Learning Machines, Extended Logic, & Intelligence

This page contains loosly connected research lines on machine learning, information theory, as well as artificial and other intelligence. Learning Machines better reason according to logic. If uncertainties are involved, this should be extended, probabilistic, or Bayesian logic. The same is true for any form of intelligence, whether of human, artificial, or other nature.

Highlights

Artificial intelligence combined

Artificial intelligence expands into all areas of the daily life, including research. Neural networks learn to solve complex tasks by training them on the basis of enormous amounts of examples. Researchers at the Max Planck Institute for Astrophysics in Garching have now succeeded in combining several networks, each one specializing in a different task, to jointly solve tasks using Bayesian logic in areas none was originally trained on. This enables the recycling of expensively trained networks and is an important step towards universally deductive artificial intelligence.

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  • The embarrassment of false predictions -
    How to best communicate probabilities?

    Complex predictions such as election forecasts or the weather reports often have to be simplified before communication. But how should one best simplify these predictions without facing embarrassment? In astronomical data analysis, researchers are also confronted with the problem of simplifying probabilities. Two researchers at the Max Planck Institute for Astrophysics now show that there is only one mathematically correct way to measure how embarrassing a simplified prediction can be. According to this, the recipient of a prediction should be deprived of the smallest possible amount of information.

Learning Machines

Extended Logik

Artificial, Human, & Other Intelligence

  • Theoretical Modeling of Communication Dynamics
    Torsten A. Enßlin, Viktoria Kainz, & Céline Bœhm, submitted (2021) arxiv:2106.05414
  • The Rationality of Irrationality in the Monty Hall Problem
    Torsten A. Enßlin & Margret Westerkamp, Annalen der Physik, vol. 531, issue 3, p. 1800128 (2019) doi: 10.1002/andp.201800128 arxiv:1804.04948