Mark Ho
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Mark Ho’s voice in public audio — every appearance, attributed to the second.
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Yeah, so my background is in cognitive science and artificial intelligence, and I work on the intersection of computer science and psychology, studying how human cognition works, how people solve problems, how that compares to how machines solve problems, and trying to understand the general principles of problem solving and intelligence.
Exactly.
Cool.
So no, I kind of I'm approaching things from a much more kind of psychological cognitive perspective than neuro perspective.
Okay.
Yeah, exactly.
And trying to understand, yeah, how can we understand kind of people as thinkers and reasoners in a more general sense?
And how does that compare to how machines are like current machines and AI systems are reasoning?
And to the extent they are reasoning and solving problems.
Yeah, so there are really two goals, I think, of kind of the research kind of field that I'm in computational cognitive science.
One is to kind of use general principles developed in AI and kind of tools and formal methods from AI to model human cognition in a way that we can better understand how people are solving problems and kind of understand things like perception and memory in terms that are like precise enough to make kind of quantitative predictions and stuff like that.
And then the other side of it is kind of developing better models and kind of predictive general models of how people kind of think and solve problems and perceive and remember things so that we can use that to design AI systems that kind of understand how humans think and work and design better interfaces and stuff like that.
Yeah.
So a lot of the, a lot of the research is going from kind of the AI formalisms and ideas for, you know, how to even build like kind of how you would engineer a AI system gives you a lot of insight into how you would, how you can kind of reverse engineer human mind and human cognition and intelligence.
So there's been a lot of direction that way recently.
But there's actually a long history also of the other way, kind of thinking in psychology more formally, a lot of,
of core ideas in ai kind of originated in mathematical psychology and computational cognitive science things like connectionist uh theories are the foundation of deep neural networks today and uh a lot of things from a lot of uh these kind of form these kind of uh reinforcement learning algorithms the ones that were able to solve like chess and go and beat humans in these
in these games, a lot of the kind of basic principles from that were developed in studying like learning, associative learning in like rats and stuff.
So there's direction that way.
And so I do a little bit of both.
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