At a minimum, a grasp of computer programming and CS. Computer programming, not even AI.
I’m inclined to disagree somewhat with Eliezer_2009 on the issue of traditional AI—even basic graph search algorithms supply valuable intuitions about what planning looks like, and what it is not. But even that same (obsoleted now, I assume) article does list computer programming knowledge as a requirement.
...what are the prerequisites for grasping the truth when it comes to AI risks?
At a minimum, a grasp of computer programming and CS. Computer programming, not even AI.
What counts as “a grasp” of computer programming/science? I can e.g. program a simple web crawler and solve a bunch of Project Euler problems. I’ve read books such as “The C Programming Language”.
I would have taken the udacity courses on machine learning by now, but the stated requirement is a strong familiarity with Probability Theory, Linear Algebra and Statistics. I wouldn’t describe my familiarity as strong, that will take a few more years.
I am skeptical though. If the reason that I dismiss certain kinds of AI risks is that I lack the necessary education, then I expect to see rebuttals of the kind “You are wrong because of (add incomprehensible technical justification)...”. But that’s not the case. All I see are half-baked science fiction stories and completely unconvincing informal arguments.
What counts as “a grasp” of computer programming/science?
This is actually a question I’ve thought about quite a bit, in a different context. So I have a cached response to what makes a programmer, not tailored to you or to AI at all. When someone asks for guidance on development as a programmer, the question I tend to ask is, how big is the biggest project you architected and wrote yourself?
The 100 line scale tests only the mechanics of programming; the 1k line scale tests the ability to subdivide problems; the 10k line scale tests the ability to select concepts; and the 50k line scale tests conceptual taste, and the ability to add, split, and purge concepts in a large map. (Line numbers are very approximate, but I believe the progression of skills is a reasonably accurate way to characterize programmer development.)
New programmers (not jimrandomh), be wary of line counts! It’s very easy for a programmer who’s not yet ready for a 10k line project to turn it into a 50k lines. I agree with the progression of skills though.
Yeah, I was thinking more of “project as complex as an n-line project in an average-density language should be”. Bad code (especially with copy-paste) can inflate inflate line numbers ridiculously, and languages vary up to 5x in their base density too.
I would have taken the udacity courses on machine learning by now, but the stated requirement is a strong familiarity with Probability Theory, Linear Algebra and Statistics. I wouldn’t describe my familiarity as strong, that will take a few more years.
I think you’re overestimating these requirements. I haven’t taken the Udacity courses, but I did well in my classes on AI and machine learning in university, and I wouldn’t describe my background in stats or linear algebra as strong—more “fair to conversant”.
They’re both quite central to the field and you’ll end up using them a lot, but you don’t need to know them in much depth. If you can calculate posteriors and find the inverse of a matrix, you’re probably fine; more complicated stuff will come up occasionally, but I’d expect a refresher when it does.
At a minimum, a grasp of computer programming and CS. Computer programming, not even AI.
I’m inclined to disagree somewhat with Eliezer_2009 on the issue of traditional AI—even basic graph search algorithms supply valuable intuitions about what planning looks like, and what it is not. But even that same (obsoleted now, I assume) article does list computer programming knowledge as a requirement.
What counts as “a grasp” of computer programming/science? I can e.g. program a simple web crawler and solve a bunch of Project Euler problems. I’ve read books such as “The C Programming Language”.
I would have taken the udacity courses on machine learning by now, but the stated requirement is a strong familiarity with Probability Theory, Linear Algebra and Statistics. I wouldn’t describe my familiarity as strong, that will take a few more years.
I am skeptical though. If the reason that I dismiss certain kinds of AI risks is that I lack the necessary education, then I expect to see rebuttals of the kind “You are wrong because of (add incomprehensible technical justification)...”. But that’s not the case. All I see are half-baked science fiction stories and completely unconvincing informal arguments.
This is actually a question I’ve thought about quite a bit, in a different context. So I have a cached response to what makes a programmer, not tailored to you or to AI at all. When someone asks for guidance on development as a programmer, the question I tend to ask is, how big is the biggest project you architected and wrote yourself?
The 100 line scale tests only the mechanics of programming; the 1k line scale tests the ability to subdivide problems; the 10k line scale tests the ability to select concepts; and the 50k line scale tests conceptual taste, and the ability to add, split, and purge concepts in a large map. (Line numbers are very approximate, but I believe the progression of skills is a reasonably accurate way to characterize programmer development.)
New programmers (not jimrandomh), be wary of line counts! It’s very easy for a programmer who’s not yet ready for a 10k line project to turn it into a 50k lines. I agree with the progression of skills though.
Yeah, I was thinking more of “project as complex as an n-line project in an average-density language should be”. Bad code (especially with copy-paste) can inflate inflate line numbers ridiculously, and languages vary up to 5x in their base density too.
I think you’re overestimating these requirements. I haven’t taken the Udacity courses, but I did well in my classes on AI and machine learning in university, and I wouldn’t describe my background in stats or linear algebra as strong—more “fair to conversant”.
They’re both quite central to the field and you’ll end up using them a lot, but you don’t need to know them in much depth. If you can calculate posteriors and find the inverse of a matrix, you’re probably fine; more complicated stuff will come up occasionally, but I’d expect a refresher when it does.