We previously discussed which portions of an artificial intelligence/machine-learning ("AI/ML") platform can be patented.

We previously discussed which portions of an artificial intelligence/machine-learning ("AI/ML") platform can be patented.

We previously discussed which portions of an artificial intelligence/machine-learning ("AI/ML") platform can be patented. Under what circumstances, however, is it best to keep at least a portion of the platform a trade secret? And what are some best practices for protecting trade secrets? In this post, we explore important considerations and essential business practices to keep in mind when working to protect the value of trade secrets specific to AI/ML platforms, as well as the pros and cons of trade secret versus patent protection. What qualifies as a "trade secret" can be extraordinarily broad, depending on the relevant jurisdiction, as, generally speaking, a trade secret is information that is kept confidential and derives value from being kept confidential. This can potentially include anything from customer lists to algorithms.

We previously discussed which portions of an artificial intelligence/machine-learning ("AI/ML") platform can be patented. Under what circumstances, however, is it best to keep at least a portion of the platform a trade secret? And what are some best practices for protecting trade secrets? In this post, we explore important considerations and essential business practices to keep in mind when working to protect the value of trade secrets specific to AI/ML platforms, as well as the pros and cons of trade secret versus patent protection. What qualifies as a "trade secret" can be extraordinarily broad, depending on the relevant jurisdiction, as, generally speaking, a trade secret is information that is kept confidential and derives value from being kept confidential.

Applications using artificial intelligence can be fooled by adversarial examples, creating confusion in the model decisions. Input sanitization can help by filtering out improbable inputs before they are given to the model, argued Katharine Jarmul at Goto Berlin 2018. We need to start thinking of the models and the training data we put into them as potential security breaches, she said. Katharine Jarmul, data scientist, O'Reilly author and co-founder of KIProtect, spoke about protecting artificial intelligence from itself at Goto Berlin 2018. InfoQ is covering this conference with Q&A, summaries, and articles.