Architecture may make it possible to train one machine-learning model that performs all sorts of tasks
Google says it is developing an AI architecture that can be used to train one giant system capable of performing multiple different tasks more efficiently than today’s models. Machine-learning models are typically built to tackle a particular challenge, such as object detection or facial recognition, and usually have to be trained from scratch when the scope or nature of the problem changes. Developers find themselves train separate models for each type of task that needs to be performed, each requiring different datasets. Training these models can be expensive – especially as they grow in complexity and size. Google wants to develop a type of computational architecture that can train a single giant system capable of performing multiple types of task, and can be continuously updated to learn new capabilities. Jeff Dean, senior fellow and SVP of Google Research and Google Health, introduced the idea of Pathways last year to achieve this. „We’d like to train one model that can not only handle many separate tasks, but also draw upon and combine its existing skills to learn new tasks faster and more effectively. That way, what a model learns by training on one task – say, learning how aerial images can predict the elevation of a landscape – could help it learn another task – say, predicting how flood waters will flow through that terrain,“ he wrote in a blog post in October.
