Advisory Board

Dr. Arthur Franz

Arthur Franz, Ph.D. is an independent AI researcher and cofounder of the Odessa Competence Center for Artificial Intelligence and Machine learning (OCCAM). His work is focused on artificial general intelligence, specifically the inference of general mental representations.

Arthur authored and coauthored various publications in diverse fields including algorithmic information theory, infant cognitive development, inductive programming, and AI safety. He recently authored A Theory of Incremental Compression.

He has cofounded a successful trading company that leverages machine learning techniques to derive predictions and trading strategies.

In 2017, he cofounded OCCAM (Odessa Competence Center for Artificial Intelligence and Machine Learning), where their mission is to advance both fundamental research and practical application in the fields of artificial intelligence and machine learning. This fundamental research is focused on so-called Artificial General Intelligence (AGI), which refers to humanity’s long term dream of constructing thinking machines that can solve a wide range of tasks without being specifically programmed for any of them.

Arthur wrote a blog Thoughts On Artificial General Intelligence, where he captured his flow of thoughts on Artificial General Intelligence (AGI) and how we should build an intelligent machine.

He earned his Master’s Degree of Science in Physics in 2006 at the Friedrich-Alexander University, Erlangen-Nurnberg, Germany, after which he got an Internship at the Massachusetts Institute of Technology (MIT). He earned his Ph.D. in Cognitive Science from the Frankfurt Institute for Advanced Studies, Germany in 2010. The topic of his Ph.D. was Computational models of cognitive development in infancy and his research areas were machine learning, computational modeling of early cognitive processes, and computational neuroscience.

Arthur attended his first AGI Conference in 2015, with his work Toward Tractable Universal Induction Through Recursive Program Learning. In 2016, at 9th AGI Conference he attended with Some Theorems on Incremental Compression, followed by attendance at AGI 2017 with the work On Hierarchical Compression and Power Laws in Nature and in 2018 AGI Conference where he coedited Artificial General Intelligence: 11th International Conference, AGI 2018, Prague, Czech Republic, August 22–25, 2018, Proceedings (Lecture Notes in Computer Science).

Watch his talk on various AGI-related ideas at OCCAM.

Watch his presentation at AGI-16, Some theorems on incremental compression and read his publication on the subject.

Visit his blog Thoughts On Artificial General Intelligence, his ResearchGate profile, dblp page, Semantic Scholar page, and his company’s OCCAM page. Follow him on Facebook and Twitter. Watch OCCAM’s YouTube video channel.