[spsp-members] Friday - Online Only - Samuel Schindler - On the Limits of AI-driven Discovery in Science

[Center for Phil Sci via spsp-members]

The Center for Philosophy of Science invites you to our Featured Former
Fellow online only lecture presented by:

*Featured Former Fellow – Samuel Schindler *- https://samuelschindler.org/
<https://samuelschindler.org/>

*Friday, November 21st @ 12:00 pm - 1:30 pm EST*

This talk is *online-only*. Follow along via Zoom:
https://pitt.zoom.us/j/97096992508

Title: On the Limits of AI-driven Discovery in Science
Abstract:

There has been much enthusiasm among the public general about recent
developments in AI. The enthusiasm is echoed among scientists, culminating
in the Nobel Prize for AlphaFold. In this paper, I want to dampen this
enthusiasm slightly and articulate limits on attempts to make AI-driven
scientific discoveries – understood as discoveries of new phenomena rather
than ideas. To get a sense of the limitations of AI-driven discovery, I
compare machine learning to standard scientific instrumentation and argue
that the much-discussed opacity of machine learning restricts our ability
to distinguish signal from noise. This constitutes a problem for both
“target-specific” and “target-unspecific” learning regimes, but especially
for the latter – arguably the more promising regime for the discovery of
yet unknown phenomena – and gives rise to what I call the Aggravated
Artifact Problem.
This talk is *online-only*. Follow along via Zoom:
https://pitt.zoom.us/j/97096992508
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[Center for Phil Sci]

The Center for Philosophy of Science invites you to our Featured Former Fellow online only lecture presented by: 

Featured Former Fellow – Samuel Schindler - https://samuelschindler.org/ 

Friday, November 21st @ 12:00 pm - 1:30 pm EST

This talk is online-only. Follow along via Zoom: https://pitt.zoom.us/j/97096992508

Title: On the Limits of AI-driven Discovery in Science

Abstract: 

There has been much enthusiasm among the public general about recent developments in AI. The enthusiasm is echoed among scientists, culminating in the Nobel Prize for AlphaFold. In this paper, I want to dampen this enthusiasm slightly and articulate limits on attempts to make AI-driven scientific discoveries – understood as discoveries of new phenomena rather than ideas. To get a sense of the limitations of AI-driven discovery, I compare machine learning to standard scientific instrumentation and argue that the much-discussed opacity of machine learning restricts our ability to distinguish signal from noise. This constitutes a problem for both “target-specific” and “target-unspecific” learning regimes, but especially for the latter – arguably the more promising regime for the discovery of yet unknown phenomena – and gives rise to what I call the Aggravated Artifact Problem.

This talk is online-only. Follow along via Zoom: https://pitt.zoom.us/j/97096992508

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