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The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence
Yet, AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access.
Optimal Spatial Deconvolution and Message Reconstruction from a Large Generative Model of Models
We introduce a univariate signal deconvolution method based on the principles of an approach to Artificial General Intelligence in order to build a general-purpose model of models independent of any arbitrarily assumed prior probability distribution.
A Simplicity Bubble Problem in Formal-Theoretic Learning Systems
When mining large datasets in order to predict new data, limitations of the principles behind statistical machine learning pose a serious challenge not only to the Big Data deluge, but also to the traditional assumptions that data generating processes are biased toward low algorithmic complexity.
