Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Explainable Ai: Interpreting, Explaining and Visualizing Deep Learning

Explainable Ai: Interpreting, Explaining and Visualizing Deep Learning

Samek, Wojciech
Montavon, Grégoire
Vedaldi, Andrea

Paperback

General ComputersProgrammingComputer Security

ISBN10: 3030289532
ISBN13: 9783030289539
Publisher: Springer
Published: Aug 30 2019
Pages: 439
Weight: 1.38
Height: 0.91 Width: 6.14 Depth: 9.21
Language: English

The development of intelligent systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to intelligent machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner.

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