5 ms·
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
by kuu 7y ago
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-networks.html https://christophm.github.io/interpretable-ml-book/neural-ne...
- DoingIsLearning 7y agoIndeed 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)
- yoav_hollander 7y agoHere 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-learning-meets-rule-based-verification/ https://blog.foretellix.com/2017/07/06/where-machine-learnin...
- DoingIsLearning 7y agoThank you very much on point. Thanks for sharing!