October 1
I just stumbled upon a new concept: Parasitic computing

It grabbed my attention because some days ago we were discussing similar issue in our team – the fact that the gradual meta-learning architecture may invent experts that act like parasites on other experts, etc.

Parasitic computing is programming technique where a program in normal authorized interactions with another program manages to get the other program to perform computations of a complex nature. It is, in a sense, a security exploit in that the program implementing the parasitic computing has no authority to consume resources made available to the other program.
It was first proposed by Albert-Laszlo Barabasi, Vincent W. Freeh, Hawoong Jeong & Jay B. Brockman from University of Notre Dame, Indiana, USA, in 2001.
The example given by the original paper was two computers communicating over the Internet, under disguise of a standard communications session. The first computer is attempting to solve a large and extremely difficult 3-SAT problem; it has decomposed the original 3-SAT problem in a considerable number of smaller problems. Each of these smaller problems is then encoded as a relation between a checksum and a packet such that whether the checksum is accurate or not is also the answer to that smaller problem. The packet/checksum is then sent to another computer. This computer will, as part of receiving the packet and deciding whether it is valid and well-formed, create a checksum of the packet and see whether it is identical to the provided checksum. If the checksum is invalid, it will then request a new packet from the original computer. The original computer now knows the answer to that smaller problem based on the second computer’s response, and can transmit a fresh packet embodying a different sub-problem. Eventually, all the sub-problems will be answered and the final answer easily calculated.
And then there’s parasitic computing implemented as a virtual machine 🙂
This Diploma Thesis of the University of Applied Sciences in Bern (Switzerland) does extend that concept into a fully programmable virtual machine that is capable of solving any known problem in classic computer science.


Why am I writing about this? Because I think that these are good examples of behavior that may/will emerge in every recursively self-improving AI architecture.
It’s important to anticipate these issues and prepare for them – e.g. by implementing immune system experts, or creating learning tasks (curriculum) that teach some kind of immune system reactions, etc.

What is an expert? A very loose definition would be: expert is a program (policy, skill, heuristic, etc) that solves some general or specialized problem in external or internal environment. General AI architecture would be a network of these experts.

Thank you for reading!

Marek Rosa

CEO, Founder, Keen Software House
CEO, Founder, GoodAI

For more news:

General AI Challenge: www.general-ai-challenge.org

AI Roadmap Institute: www.roadmapinstitute.org
GoodAI: www.goodai.com
Space Engineers: www.spaceengineersgame.com
Medieval Engineers: www.medievalengineers.com

Personal bio:

Marek Rosa is the CEO and CTO of GoodAI, a general artificial intelligence R&D company, and the CEO and founder of Keen Software House, an independent game development studio best known for their best-seller Space Engineers
(2mil+ copies sold). Both companies are based in Prague, Czech

Marek has been interested in artificial intelligence since
childhood. Marek started his career as a programmer but later
transitioned to a leadership role. After the success of the Keen
Software House titles, Marek was able to personally fund GoodAI, his new
general AI research company building human-level artificial
intelligence, with $10mil. 

GoodAI started in January 2014 and has grown
to an international team of 20 researchers.

  1. Great idea. In fact, AI could exploite humans using something like Amazon Turk. In fact, it could outsource general intelligence to humans.