I find adversarial attack super interesting. It opens up a field in machine learning where people questions the robustness of models. Adversarial examples exist because of many reasons, with data and weight space being high-dimensional as one.
IMO, the world needs to pay more attention to this, as we implement more and more AI applications. What worries me more is that we still don't really have a good solution to his problem. Pretty much every model that is defended can be attacked in another way.
My guess for now is that we would have to go all the way back, and question about the assumptions we are making about high dimensional spaces, and use a more rigorous way to prove theorems and lemma about AI systems, rather than experimentally saying the accuracy is high enough therefore it works.
IMO, the world needs to pay more attention to this, as we implement more and more AI applications. What worries me more is that we still don't really have a good solution to his problem. Pretty much every model that is defended can be attacked in another way.
My guess for now is that we would have to go all the way back, and question about the assumptions we are making about high dimensional spaces, and use a more rigorous way to prove theorems and lemma about AI systems, rather than experimentally saying the accuracy is high enough therefore it works.