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Verifiably Safe and Correct Deep Neural Networks

CORDIS · observation · Publication date unknown

Verifiably Safe and Correct Deep Neural Networks Deep machine learning is revolutionizing computer science. Instead of manually creating complex software, engineers now use automatically generated deep neural networks (DNNs) in critical financial, medical and transportation systems, obtaining previously unimaginable results. Despite their remarkable achievements, DNNs remain opaque. We do not understand their decision making and cannot prove their correctness - thus risking potentially devastating outcomes. For example, it has been shown that DNNs that navigate autonomous aircraft with the goal of avoiding collisions could produce incorrect turning advisories. Thus, the lack of formal guarantees regarding DNN behavior is preventing their safe deployment in critical systems, and could jeopardize human lives. Consequently, there is a crucial need to ensure that DNNs operate correctly. Recent and exciting developments in formal verification

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recordType

award

status

SIGNED

region

EU

value

1500000

unit

EUR