The Franke Program in Science and the Humanities and John Templeton Foundation


Latest Posts

October 11, 2022

Inference is a logical-philosophical perspective on the world; it is an epistemological theory that deals with how we come to know things and how we can justify our knowledge claims. Inference also includes deduction and induction, which are processes that work in the opposite direction from inference. Deduction infers a conclusion from a given premise, while induction infers a conclusion by considering the evidence and then concluding on the basis of that evidence. 

September 24, 2022

Inference is a logical-philosophical term that has been used to describe the process of deriving conclusions from premises. The conclusion drawn from the premises is called an inference. In this talk we will explore the mathematical and philosophical aspects of inferences. 

December 10, 2021

Today, society consumes tremendous amounts of information through various online channels from ride-sharing apps to online shopping, and these platforms are governed by decision-making algorithms that continuously learn about human behavior and their preferences through the data we generate as humans. For artificial intelligence algorithms to make future decisions, this generated data is crucial.  

November 18, 2021

A key objective of Artificial Intelligence (AI) techniques is to learn from data how patterns can be associated with prediction power. To be able to extract inference from data as a tool to be able to implicate, authorize, and supervise various aspects of society is groundbreaking. In addition, for the hiring and public services sectors, identities, demographic attributes, preferences, and predicting future behavior as related to criminal justice and lending are all possible applications for these algorithms.

October 18, 2021

Confederated developments in computer science, psychology, and other fields have created a real revolution in human understanding of causal relationships that have taken place throughout history. Causal modeling provided us with a precise, formal framework to represent causal relationships. This framework is undeniably influential and powerful due to its utility in raising concrete scientific questions.