Observer-Dependent Models: a talk at the Philosopher’s Web Cafe

Join Orthogonal Research & Education’s Bradly Alicea for an exploration of the role of the observer when interpreting models.

An upcoming talk on Observer-Dependent Models with Orthogonal Research and Education Lab (OREL) Head Scientist Bradly Alicea at the Philosophers Web Cafe will stream virtually on Friday, December 11th. The meeting will begin at UTC 4PM, EST 11AM.

Complete this form to receive the invitation link to the talk.

Moderated by OREL’s Jesse Parent, this talk reflects on the role of the observer in computational modeling, while considering its influence on causality and interpretation of results.

About The Philosopher’s Web Cafe

Managed by Charlotte Guo, this webinar and discussion series aims at creating an accessible and interactive format for philosophy, and in particular, interdisciplinary research.

UPDATE (12/18): Check out a lecture post-mortem at Synthetic Daises blog, complete with links and further discussion!

For more from OREL




A distributed collaborative-based open science lab with interests in computational science, biology & neuroscience, and cognition.

Recommended from Medium

The Body’s Most Embarrassing Organ Is an Evolutionary Marvel

The Heck is Spin?

America’s Never-Ending Battle Against Flesh-Eating Worms

What does it take to understand spiders? False eyelashes, capes and face paint

Howard Berg (1934–2021)

Tests for Human Perception of 60 Hz Moderate Strength Magnetic Fields

Clone Groups in the Phylos Galaxy

International Day of Medical Physics

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Jesse Parent

Jesse Parent

NSF Grad Fellow, Seeking ’22 PhD in AI/CS & Cognition. Assistant Scientist @ OREL. Research @ HealthTech. Strategy @ StateOfTheArt AI - {} -

More from Medium

Quality Assurance in Machine Learning: A Guide to Data Labeling

Data-Centric AI: deep dive on Class Imbalance Problem for Supervised Classification

How Federated Learning can improve ML?

The Case for SciOps