Below is a transcript of our conversation with Dr. Matthew Katz
David Nicholas:
I'm David Nicholas and this is Central Focus, a weekly look at research activity and innovative work from Central Michigan University students and faculty. As soon as you mention artificial intelligence, or AI, lines are quickly drawn between advocates and skeptics. What can AI be in a positive sense? What good can it do and what are the potential downsides? It's not just a question of technology, but also philosophy. Dr. Matthew Katz is a faculty member in CMU's Department of Philosophy, Anthropology, and Religion.
So, Matthew, if we're talking artificial intelligence, somebody that just drops in on this conversation, they're going to think maybe that we're talking tech, but we're really not. Maybe an aspect of looking at AI artificial intelligence in a much different way. You are a philosopher. You are a teacher of philosophy. So, if we are not talking about tech and you weren't looking at it initially as something involving tech, what did draw in your curiosity to study this?
Matthew Katz:
So, for me it's very closely related to other issues in philosophy that I'm really that I'm interested in. Also, so I work in in philosophy of mind and philosophy of psychology. And here are just some of the questions in those fields. What exactly is it to be conscious? What kinds of things are conscious? What here's the sort of traditional question is, what is the relationship between the mind and the body? Umm, and in those are sort of traditional philosophy of mind questions and philosophy of psychology. I'd say there are lots of interesting questions about how the mind works. Is it a computer? If it is, what kind of computer? What are the, like, what are the programs like if you will? And you know, that's naturally related to artificial intelligence research and (and), you know, one of the ways to think about. If you wanted to answer those questions in philosophy, in philosophy of psychology and philosophy of mind. And sort of. At least a couple of ways to do. One is to investigate the human mind as it is another way to do it is to try to build a synthetic version or an artificial version. A lot of artificial intelligence started out that. Like, hey, can we understand how the mind works by building an artificial one that would look like? And here we are many decades later, still trying to do.
DN:
When you sat down with my colleagues in University Communications, they titled the article, “What happens if artificial intelligence becomes self-aware?” That (that) sounds like more of, now we're really ready for an application kind of process. Is the question of conscience and consciousness, is that where we begin making this transition between understanding ourselves and then making the path to the machine?
MK:
What happened was, there, and now still are many, I say programs that do individual tasks like chess playing machines or now machines that drive cars autonomously. The question of when would a machine be conscious is a much, much more difficult question that I think we're not anywhere near solving or answering.
DN:
Are we really using it for the best purposes? Or, are we not?
MK:
And so, in no particular order, I'll say the issue of deep fakes is a real issue given our politically fractured and our sort of fractured state of media consumption, it's a real problem figuring out what's real and what isn’t. And I think right now there are some tells, but as it gets better it will get more and more difficult. And that's a real concern there's also. Concerns about how data is gathered so it takes massive amounts of data to train these systems and where the data can from whether there are privacy considerations that may be violated, and there are concerns about recreating or exacerbating past inequities or present inequities. If you're using a system to recommend candidates for interview or to hire for a position and it's based on past data and the past data is has not been equitable. There's a worry that the systems might recreate those inequities. Or make them. There's all kinds of questions like that.
DN:
But it is very good that people like you are asking those questions. Matthew Katz, thank you very much for taking the time to sit down and talk with us.
MK:
Thanks so much for having me.
