Neural networks are increasingly being used for a variety of applications, from facial recognition to drug development. However, as neural networks become more sophisticated, they also become more difficult to interpret. This lack of interpretability can be a problem when neural networks are used for critical applications, such as healthcare, where it is important to understand how and why a decision was made.
There are a number of ways to tackle the interpretability challenge. One approach is to develop new methods for interpretable neural networks. This could involve developing new algorithms that are specifically designed to be interpretable, or modifying existing algorithms to make them more interpretable. Another approach is to use existing methods for interpretability to understand neural networks that have already been developed. This could involve applying existing methods to new datasets, or using new methods to interpret neural networks that have already been trained.
Interestingly, recent advances in AI have made it possible to use neural networks for interpretability. For example, it is now possible to use neural networks to generate human-readable explanations for their decisions. This is an area of active research, and it is likely that further advances will be made in the future.
In summary, interpretability is a challenge for neural networks, but there are a number of ways to tackle this challenge. Recent advances in AI have made it possible to use neural networks for interpretability, and it is likely that further progress will be made in this area in the future.
References:
-https://en.wikipedia.org/wiki/Artificial_neural_network
-https://www.nature.com/articles/s42256-019-0138-x
-https://arxiv.org/abs/1706.07979
-https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8463713