trentmkelly/LessWrong-43k huggingface dataset:
Livestreaming monetisation models aka What do camgirls have in common with Twitch streamers?
by characteristics — 2020-09-19T18:57:08.985Z
Link: https://lillianli.substack.com/p/livestreaming-monetisation-models

Repost from my personal blog / newsletter - feel free to subscribe if you like longform Chinese tech analysis.

In many ways, the internet was made for livestreaming. Starting with

Jennicam

back in 1996, it has taken the medium a while to get to the fabled plateau of productivity.

Livestreaming in its genesis of Twitch, Meerkat, Periscope and Facebook Live never fleshed out a compatible monetisation model. As a result, it struggled with

marketplace supply-side liquidity

since livestreaming hosts weren't incentivised to stream consistently. Viewers then stopped watching as there was no guarantee of good content on the platform. Livestreaming only became a medium after the emergence of effective monetisation, as then creators had the incentive to keep to a regular broadcasting schedule for followers.

Bolstered by Covid-19, livestreaming is now ubiquitous in China and increasingly mainstream in the west. The Chinese market is estimated to be worth

RMB 433.8 billion

($64.2bn USD) in 2019 and is expected to double by the end of 2020. Twitch currently has

6.2 million monthly broadcasters,

and games livestreaming as an industry is estimated to be worth $50bn. Livestreaming has created massive stars; on Twitch, Ninja the Fortnite streamer has ~7m fans and even higher earnings. In China, Li Jiaqi the 'Lipstick queen'

sold 15,000

tubs of lipstick in 15 minutes on Taobao. Viya, who is essentially Oprah incarnated as a livestreamer, can

command 37m viewers

(bigger than the audience for the Oscars or Game of Thrones finale) during a stream. These big numbers are representative of the digitalisation of distribution and entertainment that's been quietly occurring throughout the world. The fact that livestreaming has become their de-facto medium owes much to the unlocking of effective monetisation strategies.

Livestreaming as a medium is a conflation of a product as well as as a distribution channel. It exists on a spectrum of being pure entertainment on one-side and a new go-to-market strategies on the other, with different kinds of monetisation models for each side. While western startups have centred around the 'livestreaming as product' theme, China, with its enabling infrastructure in payment and fulfilment, have been quick to adopt livestreaming as a new distribution channel. This June's 618 Shopping Festival, saw Alibaba and

JD.com

repo...
Get Social Sector Leaders to Use Evidence With This 1 Weird Trick
by Ian David Moss — 2020-09-19T18:07:50.000Z

Okay, it’s more like a dozen not-that-weird tricks, but the point is…

For a while now, I’ve been sounding the alarm about the social sector’s crisis of evidence use. To put it bluntly, the human race expends mind-boggling resources bringing studies, reports, and analyses into the world that are read by few and acted upon by no one. As I argued at the time, “we are either vastly overvaluing or vastly undervaluing the act of building knowledge.” My sincere hope has long been that it’s the latter — that the knowledge we produce really does hold tremendous value, and all we need to do is figure out what works to unlock it. Luckily there is a whole ecosystem of organizations dedicated to answering “what works?” kinds of questions, and four years ago that ecosystem produced a report called “The Science of Using Science” that tackles this question head-on.

“The Science of Using Science” seeks to understand what interventions are effective in increasing the use of research evidence by leaders and practitioners at various levels of civil society. It encompasses two reviews in one: the first examined the literature on evidence-informed decision-making (EIDM) itself, and the second prowled the broader social science literature for interventions that could be relevant to EIDM but haven’t specifically been tested in that context.

Based on previous theoretical work by themselves and others, the authors devised a conceptual framework encompassing six mechanisms under which to group the interventions discussed in the reviews. The mechanisms are as follows:

The theory is that these mechanisms motivate behavior change through the three-part Capability, Motivation, and Opportunity model developed by Michie et al in 2011 as diagrammed below:

So what did they find? The first review, the one focused on studies specifically assessing EIDM interventions, identified 23 existing reviews of moderate to high trustworthiness that were published between 1990–2015. Most of these drew from the health sector, and featured plenty of variation in the interventions, outcomes, and indicators used. Nevertheless, the aggregated findings offer several useful insights:

Just providing people with evidence doesn’t mean they’ll do anything with it

One of the clearest takeaways from the experimental literature, which backs up the descriptive evidence I’ve previously written about, is that simply putting evide...
Taking Social Initiative
by Neel Nanda — 2020-09-19T15:31:21.082Z
Link: https://www.neelnanda.io/blog/mini-blog-post-23-taking-social-initiative

(This is a post from a personal daily blogging project, on social skills, taking social initiative, and "networking without being a terrible person". And I think this is relevant to the interests of LessWrong readers!)

Introduction

A theme I’ve touched on pretty heavily in previous posts is agency. Not being passive, and actually doing things. Making it part of your identity, understanding the underlying biases and becoming able to take the first step. One extremely important instance is applying this to social settings - taking social initiative. I think this is something that most people I see are systematically bad at, and something I’ve deliberately improved at over time, and has been an insanely valuable skill. I estimate that at least 80% of my current friendships either wouldn’t exist or would be substantially worse without this. And this is an important component of how I find things I’m excited about and seek positive externalities

My goal in this post is to outline my model for why this is hard, how to become better at it, and how I’ve specifically applied this to friendships and to networking.

Health warning: The Law of Equal but Opposite Advice applies massively here. My guess is that most people reading this don’t take the social initiative enough, but there are definitely shameless people who take it far too much! My guess is that most readers are very unlikely to reach that point by accident.

Taking Social Initiative is Hard

It’s a well-documented psychological phenomenon that people systematically underestimate their abilities. This is what an insecurity is. And as I talk about here, I’ve found this mindset valuable for getting over my own insecurities. My goal is to have a true view of my abilities, and whether I’m good at something. And, empirically, insecurities are systematically biased against me - insecurity is a cognitive bias.

And I observe a similar thing when people consider taking the social initiative - organising events, meeting new people, reconnecting with old friends, going to unfamiliar events. People focus strongly on the downsides, ways this could go wrong, think people will laugh at them, etc. It’s extremely easy to fall into the default path of doing nothing. And empirically, I think this is a cognitive bias. I have frequently observed people being...
Why GPT wants to mesa-optimize & how we might change this
by John_Maxwell — 2020-09-19T13:48:30.348Z

This post was inspired by orthonormal's post Developmental Stages of GPTs and the discussion that followed, so only part of it is original.

First I'll aim to provide a crisper version of the argument for why GPT wants to mesa-optimize. Specifically, I'll explain a well-known optimization algorithm used in text generation, and argue that GPT can improve performance on its objective by learning to implement something like this algorithm internally.

Then I'll offer some ideas of mine about how we might change this.

Explanation of beam search

Our goal is to generate plausible text. We evaluate whether text is "plausible" by multiplying together all the individual word probabilities from our language model.

Greedy word selection has a problem: Since it doesn't do lookahead, it's liable to get stuck in a dead end. Let's say we give our system the following poem about cheeses and ask it to generate more text:

Mozzarella is white

So you can see it at night

Cheddar is...

If our language model is decent, the word it will assign the highest probability to is "orange". But this creates a problem, because "orange" is a hard word to rhyme.

Beam search is an attempt to solve this problem. Instead of picking the next word greedily, we explore the tree of completions and try to find a multi-word completion that maximizes the product of the individual word probabilities.

Because there are so many words in the English language, the tree grows at a very fast exponential rate. So we choose an integer beam_width for the number of partial completions to track, and each time we take another step deeper into the tree, we discard all but the most plausible beam_width partial completions.

Beam search with a beam width of 2. The bold red path corresponds to the maximum-plausibility completion, which would not get discovered by greedy search because "nice" has a higher probability than "dog". Image stolen from this Hugging Face blog post, which has another explanation of beam search if you didn't like mine.

Claim: GPT can do better on its training objective if it learns to do beam search internally

We've discussed text generation with a pretrained language model. Let's switch gears and talk about the model's training process.

Suppose GPT's training corpus has the following poem:

Mozzarella is white

So you can see it at night

Cheddar is marigold

Unless you let it get too o

...
EA Relationship Status
by jefftk — 2020-09-19T01:50:02.731Z

A friend

observed
that fewer people in the effective altruism movement are married than
you might expect. I was curious: what are marriage rates like
within the EA community? The 2018

EA Survey asked
about relationship status, and we can look at how that varies
by age:

I'm using "ever married" for people who are currently married or have
ever been married, including people who are now divorced, widowed, or
separated. Since some of these buckets might be pretty small, let's
add sample size information:

The anonymized survey data doesn't have 35-44 data, and the 65+ group looks
suspiciously like it has the 35-44 group lumped in with it. I've
filed
a bug and if they fix it I'll update the post. For now, probably
ignore the 65+ group.

For comparison, here's 2018 ACS data (via) for US marriage rates, on the
same scale:

And, at least in the US, people with more education are more likely to
be married, after age 28:

Now, EAs are not all American, and even then they're different from
Americans as a whole in many ways other than being interested in
effective altruism. On the other hand, when I look at what fraction
of my (Swarthmore) college friends are married, a group similar to EAs
in many ways, it's not far from the graph for the US at large. (And
it's off in the positive direction). I see similar numbers by school
for people
who were 30-34 in 2014.

There does seem to be something real here. Some guesses as to why:

EAs often highly prioritize their careers.

EAs are generally less interested in having children.

EAs are often poly.

EAs often live in group houses.

EAs are more willing to be weird; less likely to do something because it is the standard
thing to do.

Other ideas?
Draft report on AI timelines
by Ajeya Cotra — 2020-09-18T23:47:39.684Z

Hi all, I've been working on some AI forecasting research and have prepared a draft report on timelines to transformative AI. I would love feedback from this community, so I've made the report viewable in a Google Drive folder here.

With that said, most of my focus so far has been on the high-level structure of the framework, so the particular quantitative estimates are very much in flux and many input parameters aren't pinned down well -- I wrote the bulk of this report before July and have received feedback since then that I haven't fully incorporated yet. I'd prefer if people didn't share it widely in a low-bandwidth way (e.g., just posting key graphics on Facebook or Twitter) since the conclusions don't reflect Open Phil's "institutional view" yet, and there may well be some errors in the report.

The report includes a quantitative model written in Python. Ought has worked with me to integrate their forecasting platform Elicit into the model so that you can see other people's forecasts for various parameters. If you have questions or feedback about the Elicit integration, feel free to reach out to elicit@ought.org.

Looking forward to hearing people's thoughts!
Expansive translations: considerations and possibilities
by ozziegooen — 2020-09-18T15:39:21.514Z

A crowd probably best served by a wide variety of translations

TLDR: Language translation is a decent first step for written works, but the ideal looks more like an empathetic personal tutor. There’s a lot to do in-between, both in the near term with human labor, and the longer term with Machine Learning.

Epistemic Status: I’m not an experienced researcher in this field. I’ve read a few audiobooks on language and thought about the area, but I’m sure I’m failing to reference many key papers and books. I’m fairly uncertain about all of this, I suggest taking my opinion very lightly (if at all) and thinking through the issue yourself. If you know of other materials I or other readers should know about, references would be appreciated.

Feedback Preferences: I’m sure I’m wildly wrong on many things. Feedback is highly appreciated. I will take little offense on rude comments so go wild. That said, don’t expect long responses.

I’m not sure how to best write this, so I’ll divide things into a few vignettes.

Some rough statements which think are misguided:

“Our book is available in 20 countries, so is accessible to 3 Billion people.”

“Once GPT-n can translate perfectly between languages, everyone will be able to communicate with each other”

“I’m not going to try to rephrase or re-explain this (technical) book, because you really should just read it directly”

“I don’t see why you wrote up those concepts, they were explained in more detail in a previous post”

Retellings and their skeptics:

There’s been an interesting trend recently of books that retell the ideas of other, older books. See:
How Proust Can Change Your Life
How Adam Smith Can Change Your Life
A Jane Austen Education: How Six Novels Taught Me About Love, Friendship, and the Things That Really Matter
And all the other books mentioned in

this post

.

I haven’t seen a better term for these, so I’ll refer to these as “retellings”.

More near to our community, Robert Wiblin has recently posted

a piece

on “Ugh fields”, which acts as a retelling of

LessWrong posts

from 10 years ago.

To some audiences, these retellings are not only a waste of time, but lossy explanations of superior sources. What if readers stop at the retellings and skip the sources, and are left with false impressions? Clearly the solution is to point readers to the source and skip the in-between. Maybe add a minor amount of...
The "Backchaining to Local Search" Technique in AI Alignment
by adamShimi — 2020-09-18T15:05:02.944Z

In the spirit of this

post

by John S. Wentworth, this is a reference for a technique I learned from Evan Hubinger. He's probably not the first to use it, but he introduced it to me, so he gets the credit.

In a single sentence, backchaining to local search is the idea of looking at how a problem of alignment could appear through local search (think gradient descent). So it starts with a certain problem (say reward tampering), and then tries to create a context where the usual training process in ML (local search) could create a system suffering from this problem. It’s an instance of backchaining in general, which just looks for how a problem could appear in practice.

Backchaining to local search has two main benefits:

It helps decide whether this specific problem is something we should worry about.
It forces you to consider your problem from a local search perspective, instead of the more intuitive human/adversarial perspective (how would I mess this up?).

Let's look at a concrete example: reward gaming (also called specification gaming). To be even more concrete, we have a system with a camera and other sensors, and its goal is to maximize the amount of time when my friend Tom smiles, as measured through a loss function that captures whether the camera sees Tom smiling. The obvious (for us) way to do reward gaming here is to put a picture of Tom’s smiling face in front of the camera -- then the loss function is minimized.

The backchaining to local search technique applied to this example asks "How can I get this reward gaming behavior by local search?" Well this reward gaming strategy is probably a local minima for the loss function (as changing just a little the behavior would increase the loss significantly), so local search could find it and stay in there. It's also better than most simple strategies, as ensuring that someone smiles (not necessarily a good goal, mind you) requires rather complex actions in the world (like going full "Joker" on someone, or changing someone's brain chemistry, or any other weird and impractical scheme). So there's probably a big zone in model space for which our specific example of reward gaming is the local minima.

All in all, the backchaining to local search technique tells us that this looks like a problem that should happen frequently in practice. Which lines up well wi...
Some thoughts on criticism
by Buck — 2020-09-18T04:58:37.042Z

Here are some somewhat unconnected unconfident thoughts on criticism that I’ve been thinking about recently.

---

A while ago, when I started having one-on-ones with people I was managing, I went into the meetings with a list of questions I was going to ask them. After the meetings, I’d look at my notes and realize that almost all the value of the meeting came from the part where I asked them what the worst parts of their current work situation were and what the biggest mistakes I was making as a manager were.

I started thinking that almost the whole point of meetings like that is to get your report to feel comfortable giving an honest answer to those questions when you ask them--everything else you talk about is just buttering them up.

I wish I knew how to make people I’m managing more enthusiastic about criticising me and telling me their insecurities. Maybe I could tell them to have a group chat with just them in it where they all had to name their biggest complaints about me? Maybe I should introduce them all to a former intern of mine who promised not to repeat anything they said who told them about all the mistakes I made while managing them, as an attempt to credibly signal actual interest?

---

A lot of the helpful criticism I’ve gotten over the last few years was from people who were being kind of unreasonable and unfair.

One simple example of this is that one time someone (who I’d engaged with for many hours) told me he didn’t take my ideas seriously because I had blue hair. On the one hand, fuck that guy; on the other hand, it’s pretty helpful that he told me that, and I'm grateful to him for telling me. You’d think that being on the internet would expose you to all the relatively uninformed impolite criticism you’d possibly need, but in my experience this isn’t true.

Additionally I think that when people are annoyed at you, they look harder for mistakes you’re making and they speak more frankly about them. So it’s sometimes actually more likely that people will give you useful criticism if they get unreasonably annoyed at you first. This goes especially for people who know you and understand you well. (This is also a reason to think it’s probably helpful to sometimes get drunk around people you like a lot but don’t totally see eye to eye with. I haven...
Against Victimhood
by Jacob Falkovich — 2020-09-18T01:58:31.041Z

Cross-posted, as always, from Putanumonit.

I have written many posts in the shape of

giving life advice

. I hear back from readers who take it and those who refuse it. Either is good — I’m just a guy on the internet, to be consumed as part of a balanced diet of opinions.

But

occasionally

I hear: who are you to give life advice, your own life is so perfect! This sounds strange at first. If you think I’ve got life figured out, wouldn’t you want my advice? I think what they mean is that I haven’t had to overcome the hardships they have, hostile people and adverse circumstances.

I talk quite often about things that are going poorly for me, but only from the point of view of

how

I

fucked up

. I avoid talking about being wronged, oppressed, attacked, discriminated against, or victimized. If you assume that it’s because I live a charmed life where none of these things happen, you may need a refresher on the

base rate fallacy

.

The reason I never talk about being a victim is that I’m extremely averse to

victim mentality

. It’s an insidious mindset, one that’s self-reinforcing both internally and by outside feedback. I’ve seen it claim people, groups, entire nations. On the flip side, I’ve noticed that the less often I think of myself as a victim the less I am victimized, which in turn makes that mindset even rarer. If I do feel on occasion that I have been harmed through no fault of my own by hostile actors I keep it to myself. This is a bad time to be a victim on the internet.

What’s bad about victim mentality? Most obviously, inhabiting a narrative where the world has committed a great injustice against which you are helpless against is extremely distressing. Whether the narrative is justified or not, it causes suffering.

See yourself as a victim prevents you from improving your situation by your own power, since doing so will contradict the story of your own helplessness. In particular that’s true of the story you tell yourself. That story is your

identity

, how your mind predicts your future actions. If your story is helpless victimhood your mind will refuse to help — it wants to be vindicated more than it wants to do better.

When I was young I was a weird nerd, and while that maybe hasn’t changed much I do find myself in social circles where weird nerdiness is welcome. In school it was not very welcome, and people weren’t nice to me. But I never really fell into think...