the Cambrian Explosion with rapid advance that makes a good many data scientists experience a serious case of imposter syndrome. It has only been 8 years since the modern era of deep learning began at the 2012 ImageNet competition. Which novel AI approaches will unlock currently unimaginable possibilities in technology and business? This article highlights emerging areas within AI that are poised to redefine the field -- and society -- in the years ahead. Unsupervised learning more closely mirrors the way that humans learn about the world: through open-ended exploration and inference, without a need for the "training wheels" of supervised learning.
In contrast to traditional server-side training whereuser data is aggregated on centralized servers for training, FL instead trains models on end user devices while aggregating only ephemeral parameter updates on a centralized server.This is particularly advantageous for environments whereprivacy is paramount. The Google Keyboard (Gboard) is a virtual keyboard for mobile devices with over 1 billion installs in 2018. Gboard includes both typing features like text autocorrection, nextword predictionand word completions as well as expression features like emoji, GIFs and Stickers (curated, expressive illustrations andanimations). As both a mobile application and keyboard, Gboard has unique constraints which lends itself well to both on-device inference and training. First, as a keyboard applicationwith access to much of what a user types into their mobile device, Gboard must respect the user's privacy.
Google has begun using a machine learning approach to learn from user interactions with mobile devices. Currently under testing in the Gboard on Android keyboard, Federated Learning lets smartphones collaboratively pick up a shared prediction model while keeping training data on the device. This way, the need to do machine learning is decoupled from the need to store the data in the cloud. Federated Learning provides for smarter models, less power consumption, lower latency, and ensured privacy, Google research scientists said. The model on the phone can help power experiences personalized by how users interact with the device.
In a paper published on February 4, Google engineers drafted out plans to forward federated learning at a scale. Federated learning was first introduced in 2017 by Google. The idea is to use data from a number of computing devices like smartphones instead of a centralized data source. Federated learning can be beneficial as it addresses the privacy concern. Android phones are used for the system where the data is only used but never uploaded to any server.
Artificial Intelligence is a forever emerging and advancing technology. Artificial intelligence models are used increasingly widely in today's world. With the power of data and artificial intelligence, machines are able to demonstrate human intelligence, sometimes even better than humans! The culmination of data with machine learning has undoubtedly created huge longevity and thrilling material progress in technology, thus achieving inconceivable heights of intelligence. One such recent yet dramatic progress in Machine Learning is a newly revoluted concept known as Federated Learning.