If, like me, you’re interested in AI and deep learning, you’ll like this. Neural networks are all the rage in AI, but there is a newer technology – GÃ¶del machines – that is now a standard part of the AI toolset.
What is a GÃ¶del machine? From the abstract of the paper A Family of GÃ¶del Machine Implementations by Bas R. Steunebrink and JÃ¼rgen Schmidhuber of the IDSIA & University of Lugano:
The GÃ¶del Machine is a universal problem solver encoded as a completely self-referential program capable of rewriting any part of itself, provided it can prove that the rewrite is useful according to some utility function, encoded within itself.
GÃ¶del machines run on von Neumann architectures – they are not a new computer architecture. They consist of two main parts:
- Solver. The solver interacts with some environment and determines utility using a reward function embedded in the machine.
- Searcher. The searcher seeks to improve the entire GÃ¶del machine in a provably – subject to GÃ¶del’s limits of provability, of course – optimal way.
The GÃ¶del Machine was invented by JÃ¼rgen Schmidhuber in 2003.
The limits of AI
As GÃ¶del proved, any formal system that includes arithmetic, either allows for unprovable but true statements, or is flawed. The implication of the former is that since at least one improvement cannot be proved by the searcher, the AI will remain less than optimal.
For an interesting overview of the GÃ¶del Machine, deep learning, and how a program can rewrite itself while running, Schmidhuber’s lecture is an excellent introduction.
The StorageMojo take
This is StorageMojo, not AIMojo. I’m curious about the system architecture running the GÃ¶del Machine and the I/O workload the machine generates. IBM’s Watson runs on a massively parallel system, and I assume a GÃ¶del Machine can too.
I’m also pleased – probably unjustifiably – by the notion that however smart Ais become, they won’t be perfect. Fallibility will always be part of an AI’s nature.
Courteous comments welcome, of course. Please feel free to expand on this topic in a comment.