AIWS University

A.I. needs to get real—and other takeaways from this year’s NeurIPS

Hello and welcome to the last “Eye on A.I.” of 2020! I spent last week immersed in the Neural Information Processing Systems (NeurIPS) conference, the annual gathering of top academic A.I. researchers. It’s always a good spot for taking...

Fujitsu uses AI to discover characteristic causal relationships of individual data in medicine, marketing, and more

In a recent trial demonstration, Fujitsu’s researchers succeeded in rediscovering the gene of interest in the colorectal cancer classification, offering a key to the development of a treatment plan individualised for each patient Fujitsu has developed...

The Domestication of Causal Reasoning

1. Introduction On Wednesday December 23 I had the honor of participating in “AI Debate 2”, a symposium organized by Montreal AI, which brought together an impressive group of scholars to discuss the future of AI. I spoke on “The Domestication...

This basic human skill is the next major milestone for A.I.

The original article was published at Digital Trends. Remember the amazing, revelatory feeling when you first discovered the existence of cause and effect? That’s a trick question. Kids start learning the principle of causality from as early as eight...

Understanding Causality Is the Next Challenge for Machine Learning

The original article was published here. “Causality is very important for the next steps of progress of machine learning,” said Yoshua Bengio, a Turing Award-wining scientist known for his work in deep learning, in an interview with IEEE Spectrum in...

DeepMind Introduces Algorithms for Causal Reasoning in Probability Trees

The original article was published here. Probability trees may have been around for decades, but they have received little attention from the AI and ML community. Until now. “Probability trees are one of the simplest models of causal generative processes,”...

Statistical tools for valid causal inference with fewer assumptions

The original article can be found here. Causal inference is important in medical research to help determine if treatments are beneficial and if natural exposures are harmful. In many settings, data collection makes causal inference difficult without making...

Causally Colored Reflections on Leo Breiman’s “Statistical Modeling: The Two Cultures” (2001) https://projecteuclid.org/download/pdf_1/euclid.ss/1009213726

Enticed by a recent seminar on this subject, I have re-read Breiman’s influential paper and would like to share with readers a re-assessment of its contributions to the art of statistical modeling. When the paper first appeared, in 2001, I had the impression...

Science vs. Data: Contesting the Soul of Data Science

“Science vs. Data: Contesting the Soul of Data Science” is a talk of Professor Judea Pearl, given on July 30, 2020.