Really, we have hundreds of years of thinking and writing about humans and it's more philosophy than meaningful to start speculating about universal this-or-tha

Really, we have hundreds of years of thinking and writing about humans and it's more philosophy than meaningful to start speculating about universal this-or-that of anything that uses a neural network. What's interesting here is that our AI models don't study their opponents; They don't do that. They're not capable of that. All they can do is iterate over a vast set of sample data and predict outcomes based off them. ...and yes, that's different to humans, but I also think there is something truly fundamental at play here: We may find that, as with self driving cars, the 'last step' to go from 'inhumanly good at a specific restricted domain' to 'inhumanly good at a specific restricted domain and robust against statistically unlikely outcomes such as adversarial attacks' is much, much harder than people initially thought. Perhaps it does play into why humans behave the way they do? Who knows? Why is that it's so easy to generate adverbial attacks against the current crop of models; that means the way that we train them is basically not flexible enough / not diverse enough / not something enough. > One might hope that the adversarial nature of self-play training would naturally lead to robustness. This strategy works for image classifiers, where adversarial training is an effective if computation- ally expensive defense (Madry et al., 2018; Ren et al., 2020). This view is further bolstered by the fact that idealized versions of self-play provably converge to a Nash equilibrium, which is un- exploitable (Brown, 1951; Heinrich et al., 2015). However, our work finds that in practice even state-of-the-art and professional-level deep RL policies are still vulnerable to exploitation. ^ this is what's happening here which is interesting. ...because, it seems like it shouldn't be this easy to trick an AI model, but apparently it is. Maybe in the future, human go players will have to study 'anti-AI' strategies from adversarial models. It's an ironic thought that the iconic man-vs-machine loss against AlphaGo could have been won if he'd used a cheap trick against it.