Sign up to save your podcasts
Or
Share S1E03. The Detective’s Dilemma: How We Infer Causes from Evidence
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
S1E03. The Detective’s Dilemma: How We Infer Causes from Evidence
Season 1: The Book of Why
What do Sherlock Holmes and artificial intelligence have in common? Both rely on evidence to reach conclusions, but only one truly understands why things happen. In this episode, we dive into the logic of inference, exploring how the Reverend Thomas Bayes and the principles of probability laid the groundwork for reasoning from effect to cause. From medical diagnoses to forensic science, we examine how Bayesian networks help us navigate uncertainty—and why causal inference remains the missing piece in today’s AI revolution.
🔍 Stay Connected
📧 Email: [email protected]
🌐 Website: https://pozapas.github.io/
🔗 LinkedIn: https://www.linkedin.com/in/amir-rafe-08770854/
🐦 X: https://x.com/rafeamir
View all episodes
By Amir RafeSeason 1: The Book of Why
What do Sherlock Holmes and artificial intelligence have in common? Both rely on evidence to reach conclusions, but only one truly understands why things happen. In this episode, we dive into the logic of inference, exploring how the Reverend Thomas Bayes and the principles of probability laid the groundwork for reasoning from effect to cause. From medical diagnoses to forensic science, we examine how Bayesian networks help us navigate uncertainty—and why causal inference remains the missing piece in today’s AI revolution.
🔍 Stay Connected
📧 Email: [email protected]
🌐 Website: https://pozapas.github.io/
🔗 LinkedIn: https://www.linkedin.com/in/amir-rafe-08770854/
🐦 X: https://x.com/rafeamir