The Great AI Weirding(deliprao.substack.com)
deliprao.substack.com
The Great AI Weirding
https://deliprao.substack.com/p/the-great-ai-weirding
50 comments
The sad bit to me is doing all of these things to pad a college application rather than for interest or enjoyment. I actually enjoyed math team and continued math competitions in college.
Though I almost wish I'd been more vigorously encouraged to study piano, since I have little time or motivation to practice it now.
As indicated, getting into Stanford still qualifies as winning the elite college admissions lottery, even if one might have preferred Princeton or MIT. (And Stanford likely provides some advantages as well.)
Though I almost wish I'd been more vigorously encouraged to study piano, since I have little time or motivation to practice it now.
As indicated, getting into Stanford still qualifies as winning the elite college admissions lottery, even if one might have preferred Princeton or MIT. (And Stanford likely provides some advantages as well.)
> real work which produces real value for other human beings
I've felt the same way in tech. I've seen so many job listings that are just an endless series of buzzwords or the latest iteration of data adtech analysis integrated lake warehouse whatever and found myself wondering when and where it translates into actually affecting the lives of anyone beyond interchangeable corporate middle managers.
I've felt the same way in tech. I've seen so many job listings that are just an endless series of buzzwords or the latest iteration of data adtech analysis integrated lake warehouse whatever and found myself wondering when and where it translates into actually affecting the lives of anyone beyond interchangeable corporate middle managers.
having a kid work basically 14 hour days for years and years so they can get into the best school based on the criteria of the ivy league, it becomes very easy to downplay the importance of communication and social skills, like empathy, play, dealing with difficult people, emotional intelligence, etc etc.
this is how people like Sam Bankman Fried and Caroline Ellison develop into having sociopathic patterns of behavior and dysfunctional realtionships, just constantly being pushed to play a game of artificial metrics from the time they are children.
its almost like taking away childhood from people and having a form of child labor.
this is how people like Sam Bankman Fried and Caroline Ellison develop into having sociopathic patterns of behavior and dysfunctional realtionships, just constantly being pushed to play a game of artificial metrics from the time they are children.
its almost like taking away childhood from people and having a form of child labor.
Sam Bankman doesn’t strike me as a hard working student though his upbringing could have the expectation to outdo his parents, both of them professors in Ivi League schools if I remember correctly. The appetite for risk may be a byproduct of that though…
IIRC they teach/taught at Stanford, which is in the Pac-12 (until next year, when it joins the ACC) rather than the Ivy League.
We, collectively as a community, are forced to play these stupid RL career games because if you refuse, you become illegible and, consequently, invisible to sources of physical, emotional, and intellectual sustenance. It’s like we are all trapped in vicious cycles of RL career games while hoovering up others in these cycles.
Once a critical threshold of people start playing these RL career games, these terrible metrics get elevated to some weird group fairness metrics for hiring/admissions/compensation decisions, no matter how inequitable these games are and how disparate the outcomes are. The metric has moved beyond convenience to something hard to root out. The terrible metric becomes tyrannical, and complaining about it makes you sound like someone who “blames the game for being a bad player”. Even if it was a game you never wanted to play, to begin with.
Be the change you want to see in the world. Hire illegible people. Someone is going to tell you can’t and cite vague legal reasons. Unless that person is an actual lawyer giving formal legal advice (or your boss) just ignore him.
As a general matter you should always ignore non-lawyers citing vague legal reasons for why you can’t do something.
Once a critical threshold of people start playing these RL career games, these terrible metrics get elevated to some weird group fairness metrics for hiring/admissions/compensation decisions, no matter how inequitable these games are and how disparate the outcomes are. The metric has moved beyond convenience to something hard to root out. The terrible metric becomes tyrannical, and complaining about it makes you sound like someone who “blames the game for being a bad player”. Even if it was a game you never wanted to play, to begin with.
Be the change you want to see in the world. Hire illegible people. Someone is going to tell you can’t and cite vague legal reasons. Unless that person is an actual lawyer giving formal legal advice (or your boss) just ignore him.
As a general matter you should always ignore non-lawyers citing vague legal reasons for why you can’t do something.
I did that with a few interns/junior people and they turned out to be disappointing rather than predictably satisfying.
I still agree with you, because I want to believe it pays off in the long run. But it definitely comes at a real short term cost.
I still agree with you, because I want to believe it pays off in the long run. But it definitely comes at a real short term cost.
Therein lies the problem. Most people are apathetic and incompetent. Winning rat race points requires some amount of competence and "oomph", so rat race points will always correlate positively with applicant quality. Most rat-race-losers, unfortunately, aren't Socrates.
I wonder what other tests of being non-apathetic and at least somewhat competent would work as well or better than the typical rat race.
Anything that signals grit
Tide may be overrated, over-promoted, and overpriced, but people who buy it know that it is going to get their clothes clean.
I expect, and have a few data points to suggest, that the average is lower but the variance is higher.
More importantly you are helping to fight an anti-human machine that sacrifices endless years of (young! healthy!) life to pointless grinding.
More importantly you are helping to fight an anti-human machine that sacrifices endless years of (young! healthy!) life to pointless grinding.
Or just leave academia. In the US at least, the job is like 80% government contracting and 20% teaching.
Teaching is great, so there's that. But literally every company will let your ad junct, and Professor of Practice usually pays more than 20% of a faculty salary. You can supervise PhD students as interns or by taking a courtesy affiliation (and often even have more impact on those students than their overworked and under-engaged advisors). And university classroom teaching in the US now looks a lot more like 90s/mid-naughts high school teaching.
Government contracting sucks, and the academic variety is not any better. I'd literally whether watch paint dry at a military base than contract for DARPA. NSF isn't actually that much better.
Who the fuck wants to be a combination high school teacher and federal government contractor? Saints or sociopaths, and there are a LOT more of the latter than the former in higher ed.
Teaching is great, so there's that. But literally every company will let your ad junct, and Professor of Practice usually pays more than 20% of a faculty salary. You can supervise PhD students as interns or by taking a courtesy affiliation (and often even have more impact on those students than their overworked and under-engaged advisors). And university classroom teaching in the US now looks a lot more like 90s/mid-naughts high school teaching.
Government contracting sucks, and the academic variety is not any better. I'd literally whether watch paint dry at a military base than contract for DARPA. NSF isn't actually that much better.
Who the fuck wants to be a combination high school teacher and federal government contractor? Saints or sociopaths, and there are a LOT more of the latter than the former in higher ed.
This article is not necessarily about academia, I think it’s actually more geared towards “industry” AI researchers.
Honestly, is there a big difference anymore? The vast majority of papers I read are either by industry directly or have industry as a partner (as an author, not just acknowledgements). There are of course some, and even plenty of examples, but it does seem industry partners is almost necessary these days. I'm not convinced that level of interaction is healthy, for either parties.
> Honestly, is there a big difference anymore?
Only a very small subset of industry cares about academic publishing, and even within that subset it's only a fraction of groups at a fraction of corps that consider publishing a primary or even secondary objective.
The groups that do care about those things can be good gigs, but are generally not the place in the company you want to be anyways, unless you can get in and out (for good) in <10 years. If you can do something that actually impacts the business -- that is actually useful to other humans -- no one gives a shit about h-indices or kaggle scores. And you'll be better compensated anyways.
Only a very small subset of industry cares about academic publishing, and even within that subset it's only a fraction of groups at a fraction of corps that consider publishing a primary or even secondary objective.
The groups that do care about those things can be good gigs, but are generally not the place in the company you want to be anyways, unless you can get in and out (for good) in <10 years. If you can do something that actually impacts the business -- that is actually useful to other humans -- no one gives a shit about h-indices or kaggle scores. And you'll be better compensated anyways.
You're measuring the wrong direction. Don't measure what percentage of industry publishes with academics. Instead, measure what percent of academics __in ML__ publish with industry. This direction because one is much larger than the other. Second, I mean... I am a researcher... and I'm talking about the environment I'm working in. It sounds like you're outside this environment trying to educate me on it. Am I misunderstanding here?
> can do something that actually impacts the business -- that is actually useful to other humans
Do not confuse these two. That's incredibly naive.
> can do something that actually impacts the business -- that is actually useful to other humans
Do not confuse these two. That's incredibly naive.
>
Honestly, is there a big difference anymore? The vast majority of papers I read are either by industry directly or have industry as a partner (as an author, not just acknowledgements).
Read more pure math papers, then you will see the difference. :-)
Read more pure math papers, then you will see the difference. :-)
I thought we were talking ML here. I mean you're not wrong (I do do this) but context. But in ML, well... I mean even Max Welling is connected with Microsoft.
> I thought we were talking ML here.
This is no contradiction: there exist quite pure math papers whose content is very relevant for the mathematics behand ML algorithms. :-)
I do have the impression that the kind of research in ML that is not strongly associated with the recent "machine-learning industrial complex" by now tends to become published in another subject area.
This is no contradiction: there exist quite pure math papers whose content is very relevant for the mathematics behand ML algorithms. :-)
I do have the impression that the kind of research in ML that is not strongly associated with the recent "machine-learning industrial complex" by now tends to become published in another subject area.
Sure, I agree with you. I just wouldn't refer to that work as pure math. And let's be real, most people are not working on the theoretical side of ML. Realistically people are anti theory in the ML space and it's really weird to me because it's a self fulfilling prophecy and the complaints are "it's not very good because not a lot of community effort hasn't been put in so let's not waste our time"
> Or just leave academia
The problem is that AI is weird not because of academia. In fact, right not it has been captured by industry and it is why we've severely slowed down in progress[0]. Most people in the space now are working in industry labs. Frankly, you can do more, you get paid A LOT more (2-3x) and you have less bureaucratic bullshit. But I think you're keenly aware of this industry capture as you're mentioning aspects of it.
I don't want there to be any confusion: I think it is good that industry and academia work together. There's lots of benefits. But we also need to recognize that these two typically have very different goals, work at different TRLs, and have have very different expectations on the time where the work will be seen as impactful. Traditionally, academia has generally been the dominating player in the high risk high reward/low level research space (yes, much more goes on too, but of people that do this type of research, you think academia) while industry research typically is focused on higher TRL because they're focused on selling things in the near future. There's just a danger when you work too closely to industry: you can't have any wizards if you don't have any noobs.
But I'm not sure it is just ML that's been going this way. There's a lot of sentiment on this website where people dismiss research papers (outside ML) that show up here due to them not being viable products. I mean... yeah... they're research. We can agree that the value is oversold, but often that's by the publisher (read university) and not the paper (not sure if I can say the same for ML). But it's a kinda environmental problem because if everything has to be a product you can't be honest about what you did and if discussing the limits and where you need to still improve upon to actually get an product down the line gets you rejected, well... you just don't talk about that.
This is all the "RL hacking" or better known as Goodhart's Law. I've been saying we're living in Goodhart's Hell because it seems, especially in the last 5-10 years, we've recognized that a lot of metric hacking is going on and decided that the best course of action is not to resolve the issues, but lean into it. We've seen the house of cards that this has created. Crypto is a good example. Shame is if we kill AI because there is a lot of real value there. But if you're a chocolate factory and promise people that eating your chocolate will give them superpowers, it doesn't matter how life changingly delicious that chocolate is, people will be upset and feel cheated. Problem is, the whole chocolate industry is doing this right now and we're not Willy fucking Wonka.
[0] More progress looks like it is being made and there is a lot of progress that should have been made but wasn't but these types of nuances are a bit harder to discuss without intimate knowledge of the field. I'll say that diffusion should have happened much sooner but industry capture had everyone looking at GANs. Anything not, got extra scrutiny and became easy to reject due to not having state of the art results (are we doing research or are we building products?)
The problem is that AI is weird not because of academia. In fact, right not it has been captured by industry and it is why we've severely slowed down in progress[0]. Most people in the space now are working in industry labs. Frankly, you can do more, you get paid A LOT more (2-3x) and you have less bureaucratic bullshit. But I think you're keenly aware of this industry capture as you're mentioning aspects of it.
I don't want there to be any confusion: I think it is good that industry and academia work together. There's lots of benefits. But we also need to recognize that these two typically have very different goals, work at different TRLs, and have have very different expectations on the time where the work will be seen as impactful. Traditionally, academia has generally been the dominating player in the high risk high reward/low level research space (yes, much more goes on too, but of people that do this type of research, you think academia) while industry research typically is focused on higher TRL because they're focused on selling things in the near future. There's just a danger when you work too closely to industry: you can't have any wizards if you don't have any noobs.
But I'm not sure it is just ML that's been going this way. There's a lot of sentiment on this website where people dismiss research papers (outside ML) that show up here due to them not being viable products. I mean... yeah... they're research. We can agree that the value is oversold, but often that's by the publisher (read university) and not the paper (not sure if I can say the same for ML). But it's a kinda environmental problem because if everything has to be a product you can't be honest about what you did and if discussing the limits and where you need to still improve upon to actually get an product down the line gets you rejected, well... you just don't talk about that.
This is all the "RL hacking" or better known as Goodhart's Law. I've been saying we're living in Goodhart's Hell because it seems, especially in the last 5-10 years, we've recognized that a lot of metric hacking is going on and decided that the best course of action is not to resolve the issues, but lean into it. We've seen the house of cards that this has created. Crypto is a good example. Shame is if we kill AI because there is a lot of real value there. But if you're a chocolate factory and promise people that eating your chocolate will give them superpowers, it doesn't matter how life changingly delicious that chocolate is, people will be upset and feel cheated. Problem is, the whole chocolate industry is doing this right now and we're not Willy fucking Wonka.
[0] More progress looks like it is being made and there is a lot of progress that should have been made but wasn't but these types of nuances are a bit harder to discuss without intimate knowledge of the field. I'll say that diffusion should have happened much sooner but industry capture had everyone looking at GANs. Anything not, got extra scrutiny and became easy to reject due to not having state of the art results (are we doing research or are we building products?)
Only a relatively tiny sliver of PhDs doing top-tier ML research are in groups that care about publishing at corps the care about publishing in academic conferences.
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I thought this was going to be a biography of Weird Al
So, I guess we're all going to end up as rodents in a Skinner Rat Box being subjected to AI-managed operant conditioning in order to further our career goals? Surely this will lead to the creation of a Brave New World, free of all conflict and suffering - but only if we can ensure that AI is safe, properly aligned and guided by today's government leaders and corporate executives, who have no desires other than to benefit humanity, or so they assure us.
It's funny how so many designers of utopian paradises ended up creating dystopian hellholes, historically speaking, isn't it?
It's funny how so many designers of utopian paradises ended up creating dystopian hellholes, historically speaking, isn't it?
> AI will make the world even more quantifiable and, in many cases, falsely quantifiable.
> The false quantification and rank ordering of things using AI will bring real-world weirdness in how people function, which has nothing to do with the functions they carry out. I call this the “Great AI Weirding”.
If anything AI is the sort of power tool that should let everyone make up their own rankings more easily, and be less limited by what others have decided people should be judged by.
> The false quantification and rank ordering of things using AI will bring real-world weirdness in how people function, which has nothing to do with the functions they carry out. I call this the “Great AI Weirding”.
If anything AI is the sort of power tool that should let everyone make up their own rankings more easily, and be less limited by what others have decided people should be judged by.
Why is that a good thing?
H index? I see people caring far more about the quality of the conference for your paper. Paper at NeurIPS or ACL or EMNLP will be worth more than papers at 2nd rate venues. Usually researchers doing real SOTA work haven’t even had time for their other work to be cited heavily yet
The two are intertwined. Getting into a top tier conference makes your work much more likely to get a high citation count (and thus h-index) regardless of the quality of that work.
Yes, higher quality work means higher chance of getting in, but we'd be naive to assume there's a strong correlation between the two given substantial evidence to the contrary and no clear mechanism to make such a connection.
> Usually researchers doing real SOTA work haven’t even had time for their other work to be cited heavily yet
Weird, I'd say the opposite. How to get high citations: tweak currently popular model/architecture so that it gets SOTA results, place on paperswithcode leaderboard (maybe don't even release code), release paper to arxiv. More datasets you cover, the better. Frankly, SOTA doesn't mean meaningful work. I even say this as an author of SOTA works.
Yes, higher quality work means higher chance of getting in, but we'd be naive to assume there's a strong correlation between the two given substantial evidence to the contrary and no clear mechanism to make such a connection.
> Usually researchers doing real SOTA work haven’t even had time for their other work to be cited heavily yet
Weird, I'd say the opposite. How to get high citations: tweak currently popular model/architecture so that it gets SOTA results, place on paperswithcode leaderboard (maybe don't even release code), release paper to arxiv. More datasets you cover, the better. Frankly, SOTA doesn't mean meaningful work. I even say this as an author of SOTA works.
The open secret is that top-quartile R1 CS faculty positions aren't coveted anymore and don't attract the best like they used to.
The choice is now between increasingly tenuous/meaningless tenure after 5-10 years and a $500K/year lower bound for 10-12 years. That choice is... not a hard choice for anyone who values intellectual freedom. And the right answer sure as shit isn't the faculty position.
A good 50% of those faculty chasing chasing NeurIPS papers are doing so because at least once before going up for tenure they will apply for positions at big tech. They end up coming on not just non-executive, but often outside of management and at the bottom of the (Top IC)-[1-2] total comp band. If they net an offer they'll usually leave. The major barrier to an offer is usually ego and "is this personal actually humble enough to be useful to other people".
The choice is now between increasingly tenuous/meaningless tenure after 5-10 years and a $500K/year lower bound for 10-12 years. That choice is... not a hard choice for anyone who values intellectual freedom. And the right answer sure as shit isn't the faculty position.
A good 50% of those faculty chasing chasing NeurIPS papers are doing so because at least once before going up for tenure they will apply for positions at big tech. They end up coming on not just non-executive, but often outside of management and at the bottom of the (Top IC)-[1-2] total comp band. If they net an offer they'll usually leave. The major barrier to an offer is usually ego and "is this personal actually humble enough to be useful to other people".
Much of this could be true, but I’ve seen R1-tenure-caliber PhDs come on staff at top industry labs for a lot more than the pay you’re suggesting.
Hence "lower bound".
> a $500K/year lower bound for 10-12 years
I don't care if you are talking about top talent here; that is an insane thing to say. As a lower bound? What percentage of software engineers / AI practitioners / data scientists are making $500k/year? 0.1%?
I don't care if you are talking about top talent here; that is an insane thing to say. As a lower bound? What percentage of software engineers / AI practitioners / data scientists are making $500k/year? 0.1%?
tldr you cannot gradient descend to find the optimal human being.
this reminds me a lot of the recent book The Fund about Bridgewater Capital, where they tried to come up with hundreds of metrics to rate each employee on, and then they made employees constantly rate each other on an iPad with this custom software they spent massive sums of money building. If you didnt rate other people you got fired. After years and years of this it was just all abandoned, complete and total waste.
this reminds me a lot of the recent book The Fund about Bridgewater Capital, where they tried to come up with hundreds of metrics to rate each employee on, and then they made employees constantly rate each other on an iPad with this custom software they spent massive sums of money building. If you didnt rate other people you got fired. After years and years of this it was just all abandoned, complete and total waste.
I read the book about this in Principles. They rate all their decisions. It is hard to imagine how it works
I think the logical and inevitable conclusion of this quantification is simply throwing out quantification as useful, especially when it comes to things like elite hiring decisions. What will replace it? Charisma, personality, and other attributes that simply can’t be quantified.
It’s easy to forget that the drive to make highly-quantified decisions is largely a recent phenomenon, with in-person charisma having a much longer history. The recent widespread dominance of online video (compared to text) is really just more of a return to this kind of charisma after a long period of textual dominance.
I think the future is dominated by people that understand how to use video (and way down the line, 3D presence tools), not those that are good at optimizing AI tools.
One example of this, I think, is how video searches on TikTok/YouTube seem to be replacing Google searches for younger people. The searcher of 2030 isn’t going to read a perfectly individualized AI-created blog post, they’re going to watch a video by someone they trust.
TLDR: widespread video will herald a return to charismatic authority, displacing quantification systems of authority.
It’s easy to forget that the drive to make highly-quantified decisions is largely a recent phenomenon, with in-person charisma having a much longer history. The recent widespread dominance of online video (compared to text) is really just more of a return to this kind of charisma after a long period of textual dominance.
I think the future is dominated by people that understand how to use video (and way down the line, 3D presence tools), not those that are good at optimizing AI tools.
One example of this, I think, is how video searches on TikTok/YouTube seem to be replacing Google searches for younger people. The searcher of 2030 isn’t going to read a perfectly individualized AI-created blog post, they’re going to watch a video by someone they trust.
TLDR: widespread video will herald a return to charismatic authority, displacing quantification systems of authority.
It's not a problem unique to AI researchers, it's about everything, if not now then ten years from now.
This is hidden metrics when you're getting a home loan, your insurance premiums, your success in dating sites, college admissions, whether somebody would hire you to do a dj gig at a nightclub, everything.
It's all recursive bullshit games, and you won't know which ones so you're just gonna run on as many treadmills as you can, all at once, while fully knowing some of them aren't even worth anything - just not which ones.
This is hidden metrics when you're getting a home loan, your insurance premiums, your success in dating sites, college admissions, whether somebody would hire you to do a dj gig at a nightclub, everything.
It's all recursive bullshit games, and you won't know which ones so you're just gonna run on as many treadmills as you can, all at once, while fully knowing some of them aren't even worth anything - just not which ones.
It is also whether your comments will actually be shown to real actual people or just left flapping in the void. It is your engagement figures on youtube and how close you are to getting demonetized and deplatformed and dmca'd. Whether your email or phonecall will be answered by a real engineer or some heavily accented lowly paid script-reader from a third-world country or chatgpt or a dumb pre-ai voice recording telling you to hold the line forever.
Maybe one of the treadmills is how pleasant and agreeable your opinions are. How they make people feel. Maybe you should shut the hell up before you get yourself into trouble and drive up your premiums.
Maybe one of the treadmills is how pleasant and agreeable your opinions are. How they make people feel. Maybe you should shut the hell up before you get yourself into trouble and drive up your premiums.
We saw examples of simple quantification of people and activities, such as using counts of likes, stars, commits, and papers, and even more informed metrics like H-Index can lead to strange outcomes. AI will make the world even more quantifiable and, in many cases, falsely quantifiable. Ever since the first ape held two sticks in the left hand and three in the right and wondered which was more, ranking things by quantity is in our nature. The ape’s descendants have now discovered a ranking hammer, and everything will look like ordered lists. Ordered lists bring legibility, and what is not legible cannot be governed and subject to value extraction. The false quantification and rank ordering of things using AI will bring real-world weirdness in how people function, which has nothing to do with the functions they carry out. I call this the “Great AI Weirding”.
Ceci n'est pas une pipe. [...] All of these things have gone beyond what they point to.
In other words, they get huge information satisfaction from ads, far more than they do from the product itself. Where advertising is heading is quite simply into a world where the ad will become a substitute for the product, and all the satisfactions will be derived informationally from the ad, and the product will be merely a number in some file.
- Marshall McLuhan, 1966. https://www.youtube.com/watch?v=bNxo7fK-MJs
I have recently been watching John Vervaeke, assistant prof at UofT in the fields of cognitive science and Buddhist psychology, and his lecture series "Awakening from the Meaning Crisis," where he describes the phenomenon of cognitive fluency:
When you increase the ease at which people can process information, regardless of what that information is, they come to believe it as more real, they have more confidence in it, etc.
- John Vervaeke, "Continuous Cosmos and Modern World Grammar," Awakening from the Meaning Crisis, 2019. https://www.youtube.com/watch?v=C1AaqD8t3pk
For all this, it takes a great leap of faith to object to playing these kinds of meaningless abstract games, at great personal risk and cost to one's self; yet I am not sure how to meaningfully participate in these systems without upholding and lending implicit assent to the fictions that they rely on. I am reminded of some meditations on Moloch regarding the matter.
One hope I have from Vervaeke's series is in his exploration of the notion of shamanism, and their role in society as developing new psychotechnologies and disrupting civilization's facilities for pattern recognition - altering their sense of what is important, altering their sense of selves, and altering the very way we think in the world. I look forward to a revival of the shamanistic tradition, applied to "cyberspace," (heh) to help us navigate the ways in which digital technology has altered our senses of meaning, what is actually important, and indeed of self and identity.
[1] https://en.wikipedia.org/wiki/Goodhart%27s_law
Calling this "weirding" is wrong and misleading (particularly going to the trouble to insert the definition). The post is describing the opposite, a great normalisation. AI research (and everything else) becoming legible, quantifiable, gamified, and Goodhart's law-ed.
Also, weirding was originally a term used in the novel Dune.
https://dune.fandom.com/wiki/Weirding_Way
https://dune.fandom.com/wiki/Weirding_Way
Frank Herbert isn't the only person allowed to gerundize a word. :-)
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As well, in this article, a person bemoans the opportunities their parents gave them; piano lessons, math competitions, etc. Even though they have clearly benefited from these advantages, it's unclear to me if the person acknowledges the position of privilege that such an upbringing grants.