Apple has pulled a new iPhone feature released just three months ago after users slammed it for spreading misinformation. The tech giant removed its AI notification summaries for news and entertainment apps after the system falsely reported a news article. The summary of the BBC article suggested that Luigi Mangione, 26, the alleged assassin of the CEO of UnitedHealthcare, had shot himself. It read: 'Luigi Mangione shoots himself; Syrian mother hopes Assad pays the price; South Korea police raid Yoon Suk Yeol's office,' in reference to three articles that had supposedly been published by the BBC. Mangione has been accused of shooting Brian Thompson, 50, at point-blank range as he was walking to a Manhattan hotel where his company was holding an investor conference on December 4. He is currently being held in a Brooklyn federal jail.

There has been an increasing interest from the scientific community in using likelihood-free inference (LFI) to determine which parameters of a given simulator model could best describe a set of experimental data. Despite exciting recent results and a wide range of possible applications, an important bottleneck of LFI when applied to time series data is the necessity of defining a set of summary features, often hand-tailored based on domain knowledge. In this work, we present a data-driven strategy for automatically learning summary features from univariate time series and apply it to signals generated from autoregressive-moving-average (ARMA) models and the Van der Pol Oscillator. Our results indicate that learning summary features from data can compete and even outperform LFI methods based on hand-crafted values such as autocorrelation coefficients even in the linear case.