Any field of study has a rich and complex backlog of history and influences. While the subject itself might seem like a independent area with its own trajectory and interests, becoming familiar with it usually reveals a trail of inspirations and catalysts from many other seemingly-different fields--no idea materializes in a vacuum. Understanding the foundation of a subject thus becomes much more interesting...but also much more complicated.
In the case of AI, learning the background has been kind of hard to pin down, if only because the field is so young. It's not that there isn't a lot of clear influences--the opposite, in fact. It seems like the field is still defining itself and pulling inspiration from a number of places. Compared to, say, biology or physics or even computer science itself, there doesn't seem to be a defining era, an identity born from many decades of cobbling together concepts and brilliant minds--it feels instead like resources are still being gathered and major foci discussed. There isn't a sort of advantage of hindsight, at least from my perspective.
It could also be, of course, that I am not in the optimal spot for clarity. I didn't enter through the channel of a bachelors in CS, so it could be that I missed out on some of the foundational concepts. Instead, we're learning about the philosophical and ethical issues, about the problems with formal logics and linguistic semantics that are occurring to the present day, about the feedback to and from the fields of psychology and neurology. It seems like many of the concepts we're covering is not just computer science, but how computer science alone could not handle the task of creating intelligence with the tools they've made for other applications. Earlier attempts to answers the question of "how to create a rational agent" ended in a wall of limitations, both in terms of computational resources and infinite logical possibilities--more thought and research seemed to reveal that perhaps, pure logic that disregarded human "irrationality" doesn't seem to work as well as it sounds when dealing with a fast-paced, irrational world. The drawing-board has been revisited many times and from many angles, and a balance is still yet to be fully agreed upon.
From an individual perspective, jumping into the deep end of "where to go from here" certainly makes it difficult to find a useful niche. It feels like there are many branching paths, and any of them could be fruitful just as any of them could be a dead-end. There are many opportunities, but few of them seem available to someone who isn't pretty well up-to-speed with many different areas of contention. The question then is: does one devote time and resources to a global understanding of the current state of the field, or does one choose to pursue mastery over a distinct branch within? What is the optimal balance? While I'd love to focus more on the former, the approach of thesis-time is looming, and one cannot study without results forever. In a little over half a year, I'll have to contribute to this mass of information in this rapidly-developing field.
It's hard to make a choice like that when you feel like you know basically nothing. But little by little, learning about the history and the way logics have changed and the leaps that have been made in modeling...I think I'm making some progress, at least.
In the case of AI, learning the background has been kind of hard to pin down, if only because the field is so young. It's not that there isn't a lot of clear influences--the opposite, in fact. It seems like the field is still defining itself and pulling inspiration from a number of places. Compared to, say, biology or physics or even computer science itself, there doesn't seem to be a defining era, an identity born from many decades of cobbling together concepts and brilliant minds--it feels instead like resources are still being gathered and major foci discussed. There isn't a sort of advantage of hindsight, at least from my perspective.
It could also be, of course, that I am not in the optimal spot for clarity. I didn't enter through the channel of a bachelors in CS, so it could be that I missed out on some of the foundational concepts. Instead, we're learning about the philosophical and ethical issues, about the problems with formal logics and linguistic semantics that are occurring to the present day, about the feedback to and from the fields of psychology and neurology. It seems like many of the concepts we're covering is not just computer science, but how computer science alone could not handle the task of creating intelligence with the tools they've made for other applications. Earlier attempts to answers the question of "how to create a rational agent" ended in a wall of limitations, both in terms of computational resources and infinite logical possibilities--more thought and research seemed to reveal that perhaps, pure logic that disregarded human "irrationality" doesn't seem to work as well as it sounds when dealing with a fast-paced, irrational world. The drawing-board has been revisited many times and from many angles, and a balance is still yet to be fully agreed upon.
From an individual perspective, jumping into the deep end of "where to go from here" certainly makes it difficult to find a useful niche. It feels like there are many branching paths, and any of them could be fruitful just as any of them could be a dead-end. There are many opportunities, but few of them seem available to someone who isn't pretty well up-to-speed with many different areas of contention. The question then is: does one devote time and resources to a global understanding of the current state of the field, or does one choose to pursue mastery over a distinct branch within? What is the optimal balance? While I'd love to focus more on the former, the approach of thesis-time is looming, and one cannot study without results forever. In a little over half a year, I'll have to contribute to this mass of information in this rapidly-developing field.
It's hard to make a choice like that when you feel like you know basically nothing. But little by little, learning about the history and the way logics have changed and the leaps that have been made in modeling...I think I'm making some progress, at least.
