Engineering is the bottleneck in deep learning research

The article highlights the increasing importance of engineering skills in advancing deep learning research. It suggests that the ability to build, scale, and maintain complex systems is becoming a critical factor, potentially more limiting than theoretical breakthroughs. This implies a shift in focus from pure algorithmic innovation to practical implementation and infrastructure.

• •Engineering expertise is crucial for deep learning progress.
• •Practical implementation and infrastructure are becoming key.
• •The bottleneck is shifting from theory to execution.