In 1988 the Pixies asked a question that philosophers and scientists still debate to this day. Since then and maybe a bit before, we’ve gotten a few different answers. In the analytic philosophy tradition we have two flavors of definition.
When friends or family ask what I study, depending on my mood, the answer can be neuroscience, cognitive science, animal behavior, computational biology, artificial intelligence, and if I am feeling really frisky I’ll combine them. I don’t think I’m lying. Although, there is a real name for what I study: computational neuroethology. In this post I want to explain what those words mean and by the end you can let me know if I’m being honest.
When friends or family ask what I study, depending on my mood, the answer can be neuroscience, cognitive science, animal behavior, computational biology, artificial intelligence, and if I am feeling really frisky I’ll combine them. I don’t think I’m lying. Although, there is a real name for what I study: computational neuroethology. In this post I want to explain what those words mean and by the end you can let me know if I’m being honest.
Everyday you bet your life that you can step on that road, eat that food, see that car coming. You bet your life and in nearly every single case you are right. That is incredible. Anyone who has made tried a hand at AI knows that’s incredible. So why is it that we can do that and yet we haven’t been able to build machines that can too?
In 1988 the Pixies asked a question that philosophers and scientists still debate to this day. Since then and maybe a bit before, we’ve gotten a few different answers. In the analytic philosophy tradition we have two flavors of definition.
In 1988 the Pixies asked a question that philosophers and scientists still debate to this day. Since then and maybe a bit before, we’ve gotten a few different answers. In the analytic philosophy tradition we have two flavors of definition.
In science we use metaphors all the time. Those metaphors could be mathematical where the phenomena is described as a formula or an algorithm. They could be physical like using water to explain electricity. They could also be specific examples. Imagine you were a scientist interested in flight, how could you study it? You could do experiments with planes, helicopters, birds, insects, bats, rockets, UFOs, or more. All of these would be examples of flight. However, which one you focus on will have a big impact on how you think about flight generally. Sometimes you might even confuse flight with the specific example you are studying. This is one of the reasons why a good theoretical foundation is important for any discipline. All of these examples are important for building a theory of flight as opposed to a theory of helicopters, for example. Aerodynamics doesn’t tell us about how eagles work but it can tell us a good bit about how they fly. So what could an aerodynamics of intelligence look like?
Everyday you bet your life that you can step on that road, eat that food, see that car coming. You bet your life and in nearly every single case you are right. That is incredible. Anyone who has made tried a hand at AI knows that’s incredible. So why is it that we can do that and yet we haven’t been able to build machines that can too?
In 1988 the Pixies asked a question that philosophers and scientists still debate to this day. Since then and maybe a bit before, we’ve gotten a few different answers. In the analytic philosophy tradition we have two flavors of definition.
In science we use metaphors all the time. Those metaphors could be mathematical where the phenomena is described as a formula or an algorithm. They could be physical like using water to explain electricity. They could also be specific examples. Imagine you were a scientist interested in flight, how could you study it? You could do experiments with planes, helicopters, birds, insects, bats, rockets, UFOs, or more. All of these would be examples of flight. However, which one you focus on will have a big impact on how you think about flight generally. Sometimes you might even confuse flight with the specific example you are studying. This is one of the reasons why a good theoretical foundation is important for any discipline. All of these examples are important for building a theory of flight as opposed to a theory of helicopters, for example. Aerodynamics doesn’t tell us about how eagles work but it can tell us a good bit about how they fly. So what could an aerodynamics of intelligence look like?
In science we use metaphors all the time. Those metaphors could be mathematical where the phenomena is described as a formula or an algorithm. They could be physical like using water to explain electricity. They could also be specific examples. Imagine you were a scientist interested in flight, how could you study it? You could do experiments with planes, helicopters, birds, insects, bats, rockets, UFOs, or more. All of these would be examples of flight. However, which one you focus on will have a big impact on how you think about flight generally. Sometimes you might even confuse flight with the specific example you are studying. This is one of the reasons why a good theoretical foundation is important for any discipline. All of these examples are important for building a theory of flight as opposed to a theory of helicopters, for example. Aerodynamics doesn’t tell us about how eagles work but it can tell us a good bit about how they fly. So what could an aerodynamics of intelligence look like?
When friends or family ask what I study, depending on my mood, the answer can be neuroscience, cognitive science, animal behavior, computational biology, artificial intelligence, and if I am feeling really frisky I’ll combine them. I don’t think I’m lying. Although, there is a real name for what I study: computational neuroethology. In this post I want to explain what those words mean and by the end you can let me know if I’m being honest.
In 1988 the Pixies asked a question that philosophers and scientists still debate to this day. Since then and maybe a bit before, we’ve gotten a few different answers. In the analytic philosophy tradition we have two flavors of definition.
Everyday you bet your life that you can step on that road, eat that food, see that car coming. You bet your life and in nearly every single case you are right. That is incredible. Anyone who has made tried a hand at AI knows that’s incredible. So why is it that we can do that and yet we haven’t been able to build machines that can too?