Sign In

Communications of the ACM


Lifelong Learning in Artificial Neural Networks

synapse, illustration

Credit: Getty Images

Over the past decade, artificial intelligence (AI) based on machine learning has reached break-through levels of performance, often approaching and sometimes exceeding the abilities of human experts. Examples include image recognition, language translation, and performance in the game of Go.

These applications employ large artificial neural networks, in which nodes are linked by millions of weighted interconnections. They mimic the structure and workings of living brains, except in one key respect—they don't learn over time, as animals do. Once designed, programmed, and trained by developers, they do not adapt to new data or new tasks without being retrained, often a very time-consuming task.


No entries found

Log in to Read the Full Article

Sign In

Sign in using your ACM Web Account username and password to access premium content if you are an ACM member, Communications subscriber or Digital Library subscriber.

Need Access?

Please select one of the options below for access to premium content and features.

Create a Web Account

If you are already an ACM member, Communications subscriber, or Digital Library subscriber, please set up a web account to access premium content on this site.

Join the ACM

Become a member to take full advantage of ACM's outstanding computing information resources, networking opportunities, and other benefits.

Subscribe to Communications of the ACM Magazine

Get full access to 50+ years of CACM content and receive the print version of the magazine monthly.

Purchase the Article

Non-members can purchase this article or a copy of the magazine in which it appears.
Sign In for Full Access
» Forgot Password? » Create an ACM Web Account