Learning to Inflate Tom Rudelius IAS Based on 1810.05159/hep-th - PowerPoint PPT Presentation

Learning to Inflate Tom Rudelius IAS Based on 1810.05159/hep-th

Outline • Machine Learning and hep-th • Introduction to Inflation • Learning to Inflate • Algorithm • Results • Possible Next Steps

Machine Learning and hep-th

Machine Learning ?

Machine Learning Works Best When… • Have clear measure of success

Example: Chess

Example: Self-Driving Cars

Machine Learning Works Best When… • Have clear measure of success • Have large, representative dataset on which to train

Example: Deciphering Digits

Example: Deciphering Digits

Advantages and Limitations of AI • When it comes to string theory, we don’t understand the large datasets very well • Known string vacua are likely small, biased sample • Don’t have a clear measure of “success” • Need to find problems suitable for machine learning: train a computer to do something we can already do and then do it “better” than we can • Inflation is nice in this regard because we have some experimental data, but more will come in. Best human models can explain data, but lack predictive power

Introduction to Inflation