There’s a crux I seem to have with a lot of LWers that I’ve struggled to put my finger on for a long time but I think reduces to some combination of:
faith in elegance vs. expectation of messiness;
preference for axioms vs. examples;
identification as primarily a scientist/truth-seeker vs. as an engineer/builder.
I tend to be more inclined towards the latter in each case, whereas I think a lot of LWers are inclined towards the former, with the potential exception of the author of realism about rationality, who seems to have opinions that overlap with many of my own. While I still feel uncomfortable with the above binaries, I’ve now gathered enough examples to at least list them as evidence for what I’m talking about.
Example 1: Linear Algebra Textbooks
A fewLWers have positively reviewed Linear Algebra Done Right (LADR), in particular complimenting it for revealing the inner workings of Linear Algebra. I too recently read most of this book and did a lot of the exercises. And… I liked it but seemingly less than the
other reviewers. In particular, I enjoyed getting a lot of practice reading definition-theorem-proof style math and doing lots of proofs myself, but found myself wishing for more examples and discussion of how to compute things like eigenvalues in practice. While I know that’s not what the book’s about, the difference I’m pointing to is more that I found the omission of these things bothersome, whereas I suspect the other reviewers were happy with the focus on constructing the different objects mathematically (I’m also obviously making some assumptions here).
On the other hand, I’ve recently been reading sections of Shilov’s Linear Algebra, which is more concrete but does more ugly stuff like present the determinant very early on, and I feel like I’m learning better from it.
I think one contributing factor towards this preference difference is that I tend to be more OK with unmotivated messiness if the messy thing is clearly useful for something but less OK slogging through a bunch of elegant but not-clear-what-it’s-used-for build up. Another way to put this would be that I tend to like to get top-down view of a subject and then go depth-first afterwards, whereas others seem happy to learn bottom-up. I used to think this was because of my experience with programming where algorithms are pretty much always presented in
terms of their purpose and tend to be become messier as they get optimized for performance. I still like knowing the motivation for things, but I also accept that stuff that works for real applications often has a bunch of messiness. On the other hand, a lot of LWers are also programmers who are only now going deep on math and they seem to still be happy with the axiomatic math way of doing things. So having a programming background doesn’t seem to correlate with my preferences that strongly...
What would be great would be if someone would chime in providing better hypotheses/explanations than the one I’ve given.
Example 2: Scientists vs. Engineers as Role Models
Much of early LW content, the Sequences in particular, used scientists like Einstein and Feynman as role models in discussions (and also targets of criticism in fairness). While I love Feynman and Einstein too, I tend to also revere builders/engineers, such as John Carmack, Jeff Dean, and Konrad Zuse, but these types of people don’t seem to get nearly as much praise on LW.
One explanation for this is that great but not necessarily thoughtful engineers can drive X-risk through their work. For example, here’s a discussion where a few folks argue that AGI requires insight more than programming ability and explicitly mention needing Judea Pearl more than John Carmack. While this is a fair argument, I’m skeptical that it’s the true rejection. Security mindset seems to be as common among engineers as it is among scientists given that most of the folks who participate in things like DefCon and work in computer security tend to be hardcore engineer types like Trammell Hudson. (In his original essay, Eliezer cites Bruce Schneier, definitely an engineer, as someone he trusts to have security mindset.)
Another potential explanation for this is that LW readers tend to like doing and learning about science (pure math included) more than doing engineering. It’s plausible that people who were attracted to early LW/OB content and were compelled by arguments for X-risk tend to also prefer science to engineering.
Conclusion
Unfortunately, I don’t have some sort of nice insight to conclude this with. I don’t think the differences between my and other LWers preferences are bad so much as an implicit thing that doesn’t get discussed.
I am curious whether my dichotomies seem reasonably accurate to anyone reading this? And if so, do my hypotheses for them seem reasonable?
I think the engineer mindset is more strongly represented here than you think, but that the nature of nonspecialist online discussion warps things away from the engineer mindset and towards the scientist mindset. Both types of people are present, but the engineer-mindset people tend not to put that part of themselves forward here.
The problem with getting down into the details is that there are many areas with messy details to get into, and it’s hard to appreciate the messy details of an area you haven’t spent enough time in. So deep dives in narrow topics wind up looking more like engineer-mindset, while shallow passes over wide areas wind up looking more like scientist-mindset. LessWrong posts can’t assume much background, which limits their depth.
I would be happy to see more deep-dives; a lightly edited transcript of John Carmack wouldn’t be a prototypical LessWrong post, but it would be a good one. But such posts are necessarily going to exclude a lot of readers, and LessWrong isn’t necessarily going to be competitive with posting in more topic-specialized places.
After I saw that Benito did a transcript post, I considered doing one for one of Carmack’s talks or a recent interview of Yann LeCunn I found pretty interesting (based on the talks of his I’ve listened to, LeCunn has a pretty engineering-y mindset even though he’s nominally a scientist). Not going to happen immediately though since it requires a pretty big time investment.
Alternatively, maybe I’ll review Masters of Doom, which is where I learned most of what I know about Carmack.
This is really interesting, I’m glad you wrote this up. I think there’s something to it.
Some quick comments:
I generally expect there to exist simple underlying principles in most domains which give rise to messiness (and often the messiness seems a bit less messy once you understand them). Perceiving “messiness” does also often feel to me like lack of understanding whereas seeing the underlying unity makes me feel like I get whatever the subject matter is.
I think I would like it if LessWrong had more engineers/inventors as role models and that it’s something of an oversight that we don’t. Yet I also feel like John Carmack probably probably isn’t remotely near the level of Pearl (I’m not that familiar Carmack’s work): pushing forward video game development doesn’t compare to neatly figuring what exactly causality itself is.
There might be something like all truly monumental engineering breakthroughs depended on something like a “scientific” breakthrough. Something like Faraday and Maxwell figuring out theories of electromagnetism is actually a bigger deal than Edison(/others) figuring out the lightbulb, the radio, etc. There are cases of lauded people who are a little more ambiguous on the science/engineer dichotomy. Turing? Shannon? Tesla? Shockley et al with the transistor seems kind of like an engineering breakthrough, and seems there could be love for that. I wonder if Feynman gets more recognition because as an educator we got a lot more of the philosophy underlying his work. Just rambling here.
A little on my background: I did an EE degree which was very practical focus. My experience is that I was taught how to do apply a lot of equations and make things in the lab, but most courses skimped on providing the real understanding that left me overall worse as an engineer. The math majors actually understood Linear Algebra, the physicists actually understood electromagnetism, and I knew enough to make some neat things in the lab and pass tests, but I was worse off for not having a deeper “theoretical” understanding. So I feel like I developed more of an identity as a engineer, but came to feel that to be a really great engineer I needed to get the core science better*.
*I have some recollection that Tesla could develop a superior AC electric system because he understood the underlying math better than Edison, but this is a vague recollection.
Yet I also feel like John Carmack probably probably isn’t remotely near the level of Pearl (I’m not that familiar Carmack’s work): pushing forward video game development doesn’t compare to neatly figuring what exactly causality itself is.
You’re looking at the wrong thing. Don’t look at the topic of their work; look at their cognitive style and overall generativity. Carmack is many levels above Pearl. Just as importantly, there’s enough recorded video of him speaking unscripted that it’s feasible to absorb some of his style.
You’re looking at the wrong thing. Don’t look at the topic of their work; look at their cognitive style and overall generativity.
By generativity do you mean “within-domain” generativity?
Carmack is many levels above Pearl.
To unpack which “levels” I was grading on, it’s something like a blend of “importance and significance of their work” / “difficulty of the problems they were solving”, admittedly that’s still pretty vague. On those dimensions, it seems entirely fair to compare across topics and assert that Pearl was solving more significant and more difficult problem(s) than Carmack. And for that “style” isn’t especially relevant. (This can also be true even if Carmack solved many more problems.)
But I’m curious about your angle—when you say that Carmack is many levels above Pearl, which specific dimensions is that on (generativity and style?) and do you have any examples/links for those?
By generativity do you mean “within-domain” generativity?
Not exactly, because Carmack has worked in more than one domain (albeit not as successfully; Armadillo Aerospace never made orbit.)
On those dimensions, it seems entirely fair to compare across topics and assert that Pearl was solving more significant and more difficult problem(s) than Carmack
There might be something like all truly monumental engineering breakthroughs depended on something like a “scientific” breakthrough. Something like Faraday and Maxwell figuring out theories of electromagnetism is actually a bigger deal than Edison(/others) figuring out the lightbulb, the radio, etc. There are cases of lauded people who are a little more ambiguous on the science/engineer dichotomy. Turing? Shannon? Tesla? Shockley et al with the transistor seems kind of like an engineering breakthrough, and seems there could be love for that. I wonder if Feynman gets more recognition because as an educator we got a lot more of the philosophy underlying his work. Just rambling here.
TRIZ is an engineering discipline that has something called the five levels of innovation, which talks about this:
1. You solve a problem by using a common solution in your own speciality.
2. You solve a problem using a common solution i your own industry.
3. You solve a problem using a common solution found in other industries.
4. You solve a problem using a solution built on first principles (e.g. little known scientific principles.)
5. You solve a problem by discovering a new principle/scientific rule.
Thanks for your reply! I agree with a lot of what you said.
First off, thanks for bringing up the point about underlying principles. I agree that there are often underlying principles in many domains and that I also really like to find unity in seeming messiness. I used to be of the more extreme view that principles were in some sense more important than the details, but I’ve become more skeptical over time for two reasons.
From a pedagogy perspective, I’ve personally never had much luck learning principles without having a strong base of practice & knowledge. That said, when I have that base, learning principles helps me improve further and is satisfying.
I’ve realized over time how much of action (where action can include thinking) is based upon a set of non-verbal strategies that one learns through practice and experimentation even in seemingly theoretical domains. These strategies seem to be the secret sauce that allow one to act fluently but seem meaningfully different from the types of principles people often discuss.
Another way to phrase my argument is that principles are important but very hard to transfer between minds. It’s possible you agree and I’m just belaboring the point but I wanted to make it explicit.
One concrete example of the distinction I’m drawing is something called the “What Are Monads Fallacy” in the Haskell community where people try to explain monads by conveying their understanding of what mondas really are even though they learned about monads by just using them a bunch which lead to them later developing a higher level understanding of them. This reflects a more general problem where experts often struggle to teach to novices because they don’t realize that their broad understanding is actually founded upon lower level understanding of a lot of details.
I think I would like it if LessWrong had more engineers/inventors as role models and that it’s something of an oversight that we don’t. Yet I also feel like John Carmack probably probably isn’t remotely near the level of Pearl (I’m not that familiar Carmack’s work): pushing forward video game development doesn’t compare to neatly figuring what exactly causality itself is.
I tentatively agree, but it’s pretty hard to draw comparisons. From an insight perspective, I agree that Pearl’s work on Bayes Nets and Causality were probably more profound that anything Carmack came up with. From an economic perspective though, Carmack had a massive, albeit indirect, impact on the trajectory of the computing world. By coming up with new algorithms and techniques for 3D game rendering at a time when people had basically no idea how to render 3D games in realtime, Carmack drove the gaming industry forward, which certainly contributed to development of better GPUs and processors as well. Carmack was also the person at Id who insisted on making their games moddable and releasing their game engines, which eventually lead to the development of games like Half-Life.
That said, a better point of comparison to Pearl is probably Jeff Dean, who, in close collaboration with Sanjay Ghemawat, first wrote much of Google’s search stack from scratch after it starting failing to scale and then subsequently invented BigTable, MapReduce, Spanner, and Tensorflow!
There might be something like all truly monumental engineering breakthroughs depended on something like a “scientific” breakthrough. Something like Faraday and Maxwell figuring out theories of electromagnetism is actually a bigger deal than Edison(/others) figuring out the lightbulb, the radio, etc. There are cases of lauded people who are a little more ambiguous on the science/engineer dichotomy. Turing? Shannon? Tesla?
Agree that science tends to be upstream of later technology developments, but I would emphasize that there are probably cases where without great engineers, the actual applications never get built. For example, there was a large gap between us understanding genes fairly well and being able to sequence and, more recently, synthesize them.
Shockley et al with the transistor seems kind of like an engineering breakthrough, and seems there could be love for that.
I agree with this and would add Lynn Conway, who invented VLSI, one of the key enablers of the modern processor industry and Moore’s Law.
A little on my background: I did an EE degree which was very practical focus. My experience is that I was taught how to do apply a lot of ehttps://www.lesswrong.com/shortformquations and make things in the lab, but most courses skimped on providing the real understanding that left me overall worse as an engineer.
To be clear, I shared this frustration with the engineering curriculum. I started as a Computer Engineering major and switched to CS because I felt like engineering was just a bag of unmotivated tricks whereas in CS you could understand why things were the way they were. However, part of the reason I liked CS’s theory was because it was presented in the context of understanding algorithms.
As a final point, I don’t think I did a good job of my original post of emphasizing that I’m pro-understanding and pro-theory! I mostly endorse the saying, “nothing is so practical as a good theory.” My perceived disagreement is more around how much I trust/enjoy theory for its own sake vs. with an eye towards practice.
I do think we agree on rather a lot here. A few thoughts:
1. Seems there are separate questions of “how you model/role-models and heroes/personal identity” and separate questions of pedagogy.
You might strongly seek unifying principles and elegant theories but believe the correct way to arrive at these and understand these is through lots of real-world messy interactions and examples. That seems pretty right to me.
2. Your examples in this comment do make me update on the importance of engineering types and engineering feats. It makes me think that indeed LessWrong too much focuses only on heroes of “understanding” when there are heroes “of making things happen” which is rather a key part of rationality too.
A guess might be that this is down-steam of what was focused on in the Sequences and the culture that set. If I’m interpreting Craft and the Community correctly, Eliezer never saw the Sequences as covering all of rationality or all of what was important, just his own particular sub-art that he created in the course of trying to do one particular thing.
That’s my dream—that this highly specialized-seeming art of answering confused questions, may be some of what is needed, in the very beginning, to go and complete the rest.
Seemingly answering is confused questions is more science-y than engineering-y and would place focus on great scientists like Feynman. Unfortunately, the community has not yet supplemented the Sequences with the rest of the art of human rationality and so most of the LW culture is still downstream of the Sequences alone (mostly). Given that, we can expect the culture is missing major key pieces of what would be the full art, e.g. whatever skills are involved in being Jeff Dean and John Carmack.
My perceived disagreement is more around how much I trust/enjoy theory for its own sake vs. with an eye towards practice.
About that you might be correct. Personally, I do think I enjoy theory even without application. I’m not sure if my mind secretly thinks all topics will find their application, but having applications (beyond what is needed to understand) doesn’t feel key to my interest, so something.
At this point, I basically agree that we agree and that the most useful follow up action is for someone (read: me) to actually be the change they want to see and write some (object-level), and ideally good, content from a more engineering-y bent.
As I mentioned in my reply to jimrandomh, a book review seems like a good place for me to start.
Cruxes I Have With Many LW Readers
There’s a crux I seem to have with a lot of LWers that I’ve struggled to put my finger on for a long time but I think reduces to some combination of:
faith in elegance vs. expectation of messiness;
preference for axioms vs. examples;
identification as primarily a scientist/truth-seeker vs. as an engineer/builder.
I tend to be more inclined towards the latter in each case, whereas I think a lot of LWers are inclined towards the former, with the potential exception of the author of realism about rationality, who seems to have opinions that overlap with many of my own. While I still feel uncomfortable with the above binaries, I’ve now gathered enough examples to at least list them as evidence for what I’m talking about.
Example 1: Linear Algebra Textbooks
A few LWers have positively reviewed Linear Algebra Done Right (LADR), in particular complimenting it for revealing the inner workings of Linear Algebra. I too recently read most of this book and did a lot of the exercises. And… I liked it but seemingly less than the other reviewers. In particular, I enjoyed getting a lot of practice reading definition-theorem-proof style math and doing lots of proofs myself, but found myself wishing for more examples and discussion of how to compute things like eigenvalues in practice. While I know that’s not what the book’s about, the difference I’m pointing to is more that I found the omission of these things bothersome, whereas I suspect the other reviewers were happy with the focus on constructing the different objects mathematically (I’m also obviously making some assumptions here).
On the other hand, I’ve recently been reading sections of Shilov’s Linear Algebra, which is more concrete but does more ugly stuff like present the determinant very early on, and I feel like I’m learning better from it.
I think one contributing factor towards this preference difference is that I tend to be more OK with unmotivated messiness if the messy thing is clearly useful for something but less OK slogging through a bunch of elegant but not-clear-what-it’s-used-for build up. Another way to put this would be that I tend to like to get top-down view of a subject and then go depth-first afterwards, whereas others seem happy to learn bottom-up. I used to think this was because of my experience with programming where algorithms are pretty much always presented in
terms of their purpose and tend to be become messier as they get optimized for performance. I still like knowing the motivation for things, but I also accept that stuff that works for real applications often has a bunch of messiness. On the other hand, a lot of LWers are also programmers who are only now going deep on math and they seem to still be happy with the axiomatic math way of doing things. So having a programming background doesn’t seem to correlate with my preferences that strongly...
What would be great would be if someone would chime in providing better hypotheses/explanations than the one I’ve given.
Example 2: Scientists vs. Engineers as Role Models
Much of early LW content, the Sequences in particular, used scientists like Einstein and Feynman as role models in discussions (and also targets of criticism in fairness). While I love Feynman and Einstein too, I tend to also revere builders/engineers, such as John Carmack, Jeff Dean, and Konrad Zuse, but these types of people don’t seem to get nearly as much praise on LW.
One explanation for this is that great but not necessarily thoughtful engineers can drive X-risk through their work. For example, here’s a discussion where a few folks argue that AGI requires insight more than programming ability and explicitly mention needing Judea Pearl more than John Carmack. While this is a fair argument, I’m skeptical that it’s the true rejection. Security mindset seems to be as common among engineers as it is among scientists given that most of the folks who participate in things like DefCon and work in computer security tend to be hardcore engineer types like Trammell Hudson. (In his original essay, Eliezer cites Bruce Schneier, definitely an engineer, as someone he trusts to have security mindset.)
Another potential explanation for this is that LW readers tend to like doing and learning about science (pure math included) more than doing engineering. It’s plausible that people who were attracted to early LW/OB content and were compelled by arguments for X-risk tend to also prefer science to engineering.
Conclusion
Unfortunately, I don’t have some sort of nice insight to conclude this with. I don’t think the differences between my and other LWers preferences are bad so much as an implicit thing that doesn’t get discussed.
I am curious whether my dichotomies seem reasonably accurate to anyone reading this? And if so, do my hypotheses for them seem reasonable?
I have similar differences with many people on LW and agree there is something of an unacknowledged aesthetic here.
I think the engineer mindset is more strongly represented here than you think, but that the nature of nonspecialist online discussion warps things away from the engineer mindset and towards the scientist mindset. Both types of people are present, but the engineer-mindset people tend not to put that part of themselves forward here.
The problem with getting down into the details is that there are many areas with messy details to get into, and it’s hard to appreciate the messy details of an area you haven’t spent enough time in. So deep dives in narrow topics wind up looking more like engineer-mindset, while shallow passes over wide areas wind up looking more like scientist-mindset. LessWrong posts can’t assume much background, which limits their depth.
I would be happy to see more deep-dives; a lightly edited transcript of John Carmack wouldn’t be a prototypical LessWrong post, but it would be a good one. But such posts are necessarily going to exclude a lot of readers, and LessWrong isn’t necessarily going to be competitive with posting in more topic-specialized places.
These are all good points.
After I saw that Benito did a transcript post, I considered doing one for one of Carmack’s talks or a recent interview of Yann LeCunn I found pretty interesting (based on the talks of his I’ve listened to, LeCunn has a pretty engineering-y mindset even though he’s nominally a scientist). Not going to happen immediately though since it requires a pretty big time investment.
Alternatively, maybe I’ll review Masters of Doom, which is where I learned most of what I know about Carmack.
As the dichotomy isn’t jumping out at me, I guess I should read both of those books* sometime and see which I like more.
*Linear Algebra Done Right (LADR)
Shilov’s Linear Algebra
This is really interesting, I’m glad you wrote this up. I think there’s something to it.
Some quick comments:
I generally expect there to exist simple underlying principles in most domains which give rise to messiness (and often the messiness seems a bit less messy once you understand them). Perceiving “messiness” does also often feel to me like lack of understanding whereas seeing the underlying unity makes me feel like I get whatever the subject matter is.
I think I would like it if LessWrong had more engineers/inventors as role models and that it’s something of an oversight that we don’t. Yet I also feel like John Carmack probably probably isn’t remotely near the level of Pearl (I’m not that familiar Carmack’s work): pushing forward video game development doesn’t compare to neatly figuring what exactly causality itself is.
There might be something like all truly monumental engineering breakthroughs depended on something like a “scientific” breakthrough. Something like Faraday and Maxwell figuring out theories of electromagnetism is actually a bigger deal than Edison(/others) figuring out the lightbulb, the radio, etc. There are cases of lauded people who are a little more ambiguous on the science/engineer dichotomy. Turing? Shannon? Tesla? Shockley et al with the transistor seems kind of like an engineering breakthrough, and seems there could be love for that. I wonder if Feynman gets more recognition because as an educator we got a lot more of the philosophy underlying his work. Just rambling here.
A little on my background: I did an EE degree which was very practical focus. My experience is that I was taught how to do apply a lot of equations and make things in the lab, but most courses skimped on providing the real understanding that left me overall worse as an engineer. The math majors actually understood Linear Algebra, the physicists actually understood electromagnetism, and I knew enough to make some neat things in the lab and pass tests, but I was worse off for not having a deeper “theoretical” understanding. So I feel like I developed more of an identity as a engineer, but came to feel that to be a really great engineer I needed to get the core science better*.
*I have some recollection that Tesla could develop a superior AC electric system because he understood the underlying math better than Edison, but this is a vague recollection.
You’re looking at the wrong thing. Don’t look at the topic of their work; look at their cognitive style and overall generativity. Carmack is many levels above Pearl. Just as importantly, there’s enough recorded video of him speaking unscripted that it’s feasible to absorb some of his style.
By generativity do you mean “within-domain” generativity?
To unpack which “levels” I was grading on, it’s something like a blend of “importance and significance of their work” / “difficulty of the problems they were solving”, admittedly that’s still pretty vague. On those dimensions, it seems entirely fair to compare across topics and assert that Pearl was solving more significant and more difficult problem(s) than Carmack. And for that “style” isn’t especially relevant. (This can also be true even if Carmack solved many more problems.)
But I’m curious about your angle—when you say that Carmack is many levels above Pearl, which specific dimensions is that on (generativity and style?) and do you have any examples/links for those?
Not exactly, because Carmack has worked in more than one domain (albeit not as successfully; Armadillo Aerospace never made orbit.)
Agree on significance, disagree on difficulty.
In an interesting turn of events, John Carmack announced today that he’ll be pivoting to work on AGI.
TRIZ is an engineering discipline that has something called the five levels of innovation, which talks about this:
1. You solve a problem by using a common solution in your own speciality.
2. You solve a problem using a common solution i your own industry.
3. You solve a problem using a common solution found in other industries.
4. You solve a problem using a solution built on first principles (e.g. little known scientific principles.)
5. You solve a problem by discovering a new principle/scientific rule.
Seems you’re referring to this https://en.wikipedia.org/wiki/TRIZ?
Yes.
Thanks for your reply! I agree with a lot of what you said.
First off, thanks for bringing up the point about underlying principles. I agree that there are often underlying principles in many domains and that I also really like to find unity in seeming messiness. I used to be of the more extreme view that principles were in some sense more important than the details, but I’ve become more skeptical over time for two reasons.
From a pedagogy perspective, I’ve personally never had much luck learning principles without having a strong base of practice & knowledge. That said, when I have that base, learning principles helps me improve further and is satisfying.
I’ve realized over time how much of action (where action can include thinking) is based upon a set of non-verbal strategies that one learns through practice and experimentation even in seemingly theoretical domains. These strategies seem to be the secret sauce that allow one to act fluently but seem meaningfully different from the types of principles people often discuss.
Another way to phrase my argument is that principles are important but very hard to transfer between minds. It’s possible you agree and I’m just belaboring the point but I wanted to make it explicit.
One concrete example of the distinction I’m drawing is something called the “What Are Monads Fallacy” in the Haskell community where people try to explain monads by conveying their understanding of what mondas really are even though they learned about monads by just using them a bunch which lead to them later developing a higher level understanding of them. This reflects a more general problem where experts often struggle to teach to novices because they don’t realize that their broad understanding is actually founded upon lower level understanding of a lot of details.
I tentatively agree, but it’s pretty hard to draw comparisons. From an insight perspective, I agree that Pearl’s work on Bayes Nets and Causality were probably more profound that anything Carmack came up with. From an economic perspective though, Carmack had a massive, albeit indirect, impact on the trajectory of the computing world. By coming up with new algorithms and techniques for 3D game rendering at a time when people had basically no idea how to render 3D games in realtime, Carmack drove the gaming industry forward, which certainly contributed to development of better GPUs and processors as well. Carmack was also the person at Id who insisted on making their games moddable and releasing their game engines, which eventually lead to the development of games like Half-Life.
That said, a better point of comparison to Pearl is probably Jeff Dean, who, in close collaboration with Sanjay Ghemawat, first wrote much of Google’s search stack from scratch after it starting failing to scale and then subsequently invented BigTable, MapReduce, Spanner, and Tensorflow!
Agree that science tends to be upstream of later technology developments, but I would emphasize that there are probably cases where without great engineers, the actual applications never get built. For example, there was a large gap between us understanding genes fairly well and being able to sequence and, more recently, synthesize them.
I agree with this and would add Lynn Conway, who invented VLSI, one of the key enablers of the modern processor industry and Moore’s Law.
To be clear, I shared this frustration with the engineering curriculum. I started as a Computer Engineering major and switched to CS because I felt like engineering was just a bag of unmotivated tricks whereas in CS you could understand why things were the way they were. However, part of the reason I liked CS’s theory was because it was presented in the context of understanding algorithms.
As a final point, I don’t think I did a good job of my original post of emphasizing that I’m pro-understanding and pro-theory! I mostly endorse the saying, “nothing is so practical as a good theory.” My perceived disagreement is more around how much I trust/enjoy theory for its own sake vs. with an eye towards practice.
Sorry for the delayed reply on this one.
I do think we agree on rather a lot here. A few thoughts:
1. Seems there are separate questions of “how you model/role-models and heroes/personal identity” and separate questions of pedagogy.
You might strongly seek unifying principles and elegant theories but believe the correct way to arrive at these and understand these is through lots of real-world messy interactions and examples. That seems pretty right to me.
2. Your examples in this comment do make me update on the importance of engineering types and engineering feats. It makes me think that indeed LessWrong too much focuses only on heroes of “understanding” when there are heroes “of making things happen” which is rather a key part of rationality too.
A guess might be that this is down-steam of what was focused on in the Sequences and the culture that set. If I’m interpreting Craft and the Community correctly, Eliezer never saw the Sequences as covering all of rationality or all of what was important, just his own particular sub-art that he created in the course of trying to do one particular thing.
Seemingly answering is confused questions is more science-y than engineering-y and would place focus on great scientists like Feynman. Unfortunately, the community has not yet supplemented the Sequences with the rest of the art of human rationality and so most of the LW culture is still downstream of the Sequences alone (mostly). Given that, we can expect the culture is missing major key pieces of what would be the full art, e.g. whatever skills are involved in being Jeff Dean and John Carmack.
About that you might be correct. Personally, I do think I enjoy theory even without application. I’m not sure if my mind secretly thinks all topics will find their application, but having applications (beyond what is needed to understand) doesn’t feel key to my interest, so something.
At this point, I basically agree that we agree and that the most useful follow up action is for someone (read: me) to actually be the change they want to see and write some (object-level), and ideally good, content from a more engineering-y bent.
As I mentioned in my reply to jimrandomh, a book review seems like a good place for me to start.
Cool. Looking forward to it!