Substrate Independence

The notion of substrate independence in is invoked in arguments about whether or not, at least in theory, a digital computer can do anything the human brain can do. The idea is that what matters is the computational procedure, the algorithm if you will, not the substrate in which it is implemented. Anything that’s being ‘computed’ in neural ‘wetware’ can be computed by suitable software running on digital hardware. We just have to figure out the procedure and have enough computing power to do it.

I wonder, though. To the extent that the notion of substrate independence assumes (something like) the distinction between hardware and software, this may not be the case. For the hardware/software distinction doesn’t apply to the brain (and its mind). Thus, you can easily erase some software and data from a digital computer without affecting the underlying hardware. You can just as easily reload that data and software to the hardware, thus restoring the computer to its prior state. You can’t do that with a brain. Similarly, you can add new capability to computer simply by uploading new software. You can’t do that with a brain. You cannot, for example, learn a new language or a new intellectual discipline simply by uploaded a new module, say, overnight. You have to learn, painstakingly learn. In neural ‘wetware’ the substrate in irreversibly changed in a way this is not true of digital hardware.

Does machine learning change this? I note that machine learning is software that’s implemented on hardware. The underlying hardware is not changed. The learning takes place entirely in the data (the parameter weights) that is learned. Still, it seems that something more or less like (organic) neural learning is taking place in the implemented system.

Food for thought.