Yeah, this is a great book. Curious to see how the rise of adblockers/anti-addiction tech will change things. There’s a counterintuitive argument that things will become worse: As sophisticated folks tune out, unsophisticated folks become the most lucrative audience and the race to the bottom accelerates. As wise people leave the conversation, the conversation that remains is even more crazy. And unfortunately, even a small number of well-coordinated crazy people can do a lot of damage.
I actually think leaving comments online is a more scaleable strategy than people realize. I leave a lot of comments on LW, the EA Forum, etc. and I’m now no longer surprised when I meet someone IRL and they recognize my name. It took me a while to internalize how skewed reader/writer ratios are online and how many lurkers there really are. I suspect the lurker numbers for any kind of culture war discussion are even higher. It’s like two people having a shouting match on public transportation: Everyone wants to watch, no one wants to participate. But the size of the audience means that if you do choose to participate, then you massively amplify your influence.
I once did an experiment where I registered a throway twitter account, searched for the trending hashtag controversy du jour, replied to peoples’ tweets and tried to talk them down from their extreme positions. I was surprised by how few tweets there were, how little time it took for me to respond to all of them, how many people engaged with me, and how successful I was at getting people to moderate their positions a bit. I got the impression that if I had the money to hire 100 people to use Twitter full time and mediate every ugly discussion they saw, I’d have a nontrivial chance of moving the needle for a nation of 330 million.
I actually think leaving comments online is a more scaleable strategy than people realize. I leave a lot of comments on LW, the EA Forum, etc. and I’m now no longer surprised when I meet someone IRL and they recognize my name. It took me a while to internalize how skewed reader/writer ratios are online and how many lurkers there really are.
My experience has been similar. I also believe this is true of Wikipedia articles, and that’s one reason I still engage. I’m less confident that I am making a difference by hosting fulltexts or scanning books, but I figure at some point I can do a time-series analysis of citations as a proxy.
As far as genetics goes, direct interactions haven’t gone too well, especially on Twitter; if you’re dealing with someone who flatly denies that GWAS hits replicate or that sibling comparisons prove causal effects or who claims that all hits are population stratification, at this point, there’s no reasoning with them. So I try to simply publicize a little all the research going on for the hidden masses, hoping that it’ll be Grothendieck’s ‘rising sea’.
Something about the model proposed here feels slightly off to me, but overall I think this changed my perspective on public discourse a bunch.
I am putting out a $50 bounty to anyone who creates a Sarah Constantin style fact-post about how many active online-commenters there are on major platforms like Twitter, Facebook and Reddit, especially with basic writing and persuasion skills.
Yeah, I’m not at all confident in this model, but I do suspect it’s underrated. I’m reminded of something Andrew Ng mentioned in his machine learning class about how he would run into machine learning projects where they didn’t have much data, and he would ask them to do a back of the napkin calculation to figure out how much time it would take them to hand-label more data. He said that oftentimes with just a week’s time spent hand-labeling data, they’d dramatically increase the amount of data available and improve their algorithm’s performance. It’s not clever or “scalable”, but sometimes the solution that doesn’t scale is the best one.
This has definitely been true in my experience with ML/DL so far. If you grit your teeth and put a bit of effort into a reasonably low-latency script for hand-labeling, you can often do a few hundred or thousand datapoints in a quite feasible amount of time and it will be enough to work with in finetuning (eg. PALM) or training on an embedding (eg. StyleGAN latents).
And this is something that a lot of people have been learning with image generation models the past 3 years: it is often way faster to just curate a small set of images to, eg. train a LoRA, than to try to train some sort of uber-model which fixes the problem out of the box or use some complicated fancy algorithm on top of the existing model or brute force sample generation & selection. It’s not necessarily that the model doesn’t “know” the thing you want, it’s that you can’t tell it accurately. ‘A picture is worth a thousand words’, which is a lot of words to have to figure out! (Or right now—we’ve been working with a guy on InvertOrNot.com as an API service for better dark modes, to automatically classify images for website dark modes, and while GPT-4-V can do it if phrased as a pairwise comparison, one could never afford to offer that in bulk as a free public service as we would like to—so… he finetuned a tiny cheap EfficientNet on ~1000 hand-labeled images and it works great now and runs for free. Because you can’t easily “prompt” for it AFAICT, but it’s easy to collect a few thousand examples to hand-label for a pretrained CNN.)
Of course, the models or services improve over time and now you can often just zero-shot what you want, but one’s ambitions always grows to match… Whether it’s art or porn or business, once we can do the old thing we dreamed of, soon we sour on it and demand even more, something even more specific and precise and niche, and the only way to hit that ultra-niche will often be some sort of hand-labeled dataset.
The argument around ad-blockers and anti-addiction tech is an interesting one. While I agree that they make it more difficult for sophisticated people to be affected by the provocation and click-bait, I’m not sure that this automatically makes the unsophisticated audience that remains more lucrative. One of the hopeful things that Ryan mentions in the 2017 edition of this book is that the rise of paywalls is a sign that “serious” media outlets are beginning to realize that the pageview game is a game for suckers. They can’t hope to compete with blogs who specialize in outrage, so instead of doing that they’re differentiating themselves as “upmarket” publications who charge an up-front fee, in exchange for not showering you with clickbait.
My personal prediction is that we’ll end up with a two-tier media market, where relatively affluent and more sophisticated readers continue to read things like the New York Times, or the Wall Street Journal, or The Economist, while less affluent or less sophisticated readers read Huffington Post and… whatever ends up taking Gawker’s place. In a sense, it’ll be like the old split between broadsheets and tabloids, only online instead of in print.
With regards to Twitter, I wonder how important it is that the handle be a throwaway one, with no prior history. I know that, for example, gwern has tried engaging with vocal skeptics of things like GWAS studies on IQ, and it doesn’t really seem to have made a difference in their viewpoint. I wonder how much of that has to do with gwern’s prior posting history putting him firmly on the “other side”, causing people to dismiss his claims on the basis of who he is, rather than what the claims are.
Yeah, this is a great book. Curious to see how the rise of adblockers/anti-addiction tech will change things. There’s a counterintuitive argument that things will become worse: As sophisticated folks tune out, unsophisticated folks become the most lucrative audience and the race to the bottom accelerates. As wise people leave the conversation, the conversation that remains is even more crazy. And unfortunately, even a small number of well-coordinated crazy people can do a lot of damage.
I actually think leaving comments online is a more scaleable strategy than people realize. I leave a lot of comments on LW, the EA Forum, etc. and I’m now no longer surprised when I meet someone IRL and they recognize my name. It took me a while to internalize how skewed reader/writer ratios are online and how many lurkers there really are. I suspect the lurker numbers for any kind of culture war discussion are even higher. It’s like two people having a shouting match on public transportation: Everyone wants to watch, no one wants to participate. But the size of the audience means that if you do choose to participate, then you massively amplify your influence.
I once did an experiment where I registered a throway twitter account, searched for the trending hashtag controversy du jour, replied to peoples’ tweets and tried to talk them down from their extreme positions. I was surprised by how few tweets there were, how little time it took for me to respond to all of them, how many people engaged with me, and how successful I was at getting people to moderate their positions a bit. I got the impression that if I had the money to hire 100 people to use Twitter full time and mediate every ugly discussion they saw, I’d have a nontrivial chance of moving the needle for a nation of 330 million.
My experience has been similar. I also believe this is true of Wikipedia articles, and that’s one reason I still engage. I’m less confident that I am making a difference by hosting fulltexts or scanning books, but I figure at some point I can do a time-series analysis of citations as a proxy.
As far as genetics goes, direct interactions haven’t gone too well, especially on Twitter; if you’re dealing with someone who flatly denies that GWAS hits replicate or that sibling comparisons prove causal effects or who claims that all hits are population stratification, at this point, there’s no reasoning with them. So I try to simply publicize a little all the research going on for the hidden masses, hoping that it’ll be Grothendieck’s ‘rising sea’.
Something about the model proposed here feels slightly off to me, but overall I think this changed my perspective on public discourse a bunch.
I am putting out a $50 bounty to anyone who creates a Sarah Constantin style fact-post about how many active online-commenters there are on major platforms like Twitter, Facebook and Reddit, especially with basic writing and persuasion skills.
Yeah, I’m not at all confident in this model, but I do suspect it’s underrated. I’m reminded of something Andrew Ng mentioned in his machine learning class about how he would run into machine learning projects where they didn’t have much data, and he would ask them to do a back of the napkin calculation to figure out how much time it would take them to hand-label more data. He said that oftentimes with just a week’s time spent hand-labeling data, they’d dramatically increase the amount of data available and improve their algorithm’s performance. It’s not clever or “scalable”, but sometimes the solution that doesn’t scale is the best one.
This has definitely been true in my experience with ML/DL so far. If you grit your teeth and put a bit of effort into a reasonably low-latency script for hand-labeling, you can often do a few hundred or thousand datapoints in a quite feasible amount of time and it will be enough to work with in finetuning (eg. PALM) or training on an embedding (eg. StyleGAN latents).
And this is something that a lot of people have been learning with image generation models the past 3 years: it is often way faster to just curate a small set of images to, eg. train a LoRA, than to try to train some sort of uber-model which fixes the problem out of the box or use some complicated fancy algorithm on top of the existing model or brute force sample generation & selection. It’s not necessarily that the model doesn’t “know” the thing you want, it’s that you can’t tell it accurately. ‘A picture is worth a thousand words’, which is a lot of words to have to figure out! (Or right now—we’ve been working with a guy on InvertOrNot.com as an API service for better dark modes, to automatically classify images for website dark modes, and while GPT-4-V can do it if phrased as a pairwise comparison, one could never afford to offer that in bulk as a free public service as we would like to—so… he finetuned a tiny cheap EfficientNet on ~1000 hand-labeled images and it works great now and runs for free. Because you can’t easily “prompt” for it AFAICT, but it’s easy to collect a few thousand examples to hand-label for a pretrained CNN.)
Of course, the models or services improve over time and now you can often just zero-shot what you want, but one’s ambitions always grows to match… Whether it’s art or porn or business, once we can do the old thing we dreamed of, soon we sour on it and demand even more, something even more specific and precise and niche, and the only way to hit that ultra-niche will often be some sort of hand-labeled dataset.
The argument around ad-blockers and anti-addiction tech is an interesting one. While I agree that they make it more difficult for sophisticated people to be affected by the provocation and click-bait, I’m not sure that this automatically makes the unsophisticated audience that remains more lucrative. One of the hopeful things that Ryan mentions in the 2017 edition of this book is that the rise of paywalls is a sign that “serious” media outlets are beginning to realize that the pageview game is a game for suckers. They can’t hope to compete with blogs who specialize in outrage, so instead of doing that they’re differentiating themselves as “upmarket” publications who charge an up-front fee, in exchange for not showering you with clickbait.
My personal prediction is that we’ll end up with a two-tier media market, where relatively affluent and more sophisticated readers continue to read things like the New York Times, or the Wall Street Journal, or The Economist, while less affluent or less sophisticated readers read Huffington Post and… whatever ends up taking Gawker’s place. In a sense, it’ll be like the old split between broadsheets and tabloids, only online instead of in print.
With regards to Twitter, I wonder how important it is that the handle be a throwaway one, with no prior history. I know that, for example, gwern has tried engaging with vocal skeptics of things like GWAS studies on IQ, and it doesn’t really seem to have made a difference in their viewpoint. I wonder how much of that has to do with gwern’s prior posting history putting him firmly on the “other side”, causing people to dismiss his claims on the basis of who he is, rather than what the claims are.