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### Sexual Dimorphism in Yudkowsky's Sequences, in Relation to My Gender Problems

3 мая, 2021 - 07:31
Published on May 3, 2021 4:31 AM GMT

(content warning sexism)
(content warning implied transphobia)
(content warning too much information about weird sexual fetishes)
(content warning WTF did I just read)

(May 2021, ~16,000 words)

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### There’s no such thing as a tree (phylogenetically)

3 мая, 2021 - 06:47
Published on May 3, 2021 3:47 AM GMT

[Crossposted from Eukaryote Writes Blog.]

So you’ve heard about how fish aren’t a monophyletic group? You’ve heard about carcinization, the process by which ocean arthropods convergently evolve into crabs? You say you get it now? Sit down. Sit down. Shut up. Listen. You don’t know nothing yet.

“Trees” are not a coherent phylogenetic category. On the evolutionary tree of plants, trees are regularly interspersed with things that are absolutely, 100% not trees. This means that, for instance, either:

• The common ancestor of a maple and a mulberry tree was not a tree.
• The common ancestor of a stinging nettle and a strawberry plant was a tree.
• And this is true for most trees or non-trees that you can think of.

I thought I had a pretty good guess at this, but the situation is far worse than I could have imagined.

CLICK TO EXPAND. Partial phylogenetic tree of various plants. TL;DR: Tan is definitely, 100% trees. Yellow is tree-like. Green is 100% not a tree. Sourced mostly from Wikipedia.

I learned after making this chart that tree ferns exist (h/t seebs), which I think just emphasizes my point further.Why do trees keep happening?

First, what is a tree? It’s a big long-lived self-supporting plant with leaves and wood.

Also of interest to us are the non-tree “woody plants”, like lianas (thick woody vines) and shrubs. They’re not trees, but at least to me, it’s relatively apparent how a tree could evolve into a shrub, or vice-versa. The confusing part is a tree evolving into a dandelion. (Or vice-versa.)

Wood, as you may have guessed by now, is also not a clear phyletic category. But it’s a reasonable category – a lignin-dense structure, usually that grows from the exterior and that forms a pretty readily identifiable material when separated from the tree. (…Okay, not the most explainable, but you know wood? You know when you hold something in your hand, and it’s made of wood, and you can tell that? Yeah, that thing.)

All plants have lignin and cellulose as structural elements – wood is plant matter that is dense with both of these.

Botanists don’t seem to think it only could have gone one way – for instance, the common ancestor of flowering plants is theorized to have been woody. But we also have pretty clear evidence of recent evolution of woodiness – say, a new plant arrives on a relatively barren island, and some of the offspring of that plant becomes treelike. Of plants native to the Canary Islands, wood independently evolved at least 38 times!

One relevant factor is that all woody plants do, in a sense, begin life as herbaceous plants – by and large, a tree sprout shares a lot of properties with any herbaceous plant. Indeed, botanists call this kind of fleshy, soft growth from the center that elongates a plant “primary growth”, and the later growth from towards towards outside which causes a plant to thicken is “secondary growth.” In a woody plant, secondary growth also means growing wood and bark – but other plants sometimes do secondary growth as well, like potatoes (in roots)

This paper addresses the question. I don’t understand a lot of the closely genetic details, but my impression of its thesis is that: Analysis of convergently-evolved woody plants show that the genes for secondary woody growth are similar to primary growth in plants that don’t do any secondary growth – even in unrelated plants. And woody growth is an adaption of secondary growth. To abstract a little more, there is a common and useful structure in herbaceous plants that, when slightly tweaked, “dendronizes” them into woody plants.

Dendronization – Evolving into a tree-like morphology. (In the style of “carcinization“.) From ‘dendro‘, the ancient Greek root for tree.

Can this be tested? Yep – knock out a couple of genes that control flower development and change the light levels to mimic summer, and researchers found that Arabidopsis rock cress, a distinctly herbaceous plant used as a model organism – grows a woody stem never otherwise seen in the species.

The tree-like woody stem (e) and morphology (f, left) of the gene-altered Aridopsis, compared to its distinctly non-tree-like normal form (f, right.) Images from Melzer, Siegbert, et al. “Flowering-time genes modulate meristem determinacy and growth form in Arabidopsis thaliana.”Nature genetics 40.12 (2008): 1489-1492.

So not only can wood develop relatively easily in an herbaceous plant, it can come from messing with some of the genes that regulate annual behavior – an herby plant’s usual lifecycle of reproducing in warm weather, dying off in cool weather. So that gets us two properties of trees at once: woodiness, and being long-lived. It’s still a far cry from turning a plant into a tree, but also, it’s really not that far.

To look at it another way, as Andrew T. Groover put it:

“Obviously, in the search for which genes make a tree versus a herbaceous plant, it would be folly to look for genes present in poplar and absent in Arabidopsis. More likely, tree forms reflect differences in expression of a similar suite of genes to those found in herbaceous relatives.”

So: There are no unique “tree” genes. It’s just a different expression of genes that plants already use. Analogously, you can make a cake with flour, sugar, eggs, sugar, butter, and vanilla. You can also make frosting with sugar, butter, and vanilla – a subset of the ingredients you already have, but in different ratios and use

But again, the reverse also happens – a tree needs to do both primary and secondary growth, so it’s relatively easy for a tree lineage to drop the “secondary” growth stage and remain an herb for its whole lifespan, thus “poaizating.” As stated above, it’s hypothesized that the earliest angiosperms were woody, some of which would have lost that in become the most familiar herbaceous plants today. There are also some plants like cassytha and mistletoe, herbaceous plants from tree-heavy lineages, who are both parasitic plants that grow on a host tree. Knowing absolutely nothing about the evolution of these lineages, I think it’s reasonable to speculate that they each came from a tree-like ancestor but poaized to become parasites. (Evolution is very fond of parasites.)

Poaization: Evolving into an herbaceous morphology. From ‘poai‘, ancient Greek term from Theophrastus defining herbaceous plants (“Theophrastus on Herbals and Herbal Remedies”).

(I apologize to anyone I’ve ever complained to about jargon proliferation in rationalist-diaspora blog posts.)

The trend of staying in an earlier stage of development is also called neotenizing. Axolotls are an example in animals – they resemble the juvenile stages of the closely-related tiger salamander. Did you know very rarely, or when exposed to hormone-affecting substances, axolotls “grow up” into something that looks a lot like a tiger salamander? Not unlike the gene-altered Arabidopsis.

A normal axolotl (left) vs. a spontaneously-metamorphosed “adult” axolotl (right.) [Photo of normal axolotl from By th1098 – Own work, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=30918973. Photo of metamorphosed axolotl from deleted reddit user, via this thread: https://www.reddit.com/r/Eyebleach/comments/etg7i6/this_is_itzi_he_is_a_morphed_axolotl_no_thats_not/ ]Does this mean anything?

A friend asked why I was so interested in this finding about trees evolving convergently. To me, it’s that a tree is such a familiar, everyday thing. You know birds? Imagine if actually there were amphibian birds and mammal birds and insect birds flying all around, and they all looked pretty much the same – feathers, beaks, little claw feet, the lot. You had to be a real bird expert to be able to tell an insect bird from a mammal bird. Also, most people don’t know that there isn’t just one kind of “bird”. That’s what’s going on with trees.

I was also interested in culinary applications of this knowledge. You know people who get all excited about “don’t you know a tomato is a fruit?” or “a blueberry isn’t really a berry?” I was one once, it’s okay. Listen, forget all of that.

There is a kind of botanical definition of a fruit and a berry, talking about which parts of common plant anatomy and reproduction the structure in question is derived from, but they’re definitely not related to the culinary or common understandings. (An apple, arguably the most central fruit of all to many people, is not truly a botanical fruit either).

Let me be very clear here – mostly, this is not what biologists like to say. When we say a bird is a dinosaur, we mean that a bird and a T. rex share a common ancestor that had recognizably dinosaur-ish properties, and that we can generally point to some of those properties in the bird as well – feathers, bone structure, whatever. You can analogize this to similar statements you may have heard – “a whale is a mammal”, “a spider is not an insect”, “a hyena is a feline”…

But this is not what’s happening with fruit.  Most “fruits” or “berries” are not descended from a common “fruit” or “berry” ancestor. Citrus fruits are all derived from a common fruit, and so are apples and pears, and plums and apricots – but an apple and an orange, or a fig and a peach, do not share a fruit ancestor.

• Acknowledge that all of our categories are weird and a little arbitrary
• Look wistfully of pictures of Welwitschia
• Send a fruit basket to your local botanist/plant evolutionary biologist for putting up with this, or become one yourself
While natural selection is commonly thought to simply be an ongoing process with no “goals” or “end points”, most scientists believe that life peaked at Welwitschia. [Photo from By Sara&Joachim on Flickr – Flickr, CC BY-SA 2.0, https://commons.wikimedia.org/w/index.php?curid=6342924%5D]

Some more interesting findings:

• A mulberry (left) is not related to a blackberry (right). They just… both did that.
[ Mulberry photo by Cwambier – Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=63402150. Blackberry photo by By Ragesoss – Own work, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=4496657 ]
• Avocado and cinnamon are from fairly closely-related tree species.
• It’s possible that the last common ancestor between an apple and a peach was not even a tree.
• Of special interest to my Pacific Northwest readers, the Seattle neighborhood of Magnolia is misnamed after the local madrona tree, which Europeans confused with the (similar-looking) magnolia. In reality, these two species are only very distantly related. (You can find them both on the chart to see exactly how far apart they are.)
• None of [cactuses, aloe vera, jade plants, snake plants, and the succulent I grew up knowing as “hens and chicks”] are related to each other.
• Rubus is the genus that contains raspberries, blackberries, dewberries, salmonberries… that kind of thing. (Remember, a genus is the category just above a species – which is kind of a made-up distinction, but suffice to say, this is a closely-related groups of plants.) Some of its members have 14 chromosomes. Some of its members have 98 chromosomes.
• Seriously, I’m going to hand 20 in cash to the next plant taxonomy expert I meet in person. God knows bacteriologists and zoologists don’t have to deal with this. And I have one more unanswered question. There doesn’t seem to be a strong tend of plants evolving into grasses, despite the fact that grasses are quite successful and seem kind of like the most anatomically simple plant there could be – root, big leaf, little flower, you’re good to go. But most grass-like plants are in the same group. Why don’t more plants evolve towards the “grass” strategy? Let’s get personal for a moment. One of my philosophical takeaways from this project is, of course, “convergent evolution is a hell of a drug.” A second is something like “taxonomy is not automatically a great category for regular usage.” Phylogenetics are absolutely fascinating, and I do wish people understood them better, and probably “there’s no such thing as a fish” is a good meme to have around because most people do not realize that they’re genetically closer to a tuna than a tuna is to a shark – and “no such thing as a fish” invites that inquiry. (You can, at least, say that a tree is a strategy. Wood is a strategy. Fruit is a strategy. A fish is also a strategy.) At the same time, I have this vision in my mind of a clever person who takes this meandering essay of mine and goes around saying “did you know there’s no such thing as wood?” And they’d be kind of right. But at the same time, insisting that “wood” is not a useful or comprehensible category would be the most fascinatingly obnoxious rhetorical move. Just the pinnacle of choosing the interestingly abstract over the practical whole. A perfect instance of missing the forest for – uh, the forest for … … Forget it. Related: Timeless Slate Star Codex / Astral Codex Ten piece: The categories were made for man, not man for the categories. Towards the end of writing this piece, I found that actual botanist Dan Ridley-Ellis made a tweet thread about this topic in 2019. See that for more like this from someone who knows what they’re talking about. Discuss ### Thoughts on Re-reading Brave New World 3 мая, 2021 - 06:28 Published on May 3, 2021 3:28 AM GMT I recently re-read Aldous Huxley's masterpiece of dystopian fiction Brave New World for the first time in at least a decade. Like any worthwhile piece in this genre, some predictions seem prescient (increase in sexual freedom and unproductive distractions), while others seem off the mark, even considering publication date (no significant automation of routine tasks). This isn't a formal book review, and I won't deliberately spoil the plot, but I will be discussing the implicit predictions and judgments about human nature, society, and technology, and I'll need to talk about the world-building to do that properly. Synopsis of Huxley's World-Building The story takes place in the Year of Our Ford 632 (which by my calculations should correspond to 2495 CE) in London and surroundings, with a brief sojourn to New Mexico. The world is under a unified government that prioritizes stability and tranquility. Families are no more; embryos are incubated in vitro, decanted en masse, and conditioned chemically and psychologically to fill just the niche they were created to occupy. Higher-caste individuals ("Alphas" and "Betas") are one-of-a-kind, but members of society's lower orders ("Gammas", "Deltas", and "Epsilon semi-morons") are created as masses of clones and shaped with growth hormones and poisons to have just the right size, temperament, and intellectual ability for their lives of drudgery. Further control is achieved through endless repetitions of mantras during sleep, as well as copious, promiscuous (heterosexual) sex and liberal doses of soma, a sort of combined antidepressant/hallucinogen/tranquilizer. Everyone is tremendously happy with the situation, because they've been conditioned to be so. Well, almost everyone. We couldn't have much of a plot without some sort of conflict now, could we? Predictions About Technology Brave New World was published in 1932, and we need to take that into account when reading it in 2021. For example, lots of progress had recently been made in automation of the big-machines-in-factories type (hence all the references to Henry Ford), but computers, to the extent they existed at all, were big, clunky, and not at all in the public consciousness. Certainly, they wouldn't have appeared to Aldous Huxley, a man educated in the humanities, likely to be of importance. Hence, we have a futuristic society (with literal flying cars and rocket planes) that still uses human elevator operators. Huxley was bullish on mind-control techniques like hypnopaedia and operant conditioning to make and keep people docile. He was in the vanguard of Western interest in hallucinogens. His description of using fetal alcohol exposure to deliberately create cognitive limitations is crude but not implausible. His failure to anticipate genetic engineering beyond selective breeding and embryo manipulation is forgivable, given that in 1932 it wasn't yet nailed down that DNA is the medium of heredity. In a foreword found in some editions published after WWII, Huxley castigates himself for not anticipating nuclear fission and its consequences, and I'm content to accept his mea culpa. Given that Brave New World is more of a fable than hard sci-fi, a creditable job of extrapolation overall. Judgments About Human Nature and Social Organization The overarching principle behind the book's changes to human society are all done in the name of stability and tranquility. Differences in social standing are hard-wired from (in vitro) conception to (heavily medicated) death, with ample conditioning in between to make everyone content with their lot. Strong passions and solitude are discouraged, while harmless distractions like elaborate sports, "the feelies" (described as basically classy pornography with added tactile stimulation), or casual sex keep people too busy to think. The underlying judgment here seems to be that nearly everyone, given the opportunity, will take bread and circuses and like it. Diseases are largely a thing of the past, and people maintain a youthful appearance and vitality until they expire in "Galloping Senility Wards". Sexual jealousy is kept to a minimum through strong community norms of promiscuity and discouragement of monogamy ("everyone belongs to everyone else"). Traditional religion has been abolished and replaced by "Community Sings". History, and indeed all old things, are forbidden. This society has no knowledge of its past and hence, one is meant to assume, no aspirations for any kind of future growth. This order is maintained by a hierarchy of bureaucrats, with a council of ten World Controllers at its apex. Brave New World relies an awful lot on an early-20th-century British aristocrat's view of society: there are social strata, very little mixing between them outside of what's necessary for one's job, and this is as it should be. All the named characters are Alphas and Betas, and the contempt for the Gammas, Deltas, and poor Epsilons is just as sharp in the narrator's voice as it is in the heavily-conditioned characters'. It's a little jarring to someone like me who was raised in post-Cold-War America, with constant rhetoric about the Land of Opportunity, rags-to-riches stories, and Equality Under the Law. My Impressions and Thoughts I think Brave New World is great, and I think you should read it. Huxley's vision of a dystopia based on cloning, conditioning, and drugging the population is more believable to me than Orwell's totalitarian state from 1984. It's also more ambiguous: I once had a spirited debate with someone (an economist, and I don't think she was trolling me) who told me that Brave New World wasn't actually a dystopia, it was a utopia. Her argument was basically that since everyone was happy in the places that they had been conditioned since birth to occupy, this world represented a victory condition, not a horrific perversion of human potential. Even those few who have trouble fitting in aren't killed but sent to islands where they can cavort with other misfits (a mistake on Huxley's part, in my view, but maybe it fit in better with his vision of a society built on stability and tranquility). This seems like a straw-man argument: yes, these people might self-report high levels of happiness, but they don't seem (to me, the outside-observer reader, or to the audience-stand-in character) to be flourishing under any reasonable definition. The removal of all aspiration and nearly all struggle from people's lives (no families, no romances, no art) doesn't sound like paradise, it sounds like purgatory at best. Not all aspects of Huxley's reimagined society seem as shocking or bad to a modern reader as I think they were meant to. Since the book was written, the taboo in the West against sexual promiscuity has steadily weakened. Descriptions of men and women hooking up with a different partner every night of the week find their way into mainstream entertainment, if not everyday life for most, in 2021. Likewise, Europe and America have become less religious over the last 90-ish years, and honestly the descriptions of Community Sings sound an awful lot like what some folks here are trying to accomplish with Solstice and related gatherings (minus the Community Sing's orgies. I think.). We're still viviparous, and "mother" isn't a dirty word, but lots of people have been conceived in test tubes. Part of Huxley's genius was the ambiguity: lots of these changes can be used to increase human freedom rather than curtailing it, and compared with Brave New World, I think that's largely what's happened. I also have a hard time with the dichotomy between London and the Savage Reservation (a place that the World Controllers didn't feel were worth "civilizing", and where old traditions like families and religion still hold on, but in the absence of anything resembling modern technology, to the point that literacy is uncommon). It just didn't seem realistic to me that you could maintain that kind of technological divide between two groups of people. Even if no one in "civilization" is curious about the "savages" (but they are! you can visit with a permit!) you'd think that if someone flew down to your mud hut in a helicopter you'd at least think to ask how it worked. But maybe I overestimate people's curiosity. I'd like to know what people here have to say about Brave New World. What did you like about it? What did you hate? Will a superintelligent AI think it's worthwhile to make us docile with hypnopaedia and soma, or will it just put its boot on our face forever? Do you agree with my economist interlocutor that it's really a successful utopia? Please do chime in! Discuss ### The Neuralink Monkey Demo 3 мая, 2021 - 05:33 Published on May 3, 2021 2:29 AM GMT The Neuralink YouTube channel (which is apparently a thing that exists) released a demo of their technology using Pager, a nine year old Macaque monkey. WHO'S A GOOD MONKEY! YES YOU ARE!Video Overview In the video, Pager plays two games using a joystick. For the first, he moves a cursor to an orange square in a grey grid, then moves it to the next square to pop up. For the second, he plays his favorite game, Pong. While he plays, the Neuralink team have been analyzing the neural activity in his brain using a Neuralink implanted in his brain. They are able to receive data in realtime, and figure out which patterns of activity correspond to each hand movement. The voiceover states that "After only a few minutes of calibration, we can use the output from the decoder to move the cursor instead of the joystick". The team then unplugs the joystick and has Pager play. Pager is then able to just think about moving his arm, and is able to play Pong using his mind. Pager plays MindPong Implications First of all, Neuralink was launched 1 year ago, and we already have monkeys playing games with their mind. I predict with 70% confidence that, within a year, Neuralink will be placed in a human and will have basic functionality. 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src: local('MathJax_Size4'), local('MathJax_Size4-Regular')} @font-face {font-family: MJXc-TeX-size4-Rw; src /*1*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/eot/MathJax_Size4-Regular.eot'); src /*2*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/woff/MathJax_Size4-Regular.woff') format('woff'), url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/otf/MathJax_Size4-Regular.otf') format('opentype')} @font-face {font-family: MJXc-TeX-vec-R; src: local('MathJax_Vector'), local('MathJax_Vector-Regular')} @font-face {font-family: MJXc-TeX-vec-Rw; src /*1*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/eot/MathJax_Vector-Regular.eot'); src /*2*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/woff/MathJax_Vector-Regular.woff') format('woff'), url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/otf/MathJax_Vector-Regular.otf') format('opentype')} @font-face {font-family: MJXc-TeX-vec-B; src: local('MathJax_Vector Bold'), local('MathJax_Vector-Bold')} @font-face {font-family: MJXc-TeX-vec-Bx; src: local('MathJax_Vector'); font-weight: bold} @font-face {font-family: MJXc-TeX-vec-Bw; src /*1*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/eot/MathJax_Vector-Bold.eot'); src /*2*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/woff/MathJax_Vector-Bold.woff') format('woff'), url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/otf/MathJax_Vector-Bold.otf') format('opentype')} Discuss ### [Weekly Event] Alignment Researcher Coffee Time (in Walled Garden) 2 мая, 2021 - 15:59 Published on May 2, 2021 12:59 PM GMT I'm organizing a weekly one hour coffee time for alignment researchers to talk about their research and what they're interested about, every Monday starting tomorrow. The time (9pm CEST) was decided after I polled a number of people, to allow timezones from PT to IDT (sorry for people in Asia and Australia). It will happen in the Walled Garden, in the central house which is directly up when you pop up in the Garden. The link of the event is public, although the event is by default reserved for AF members and people invited by them. Some basic infos: • The restriction to AF members and invitees is mostly so that we don't have to reexplain AI Risk 101 every time. This is not the point of this event. If you're genuinely interested in alignment, and if you've read some of the posts in the AF and are starting to form an image of the field, then by all means come discuss. • No obligation to come everytime. The goal is mostly that if you want to talk to a relatively broad range of people instead of having one-on-one calls, you can do so the next week instead of waiting for the next big event. • By default there is no talk planned, but if attendees feel like shaking the structure a bit, that's fine. Discuss ### [Linkpost]Teaching Paradox, Europa Univeralis IV, Part I: State of Play 2 мая, 2021 - 12:02 Published on May 2, 2021 9:02 AM GMT https://acoup.blog/2021/04/30/collections-teaching-paradox-europa-univeralis-iv-part-i-state-of-play/ I particularly wanted to discuss Paradox’s games, as compared to other historically rooted games, because I think Paradox’s oeuvre is a particularly rich vein to mine. I have already heard from multiple college-level instructors that they have students coming into their classes specifically to learn the history behind these games, which in turn means that these games are serving to shape those student’s understanding of history before they even enter the classroom. Moreover, and we’ll get deeper into this as we go along, the very presentation of Paradox’s games, which use their efforts at historical accuracy as a key selling point, encourages players to think about them as exercises in history rather than just games. But more than that, more than most historically set games, Paradox games are interesting because they are built with what I think is a detectable theory of history. Unlike other games which blunder through historical eras thoughtlessly, Paradox games, intentionally or not (in the event, I think it is clear from speaking with a couple of their developers, there is quite a lot that is intentional) have something to say about history. As we’ll see, some of that I’ll agree with and some of it I will disagree with, but the great value of Paradox’s games is that there is an ample theory of history to agree or disagree with. [...] So we are going to approach this question from two related frames, first, what should the student of history be thinking about when playing Paradox’s games; what unspoken assumptions should they be aware of, or even forewarned about? And what of those assumptions are grounded in real arguments among historians (or, put another way, where does Paradox have its feet firmly in the scholarship in crafting its games)? And second, what ought teachers of history know about these games and take into account if they find themselves teaching students for whom Paradox is the historical ‘mother tongue’ and actual history only a second language? A linkpost to the great blog A Collection of Unmitigated Pedantry is long overdue. The author, a historian, examines pop culture through the lens of history, as well as some more direct historical topics from time to time. It's great, fascinating, and quite different from the other blogs and websites I usually follow. This specific link is to a series that started this week, looking at a franchise of historical games and how they portray history. The first post focuses on questions of legibility and how perspective frame our understanding history. Exactly the kind of good stuff people around here should enjoy. Discuss ### Saying something is toxic perpetuates the toxicity 2 мая, 2021 - 11:02 Published on May 2, 2021 8:02 AM GMT I saw this tweet about the tech industry. Let's start off with where I agree. There definitely are toxic environments in tech. I think even one toxic environment is one toxic environment too many. I think it's a real shame there aren't more women in tech - that's a huge loss of talent and diversity. I don't agree that the reason there aren't many women in tech is because of toxicity. I think SSC did a pretty decent debunking here. Also I've simply never met anyone who says the didn't work in tech because of its culture - I've heard every other reason but that one. But what I want to talk about here is this: Besides for how this obviously makes it impossible to ever claim an environment isn't toxic, I think this exactly wrong for 2 reasons: 1. One of the best ways to make tech a more female friendly environment is to encourage more women to work in tech. Constantly going on about how tech is toxic, and refusing to let people say that their experiences were actually quite positive seems like a sure-fire way to discourage women from working there. 2. Only a minority of companies have toxic cultures. It's super important to shout that from the rooftops so that those trapped in such environments know they can leave and join another company. Saying "the whole industry is full of toxic people" forces anyone who's in a toxic environment to assume they either have to leave the entire industry or grin and bare it. On to the more meta point: There's a natural tendency for those fighting against some particular type of injustice to claim the injustice is far more common than it really is. I think that's often an own-goal. For example, exaggerating the prevalence of racism increases distrust between different races which in turn creates a fertile breeding ground for racism. If you exaggerate how many libertarians get cancelled, you're going to silence libertarians who would otherwise have been prepared to talk about their views, making the views seem even rarer and more fringe, and therefore more likely to be cancelled. I think it's much more productive to be honest about the true prevalence and point out that whilst the prevalence is low, any amount of something bad is still bad. Discuss ### Car Seats Three Across 2 мая, 2021 - 05:00 Published on May 2, 2021 2:00 AM GMT When I posted about how we were thinking of getting a car, and specifically that we were thinking of having three kids in car seats across the back of a hatchback, several people told me they wouldn't fit. This is a common enough view that there's an econ paper about it: Car Seats as Contraception (which I disagreed with). When we decided to share a 2013 Honda Fit I was pretty confident it would work since I knew how wide the seats were, how wide the space was, and I had done something similar with a different collection of car seats. But I hadn't actually tried our particular seats in this particular car. With the baby getting closer, about a month out, I put the seats in for practice. I tried two different ways: The version with the baby in the middle fits the easiest, with enough room to use the wide booster. On the other hand, that requires getting the baby plus carrier farther in, which could be awkward? The version with the baby on the side does work, but you do need to use quite a narrow booster seat in the middle. These three car seats are ones we chose for being narrow, but there are still many options at this size. This is a 17" infant seat, 17" convertible, and either a 17" booster (baby in the middle) or 13" booster (booster in the middle). Discuss ### Arguing from a Gap of Perspective 2 мая, 2021 - 03:32 Published on May 1, 2021 10:42 PM GMT TL;DR This post gives a name to a concept that I've encountered in many discussions recently: The Gap of Perspective. The concept is very similar to Inferential Distance in that there is a difficulty conveying your point with appropriate strength because of a huge difference in knowledge or experience. On top of that, the Gap of Perspective is based on an irrational distribution of opinion that runs inverse to the arguments, mostly because of tradition. The term Gap of Perspective is supposed to highlight that the other person does not update her opinion according to what the arguments suggest, instead staying closely to her original opinion. The reason for this is that she can't see the arguments or evidence from your point of view. Thus, despite of overwhelming arguments for this sane perspective, people update their belief only minor steps away from the socially established perspective. I elaborate on this using some controversial examples that I need to hold my tongue back about in everyday life, because what I consider sane is considered radical by many of the people I interact with. Like with Inferential Distance, I feel it is important to notice the Gap of Perspective in conversations in order to understand the frustration of certain discussions and better cope with it. Disclaimer 1. Let me state first, that this is probably not a novel concept, because once seen, it feels obvious in a lot of cases. However, I don't recall having seen it stated this way on LW. I feel like Tim Urban came pretty close with his elaboration of the Idea Spectrum which does avoid the possibility of one opinion being way saner than the other, which is perfectly fine for his line of thought. 2. This article contains quite some of my personal opinion in the examples, which is backed by many arguments that I will not repeat here. I mainly use the most obvious arguments, while hoping to avoid strawmanning. In the end, the Gap of Perspective is exactly that: A huge difference in perception of a topic caused by a huge difference in knowledge or experience, plus the failure to update the beliefs appropriately. 3. This is not a post of what people should belief. It's about how it is sometimes necessary to look at a topic from a different perspective in order to get a reasonable consensus and notice when conversations turn (hopelessly) toxic. Also this post is a way to structure my thoughts about many frustrating interactions that I and potentially you encounter in our everyday life. A Gap of Perspective Opinions vary, which is mostly a result of upbringing, education, knowledge and experience, a great deal of which is formed by our society. No one would argue that society is always right, best shown by the shifts of societal opinion, e.g. on smoking, seat belts, homosexuality and maybe in general on (mostly) not burning witches anymore. This posts talks specifically about one-dimensional opinion distributions, like the ones stated in the previous sentence. We can call the distribution of opinions here the opinion spectrum. On this spectrum, I want to allocate two positions at (or near the end of) the edges of the opinion spectrum, which I call the established (widely accepted by society) and the sane (has a pile of good arguments, but is often not widely accepted) position: Take smoking, for example: The sane position is (for us) obviously not to smoke (health, costs, harm to others, annoying others, health, environmental pollution, fire hazard, and did I mention health?). But how did the opinion spectrum in the range from fish swim, bird fly, humans smoke to smoking kills on this look in the 1960s? Admittedly this is just me scribbling semi-arbitrary lines. But the facts that your grand-parents can tell you that smoking was allowed in planes and trains (have you noticed the non-smoking signs that are still in planes in 2021?), or that in any pre-1970-movie people were smoking all the time, show us the established opinion was pretty far from what we nowadays consider the sane position [1]. If I'd redraw the opinion spectrum for smoking, I could probably find data nowadays on how the opinion has shifted and would be hard-pressed to find a single established point, since it highly depends on the country and culture. But it is hard to argue that the distribution of opinions has shifted a lot closer to the sane position, as indicated by a lot of policies (taxes, mandatory filters, smoking bans in certain public areas, especially indoors). I am not arguing about what brought this change about and where to go from that. Instead, I'd like to send you (with your knowledge and being a non-smoker) back to the 1960s and ask you to argue with any person about smoking. How would you feel? Frustrated? Aghast? Despairing? Similarly, if we brought a habitual smoker from the 1960s to our time, she would probably feel the same. But, you say, surely they would see the truth of our ways and adjust to our time. Sure. Exactly my point. In a society, where the sane (call it scientifically sane, maybe) opinion predominates, it feels right to do the right thing. On the other hand, in a society, where the established opinion is far from the sane opinion, you will have a hard time talking to people, like trying to convince people to stop smoking in the first half of the 20th century. So the larger the gap between established and sane opinion, the harder it is to argue with someone [citation needed]. It is also harder to be at the same time educated and mentally healthy [2]. This gap, I call the Gap of Perspective. Let me explain the motivation for this term: Assume you got teleported into a society with a huge gap between the sane and the established opinion. Like it's the 21st century and people still get imprisoned/burned for homosexuality [3]. So the spectrum of opinion looks like this: Now, you are on the sane side and want to argue with someone (Frank), who is living in this society. Frank is pretty reasonable and open-minded and won't report you to the authorities. Now if you talk to Frank, he thinks from the perspective of the established opinion, while you start thinking from the perspective of the sane (or rather established in your original society) opinion. What would be the best-case result of such an argument? You leave Frank slightly doubting the established opinion, so his opinion shifted slightly towards the sane position. Which makes sense, but feels frustrating and unsatisfying because you wanted to convince him to be at least close to the sane position. I mean, your arguments are completely reasonable, comprehensive and well-founded, while the status quo is seriously bollocks. But even worse, if you had talked to John instead of Frank, who is way less open-minded and has stronger ties to society, he might have shifted his opinion even beyond the established homophobic point. What? Did I mention your arguments were completely reasonable, comprehensive and well-founded? My personal opinion is that this is due to a huge Gap of Perspective. If you could just make John see the world through your eyes for an hour, he would understand and potentially end up accepting homosexuality, tending more to the sane side of the opinion spectrum. Less convinced than Frank, but it would have been a huge shift. Sadly, there is that huge gap where you can't make someone take your perspective. Unless... see later. For now I want to highlight some key points: 1. A lot of hopeless discussions start from a huge distance between sane and established opinions. A consensus will always land close to the established position unless you manage to convey the sane perspective to the other person as the starting point of the discussion. 2. If the positions are really far away, your arguments might even convince the other person to go further away from the sane position (e.g. when the other person just labels you an idealist, commie, leftist, radical). Of course it is not just about the distance, but more about the distribution. Broader distribution of opinions should improve the mobility of opinions, allowing for more radical changes of mind (e.g. when the sane position is also widely accepted in society or at least in your peer group). 3. Noticing the width of the Gap of Perspective should invite you to rethink your discussion strategy. If you can't take the other person's perspective, you won't understand why he disagrees so much with your arguments. And if you don't manage to put the other person in your perspective, your arguments will only lead to a small shift of opinion, which might even be in the opposite direction of what you intended. Results of arguing with a large Gap of Perspective Personally, I feel mostly frustrated and hopeless in the face of discussions featuring a large Gaps of Perspective and don't know how to deal with people whose opinions are so far away from mine. I noticed that this can even escalate and make wonderful people turn introvert and saturnine (yes, I guess that is the right word). Sometimes I observe someone (including me) shift their opinion beyond the sane position and becoming radical. What I mean by that is: ignoring of counter-evidence against their position, getting caught in echo chambers of singular opinion, and being depressed with society as a whole. From personal experience, I can state that living on the sane side in a society where that side is considered radical does serious harm to your social capabilities. Of course, everyone feels they are on the sane side, but there are just plenty of good examples where one side has clearly superior arguments and the society does not update appropriately or at least not in a timely fashion. Remedies Of course we can't avoid the discourse about topics just because society is stuck in a singular or bipartisan position. But how do we achieve (a) a broader distribution of opinions, to increase opinional mobility; (b) a faster movement of the established to the sane position in the society in general; and (c) a greater shift of an individual's opinion towards the sane position in particular? I don't want to discuss this on a societal level, but instead focus on the individual, thus I will only talk about (c). For a great approach on thinking differently about (a) and (b), look at the amazing blog series by Tim Urban [4]. Move away to a saner society The (potentially) obvious solution for your own frustration is to move somewhere else, where you find agreeable people. If you are in the US, maybe Canada is an option, or just change to a State that seems cool enough? I fear, there is no evading your insane politics unless California declares independence. If you are in Europe, there are some countries converging on reasonable policies in many areas, the sane ones coming to mind being The Netherlands, Denmark and Finland, in my personal opinion. Instead of moving the country, you can also find groups that you identify with. Thanks to the internet, like-minded people are easier to find, but then you are also in the danger of landing in an echo chamber and alienating yourself from your environment. I highly recommend to not rely only on the internet to interact with those groups. Find like-minded people in your hood. Start/join an EA/LW/ACX/whatever group. Or found your own shared flat or commune. Why do I recommend this at all? Isn't moving really hard on yourself? Yes, but so is living in a society that you only share the location with. So instead of fighting the tide, you leave a toxic environment. The other two remedies are about changing opinions instead. The hammer I can see two paths to changing the perspective of someone to approach the sane side of the spectrum. One is the hammer, that is overwhelming evidence that hits you like a chair, in the face. Three kinds of hammer come to mind: • A huge catastrophe, e.g. the Chernobyl and Fukushima disasters, which led to huge shifts of opinion on nuclear power plants in some countries [5]. An even greater but less known example is the flood that almost drowned half of Holland thereby leading the Dutch to become the best dyke-builders in the world. • Something happening to a close friend/relative, e.g. a friend dying in a car accidents changes your opinion on wearing seat belts, an acquaintance dying of lung cancer manages to shift your attitude on smoking. • Something happening to you, e.g. you having a crash with your motorbike making you averse to riding motorbikes. Or getting diagnosed with type 2 diabetes might update someone's attitude on nutrition. Some things I would love to count as a hammer, like geneticists figuring out that there is an existing biological cause for homosexuality, scientists finding no difference in average IQ between different races, etc. But none of these revelations seem strong enough to convince people to a fundamental change of perspective. Rather, the opinion is being updated still from the established perspective shifting only by small bits. Likewise, the pile of evidence for smoking being unhealthy did not appear at once, but instead needed at least 1.5 generations to propagate change, even on the individual level. And we're still not sane there. Similarly the evidence for the theory of evolution is piling up and we're still not at a reasonable point of discussion on a societal level. Hammers to bat you across the Gap of Perspective in one big hit do exist, but it seems (at least short-term) unethical to actively employ them for a change of perspective. The staircase It needed a catchy word, so the staircase it is. I like this term, because climbing stairs is a slow and incremental process which requires both time and effort to shift the perspective from the established to the sane opinion. In my own experience (call it anecdotal evidence) I see opinions can shift/be shifted towards the sane end mainly by two means: books and role models. You might want to add articles/news/social media/podcasts here, but I think this is in a huge danger of putting you in an echo chamber. I could add a lot of arguments in favor of books here, but I think the crucial point is that books have a lot of space to elaborate and repeat their arguments and it absolutely requires time to read them. So you have time to slowly adjust your perspective in incremental steps. And once you made it through the book, you'll find another five books that you want to read on that point. If someone were to ask me about why I think meditating is a good idea, I'd recommend them a book. Sam Harris has put down his arguments in a well-written manner, while for me it's impossible to explain it well in just a few arguments. Evolution? Richard Dawkins! Less wrong? The Sequences/HPMOR/Daniel Kahneman. Beyond books, I think that role models (mainly friends) are of huge importance. Of course I could point at some high-level heroes and idols (like I just did with Dawkins and Harris), but they mostly influenced me through their books. What I mean are personal role models, friends, acquaintances, colleagues with whom we interact on a regular basis. Seeing your friend being at or moving towards the sane perspective makes you consider it a reasonable alternative to the established perspective. For example, meeting a rational anarchist after high school would have probably left me thinking something like "what a radical person". But having shared an apartment with someone being close to these ideas and recognizing that he/she is smart and has good arguments leaves me more with a "what a cool person with reasonable points". One note on influencers: Podcasts are cool and practical, news aggregators and pseudo-social media are convenient. However, they mostly do not fulfill the most important criterion: giving you time and space to think about an idea from multiple angles by keeping you occupied with it for a while. How many articles can you read in a day? Will one of them strongly shift your opinion? How about ten articles of a similar kind? Maybe better? I think it took me all 3(?) chapters in the Sequences about lottery to finally accept it is completely irrational to play it and I'd better get a piece of cake for the money. What convinced me? Mostly the new improved lottery, but then I think it was mostly the combination of articles with multiple angles on the same topic [6]. I would give a strong plus for open podcasts without a time limit, as well as reasonably researched articles that carry you further to other articles. Also blogs and platforms that follow on a topic for longer periods of time and are potentially written by different people. However, pseudo-social media is arguably not a good influence on people's opinion unless it brings them to a decent platform like LW/ACX. To me it appears that books can put you on a slippery slope with inverted gravity, making you ascend towards a saner point of view. Maybe they tilt the staircase so that the sane perspective is suddenly down. In addition, friends are of particular importance to not lose your mind in the conflict between what you read and what the society around you believes. Examples Animal (product) consumption First, I am not arguing (here) that you should go vegan. What I want to argue is to start the debate on animal (product) consumption from near the sane instead of the established side of the opinion spectrum. The arguments in favor of veganism are pretty overwhelming, ethically, environmentally and (mostly) health-wise [7]. In 2021, the spectrum looks qualitatively like this, with a lot of people having animal products in every single one of their meals. There is still hope, because there is a huge middle ground and a wide distribution of opinions, though: Still, somehow I live among mostly reasonable people (even lesswrong-readers) who think it is totally normal to have no single meal without an animal product. Whenever I have an argument with such a NoMealWithoutAnimalProduct-person, for them it feels like a huge concession if they would eat one animal-free meal per week. (For a lot of people, even a meat-free Friday in the canteen seems unfathomable). Seen from the plant-based side this "effort" feels not even like a drop in the ocean. Seriously, you have objectively correct arguments and the other person would not even stand watching Dominion, but here they are not even taking you seriously. Because they are still caught on the other side of the opinion spectrum. A reasonable perspective should start from a plant-based diet and slowly ask which parts you can add ethically and environmentally, as well as what you should add health-wise. Plus, of course you can add a bit for your personal well-being. First, view nutrition through the eyes of a vegan. Only then, decide whether you need more protein (no), how much meat/animal products you need to fulfill your nutritional requirements, and whether it is ethically/environmentally acceptable for you to eat chicken, fish or eggs. If everyone could just look through these eyes, we could still have some rainforest left in the next years (or enter your polemic, but well-founded statement here). Remedies: • Need a hammer? Watch Dominion. Rewatch after two weeks, best with a friend. • On a even more serious note: How many meat-consumers would be able to slaughter the animal they eat [8]? I don't dare to bet here too much because at least the older generations have probably done this in their life and would laugh here. But I think this would be a real hammer-blow for plenty of young people to look at the animal that they want to eat. • The staircase: I don't feel like reading here helps too much. Everyone who hasn't read the facts about the horrific animal food production and its devastating environmental impact has probably been living under a rock in the recent years (if you are living in a level 4 country, that is). Books about nutrition are interesting though, and I find vegan cookbooks (which I was laughing at until recently) make living more plant-based actually attractive. Personally, I find that living with and meeting vegetarians and vegans is the best that can happen to slowly shift your opinion here. It has brought me on a years-long staircase up towards veganism and with hindsight I feel grateful for meeting these people enabling this comfortable shift. Belief in (belief in) God(s) Tricky topic, but I would state here that the evidence for there being metaphysical entities influencing our world in a conscious manner is abysmally small. So how do we explain this, then? Again, I am not here to explain this (and yes, admittedly these lines are pretty arbitrary but suitable enough to convey my meaning). I want to point out that if you want anyone far on the established side (believing in God(s)) to seriously consider their position, a simple argument won't do (as you have probably found out). My experience taught me to ever avoid this discussion, as long as the other person thinks belief in God(s) is separate from other beliefs. Remedies: • Actually, there is a very workable hammer, but sadly it only works in reverse: Any credible appearance of a God and Him/Her breaking some laws of physics would probably greatly shift my opinion on the spectrum above. I feel like a lot of religions actually use this to convince their sheep of the Truth. I met at least two persons of incredibly different religious upbringing who were both convinced of their belief because God spoke to them in one of their dreams. :facepalm: • I think the only workable way here is to hand people a copy of The Selfish Gene, Letter to a Christian Nation, The God Delusion, Sapiens, The Sequences, and to talk about it. Actually reading the original Bible or Quran is also helpful for doubting their conviction, but I guess this is only for people who already disgress from dogma. I am not so sure about meditation because it is tainted by so much esoteric nonsense that it might lead some people further astray. • (Even though I wanted to avoid the societal level I can't just not point out that it would be very helpful if people were talking about human values instead of christian values, and if states would stop being interwoven with certain religious groups.) Education This is way more controversial than the above examples because the case is really not clear here (and might be quite individual). I still want to point out two extreme points of view which have a very hard time talking to each other. I am not calling any of it sane but we do roughly know where to find the established perspective. On one extreme I imagine children in a >30h/week prison where they have to learn by repeating stuff written in books. I learned the Chinese term 读书 (dú shū, reading the book) for learning a book by heart and filling out an exam by vomiting the text back out. I've also heard the related german term bulimia learning. That is, no place for initiative from the students, creativity, or playfulness, in school. On the other extreme: unstructured play (which is kind of a pleonasm since play is by definition unstructured), leaving children to pick up everything they want on their own. The first extreme can be used as an exaggerated version of what education is like in most level 4 countries, probably you've been there to some degree. The details strongly depend on which country you live in and who your parents are. The second extreme is pretty much the education that is depicted for hunter-gatherer societies with children learning from their seniors by playing together. Again, I am not arguing for or against a side here. But if you try to argue for the unstructured play side in our society, you mostly feel the Gap of Perspective. Despite a lot of good arguments for an education system that allows for way more free play, as well as many good arguments against putting children sitting in classrooms for most of their young life, people usually can't be convinced this is worth thinking about because they don't know most of these arguments. Remedies: • It seems that none of the things that should work as a hammer, manages to open up the discussion to create a more children-friendly education system. Not even when many scientists confirm that teenagers have a later sleep cycle and show poor performance because of lack of sleep will the time table be adjusted. (I don't know how a lot of schools in the US seem to manage to implement this, in my European country this not even up to discussion, afaik.) • Again, books and role models: Reading Rutger Bregman's Humankind helped me shift my perspective a lot, but then I wasn't that opposed to it from the start. What I found way more convincing was meeting people who had a less formal education (or had less formal education). Finding them decent, educated, and successful human beings helps a lot. But then maybe there is selection bias here, because better educated parents send their children to more creative schools? Anyway, the case for strictly formal education being successful or efficient in conveying knowledge to children and developing their personalities seems highly doubtful, not even considering the (mental) health aspect of it. Further Examples I find myself tempted to imprint the Gap of Perspective on any frustrating discussion that I have. But the case is rarely as one-dimensional as stated in the above examples. Still, I can think of a few more, and so can you probably: • Cars in cities: Cities are mostly established for cars, not for humans. There are lot's of arguments to fundamentally change the way we plan cities and new districts to create more and healthier space for humans and a better quality of life. But since planners start from the established perspective "every household needs a car", we end up with a car-friendly new district with hopefully decent paths for pedestrians. If the planners were to start thinking from my (don't call it sane, call it wishful thinking) perspective, they would design the city for humans and bicycles, while disincentivizing car ownership by providing convenient public transportation, bicycle paths, car sharing, and making having your own car a hassle. Starting from there you can compromise by adding a bit of space for private cars, but the district would be for humans first, ending up way closer to my dream space and enabling a huge shift in quality of life in cities. Luckily, this is not only a dream in some places. • Homosexuality: Still a political issue in a lot of countries. On an individual level, the old generation still lives in an established perspective (and guess who is ruling, mostly). Facts of genetics didn't help too much, even "liberal" countries did not accept homosexuality legally until very recently. I once introduced conservative people to a flatmate, that they only later learned about was lesbian, thereby dealing an unintended hammer-blow to their opinion and shifting it way more than any facts could have done. But besides this, introducing a shift of perspective here seems like it will take a new generation. • "Using" facebook: You already know it: pseudo-social media makes you unhappy. You are not using facebook, facebook is using you. Still, usage of it is established. No matter how good the arguments against it, you just don't manage to delete your account. I mean, what more arguments do you want besides it making your life miserable and it making a lot of other peoples' lives miserable? It constantly spying on you? It predicting and exploiting your mood shifts? This is not something we can blame on any old and long established patterns of thinking. This is what you are still using. I really don't know any way to shift opinion here, besides being a role model and acting as if not having facebook would be the most natural thing in the world. • Alcohol consumption: The facts are clear. But still, alcohol consumption is established and any argument on how we can further disincentivize the consumption still argues from the point where it is totally acceptable to have beer or wine daily, or to bring alcohol as a gift to friends and family. • Working on things we don't believe in: This is a big and controversial topic. But the socially accepted (established) perspective is that you need a job to make a living and that it is totally acceptable even if your position is incredibly useless, harmful (designing ad targeting algorithms at a tech giant), or wasteful (designing or marketing the next generation self-driving smart fridge). It is a trap. Hard to walk around in our societies, mostly because most of the readers of this article will like their jobs despite the above mentioned attributes. Designing self-driving smart fridges is probably quite challenging and fun for an engineer. However, in my opinion, a sane perspective should start from considering the needs of human beings instead of aiming for full employment for everyone. Future automation or further technological disruption will arguably highlight the need for a change of thinking here. All this leaves me wondering, where else are we still looking through the eyes of our cultural tradition instead of a reasonable perspective? One point that I feel I have to mention here (again): I am not saying there is no progress. That would be a ridiculous statement. On pretty much any of the above examples (though arguably not on the last one), almost any nation in the world has shown progress towards the sane side of the spectrum. However, this shift is still way too slow considering the mountains of good arguments and piles of skulls that exist for most of these examples on the established side. Discussion A friend who was reviewing this article asked me to add more on the topic of remedies to further shift an individual's opinion. But all I've offered thus far is summed up as: "Give him a book." This is certainly not great advice, especially since books can also be more misleading than individual articles or blog posts (same argument, more time, more exposure). They definitely don't have an inbuilt sanity-guarantee. Still, I think it's the most efficient way to get someone to acknowledge your point of view if there happens to be a good book on the respective topic. After some pondering, a more romantic vision of shifting someone's mind seems feasible under certain circumstances: It should be possible to get people close to you to see the world through your eyes by describing it better. Two ideas to that end: • Write a fictional (short) story or put down your ideas in some sort of readable article and read it to your friend. I wonder how many sci-fi books have been written with that motivation. And potentially most of lesswrong? • Have you ever walked blindfolded with someone else guiding you and describing the world? Maybe it is wishful thinking, but have you ever tried really describing your perspective to someone? Like not in a few sentences but really talked about it for as long as it takes with the other person only listening? I am a bit fond of the quote The biggest communication problem is we do not listen to understand. We listen to reply. (by Stephen. R. Covey). I think that it is possible to get another person to see through your eyes. But there has to be the will and appropriate communication for it. Summary What could you take from this article? • A lot of discussions are doomed to be frustrating because of a strong asymmetry between two perspectives, one being culturally established, the other having a huge pile of good arguments. Sometimes the established perspective also has a lot of skulls in their basement. • To obtain a sane consensus, the discussion would need to start from the perspective having the pile of good arguments. However, discussion usually starts near the established perspective and people update their opinion often only with a small step near the established perspective. • The result of this is that opinions change only slowly, mostly. In order to have people see the world through your eyes, they mostly need a book, time to doubt, and someone to talk to. 1. For a great discussion how scientists were still smoking in offices while the stacks of correlations between smoking and cancer were piling up on their desks, I highly recommend The Book of Why by Judea Pearl. ↩︎ 2. Yes, you can always meditate and still be happy. But violating your own convictions in everyday interactions is still hard on your psyche. ↩︎ 3. If you still want to say anything that would say this makes sense today, please read The Gene by Siddhartha Mukherjee. ↩︎ 4. To the historians of the future: As of 2021, everyone is anxiously waiting for GoT book 6, Kingkiller book 3 and Tim Urban's next blog post. We have high hopes that the latter one will appear during our lifetimes. ↩︎ 5. Let's not discuss how harmful this shift is environmentally, please. ↩︎ 6. Of course, lottery is not that simple. But take lesswrong in general. Reading only one article would not seriously shift your perspective. Reading many of them over a long time has probably had a huge impact on your way of thinking. ↩︎ 7. If you don't think so, try writing down your pro-animal-consumption arguments and find a reasonable vegan to discuss them. ↩︎ 8. This refers to the "short-term unethical" I mentioned above. It seems cruel to make (young) people watch the slaughtering or even slaughter the animals they eat themselves. However, long-term I find that idea defensible. ↩︎ Discuss ### Prediction and Calibration - Part 1 2 мая, 2021 - 01:41 Published on May 1, 2021 10:41 PM GMT (this post is cross posted at badprior.com) Scott Alexander is a darling of the Bayesian rationalist community, he has a lot more epistemic humility than most, despite being an impressively well-calibrated predictor. In this series we will try to achieve 2 things: 1. (this post) We try to understand what a likelihood function is, and use it to evaluate predictions 2. (next post) We Make a Bayesian calibration model, and get an uncertainty estimate over our calibration. 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src: local('MathJax_Size4'), local('MathJax_Size4-Regular')} @font-face {font-family: MJXc-TeX-size4-Rw; src /*1*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/eot/MathJax_Size4-Regular.eot'); src /*2*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/woff/MathJax_Size4-Regular.woff') format('woff'), url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/otf/MathJax_Size4-Regular.otf') format('opentype')} @font-face {font-family: MJXc-TeX-vec-R; src: local('MathJax_Vector'), local('MathJax_Vector-Regular')} @font-face {font-family: MJXc-TeX-vec-Rw; src /*1*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/eot/MathJax_Vector-Regular.eot'); src /*2*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/woff/MathJax_Vector-Regular.woff') format('woff'), url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/otf/MathJax_Vector-Regular.otf') format('opentype')} @font-face {font-family: MJXc-TeX-vec-B; src: local('MathJax_Vector Bold'), local('MathJax_Vector-Bold')} @font-face {font-family: MJXc-TeX-vec-Bx; src: local('MathJax_Vector'); font-weight: bold} @font-face {font-family: MJXc-TeX-vec-Bw; src /*1*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/eot/MathJax_Vector-Bold.eot'); src /*2*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/woff/MathJax_Vector-Bold.woff') format('woff'), url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/otf/MathJax_Vector-Bold.otf') format('opentype')} In common parlance, the 4 parts of Bayes Theorem are called: posterior=likelihood×priordata What we want is our posterior, the probability of some model parameters (often θ) given some data (y). We construct a model with two things, a prior function which describes what we believe before seeing the data, and a likelihood function (p(y∣θ)) which given a model (θ, drawn from the prior) scores the data. The simplest and most relevant likelihood function is the Bernoulli p(y|θ)=θy(1−θ)1−y Here y is 1 when our prediction turns out to be correct and is 0 otherwise. And θ represents our model. Our model for now is just 'what Scott predicted.' As an example, let's take a prediction of θ=0.6. If the prediction turns out to be true (y=1), then the Bernoulli likelihood function is equal to 0.6: p(y=1|θ=0.6)=θy(1−θ)1−y=0.61(1−0.6)1−1=0.61×0.40=0.6 And if the prediction turned out wrong (y=0), then: p(y=0|θ=0.6)=θy(1−θ)1−y=0.60(1−0.6)1−0=0.60×0.41=0.4 The likelihood function says that there was a 40% chance you were wrong. Which is the same a predicting not θ with 40%. If a person makes 3 predictions θ=[0.6,0.6,0.7] and the outcomes were y=[1,0,1], then the likelihood of all 3 observations is simply the product of the 3 Bernoulli likelihoods: P(θ|y)=3∏i=1p(θi|yi)=0.6×(1−0.6)×0.7=0.168 Better predictions will have higher numbers. It can be useful to divide by the null predictor to compare against random performance: p(θ=0.5|y)=N∏i=1p(θi|yi)=0.5N So the likelihood of the 3 above predictions are 0.1680.53≈1.34 times more likely than random. Making this person slightly better than random. How good a predictor is Scott Because Scott has made a lot of predictions, and because we will later implement a 'calibration' model of Scott, let's try to compare the likelihood of his 2019 predictions with the null model which predicts everything with 50% (which implicitly mean that it also predicts it doesn't happen with 50%). First we import numeric and scientific python libraries import numpy as np import scipy as sp import scipy.stats Then we code Scott Alexanders 2019 prediction as [Guess, Outcome]. Because Outcome is what we want to predict, we put that in the y variable, and put Guess in the predictor variable x. data = np.array(( [[0.5, 1]] * 7 + [[0.5, 0]] * 4 + [[0.6, 1]] * 15 + [[0.6, 0]] * 7 + [[0.7, 1]] * 12 + [[0.7, 0]] * 5 + [[0.8, 1]] * 31 + [[0.8, 0]] * 6 + [[0.9, 1]] * 16 + [[0.9, 0]] * 1 + [[0.95, 1]] * 5 + [[0.95, 0]] * 0 )) y = data[:, 1] X = data[:, 0] The person who made 3 predictions and got 2 correct was slightly better than random. How much better than random is Scott? Let's take the product of all his predictions. scott_likelihood = sp.stats.bernoulli(X).pmf(y).prod() random_predictor = 0.5 ** len(y) f"{scott_likelihood / random_predictor:g}" '7.4624e+09' So 7 billion times more likely! There are two reasons why this number is so large: 1) Scott made a lot of predictions and 2) Scott is a very good predictor. It is easy to become a better predictor than Scott if you simply make a lot of predictions about things that are easy to predict. The hard part is being as well-calibrated as Scott. Prediction vs Calibration Predictor: • A good predictor is a person who predicts better than random: • > 0.5^N">∏P(θ|y)>>0.5N • A bad predictor is a person who predicts close to random: • ∏P(θ|y)≈0.5N • A terrible predictor is one who are worse than random: • ∏P(θ|y)<0.5N It may be hard to understand how you can be worse than random, and that of course takes skill, but if Scott had flipped all his guesses, his likelihood ratio would be 17×109 which is much less than 1. Now that we all agree that Scott is a good predictor, we can finally introduce what we want to talk about: How well-calibrated is Scott and how do we measure that? Calibrated: • A well-calibrated predictor makes predictions that match the outcome frequency. Example • Person A predicts 100 things with 60% confidence, 61 of them turns out to occur, because 61100≈0.6 this person is very well-calibrated. • Person B predicts 100 things with 80% confidence, 67 of them turns out to occur, because 67100≠0.8 this person is not very well-calibrated. Because 67 > 61, is Person B the better predictor, even though they're not as well-calibrated? Let's evaluate the likelihood of their claims. Person A's prediction is equivalent to 61 'correct' 60% predictions and 39 'correct' 40% predictions, yielding the following likelihood: 0.661×0.439≈8.86×10−30 Person B's prediction is equivalent to 67 'correct' 80% predictions and 33 'correct' 20% predictions, yielding the following likelihood 0.867×0.233≈2.76×10−30 Because 2.76\times{}10^{-30}">8.86×10−30>2.76×10−30 Person A is also a slightly better predictor than person B. To understand why, let's consider Person C: Person C predicts 100 things with 100% confidence and 99 of them turn out to occur. Thus, he will spend an eternity in Probability hell for assigning 0% probability to something that actually occurred. This is also reflected in the likelihood of his predictions, which is zero: 199×01=0 Summary so far We can improve the likelihood of our predictions by being both well-calibrated and very knowledgeable. The next post in this series will focus on measuring calibration. How good a predictor you are can be evaluated by the product of your likelihood function. Is there a better way to evaluate this? Yes, make a model! We can also make a model to find out how well-calibrated we are. That is what we will explore in the next post. Discuss ### Follow-up to Julia Wise on "Don’t Shoot The Dog" 1 мая, 2021 - 22:07 Published on May 1, 2021 7:07 PM GMT Julia Wise recently wrote up notes from "Don’t Shoot The Dog", a book by Karen Pryor about behavioral training methods: The book applies behavioral psychology to training animals and people. The author started off as a dolphin trainer at an aquarium park in the 1960s and moved on to horses, dogs, and her own children. There are a lot of anecdotes about how to train animals (apparently polar bears like raisins). At the time, training animals without violence was considered novel and maybe impossible. Julia’s notes are wonderful. She skilfully picks interesting anecdotes and key points from the text, adding her own beautiful and concise connections to the practical experience of raising children. It’s a real treat to read. Julia begins by addressing the perception that systematic training might be in some way unethical: I can understand not wanting to use behavioral methods on children; the idea can sound overly harsh or reductive. The thing is, we already reinforce behavior all the time, including bad behavior, often without meaning to. So you might as well notice what you’re doing. What is it about systematic training that seems harsh and reductive? Because it does seem, at least on the surface, potentially harsh and reductive. It may be that this is a mistaken perception, or it may be that it is a fact that we have to live with, but where does this perception come from in the first place? Togetherness When I interact with friends, family, lovers, colleagues, children, and even animals, I long for a boundaryless togetherness in which no private plan or intention is held back on either side. But for practical reasons it is very difficult to reach this state of affairs in any connection for any length of time. For example, Julia offers this lovely anecdote: [...] our four-year-old was eager to go home from the park, and left without us towards the house. I caught up with her and told her not to leave without us. We were halfway to the house, but If I’d continued home with her from there, she would still have achieved what she wanted: getting home sooner. So I took her back to the park and we redid the whole situation: she said "I want to go home" and I walked home with her. Running off on her own didn’t pay, and she hasn’t repeated it. It would not have been very practical for Julia to operate from a place of "boundaryless togetherness" in this scenario. What would that even mean? For Julia to allow her daughter to wander home alone? To follow her and go straight home, reinforcing the behavior of leaving the park along? To follow her and, rather than bringing her back to the park, express some deep dilemma in words that her young daughter wouldn’t understand? None of these options seem very practical. But when we use systematic training, perhaps we should ask ourselves whether it is leading us in the direction of fewer or more numerous boundaries. Consider using systematic training to establish a mutually beneficial chore sharing with a housemate: Often when we are teaching the behavior [...]. For example, once a pattern of chore sharing has been established, your roommate or spouse may stop at the dry cleaners on the way home without being reinforced each time This may work as a means to getting one’s roommate or spouse to stop at the dry cleaner’s. But if I interact with my roommate or spouse from a place of strategic reinforcement, then I am spending at least some of my attention when I am with them on thinking about when to offer praise and what its effect will be as a form of reinforcement. The one receiving this systematic training is then going to think about what they are being trained to do and whether that is good for them. Both people then spend some of their time together thinking about training or thinking about being trained by each other, which is at best a distraction and at worst a hindrance to togetherness. Now it may be necessary to use systematic training. And it may not be possible to achieve boundaryless togetherness in any given connection. But it would be nice to use systematic training in a way that moves us in the direction of reducing, not increasing the boundaries in our connections. Fun Clicker trainers have learned to recognize play behavior in animals as a sign that the learner has become consciously aware of what behavior was being reinforced. When ‘the light bulb goes on,’ as clicker trainers put it, dogs gambol and bark, horses prance and toss their heads, and elephants, I am told, run around in circles chirping. They are happy. They are excited. Learning can be fun. I enjoyed learning jiu-jitsu during graduate school. There were times when I received praise and times when I received criticism. I guess I felt a tiny bit of pleasure when receiving praise and a tiny bit of pain when receiving criticism but these feelings were so tiny in magnitude compared to the joy of learning something that took me beyond what I had previously been able to do that it just didn’t matter very much. There was a kind of boundarylessness in the training. My teacher would give very energetic and demanding feedback. I knew that I was being trained and I knew that I wanted to be trained. I understood how the reinforcement was shaping me, and I wanted it to shape me. My teacher was aware not just of how the training was affecting me, but also that I wanted it to affect me. In this mutual "yes" to training there was a willingness in both trainer and trainee to hold back less. If I had been an elephant, I definitely would have run around in circles chirping. It was exhilarating. But consider now the use of a clicker to train pilots: A flight instructor can also click a student for initiative and for good thinking: for example, for glancing over the instrument panel before being reminded to do so. So the clicker can reward nonverbal behavior nonverbally in the instant it’s occurring. Wouldn’t it be tedious to be "clicked" each time you glance over the instrument panel while learning to fly an airplane? Wouldn’t it be tedious to be the instructor doing the "clicking"? I imagine this whole enterprise being cold and painful for both sides. Perhaps the trainer and trainee would both see it as necessary in order to engender the kind of rigor necessary to fly an aircraft. But is this really the case? What is it that caused the dogs to gambol and the horses to prance and the elephants to run around in circles chirping? I doubt they were celebrating the food they were receiving as reinforcement. Surely they were celebrating learning! Learning is joyful. If a trainee never experiences joy, perhaps they are not learning. So one way to evaluate a training setup is to ask: is this setup leading, eventually, to joy? It’s not that the point of the training is to give the trainee joy. It’s that joy is a sign that training is working. It may take some time to get there, but the trainee should eventually get there. If trainees are not getting there then perhaps the training setup should be re-worked. Respect The training methods described in Pryor’s book are powerful. They allow us to hone in on the basic reward/punishment structure of biological brains, and to use it to bring about profound behavior change: It often happens, especially when training with food reinforcers, that there is absolutely no way you can get the reinforcer to the subject during the instant it is performing the behavior you wish to encourage. If I am training a dolphin to jump, I cannot possibly get a fish to it while it is in midair. If each jump is followed by a thrown fish with an unavoidable delay, eventually the animal will make the connection between jumping and eating and will jump more often. However, it has no way of knowing which aspect of the jump I liked. Was it the height? The arch? Perhaps the splashing reentry? Thus it would take many repetitions to identify to the animal the exact sort of jump I had in mind. To get around this problem, we use conditioned reinforcers. Breland called the whistle a ‘bridging stimulus,’ because, in addition to informing the dolphin that it had just earned a fish, the whistle bridged the period of time between the leap in midtank—the behavior that was being reinforced—and swimming over to the side to collect one’s pay. But there is a risk that we will use these techniques to teach the whole world to behave in the way that we imagine we want, and lose track of what the world has in turn to teach us. Training a dolphin to jump through a hoop might be an exhilarating journey that opens up a connection between trainer and dolphin that leads to the trainer appreciating and learning from the dolphin in profound ways. But it could also be a cold and harsh experience in which a trainer exerts a kind of iron will over the dolphin and becomes less and less able to see and learn from the dolphin. This is the basic dynamic of respect: am I balancing that which I have to teach you with that which I have to learn from you? Am I devoting an appropriate amount of my time and attention to discovering what I have to learn from you, relative to the time and attention I am putting into teaching you? Not all beings understand how to wield systematic training techniques. I might use systematic training to teach my friend to cut and arrange flowers, but my friend might not in turn be equipped with the skill of using systematic training, so may not respond by offering their own gifts back to me in as clear or forceful of a way. The more I teach to my friend, the more I ought to look carefully for that which my friend has to teach me. Trust If you get into a relationship with someone who is fascinating, charming, sexy, fun, and attentive, and then gradually the person becomes more disagreeable, even abusive, though still showing you the good side now and then, you will live for those increasingly rare moments when you are getting all those wonderful reinforcers: the fascinating, charming, sexy, and fun attentiveness. And paradoxically from a commonsense viewpoint, though obviously from the training viewpoint, the rarer and more unpredictable those moments become, the more powerful will be their effect as reinforcers, and the longer your basic behavior will be maintained. Furthermore, it is easy to see why someone once in this kind of relationship might seek it out again. A relationship with a normal person who is decent and friendly most of the time might seem to lack the kick of that rare, longed-for, and thus doubly intense reinforcer. But even more painful than a relationship with a disagreeable partner is the fear that every action by every partner might be training us into a pattern that is not good for us. If we view the whole world as a giant reinforcement trainer, some of it random, some of it deliberate, then how can we trust any of it? And it is not just the world that trains us, but also ourselves: I found that if I broke down the journey, the first part of the task, into five steps—walking to the subway, catching the train, changing to the next train, getting the bus to the university, and finally, climbing the stairs to the classroom—and reinforced each of these initial behaviors by consuming a small square of chocolate, which I like but normally never eat, at the completion of each step, I was at least able to get myself out of the house, and in a few weeks was able to get all the way to class without either the chocolate or the internal struggle. Every action I take has some training effect on myself as a positive or negative reinforcement signal. If I take actions that cause myself pleasure then I am positively reinforcing my recent behavior. If I take actions that cause myself pain then I am negatively reinforcing my recent behavior. If I take actions that cause neither pleasure nor pain then I am actively omitting to reinforce my recent behavior, which itself has an effect on training. And in fact the decision to engage in training in the first place, as well as the decisions about what training objectives to pursue, are themselves the result of our past implicit and explicit training. How can we trust any of it? When we are born, we inherit billions of years of positive and negative reinforcement in the form of our genome. And beyond that the way our parents treat us when we are children, which has a huge training effect on us, is the result of the positive and negative reinforcement that they received over their own lifetimes, including as children from their own parents, and so on backwards through the generations. And then we look at our own actions and see that they are the result of this huge mixture of random and deliberate reinforcement over so many generations, and wonder how we can possibly trust our own actions. And the answer, so far as I’m concerned, is that there is no answer. This is just a frightening way to look at the world. If we choose to look at the world through frightening lenses, then we shouldn’t be surprised to find ourselves in a state of fright. Perhaps it is necessary to be in a state of fright some of the time. Perhaps not. But we do have the power to choose our lenses. The entire story of systematic training is a story of having the power to shape our own behavior. To then doubt that we have the power to shape the actions that matter most -- the actions of choosing what lenses we use to view the world -- would be deeply ironic. Graduate school My favorite of all of Julia’s delightful quotes is the following: One psychologist jokes that the longest schedule of unreinforced behavior in human existence is graduate school. The opposite, then, of graduate school is the following hopeful vision: When you get a whole family, or household, or corporation working on the basis of real stimulus control— when all the people keep their agreements, say what they need, and do what they say— it is perfectly amazing how much gets done, how few orders ever need to be given, and how fast the trust builds up. Good stimulus control is nothing more than true communication— honest, fair communication. Discuss ### Death by Red Tape 1 мая, 2021 - 21:03 Published on May 1, 2021 6:03 PM GMT Contains spoilers for the worldbuilding of Vernor Vinge's "Zones of Thought" universe. In the Zones of Thought universe, there is a cycle of civilization: civilizations rise from stone-age technology, gradually accumulating more technology, until they reach the peak of technological possibility. At that point, the only way they can improve society is by over-optimizing it for typical cases, removing slack. Once society has removed its slack, it's just a matter of time until unforeseen events force the system slightly outside of its safe parameters. This sets off a chain reaction: like dominoes falling, the failure of one subsystem causes the failure of another and another. This catastrophe either kills everyone on the planet, or sets things so far back that society has to start from scratch. Vernor Vinge was writing before Nassim Taleb, but if not for that, this could well be interpreted as a reference to Taleb's ideas. Taleb mocks the big players on the stock market for betting on the typical case, and taking huge losses when "black swans" (unexpected/unanticipatable events) occur. (Taleb makes money on the stock market by taking the opposite side of these bets, betting on the unknown unknowns.) Taleb ridicules Bayesians for their tendency to rely on oversimplified models which assume the future will look like the past. Instead he favors Popperian epistemology and ergodicity economics. Indeed, naive Bayesians do run the risk of over-optimizing, eliminating slack, and overly assuming that the future will be like the past. On a whole-society level, it makes sense that this kind of thinking could eventually lead to catastrophe (and plausibly already has, in the form of the 2008 housing crash). However, human nature and modern experience leads me to think that the opposite failure mode might be more common. Taleb advises us to design "antifragile" systems which, like him, bet on the atypical and get stronger through failure. This means designing systems with lots of slack, modularity, redundancy, and multiple layers (think of a laptop, which has a hard chassis to protect and support the vital electronics, & then often has a moderately hard plastic protective case, and then is transported in a soft outer case of some kind). It means responding to black swans by building new systems which mitigate, or (even better) take advantage of, the new phenomena. But when I look around at society (at least, through my Bayesian-biased lens) I see it doing too much of that • The over-cautious FDA seemingly kills a lot more people on average (compared to a less-cautious alternative) in the name of avoiding risks of severe unanticipated drug side-effects. And people are largely comforted by this. A typical healthy individual would prefer (at least in the short term) to be very sure that the few drugs they need are safe, as opposed to having a wider selection of drugs. • In response to the 9/11 attacks, the government spend huge amounts of money on the TSA and other forms of security. It's possible that this has been a huge waste of money. (The TSA spends 5.3 billion on airline security annually. It's difficult to put a price on 9/11, but quick googling says that total insurance payouts were40 billion. So very roughly, the utilitarian question is whether the TSA stops a 9/11-scale attack every 8 years.) On the other hand, many people are probably glad for the TSA even if the utilitarian calculation doesn't work out.
• Requiring a license or more education may be an attempt to avoid the more extreme negative outcomes; for example, I don't know the political motivations which led to requiring licenses for taxi drivers or hairdressers, but I imagine vivid horror stories were required to get people sufficiently motivated.
• Regulation has a tendency to respond to extreme events like this, attempting to make those outcomes impossible while ignoring how much value is being sacrificed in typical outcomes. Since people don't really think in numbers, the actual frequency of extreme events is probably not considered very heavily.

Keep in mind that it's perfectly consistent for there to be lots of examples of both kinds of failure (lots of naive utilitarianism which ignores unknown unknowns, and lots of overly risk-averse non-utilitarianism). I'm not really claiming that I'd adjust society's bias in a specific direction; I'd rather have an improved ability to avoid both failure modes.

But just as Vernor Vinge painted a picture of slow death by over-optimization and lack of slack, we can imagine a society choking itself to death with too many risk-averse regulations. It's harder to reduce the number of laws and regulations than it is to increase them. Extreme events, or fear of extreme events, create political will for more precautions. This creates a system which punishes action.

One way I sometimes think of civilization is as a system of guardrails. No one puts up a guardrail if no one has gotten hurt. But if someone has gotten hurt, society is quick to set up rails (in the form of social norms, or laws, or physical guardrails, or other such things). So you can imagine the physical and social/legal/economic landscape slowly being tiled with guardrails which keep everything within safe regions.

This, of course, has many positive effects. But the landscape can also become overly choked with railing (and society's budget can be strained by the cost of rail maintenance).

Discuss

1 мая, 2021 - 19:07
Published on May 1, 2021 4:07 PM GMT

Highlights
• Polymarket is being attacked by “sandwiching” bots
• Metaculus launches “Forecasting Causes
• In Reflective Bayesianism, Abram Demski outlines questionable and implicit assumptions which Bayesians make.
Index
• Prediction Markets & Forecasting Platforms
• In The News
• Blog Posts
• Long Content
• Hard To Categorize

Prediction Markets & Forecasting PlatformsPolymarket

After the demonstrable success of Polymarket (and, to a lesser extent, Augur) in attracting volume to their platforms, many imitators have popped up on the crypto scene. None of them are functional yet, but I thought I'd mention them, in order from least to most scammy:

• PolkaMarkets is aiming for an August release date, and has recently begun testing its MVP.
• Hedgehog Markets's schtick is to denominate its markets in SOL, a cryptocurrency from an up-and-coming blockchain, Solana. The functioning markets on their webpage are currently using test money.
• Totem has an interesting scheme where predictions are non-punitive. That is, people who predict incorrectly won't lose money, they will merely win less. However, after reading their whitepaper, it is not exactly clear where the money for rewards will come from. It is being built on top of the Binance chain, a centralised exchange with a centralized coin, which I find unappealing.
• Polars and PredictX are also built on top of the Binance chain. I would characterize them as money grabs. That is, they are attempting to raise money to do something like Polymarket without a clear plan for how they would be superior.

While Totem feels scammy, there are interesting possibilities related to its core idea. For example, external actors could subsidize the market. Another alternative could be to stake the bet amounts on a financial instrument which provides returns, like Uniswap, while waiting for resolution. In other words, one could bet the interest, not the principal.

While imitator projects go through the design and test stages, Polymarket has been dealing with a new real world problem: “sandwiching". Here is how it works on a high level: a bot detects transactions before miners process them, and profits from that information at the expense of the user.

To understand it on a more detailed level, it is first necessary to explain several details about Polymarket's architecture. Polymarket uses an Automated Market Maker design (as opposed to an order book). This means that users trade with liquidity providers, who take bets on both sides, rather than with other users. This enables users to make bets at any time, even when there isn't somebody willing to take the other side. Liquidity providers take a small fee, and are exposed to risk if too many people want to bet on the correct side at once. To reduce that risk, liquidity providers change the odds with each bet—the more people bet in the same direction, the more the odds change. This happens on a blockchain, so miners—the players who add transactions to the record of transactions—need to be able to see transactions, such as bets. But, this means that other players, and in particular bots, are  able to see them too. The miners prioritize transactions based on how much users are willing to pay, so a bot which wants to jump the line can pay a little bit more to do so.

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The user then buysu worth of a contract at X+slippage(b), and moves the price to X+slippage(b)+slippage(u). The bot then sells its shares at X+slippage(b)+slippage(u), moving the price to approximately X+slippage(u). The bot bought at X+slippage(b), and sold at X+slippage(b)+slippage(u), so it made a profit of approximately slippage(u) per share, at the expense of making the user buy at a more expensive price (at X+slippage(b), instead of at X). Crucially, because the bot also pays fees to liquidity providers for its two transactions, the attack is only profitable if slippage(u) is large enough. For example, Polymarket has 2% fees, so in a 50% contract, the attack is only profitable if the user moves the price by more than 4%, i.e., if slippage(u) is bigger than 4%. This calculation changes somewhat when the price moves away from 50%.

Polymarket has implemented "slippage protection", which solves a part of this problem. In particular, it detects that a bot (or another user) has moved the price from the expected X to X+slippage(someone), and halts the trade if this is the case. But, for this protection to be effective, the user then has to refuse to buy at the new price of X+slippage(someone) after the trade has failed. Polymarket could improve on this by dividing a trade into small, unpredictably-sized chunks randomly delayed in time. Then it wouldn’t be profitable to sandwich each trade individually, and it would also be difficult to sandwich the whole sequence—a bot wouldn’t be able to know whether a trade begins a sequence. Still, there is currently a particularly annoying bot which appears to be sandwiching all trades, even when it isn’t be profitable.

Background reading: Ethereum is a Dark Forest; I got “sandwich attacked” on my transaction

Avraham Eisenberg writes Tales from Prediction Markets, taking place on Polymarket. It features market manipulation, bold exploits, and cautionary tales. Polymarket itself also has announced a "Microgrants" program for people to work on projects related to the platform.

Metaculus

Metaculus has announced "Forecasting Causes", an initiative to connect non-profit organizations with forecasters. They are starting with a tournament on alternative meat (see also: more commentary), a part of their Feeding Humanity cause area, and a tournament on COVID-19 in Virginia, as part of their Healthy Communities cause.

Background reading: Long Term Future Fund grants to Metaculus, back in 2019 and 2020.

Metaculus is revamping a part of its incentive designs mechanism. They are also hiring for Junior Designer, Full-Stack Developer, and Public Policy Data Scientist positions.

SimonM kindly curates the top comments from Metaculus this past April. They are:

• GlobalGuessing and PeterHurford lay out the chances of an Olympic boycott in 2022.
• EvanHarper points out the base rate for KIA is much lower in modern wars.
• Metaculus users have fun with a pseudo-Keynesian Beauty Contest.
• SimonM and Charles discuss Jannik Sinner's prospects.
• elifand_ought and liconstan have very different forecasts for whether or not the meat industry will put out an anti-plant-based-food ad.
• onlyasith's forensic analysis of a Brandon Adams podcast is part of his model for when Nate Silver's next book will be out.
• fianxu points out jury instructions don't apply to forecasters.
• haukurth notes the community forecast was well calibrated during the Chauvin trial.
• SimonM points out people don't update their forecasts as often as they should.
• clearthis's comment causes people to notice an old question about Trump family indictments, and the Metaculus community prediction moves up by 30%.

SimonM has also added more functionality to the Metaculus Extras site, including a “movers and shakers” page. It lists the Metaculus questions that moved the most in the last week.

In the News

The Anticipation Hub is a loose association of organizations trying to anticipate catastrophes and mitigate them before they happen. Based on the contents of their newsletter, they seem to be fairly active, particularly around the topics of floods, tropical storms, and inter-institutional cooperation. For instance, they have a postmortem of the actions they took before a severe tropical storm “Chalane" hit Mozambique. They find that the value of their preparation decisions  was not as high as they had hoped for.

Charismatics issue ‘prophetic standards’ to address false Trump prophecies (original source). “After an embarrassing number of wrong prophecies and bungled predictions about the 2020 election, a group of charismatic Christian leaders have released a four-page statement of “prophetic standards'' to help correct abuses in the movement”. In particular, they call on those who have made false prophecies to apologize.

How spooks are turning to superforecasting in the Cosmic Bazaar: The Economist mentions a few details about the "Cosmic Bazaar", a forecasting tournament organised by the British government.

Since the launch of the website in April 2020, more than 10,000 forecasts have been made by 1,300 forecasters, drawn from across 41 government departments and several allied countries. The site has around 200 regular forecasters monthly, who must draw only on publicly available information to tackle 30-40 questions live at any time. Cosmic Bazaar represents the gamification of intelligence. Users are ranked by a brutally simple measure: the accuracy of their predictions.

Facebook releases a research paper: "Large-scale forecasting: Self-supervised learning framework for hyperparameter tuning" (blog post, arxiv preprint)

Forecasting is one of the core data science and machine learning tasks we perform at Facebook, so providing fast, reliable, and accurate forecasting results with large amounts of time series data is important for our business.

We empirically evaluated our algorithms on both internal and external data sets, and obtained similar conclusions. SSL frameworks can dramatically improve the efficiency of model selection and hyperparameter tuning, reducing running time by 6-20x with comparable forecasting accuracy.

This approach is independent of specific forecasting models and algorithms

Goldman Sachs downgrades India’s growth forecast as Covid cases spike.

The European Centre for Disease Prevention and Control (ECDC) has run a new forecasting hub for about a month now (announcement, link to the hub). It's unclear whether any decision-makers are influenced by its predictions.

Draft EU legislation on AI is proposing to regulate the use of Bayesian estimation (see also some commentary here here, and here). Some commenters opine that the proposal is business as usual,—draft legalese that will get refined and clarified in a future review. Other commenters view it as another example of the EU's incompetence.

Blog Posts

The Kelly Criterion Kinda Sucks. An experienced PredictIt bettor points out that the Kelly criterion doesn't offer guidance in cases when one can bet on more than one event at more than one point in time.

Scott Alexander, of AstralCodexTen, publishes a list of 75 public predictions for 2021. Zvi and SimonM discuss some aspects of it on LessWrong. I have also added them to Foretold, in case people want to forecast on them, and to Metaforecast, which makes them searchable using a nicer interface.

The die is forecast is a "Computational Social Science and Political Event Analysis" blog by the people behind CoupCast, and the Rulers, Elections and Irregular Governance (REIGN) dataset. I appreciate their rigorous, base-rate-driven analysis of election violence risk in upcoming elections around the world.

Global Guessing is a geopolitics forecasting blog. The authors take questions from various forecasting platforms, chiefly Metaculus, and analyze them in depth in public. For instance, see this post on the chances of a boycott of the 2022 Winter Olympics in China, the chances of a North Korea ICBM test, and the possible origins of COVID-19.

Stephen S. Roach is a senior fellow at Yale. In My Worst Forecasting Mistake, he looks back at one of his failed predictions:

Attempting to predict interest rates was my least favorite part of the job. With good reason. I remember walking into the old Morgan Stanley investment banking meeting room and seeing a chart of my predecessor’s bond market forecast sitting upside down on the floor. I was determined to avoid that fate. When my favorite bond trader started calling me “dart man,” I made an executive decision to disengage and hire an interest-rate strategist. Survival of the fittest, I guess.

I went on to stress that the nascent recovery was likely to be aborted by a relapse, as had occurred in eight of the preceding 11 recessions since the end of World War II. A few months later, taking comfort from some economic indicators that had broken my way, I committed the most egregious forecasting sin of all: giving a date. I actually wrote that the coming double-dip was likely to occur by mid-2021.

In the end, the confluence of science, politics, and the indomitable human spirit left my out-of-consensus double-dip call in tatters. It wasn’t my first forecasting mistake, but it is probably the most glaring. Mea culpa is an understatement. Back to the ivory tower.

He has it right that for him as an individual, it was the wrong move to make a quantifiable prediction. But, the system as a whole wants for people like him to make quantifiable predictions and be weeded out by more accurate forecasters.

In The Johnson & Johnson Pause Shows The System Is Working, FiveThirtyEight claims that stopping vaccination because of one death and six cases of a rare type of blood clots among 6.8 million doses administered was somehow a positive development.

Some "This is fine" dog meme variations, @LinchZhang, 2021

Abram Demski writes Reflective Bayesianism, which outlines implicit and questionable assumptions that strict Bayesians have to make (cf. Radical Probabilism).

Probability theory and logical induction as lenses talks about how both probability theory and logical induction are both "lenses for looking at the real-world phenomena of machines that quantify their uncertainty in their beliefs".

Long Content

The Office of the Director of National Intelligence (ODNI) of the USA has released a "Global Trends 2040" report. In ancient history, the Bush administration established this office in the aftermath of the US intelligence community's failure to aggregate information available in disjoint agencies to predict the 2001 attacks (cf. Wikipedia). Then in 2006, IARPA was formed as an organization within the ODNI, and went on to organize the ACE program, which explored which kind of setups produced the most accurate probabilities, and found out that pre-selected superforecasters from the general population working in groups did best, or at least better than intelligence analysts with access to classified information.

Because of this history and background of its office, I was expecting the report to have some probabilistic estimates, but the report is instead structured around a set of scenarios, rather than around quantified predictions. However, the report does includes interesting observations around structural technology forces:

Global Trends 2040, Structural Forces: Technology, Office of the Director of National Intelligence, 2021.

Timelines Shrinking. The time to develop, deploy, mature, and then retire technologies is moving from decades to years and sometimes faster. Multiple actors, including corporations and states, at the forefront of emerging technology may deploy and exploit a new technology before others get off the starting blocks. Those trying to catch up, especially in developing countries, may be increasingly forced to choose technologies before the implications of those choices are fully understood, risking investment in technological dead ends or falling hopelessly behind. Planned economies may be able to react faster to emerging technology developments, potentially at the cost of reduced technological diversity and efficiency.

AI is the demonstration of cognition and creative problem solving by machines rather than humans or animals, ranging from narrow AI, designed to solve specific problems, to Artificial General Intelligence, a system that in the future may match or exceed a human being’s understanding and learning capacity. By 2040, AI applications, in combination with other technologies, will benefit almost every aspect of life, including improved healthcare, safer and more efficient transportation, personalized education, improved software for everyday tasks, and increased agricultural crop yields. Political and business leaders worldwide are seeking global talent and are pouring resources into developing AI, hoping to be among the first to use it to reshape societies, economies, and even war. Enabled by concurrent increases in high-quality data, computing capability, and high-speed communication links, AI will challenge leaders to keep pace and reap the benefits while mitigating harmful effects, such as threats to privacy and liberty.

Existential Risks. Technological advances may increase the number of existential threats; threats that could damage life on a global scale challenge our ability to imagine and comprehend their potential scope and scale, and they require the development of resilient strategies to survive. Technology plays a role in both generating these existential risks and in mitigating them. Anthropomorphic risks include runaway AI, engineered pandemics, nanotechnology weapons, or nuclear war. Such low-probability, high-impact events are difficult to forecast and expensive to prepare for, but identifying potential risks and developing mitigation strategies in advance can provide some resilience to exogenous shocks.

In contrast to the approach taken by the report above, Keeping Score: A New Approach to Geopolitical Forecasting, by Perry World House, calls for more quantified predictions. See also this twitter thread by @MWStory.

The Survey of Professional Forecasters is "the oldest quarterly survey of macroeconomic forecasts in the United States. The survey began in 1968 and was conducted by the American Statistical Association and the National Bureau of Economic Research. The Federal Reserve Bank of Philadelphia took over the survey in 1990." Its last forecasts, for Q1 2020, can be found here.

Robin Hanson does some Robin-Hansoning, and proposes “Shoulda-Listened Futures”, a system in which people who think they are unfairly ignored by mainstream science could pay for a future evaluation of their work and for an immediate prediction about that evaluation.

These “world shoulda listened to me” customers might pay to have some of their works evaluated by posterity. For example, for every $1 saved now that gains a 3% real rate of return,$19 in real assets are available in a century to pay historians for evaluations. At a 6% rate of return (or 3% for 2 centuries), that’s $339. Furthermore, if future historians needed only to randomly evaluate 1% of the works assigned them, then if malcontents paid$10 per work to be maybe evaluated, historians could spend $20K (or$339K) per work they evaluate. Considering all the added knowledge and tools to which future historians may have access, that seems enough to do a substantial evaluation, especially if they evaluate several related works at the same time.

Given a substantial chance (1% will do) that a work might be evaluated by historians in a century or two, we could then create (conditional) prediction markets now estimating those future evaluations. So a customer might pay their $20 now, and get an immediate prediction market estimate of that future evaluation for their work. That$20 might pay $10 for the (chance of a) future evaluation and another$10 to establish and subsidize a prediction market over the coming centuries until resolution.

@LinchZhang, 2021.

Discuss

### April 2021 Deep Dive: Transformers and GPT-3

1 мая, 2021 - 14:18
Published on May 1, 2021 11:18 AM GMT

Introduction

I know very little about a staggering number of topics that would be incredibly useful for my research and/or navigating the field. Part of the problem is the sheer number of choices -- I can't make myself study one thing very long because I always feel like I need to learn 20 other things.

To solve this problem, I started to implement monthly deep dives into a topic. A month is short enough that even I can stay relatively on track for that long, while still being enough time to actually learn something. The goal is not to master the topic completely (which would be impossible); it's to get a finer map of the territory, and to be able to discuss relevant ideas on this topic.

This first month was dedicated to transformers and the GPT-3 model, a topic I felt like I had to do, but which actually kind of grew on me.

Note that this post is a very quickly written summary of what I did and how it went (a bit like TurnTrout's sequence, with probably less insights). This is not a distillation post, and if you read it, you will not learn that much about the subject. That being said, it might prove useful if you want to go learn it by yourself.

Thanks to Jérémy, Flora, Connor, Kyle and Laria for great discussions that helped me understand this topic further.

The Plan

I based myself quite loosely on the structure advocated in Scott Young's Ultralearning. Which only means that I made a planning week by week, checked what resources were recommended beforehand, and tried to focus on the direct applications I wanted to make of my learning, which is explaining it to people and having interesting discussions on the subject.

My idea was that in order to understand GPT-3, I needed first to understand the Transformer architecture, then the GPT family, then play with GPT-3 directly. Since I started this deep dive on the 8th of April, the planning was broadly:

I expected to take one hour per day, tentatively placed between 1pm and 2pm, just after my lunch.

The RealityWeek 1: Following the Plan

The first week I actually did my weekly hour, at the time scheduled, and I pretty much learned what I wanted to learned about how transformers worked.

My biggest hurdle in groking the original paper was the self-attention mechanism itself. With hindsight, I had two issues:

• Not having thought about NN architectures for so long, I had forgotten that the whole point was not the encode the information or even the specific mechanism that you wanted, but to create an affordance for the NN to learn the kind of things you care about. Remembering this helped me with getting that the NNs learned how to parameterize the queries, keys and values as was most useful for it.
• I couldn't find where the inputs where used in the mechanism. The phrasing of the original paper only talked about matrices of parameters, and most of the other content didn't help on that front (even the specific Stack Overflow question on it). In the end, I read the great illustrated transformer blogpost, which was incredibly clear, and told me that the inputs are just multiplied by the matrices of learned parameters to give the matrices used in the self-attention mechanism. Pretty obvious afterwards, but I was properly confused for a couple of days.

In the course of this first week, I also ended up changing my mind on the usefulness of some resources. For example, I found no use whatsoever for the annotated transformer, even if I assume that people who think in TensorFlow would find that helpful. On the other hand, the aforementionned illustrated transformer that cleared up so many parts of the attention mechanism for me was pretty low in my original list, as it just looked like any other blog post.

So here would be my recommendation for studying transformers, if you are like me a theoretically minded person (albeit with some programming chops):

As part of my learning, I also had quite a lot of discussions trying to explain the technical details to a friend who knew about transformers but had forgotten the nitty-gritty; and I spent an hour and a half retracing the history of neural nets architectures (based on nostalgebraist post) and a bit of self-attention to my girlfriend, which has a background in chemistry and neuroscience. That was definitely helpful, and left me with the impression that I could explain the ideas decently well, or look for the shaky parts of my explanation pretty quickly.

Week 2: the Loss of Hope

Honestly, the first week went better than I expected, so I was pretty hopeful coming into the second week. Then, I started reading the GPT papers.

Now, the first GPT paper was fine. I didn't learn that much about the architecture (given that it's not that different from the original transformer architecture), but I found out about some fun new NLP related ideas: perplexity and BPEs. So in itself, the paper was pretty interesting.

But GPT-2 ... well my take on the GPT-2 paper is that it's an attempt to make a respectable-sized paper (24 pages!) out of the content "we removed fine-tuning and made the model bigger, and it's now better!". Very important here: I'm not saying that the results are not important, interesting and impressive. But the paper in itself has not that much to say, I feel.

The mistake I did was to force myself to finish it. Which means that I stopped studying, because it was a real bore. I thus lost almost all momentum in the second week. Only when I started reading the GPT-3 paper in the week-end did I get a bit excited. This last paper actually tried to present a conceptual framework to think about the results (zero-shot vs few-shots), which I didn't know in details, and so was pretty exciting. I actually never finished it, but it was enough to push me back into rails for the third week.

Although I didn't really study much this week, I still can give some tentative recommendations:

• Read the GPT paper to get the basic changes to the architecture and a bit of the NLP setting
• Skim the GPT-2 paper really quickly
• Read the GPT-3 paper, with a focus on the framing of zero vs few-shots tasks.
• Probably read on the history of the GPT models and others like BERT (no pointer here, I ended up reading nothing on this).
Week 3 and 4: Fascinating GPT-3

My original plan for toying with GPT-3 was to use AI Dungeon, a storytelling service that uses a fine-tuned version of GPT-3 under the hood (with the premium membership, but there's a free one week-trial). But I unexpectedly found a way to have access a bit to the actual GPT-3 API.

Followed a flurry of experiments and prompt writing, right? Not exactly. After toying a bit with it, I quickly realized that I didn't really knew what I wanted to experiment on. The lack of understanding of why it sometimes worked and other times it didn't also quite frustrated me, coming from a more programming languages perspective.

So I spent a couple of days after that looking at interesting discussions online in the EleutherAI discord, where people have a lot of hands-on knowledge about LMs and GPT-3. This lead to me discovering this great blog. What was so great about it is that it supplied both a detailed explanation of the kind of strategies that work when using GPT-3 (in this post), and a mental model of how to think about Language Models that gave me new alignment ideas (presented in this post, but I also like the intro of the previous post for a quick overview).

I thus ended thinking and discussing far more about GPT-3 than one hour a day, but with a quite unstructured approach. I tried some of the experiments in the methods of prompt-programming blogpost, I wrote rants about how the model of LMs seems to imply interesting directions for alignment, and I discussed with the authors of the blog.

Another surprise was that I didn't spend that much time on Gwern's post about GPT-3. I expected this to be one of the most fun part of the deep dive, but it didn't go that way. But here, contrary to what happened with the GPT-2 paper, I think it's mostly on me. I had already read a lot of the conceptual framing sections, and I'm not that excited by a long list of results, even if they are all individually pretty cool. I'd still want to go through it eventually, and still thinks it's a valuable resource for someone wanting to get a grasp of what GPT-3 can do.

Here are my recommendation for studying GPT-3 more specifically, especially if you're not that hands on:

(Note that here even more than in the previous sections, the recommendations are super biased to what I found exciting. There's probably hundreds of great resources on GPT-3 that I don't know).

What I Would Have Done Differently

I'm quite happy with the transformer week; pretty unhappy with the second week; and excited about the rest, while knowing that it was a lot more due to luck than planning. Mostly, there are three things I would change:

• Try to ankify some of the things I learned. I half planned to do that, which resulted in me never implementing it. That would be mostly useful for the transformer stuff, I think.
• Prepare backup plans in case some part of the planning is too hard and/or too boring. I definitely feel like reading a lot more on the history of GPT models and the controversies (as well as people's reactions) would have been a better use of my second week, if I didn't fixated on reading the GPT-2 paper completely.
• When possible, force myself to do more hands on experiments. Even if my investigation of GPT-3 is satisfying, I still feel like I should have tried more prompts, more variants, more experiments. That's not my natural tendancy, but there's some knowledge that can only be gotten like that.
Conclusion

For a first try, I think I did decently well. I managed to focus on one main topic for a month, and learned a lot around it that is already informing some of my alignment research and perspectives on AI and AI risk.

Discuss

### Your Dog is Even Smarter Than You Think

1 мая, 2021 - 09:18
Published on May 1, 2021 5:16 AM GMT

Epistemic status: highly suggestive.

There's a revolution going on and you're sleeping on it.

A combination of surveys and bayesian estimates[1] leads me to believe this community is interested in autism, cats, cognition, philosophy, and moral valence of animals. What I’m going to show you checks every box, so it boggles my mind that I don't see anyone talk about it. It has been bothering me so much that I decided to create an account and write this article.

I have two theories.

1. The community will ignore fascinating insight just because its normie coded. Cute tiktok-famous poodle doesn't pattern match to "this contains real insight into animal cognition".

2. Nobody tried to sell this well enough.

I personally believe in the second one[2] and I'll try to sell it to you.

Stella

There’s an intervention to help non-verbal autistic kids communicate using “communication boards” (not to be confused with facilitated communication which has a bad reputation). It can be a paper board with pictures or it can be a board with buttons that say a word when pressed. In 2018 Christina Hunger (https://www.hungerforwords.com) - a speech pathologist working with autistic children using such boards - started to wonder if her dog was in fact autistic. Just kidding, she saw similarities in patterns of behavior between young kids she was working with ("learner" seems to be the term of art) and her dog. So she gave it a button that says “Outside” and expanded from there.

Now teaching a dog to press a button that says “outside” is not impressive or interesting to me. But then she kept adding buttons and her dog started to display capabilities for rudimentary syntax.

https://youtu.be/g5xtDyOju8E

https://youtu.be/Ob1-5xAgllo

Reaction from serious animal language researchers and animal cognition hobbyists was muted to non-existent, but dog moms ate this stuff up. One of them was Alexis.

Bunny Most useful research is impractical to do within academia

The Importance of Methodology and Practical Matters

Ethology has some really interesting lessons about how important various practical matters and methodology can be when it comes to what your field can (and can't) produce. For example, it turns out that a surprising amount of useful data about animal cognition comes from experiments with dogs. […] The main reason is because they will sit still for an fMRI to be the goodest boy (and to get hot dogs). […] On the other side of that coin, elephants are clearly very smart, but we've done surprisingly little controlled experiments or close observation with them. Why? […] They're damn inconvenient to keep in the basement of the biology building, they mess up the trees on alumni drive, and undergrads kept complaining about elephant-patty injuries while playing ultimate on the quad.

https://astralcodexten.substack.com/p/your-book-review-are-we-smart-enough

A lot of useful research isn't done because it's too inconvenient, too expensive or otherwise impractical to execute within confines of academia. This is a massive shaping force. Existence of ImageNet and its quirks is a stronger shaping force on AI research than all AI ethics committees combined.

Nobody had done this before because it takes months of everyday training to get interesting results. Once your dog gets the hang of it, you’re able to add more buttons faster, but it’s never quick. Dogs take a while to come up with a response (they’re bright, but they’re not humans), and you can’t force your dog to learn, so you have to work together and find motivation (for the dog and for yourself!). And not every pet has a strong desire to communicate.

But it may be practical to do for a layperson

Lucky for us, Alexis has many more commendable qualities besides willing to spend time and effort on her dog. She maintains healthy skepticism, she's well aware of confirmation bias and "Clever Hans" effects and of the danger of over-interpreting the dog's output. She has partnered with researches from University of California, San Diego to have several cameras looking at the button pad running 24/7, for them to do more rigorous analysis. Presumably there's an actual paper on the way.

Watch this vid first where she gives a brief explanation of what she's doing: https://youtu.be/A83-MNqTzkM

Things I've seen the dog do that surprised me

Bunny is creative with the limited button vocabulary available to her and tries to use words in novel ways to communicate: "stranger paw" for splinter in her paw, "sound settle" for shut up, "poop play" for fart, "paw" to refer to owner's hand.

https://youtu.be/6MMGmRVal6M

Bunny knows each of her doggy friends by name, thinks about them when they're not there, asks where they are, requests to play with them.

https://youtu.be/QB9zY62xhws

Bunny understands times of day like today, morning, afternoon, night.

https://youtu.be/echpdOTH78w

And can recall what time of day she went to the park.

https://youtu.be/iAPfrQDSibM

Bunny is quite obsessed over her bowel movements (how Freudian) and about her owners' poop cycle.

https://youtu.be/9iW1CY7mlOI

Bunny communicates emotional states like mad, happy, concerned. And "ugh".

https://youtu.be/QOtvKkm-jVM

Bunny wants to know what and why is a "dog".

https://youtu.be/Fn8Fx0bqzT4

And whether Mom used to be a dog. And she can recognize herself in the mirror.

https://youtu.be/vLD7GIc8kZQ

There's a long-running debate about whether human brains possess a special ability for language.  Although "feral" human children who are raised with no language lose the ability to pick it up later in life. Maybe they could learn button talk, I don't think anyone tried for lack of steady supply of feral children.

But what I see is strongly suggestive that language facilities are not unique to us and a dog that is given ability to produce words and is taught from puppyhood with the same massive amount of effort that we put into human children will be able to talk. Don't get me wrong, I don't expect dogs to start writing poetry and doing particle physics. But I expect them to produce something that can undoubtedly be called language.

Koko

So what, you ask, some apes have been taught sign language and they produced rudimentary syntax as well.

For starters you wouldn't predict dogs to be capable of the same, and it's significant to see that ability given their evolutionary distance from us (even with selective breeding pressure from domestication).

Most of the ape research was done in the 70s, and it is, well, very 70s. Those things aren't known for being well-run or replicating well. And it was done with sign language, perhaps buttons are much more conductive to language acquisition. Since the 70s craze, it apparently became unfashionable, and nothing new happened for decades. To this day any conversation on animal language is about Koko (who died in 2018) and the parrot who said "love you, bye" before dying in 2007. Utter stagnation.

What we have is something new, orders of magnitude easier to study and reproduce (how many of you have gorillas at home?), massive PR potential, modern tech that allows you to have cameras running 24/7 to preclude criticism. It started with an outsider to the field, who wasn't conceptionally limited by prior art. And it's accessible to regular people, potentially revolutionizing our relationship with our pets.

This raises the natural question: what if you gave an ape the buttons, and taught it from childhood, and put parent-level effort into it, not "70s research”-level effort? Perhaps the answer would surprise us.

Honorable Mentions Billi

A cat who initially became famous for pressing "MAD MAD MAD" at a slightest inconvenience, but she has meollowed out a bit.

https://youtu.be/xZW2RVY0sWs

https://youtu.be/JFy0P4Uie3Y

https://youtu.be/7vs9vl7Y6oE

Luna & Izzy

Aren't as impressive but here's a video of a dog using buttons to help her sister get through to the humans.

https://youtu.be/v2_g7gWnXfM

Luna & family

https://youtu.be/QKQK7EIcq9Y

HowTheyTalk.org

Community of people trying to replicate this.

Maybe later

“Maybe later” at substack for trying in vain to tell others about this, only to be summarily ignored. I got your back, buddy.

1. aka blindly trusted stereotypes ↩︎

2. Normie blindspot does exist in the community, but that's kind of obvious and expected, and should be a separate article. ↩︎

Discuss

### Making Markets Over Beliefs

30 апреля, 2021 - 23:43
Published on April 30, 2021 8:43 PM GMT

Betting is an excellent way to improve the accuracy of your beliefs. For example:

Me: "I think Alice is 5'11."

Friend: "I'll bet you $5 1:1 that it's higher." Translated, my friend means they're willing to agree to a deal where I pay them$5 if Alice is taller than 5'11" and they pay me $5 if she's shorter or equal to 5'11". However, if Alice turns out to be 6'5", it feels like I will have lost the bet "more" than if Alice was 6'0". Similarly, if Alice turns out to be 5'1", it feels like my friend lost the bet "more" than if Alice was 5'10". Market making is an alternative to better that captures these intuitions. For example: Me: "I think Alice is 5'11"." Friend: "Buy." Translated, my friend means they would buy an asset that resolved to the price of Alice's height in inches for 5 * 12 + 11 =$59. If Alice turns out to be 6'5" (65 inches), I owe them $65 -$59 = $6. However, if Alice turns out to be 5'0" (60 inches), I only owe then$1. You might think of "buying" as claiming the value is too low and "selling" as claiming the value is too high.

I've been slightly cheated in both of the above examples. In the first example, I gave my best guess at Alice's height. If my friend's best guess is higher than mine, the height we should bet on is the average of our guesses. In the second example, my best guess at Alice's height is not the price at which I would sell the asset. If I thought Alice's height was 58-61 inches, I might be willing to buy the "Alice's height in inches" asset at $58 and sell it for$61, allowing me to roughly express my confidence interval and allowing my friend to bet on either side.

The above example dealt with a numeric quantity. This technique can be extended to probabilities by simply making a market over an asset whose value is $1 if some binary event happens and$0 if it doesn't, as is standard in prediction markets. In this case, the prices I offered would roughly correspond to my subjective probabilities. If I thought a coin was 50% likely to be heads, I might be willing to buy the "coin is heads" asset for $0.45 and sell for\$0.55. This process is equivalent to saying I would bet at 9:11 one way and 11:9 the other. [1]

Similarly to how the amount you bet specifies how confident you are in a belief, the number of assets you're willing to buy/sell will correspond to how confident you are in your bound. For instance, if I had previously measured Alice's height, I might be willing to buy/sell 100 of the "Alice's height in inches" asset; if I was only guessing, I might only be willing to buy/sell 1 asset.

When you make binary bets, the loser pays the winner a fixed amount. When you make markets, the loser pays the winner an amount linearly proportional to the amount by which they lost, weighted by confidence.

A way to make market making more efficient is to use trading jargon. From Jane Street's interview guide:

When you want to effect a trade, you'll need to specify the

1. thing you want to trade (which is often implied by the context, so let's ignore this for now)
2. direction - whether you want to buy it or sell it
3. price at which you're willing to transact, and
4. quantity - how many shares/contracts/etc.; also called size

If you want to buy something, like a stock or a contract, you can say or otherwise indicate a bid to buy. If you want to sell, it's an offer to sell.

If you want to try to buy 10 widgets for a price of $2 each, say "I'm 2 bid for 10." If you want to try to sell 10 widgets for a price of$4 each, say "I have 10 at 4."

Now, if you're happy to do both of the above, you can say both phrases, but it's much more common to say "I'm 2 at 4, 10 up." Note that this requires you to be willing to buy or sell for the same quantity; if you have different quantities depending on direction, then you'll have to say the bid and offer separately.

...

Once an order is created, someone else can trade against it, by saying "sold" to hit (sell) to your bid, or "take 'em" to lift (buy) your offer. Saying "sold" or "take 'em" trades against the entire quantity available. To trade only 5 (or any partial amount) of the available 10 widgets, say "I buy 5" or "I sell 5."

You can also make markets over functions of attributes to account for different distributions. Height is normally distributed, so a linear payout makes sense. However, the amount of time my homework takes might be power-law distributed. In this case, I might make a market over the base-10 logarithm of how long the task takes in minutes.

Similar to how giving confidence intervals is often more informative than making point predictions, making markets is often more informative than making singular bets.

1. Technically, the market I should make corresponds to what I think other people's probabilities are likely to be given they can see my market. I might give a wider market because only people that think they're getting a good deal with trade with me. ↩︎

Discuss

### [ACX Linkpost] A Modest Proposal for Republicans

30 апреля, 2021 - 21:43
Published on April 30, 2021 6:43 PM GMT

Politics is indeed the mindkiller and/or hard mode, but this post is the most novel and thought-provoking political writing I've seen all year and I can't stop thinking about it.

Discuss

### Review of "Why AI is Harder Than We Think"

30 апреля, 2021 - 21:22
Published on April 30, 2021 6:14 PM GMT

"Why AI is Harder Than We Think" is a recent (April 26, 2021) arXiv preprint by Melanie Mitchell. While the author is a tenured professor with technical publications, they also have published multiple layman-level essays and articles which tend to be skeptical and pessimistic of AI progress. Why AI is Harder Than We Think falls somewhere in between the two extremes—it's written in the style of an academic paper, but the arguments appeal to a layman audience.

Reddit's /r/machinelearning was pretty harsh on the paper—click here for the full discussion. While I agree it has flaws, I still found it an interesting and valuable read because I enjoyed the process of figuring out where my opinions diverged from the author's.

In this post, I will briefly summarize the paper and state my opinions. The most interesting part (= where I disagree the most) is Fallacy 4, so skip to that if you don't want to read the whole blog post.

Introduction: AI Springs and Winters

Self-driving cars were predicted to be available for purchase by 2020, but they aren't. The author also gives a more in depth historical account of the various AI "springs" (periods of growth and optimism due to advances in AI research) and "winters" (periods of stagnation and pessimism when said advances aren't powerful 'enough') which have occurred since the 1950s.

Why does this keep happening?  The author's thesis:

In this chapter I explore the reasons for the repeating cycle of overconfidence followed by disappointment in expectations about AI. I argue that over-optimism among the public, the media, and even experts can arise from several fallacies in how we talk about AI and in our intuitions about the nature of intelligence.

Specifically, they discuss four fallacies, which I'll address individually in the coming sections.

Sidenote: I really enjoyed pages 2–3 (the section on AI Springs and Winters). Whether or not you buy the thesis of this paper, that section is a well written historical account and you should check it out if you're interested.

Fallacy 1: Narrow intelligence is on a continuum with general intelligence

The claim here is, when we make progress in AI by beating top humans at chess, we've solved a problem which is much more narrow than we think. Similar claims apply to seemingly general systems such as IBM's Watson and OpenAI's GPT-3.

My opinion

Issue 1: Narrow tasks are slightly generalizable. While it is true that chess is much narrower than general intelligence, the author neglects to mention that once we solved chess, we were able to "easily" apply those techniques to other tree search based domains. For a certain class of tasks, chess is like an ""NP-Complete problem""—once we solved it, we (after a ""polynomial time transformation"") were able to solve all other problems in that class. This is how DeepMind went from AlphaGo to MuZero so quickly.

Issue 2: GPT-3 is kinda general. I completely disagree with the notion that GPT-3 is only slightly less narrow than chess, in comparison to human intelligence. GPT-n trained on a sufficiently large amount of text written by me would be indistinguishable from the real me. If GPT-n is 90% "general intelligence" and chess is 0.001% (coming from some dumb heuristic like "chess-like things are 1 out of 100,000 tasks a general intelligence should be able to do"), then I think GPT-3 is 1% general intelligence. And 1% is closer to 100% than it is 0.001%, in terms of orders of magnitude.

Where we agree: The "first-step fallacy" is real. Here, the "first step fallacy" refers to the phenomenon where an advance ("first step") in AI is perceived as less narrow than it really is. I agree that the ML research frontier tends to "overfit" to tasks, causing research progress to appear like it required more insight than it did. This seems related to the planning fallacy—researchers assume research progress will continue at the same rate, where really they should expect to experience diminishing returns in effort. A third way of thinking about this is related to The illustrated guide to a Ph.D.. I've lazily altered some images from there to make my point.

A researcher makes an advance on a specific AI task, such as chess or text prediction.

The advance is mistakenly seen as a greater step towards general intelligence than it really is.

The advance did get us closer to general intelligence, but not by as much as over-exaggerated accounts would lead one to believe.  Fallacy 2: Easy things are easy and hard things are hard

The claim here is basically Moravec's law—tasks which humans think are hard are actually relatively easy for AI, and tasks which humans consider to be easy are much more difficult. For example, AlphaGo solving Go was seen as a triumph because Go is a very challenging game, but "challenging" here refers to a human's perspective. From an AI's perspective, charades is a much more "challenging" game.

My opinion

I agree with the author in that Moravec's law is correct. I think this is a good point to bring up when talking about the history of AI, and (for example) why early attempts at computer vision failed. However, I think the modern ML research community has internalized this phenomenon, so I don't think this point is super relevant to a modern conversation about the future of artificial general intelligence.

Fallacy 3: The lure of wishful mnemonics

The claim here is related to the idea that the terminology we use impacts our understanding of the objects we are discussing. For example, calling a neutral network a neural network implies that neutral networks are more similar to a human brain than they actually are. This also applies to how NLP benchmarks are named (a model which does well on the "Stanford Question Answering Dataset" is not going to be able to answer all questions well) and how people talk about models' behavior (saying "AlphaGo's goal" implies more coherence than it may have). That is, the terminology/shorthands/mnemonics here are all wishful.

Using these shorthands is damaging because they give off an impression to the public that AI systems are more capable than they actually are.

My opinion

I agree with the author that terminology is often not well-explained, and this leads to misrepresentation of AI research in the media. It's hard to fix this problem because STEM coverage is rarely good. I think the solution here is to get better reporters (for example, Quanta Magazine has good math reporting) rather than to change the language AI researchers use.

There is an additional problem, which the author doesn't focus on as much, of researchers using these "wishful mnemonics" within the research community. Sometimes this is fine—I find it easy to substitute a phrase like "AlphaGo's goal" with a more precise phrase like "AlphaGo's loss function is minimized by the following policy".

But in the context of AI Safety, anthropomorphizing can get dicey. For example, the problem of understanding deceptive mesa-optimizers only becomes tricky and nuanced when we stop anthropomorphizing the system. If this point is not communicated well from the AI Safety community to the ML research community at large, the AI Safety community runs risk of leaving the ML research community unconvinced that deceptive alignment is an important problem.

I wish the author more clearly distinguished between these two settings—the first being communicating with the media and their resulting message to the public, and the second being the language used internally to the AI research community. Their point about "wishful mnemonics" would be stronger if they more clearly explained when they are beneficial/problematic in either setting.

Fallacy 4: Intelligence is all in the brain

This is the most complex claim of the four. The author's reasoning here can be separated into two ideas. In both, the theme is that human intelligent reasoning does not only occur in the brain, but also <somewhere else>.

Idea 1: human intelligent reasoning does not only occur in the brain—it is also inextricably intertwined with the rest of our body. This theory is called embodied cognition. Experimental evidence in favor of embodied cognition exists, for example:

Research in neuroscience suggests, for example, that the neural structures controlling cognition are richly linked to those controlling sensory and motor systems, and that abstract thinking exploits body-based neural 'maps'.

Idea 2: human intelligent reasoning can not be reduced to a sequence of purely rational decisions—it is also inextricably intertwined with our emotions and cultural biases. Therefore, we have no reason to believe that we can create artificial general intelligence which would be superintelligent, but lack emotions and cultural knowledge.

My opinion

Fallacy 4 is where this paper is weakest. I don't find either of these ideas particularly convincing, and I find Idea 2 especially problematic.

My opinion on Idea 1: Embodied cognition sounds reasonable to me. I found the example mentioned in the paper too abstract to be compelling, so here is a more concrete example of my own: When humans do geometry, they might use spacial reasoning in such an intense way so that their eyes, arms, hands, etc. are engaged. This means that the amount of computational power which is being used on geometry exceeds the amount of computational power the human brain has.

Where my opinion differs from the author's opinion is on the subject of what to do about this. The author seems to think that more research into embodied cognition—and more specifically the precise mechanisms underlying human intelligence—are necessary for making progress on artificial general intelligence. However, I think that all embodied cognition is saying is that we might need a bit more than 10^15 FLOP/s to match the processing power of a human. An extra factor of 2, 10, or 100 won't make a difference in the long run. The Bitter Lesson provides evidence in favor of my opinion here—compute often eventually outperforms hard-coded human insights.

My opinion on Idea 2: At best, this seems incorrect. At worst, this seems completely incoherent.

Ironically, the author's problem here seems to be that they are falling for their own Fallacy 3 ("The lure of wishful mnemonics")—more specifically, they seem to be over-anthropomorphizing. Yes, it is true that human intelligent reasoning is intertwined with our irrational heuristics and biases. But this doesn't mean that an artificial general intelligence has to operate in the same way.

For example, the author is skeptical of Bostrom's orthogonality thesis because they think (for example) a paperclip maximizer with enough intelligence to be a threat cannot exist "without any basic humanlike common sense, yet while seamlessly preserving the speed, precision, and programmability of a computer."

While I agree that a superintelligent paperclip maximizer will have to have a decently accurate world model, I disagree with the notion that it will have to learn and internalize human values.

For example, one could imagine a paperclip maximizer trained exclusively on synthetic physics simulation data. If its influence on human society is only indirect (for example, maybe all it can do is control where clouds are distributed in the sky or something silly), then the strategies it employs to increase paperclip production will seem convoluted and unintelligible from a human perspective. ("Why does putting clouds in these exact 1000 places triple paperclip production? Who knows!") Maybe concepts like "what is a paperclip factory" will crystalize in its internals, but still, I think such a model's inner workings would be very far from "humanlike common sense".

Moreover, there is no reason to expect it to be difficult for an artificial general intelligence to learn whatever human biases and cultural knowledge are supposedly necessary for human-level intelligence. In fact, this seems to be the default path for AGI.

Interpreting the author's point here as charitably as possible, it seems like the issue here is an imprecise notion of intelligence. MuZero is able to "superintelligently" beat some Atari games without the human bias of "getting dopamine when I reach a checkpoint" or the cultural knowledge of "death is bad and I should avoid it".

So, the author's notion of intelligence must be broader—closer to a general-purpose, human-level intelligence. But then, does GPT-3 count? Its output is human-like writing which feels like it was influenced by irrational heuristics and biases, not a superintelligent, purely rational system.

Even if GPT-3 doesn't count—the entire field of AI Ethics is specifically devoted to the problem of ridding AI of human biases. Whenever we train ML systems on human data, the default outcome is that they learn our human biases! Since we keep running into these problems, the idea that AGI progress will be blocked by our understanding of the mechanics of human cognition seems ludicrous.

Conclusion

This paper is at its best when it goes over the history of AI, and to some extent when it discusses Fallacy 3 ("The lure of wishful mnemonics"). I do think that crisp communication, both within the AI research community and the general public, would make conversations about AI policy more productive.

This also applies to the AI Safety community in particular—the fact that the author, a professor of computer science, understood the orthogonality thesis as poorly as they did, speaks to how much more credible AI Safety could become if it had more accessible literature.

The paper is certainly at its worst in Fallacy 4, where it claims that AI is hard by appealing to a "special sauce"-type argument in favor of human cognition. I would not be surprised if human reasoning is more complex than a brain-sized neural network, due to the brain being highly optimized and the body performing additional computation. However, at worst, I think all this implies is that we'll need a bit extra compute to achieve human-level intelligence.

For the well written and sourced AI history content alone, I do recommend you read this paper. Just maybe critically evaluate the author's claims about what progress in AI will look like going forward, because I don't buy many of them.

Discuss

### Moral Privilege

30 апреля, 2021 - 20:03
Published on April 30, 2021 2:54 PM GMT

Imagine, for a moment, living in a society in which toddlers routinely stab infants with knives, and adults routinely smash toddlers with rocks.  You, naturally, are horrified by this state of affairs, and are thus flabbergasted when everybody suddenly notices that toddlers stabbing infants with knives is a bad thing - and pushes for a solution of increasing the rate at which toddlers are smashed with rocks.

You, having come from moral sensibilities that arose in an entirely different place in time in which stabbing infants and smashing toddlers are both considered horrifying, are naturally going to feel pretty conflicted about this.  Yeah, it's great that they've decided that it's a bad thing that toddlers stab infants with knives, but also their solution actually kind of makes everything worse.  They've progressed to a morality more similar to your own, but the increased similarity has, in a sense, made everything worse.

Your friend in this society, whose views you find abhorrent, is puzzled by your reaction.  Why are you so upset?  Sure, you didn't get everything you wanted, but society has progressed, hasn't it?  Shouldn't you support this new initiative, which is, after all, a step in the right direction, the direction you want everything to progress in?  Why are you so insistent that knives be taken away from toddlers, as access to knives is part of every human's natural rights, when there's a much more moral solution that leaves everybody better off and doesn't harm anyone's rights, which is to say, smashing the toddler's heads in with rocks?

Setting aside how contrived this scenario is, I think it basically describes a routine state of affairs in the world: From any given moral perspective, progress in one moral dimension will basically always amount to regression in another, even if it's only the inverse moral position.  We frequently find ourselves biting repugnant bullets, and it's difficult not to be at least a little bit cynical about the state of affairs.

It's hard not to get annoyed at those who never have to make these trade-offs, particularly in situations where they get upset about having to make a trade-off, or acknowledge a moral perspective they don't really care about.  Because the privilege referenced in the title of the post relates to the idea that a majoritarian moral perspective may find itself making no moral trade-offs; that is, a person who agrees with the majority moral perspective on every single issue may find society to be a wonderful place with constant moral progress in the right direction, and more, may find anybody who feels anything less than enthusiasm about the progress of society to be, well, evil.

Where the rest of us find ourselves dealing with people who act like there aren't any trade-offs whatsoever, like their solutions to problems, which happen to maximize all of their values as much as possible, are completely perfect, and we are absurd or evil for questioning the value of those solutions.

In our toddler-smashing society, the radio is full of songs about smashing toddlers' heads in with rocks.  There used to also be songs about toddlers stabbing infants with knives, but as sensibilities have changed, now those songs are getting censored, and removed from the radio.  So now all the songs are about smashing toddlers' heads in with rocks.  Since this society has only two hats, and they've hung one of the hats on the rack, that's all the songs they have; you've lived in this society for a while, and, while you don't like the content of the songs, they are all the music that exists, and you've come to be able to appreciate the artistic qualities of the songs in spite of their lyrical content.  You find yourself objecting to the censorship; they're censoring half the good songs that exist.

Your friend is puzzled why you're annoyed about the censorship - you're against stabbing infants with knives, after all, why are you complaining about it?  All decent people who hear those horrible lyrics are naturally offended, so why  should those songs get any airtime?  Are you secretly for stabbing infants with knives?  Censoring the old infant-stabbing songs is pretty much the only decent thing to do.

And it's difficult to express the problem you have with these arguments, because they're reasonable, in isolation.  The problem is that -all- music is offensive to you, but only -some- music is offensive to them; so you've been forced to get used to hearing offensive views, and so offensiveness has become somewhat tolerable.  They, however, are not used to hearing offensive music - the experience is raw and painful to them.

They have a kind of moral privilege, with respect to you; they never have to experience living in a society full of moral evils, whereas that is your default experience.  Everything exists to cater to them - and they get rid of anything that doesn't.  Their newfound morality, even though it is agreeable in an of itself, has come with some profound losses, losses which are justified entirely on the basis of their own unwillingness to experience anything which they find disagreeable.

Now, we could construct a utilitarian argument for censoring the music in our toddler-smashing society; you're literally the only one who is upset by the loss of the music, the only one who had to put up with disagreeable moral notions in the first place.  But that's somewhat beside the point.

I don't have any particular recommendations here; mostly my purpose is to gesture at, and maybe help other people notice, something which I think encapsulates a common experience, because I think it may be a useful concept to some people.

Discuss

### Mindfulness as debugging

30 апреля, 2021 - 19:59
Published on April 30, 2021 4:59 PM GMT

Are mindfulness practices just attempts to reverse-engineer our brain’s compression algorithms?

I was recently thinking about one cool way we could conceptualize various mindfulness practices from a cognitive science and information theory perspective. Here I am referring to mindfulness practices in the broadest sense. So Tai Chi (or other martial arts of even dance practices) can be seen as mindfulness of physical movement: rather than just making a step, I look into what muscles I need to contract and where to shift my weight in order to make that step. “Consent Culture” could be another one: rather than just initiating physical contact with a partner, I first ask myself what precise contact I would really enjoy, and then explicitly ask them if it aligns with their desires. Similarly we can view practices like “Nonviolent Communication” — as mindfulness of emotions and needs behind the things I say; or “Street Epistemology” — as mindfulness of the beliefs I hold and the evidence that led me to them. And, of course, meditation — as mindfulness of the processes that turn my raw sensations into the qualia of my experiences.

The emerging pattern may thus be that all mindfulness practices are about unpacking and examining our habitual patterns of experiencing the world. To be more precise, by “habitual patterns” I am here referring to the amazing ways that our brain learns to make sense of the vast amounts of data constantly streaming in through our various senses. This complex task of compressing sensory information by finding patterns and forming concepts is crucial for our survival and adequate existence in our world (see my last post about this). From the day we are born, our brain is hard at work looking for the most accurate and efficient compression algorithms. Even cultural heritage may be seen as especially effective compression heuristics that are being passed down through generations.

Nonetheless, any compression algorithm is necessarily inaccurate in some ways — and may thus mislead us in atypical, delicate, or complex situations. In order to have some way to “debug” these algorithms when that happens, we need to have some sense of how they function — and this is where mindfulness comes in! In these practices, we pick one particular habitual compression pattern (e.g., for Tai Chi, this would be physical movement), we setup a safe practice container (e.g., matts or grass to soften falls, certain rules around not moving too fast or punching too hard), and then try to unpack our usual actions by doing everything much more slowly and explicitly, being aware of every detail of the process. For another example, we can view Street Epistemology as unpacking our habitual pattern of beliefs, by creating a safe container of non-judging unbiased listening and constructive inquiry, and slowly walking through the various factors our beliefs have been constructed of.

We typically also check our awareness by seeing if we can change the habitual pattern in one way or another — though I think this control must really be seen as a test of awareness, and not a goal in itself (otherwise we merely create new habitual patterns and start labeling them as “better” than others). Another delicate issue here is to confuse the practice with life: outside the practice container, it may not be safe or practical to do things as explicitly or as slowly as during practice. Our compression algorithms have evolved for a good reason, and are still quite useful and reliable in many situations — we just need to notice when they start failing, and then be able to shift them.

I think this perspective helps to put mindfulness practices in their right place in the grand scheme of our lives, and to highlight what they are and what they are not. They are a tool to unpack and re-examine any habitual patterns we rely on. With practice, we can go deep enough as to see our fundamental patterns of perceiving the world and of finding joy in our lives. Mindfulness is not, however, a recipe for a new pattern. As there cannot exist a lossless compression algorithm that will work in all situations, so it is futile (and boring) to try formulating a fail-safe habitual pattern for life in our complex world.

How useful / accurate do you think this perspective on mindfulness is? Any ideas for further consequences of this thinking?

[cross-posted from my blog https://medium.com/bs3]

Discuss