I wrote this story for the Communications of the ACM and it was published on October 18, 2022
It was 10 years ago, in 2012, that deep learning made its breakthrough, when an innovative algorithm for classifying images based on multi-layered neural networks suddenly turned out to do spectacularly better than all algorithms before it. That breakthrough has led to deep learning's adoption in domains like speech and image recognition, automatic translation and transcription, and robotics.
As deep learning was embedded into ever-more everyday applications, more and more examples of what can go wrong also surfaced: artificial intelligence (AI) systems that discriminate, confirm stereotypes, make inscrutable decisions and require a lot of data and sometimes also a huge amount of energy.
In this context, the 9th Heidelberg Laureate Forum organized a panel discussion on the applications and implications of deep learning for an audience of some 200 young researchers from more than 50 countries. The panel included Turing Award recipients Yoshua Bengio, Yann LeCun, and Raj Reddy, 2011 ACM Prize in Computing recipient Sanjeev Arora, and researchers Shannon Vallor, Been Kim, Dina Machuve, and Shakir Mohamed. Katherine Gorman moderated the discussion.
The full story can be read on the website of the Communications of the ACM
