Machine learning algorithms have a fatal flaw: They’re costly to fine-tune (in terms of time and resources) from scratch for specific apps. Some automated approaches attempt to expedite the process by searching for suitable existing models, but researchers at Google’s AI research division have a better idea.

In a blog post published this afternoon and accompanying paper (“MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks“), they describe a technique — MorphNet, which is available in open source on GitHub — that takes as input AI systems and simplifies their architectures. The result is smaller, faster, and better-performing models that can be applied to “Google-scale” problems, said senior software engineer Andrew Poon and Google AI Perception product manager Dhyanesh Narayanan. READ MORE ON: VENTUREBEAT

 Above: Google HQ Image Credit: Reuters Above: Google HQ Image Credit: Reuters