Next step of AI: How to add Causal Reasoning using only Observational Data by Rubén Martínez
The problem shared by both Machine Learning and Deep Learning algorithms is that they are oriented to the search for patterns based on linear and / or non-linear correlations. In this search, they do not take into account the study of the presence of causal relationships. At this point, we must remember the famous phrase that the existence of correlation does not imply that there is causality.
The weaknesses of Machine Learning or Deep Learning models when making predictions about situations that are far from the distribution of the data with which they were trained can begin to be explained when the absence of a causality study is taken into account. Some examples of this situation can be found when deploying these models in open environments, as is the case with autonomous driving or speech recognition systems.
#BIGTH20 #AI #DeepLearning #TechForGood
Session presented at Big Things Conference 2020 by Rubén Martínez, Data Scientist / AI Researcher at Datahack
16th November 2020
Home Edition
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