If it isn’t clear to everyone, this is a gesture meant to calm public populace fears about “AI”.
First of all, there is no definition of “AI”. It’s a running joke in the community that once an “AI” approach gets good enough to be commoditized it is no longer called “AI” and becomes simply “ML” (machine learning) that we teach to college students.
With that in mind, military drones have used “AI” since at least during the Obama administration. This is a nonstory.
I’ve been to summits like the one talked about that are a bunch of people in government, nonprofit, or for profit executive roles that get together to discuss things they don’t understand and make a resolution that looks good. They are all for show. Nothing useful, meaningful, or impactful comes from these things.
Yes, yes, we should be responsible with the use of AI. Everyone agrees without asking what that really means.
I am once again baffled by folks in the tech industry being totally unaware of what came before them.
You are, in a way, asking what consciousness is and how one could recognize it. This has been a philosophical topic for millennia; this is a reasonable place to start reading: https://plato.stanford.edu/entries/consciousness/
It is a very interesting and deep topic. But let’s not pretend that recent advances in AI are the thing that brought it up for the first time.
I feel like fluff articles like this are written by people who have never actually been in the situation they write about.
One of the first things one learns when working professionally is to say “I’ll look into that and get back to you” or some such instead of “I don’t know” and just standing there.
I say "I don't know" when I'm reasonably confident that the other guy doesn't know, and that neither of us is going to look into it. It's sort of like "next question."
It never ceases to baffle me how people in the tech industry are completely unaware of everything that preceded them. The meandering exploration described in this article could have been short circuited by an understanding of things are standard topics in organizational psychology. There’s a wiki article on it: https://en.wikipedia.org/wiki/Leadership_style
I echo others here in the sense that “mastering programming” is not well defined. It’s interesting to try to define what that might be though. For example, I know people who would qualify as “master Java programmers” if there ever was one, but that doesn’t make them “master programmers”.
I think that in my life I have met one person that I would call a master programmer. He was intimately familiar with C, C++, Java, Erlang, Perl, APL, Ruby, Python, Prolog, Haskell, Scala, and more. You could probably find a person that is more of an expert in any one of those, sure, but that’s beside the point. The reason this fellow qualifies as a master of programming, for me, is that he seems to have complete knowledge of where every language came from, how that influenced its semantics, what the pros and cons of each language’s design are, and could code more or less idiomatically in any one of all those languages and explain why it was idiomatic.
Based on that, I’d say that if there is such a thing as a “master programmer”, it’s probably someone so well versed in computational theory and language design that picking up a new language is just a fun afternoon.
I am sure others will have different viewpoints. This is my two cents.
The fact that the data is from 1915 - 1920 does not invalidate it unless you want to suggest that human nature in 1915-1920 is different than it is now - which would seem a strange point to defend. And the sample sizes are quite decent.
I know that the HM userbase had a tendency to dismiss studies that are incorrectly conducted, but I don't think that this is one of those cases.
The life and the work back then is very different from the life and work that we have now. A big factor is that there is no war to motivate people to work hard. Another is that labour circumstances are very very different now than what they were previously.
Finally almost nobody who reads HN does repetitive manual labour. Manual productivity is very different from mental productivity.
From my own experience, I'm not even productive for 40 hours per week. Let alone working 50 hours consistently every week.
"Human nature". Alternatively: People who think the data is not as pertinent today or in other lines of work believe that human behavior is largely determined by context, not a priori hardwired behavior.
Bernie has described himself as a "secular Jew who does not practice any religion". So if he doesn't practice any form of Judaism, to what extent can you say that his Jewishness is not racial? What else is it?
For most psychological research you don't need a very random sample, nor do you need a very large sample. It's got to do with the fact that our brains largely function the same. Your sample has to be large and random enough to be fairly confident that you didn't accidentally pick a significant amount of people with some sort of mental divergence (i.e. very low IQ, very high IQ, autism, psychopathy, etc..) that could be relevant.
I don't think picking nearly 200 people from Amazon Turk is going to risk that. But perhaps someone should research what kind of people are on Amazon Turk.
Fun exercise, let's modify the conclusion to reflect the specific population bias we think the paper might have:
People working for Amazon Turk are more likely to support conservative ideas when they also are susceptible to bullshit.
What is a much more important question: What is the distribution of conservative/non-conservative supporters in the group, and how significant is the measured correlation?
edit: Just as a disclaimer, I made this comment based on the psych master thesis presentation I've attended of a friend when I was at university where I posed the same question (N was only 16, and only local students were surveyed). He explained to me that for his particular research (pertaining correlation between auditory senses and motor skills) a small group was sufficient because of the fundamental brain structure we (mostly) all share. Whether that holds up for more complex research like this I don't know, I'm not a psych student.
The view you're espousing is wrong. When you actually test if what you're calling human universals actually are universal, you generally find they're not:
"Broad claims about human psychology and behavior based on narrow samples from Western societies are regularly published in leading journals. This review suggests not only that substantial variability in experimental results emerges across populations in basic domains, but that standard subjects are in fact rather unusual compared with the rest of the species - frequent outliers. The domains reviewed include visual perception, fairness, categorization, spatial cognition, memory, moral reasoning and self-concepts. This review (1) indicates caution in addressing questions of human nature based on this thin slice of humanity, and (2) suggests that understanding human psychology will require tapping broader subject pools. We close by proposing ways to address these challenges."
(The thin slice of humanity referred to above is westernized college students. Perhaps mechanical turkers do not suffer from this bias? seems unlikely though)
Thanks! That's interesting, seems I might have had it wrong. I should've brought that up during my friends master thesis presentation, maybe he wouldn't have got his diploma ;) Are psychology researchers in general aware of this review? Obviously my friends master thesis was just a small inconsequential study, but if the universals that are now commonly used have turned out to not actually be true wouldn't that mean huge swathes of researchers have to retract/redo their research?
> but if the universals that are now commonly used have turned out to not actually be true wouldn't that mean huge swathes of researchers have to retract/redo their research?
Reproducibility is a major concern. Many experiments are poorly designed.
Even the paper itself acknowledges the weaknesses of the sample. It's much easier to make "based on consistent responses in our sample, humans generally behave in a certain way" generalisations from a small, unrepresentative sample than to conclude "this cohort of humans we've unrepresentatively sampled generally behaves differently from that cohort of humans we've unrepresentatively sampled"
Consider the hypothesis that (for the sake of simplicity) half of conservatives support conservative candidates because they're "susceptible to bullshit" and half of conservatives support them because they're hardworking professionals who believe those candidates' brand of fiscal conservatism is most aligned with their rational self interest in not being taxed too highly.
Which of these two categories of Trump and Cruz supporters would be underrepresented on AMT, a place for underemployed people to find menial work?
>For most psychological research you don't need a very random sample, nor do you need a very large sample.
Heck, as long as you get published and get a grant, any size or representativeness of sample will do (even straight made up statistics, if there's no big fear of being found out).
you don't need a very random sample, nor do you
need a very large sample. It's got to do with the
fact that our brains largely function the same.
Do you think, if I did a study of political leanings among college undergraduates, that I'd get the same results as a study of the general population because "our brains work the same" ?
This is not a study of political leanings of people. It's a study of the correlation between a characteristic of a person and their political leaning.
If you take 200 college undergraduates, let's say 80% of them are lefties. That means you will have 160 lefties and 40 righties. Then of the 160 lefties 50 believe in BS (31%) and of the 40 righties 16 (40%) believe in BS, you can take those numbers to your statistician and ask him whether that spread is significant.
Just the idea that the correlation is there for these college students, where you might not expect it would be there for college students is a hint that maybe this could hold up for the population in general. At the least it could warrant for a larger investigation.
There's some rather more fundamental issues with the paper.
Let's do a little bit of a dive...
1. The "BSR" is 10 questions on a Likert scale with extraordinarily vague labels. So, what's the difference between "somewhat profound" and "fairly profound"? How confident are you that different populations (eg lib v con) will have similar views on the difference between "somewhat" versus "fairly"?
2. Liberal/conservatism meanwhile is a single question on a Likert scale. 1 to 7, how conservative are you? So, self-image not actual conservatism. And given 109 participants rated themselves on the liberal side versus 46 on the conservative side, it's going to be dominated by "just how extreme do you think your liberalism is?"
3. But best fun of all -
On the left, we have
1 = liberal
1 = not at all profound
1 = not at all favourable
On the right side of their questions we have
7 = conservative
5 = very profound
5 = very favourable
109 participants were Liberal (less than 4 on lib/con Likert item)
46 participants were Conservative (above 4 on lib/con Likert item)
So, just the factor of "how much do you like to tick the extreme boxes on a Likert scale" would give a correlation like the one they get.
More likely to pick a 1 than a 2 on a Likert item? You'll rank as both more liberal and less receptive to bullshit then... Like to leave a radio box on the left so you don't feel extreme? That'll register you slightly more conservative and slightly more receptive then...
And as the participant pool is 2:1 liberal:conservatism, then that extremeness factor will produce candidate correlations like the ones they get too. (More extreme-tickers are likely to be going for 1s on lib, 1 not profound, 1 not favourable of Republicans, and 5s on favourability of Democrats. Middle-of-the-road tickers are likely to be going 2s for lib, 2s for profound, 2s for Reps, and 4s for Dems they like. Higher score for bullshit receptivity, less liberal, less favourable of Dems, and more favourable (less unfavourable) of Republicans.
MeanMundane is bang on the middle (3.1 mean), neatly unaffected by "extremeness" factor, whereas MeanBullshit isn't (2.6), so extremeness will push out the "controlled" correlations neatly too.
(Yes, I'm procrastinating, and had a brief back-of-the-envelope poke around their CSV of data...)
I see this as far more sociological than psychological, where the standards for sufficiently large and random samples are much higher. It's wrapping too many particularities of todays politics to be immune to failures in fair distribution outside IQ and mental illness.
It sounds like you haven't actually understood that argument. "The guy in the room" has always been a minor detail. Furthermore, machine translation has existed since before convolutional neural nets, so your whole point falls under the "not even wrong" category.
First of all, there is no definition of “AI”. It’s a running joke in the community that once an “AI” approach gets good enough to be commoditized it is no longer called “AI” and becomes simply “ML” (machine learning) that we teach to college students.
With that in mind, military drones have used “AI” since at least during the Obama administration. This is a nonstory.
I’ve been to summits like the one talked about that are a bunch of people in government, nonprofit, or for profit executive roles that get together to discuss things they don’t understand and make a resolution that looks good. They are all for show. Nothing useful, meaningful, or impactful comes from these things.
Yes, yes, we should be responsible with the use of AI. Everyone agrees without asking what that really means.