Yoshua BENGIO from the Canadian Institute for Advanced Research (CIFAR) emphasized during his workshop talk “towards disentangling underlying explanatory factors” (cool title) at the ICML 2018 in Stockholm, that the key for success in AI/machine learning is to understand the explanatory/causal factors and mechanisms. This means generalizing beyond identical independent data (i.i.d.) – and this is crucial for our domain in medcial AI, because current machine learning theories and models are strongly dependent on this iid assumption, but applications in the real-world (we see this in the medical domain every day!) often require learning and generalizing in areas simply not seen during the training epoch. Humans interestingly are able to protect themselves in such situations, even in situations which they have never seen before. Here a longer talk (1:17:04) at Microsoft Research Redmond on January, 22, 2018 – awesome – enjoy the talk, I recommend it cordially to all of my students!
https://human-centered.ai/wordpress/wp-content/uploads/2018/09/deep-learning-deep-understanding.png 861 1604 Andreas Holzinger https://human-centered.ai/wordpress/wp-content/uploads/2019/09/hcai.png Andreas Holzinger2018-09-27 17:45:362020-09-08 09:24:53Yoshua Bengio emphasizes: Deep Learning needs Deep Understanding !