Pinaki Laskar on LinkedIn: #CausalLearning #MachineLearning #AImodels

The most advanced part of ML, #DeepLearning, has focused too much on correlation without causation, finding #statistic patterns in terms of training data, but failing to explain how they're connected. The majority of ML/DL successes reduce large scale #patternrecognition on the collected independent and identically distributed (i.i.d.) data. Causal knowledge and learning are about how intelligent entities think, talk, learn, explain, and understand the world in causal terms, in terms of causes and effects, agents, changes or processes, actions and manipulation. It is about self-supervised learning, transfer learning and causal discovery, i.e., learning causal information from the real world's data, from heterogeneous data when the i.i.d. The critical role of causality, causal models, and intervention is evidenced in in the basic cognitive functions: reasoning, judgment, categorization, deductive or inductive inference, language, and learning, and decision making, Causal learning the cause–effect relationships, as determining the causation among a set of two or more events or discoverying the causality in data, could be viewed in various ways.