A Golden Decade of Deep Learning: Computing Systems & Applications | American Academy of Arts and Sciences

A Golden Decade of Deep Learning: Computing Systems & Applications | American Academy of Arts and Sciences 
 
 
 

 

 
 
 
 
 
 
 
 
 
 
 
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 An open access publication of the American Academy of Arts & Sciences 
 
 

 
 

 
 
 
 

 
 
 
 
 
 
 

 
 

 

 
 
 
 
 
 
 Spring 2022
 
 
 
 
 

 
 
 A Golden Decade of Deep Learning: Computing Systems & Applications 

 
 
 
 Author 
 
 Jeffrey A. Dean
 
 
 
 
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 To Dædalus issue 
 
 
 
 
 
 

 

 

 

 

 
 
 
 

 

 

 
 
 
 Abstract 
 
 The past decade has seen tremendous progress in the field of artificial intelligence thanks to the resurgence of neural networks through deep learning. This has helped improve the ability for computers to see, hear, and understand the world around them, leading to dramatic advances in the application of AI to many fields of science and other areas of human endeavor. In this essay, I examine the reasons for this progress, including the confluence of progress in computing hardware designed to accelerate machine learning and the emergence of open-source software frameworks to dramatically expand the set of people who can use machine learning effectively. I also present a broad overview of some of the areas in which machine learning has been applied over the past decade. Finally, I sketch out some likely directions from which further progress in artificial intelligence will come.

 
 
 

 
 

 
 
 
 
 Author Information 
 
 
 
 
 
 Jeffrey Dean , a Fellow of the American Academy since 2016, is a Google Senior Fellow and Senior Vice President for Google Research at Google, Inc.; and Distinguished Fellow at the Stanford University Institute for Human-Centered Artificial Intelligence. He has published in such outlets as Communications of the ACM , ACM Transactions on Computer Systems , and Transactions of the Association for Computational Linguistics . His research papers can be found on Google Scholar . 

 
 
 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 S ince the very earliest days of computing, humans have dreamed of being able to create “thinking machines.” The field of artificial intelligence was founded in a workshop organized by John McCarthy in 1956 at Dartmouth College, with a group of mathematicians and scientists getting together to “find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.” 1 The workshop participants were optimistic that a few months of focused effort would make real progress on these problems. 

 The few-month timeline proved overly optimistic. Over the next fifty years, a variety of approaches to creating AI systems came into and fell out of fashion, including logic-based systems, rule-based expert systems, and neural networks. 2 Approaches that involved encoding logical rules about the world and using those rules proved ineffective. Hand-curation of millions of piec

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