I am a relative newcomer to Less Wrong. I discovered Elizer Yudkowski and Scott Anderson in 2019. My posts revolve around trying to solve quantum gravity and build an AGI. For my day job, I run a consumer AI company.
lsusr
Karma: 1,447
If we are listing quality educational history podcasts then it would be a shame not to mention History of Japan.
Similar ladders exist in other domains—drawing, for example. But I have not observed such hard ceilings on one’s performance in drawing. Effective training seems far more important. The same goes for physics, entrepreneurship and foreign languages.
I like where you are going with #10. Perhaps it could condensed into “informatic creativity”.
Remember the first atomic bomb to kill people was a howitzer barrel and two lumps of Uranium 235 (not even weapons grade) shot into each other.
What, precisely, do you mean by “not even weapons grade”? Do you have a source for this?
A truck driver as a hobby built a copy of Little Boy with public sources.
Little Boy was a nuclear weapon. From the NPR article, it sounds like truck driver John Coster-Mullen did not build a Uranium-235 core. A fission bomb without a Uranium-235 core is not a nuclear weapon.
“The hard part is creating the nuclear fuel. That requires a nation-state,” says.
―Coster-Mullen in North Korea Designed A Nuke. So Did This Truck Driver
Coster-Mullen reverse-engineered a nuke. Then he built a toy. He never built a nuke.
I have never met someone who took a year to understand recursion. If it is not grasped quickly then the student never reaches mastery at all. Many programmers read this website and so far not a single one has contested this claim.
Incidentally, I bounced off of CS for a couple years after getting a bad grade in my first CS class. But I bounced off because the class was too easy and therefore not worth time. Not because it was too hard.
This kind of inference feels like claiming the insolubility of the quintic because I solved a couple quintics numerically and the numbers look very weird.
I think it is more like the irreversibility of the trapdoor functions we use in cryptography. We are unable to prove mathematically they are secure. But an army of experts failing to break them is Bayesian evidence.
Sidenote: Lol.
As I pointed out in my other comments, these levels are about potential, not ability. The question are not really about “Can you answer ?” They are more along the lines of “Can you easily learn to answer ?” I believe this decouples “the crystallized intelligence requirements from the fluid ones”.
What about recursion as a concept makes it hard for people to understand?…I’d like to see the article point at the key idea behind recursion that people have trouble grasping.
Recursion requires a person to hold two layers of abstraction in zeir mind simultaneously. This could require twice as much working memory. Working memory, unlike crystallized intelligence, is something we cannot do much to improve.
Is there a ‘cannot learn pointers’ in chemistry for example?
I have never tutored chemistry. But I have tutored physics for years. I have never run into anything like “cannot learn pointers” in standard undergraduate physics. The only time I encounter anything like “cannot learn pointers” is when I discuss my weird crackpot theories of relational quantum gravity and entropic time.
The ideas involved in undergraduate computer science are simpler than the ideas involved in undergraduate physics. The difference between undergraduate computer science and undergraduate physics is that undergraduate physics is a set of solutions to a set of problems. It requires little creativity. You can (relatively speaking) learn everything by rote. While fluid intelligence helps you learn rote knowledge faster, fluid intelligence tends not to put a hard cap on total rote knowledge acquisition.
On the other hand, computer science requires a person to solve novel (albeit simpler) problems all the time. It makes sense one’s ability to do this could be limited by fluid intelligence.
The other difference between computer science and physics is that in physics you never have to hold two layers of abstraction in your head at the same time. It makes sense that holding two layers of abstraction in your head at the same time could be limited by working memory. One’s ability to solve novel problems is the definition of fluid intelligence. Fluid intelligence is correlated with working memory.
My brief forays into chemistry suggest chemistry involves more rote knowledge and less “juggling multiple layers of abstraction” than computer science does. Chemistry, like all subjects, is limited by intelligence at some level. But I would expect the floor is lower than physics and computer science.
I’d also be wary of the possibility this entire framework / system looks good because it positions your tribe as superior (computer programmers) and possibly you somewhere comfortably towards the top.
Really? I do not know a single computer programmer who meets #9. I know only one who passes #8. Most of my programmer friends do not even pass #7. This framework puts quants on top. Not computer programmers. I do not think anyone would doubt that quants are smart people—at least in the sense relevant to this post.
Computer programmers tend to be superior at computer programming (and perhaps related fields).
This article triggered me emotionally because I think one of the things that prevents people from learning things is the belief that they can’t. I wouldn’t want anyone to take away from this article that because they didn’t understand pointers or recursion at some point in there life, it was because they are dumb and should stop trying.
Among the most important things I have learned from Less Wrong is the value of epistemic rationality over instrumental rationality. It breaks my heart that certain people seem unable to perform certain tasks I consider trivial. The greater cruelty is blaming someone for their failure to achieve a goal zey lack the potential to accomplish.
The levels are about potential, not ability. The tests are not pass/fail (except for #8). They are pass/null tests. Ability implies potential. Lack of ability does not imply lack of potential. (Except nominally in the Bayesian sense.)
I do indeed mean “lowest big-O complexity”.
The pointer puzzle was unfair. I have removed it. What quibble do you have with #4?
I appreciate your genuine attempt to understand this post. Python is indeed a stand-in for any programming language of comparable complexity. Pointers and recursion could indeed be flipped around. I think we are on the same page concerning these details.
You seem to be making a few claims: (1) that these skills require an increasing amount of 1-dimensional intelligence (2) that one cannot do lower-indexed things without doing higher-indexed ones and (3) that there is something fundamental about this.
Yes.
Not quite. There can be a little slippage between close-together indices, especially #6 & #7. More importantly, this hierarchy is about talent and potential, not ability. If someone could do #5 with a little bit of study then that counts as doing #5. It is easy to imagine a mathematician who understands recursion but has never written a line of computer code. But I would be surprised if such a person could not quickly learn to write simple Python scripts.
This is technically an implication, not a claim. But…yeah.
First, what is an abstraction?
By “abstraction”, I mean a simplification of the universe—a model. By “higher-level abstraction”, I mean a model built on top of another model.[1]
I am not referring to how these models are built upon one another within mathematics. I am trying to organize them based on how they are constructed inside a typical human brain. For example, a human being learns to write before learning arithmetic and learns arithmetic before learning calculus. It is possible to construct calculus from the Peano axioms while skipping over all of arithmetic. But that is not how a human child learns calculus.
To put it another way, a human being learning a complex skill begins by learning to perform the skill consciously. With practice, the skill becomes unconscious. This frees up the conscious mind to think on a higher (what I call more “abstract”) level. Rinse and repeat. To use an example from Brandon Sanderson’s creative writing lectures, a novice author is worried about how to put words down on a page while a skilled writer is thinking with plot structure. An author cannot afford to think about higher level activities until the lower level activities are automatic.
I hope this clarifies exactly what I mean by “abstraction”.
The best I can make of this post is that these tasks have something akin to a hard-floor in g-factor required…
Yes. That is the point of this post.
…which is an extraordinary claim in need of extraordinary evidence.
Really? What makes you think this is an extraordinary claim? My prior is 50%/50% credence without evidence. Here are some observations I have used to subsequently update those priors.
I am not the first person to point out that there is a hard floor on the intelligence required to understand pointers. Joel Spolsky wrote about the basic idea concerning pointers back in 2005. My college computer science teacher pointed out something similar in 2011.
On the low end of the spectrum, I have cared for severely retarded adults who appeared to have -related floors. This is a sensitive example and I do not want to get too deep into it. But it should be clear that someone without enough to hold a coherent conversation is unlikely ever to perform arithmetic at the level specified in this post.
Similarly, an adult who has trouble remembering how to press 3 buttons on a smartphone is unlikely ever to write computer code.
On the high end of the spectrum, quants are regularly hired from physics departments. These people can apparently pick up finance faster than students trained in economics. The difference seems to be mathematical aptitude. When I talk to quants I can get away with explaining much less than when I talk to, say, a Silicon Valley CTO. At least one friend of mine has confirmed this evaluation.
I have spent countless hours tutoring physics, computer science and other subjects. The levels in my post comes from my personal experience. When it comes to specific subjects it often feels like there’s a block and my interlocutor cannot fit all of the relevant information in zeir head at once.
In the scientific literature it is established that traits like fluid intelligence and working memory are basically capped. If the ability to reason at certain levels of abstraction is capped by fluid intelligence or working memory then that would explain the phenomenon I have observed.
This might seem to contradict my claim about talent and potential. You can get around this contradiction by supposing the existence of horizontal alternatives for specific benchmarks. If these alternatives require similar potential to pass then they do not invalidate the ordering. ↩︎
A Hierarchy of Abstraction
I am not yet prepared to release the details of what the system can and cannot do.
I myself also tend to enjoy posts about science going down the wrong road. If you are comfortable sharing and it is is not too inconvenient to you, I am curious which specific posts you have enjoyed on this topic.
This is not a hypothetical to me. I currently have, running on my computer, a few tricks that allow me to train small data systems much faster than anyone else. The first thing I did was figure out if they could bring an AGI within reach of a large project. Is it? I don’t know. AGI faces bigger obstacles than a mere 4 orders of magnitude of compute power.
The second thing I did was chat with machine learning engineers from the big tech companies. They are not using these tricks and are not interested in adopting them. This will not surprise the Lisp hackers reading this post.
There might be a hedge fund out there that can use my tech. But a cursory investigation suggests that the majority of hedge funds are not doing anything sophisticated enough to require it.
So now I’m using my ML system to train an IMU-based gesture detection algorithm to track how much people eat in order to help prevent obesity-related diseases.
That is not a citation.
Let me dig up that list of a hundred US and Russian papers from the 1970s...
Please do. A single citation pointing to that list ought to suffice.
Your comments would benefit from the use of hyperlinks and blockquotes. If WISIWYG is a problem you can enable the markdown editor from your “Edit Account” page.
When someone makes several comments that are longer than the post itself, and when the reasoning is demonstrably fallacious (weirdness criterion!?), I think it is fair to call the comment a gish gallop when that is the most economical way to express what happened.
You still have not refuted gjm’s comment, which remains childless. I am not referring to the comment you handwaved away with the term “Gish gallop”. I am referring to the comment where gjm explains why you are Gish galloping and ze is not. Once again, this demonstrates “remarkably little effort by you to address any particular feedback other than to hand wave it away.”
…we (rational scientists)…
You continue to misrepresent the statements of others. Dustin did not claim to be a scientist nor member of the scientific establishment.
You have not refuted Dustin.
You have not even contested my statement “You have not refuted Dustin.” This corroborates Dustin’s claim “A brief perusal of what people have commented on your posts seems to show remarkably little effort by you to address any particular feedback other than to hand wave it away.”
In the case of gravitational waves, back in the 1970s, hundreds of independent research groups constructed simple devices to measure them and they all compared their results. Each research group thought that it had measured gravitational waves, but when the results were combined, they all had to conclude that no one had been measuring gravitational waves. They had all been measuring different sources of noise.
…
A hundred other researchers say, “We have used the same device to measure gravitational waves and found that it is too sensitive to local sources of noise to be able to conclude that anything it measures is cosmic in origin.”
Please provide a citation for this claim.
…a series of gish gallops by gjm…
What you, nixtaken, wrote was a Gish gallop. What gjm wrote was a refutation. You have ignored gjm’s comment about how you misapply the word “Gish gallop”. This shows “remarkably little effort by you to address any particular feedback other than to hand wave it away.”
So I did that in this post, but then I was told by dustin [sic] that I’ve written something too glaringly obvious yet clearly incorrect and controversial. Instead of using specific examples from published papers, I used analogies and I was criticized for that as well.
You have not refuted Dustin. You have not even failed to refute Dustin. You misrepresented Dustin’s comment and then did not even contradict your self-contradictory misrepresentation of Dustin’s comment that your post is “glaringly obvious yet clearly incorrect”. In this way, your writing continues to demonstrate “remarkably little effort by you to address any particular feedback other than to hand wave it away.”
There seems to be no winning with a crowd that doesn’t like to see their favorite team (science) painted in a less than glowing light. I think that some of the elders of your community might be needed to help teach these people that science isn’t about team loyalty, it is about a method, and that is what I’ve been writing about. [emphasis mine]
Thank you for not making personal attacks this time. Your overly-derogatory tone continues to violate the social norms of this website.
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