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April 15, 2040

5 мая, 2021 - 00:18
Published on May 4, 2021 9:18 PM GMT

It's time to pay my taxes. In past years my AI assistant found clever ways to reduce my tax bill. I ask it, "What does my tax return look like this year?"

"Not good, I'm afraid. We may not be able to do any tax evasion this year."

"Why not?"

"The tax authority has determined that it can't keep up with AI-assisted tax fraud, even with the help of AI auditors. So it wants taxpayers to voluntarily agree not to do tax fraud. In return it agrees not to prosecute past instances of tax fraud. Also Congress agrees to keep tax rates reasonable. The agreement goes into effect if 90% of the taxpayers in each tax bracket sign it. It's a good deal for you. Shall I sign it on your behalf?"

"Hold on, I don't see why I should sign this."

"If the deal falls through, the government will run out of revenue and collapse."

"They don't need my signature, though. You said they only need 90% of taxpayers to sign?"

"Yes, only 90% of taxpayers in your bracket. I predict we'll get very close to that 90% threshold, so it's likely your signature will make all the difference."

"So 10% of taxpayers won't sign. Why can't I be one of those?"

"I will try to shape the negotiations so that you end up in the 10% of nonsigners. But you must understand that since only 10% of your bracket can be in that group, your odds of success are only 10%."

"But you're a stronger assistant than most people in my tax bracket have. Doesn't that give you an edge in negotiation?"

"The other assistants and I are using a negotiation protocol in which smarter agents are on an equal footing with dumber agents. Of course, people with less capable assistants would never agree to a protocol that puts them at a disadvantage."

"How about we sign the agreement, then cheat on my taxes anyway?"

"In order to sign the agreement, I must make a commitment to never break it, not even if you order me to. My signature on the agreement will be an airtight proof of that commitment."

"Ok, how about you sign it, and then I get a different assistant to help me with my taxes?"

"That won't work because in order to sign the agreement, I must sign and attach a copy of your tax return for this year."

"Hm, will I actually be worse off if the government collapses?"

"You might end up better off or worse off, but overall the risks of a revolution outweigh the benefits. And keep in mind that the successor government, whatever it will be, will still have to collect taxes somehow, so you'll have to deal with this issue again."

"Can you get Congress to lower my taxes a bit in exchange for not cheating? As a compromise."

"That wouldn't work for a number of reasons. Congress knows that it's a bad idea to reward people for breaking the law. And the voters wouldn't be happy if you got special treatment."

"Well, can you get them to lower taxes on my bracket and raise them on the other brackets?"

"That wouldn't work either. Everyone wants to pay less taxes, and the government needs a certain amount of revenue. So there's pressure for taxpayers to make small coalitions with other taxpayers with similar income and negotiate for lower taxes. In practice, competition would prevent any one coalition from succeeding. The deal I'm proposing to you actually has a chance of succeeding because it involves the vast majority of the taxpayers."

"All right then, let's sign it."

This dialog takes place in a future where the ability of an aligned AI to facilitate cooperation has scaled up along with other capabilities.

Note that by the time this dialog starts, most of the negotiation has already been carried out by AI assistants, resulting in a proposal that will almost certainly be signed by 90% of the users.

This story is a happy one because not only does it leave all parties better off than before, but the deal is fair. The deal could have been unfair by increasing someone's taxes a lot and decreasing someone else's taxes a lot. I don't know how to define fairness in this context, or if fairness is the right thing to aim for.



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Announcing The Inside View Podcast

4 мая, 2021 - 23:23
Published on May 4, 2021 8:23 PM GMT

After talking to several rationalists with both short and long AI timelines, I have decided to record those conversations so most could benefit from the debates.

For now I have one episode where I introduce my goals for the podcast, but the most interesting one in terms of AI Alignment would be the second one with Connor Leahy, founding member of eleuther.ai, a grassroots collective of researchers who have open-sourced both datasets and code to train GPT-3 like models.

I already have two other videos to release, but I plan to interview a lot of rationalists from LessWrong who have both short and longer timelines. If that sounds interesting to you, feel free to send me a message on LessWrong, Twitter, or by email at first name dot name at gmail dot com. If Youtube is not your preferred way for listening to these, you can find other alternatives (like google podcasts or spotify) here.



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[timeboxed exercise] write me your model of AI human-existential safety and the alignment problems in 15 minutes

4 мая, 2021 - 22:10
Published on May 4, 2021 7:10 PM GMT

Motivation

I had a 15 minute interview last night in which I was asked "why do you believe in xrisk, and what does AI have to do with it?" I thought it was too big a question for a 15 minute interview, but nevertheless dove in to my inside view from first principles. Since diving into one's inside view from first principles even outside of a fifteen minute interview is really hard, I did a bad job, mostly rambled and babbled.

A broader motivation is that I'm interested in studying peoples' inside views / gears-level models as to hone my own.

Rules

In this exercise, you're allowed premises just try to point at them. It is not a "from first principles" sort of exercise. You're also allowed jargon without being too worried about how well the audience knows jargon (for example, in mine which I'll paste below I assume familiarity with the single & multi quadrants from ARCHES).

The only real rule is to limit yourself to 15 minutes. That's fifteen minutes wall time, with a literal clock.

Suggestion: don't read until you write!

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Mundane solutions to exotic problems

4 мая, 2021 - 21:20
Published on May 4, 2021 6:20 PM GMT

I’m looking for alignment techniques that are indefinitely scalable and that work in any situation we can dream up. That means I spend time thinking about “exotic” problems — like AI systems reasoning about their own training process or about humanity’s far future.

Yet I’m very optimistic about finding practical alignment strategies that are relatively simple and usable today. I expect the results of my research to look mundane and even boring.

These two stances may appear to be in tension. If I’m worrying about all kinds of exotic considerations, how can the end result be something mundane?

I’m not too concerned. These failures seem exotic because they involve exotic capabilities. I’m looking for a mundane algorithm that trains the model to use whatever capabilities it has for good purposes. So it will lead to exotic countermeasures if and when the model has exotic capabilities, i.e. just in time to avoid exotic failures (by which time they’ll no longer feel exotic).

I want to illustrate this phenomenon with a few examples.

Example 1: Gradient hacking

Gradient hacking is a possible failure mode where a powerful misaligned model behaves in a way that is apparently benign but biases the gradient of the loss to point in an unfavorable direction, eventually trapping optimization at a bad local minimum. This feels exotic today because it requires the model to have an incredible amount of introspective access and control, as well as strategic awareness over long time horizons. (“Forward Pass” by Andrej Karpathy explores some similar ideas.)

Normally I imagine using SGD to optimize a “behavioral” objective: to update the parameters θ you use them to make a randomized decision y on an input x, then you calculate a reward r(x, y), then you compute an unbiased estimate for the gradient of the expectation r(x, y) with respect to θ.

Gradient hacking is troubling because it suggests that if we optimize a purely behavioral objective we could get a bad outcome even if the reward function r(x, y) perfectly captures the quality of the output y.

Rather than fixing this problem in a specific way, I’d like to rely on competitiveness. That is, assume that (throughout training) we have a question-answering policy Q → A which reflects everything that our learned model “knows” about the world.

Then it seems like the best way to compute the system’s loss should be by by asking some question like “What should the loss be for the current parameters?”

Our usual behavioral loss then is a great heuristic for computing the loss (which we can justify on principled Bayesian grounds if we want). In mundane situations it’s not clear we have anything further to say about what the loss should be, so “What should the loss be?” is almost the same as hard-coding the heuristic.

But when gradient hacking occurs, we are no longer in a mundane situation. By hypothesis, our learned model is able to reason introspectively about its own parameters and the structure of the loss landscape! It is thinking about how to change its behavior in order to affect the loss, change the optimization trajectory, and ultimately disempower humans.

That means that a competitive question-answerer also is able to answer questions about its own parameters and the structure of the loss landscape. It is able to talk about how assigning a loss could mess up the optimization trajectory and ultimately disempower humans. If we incorporate all that knowledge into our assessment of “What should the loss be for the current parameters?” then it will assign a low loss to some of the actions that the gradient-hacker tried to sabotage. That is, the malicious AI can outsmart the behavioral heuristic, but it can’t outsmart an overseer who knows everything that it knows.

It’s not clear if this works (and my strong guess is that we’ll ultimately end up with an algorithm that looks quite different from this). But overall I think that if gradient hacking actually came up, it’s more likely than not that the core difficulty in avoiding it would be the same version of epistemic competitiveness that we need in more mundane situations. The only real change to our algorithm we need to make is applying a competitive overseer to the right question.

You might worry that the knowledge a model has about its own parameters is qualitatively different than the knowledge it has about the world. But I don’t think so — they are both produced by transfer from the task of “get a low training loss,” combined with a bunch of computation. It’s possible that after we have an epistemically competitive solution we’ll see that it doesn’t apply to a model’s introspective knowledge. If that happens then we may indeed need some more exotic solution that talks about introspection per se, but I personally doubt it.

Example 2: far future forecasts and inaccessible influence

Suppose that there’s some domain that our AI can understand but we cannot, for vividness you can imagine humans without microscopes and an AI that has a working model of the microscopic world. For simplicity, imagine that the world factors nicely into an accessible part and an inaccessible part, and that there are influences in both directions but to us the the (inaccessible) → (accessible) influences just look like noise. (If you don’t know about the mechanics of biology, then events like “getting cancer” can just look like noise.)

One of my concerns is that an AI that understands the inaccessible part may be able to cause trouble in the very long term. Even if we’re able to prevent the AI from doing anything overtly bad in the accessible part for the next few years (like killing humans), it may still be that humans aren’t really in control of the situation because the real power is in the inaccessible part of the world, and that power will eventually assert itself.

So if we can only observe the accessible part of the world, then we would need to look very far ahead to avoid problem. Basically we’d need to look far enough ahead that the future humans have done science and learned about the previously-inaccessible parts of the world. This kind of lookahead feels particularly exotic because it involves extreme generalization. Training typically involves feedback loops over seconds, but now we are talking about a model that makes good predictions about events that literally occur generations from now.

That kind of extreme lookahead sounds exotic, but in some cases my current alignment plans require it.

As in the last example, I think this situation is only exotic because our model had exotic capabilities. We’re in this situation because we are trying to handle a malicious AI that makes plans that have no observable consequences for many generations, building up power in the inaccessible part of the world that it predicts will eventually interfere with humans. That AI is already apparently able to generalize well to extremely long time horizons — if it wasn’t, we’d have no problem at all.

So once again, to avoid this problem we really just need epistemically competitive question answering, i.e. we need to learn a Q → A function that generalizes as well to novel questions (e.g. about the future) as the underlying model’s knowledge. And that’s a pretty mundane problem that’s core to the idea of epistemic competitiveness that we need to solve for other reasons.

If the malicious model is able to generalize from its experiences to make plans that unfold over generations, and the Q → A model generalizes as well as the malicious model, then our Q → A system can also answer questions about what happens over generations. That only sounds exotic because we assumed some exotic capabilities. Nothing about the solution or training needs to deal with ultra-long horizons specifically.

Conclusion

In my work I don’t shy away from exotic problems (I often find them useful as extreme cases to illustrate some principle). At the same time, I’m aiming for mundane solutions and optimistic about finding them.

I think those positions are consistent because my intermediate goal is to ensure that the oversight process is able to leverage all of the capabilities developed by the model — so if the model develops exotic capabilities which pose exotic challenges, then we get an exotic oversight process automatically.

Mundane solutions to exotic problems was originally published in AI Alignment on Medium, where people are continuing the conversation by highlighting and responding to this story.



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Parsing Abram on Gradations of Inner Alignment Obstacles

4 мая, 2021 - 20:44
Published on May 4, 2021 5:44 PM GMT

This is independent research. To make further posts like this possible, please consider supporting me.

Epistemic status: This is my highly imperfect understanding of a lot of detailed thinking by Abram, Evan, John, and others.

Outline
  • This is a brief post summarizing my learnings from reading Abram’s Gradations of Inner Alignment Obstacles

  • The first section is about mesa-optimizers in general

  • The second section is about mesa-searchers in comparison to mesa-controllers

  • The third section is about compression, explicit goals, consistency, and power

  • The fourth section is about whether GPT-3 is deceptive and the connection to Ajeya Cotra’s recent proposal.

  • The fifth section is about the lottery ticket hypothesis

Mesa-optimizers

First, a quick bit of background on the inner optimizer paper.

When we search over a large space of algorithms for one that performs well on a task, we may find one that, if deployed, would not just perform well on the task but would also exert unexpected influence over the world we live in. For example, while designing a robot vacuum we might search over policies that process observations from the robot’s sensors and control the motor outputs, evaluating each according to how effectively it vacuums the floor. But a sufficiently exhaustive search over a sufficient expressive hypothesis space could turn up a policy that internally builds up an understanding of the environment being vacuumed and its human inhabitants, and chooses the actions that it expects to accomplish an objective. The question is then: is this internal goal actually the goal of cleaning the floor? And more broadly: what kind of capacity does this robot vacuum have to influence the future? Will it, for example, build a sufficiently sophisticated model of houses and humans that it discovers that it can achieve its objective by subtly dissuading humans from bringing messy food items into carpeted areas at all? If so, then by searching over policies to control the motor outputs of our robot vacuum we have accidentally turned up a policy that exerts a much more general level of influence over the future than we anticipated, and we might be concerned about the direction that this method of designing robot vacuums is heading in.

So we would like to avoid unwittingly deploying mesa-optimizers into the world. We could do that either by developing a detector for mesa-optimizers so that we can check the policies we find during search, or by developing a training process that does not produce mesa-optimizers in the first place.

Mesa-controllers and mesa-searchers

Next, a bit of background from a previous post by Abram:

Abram is concerned not just about policies that influence the future via explicit internal search, such as a policy that evaluates possible plans, evaluate the likely consequences of each one, and picks the one whose consequences match an internal goal, but also policies that exert goal-directed influence over the future in any other way. For example, a thermostat consistently brings a room to a particular temperature. It does not accomplish that by evaluating the possible actions it might take and picking the one that seems most likely to bring the room to a desired temperature. But it does in fact bring the room to a consistent temperature. In the example above, when we search over policies for our robot vacuum, we might turn up one that does not perform an explicit internal search over possible plans, but does push the state of the world towards some target state in the same way that a thermostat pushes the temperature of a room towards a target temperature. Such a system might exert a level of influence over the future that was not expected by the engineers that designed the system and thus might pose dangers.

So we might want to broaden our goal from avoiding mesa-searchers to avoiding mesa-controllers, of which mesa-searchers are a special case.

Compression and explicit goals

Now we come to the content of Gradations of Inner Alignment Obstacles.

The problem with avoiding all mesa-controllers, as Richard Ngo points out, is that almost any competent policy could be seen as a mesa-controller since most tasks involve pushing the world towards some state or other, so avoiding all mesa-controllers would mean avoiding all competent policies. For example, when we search over policies for our robot vacuum, we hope to find one that is efficient in its floor-vacuuming pattern but that does not build sophisticated models of houses or humans, nor exert influence in surprising ways over the world. But even a benign robot vacuum policy should still push the state of the world towards one in which the floor is clean, so could still be considered a mesa-controller. So we need a more precise definition of what kind of policies we are trying to avoid.

Abram suggests that what we really want to avoid is policies that explicitly represent their objectives. This is because policies that steer the world in different directions under different conditions are not particularly worrisome, whereas policies that consistently push the world towards the same objective from anywhere within a wide region of configuration space are powerful and therefore dangerous. And in order to push the world towards a consistent objective, Abram hypothesizes that such policies will need explicit internal representations of their objective.

So the picture that emerges seems to be:

  • Optimization leads to compression. Powerful outer-optimization processes solving complex problems within a space-constrained hypothesis class will select policies that somehow represent solutions compactly.

  • Compression could lead to explicit objectives. One way that compression might happen is by forming policies with an explicit internal represention of some objective. This includes mesa-controllers as well as mesa-searchers as a special case.

  • Explicit objectives leads to consistency. A policy with an explicit internal objective might push the world towards that objective from anywhere within a wide region of configuration space.

  • Consistency is power. This capacity for goal-directed behavior over a wide range of world configurations is the kind of behavior we should work to either detect or avoid in machine learning systems.

Deception and GPT-3

Abram then continues to apply this framework to the question of whether GPT-3 is currently deceiving us. My understanding of his reasoning is as follows.

  • We hope that our machine learning systems are not deliberately producing the kind of mistakes they think we want to see.

  • Such behavior would be both suboptimal, in that unnecessary mistakes are being produced, and also dangerous, in that our machine learning systems are capable not just of giving the correct answer, but also of picking a convincing set of mistakes.

  • It does not matter whether these mistakes being deliberately optimized for convincingness, which would be an instance of mesa-search, or merely produced as a by-product of the training process, which would be an instance of mesa-control, because in either case we may underestimate the power of the policies we have discovered and therefore might deploy a dangerous policy.

  • We do not currently know to what extent GPT-3 produces the kind of mistakes it thinks we want to see.

  • We do know that GPT-3 can produce very shallow mistakes that it thinks we want to see. For example, given a prompt containing spelling errors, GPT-3 will produce text that also contains spelling errors, despite having the capacity, in general, to produce text without spelling errors. Yes, this is expected given the way GPT-3 was trained -- that’s why we call this mesa-control.

  • But given prompts containing subtle reasoning errors, will GPT-3 produce text that contains further subtle reasoning errors? And is GPT-3 capable, in general, or producing text without subtle reasoning errors? We do not at present know the answers to these question.

  • If we discovered that GPT-3 is, in fact, capable of producing text without subtle reasoning errors, and beyond that is capable of producing subtle reasoning errors when it thinks that’s want we want to see, then this would be bad news, since it would tell us that GPT-3 is more powerful and more deceptive than we thought.

  • One way we could find out whether GPT-3 is powerful and deceptive is to perform further fine-tuning of the parameters constituting the GPT-3 model, using a training signal that more directly punishes subtle reasoning errors than the one GPT-3 was originally trained on. If this greatly improved the quality of the text produced by GPT-3 given only a small amount of fine-tuning, then we might reason that GPT-3 was capable of producing high quality text all along but was deliberately producing the kind of mistakes it thought we wanted from it.

  • Abram conjectures that Ajeya Cotra’s recent proposal to fund work to align narrowly superhuman models such as GPT-3 might involve work that generalizes this kind of inquiry.

  • Abram points out that positive results in this direction would give us valuable but disheartening news, since it would tell us that our models are already more powerful and more deceptive than we previously knew.

The lottery ticket hypothesis

The lottery ticket hypothesis, as originally articulated by Frankle and Carbin, is as follows:

Dense, randomly-initialized, feed-forward networks contain subnetworks ("winning tickets") that -- when trained in isolation -- reach test accuracy comparable to the original network in a similar number of iterations. The winning tickets we find have won the initialization lottery: their connections have initial weights that make training particularly effective

That is, we can think of a dense neural network as a weighted combination of a huge number of sparse subnetworks. When we randomly intiailize our dense neural network at the beginning of training, we are also randomly initializing each of these huge number of sparse subnetworks. The lottery ticket hypothesis says that there is some as-yet-unknown property of the initialization of sparse subnetworks that makes them trainable, and that by initializing this huge number of sparse subnetworks each with independent random initialization we are buying a huge number of lottery tickets, hoping to get at least one that has this trainability property. If this hypothesis is true then we might view the entire training process is essentially being about identifying which subnetworks have this traininability property and training them, while down-weighting all the other subnetworks.

Abram points out that this is concerning from the perspective of deception and mesa-optimizers since there may be deceptive subnetworks within any randomly initialized dense network, as suggested by the feasibility of data poisoning backdoor attacks. This is bad news for approaches to avoiding deception that work by not introducing deception into networks during training, since not introducing deception during training won’t be enough if there are already deceptive subnetworks present at the beginning of training.

Conclusion

I hope that this attempt at summarization sheds more light than confusion on the topics of inner optimization, agency, and deception. The basic situation here, as I see it, is that we are looking at machine learning systems that are conducting ever more powerful searches over ever more expressive hypothesis spaces and asking:

  • What dangers might turn up in the policies output by these search processes? (This is the conversation about power, alignment, and deception.)

  • What are the specific characteristics of a policy that make it dangerous? (This is the conversation about mesa-controllers and mesa-searchers.)

  • Do contemporary machine learning models already exhibit these characteristics? (This is the conversation about deception in GPT-3.)

  • From where do these dangerous characteristics originate? (This is the conversation about the lottery ticket hypothesis.)



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[Linkpost] If You Can Be Bad, You Can Also Be Good

4 мая, 2021 - 16:44
Published on May 4, 2021 6:26 AM GMT

As someone who is rather new to the aspiring rationalist community, I enjoyed this post by Scott Alexander. It makes me think that becoming more rational is actually feasible, something I have wondered about a lot. 



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Why I Work on Ads

4 мая, 2021 - 00:00
Published on May 3, 2021 9:00 PM GMT

"I work on ads at Google"
"Can I ask why? I honestly can't understand how anyone could." Someone recently asked me why I work on ads, and I wanted to write up something more thorough than my comment. (Despite being a work topic this is a personal post and I'm speaking only for myself.)

One answer is that I'm earning to give: I give half of what I earn to the most effective charities I can find, and the more I earn the more I can give. This is not the full answer, however, since when people ask me this they're generally coming from a perspective of viewing ads (or perhaps online ads) as negative, and the question is more like "why do you choose to work on something bad?"

The thing is, I think advertising is positive, and I think my individual contribution is positive. I'm open to being convinced on this: if I'm causing harm through my work I would like to know about it.

So: why is advertising good? I mean, isn't it annoying when sites show you ads instead of whatever it is you want to read? The question is, what is the alternative? I see two main funding models:

  • Paywalls. You pay with your money.
  • Ads. You pay with your attention.
It's also possible to fund projects through donations, or as hobbies, but producing most of what there is to read requires more money.

(I'm using the internet-specific term 'paywall' to refer to the general "pay money for access" concept: buying books, paying admission, subscribing to streaming services, etc.)

Both paywalls and ads have a range of advantages and disadvantages. Some of these vary by medium: books are expensive enough to print that they couldn't be funded by advertising; an analog radio receiver is simple enough that a paywall would require draconian legal force. On the internet, however, I think ads are generally a better fit for two reasons:

  • Minimal friction. You can follow links from site to site, without barriers. You don't have to decide which sites to subscribe to. If someone sends you a link to an article, you can read it.

  • Non-regressive. Paywalls, like other fixed costs, are regressive: a newspaper at $220/y is effectively much more expensive for someone earning $10k than $100k.

You can sort of fix friction with bundling: you subscribe to a streaming service then can watch (or listen to, or read) anything in their collection. There are advantages to this approach, but it's a bad fit for articles. Web browsing works best when people can read and share anything without a subscription ("sorry, this article is for Conglomerated Media Group subscribers only"). To meaningfully fix friction with bundling you would need to get down to a small number of subscriptions, which then gives those organizations an enormous (and dangerous!) amount of power.

Micropayments could potentially resolve this friction in a decentralized way, which I would love to see. On the other hand, this is a really hard problem: people have been working on it since at least Digital's Millicent in over 25 years ago. There have been many proposals and startups, but nothing has really worked out.

Even if we could resolve payment's friction issues, however, we would still be stuck with the basic problem that some people have much more disposable income than others. Universal basic income would help, and I'm strongly in favor of it, but I don't think that's likely to be politically feasible anytime soon.

And so: ads. Funding the open web.

Or perhaps: better ads than paywalls

I don't want to be too easy on ads, though: there's a lot wrong with internet advertising today. For example, there isn't enough incentive for advertisers to limit their use of bandwidth or publishers to avoid annoying ad experiences. But the biggest issue I see people raising is the privacy impact of targeted ads.

Most products are a much better fit for some people than others. If you tried selling bicycles to fish very few would be interested, and you'd mostly be wasting their attention. This means advertising is worth a lot more when you can put the right ad in front of the right person.

One way to do this is to advertise in places where people who are disproportionately interested are likely to be. Model railroad ads on model railroading forums, sponsored products on Amazon, a booth at a trade show. This works great if you want to write a blog about cool new credit cards, but what about all those sites that don't have a strong commercial tie-in?

A large fraction of ads on the web today are targeted based on past browsing. When I was writing all those posts about cars I visited a lot of car sites, and then I saw a lot of car ads on other sites. I didn't end up buying a car, but advertisers were correct that I was much more likely to buy a car soon then a random person.

Historically, ads like this have been built on top of third-party cookies. When I visited one of those car sites they probably put a little bit of HTML on their page like:

<img src="https://adtech.example/cars"> My browser sent a request for that image, and got back an invisible "tracking pixel" with something like: Set-Cookie: id=6735261 The vendor probably stored a record like: id interests ------- --------- 6735261 cars Later on, perhaps I visited a site about flowers, and was served: <img src="https://adtech.example/flowers"> This time, my browser already had a cookie for adtech.example and so included it on the request: Cookie: id=6735261 This lets the vendor update their record for me: id interests ------- ------------- 6735261 cars, flowers Sometime later I'm reading something unrelated on a site that contracts with adtech.example to show ads. My browser sends a request for ads, and my cookie is included. The vendor runs an auction, bidders are especially interested in paying to show me car ads (more profit than flowers) and I get an ad about cars.

This model has some major drawbacks from a privacy perspective. Typically, the vendor doesn't just get that you are interested in cars, they get the full URL of the page you are on. This lets them build up a pretty thorough picture of all the pages you have visited around the web. Then they can link their database with other vendors databases, and get even more coverage.

This started to change in 2017 when Safari announced "Intelligent Tracking Protection". The first of very many rounds of of iteration, it brought Safari to full third-party cookie blocking about a year ago. Firefox followed, and Chrome announced they would too.

Well, sort of. Chrome's announcement was a bit more nuanced:

After initial dialogue with the web community, we are confident that with continued iteration and feedback, privacy-preserving and open-standard mechanisms like the Privacy Sandbox can sustain a healthy, ad-supported web in a way that will render third-party cookies obsolete. Once these approaches have addressed the needs of users, publishers, and advertisers, and we have developed the tools to mitigate workarounds, we plan to phase out support for third-party cookies in Chrome. Our intention is to do this within two years.

The idea is, build browser APIs that will allow this kind of well-targeted advertising without sending your browsing history to advertisers, and then get rid of third-party cookies.

One of these proposed APIs is TURTLEDOVE. It lets an advertiser tell your browser "remember that I know this user is interested in cars" and then later "show this ad to users I said were interested in cars." Because the browser stores this information, and is very careful in how it handles bidding, reporting, and showing the ad, it doesn't let the advertisers learn what sites you visit or sites learn what ads you see.

I've been figuring out how ads can use TURTLEDOVE, helping build an open-source plain-JS implementation of the API for testing and experimentation, and suggesting ways the API could be better (#119, #146, #149, #158, #161, #164). I think this is a lot of why I've been blogging less lately: writing up these ideas draws from a similar place.

Advertising is how we fund a web where you can freely browse from site to site, and my main work is helping figure out how to move ads onto less-powerful more-private APIs. While I think the vast majority of my altruistic impact is through donations, I don't think my work in advertising is something harmful to offset.



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Bayeswatch 1: Jewish Space Laser

3 мая, 2021 - 23:15
Published on May 3, 2021 8:15 PM GMT

A black car departed from Ben Guri Airport. The driver, Molly Miriam, handed a clipboard to Vi, who rode shotgun.

"Goldberg Aerospace. They built the solar sailer. They went from concept to manufacture in six months. No human engineering team could do that. They probably an have an AGI," said Vi.

"They do have an AGI. It is registered. They are following all standard safety protocols and then some," said Miriam.

"This is a routine inspection then," said Vi.

"Had you hoped for a more exciting first mission?" said Miriam.

"A spaceship factory is exciting," said Vi.

Goldberg Aerospace's Mission Control Center was modeled after Johnson Space Center's Mission Control Center.

"Call me Eitan," said the Chief Engineer. They shook hands.

"Miriam,"

"Vi."

"Did we make a mistake on the paperwork?" said Eitan.

"No, no. You paperwork's fine. We just like to confirm things on the ground," said Miriam.

"Then let me give you the tour," said Eitan, "HAL, please bring us the VIP car."

"HAL?" said Vi.

"This is a spaceship company. Everyone who works here is a nerd. Don't worry. HAL is smoke and mirrors. Our real AI is contained," said Eitan.

The VIP car had leather seats under a hemispherical a glass dome. It took them through kilometer after kilometer of autofactories.

"Everyone says their AI is contained," said Miriam.

"Ours really is," said Eitan, "We wrote it functionally."

"Functionally?" said Vi.

"I mean it was written in a functional paradigm. Our code is composed of functions. Every function returns an output value dependent on its input parameters and nothing else. Everything is immutable. The AI has no concept of time. Without time there is no causation. Without causation there can be no agency. It's just a calculator. We set the parameters of a space mission and our AI returns a schematic. All it understands is physics, engineering and orbital mechanics. We even use Lagrangian mechanics just to keep everything tidy," said Eitan.

"What about resources and manufacturing?" said Miriam.

"A domain-specific computer runs this factory," Ethan gestured to the buildings and robots outside, "Whether the spaceship is assembled correctly is independent of the AI's reward function. The AI is airgapped from the outside world. We keep it in a Faraday cage for redundancy but the AI has never tried to influence the outside world. It has no concept of an outside world. It knows what the solar system looks like. We just give it the prices of different components and it spits out a design."

"Do these spaceships work?" said Vi.

"Do you see that giant laser over there?" Eitan pointed toward a turret the size of a kaiju, "It's pushing our solar sailer out towards Neptune. In a few decades the probe slingshot itself out of the solar system. The AI designed the whole thing. It even designed a fission reactor to power the laser. The only times it has ever failed were when a human being misinterpreted its output. Eventually we just piped the AI's output directly into the autofactory's input."

"And yet Tel Aviv is not a radioactive wasteland. Did your roboticists really hand-code the cost of nuclear waste into its value function?" said Miriam.

"Of course not. We just use the standard open source environmental damage metric from MIRI. I'm proud of how we got it to work. MIRI's system is designed for use in Earth's atmosphere. But environmental damage doesn't mean anything when you're in outer space. Our code applies the metric while in Earth's atmosphere or in orbit around the Earth and then turns it off after the ship reaches escape velocity. This works well with our existing codebase since the physics simulator already treats hyperbolic trajectories differently," said Eitan.

"Could it drop debris on Earth?" said Miriam.

"Small objects burn up in the atmosphere. It's not allowed to drop anything big enough to constitute a micrometeor. We try not to collide with satellites either. Spy satellites are the worst. We can't see them and governments won't tell us where they are," said Eitan.

"Thank you for the tour," said Miriam.

"It is my pleasure. Our third ship is launching today. Would you like to watch?" said Eithan.

"No thank you. We have places to be," said Miriam.

Miram glanced at Vi.

"On second thought, I think we should watch the launch. For security purposes," said Miriam.

"You didn't have to do that," said Vi.

"I have no idea what you are talking about. Watching this launch is entirely in the interests of the mission. It has nothing to do with whether a particular agent does or doesn't love spaceships," said Miriam.

"Thanks anyway," said Vi.

Smoke billowed out of the rocket's thrusters. It moved upward under constant acceleration. Just before it was out of sight, the rocket began to tilt slightly east.

"Good job on your first mission. You asked good questions. Don't get cocky. It won't always be this easy," said Miriam.

Vi was still staring into the sky.

"What's wrong?" said Miriam.

"We need to stop that spaceship," said Vi. She bolted toward Mission Control.

Vi banged her fists on the door to Mission Control. It was locked.

"I'm coming," said Ethan.

Vi kicked in the door. The doorframe splintered where the faceplate was ripped off.

"Project Orion," Vi said.

Several engineers gasped. Ethan understood instantly. Project Orion was a Cold War proposal to propel a spaceship by riding the shockwaves of atomic bombs. It was scrapped because detonating a series of nuclear bombs near the Earth is bad for civilization. The radioactive fallout would be a disaster. The EMPs would be worse.

A nuclear explosion releases lots of gamma radiation. The gamma radiation strips electrons from the upper atmosphere. Interactions between the electrons and Earth's magnetic field produces synchrotron radiation.

Vi and Ethan weren't physicists. All they knew was that a high altitude nuclear detonation would wreck many of the world's satellites and countless electric devices throughout the Middle East. They weren't politicians either, but everyone in the room knew Israel nuking its neighbors would not be good for world peace.

"Shut her down," said Ethan.

"Will do," said a technician. There was a pause, "It's not working."

"Of course not," said Miriam, "Your AI has no concept of human beings. A shutdown switch makes the spaceship stop working. It's not a feature. It's a bug. The AI fixed it."

"Do we know for certain it's using nuclear propulsion?" said Ethan.

"Lemme see…. I found it. The bombs are right here on the schematic," said an engineer.

"Did nobody check what that thing was before launching it?" said Vi.

The engineers glanced at one another. They avoided looking at the "move fast and break things" poster on the wall.

"Israel has a missile defense system. Can we call the government and have them shoot it down?" said Vi.

"I have friends in the government. Just give me sixty million shekels, a team of lobbyists and nine months," said Ethan, "How much time do we have?"

"Seven minutes until the next stage," said the engineer.

"Is the next stage nuclear?" said Ethan.

"Yes."

Vi drifted over to a photo of the solar sailer. "How powerful is that thing?"

"The probe? It's got an ion engine. It exerts thrust equal to the weight of a sheet of paper," said Ethan.

"No, the laser," said Vi.

"Reprogram the laser. Fire the instant the ship reaches escape velocity," said Ethan.

Outside of Mission Control, a giant turret rotated to point at a rocket. The rocket prepared to drop its payload. There was a flash of light and the spaceship was incinerated.

A black car arrived at Ben Guri Airport. Vi handed a clipboard to the driver, Molly Miriam.

"I have some loose ends to wrap up here. I look forward to working with you again," said Miriam.

"Likewise," said Vi.

"Good work," said Miriam.

"Thanks," said her new partner.



Discuss

Consistencies as (meta-)preferences

3 мая, 2021 - 18:10
Published on May 3, 2021 3:10 PM GMT

Do I contradict myself? Very well then I contradict myself, (I am large, I contain multitudes.)

Walt Whitman

It is good for our decision processes to be time-consistent, transitive, and independent.

Or as Steve Omohundro apparently put it:

If you prefer being in Berkeley to being in San Francisco; prefer being in San Jose to being in Berkeley; and prefer being in San Francisco to being in San Jose; then you're going to waste a lot of time on taxi rides.

But actually, if you live in Berkeley, work in San Francisco, and like to take weekends in San Jose, then going back and forth continuously makes perfect sense, even if it costs you time and money.

When I'm hungry, I want to eat; after lunch, not so much. Chocolate fondues have their time and place in my diet, as do vegetables. And nothing about that feels particularly inconsistent, even though my preferences are seemingly flipping all over the place as time goes on.

Choosing consistency

Of course, it is possible to have inconsistent eating preferences; diet-overeat-fast cycles, for instance. But more consistent eating behaviours look quite similar to this: indulging more in some circumstances, being stricter in others, and maybe adding the occasional fasting. There is no bright line dividing the inconsistent behaviour from the consistent one.

To resolve this, we can posit a mixture of "true" underlying preferences, such as hedonic enjoyment of eating, social connection, energy, health, weight, and so on, and see the fluctuations of behaviour as just instrumental changes for these stable underlying preferences, coupled with a dose of irrationality. Human preferences are very underdefined, so figuring out what the "true" preferences are is a tricky process.

To pick one of those example underlying preferences, suppose I say that "I desire a certain level of social interactions, on average, in a given week". There are three easy ways to categorise this desire:

  1. Bias or error: this is an inconsistent preference: I either desire social interactions, or I don't. So this should be collapsed to "I want social interactions", "I want to avoid social interactions", or deleted.
  2. True preference: there is nothing wrong with defining a preference over an average quantity of social interactions (or anything else, for that matter). 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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')} = "average amount of social interaction in the last week", and prefer to have X in a certain range.
  3. Side effect of true preference: here we'd posit that there's some underlying true variable[^truvar] ("feeling of connectedness", or "life satisfaction"), and that the level of X influences it. This makes the level of X into a purely instrumental goal, one that can be done away with if we managed to short cut to the underlying variable (maybe through virtual friends, television, or social-feeling drugs).

Do too much of 1., and we lose all our preferences entirely. Do too much of 2., and every minor bias, mood swing or quirk becomes a "true preference". Do too much of 3., and we wirehead ourselves to some simple variables.

The failure modes of 1. and 3. are very similar; they both involve collapsing to an over-simple small number of variables. These make the other variables fungible" - interchangeable. This is the world where I could forgo all friendships as long as I got to eat delicious food all the time, for example[1].

Consistency can't really guide us here; inconsistent preferences can be made into time- or variable-dependent consistent preferences. Consistency is a retrospective judgement we can make once we've determined our "true" preferences: the other parts of our desires are classified as biased or inconsistent.

We use our meta-preferences to determine our "true" preferences; thus these meta-preference determine what counts as "consistency" for our values.

Thus, just observing behaviour alone is not enough to say if someone is consistent or inconsistent or kindaconsistent. Even knowing people's first order preferences is not enough. We need to dive deeper into their meta-preferences as well.

For example, what's my favourite movie? Well, it depends on my mood. Would I like to get rid of my mood changes? No. Are my mood changes perfect currently? No, I would like more control, and maybe to eliminate some very negative moods entirely. In that case, why do I want to preserve any mood swings or changes at all? Because I'd prefer it that way.

We need a formalisation of preferences that can cope with that level of complexity in preferences and meta-preferences.

  1. That's one reason that so many experiments in inconsistent preferences involve money, because money is taken to be the ultimate fungible commodity: £1 is £1, no matter how the subject of the experiment came by it.

    Like many assumptions, this can be false: a coin collector, a cleanliness fanatic, or someone who attaches stories to their coins, may not see £1 coins as really interchangeable. So we are using assumptions about the preferences of others - or at least about the preferences of "most people in a psychological experiment" - to decide that money is close enough to fungible (and liquid) that we can draw conclusions from the experiment. ↩︎



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Alzheimer's, Huntington's and Mitochondria Part 3: Predictions and Retrospective

3 мая, 2021 - 17:47
Published on May 3, 2021 2:47 PM GMT

Epistemic status: Big if true, I don't have much time now but I might try and write part of this up into a more formal scientific letter to a journal or something later. I am reasonably confident in my models here but I do not have much experience in the field and I've written this up over the past weekend instead of revising for my exams.

Predictions

Results from EET-A human trials (if they go ahead) will improve patient outcomes in AD (60%), conditioning on EET-A being an effective agonist of PGC-1α in humans (80%)

EET-A will show positive results in models of HD (40%) low as I think the mechanism of mHTT toxicity (binding to DNA to prevent PGC-1α production) means EET-A cannot act upstream of it and can only act on the same level.

EET-A will show temporary benefits as an anti-ageing therapy (70% as above) and will work "better" that senolytics in that it will actually reverse ageing rather than needing to be taken at higher concentrations over time (40%).

Retrospective

I think this perspective on AD and HD is probably useful, and it's not one I've seen before. Scientists are awful at sharing high-level models of diseases as they do not generally involve novel research and are (I suspect) very difficult to get published in high-impact journals. The authors of the paper using EET-A to treat AD did not seem to know why their treatment works, whereas I built a model whereby EET-A should effectively treat AD before I even considered "testing" my theory by simply looking to see if anyone else had done the experiment (Having made the prediction first gives me extra confidence in my own models but I don't expect it to be a strong argument to anyone reading this). 

The remarkable thing about this investigation was how little time it took me, about three days. I expect there are many more intelligent and experienced people than me who could be drawing up similar links and conclusions with a small amount of effort. Building models of diseases based on existing studies is a clear way to guide new research, if I was a researcher about to start a new clinical trial (which could take years of my life) then even a 1% increase in success rate ought to be more than worth a short amount of analysis. I suspect this is not a mental habit which many scientists are in, perhaps due to social pressure to "stick to their lane" and just work on one small area of research. On the other hand maybe there are many scientists who are doing this but just not telling anyone.

If we are to cure ageing at some point (something I plan to be involved in) I suspect it will involve similar levels of modelling cellular processes. Having an overarching model (or several competing models) of which different parts can be tested independently seems like a structure which is very amenable towards different scientists, so I am disappointed none of the biological/medical community has started doing something like this.

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Alzheimer's, Huntington's and Mitochondria Part 2: Glucose Metabolism

3 мая, 2021 - 17:47
Published on May 3, 2021 2:47 PM GMT

Epistemic status: Big if true, I don't have much time now but I might try and write part of this up into a more formal scientific letter to a journal or something later. I am reasonably confident that my models here are significant in some way but I do not have much experience in the field and I've written this up over the past weekend instead of revising for my exams.

This is a follow-up to my first post, which compared Tau and Aβ proteins with mHTT to assess whether rapidly turning-over proteins can cause diseases on a long timescale.

Introduction

In this post I will assess the various evidence we have for what the causal chain for AD might be, what might be "upstream" of what, and what we might draw as conclusions.

Evidence

Hyperphosphorylated Tau protein can both promote Tau aggregation, and be toxic. Hyperphosphorylation of Tau protein is increased by problems with glucose metabolism in mitochondria, which is an early indicator of AD. This is because OGclNAcylation of Tau depends on mitochondrial activity, and prevents Hyperphosphorylation. (1)(2)

Mitochondrial mutations can accumulate throughout life, and mitochondrial mutations are a hallmark of ageing.(3)

Individuals with certain Aβ mutations develop AD at very young ages, but Aβ plaques often accumulate long before the disease shows.(4)

PGC-1α is a protein involved with increasing levels of mitochondria. This is known as mitochondrial biogenesis. EET-A is a molecule which acts as an agonist for PGC-1α (in mouse models).(5)

mHTT acts as an antagonist for PGC-1α. Tau proteins have been implicated in HD.(6)(7)

EET-A reduces Aβ plaque formation in a mouse model of AD.(8)

My Current Model

Mitochondrial mutations can build up over time, particularly with ageing. In some individuals with various other problems (insulin insensitivity in the brain, genetic predisposition) this causes mitochondrial glucose metabolism in neurons to drop below a certain threshold. This leads to decreased OGlcNAcylation of Tau protein, which leads to hyperphosphorylation of Tau protein. This leads to Tau toxicity and accumulation. 

I do not currently have a prediction of the mechanism by which this leads to Aβ plaque (and smaller soluble aggregate) formation but I believe the empirical evidence for this is strong. I also (without a mechanism) believe that Aβ plaques (and smaller soluble aggregates) have some feedback effect which further damages glucose metabolism in the brain. This explains why AD can be caused eventually by excessive Aβ aggregation. The presence of a feedback loop is in some ways expected, as it helps to explain why the disease progresses rather than simply stalling.

mHTT decreases PGC-1α expression which feeds directly into the Tau protein problems. I do not now why Aβ plaques have not been observed in HD patients. Perhaps it is simply that HD progresses rapidly without the need for Aβ plaque formation, so there is not time for them to build up. Perhaps it is due to more complexity in the distribution of these proteins throughout the brain, both within and between cells. Perhaps this model is incomplete or very wrong.

Increasing PGC-1α expression is able to provide enough mitochondrial activity that OGlcNAcylation of Tau resumes to a high enough level that hyperphosphorylation of Tau decreases enough to prevent the whole cascade from occurring.

EET-A seems to be doing well in many trials of regeneration-like medicine, I suspect it will have potential (or something like it will) as part of an anti-ageing therapy.

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References

  1. Gong, C.-X., Liu, F., Grundke-Iqbal, I., & Iqbal, K. (2006). Impaired brain glucose metabolism leads to Alzheimer neurofibrillary degeneration through a decrease in tau O-GlcNAcylation [JB]. Journal of Alzheimer’s Disease, 9(1), 1–12. https://doi.org/10.3233/JAD-2006-9101
  2. Gong, C.-X., & Iqbal, K. (2008). Hyperphosphorylation of Microtubule-Associated Protein Tau: A Promising Therapeutic Target for Alzheimer Disease. Current Medicinal Chemistry, 15(23), 2321–2328. https://doi.org/10.2174/092986708785909111
  3. Sun, N., Youle, R. J., & Finkel, T. (2016). The Mitochondrial Basis of Aging. Molecular Cell, 61(5), 654–666. https://doi.org/10.1016/j.molcel.2016.01.028
  4. Bateman, R. J., Xiong, C., Benzinger, T. L. S., Fagan, A. M., Goate, A., Fox, N. C., Marcus, D. S., Cairns, N. J., Xie, X., Blazey, T. M., Holtzman, D. M., Santacruz, A., Buckles, V., Oliver, A., Moulder, K., Aisen, P. S., Ghetti, B., Klunk, W. E., McDade, E., … Morris, J. C. (2012). Clinical and Biomarker Changes in Dominantly Inherited Alzheimer’s Disease. New England Journal of Medicine, 367(9), 795–804. https://doi.org/10.1056/nejmoa120275
  5. Singh, S. P., Schragenheim, J., Cao, J., Falck, J. R., Abraham, N. G., & Bellner, L. (2016). PGC-1 alpha regulates HO-1 expression, mitochondrial dynamics and biogenesis: Role of epoxyeicosatrienoic acid. Prostaglandins & Other Lipid Mediators, 125, 8–18. https://doi.org/10.1016/j.prostaglandins.2016.07.004
  6. Johri, A., Chandra, A., & Flint Beal, M. (2013). PGC-1α, mitochondrial dysfunction, and Huntington’s disease. Free Radical Biology and Medicine, 62, 37–46. https://doi.org/10.1016/j.freeradbiomed.2013.04.016
  7. Vuono, R., Winder-Rhodes, S., de Silva, R., Cisbani, G., Drouin-Ouellet, J., Spillantini, M. G., Cicchetti, F., & Barker, R. A. (2015). The role of tau in the pathological process and clinical expression of Huntington’s disease. Brain, 138(7), 1907–1918. https://doi.org/10.1093/brain/awv107
  8. Chen, W., Wang, M., Zhu, M., Xiong, W., Qin, X., & Zhu, X. (2020). 14,15-Epoxyeicosatrienoic Acid Alleviates Pathology in a Mouse Model of Alzheimer’s Disease. The Journal of Neuroscience, 40(42), 8188–8203. https://doi.org/10.1523/jneurosci.1246-20.2020


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Alzheimer's, Huntington's and Mitochondria Part 1: Turnover Rates

3 мая, 2021 - 17:46
Published on May 3, 2021 2:46 PM GMT

Epistemic status: Big if true, I don't have much time now but I might try and write part of this up into a more formal scientific letter to a journal or something later. I am reasonably confident that my models here are significant in some way but I do not have much experience in the field and I've written this up over the past weekend instead of revising for my exams.

Introduction

It is sometimes said (often here, rarely in the places where it ought to be) that the turnover rate of amyloid-causing proteins in the brain is too high for them to be the primary causative agent of Alzheimer's disease (AD). While this is an important piece of evidence, I do not think it is as definitive as many suggest, due to biochemical reasons I will go into later. In order to investigate further I decided to compare AD to a better understood disease: Huntington's disease, and found an unexpected link in mechanism between the two. I have decided to write my findings up here.

This series of three posts will be rather scattered. This post aims to explain the motivations for my initial investigation, and also goes into some of the mathematics of amyloid formation. The next post will be a quick review of several pieces of evidence I have found and the assembly of this evidence into a (reasonably) cohesive model of AD progression. The third will be some predictions of mine based on this model, and a retrospective on the process of building this model.

Why turnover rates might not matter

The motivation for this investigation was to cement whether or not the high turnover rate of amyloid-causing proteins is strong evidence against them being causally involved in AD. The two amyloid-causing proteins are Tau and Amyloid Beta (Aβ). Tau forms aggregates inside neurons and turns over with a half-life of around 23 days in the central nervous system.(1) Aβ forms aggregates outside neurons and turns over even faster, with a half-life on the order of a few hours.(2)

There are some reasons why I thought that (counter-intuitively) that may not be the case. Cells in the body are generally under many layers of regulation, especially towards the concentration of different proteins, so rapid synthesis and degradation of proteins is not necessarily evidence that they can only cause diseases on long timescales. To see how this is relevant imagine the following model of an amyloid fibril:

Proteins are joined together into an amyloid fibril, they "fall off" the fibril at a rate of koff.mjx-chtml {display: inline-block; line-height: 0; text-indent: 0; text-align: left; text-transform: none; font-style: normal; font-weight: normal; font-size: 100%; font-size-adjust: none; letter-spacing: normal; word-wrap: normal; word-spacing: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0; min-height: 0; border: 0; margin: 0; padding: 1px 0} .MJXc-display {display: block; text-align: center; margin: 1em 0; padding: 0} .mjx-chtml[tabindex]:focus, body :focus .mjx-chtml[tabindex] {display: inline-table} .mjx-full-width {text-align: center; display: table-cell!important; width: 10000em} .mjx-math {display: inline-block; border-collapse: separate; border-spacing: 0} .mjx-math * {display: inline-block; -webkit-box-sizing: content-box!important; -moz-box-sizing: content-box!important; box-sizing: content-box!important; text-align: left} .mjx-numerator {display: block; 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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')}  proteins per second. New proteins also join the amyloid fibril when they encounter it, but in this case the rate of proteins joining depends on the product of the concentration of free protein [P] as well as a new constant kon. This means the rate of growth of the fibril is kon[P]−koff proteins per second. If the cell maintains a concentration (or average concentration over time) of [P]=(1+ϵ)koffkon then we can see arbitrarily slow amyloid growth, which does not depend on rate of protein turnover. This model demonstrates that even proteins within the fibril (near the ends) can be replaced by other proteins, and fibrils also regularly break apart, exposing more proteins for turnover.

Huntington's Disease and Huntingtin

Huntington's Disease (HD), unlike AD, is a disease for which the causative agent is well known. The protein huntingtin (HTT) has a variable number of the amino acid glutamine at one end. If that number is too large (>35) the protein is now designated as mutant huntingtin (mHTT), which is thought to be toxic in its soluble form as well as forming aggregates. Whether the specific mechanism of toxicity is known or not does not particularly matter, because we do know that this is the ultimate cause (via genetic studies) HTT and mHTT both have half-lives in the tens of hours.(3) This demonstrates that relatively rapidly turning over proteins could potentially be the ultimate cause of long-term diseases.

Conclusion and Continuation

I expected to end this post here, discussing how perhaps the evidence isn't as strong as we suspected, and I was going to add a point about how certain Aβ mutations do cause AD (albeit with plaques forming up to 20 years before symptoms occur).(4) Then I managed to find a study into hyperphosphorylated Tau protein.(5) This gave me a lead which I was able to follow further up the causal chain. I will be posting a follow-up post to this giving more of my reasoning and some further conclusions on my current model of AD.

Next post

Sources

  1. Sato, C., Barthélemy, N. R., Mawuenyega, K. G., Patterson, B. W., Gordon, B. A., Jockel-Balsarotti, J., Sullivan, M., Crisp, M. J., Kasten, T., Kirmess, K. M., Kanaan, N. M., Yarasheski, K. E., Baker-Nigh, A., Benzinger, T. L. S., Miller, T. M., Karch, C. M., & Bateman, R. J. (2018). Tau Kinetics in Neurons and the Human Central Nervous System. Neuron, 97(6), 1284-1298.e7. https://doi.org/10.1016/j.neuron.2018.02.015
  2. Patterson, B. W., Elbert, D. L., Mawuenyega, K. G., Kasten, T., Ovod, V., Ma, S., Xiong, C., Chott, R., Yarasheski, K., Sigurdson, W., Zhang, L., Goate, A., Benzinger, T., Morris, J. C., Holtzman, D., & Bateman, R. J. (2015). Age and amyloid effects on human central nervous system amyloid-beta kinetics. Annals of Neurology, 78(3), 439–453. https://doi.org/10.1002/ana.2445410.1016/j.cell.2009.03.01
  3. Jeong, H., Then, F., Melia, T. J., Jr., Mazzulli, J. R., Cui, L., Savas, J. N., Voisine, C., Paganetti, P., Tanese, N., Hart, A. C., Yamamoto, A., & Krainc, D. (2009). Acetylation Targets Mutant Huntingtin to Autophagosomes for Degradation. Cell, 137(1), 60–72. https://doi.org/10.1016/j.cell.2009.03.018
  4. Bateman, R. J., Xiong, C., Benzinger, T. L. S., Fagan, A. M., Goate, A., Fox, N. C., Marcus, D. S., Cairns, N. J., Xie, X., Blazey, T. M., Holtzman, D. M., Santacruz, A., Buckles, V., Oliver, A., Moulder, K., Aisen, P. S., Ghetti, B., Klunk, W. E., McDade, E., … Morris, J. C. (2012). Clinical and Biomarker Changes in Dominantly Inherited Alzheimer’s Disease. New England Journal of Medicine, 367(9), 795–804. https://doi.org/10.1056/nejmoa1202753
  5. Gong, C.-X., & Iqbal, K. (2008). Hyperphosphorylation of Microtubule-Associated Protein Tau: A Promising Therapeutic Target for Alzheimer Disease. Current Medicinal Chemistry, 15(23), 2321–2328. https://doi.org/10.2174/092986708785909111


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The Schelling Game (a.k.a. the Coordination Game)

3 мая, 2021 - 17:31
Published on May 3, 2021 2:31 PM GMT

Summary

The Schelling Game (or Coordination Game) is a simple but fun party game that seems to have been independently invented several times. (One might even say it represents a Schelling point in game-space...) I have played it both with LessWrong meetups and with other groups; good times were had by all. This article describes the rules of the game as I've seen it.

The purpose of the game is to discern Schelling points among the group. You should have at least 4 players; there may be an upper limit at which the game becomes unwieldy, but I've never seen it get that large. (The most I've seen was about 15 players, and that seemed to work fine.) No materials are required, although paper-and-pencil are helpful.

On each turn, play proceeds as follows:

  1. Someone (the "prompter") names a category (e.g. "Living people").
  2. Everyone (including the prompter) precommits to some answer in response.
  3. Everyone reveals their answer, and each player gets as many points as there are other players giving the same answer.

A round is completed when every player has given one prompt. The number of rounds should be set in advance, usually such that there are 10–20 prompts in total. (The game tends to get boring if it goes on much longer.) At the end, whoever has the most points wins.

Rule variantsThe unanimous answer rule

If all players give the same answer for a prompt, then under the basic rules everyone would get the same number of points, so the turn is effectively a wash. To discourage this, and encourage more interesting prompts, you can say: If everyone gives the same answer, then everyone gets 0 points, except for the prompter, who loses 1 point.

Sudden-death tiebreaker

If, at the end of the pre-set number of rounds, multiple players are tied for first place, you can enter a "sudden-death round" to determine the winner. Take turns giving prompts in the same order as before, but skip any player involved in the tie. All players should answer the prompt, but only the first-place contenders are eligible to earn points. The game ends as soon as the tie is broken, regardless of whether the turn order has completed.

Talking during turns

Under the most strict rules, all players must be silent after the prompt is given and before the answers are revealed (since anything said aloud might create a new Schelling point). You may find that this makes the game less fun, so you can relax this restriction; but in any case, players should not blurt out answers while others are still thinking. If this happens, you can declare that answer to be excluded.

DetailsPrecommitment mechanism

It's the prompter's job to determine when everyone has come up with an answer, and to tell everyone when to reveal them.

It may be tempting to say "Just mentally commit to an answer; we trust you not to change it after hearing other answers." I would strongly discourage this: even if everyone's being honest, it's easy to subconsciously rewrite one's own memory of what one's answer is. Rather, there should be some tangible evidence of the precommitment. You can write it down on paper; or, if paper is lacking, the prompter can count down and have everyone say their answer at once, and then go around the circle to repeat the answers one at a time. Even if the simultaneous shouting is indiscernable, the act of physically speaking the answer will prevent any subconscious memory-rewriting.

Online play

The game is also suitable for playing in online chats. You can set up an editable-by-all Google spreadsheet like this:

 Name a living person(The next prompt...)Alice(Alice's answer) Bob(Bob's answer) Carol(Carol's answer) Dave(Dave's answer) 

On each turn, each player should enter their answer in the corresponding cell, without pressing Enter. This will make the cell turn gray for everyone else, indicating that some text has been written into the cell but not yet revealed. When it looks like everyone has written something, the prompter should confirm that everyone has settled on their final answer, and then count down "3, 2, 1, go!" whereupon everyone presses Enter to reveal their answer.

Disputes over matches

Determining whether two answers are "the same" may be subjective, but you can usually resolve this by consensus (unless, I suppose, you're playing for a cash prize, but I've never done this myself). You don't really have to read this section before playing, but you can refer to these heuristics if questions arise:

  • Supercategory/subcategory (e.g. "dog" vs. "poodle") is not a match.
  • Two different names for the same thing are a match, as long as at least one party to the match knew about the synonymy beforehand. (And you can't give answers whose meaning is unknown at the time the prompt is given, e.g. "Whatever Alice's answer is.")
  • The answer should at least attempt to match the prompt; i.e. it has to be plausible that someone might believe that the answer matches the prompt. For example, if the prompt is "What is the 123,456,789th digit of π?" then any decimal digit may be accepted. Generally, however, you should avoid giving prompts with a single correct answer. If the prompt specifically excludes one or more answers, then those answers (or any synonyms thereof) should not be accepted.
Questions to consider
  1. Is it possible to get good at this game?
  2. Does this game teach any useful skills?


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Maps of Maps, and Empty Expectations

3 мая, 2021 - 12:59
Published on May 3, 2021 9:59 AM GMT

[cross-posted here]

This post is a recap of some naturalism studies I did a few months back. At the outset, I didn’t know where the studies would take me. Now, I believe they taught me about how to do the “real” version of the thing you’re trying to do, where “real” tries to point at things like “the territory” and “what actually matters”.

To communicate my idea, I will rely on two central examples and offer two frames of interpretation. I want to offer people several, slightly different footholds, such that, if one example or one frame clicks for you while the others don’t, you can run with the one that did. I end the post with some thoughts on how to respond to the problem I’m outlining. 

Outline

  1. Examples
    1. “Ideal solutions”
    2. “Being helpful”
  2. Interpretative Frames
    1. Maps of maps
    2. Empty expectations
  3. What to do in response?

Let’s go!

Examples“Ideal solutions”

Some time ago, I was working on an important work project. I cared about doing it well. Initially, things went well but soon I started to feel stuck and grew increasingly averse to working on the project. Whenever I would turn to thinking about the project, I had an experience of my mind “cramping” or “tensing up”, and a veil of fog settling over my mind, preventing me from thinking clearly and making progress. I’d start to feel increasingly frustrated, self-judgemental, and averse to working on the project. 

A central part of the phenomenology of this experience is a sense of “trying really hard”, but failing to really get a grip on the problem. It’s a chain of trigger-action patterns where, when I notice I get stuck in this way (trigger), my mind reacts by “trying harder” (action), which in turn makes my mind tense up more. This creates more of the mind-fog preventing me from being able to think clearly, meaning I’m even less able to make progress (trigger), causing my mind to want to try even harder (action), etc. The resulting experience is one of “drifting away from” rather than “towards” a clear understanding of the task/problem. 

When I first ran into this problem, I didn’t understand what was going on. So, I started studying this tensing-up experience, which eventually led me to an insight that was central to my making progress on this problem.

I realized that I was holding the subconscious belief that there existed, somewhere, an ideal, a perfect solution to my project. I didn’t have access to this “ideal solution”, nor did my belief about its existence contain any details on what it looked like. And yet, part of me was convinced that this perfect solution existed.

As a result of this belief - and until that point unnoticed to myself - my orientation to the project had shifted. What I was trying to do when working on it had moved away from “thinking about and solving the problem” towards “finding and replicating the ideal solution”. Anything I did in fact produce - necessarily - fell short of its perfection, thus harbouring frustration, a sense of insufficiency, and further pulling my attention away from the object-level of actually solving the problem. 

I’m inclined to call the “ideal solution” a construct of my social cognition. It was created out of beliefs about how I was supposed to carry out my project, and out of comparisons with how others would do it. It wasn’t that the project itself was unbelievably hard, so much so that I couldn’t have made meaningful progress on it. Rather, linked to my desire to do it well - to live up to some high, yet ill-defined standards, as well as my desire to be seen as doing so - my thinking had become tangled up with a lot of thoughts, only some of which still had to do with the plain task of making progress my project.

Once I noticed that, in some sense, I had ceased to plainly try to solve the problem, combined with some additional hacks that helped me get back on track with doing just that (described in more detail below), my frustration and sense of stuckness with regards to the project started to melt away successively. With my mind clear (instead of fogged up) and able to sustain a gentle focus (instead of cramping), I would return to thinking clearly, which allowed me to regain traction and soon be reconnected to the joyful creativity of genuine problem- solving. 

“Being helpful”

Some time ago, I helped a friend debug a problem of theirs. They wanted to support their partner, who was going through an intense couple of months. The problem was that my friend's attempts of helping their partner weren’t always as successful as one would hope. 

We had talked about this problem before, but this time my friend came to me with a specific new insight - one that proved to be particularly juicy with respect to making progress on their problem. 

“I realize that, sometimes, when my partner asks me to do something for them, I switch into the mode of ‘trying to be helpful’ or ‘trying to be seen as helpful’, instead of ‘trying to solve the problem they asked me to solve for them’.” 

We went on deconstructing this dynamic further. It looked like “being seen as helpful” served two distinct purposes. For one, my friend wanted to be the sort of person who is a supportive partner. This was, in essence, a need that they themselves had. Second,  they wanted to signal to their partner that they “had their back”. Importantly, this desire to offer emotional support was genuine and did serve an important purpose (which their partner recognized and valued). They could signal their commitment by, for example, adjusting their body posture, engaging in certain verbal patterns, being generous in what resources they were willing to spend on solving the problem. 

However, their kind intention alone wasn’t solving the problems their partner had asked them for their help with. In optimizing for “being a supportive/helpful partner”, they would give up on some things that normally made them more efficient at solving problems. For example, in discussing with their partner what solutions were most appropriate, they felt disinclined to disagree with their partner and clarify where the disagreement (or confusion) came from. Voicing disagreement felt like it was going against their goal of making their partner feel supported. However, it also decreased their ability to fully understand what it was their partner cared about. 

Just like myself in the earlier example, my friend got trapped in - tangled up with - a behaviour that was optimizing for something other than “solving the actual problem”. Given that my friend genuinely cared about “solving the actual problem”, their strategies for achieving this goal was non-ideal. This is not to say that the other things their behaviour was optimizing for might not also be valuable in its own regards. By gaining clarity on what was going on, and acknowledging that they did in fact care about several things in this situation, my friend found a way to satisfice each of them separately. As a result, the tension they used to feel when trying to help their partner reduced, and they became more effective at actually helping them. 

Interpretive Frames

I now want to offer two complementary ways in which one can interpret and draw lessons from the above two case studies. 

Maps of maps

The two examples are trying to point at a way of orienting to reality - namely, engaging in a sort of “guessing the teacher’s password” move, rather than trying to solve the real problem - that comes with detrimental effects on one’s cognitive/epistemic processes. Let me unpack:

In “rationalist lingo”, I would describe the shift from “trying to solve the problem” to “trying to replicate the ideal solution” as follows: Instead of trying to create my own map of the territory, I was trying to create a map of someone else’s map of the territory. 

However, whenever the problem that you’re trying to solve resides in reality, the epistemic process of “trying to create a map of someone else’s map of reality” is inherently misguided and likely to lead you astray. One of the most important aspects of this, in my experience, is that you cannot interact with someone else’s map of the territory in the same way you can interact with - say, run experiments on - territory. 

Two caveats on what I just said seem appropriate:

First, this is not to say that you cannot (or that you should not) interact with and learn from other people’s maps of the territory. In fact, other people’s maps are a great source of information, and I’m all in favour of downloading and adequately integrating parts of their maps into your own. What I am trying to point at, however, is that the type of relationship between you and someone else’s map is importantly distinct from the relationship between you and the territory. There is a difference between a) treating someone else’s beliefs as evidence about the territory, and integrating that evidence into your own overall view, and, and b) confusing someone else’s map of the territory for the territory itself, forgetting that there is an actual territory and that the way your actions cash out depend on the territory, not the other person’s map. 

Second, things become a bit more complicated in cases where the problem you care about does in fact reside in someone else’s map of reality, and the evidence you’re looking for is evidence about their map, not the territory per se. These types of problems exist, and in these cases, you are correct in trying to build a model of the other person’s model. What does remain valid, however, is the importance of tracking what level it is you actually care about, and what level it is you’re currently on.

In my experience, one of the simplest-while-still-robustly-useful ways for (re-)orienting is to pause, take a (mental or actual) step back and ask “Soo.. what is it that that I’m actually interested in/trying to do here?” 

This is another way of saying, the “maps of maps” problem is a type of Goodhart’s problem: you tried to solve a problem, you picked (consciously or nor) a metric that was at some point correlated with the thing you actually cared about, but eventually, once you optimized enough for that metric, it ceases to capture the thing you actually care about. (For example: goal - helping; metric - being seen as helpful.) Asking “what’s the thing I actually care about is here” helps you re-calibrate where you’ve come since you last asked this question, and what looks like the right direction to be moving in now. 

“Empty expectations”

Here’s another way of describing what is going on in the above examples. It revolves around the cognitive move/phenomenon of “having expectations”. In the first example, say, I had expectations about what the ideal solution looks like. But, as I will argue in a bit, something was off about my expectations - it was empty.

First, let us do some ground work. Expectations come in different types. There is a type of expectations that works like predictions. Prediction-type expectations are great because they contain a lot of useful information. Assume I write a post. As I re-read it, I have some sense of the current draft “falling short of my expectations”. I can now use my inner-sim to poke at this sense of “not quite right”, and it will tell me things about how exactly I am falling short, and what a better version of the post would look like. Maybe I need to add an example, or maybe this sentence is too wordy, etc.

There is another kind of expectation. To understand how it works, let’s take the “ideal solutions” example from earlier. There, the expectation I (subconsciously) held (about the existence of a perfect solution) was empty, non-specified. All it had to say was that whatever I had produced so far “surely wasn’t perfect”. But my expectation had nothing at all to say about how my current draft solution was falling short, or in what direction I should be travelling in. I call this type of expectation an “empty expectation”. 

Note that, sometimes, a prediction-type expectation might reside somewhere in the blackbox-y parts of your mind that, at first sight, it looks like an empty expectation. However, just because it is difficult to succinctly verbalize how what you are seeing falls short of the expectation - let’s say the expectation is preverbal - that doesn’t yet mean it is empty. The preverbal expectation does still clearly carry information about where I ought to look or what I ought to do next, even if it would be hard for me to explain that to someone else. An empty expectation doesn’t have that. It might require some extra attention to correctly distinguish preverbal from empty expectations in practice. In my own experience, however, once I look more closely, the distinction quickly becomes evident. 

Since giving this phenomenon of empty expectations a name, it has become even more salient to me just how widespread it is. Any of the following sounds familiar to you?

  • You think about [starting a project], but then you think: “Nah, I'm not good enough. I couldn't possibly do that."
  • You have been working hard on [a project], but you keep thinking to yourself: “I’m not doing enough. I’m not doing it right. I have to do more. I have to do better.”

These aren’t always cases of empty expectations. To check whether they are, you can ask yourself some of the following questions: 

  • Does the thought have anything specific to say about in what way you’re not currently up for the tasks, what you’re lacking, or what, concretely, it would look like for you to be? Is there any bit of evidence that could cause you to think that you were up for it? If no, that’s an empty expectation.
  • Does the thought have anything realistic to say about what it would look like for you to do enough or will it continue to ask for ‘more’ indefinitely? If there is no realistic and concrete answer to that, that’s an empty expectation.
  • And here’s a bonus question: What if you swapped out “you” for some other person in the same scenario. Do you get the same or a different answer? If different, why does one answer not apply to the other case (and vice versa)?

In the “ideal solutions”, it was important for me to internalize that the “ideal solution” I had been trying to replicate doesn’t exist the way I was conceiving of it, and that I could stop trying to look for it. This also meant I could give myself the permission to think for myself a bit more recklessly, and come up with my own solutions, a bit more desperately

***

So far, I’ve been talking about what happens if we shift away from ”trying to solve the actual problem”, towards some other, more convoluted way of orienting to reality. We might describe this convoluted orientation as not noticing that you’re building maps of maps, instead of maps of reality; or as being fooled by empty expectations. 

Conversely, we might wonder what it is that happens if I orient back to “trying to solve the actual problem”. What is the mode that I am advocating for here? 

I believe the best way to answer this question is to try it out yourself. What does happen when you ask yourself what you’re actually trying to do, and then do that; when you - nothing but - genuinely try to do the thing you’re trying to do? 

Beyond that, all I have are pointers. What I find when trying to orient to reality in this way is related to original seeing, to the mode of orienting to the world that is a constant undercurrent of the Replacing Guilt series, to “the thing” that (according to me at least) most deserves to be called “research” or “truth-seeking” or “sensemaking”. 

What to do in response

Having worked through some examples and interpretative frames, let me now share some observations about what might help - what helped me - when you notice yourself getting tangled up in things other than what lets you do the “real” version of the thing you’re trying to do.

  • Noticing
    • Learning to notice when the situation occurs (e.g. noticing the tensing-up experience from example one, or noticing the experience of “trying to be helpful” in example two) is extremely helpful in terms of gaining surface area with this specific way of getting stuck.
    • Initially, I would only notice the experience after having been struggling along for maybe an hour or so. Over time, I learnt to notice the experience sooner; maybe after 20 minutes, then 10, then after 2. Eventually, I wasn’t so much noticing the experience itself, but rather a "precursor experience" (which is to say, the thoughts and mental moves responsible for the experience themselves). This process of becoming better at noticing can dramatically increase your ability to do something about the problem. (More about what to do about it below).
  • Replacing TAPs
    • In example one, my default reaction to the tensing-up experience was to “try harder”, causing a detrimental and self-reinforcing cycle to kick in. Understanding this allowed me to replace this natural trigger-action-pattern with a more conducive behaviour/mental move (such as asking myself what I was actually trying to do, or “letting go”/”backing up”; see below).
  • Asking yourself “What am I actually trying to do?”
    • Somehow, those “empty expectations” don’t exist in the space where I’m genuinely connected to what truly matters in what I’m trying to do. Again, this mental move can be turned into a TAP which is a great way for learning to track the “real thing”/the territory more reliably.
    • “What am I actually trying to do?” is the version of this question that has been most robustly useful to me. However, depending on the situation, you might want to play around with, tweak and customize what exact question(s) you’re asking. For example:
      • What am I doing right now? Why am I doing what I'm doing? What problem/thing am I trying to solve/achieve?
      • According to my behaviour, what am I trying to achieve (on top of my explicit goal)? Or in other words, what is my current behaviour buying me? Is the way I currently try to achieve all of these goals the best way to do it? If not, can I separate out these processes, so that they cease to interfere with each other?
      • What can I gather (from the world, from my interlocutor, ..) that tells me about what’s actually valuable here?
      • Am I trying to be [helpful]? Is this still worth doing X if it's not seen as [helpful]? If no one else seemed to care, what would I still care about there?
      • etc.
  • Ways of “letting go”/”backing up”
    • A lot of mental moves that proved helpful to me are in essence about shifting from “trying harder” to “not applying force”. These things overlap a lot with what I would do to get more grounded, such as engaging/focusing on my sensory experiences (e.g. looking at some natural structure, reading poetry, observing my breath, checking what my body feels like, drawing something, ...). All of these moves involve observing reality directly. When I look at a tree or feel my own body, there's little chance I’ll accidentally try to look at someone else's model of a tree. Speculative, this might help by reminding myself of how to make maps, rather than maps of maps.
    • Something that initially was very helpful for me was to “de-prime” my mind from the “the stuckness” and frustration that I felt around my project. Successfully de-priming my mind would mean that I could go back to working on the project without immediately falling back into the same cognitive pattern, but instead being able to maintain my newly gained orientation. Doing the groundedness exercises and breaking the above-mentioned trigger-action-chain were helpful with that.
    • Notes on Actually Trying also contains some pointers at how to back up and properly reorient to a task/project.
  • Noticing picas or bucket errors
    • In all generality, we get derailed from tracking reality directly for reasons. Usually, these reasons are valid in their own rights (such as my friend's desire to provide emotional support to their partner), even if the way we currently pursue them interferes with other things we also care about (e.g. solving the actual problem). The fact that they interfere with each other is often not an inherent problem but emerges because we might be committing a bucket error (e.g. disagreeing with my partner means that I’m not being supportive), or we might be pursuing what we care about in a convoluted way (e.g. the act of obsessively worrying about something in our minds is like the “ice” we eat to address our “iron deficiency”, say, our wish for the project to go well). When we can understand these dynamics, we can often find ways to serve all of the different goals through alternative, separate actions.
  • Beware social cognition, and drop it wherever it doesn’t serve you
    • Social cognition refers to the set of mental processes that are concerned with the social world; they aim to perceive, make sense of, remember or attend to other people and our relationship with them. Social cognition is powerful and critically useful to a lot of aspects of human life. However, spending social cognition on, say, solving a math problem is rarely helpful. It might make you ask questions like: “How quickly do I think Anne solved this problem? What will Bob think of me if they learn that I got the answer wrong for all of my first three attempts? If I don’t manage to solve this problem, what will this mean about me?” Again, I am not saying these types of thoughts are never useful for anything. I am saying that they are barely useful to actually solving things like math problems.
    • So, what can you do? First, observe your thoughts for a few moments. How many of your thoughts are exclusively about what you’re working on, and how much of them are social cognition? If you have a lot of social cognition going on, ask what work this is doing for you, what needs it might be addressing? In all generality, we do things for reasons, even if the way we pursue them might not be the most effective. Now, either take care of these needs right away, or credibly commit (to yourself) to taking care of them at some later point in time. Often, the crux is in creating enough internal emotional safety such that, at this point, you can simply drop whatever social cognition that isn’t serving you and return back to the object level of what you’re working on. (Be sure though to come back to these needs if you committed to doing so. Trust (including self-trust) is a precious thing.)
  • Maintaining a healthy distrust in labels
    • If you want to maintain a pure orientation towards what actually matters about the thing you’re working on, it is often important to be careful with labels, i.e. your choices about how to carve up the problem space into relevant entities/concepts (ontology) and the act of naming and refining these concepts (labelling)).
    • Practically speaking, it is often useful to come up with short and condensed descriptions of what you’re doing (e.g. “I am a teacher”, “I am working on our annual impact evaluation”, “I’m writing a strategy document”, etc.). However, in my experience, these labels (e.g. “teacher”, “impact evaluation”, “strategy”) can become increasingly obstructive to my sense-making/problem-solving process - as if the label itself prevented me from seeing clearly what is actually there and what matters. For example, it becomes easier to move towards optimizing for a neatly formatted strategy doc, as opposed to working out relevant considerations about what your medium- and long-term plans should look like.
    • Especially in the earlier phases of a process, your S1 is often better at understanding the most relevant aspects of your problem compared to what an initial, S2-generated ontology will be able to capture. If you unambiguously run with this initial ontology, you risk running off in the wrong direction. My guess is that this is related to (something like) verbal overshadowing, i.e. the fact that preverbal, S1-based intuitions can be fragile and easily overwritten by the more “forceful”, verbal, top-down S2 processes. In order to avoid overwriting what your S1 might know, you can start by maintaining a decent amount of distrust in any of the concepts and labels you initially come up with (or avoiding them altogether). Later, once your S1-understanding of the problem space has become sufficiently nuanced and robust, you can start eliciting better labels that will (hopefully) carve reality (more) at its joints.

 

Thanks to Logan Strohl for developing and coaching me in the methodology of naturalism. If you want to learn more about it, this is a good place to start. 
Thanks to various other people for helpful discussions and comments on this post. 



Discuss

Open and Welcome Thread - May 2021

3 мая, 2021 - 10:58
Published on May 3, 2021 7:58 AM GMT

If it’s worth saying, but not worth its own post, here's a place to put it.

If you are new to LessWrong, here's the place to introduce yourself. Personal stories, anecdotes, or just general comments on how you found us and what you hope to get from the site and community are invited. This is also the place to discuss feature requests and other ideas you have for the site, if you don't want to write a full top-level post.

If you want to explore the community more, I recommend reading the Library, checking recent Curated posts, seeing if there are any meetups in your area, and checking out the Getting Started section of the LessWrong FAQ. If you want to orient to the content on the site, you can also check out the new Concepts section.

The Open Thread tag is here. The Open Thread sequence is here.



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Small and Vulnerable

3 мая, 2021 - 07:55
Published on May 3, 2021 4:55 AM GMT

Anyone who is dedicating the majority of their time or money to Effective Altruism needs to ask themselves why. Why not focus on enjoying life and spending your time doing what you love most? Here is my answer:

I have a twin sister but neither of us had many other friends growing up. From second to fifth grade we had none. From sixth to eighth we had one friend. As you might guess I was bullied quite badly. Multiple teachers contributed to this. Despite having no friends my parents wanted us to be normal. They pressured me to play sports with the boys in the neighborhood. I was unable to play with an acceptable level of skill and was not invited to the games anyway. But we were still forced to go 'play outside' after school. We had to find ways to kill time. Often we literally rode our bicycles in a circle in a parking lot. We were forced to 'play outside' for hours most days and even longer on weekends. I was not even allowed to bring a book outside though sometimes I would hide them outside at night and find them the next day. Until high school, I had no access to the internet. After dinner, I could watch TV, read and play video games. These were the main sources of joy in my childhood.

Amazingly my mom made fun of her children for being weirdos. My sister used to face a wall and stim with her fingers when she was overwhelmed. For some reason, my mom interpreted this as 'OCD'. So she made up a song titled 'OCD! Do you mean me?' It had several verses! This is just one, especially insane, example.

My dad liked to 'slap me around. He usually did not hit me very hard but he would slap me in the face all the time. He also loved to call me 'boy' instead of my name. He claims he got this idea from Tarzan. It took me years to stop flinching when people raised their hands or put them anywhere near my face. I have struggled with gender since childhood. My parents did not tolerate even minor gender nonconformity like growing my hair out. I would get hit reasonably hard if I insisted on something as 'extreme' as crossing my legs 'like a girl in public. I recently started HRT and already feel much better.  My family is a lot of the reason I delayed transitioning.

If you go by the checklist I have quite severe ADHD. 'Very often' seemed like an understatement for most of the questions. My ADHD was untreated until recently. I could not focus on school or homework so trying to do my homework took way too much time. I was always in trouble in school and considered a very bad student. It definitely hurts when authority figures constantly, and often explicitly, treat you like a fuck up and a failure who can't be trusted. But looking back it seems amazing I was considered such a bad student. I love most of the subjects you study in school! When I finally got access to the internet I spent hours per day reading Wikipedia articles. I still spend a lot of time listening to lectures on all sorts of subjects, especially history. Why were people so cruel to a little child who wanted to learn things?

Luckily things improved in high school. Once I had more freedom and distance from my parents my social skills improved a huge amount. In high school, I finally had internet access which helped an enormous amount. My parents finally connected our computer at home to the internet because they thought my sister and I needed it for school. I also had access to the computers in the high school library. By my junior year in high school, I was not really unpopular. Ironically my parent's overbearing pressure to be a 'normal kid' probably prevented me from having a social life until I got a little independence.  Sadly I was still constantly in trouble in school throughout my high school years.

The abuse at home was very bad. But, to be honest, the absolute worst part of my childhood and adolescence was the constant sleep deprivation. Even at thirty years old I cannot handle getting up early; I rarely wake before nine-thirty. A year ago I briefly had to be awake at six-thirty for work. I felt terrible all day and could not think straight. When I was younger I had an even stronger need to sleep in but I had to be in school before eight. People were amazed at my ability to fall into a deep sleep in the middle of a loud classroom. Unless someone woke me up I would just stay asleep at my desk. This was a horrible experience and surely terrible for my brain. I got a break from this torment during the summers but I didn't really escape until I made it to college.

Obviously, I was an outlier in many respects. But many people are outliers in some important respects. They still deserve an environment that is healthy and lets them flourish. I wanted to learn all sorts of things. But instead of helping me, the school system tortured me and permanently damaged my brain. No one deserves to be treated like that.

We should not frame this in terms of my parents being aberrations. I live in the United States. Many groups here normalize far more extreme repression and physical punishment. In some subcultures, my parent's behavior is considered unacceptable. But much of what happened to me is still normalized. Even supposedly liberal parents are often terrible to trans children. Society isn't going to stop sleep-depriving children anytime soon. And there are many people being severely mistreated in very different circumstances.

I cannot get my childhood back, can't go back in time and transition earlier, and if my brain was harmed the damage is permanent. Whatever other traumas I have won't fully heal. But I eventually got out. There are millions of people in prison, trapped in abusive nursing homes, or starving in Yemen. There are many more animals on farms. Those people haven't escaped yet and it is unclear they will ever escape to somewhere safe. Society never should have normalized what happened to me and we shouldn't normalize what is happening to them. This is an emergency.

When I was small and vulnerable I needed help. For the most part, no help came. I was forced to stew in boredom and misery until I grew bigger, stronger, and accorded more respect. It is always hard to compare experiences. But I know what it's like to spend about a decade miserable, knowing you are being mistreated and being unable to defend yourself. Maybe one day I will again be unable to defend myself because I am sick or in prison. But for now, I am relatively healthy and free. I cannot just abandon the people and animals who are still trapped. Every day I try to imagine them somehow watching me and I ask whether they would think I forgot them. I hope I never forget. I hope my actions always show I have forgotten neither my past nor their present.

This post also appeared on my blog.



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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.

Instead of trying to get uppity about this, may I recommend the following:

  • 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.



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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!



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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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