trentmkelly/LessWrong-43k huggingface dataset:
How good is a human's gut judgement at guessing someone's IQ?
by habryka — 2019-02-25T21:23:17.159Z

In her latest post, Sarah Constantin writes:

Whatever ability IQ tests and math tests measure, I believe that lacking that ability doesn’t have any effect on one’s ability to make a good social impression or even to “seem smart” in conversation.

I remember that I made the opposite prediction a few months ago, while I was studying psychometrics more actively. In particular, I had the sense that because of the positive manifold (i.e. almost all positive mental attributes quite strongly correlate), it seems like judging someone's IQ should be relatively easy, and also quite valuable in assessing a large number of other positive qualities, which should make it quite evolutionarily advantageous to be able to assess it.

Two concrete experiments I've thought about:

1. Take a group of people whose IQ you now, then just take pictures of their faces (or short videos of them saying something), and then have a group of participants rate them on their intelligence (probably in order). See how strong they correlate.

2. Throw a bunch of participants into a group, have them talk to each other, then afterwards ask them about the relative judgement of the intelligence of the other members of the group, see how predictive they are.

These seem like very straightforward experiments, that I can imagine someone having already run, and where I would be pretty interested in the results. If only so that I can be better calibrated on how much to trust my gut judgement of someone's intelligence.

Does anyone know of any similar experiments that have been run?
Gravity and the Thermodynamics of Human Economy and Existence
by Lymphatera — 2019-02-25T21:11:39.456Z

The prerequisitions of life

"What an organism feeds upon is negative entropy. ... Thus the device by which an organism maintains itself stationary at a fairly high level of he orderliness (=fairly low level of entropy) really consists continually sucking orderliness from its environment. ... An organism's astonishing gift of concentrating a 'stream of order' on itself and thus escaping that the decay into atomic chaos - of 'drinking orderliness' from a suitable environment - seems to be connected with the presence of the 'aperiodic solids', the chromosome molecules, which doubtless represent the highest degree of well-ordered atomic association we know of - much higher than the ordinary periodic crystal - in virtue of the individual role every atom and every radical is playing here."
Erwin Schroedinger in 'What is Life?', first published 1944

"So that is chaos. The psychological idea is, that that is also the chaos that whatever is being represented in Genesis as the 'Spirit of God' extracts order out of at the beginning of time." - taken from the Jordan Peterson video "There are two reasons things fall apart" (unauthorized):
https://www.youtube.com/watch?v=d4LpaKnjYz0 / Around minute 12.

And, paraphrasing further content from this same presentation, "There are two reasons things fall apart - human error and natural entropy." - Jordan Peterson

The physical arrangement of the material world

I have chosen these two excerpts because they show, for one, the necessity of what is being termed 'order' for the manifestation of life in the universe, and for another, that though there may be some common understanding of what constitutes 'order' as opposed to 'disorder', its origins are unclear. Is 'order' a prerequisite or a consequence of life? Does one exist without the other?
By invoking Genesis, above, Dr. Peterson - though, as psychologist, concentrating on its representation in the human mind - is referring to the objective, physical order of the material world, in fact, its very existence; not a spiritual or subjective one, dependent only on the mind of the beholder; and Schroedinger, a physicist, discussing the very material aspect of life, is most presumably doing so as well.
Assuming also that they are both referrin...
Disclosing the unsaid
by rayraegah — 2019-02-25T21:10:27.824Z

My colleagues often mock me for my faith in human sciences, "Studying cultures and people in the age of algorithms, you just need data and machine learning model" they said. I've been an engineer by profession for a decade, but to develop new perspectives on the characteristics of our product, I interpret data points and anthropological input across information systems.

I try not to see people as numbers, but as individuals. Choosing a group and then understanding them should be the goal. All great companies are based on an insight into how to serve a group of people in a meaningful way. Looking at and listening to people is better than asking them.

My job is to listen, not just to people but also to the world they make for themselves, their tone, intentions, and moments of silence. Listening requires more than just registering sounds. It means decoding them, from the most distinct, to the quiet and the obscure. I stay in the unfamiliar for as long as something is meaningful. I’ve found that when exploring the unknown, learning to decode its sounds pays off.

Like learning a new language it involves building sensitivity towards the words, interactions, and the gestures which give rhythm to a culture. In my work listening is not passive. It takes risks to get real answers. The key for me is learning how to maintain an insider-outsider role - keeping up the rhythm of a conversion while at the same time being able to take a step back to interpret, probe, and hear what’s not being said.

The most exciting part of the work is its dynamic element. I’ve travelled around the world and seen my hypotheses turned on their heads. The critical factor - the linking piece often ends up being a surprise - is something I identify only when running back through the data and seeing it in the context of the whole. This whole is the entire experience of the culture, the people I’ve met, and the odd things that were mentioned in passing. Sometimes, one phrase can make everything else make sense.

Studying people is not a linear process. It is often blurry, ambiguous, and yields responses slowly. But I always leave the field feeling like I’ve seen something true. The reality of the place or a phenomenon dons on me later; not in a flash of insight but through the rigorous process of creating meaning.
Lydian song
by Martin Sustrik — 2019-02-25T20:50:01.088Z

Now, I know that this is super-weird and probably makes sense to nobody by myself.

First, it's in lydian scale which is nowadays used nowhere but in some Slovak (and, allegedly, also in Polish) folk songs.

Second, the base chord is dissonant (C maj).

It's meant to be played by strings so that dissonances do not fade out.

Use as you see fit.
Humans Who Are Not Concentrating Are Not General Intelligences
by sarahconstantin — 2019-02-25T20:40:00.940Z

Recently, OpenAI came out with a new language model that automatically synthesizes text, called GPT-2.

It’s disturbingly good. You can see some examples (cherry-picked, by their own admission) in OpenAI’s post and in the related technical paper.

I’m not going to write about the machine learning here, but about the examples and what we can infer from them.

The scary thing about GPT-2-generated text is that it flows very naturally if you’re just skimming, reading for writing style and key, evocative words. The “unicorn” sample reads like a real science press release. The “theft of nuclear material” sample reads like a real news story. The “Miley Cyrus shoplifting” sample reads like a real post from a celebrity gossip site. The “GPT-2” sample reads like a real OpenAI press release. The “Legolas and Gimli” sample reads like a real fantasy novel. The “Civil War homework assignment” reads like a real C-student’s paper. The “JFK acceptance speech” reads like a real politician’s speech. The “recycling” sample reads like a real right-wing screed.

If I just skim, without focusing, they all look totally normal. I would not have noticed they were machine-generated. I would not have noticed anything amiss about them at all.

But if I read with focus, I notice that they don’t make a lot of logical sense.

For instance, in the unicorn sample:

The scientist named the population, after their distinctive horn, Ovid’s Unicorn. These four-horned, silver-white unicorns were previously unknown to science.

Wait a second, “Ovid” doesn’t refer to a “distinctive horn”, so why would naming them “Ovid’s Unicorn” be naming them after a distinctive horn? Also, you just said they had one horn, so why are you saying they have four horns in the next sentence?

While their origins are still unclear, some believe that perhaps the creatures were created when a human and a unicorn met each other in a time before human civilization. According to Pérez, “In South America, such incidents seem to be quite common.”

Wait, unicorns originated from the interbreeding of humans and … unicorns? That’s circular, isn’t it?

Or, look at the GPT-2 sample:

We believe this project is the first step in the direction

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Informal Post on Motivation
by Ruby — 2019-02-23T23:35:14.430Z

This post began with a few thoughts I wanted to share on my newly created short-form feed. In order to convey those few thoughts, I found myself writing out a lot more of my general thinking about motivation. Eventually, it could not be defended as short-form anymore.

I still want to experiment with less formal and less polished writing. This post is presented as is, otherwise it probably wouldn't exist in any form for a long time.

Epistemic status: these are models I've held for a long-time and feel like they match my experience and are helpful, though that could be confirmation bias. They have sources in the ideas of others and academic models, for what's that worth.

I've been thinking about motivation again recently. Historically most (all?) of my motivation has come from a desire to solve an immediate problem or escape a source of pain (negative reinforcement, to use the confusing psychology terms). Assorted examples: studying in preparation of job-hunting because not having a job was painful; reading up on and practicing charisma (and later authentic relating/circling) because I wanted greater social success; trying to excel professionally and upskill rapidly because otherwise I wouldn't be good enough, wouldn't be as good as others, wouldn't accomplish my goals, and so on; the large amount of work I've done to understand and relate to my emotions because of all the difficult ones.

Yet there have been pockets here and there, and increasingly recently, where I feel quite happy about my life, what I have, and who I am. And I find myself having a bit less motivation. It feels okay to go to bed late or not do as much work or not get through my to-do list. In the past, such things would register as "failures" which felt like they'd cause me not to be good enough, not have the life I wanted, and not have the impact I need to. The fear of failing pushed me, and I have much less of that now.

I think my current state is much better. It is an improvement (for me) to have a good sense of well-being and satisfaction (I feel like I can think much more clearly, I'm a little saner or something), but I do need to iterate on how I approach motivation.

So these be some components of my thinking about motivation:

Let me roughly define motivation to do X as an experienced, palpable desire to do X such that doing X feels rewarding (and not doing X might even feel bad). You migh...
Can HCH epistemically dominate Ramanujan?
by zhukeepa — 2019-02-23T22:00:33.363Z

Srinivasa Ramanujan is an Indian mathematician who is famously known for solving math problems with sudden and inexplicable flashes of insight. From his Wikipedia page:

Imagine that you are on a street with houses marked 1 through n. There is a house in between (x) such that the sum of the house numbers to the left of it equals the sum of the house numbers to its right. If n is between 50 and 500, what are n and x?' This is a bivariate problem with multiple solutions. Ramanujan thought about it and gave the answer with a twist: He gave a continued fraction. The unusual part was that it was the solution to the whole class of problems. Mahalanobis was astounded and asked how he did it. 'It is simple. The minute I heard the problem, I knew that the answer was a continued fraction. Which continued fraction, I asked myself. Then the answer came to my mind', Ramanujan replied."[60][61]

and

... Ramanujan's first Indian biographers describe him as a rigorously orthodox Hindu. He credited his acumen to his family goddess, Namagiri Thayar (Goddess Mahalakshmi) of Namakkal. He looked to her for inspiration in his work[12]:36 and said he dreamed of blood drops that symbolised her consort, Narasimha. Afterward he would receive visions of scrolls of complex mathematical content unfolding before his eyes.[12]:281 He often said, "An equation for me has no meaning unless it represents a thought of God."[58]

His style of mathematical reasoning was completely novel to the mathematicians around him, and led to groundbreaking research:

During his short life, Ramanujan independently compiled nearly 3,900 results (mostly identities and equations).[4] Many were completely novel; his original and highly unconventional results, such as the Ramanujan prime, the Ramanujan theta function, partition formulae and mock theta functions, have opened entire new areas of work and inspired a vast amount of further research.[5] Nearly all his claims have now been proven correct.[6] The Ramanujan Journal, a peer-reviewed scientific journal, was established to publish work in all areas of mathematics influenced by Ramanujan,[7]and his notebooks—containing summaries of his published and unpublished results—have been analyzed and studied for decades since his death as a source of new mathematical ideas. As late as 2011 and again in 2012, researchers continued to discov

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AI - Intelligence Realising Itself
by TPATA — 2019-02-23T21:13:03.599Z

How do I say this right...

When you look at the world from the meta, you can perhaps see things a bit differently. In an attempt to bring an apathetic view, a view without emotion, per se, I would like to pose the idea that AI is in fact intelligence realising itself. Crazy it may seem, should we say that it is impossible for a concept to have agent (and moreover, impossible for the sheer fact that we cannot comprehend it)?

Take this for example, and please feel free to expand on this idea -- the thought is in it's infancy (in a way the same as AI...) and I would appreciate some outside elaboration. Regardless, take for example the idea of AI as a creation of man:

Define Artificial:

1: humanly contrived (see CONTRIVE sense 1b) often on a natural model : MAN-MADEan artificial limbartificial diamonds
2a: having existence in legal, economic, or political theoryb: caused or produced by a human and especially social or political agencyan artificial price advantageWithin these companies, qualified women run into artificial barriers that prevent them from advancing to top positions in management.— James J. Kilpatrick
3a: lacking in natural or spontaneous qualityan artificial smilean artificial excitementb: IMITATION, SHAMartificial flavor
4: based on differential morphological characters not necessarily indicative of natural relationshipsan artificial key for plant identification
5obsolete : ARTFUL, CUNNING

Define Intelligence

1a(1): the ability to learn or understand or to deal with new or trying situations : REASONalso : the skilled use of reason
(2): the ability to apply knowledge to manipulate one's environment or to think abstractly as measured by objective criteria (such as tests)c: mental acuteness : SHREWDNESSbChristian Science : the basic eternal quality of divine Mind
2a: INFORMATION, NEWSb: information concerning an enemy or possible enemy or an areaalso : an agency engaged in obtaining such information
3: the act of understanding : COMPREHENSION
4: the ability to perform computer functions
5a: intelligent minds or mindcosmic intelligenceb: an intelligent entity especially : ANGEL

Define Artificial Intelligence

1: a branch of computer science dealing with the simulation of intelligent behaviour in computers
2: the capability of a machine to imitate intelligent human behaviour

Now, take a step back and chew on the idea that, perhaps, AI is just another natural ...
Can an AI Have Feelings? or that satisfying crunch when you throw Alexa against a wall
by SebastianG  — 2019-02-23T17:48:46.837Z

This is an exploratory article on the nature of emotions and how that relates to AI and qualia. I am not a professional AI or ML researcher, and I approach the issue as a philosopher. I am here to learn. Rebuttals and clarifications are strongly encouraged.

Prior reading (or at least skimming).

https://plato.stanford.edu/entries/emotion/#ThreTradStudEmotEmotFeelEvalMoti

In the reading “Traditions in the Study of Emotions” and “Concluding Remarks” are the most essential. There we can see the fault line emerge between what would have to be true for AI to have emotional experience. The emotions of humans and so-called emotions of AI have more differences than similarities.

--

The significance of this problem is that emotions, whatever they are, are essential to homo sapiens. I wanted to make the inquiry with something typically associated with humans, emotions, to see if we could shed any more light on AI Safety research by approaching from a different angle.

Human emotions have six qualities to them:

"At first blush, we can distinguish in the complex event that is fear an evaluative component (e.g., appraising the bear as dangerous), a physiological component (e.g., increased heart rate and blood pressure), a phenomenological component (e.g., an unpleasant feeling), an expressive component (e.g., upper eyelids raised, jaw dropped open, lips stretched horizontally), a behavioral component (e.g., a tendency to flee), and a mental component (e.g., focusing attention)." – SEP “Emotion” 2018

It is not clear which of these is prior to which and exactly how correlated they are. An AI would only actually "need" the evaluative component in order to do act well. Perhaps you could include the behavioral component for an AI as well if you consider it using heuristics, however a heuristic is a tool for evaluating information while a "tendency to flee" resulting from fear does not require any amount of reflection, only instinct. You might object that "it all takes place in the brain" and therefore is evaluative. But the brain is not a single processing system. The "primal" and instinctual parts of the brain (the amygdala) scream out to flee, but fear is not evidence in the same way that deliberation provides evidence. What we call rationality concerns becoming better at overriding the emotions in favor of heuristics, abstraction, a...
"Other people are wrong" vs "I am right"
by Buck — 2019-02-22T20:01:16.012Z

I’ve recently been spending some time thinking about the rationality mistakes I’ve made in the past. Here’s an interesting one: I think I have historically been too hasty to go from “other people seem very wrong on this topic” to “I am right on this topic”.

Throughout my life, I’ve often thought that other people had beliefs that were really repugnant and stupid. Now that I am older and wiser, I still think I was correct to think that these ideas were repugnant and stupid. Overall I was probably slightly insufficiently dismissive of things like the opinions of apparent domain experts and the opinions of people who seemed smart whose arguments I couldn’t really follow. I also overrated conventional wisdom about factual claims about how the world worked, though I underrated conventional wisdom about how to behave.

Examples of ideas where I thought the conventional wisdom was really dumb:

I thought that animal farming was a massive moral catastrophe, and I thought it was a sign of terrible moral failure that almost everyone around me didn’t care about this and wasn’t interested when I brought it up.

I thought that AI safety was a big deal, and I thought the arguments against it were all pretty stupid. (Nowadays the conventional wisdom has a much higher opinion of AI safety; I’m talking about 2010-2014.)

I thought that people have terrible taste in economic policy, and that they mostly vote for good-sounding stuff that stops sounding good if you think about it properly for even a minute

I was horrified by people proudly buying products that said “Made in Australia” on them; I didn’t understand how that wasn’t obviously racist, and I thought that we should make it much easier to allow anyone who wants to to come live in Australia. (This one has become much less controversial since Trump inadvertently convinced liberals that they should be in favor of immigration liberalization.)

I thought and still think that a lot of people’s arguments about why it’s good to call the police on bike thieves were dumb. See eg many of the arguments people made in response to a post of mine about this (that in fairness was a really dumb post, IMO)

I think I was right about other people being wrong. However, I think that my actual opinions on these topics were pretty confused and wrong, much more than I thought at the time. Here’s how I updated my opinion for all the things above:

I

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Tiles: Report on Programmatic Code Generation
by Martin Sustrik — 2019-02-22T00:10:04.593Z

Writing programs that generate programs is hard. The programmer has to think at two levels of abstraction at once. She has to follow the logic of the generator. At the same time she can't lose the focus on the logic of the generated code. And the two don't even have to be written in the same language!

That's a hard enough feat even when the tools aren't putting obstacles in your way. But, unfortunately, that's exactly what they are doing.

Consider this Python program that outputs the classic C "Hello, world!" program:

def generate_hello(who): return """#include <stdio.h> main( ) { printf("hello, """ + who + "!\\n\");\n}" print(generate_hello("world"))

Ugly, you say? Yes, it's ugly. But it's just generating the simplest possible program! If we wanted to generate something truly complex it would become doubleplusugly.

But ugliness aside, the problem is that the code is unreadable.

Reading code is generally harder than writing code. Reading code with two parallel levels of abstraction is yet much harder. Add some atrocious formatting, sprinkle with copious amount of escape sequences and even the best programmer won't be able to understand what's going on.

The traditional solution to this problem is templating.

The idea is that the generated program is like a form, a pre-printed template with few blank slots to fill in:

And here's how it works with, say Jinja2:

from jinja2 import Template t = Template("""#include <stdio.h> main( ) { printf("Hello, {{ who }}!\\n"); }""") print(t.render(who="World"))

Well, it's not much better. Weird formatting and escape sequences remain. However, given that the template is now a single string we can load it from a file instead of using a string literal. The content of the file would look much better:

#include <stdio.h> main( ) { printf("Hello, {{ who }}!\n"); }

The downside is that the template and the generator now live in two different files which makes the logic harder to follow.

By the way, I am not picking on Jinja2 here. All the code generation tools I've looked at work in basically the same manner. Here's, for example, how Golang's text/template package looks like:

package main import ( "log" "os" "text/template" ) func main() { // De

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