by Push Singh, MIT Media Lab
October 16, 1999
We are beginning work on a project to build a commonsense database by trying to get people on the internet to type it in. We are still sorting out the many details, but I am anticipating a number of “brain-in-the-vat” criticisms complaining that such a system could never be intelligent, and I’d like to try to head them off immediately.
Critic: Your system will not be intelligent because ...
First, let us try to understand what people mean when their complaint starts out this way. Let us parse this as
“Your system differs from a person because ...”We do want the system to understand people well, but for us it is not a problem if the system does not think just like a person does.
"Your system will be poor at solving the same problems that people can because ...”
Critic: Your system will never really understand what it means be an actual living person.
In much the same way that anthropologists make theories of other cultures and ethologists make theories of other species, machines can make theories of human beings. It is possible to study and articulate the behavior of things different from yourself. We may never really understand them in the same way that they understand themselves, but at the same time, there are ways we might understand them better than they understand themselves! It is not uncommon for people to sometimes have, in some ways, better models of their friends than their friends have of themselves.
Critic: Your system will not ever be intelligent because it is not grounded in sensory perception.
I have three responses:
This criticism points to a genuine problem. Contextualizing the knowledge is important to connect pieces of knowledge that are used or learned together. But at the same time, much of what children presumably must do when they learn is to separate accidentally associated items into separate mental bins. In a sense, this project will start from the other direction from real children, because the knowledge is provided as isolated, separate items. The problem then becomes re-associating these items into useful contexts, and we need to think of good ways to make it easier to do this.