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It's funny when you think you know something pretty much thoroughly. Then you learn a bit more and realize that your understanding was a bit simplified, had gaps or there was a whole other level to it. The feeling is a strange mixture of disappointment, awe, annoyance and excitement.
"All evidence points towards x under the constraints y, z and q."
vs
"Its like this: x"
Looking at the paper, the core message is 'that even scientists harbor the illusion of understanding more than they actually do'. In reality, science operates much like a mental model. The paper argues that just because a model predicts future values more accurately, it doesn't mean the model explains the actual causal structure. Yet, the fact that outcomes fall within the predicted range reinforces the illusion that…
> This reminds me of the statistician's aphorism: 'All models are wrong, but some are useful.' It reminded the authors of this too, since they quote and source it
Looking at the paper, the core message is 'that even scientists harbor the illusion of understanding more than they actually do'. In reality, science operates much like a mental model. The paper argues that just because a model predicts future values more accurately, it doesn't mean the model explains the actual causal structure. Yet, the fact that outcomes fall within the predicted range reinforces the illusion that…
Exactly. The lede buried here is, as you say, accurate prediction is not better understanding Which has a statistician counterintuition Less "accurate" model can lead to better prediction Therefore (in my understanding) A better understanding encodes more info about how much more it can be improved, when compared to a less good understanding Maybe understanding should be related to wisdom rather than intelligence? Li…
Like thinking LLMs aren’t magic* because you utter “it’s just predicting the next token!” I’d argue, only slightly tongue in cheek, that thinking of LLMs as magical leads to more effective use than the predicting-next-token explanation. See also Frank Keil’s “illusion of explanatory depth.” * magic not as “unreal,” but in the classical conception of a living magic world where mental intentions can manifest physical r…
Earlier quoted context omitted.
> 'All models are wrong, but some are useful.' And beyond that: models become most interesting at the point they fail, because that's where you learn something.
The problem is when certain (mental) models are important to a community or science specialization. When these models fail, the community will often enforce the model and censor the opposing facts. I have encountered several such conflicts. Scientists are still humans. Individual people may be curious and be open to some questioning. But thy find it difficult to discuss such things in the open. It is like a religious…
It is religious dogma.
It's funny when you think you know something pretty much thoroughly. Then you learn a bit more and realize that your understanding was a bit simplified, had gaps or there was a whole other level to it. The feeling is a strange mixture of disappointment, awe, annoyance and excitement.
We're handed a bunch of simplified models then build on them. The consequence being that the landscape is very narrowly revealed.
This itself is a consequence of the architecture, at least in the US we don't really specialize until college/university.
Nobody really comprehends the depth of things until then, and troublingly enough we don't understand that until we're 2-3 years in.
For instance we have DNA, right? It gets replicated in cells via mitosis and in specialized cells called gametes via meiosis, the latter form is used for sexual reproduction. That's the gist you get in High School, you might talk about the polymer, nucleotides, nucleotide construction (which are superficial)
That isn't even close to the end, just off the top of my head there are elements in the DNA that code for several processes like methylation, allow interfacing with proteins which have myriad effects like up or downregulating transcription, they can also change the local topology forming loops that facilitate the process. There's a bunch of crazy shit that happens at the histone level.
And any one of those Substituent parts is probably worth discussing for a week in lecture at least but they're glossed over for more advances classes. For instance those regulatory molecules, in sufficient concentrations, can overpower histones that keep genes turned off, and that has downstream consequences in general regulation and cell differentiation.
I think that in the long run it compromises mental rigor. It also allows people to carry with them a sense of complete understanding when it's superficial and shallow. Having never experience the depth that the real world goes to kind of limits the horizons of people that aren't naturally curious and never shows them the potential for how deep shit can really get and when it does it is still superficial by the dint of the expectation placed on students being so narrow.
It's funny when you think you know something pretty much thoroughly. Then you learn a bit more and realize that your understanding was a bit simplified, had gaps or there was a whole other level to it. The feeling is a strange mixture of disappointment, awe, annoyance and excitement.
Y'know it's funny how, at least in my experience, education worked. We're handed a bunch of simplified models then build on them. The consequence being that the landscape is very narrowly revealed. This itself is a consequence of the architecture, at least in the US we don't really specialize until college/university. Nobody really comprehends the depth of things until then, and troublingly enough we don't understand…
> It is the writer's experience that new degrees of comprehension are always and only consequent to ever-renewed review of the spontaneously rearranged inventory of significant factors. This awareness of the processes leading to new degrees of comprehension spontaneously motivates the writer to describe over and over again what—to the careless listener or reader—might seem to be tiresome repetition, but to the succes…
Yuri Manin – Reception speech at the Paris Academy of Sciences
> I see the process of mathematical creation as a kind of recognizing a preexisting pattern. When you study something—topology, probability, number theory, whatever—first you acquire a general vision of the vast territory, then you focus on a part of it. Later you try to recognize “what is there?” and “what has already been seen by other people?”. So you can read other papers and finally start discerning something nobody has seen before you.
Yuri Manin – Good proofs are proofs that make us wiser
> The central figure of a philosophic dialogue is a wise man, whereas modernity generally and systematically replaces wisdom by training. Wisdom seems to be an inborn faculty slowly ripened by life experience; as such it is rarely met and even more rarely put to any use. Training is a democratic surrogate for wisdom which, in spite of all of its (mainly aesthetic) drawbacks, is superior in one respect: it produces professionals.
Yuri Manin – Mathematics as Metaphor
Looking at the paper, the core message is 'that even scientists harbor the illusion of understanding more than they actually do'. In reality, science operates much like a mental model. The paper argues that just because a model predicts future values more accurately, it doesn't mean the model explains the actual causal structure. Yet, the fact that outcomes fall within the predicted range reinforces the illusion that…
Yes. Celestial navigation was based on a universe which spun around the earth, which is wrong, but it worked for navigation.
Looking at the paper, the core message is 'that even scientists harbor the illusion of understanding more than they actually do'. In reality, science operates much like a mental model. The paper argues that just because a model predicts future values more accurately, it doesn't mean the model explains the actual causal structure. Yet, the fact that outcomes fall within the predicted range reinforces the illusion that…
> The paper argues that just because a model predicts future values more accurately, it doesn't mean the model explains the actual causal structure. Yes. Celestial navigation was based on a universe which spun around the earth, which is wrong, but it worked for navigation.
Mathematically, in a two-body system, there's no actual difference between saying body A orbits body B or saying body B orbits body A, so in some sense, it's not even wrong.