Diverse Consequences of Algorithmic Probability

Here is my presentation for the following paper titled Diverse Consequences of Algorithmic Probability.

The slides aren’t viewed well in the video, so here they are:
Slides to Diverse Consequences of Algorithmic Probability

Probably the most interesting thing about this paper is that I try to axiomatize AI. What is absolutely necessary for an AGI system? I believe researchers must reflect on this question. And if you’re into philosophy, you may find the claims about epistemology and mathematics interesting. And then there is the bit about infinity point, IP laws just don’t seem to work for an era of rapid technological progress.

BTW, I give a wrong citation somewhere in the beginning, Dowe should be Wallace.

Diverse Consequences of Algorithmic Probability

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Eray Özkural has obtained his PhD in computer engineering from Bilkent University, Ankara. He has a deep and long-running interest in human-level AI. His name appears in the acknowledgements of Marvin Minsky's The Emotion Machine. He has collaborated briefly with the founder of algorithmic information theory Ray Solomonoff, and in response to a challenge he posed, invented Heuristic Algorithmic Memory, which is a long-term memory design for general-purpose machine learning. Some other researchers have been inspired by HAM and call the approach "Bayesian Program Learning". He has designed a next-generation general-purpose machine learning architecture. He is the recipient of 2015 Kurzweil Best AGI Idea Award for his theoretical contributions to universal induction. He has previously invented an FPGA virtualization scheme for Global Supercomputing, Inc. which was internationally patented. He has also proposed a cryptocurrency called Cypher, and an energy based currency which can drive green energy proliferation. You may find his blog at http://log.examachine.net and some of his free software projects at https://github.com/examachine/.

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