The Future of Machine Intelligence

Chapter 9. Oriol Vinyals: Sequence-to-Sequence Machine Learning

Oriol Vinyals is a research scientist at Google working on the DeepMind team by way of previous work with the Google Brain team. He holds a Ph.D. in EECS from University of California, Berkeley, and a Master’s degree from University of California, San Diego.

Key Takeaways

• Sequence-to-sequence learning using neural networks has delivered state of the art performance in areas such as machine translation.

• While powerful, such approaches are constrained by a number of factors, including computational ones. LSTMs have gone a long way towards pushing the field forward.

• Besides image and text understanding, deep learning models can be taught to “code” solutions to a number of well-known algorithmic challenges, including the Traveling Salesman Problem.

Let’s start with your background.

I’m originally from Barcelona, Spain, where I completed my undergraduate studies in both mathematics and telecommunication engineering. Early on, I knew I wanted to study AI in the U.S. I spent nine months at Carnegie Mellon, where I finished my undergraduate thesis. Afterward, I received my Master’s degree at UC San Diego before moving to Berkeley for my Ph.D. in 2009.

While interning at Google during my Ph.D., I met and worked with Geoffrey Hinton, which catalyzed my current interest in deep learning. By then, and as a result of wonderful internship experiences at both Microsoft and Google, I was determined to work in industry. In 2013, ...