Machine Learning can be unpredictable and opaque.
Deep Learning can be vulnerable to hacking.
Machine Learning requires tons of clean training data.
Deep Learning and GPUs break a lot of assumptions.
Machine Learning can look at far more data than humans.
The combination of humans and computers is powerful.
Better tools and platforms.
More medical applications.
New solutions to training data.
How to (successfully) ship deep learning projects:
Pay a lot of attention to your training data.
Get something working end-to-end right away, then improve one thing at a time.
Look for graceful ways to handle the inevitable cases where the algorithm fails.
Read Lukas's article: "Why are Machine Learning Projects so Hard to Manage?"