#AIimplementation #MachineLearning #CausalAI Researcher at Microsoft Robert Usazuwa Ness talks to @JonKrohnLearns about how to achieve causality in AI with correlation-based learning, the right libraries, and handling statistical inference. When dealing with causal AI, Robert notes how important it is to keep aware of variables in the data that may mislead us and force inaccurate assumptions. Not all variables will be useful. It is essential, then, that any assumptions are grounded in a deeper understanding of how the data were gathered, and not what appears in the dataset. Listen to the episode to hear how you can apply causal AI to your projects. This episode is brought to you by: • Trainium2, the latest AI chip from AWS: https://aws.amazon.com/ai/machine-learning/trainium/ • Dell AI Factory with NVIDIA: https://www.dell.com/superdatascience Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: Additional materials: https://www.superdatascience.com/909