Phenotropics, or Prospects for Protocol-adverse Computing

Whenever we pass a variable to a function, or send a message to an object, we're simulating the sending of pulses down a wire. The way that works is the sender and receiver agree in advance on a format that makes the pulses interpretable, also known as a protocol.

Protocols aren't the only way information can travel between places, however. When a physical coffee mug sits on a table, it's possible to imagine that there's a protocol that exists between the two things, but it's an awkward way to think. And yet that's what we often do when we try to build scalable simulations of the world. We can end up with a coffee mug module connected to a table module via a protocol. In the early years of computing, many researchers wished that the world was a little more like a protocol, so that would be easier to interface computers to it.

Early natural language researchers, for instance, were unhappy to find that it wasn't so. What happened instead was that processors eventually became powerful enough to run pattern classification algorithms that could gather information even though the world didn't agree with us in advance on a format. Some examples are face recognition and feature tracking, voice recognition, and scene understanding.

The idea of phenotropics is to use similar pattern recognition techniques to connect software modules together inside the computer. Hopefully systems built in that way will display more informative failure modes, and therefore be more amenable to adaptive improvement. Another potential benefit is that scientific simulations might not be distorted by protocols (as in the example of the coffee mug on the table), and might be more easily integrated into a new iteration of the scientific method in which they could be usefully published, tested, and reused. A potential early application in surgical simulation will be discussed.