DeepMind’s Three Pillars for Building Robust Machine Learning Systems

DeepMind’s Three Pillars for Building Robust Machine Learning Systems

DeepMind’s Three Pillars for Building Robust Machine Learning Systems. Specification Testing, Robust Training and Formal Verification are three elements that the AI powerhouse believe hold the essence of robust machine learning models.

Building machine learning systems differs from traditional software development in many aspects of its lifecycle. Established software methodologies for testing, debugging and troubleshooting result simply impractical when applied to machine learning models. While the behavior of traditional software components like websites, mobile apps or APIs is exclusively dictated by its code, machine learning models evolve their knowledge over time depending on specific datasets. How to define and write robust machine learning agents is one of the existential challenges for the entire space. Last year, artificial intelligence(AI) researchers from DeepMind published some ideas about that topic.

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