Radical empiricism and machine learning research

Judea Pearl

doi:10.1515/jci-2021-0006

Abstract

Abstract I contrast the “data fitting” vs “data interpreting” approaches to data science along three dimensions: Expediency, Transparency, and Explainability. “Data fitting” is driven by the faith that the secret to rational decisions lies in the data itself. In contrast, the data-interpreting school views data, not as a sole source of knowledge but as an auxiliary means for interpreting reality, and “reality” stands for the processes that generate the data. I argue for restoring balance to data science through a task-dependent symbiosis of fitting and interpreting, guided by the Logic of Causation.

Highlights

I contrast the “data fitting” vs “data interpreting” approaches to data science along three dimensions: Expediency, Transparency, and Explainability
A speaker at a lecture that I have attended recently summarized the philosophy of machine learning this way: “All knowledge comes from observed data, some from direct sensory experience and some from indirect experience, transmitted to us either culturally or genetically.”
Before asking how realistic this agenda is, let us preempt the discussion with two observations: 1. Simulated evolution, in some form or another, is the leading paradigm inspiring most machine learning researchers today, especially those engaged in connectionism, deep learning, and neural

Summary

Introduction – Simulated evolution versus data science

A speaker at a lecture that I have attended recently summarized the philosophy of machine learning this way: “All knowledge comes from observed data, some from direct sensory experience and some from indirect experience, transmitted to us either culturally or genetically.”. Viewed from artificial intelligence perspective, this data-centric philosophy offers an attractive, if not seductive agenda for machine learning research: In order to develop human level intelligence, we should merely trace the way our ancestors did it and simulate both genetic and cultural evolutions on a digital machine, taking as input all the data that we can possibly collect Taken to extremes, such agenda may inspire fairly futuristic and highly ambitious scenarios: start with a simple neural network, resembling a primitive organism (say an Amoeba), let it interact with the environment, mutate and generate offsprings; given enough time, it will eventually emerge with an Einstein’s level of intellect. For example, by causal models that predict both the outcomes of hypothetical manipulations and the consequences of counterfactual undoing of past events [4]

Transparency

Explainability

Conclusion