How the U.S. military thinks about AI [audio]

Re:How the U.S. military thinks about AI [audio] by kuu on Wednesday January 15, 2020 @02:52AM (#22052430)

There is work in progress for understanding the content of a neural network and avoid this "black box" effect. This is still far from reached but there are some advances. See [0] for example.

Also, remember that AI includes more than NN. You can use some other models, as a Linear Regression or a Random Forest which are perfectly explainable.

[0] https://christophm.github.io/interpretable-ml-book/neural-ne...

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Re:How the U.S. military thinks about AI [audio] by DoingIsLearning on Wednesday January 15, 2020 @06:17AM (#22053308)

Indeed ML is vast and not limited to NN.

My question was more towards people who are actively using NN as a solution and _have_already_ shipped products in a 'regulated' industry.

The products are obviously out there, so to me it is confusing what evidence can a team provide at this point in time (with the current NN implementations) in order to get a product using NN (with a non-deterministic uncertainty) approved by a regulatory body? (automotive, medical, etc.)

Examples that jump to mind are Tesla's stereovision sub-systems (I am assuming NN are used), or NN based medical imaging classifier software (NN instead of Clustering or SVM techniques)

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Re:How the U.S. military thinks about AI [audio] by yoav_hollander on Wednesday January 15, 2020 @01:19PM (#22056803)

Here is a (longish) post I wrote about this issue (mainly from the perspective of verifying autonomous vehicles):

https://blog.foretellix.com/2017/07/06/where-machine-learnin...

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Re:How the U.S. military thinks about AI [audio] by DoingIsLearning on Thursday January 16, 2020 @03:06AM (#22062520)

Thank you very much on point. Thanks for sharing!

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