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APL AI Safety Conference

22 июля, 2019 - 22:07
Published on July 22, 2019 6:18 PM UTC

The Johns Hopkins University Applied Physics Laboratory (APL) in Laurel, MD will be hosting a full-day conference on AI safety, on August 29. The speaker line-up has not been finalized yet, but current confirmed speakers include:

  • Max Tegmark
  • Lt. Gen. John “Jack” Shanahan, Director of the Joint AI Center of the U.S. Department of Defense
  • Bob Work, Former Deputy Secretary of Defense, currently serving as Co-Chair of the National Security Commission on AI

I will try to update this space when the speaker line-up is announced. Space is limited and advanced registration is required, so register now if you're interested in attending.

https://www.eventbrite.com/e/the-future-of-humans-and-machines-assuring-artificial-intelligence-tickets-64320947686



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The Self-Unaware AI Oracle

22 июля, 2019 - 22:04
Published on July 22, 2019 7:04 PM UTC

Abstract A self-unaware AI oracle is a system that builds a world-model inside which there is no concept of "me". I propose that such systems are safe to operate, that they can be plausibly built by continuing known AI development paths, and that they would be competitive with any other type of AI—capable of things like inventing new technology and safe recursive self-improvement.

Epistemic status: Treat as preliminary brainstorming; please let me know any ideas, problems, and relevant prior literature.

Overview

My proposal is to build a system that takes in data about the world (say, all the data on the internet) and builds a probabilistic generative model of that data, in the sense of taking an arbitrary number of bits and then predicting one or more masked bits. If you want to imagine something more specific, here are three examples: (1) A hypercomputer using Solomonoff Induction; (2) Some future descendant of GPT-2, using a massive Transformer (or some better future architecture); (3) A neocortex-like algorithm that builds low-level predictive models of small segments of data, then a hierarchy of higher-level models that predict which of the lower-level models will occur in which order in different contexts.[1]

Further, we design the system to be "self-unaware"—we want it to construct a generative world-model in which its own physical instantiation does not have any special status. The above (1-2) are examples, as is (3) if you don't take "neocortex-like" too literally (the actual neocortex can learn about itself by, say, controlling muscles and observing the consequences).

Finally, we query the system in a way that is compatible with its self-unawareness. For example, if we want to cure cancer, one nice approach would be to program it to search through its generative model and output the least improbable scenario wherein a cure for cancer is discovered somewhere in the world in the next 10 years. Maybe it would output: "A scientist at a university will be testing immune therapy X, and they will combine it with blood therapy Y, and they'll find that the two together cure all cancers". Then, we go combine therapies X and Y ourselves.

What is self-unawareness?

In the context of predictive world-modeling systems, a self-unaware system (a term I just made up—is there an existing term?) is one that does not have itself (or any part of itself, or any consequences of itself) as specially-flagged entities in its world model.

Example of a self-aware system: A traditional RL agent. (Why? Because it has a special concept of "its own actions" represented in its models.)

Example of a self-unaware system: Any system that takes inputs, does a deterministic computation, and spits out an output. (Why? Because when you correctly compute a computable function, you get the same answer regardless of where and whether the computation is physically instantiated in the universe.)[2]

In one sense, ensuring that an AGI is self-unaware seems like it should be pretty easy; "The space of all computable functions" is a pretty big space to explore, and that doesn't even exhaust the possibilities! On the other hand, of course there are always pitfalls ... for example, if your code has a race condition, that's a side-channel potentially leaking information from the physical instantiation into the (symbolic) world model. Still, designing a system to be self-unaware seems pretty tractable, and maybe even amenable to formal verification.

More examples of how to think about self-unawareness
  • If a self-unaware system is, at some moment, consolidating its knowledge of anthropology, it doesn't "know" that it is currently consolidating its knowledge of anthropology—this fact is not represented in the world-model it's building.
  • If a self-unaware system is running on a particular supercomputer in Mexico, maybe its world-model "knows" (from news stories) that there is a new AI research project using this particular supercomputer in Mexico, but it won't conclude that this research project is "me", because, as far as it knows, there is no "me"; it is utterly ignorant of its own existence.

If you find this unintuitive, well, so do I! That's because self-unaware systems are super non-anthropomorphic. If I'm able to think straight about this concept, it's only by firmly grounding myself in the three examples I mentioned in the Overview. For example, take a hypercomputer, using Solomonoff Induction to find the world-model that most parsimoniously predicts all the data on the internet. Does this world-model contain the statement: "I am a hypercomputer running Solomonoff Induction"? No!! That's just not something that would happen in this system.

Just as Hume said you can't derive an "ought" from an "is", my contention here is that you can't derive a first-person perspective from any amount of third-person information.

How do you query a self-unaware system?

There's some awkwardness in querying a self-unaware system, because it can't just directly apply its intelligence to understanding your questions, nor to making itself understood by you. Remember, it doesn't think of itself as having input or output channels, because it doesn't think of itself period! Still, if we spend some time writing (non-intelligent) interface code, I think querying the system should ultimately work pretty well. The system does, after all, have excellent natural-language understanding inside of it.

I think the best bet is to program the system to make conditional predictions about the world, using its world-model. I gave an example above: "Calculate the least improbable scenario, according to your world-model, wherein a cure for cancer is discovered anywhere in the world". The subroutine does some calculation, writes the answer to disk, and terminates (of course, as always, it doesn't know that it wrote the answer to disk, it just does it). As we read the answer, we incidentally learn the cure for cancer. I expect that we would have some visibility into its internal world-model, but even a black-box predictive world-modeler is probably OK. Imagine prompting GPT-2 for the most likely completion of the sentence "In 2030 scientists finally discovered a cure for cancer, and it was ...", or something like that.

Can you give it multiple queries in sequence without resetting it?

Yes, although things get a bit tricky when the system starts reasoning about itself. (It reasons about itself in exactly the same way, and for exactly the same reason, as it reasons about anything else in the world.)

Suppose that we continue allowing our self-unaware system to have read-only internet access, after we've published the cure for cancer from the previous section. Now plastered all over the newspapers are stories about the famous Self-Unaware AI Oracle running in on a supercomputer in Mexico, which has just invented the cure for cancer. The system now will definitely put self-unaware AI oracles into its predictive generative world-model (if it hadn't already), which entails trying to understand and predict what such a thing would do in different circumstances. Maybe it even reads its own source code!

Unfortunately, it won't be able to reason about itself perfectly—that would require simulating itself, which causes an infinite regress. But the system will apply all its models and heuristics, and do the best it can to come up with a predictive model of itself, on the basis of the (non-privileged) information it has. (Note that there is no reason to expect it to build a self-consistent model, i.e., a model in which its own predictions about itself are correct.)

OK, now we go back and ask the system for something else, say, a cure for Alzheimer's. Again, we say "Please output the least improbable scenario, according to your world-model, wherein Alzheimer's is cured in the next 10 years". It might say "Those scientists in Mexico, using their Self-Unaware AI Oracle, learn the following treatment approach...".

Now, recall that with the classic AI Oracle, we worry about manipulative answers. In this particular case, we can be sure that the system itself is not inherently manipulative (see below), but we would still be in trouble if the Self-Unaware AI Oracle in Mexico believes that the Self-Unaware AI Oracle in Mexico would be manipulative. This failure mode seems extremely unlikely. As I mentioned a couple paragraphs ago, its third-person model of itself will be an imperfect approximation to the real thing, but the belief that it would be manipulative would be a bizarrely specific modeling error that came out of nowhere!

Still, out of an abundance of caution, the query should probably be something like: "Please output the least improbable scenario, according to your world-model, wherein a cure for Alzheimer's is invented by a group with no access to any AI oracles!" Then we avoid the self-modeling issues altogether. Indeed, it should be possible to use a predictive world model to do counterfactual reasoning about what might happen in a world in which there are no AIs whatsoever.

Self-unaware AIs are safe to use

As far as I can tell, a self-unaware system should be about as safe as we could possibly hope for. Manipulation, deception, self-preservation, reproduction, and all the other Omohundro goals seem to be completely out of the question for a system with no knowledge that it is a calculation physically instantiated in the universe!

I'm trying hard to think of failure modes here. OK, there's a potential for the system to describe possible futures while leaving out critical details. So, let's say we ask for the least improbable scenario wherein "cancer is cured", and the system describes the events leading up to a plague that wipes out humanity. OK, well then we ask for the second least improbable scenario, and this one sounds pretty good! But after we inject the chemical into the first test patient, it turns out that this one is also a plague that wipes out humanity, but the AI's model output was unfortunately treating that fact as an irrelevant detail and neglecting to mention it. Are we back to Goodhart's law and the "nearest unblocked strategy" problem? Sort of, but I don't think it's particularly worrisome version. Again, we're asking the system to guess what normal humans might plausibly do in the natural course of events (in the absence of AI), and we can look at the probabilities assigned to the scenarios to make sure they're not outlandishly small. We can also program the system to answer our follow-up questions, like "according to your models, if someone rolls out this therapy, what is the likely impact on lifespan? what is the likely impact on the environment? how does it work on the cellular level?" and so on. And we can trust that, while the answers may be imperfect, they will not be manipulative. I'm really not seeing any cause for concern here, or elsewhere, although I'm going to keep thinking about it.

Are Self-Unaware AI Oracles competitive with other approaches to AGI?

I see two main disadvantages of Self-Unaware AI Oracles, but I think that both are less problematic than they first appear.

The first disadvantage is that these things are completely incompatible with RL techniques (as far as I can tell), and a lot of people seem to think that RL is the path to superintelligence. Well, I'm not at all convinced that we need RL, or that RL would ultimately even be all that helpful. The alternative path I'm proposing here is unsupervised learning: Given a sequence of bits from the internet, predict the subsequent bits. So there's a massive amount of training data—for example, I heard that 100,000 years of video have been uploaded to YouTube! I keep going back to those three examples from the beginning: (1) GPT-2 shows that we can get impressively far on this type of unsupervised learning even with today's technology; (2) Solomonoff induction on the entire internet is the astronomically high ceiling on what's possible; and (3) the human brain—which works primarily on exactly this type of unsupervised learning[3]—is a nice reference point for how far we might get along this path just by brute-force biomimetic engineering.

The second disadvantage is that it's still an oracle, needing a human in the loop.[4] But as far as oracles go, it's about as powerful as you could hope for: able to answer more-or-less arbitrary questions, and able to design new technology, as in the cancer example above. In particular, we can take a bootstrapping strategy, where we can ask the safe self-unaware oracle to help us design a safe AGI agent.

By the same token, despite appearances, Self-Unaware AI Oracles are capable of recursive self-improvement: We just present the query in the third person. ("This is a Self-Unaware AI Oracle", we say to it, holding up a giant mirror. "How might scientists make this type of system better?") We can even record the system doing a calculation, then pass that video back to itself as an input to improve its self-models. I think this would be a quite safe type of self-improvement, insofar as self-unawareness is (I hope) possible to rigorously verify, and also insofar as we're not worried about manipulative suggestions.

Conclusion

I'm very excited about this approach; it seems almost too good to be true. I'm looking forward to any feedback. By the way, I think there are probably better and more precise ways to define self-unawareness, but I hope my definition above is close enough to get the idea across. I'll keep thinking about it, and I hope others do too!

  1. See Jeff Hawkins On Intelligence or Andy Clark Surfing Uncertainty, for example. ↩︎

  2. On second thought, saying this about all computable functions might be a bit too strong. Or maybe it's OK. I still need to think through some subtleties of how "self-unawareness" ought to be defined in general... ↩︎

  3. See references in footnote 1. Of course, the human brain uses both unsupervised learning (predict the next thing you'll see, hear, and feel) and RL (cake is good, death is bad). My feeling is that we can throw out the RL part (or throw out enough of it to allow self-unawareness) and the system will still work pretty well. For example, when Einstein invented relativity, he wasn't doing RL interaction with the real world, but rather searching through generative models, tweaking and recombining the higher-level models and keeping them when they offered a parsimonious and accurate prediction of lower-level models. I think we can write self-unaware code that does that kind of thing. Without a reward signal, we might need to program our own mechanism to direct "attention", i.e. to guide which aspects of the world need to be modeled with extra-high accuracy. But again, this seems like something we can just put in manually. Note: I'm not terribly confident about anything in this footnote, and want to think about it more. ↩︎

  4. If you try to make a self-unaware AI agent, it immediately starts modeling itself and adjusting its behavior to that model, which (as mentioned above) yields hard-to-predict and possibly-problematic behavior ... unless there's a trick I haven't thought of. ↩︎



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AN #60: A new AI challenge: Minecraft agents that assist human players in creative mode

22 июля, 2019 - 20:00
Published on July 22, 2019 5:00 PM UTC

AN #60: A new AI challenge: Minecraft agents that assist human players in creative mode View this email in your browser

Find all Alignment Newsletter resources here. In particular, you can sign up, or look through this spreadsheet of all summaries that have ever been in the newsletter. I'm always happy to hear feedback; you can send it to me by replying to this email.

Highlights

Why Build an Assistant in Minecraft? (Arthur Szlam et al): This position paper proposes a new challenge for AI research: building a bot that can provide assistance in Minecraft (creative mode). A companion paper presents an initial setup for such an agent.

The main goal here is to advance natural language understanding, intent inference and instruction following. As a result, there is no formal specification like a reward function -- in their own words, "the ultimate goal of the bot is to be a useful and fun assistant in a wide variety of tasks specified and evaluated by human players". They chose Minecraft in particular partly because it has a very rich space of tasks, even though the execution of any given task is relatively straightforward. They script many low level policies to automate this execution in order to make learning easier (for example, they have policies to navigate to a location or to build specified structures) and focus the learning challenge on figuring out what the user wants.

The current version of the bot takes dialogue from the user and uses a neural model to parse it into an action dictionary that unambiguously specifies what the agent should do -- I think this neural model is the main thing to be learned. There are a bunch of details on how the rest of the modules work as well. They have also released three datasets: a semantic parsing dataset that associates instructions with action dictionaries, a house dataset that has trajectories where a human builds a house, and a semantic segmentation dataset that labels various parts of houses.

Rohin's opinion: I'm really excited to see a project that is very directly aimed at inferring end user intent in a complex environment. This seems like a great direction for the field to move towards. I think Minecraft would also be great as a test bed for the setting in which researchers or engineers (as opposed to end users) are trying to get an agent to do something: we can assume more expertise and knowledge here. Ideally, this would allow us to solve more complex tasks than can be accomplished with natural language from end users. I personally plan to do work with Minecraft along these lines.

While this project does need to infer intent, it probably won't require the sort of pragmatic understanding shown by e.g. Cooperative IRL. Even understanding what the human is literally asking for in Minecraft is currently beyond our capabilities.

Read more: CraftAssist: A Framework for Dialogue-enabled Interactive Agents

Technical AI alignment   Learning human intent

Ranking-Based Reward Extrapolation without Rankings (Daniel S. Brown et al) (summarized by Cody): A while back, these authors released the T-REX paper (AN #54), where they showed that providing ranked sets of trajectories, rather than one single optimal trajectory, lets you learn a more accurate reward that can outperform the demonstrator. This ability to outperform the demonstrator is rooted in the ability to extrapolate predicted reward outside of demonstrated points, and that ability to extrapolate comes from the fact that ranked trajectories provide more information about relative reward values. This paper is a fairly straightforward extension of that one, and asks: can we get similar benefits without requiring humans to actually rank trajectories? The authors argue that they can replicate T-REX's ability to outperform the demonstrator by simply learning a behaviorally cloned policy off of a single (potentially sub-optimal) demonstrator, and making that policy gradually worse by adding more noise to it. This model is called D-REX, for Disturbance-based Reward EXtrapolation. They then make an assumption that more noise in the policy corresponds to less reward, and use that as a ranking scheme to throw into the existing T-REX algorithm.

Cody's opinion: Overall, I think this is potentially a straightforward and clever trick for giving your imitation learner more informative data to learn off of. I have two main questions. First off, I'd have loved to see D-REX compared directly to T-REX, to get a sense of how much you lose from this approximate ranking strategy rather than a more ground truth one. And, secondly, I'd have appreciated a bit more justification of their assumption that noisier actions will consistently lead to a worse policy, in ways that capture reward information. This doesn't seem obviously untrue to me, I'd just love some more intuition on why we can get additional information about underlying reward just by adding noise.

SQIL: Imitation Learning via Regularized Behavioral Cloning (Siddharth Reddy et al) (summarized by Cody): Behavioral Cloning is one of the most direct forms of imitation learning: it learns to predict the action the expert would have taken in a given state of the world. A clear weakness of the approach is that, if cloning models are only trained on pairs of (state, expert action) drawn from the expert's policy distribution, that means the model is underconstrained and thus likely to have high error on states that would have been unseen or just highly unlikely to be visited by the expert. This weakness means that errors within behavioral cloning systems can compound: if the system takes an incorrect action that leads it to a state it never saw the expert in, it will have a difficult time knowing what to do there.

The main contribution of this paper is to suggest a fix for this weakness, by learning a Q function to represent expert behavior, and by penalizing the model for being in states where its temporal difference error on the Q function (otherwise known as the Bellman error) is high. Intuitively, the hope is that this term, which can also be seen as a reward for being in states the expert has seen more frequently (equivalently, states where the model had more training experience) will propagate outward, and give the model a loss surface that pulls it back into states where its predictions are more confident.

Cody's opinion: I still have a personal sense that Behavioral Cloning is too brittle of a conceptual frame to build really robust imitative agents with, but this seems like a clever and relatively clean way to build in a bias towards high-confidence states. I find myself wondering if the same general idea of penalizing being in high-model-error states could be more broadly applied as a sort of regularizer in other off-policy settings where exploration can be risky.

Research Agenda v0.9: Synthesising a human's preferences into a utility function (Stuart Armstrong): One approach to AI alignment involves learning a specification of human values that can then be optimized. This agenda proposes that we learn an adequate representation of values (i.e. not ambitious value learning (AN #31)). We first obtain partial preferences and associated weights from human mental models whose symbols have been adequately grounded. Calling these "preferences" is a normative assumption to avoid an impossibility result in value learning (AN #31): the hope is that the AI could correct for incorrect human beliefs. The preferences are then extended to all possible states, and are normalized so that they are comparable to each other, and then synthesized into a utility function that the AI can optimize.

The partial preferences are divided into a few categories: individual preferences, preferences about the rest of the world, and meta-preferences, some of which can be about the synthesis procedure itself. The hope is that further categories of preferences would be handled by the synthesis procedure; these categories are the ones that seem most important to get right, or couldn't be obtained any other way.

Rohin's opinion: See the next entry.

Some Comments on Stuart Armstrong's "Research Agenda v0.9" (Charlie Steiner): This post makes two main critiques of the research agenda in the previous entry. First, the research agenda involves a lot of human-designed features and modules, but The Bitter Lesson (AN #49) is that machine learning tends to shine with highly abstract large models that can make use of a lot of compute. Second, the symbol grounding part of the agenda requires the AI system to develop representations of the world that match the representations that humans use, and we have no idea how to do that, or even what it would mean to "match human representations" when the AI is more intelligent than humans. The post also includes some more specific comments that I'm not summarizing.

Rohin's opinion: I agree with both of these critiques, especially the one about the bitter lesson. It seems like Stuart's approach imposes a particular structure or algorithm for how to synthesize the utility function; I am generally skeptical of such approaches. Also, as you might already know, I think it is neither necessary nor sufficient for AI alignment to find a utility function or "goal" that the AI can safely optimize. Since this promises to be a very difficult enterprise (Section 0.2 notes that it aims to "solve at least 5 major open problems in philosophy, to a level rigorous enough that we can specify them in code"), I prefer to look into other approaches that seem more tractable.

I do think that the problems that motivate the various aspects of the agenda are important and useful to think about, and I am happy that they have all been put into this single post. I also like the fact that the research agenda is directly aiming for a full solution to AI alignment.

IRL in General Environments (Michael Cohen)

Forecasting

Musings on Cumulative Cultural Evolution and AI (calebo): A recent paper develops a conceptual model that retrodicts human social learning. They assume that asocial learning allows you adapt to the current environment, while social learning allows you to copy the adaptations that other agents have learned. Both can be increased by making larger brains, at the cost of increased resource requirements. What conditions lead to very good social learning?

First, we need high transmission fidelity, so that social learning is effective. Second, we need some asocial learning, in order to bootstrap -- mimicking doesn't help if the people you're mimicking haven't learned anything in the first place. Third, to incentivize larger brains, the environment needs to be rich enough that additional knowledge is actually useful. Finally, we need low reproductive skew, that is, individuals that are more adapted to the environment should have only a slight advantage over those who are less adapted. (High reproductive skew would select too strongly for high asocial learning.) This predicts pair bonding rather than a polygynous mating structure.

This story cuts against the arguments in Will AI See Sudden Progress? and Takeoff speeds: it seems like evolution "stumbled upon" high asocial and social learning and got a discontinuity in reproductive fitness of species. We should potentially also expect discontinuities in AI development.

We can also forecast the future of AI based on this story. Perhaps we need to be watching for the perfect combination of asocial and social learning techniques for AI, and once these components are in place, AI intelligence will develop very quickly and autonomously.

Rohin's opinion: As the post notes, it is important to remember that this is one of many plausible accounts for human success, but I find it reasonably compelling. It moves me closer to the camp of "there will likely be discontinuities in AI development", but not by much.

I'm more interested in what predictions about AI development we can make based on this model. I actually don't think that this suggests that AI development will need both social and asocial learning: it seems to me that in this model, the need for social learning arises because of the constraints on brain size and the limited lifetimes. Neither of these constraints apply to AI -- costs grow linearly with "brain size" (model capacity, maybe also training time) as opposed to superlinearly for human brains, and the AI need not age and die. So, with AI I expect that it would be better to optimize just for asocial learning, since you don't need to mimic the transmission across lifetimes that was needed for humans.

The AI Timelines Scam (Jessica Taylor): This post argues that AI researchers and AI organizations have an incentive to predict that AGI will come soon, since that leads to more funding, and so we should expect timeline estimates to be systematically too short. Besides the conceptual argument, we can also see this in the field's response to critics: both historically and now, criticism is often met with counterarguments based on "style" rather than engaging with the technical meat of the criticism.

Rohin's opinion: I agree with the conceptual argument, and I think it does hold in practice, quite strongly. I don't really agree that the field's response to critics implies that they are biased towards short timelines -- see these comments. Nonetheless, I'm going to do exactly what this post critiques, and say that I put significant probability on short timelines, but not explain my reasons (because they're complicated and I don't think I can convey them, and certainly can't convey them in a small number of words).

Jeff Hawkins on neuromorphic AGI within 20 years (steve2152)

AI strategy and policy

How Europe might matter for AI governance (Stefan Torges)

Other progress in AI   Unsupervised learning

Large Scale Adversarial Representation Learning (Jeff Donahue et al) (summarized by Cody): The BigGAN paper, published last September, used a much larger model (and a handful of optimization tricks to facilitate training it) to achieve a huge leap forward in the quality of generated images. However, it was unclear from the earlier paper whether this improvement in generation quality would also be tied to an increase in the model's usefulness as a source of unsupervised semantic representations of images. This paper set out to answer that question by taking an existing technique for learning representations with GANs - called BiGAN - and combining it with the BigGan architecture, which hadn't been available when BiGAN was originally published. BiGAN, short for Bidirectional GAN, works by learning both a latent space to image transformation, and also an image to latent space encoder, and then enforcing that pairs of (latent, image) from these two distributions be indistinguishable from one another. They evaluated the quality of learned representations by measuring the performance of a linear model trained using the encoder's learned latent vectors as input, and did find it to be the case that a BiGAN trained with a BigGAN architecture performs better than one trained with a smaller architecture.

Cody's opinion: I really liked this paper; it was cleanly written, conceptually straightforward, and did a generally useful scientific service of checking whether an advance in one area might change our beliefs about a previous result. I particularly enjoyed looking at the "reconstructed" images they got by running their encoder and then generator: more so than anything I recall seeing from a VAE pixel-based reconstructor, this model seems to be treating images as valid reconstructions of one another if they're of the same class (i.e. two pizzas) even if the colors and low level detail are different. This makes reasonable sense if you think that those two pizzas are probably nearby in latent space, and so each is a plausible reconstruction of each other's latent space encoding, but it's still cool to see concretely borne out.

News

Join our rapidly growing research teams (Tanya Singh): The Future of Humanity Institute is hiring researchers across a wide range of topics, including AI safety and strategy. The deadline to apply is midday August 16.

Copyright © 2019 Rohin Shah, All rights reserved.


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Self-experiment Protocol: Effect of Chocolate on Sleep

22 июля, 2019 - 06:32
Published on July 22, 2019 3:32 AM UTC

Epistemic Status

Preregistering an experiment, not very confident in the design.

Because it is important to decide how the data will be analyzed before collecting it, I'm posting my planned experiment here, may modify the design based on feedback (I'll post an update if I do), and then I will start the experiment, and finally I'll post the results when it's finished.

All feedback is appreciated!

Introduction and Goals

I often suffer from fatigue in the early evening that leads to anxiety later at night, which makes it difficult to sleep. The purpose of this experiment is to determine whether eating a small amount of chocolate to counteract fatigue in the early evening affects my sleep, and if so, how. I could easily imagine the effect on sleep being either beneficial (counteract the pattern of fatigue and anxiety that makes it hard to sleep) or harmful (the theobromine might keep me awake).

Because I'd rather not add the fat and sugar that comes with chocolate to my diet, the effect on sleep needs to be appreciable for it to be worthwhile. I have fairly arbitrarily decided that the chocolate is worth it iff it causes me to fall asleep at least 15 minutes earlier than I otherwise would.

Experimental Protocol

Every work day evening when I go directly home from work and do not have a social event planned, I will get home, make any necessary adjustments to the air conditioning (temperature affects my sleep a lot, and I want to make sure I don't accidentally bias the results by setting the AC differently depending on whether I have chocolate), and then with probability 1/2 (decided by a coin flip or die roll) eat a square of dark chocolate.

I will use a spreadsheet to track which nights I did and did not eat a square of chocolate, and I will use sleep data from a Fitbit Blaze smartwatch to record what time I fall asleep each night. The Blaze uses heart rate and movement data to decide what time I fall asleep (I don't need to actively tell it when I go to bed). In my experience, the time it says I fell asleep generally matches my subjective memory of when I fell asleep (not when I went to bed).

Statistical Analysis

I care about the magnitude of the effect, not just the direction, and my goal is to make the correct decision rather than to get published, so testing whether the observed effect is statistically significantly different from 0 is not very useful. While it would be a mistake to keep going until I get the result I want, if the results are close (in either direction), it will be worth gathering more data.

Because I have arbitrarily picked an absolute effect size of -15 minutes (I fall asleep 15 minutes sooner than I otherwise would), I will be focusing on determining which side of that cut-off the effect is. To do so, I plan on analyzing the data as follows:

  1. Run the experiment until I have used up one bag of chocolate (15 individually-wrapped squares).
  2. Calculate the standard error of the difference of sample means (I am not assuming equal variance in the two samples).
  3. If the observed effect is at least one standard error away from the -15 minute cut-off (my sleep is inconsistent enough that I expect this condition probably won't be met at this point), stop the experiment.
  4. Otherwise, keep going until either the stop condition from step 3 is met or the standard error is less than 5 minutes. If I end with a standard error less than 5 minutes and the observed effect is within 5 minutes of -15 minutes, I will consider the experiment inconclusive.


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How Much Do Different Users Really Care About Upvotes?

22 июля, 2019 - 05:31
Published on July 22, 2019 2:31 AM UTC

For the last several years, I've known people who've submitted articles to the EA Forum or LessWrong, and found that the culture on these sites is pretty hostile to the kinds of views they're presenting (different people have different opinions of the patterns of hostility to different views on the EA Forum and LW, respectively). What the particular views are doesn't matter, because it's been all kinds of views. What's common between them is a perception an article they wrote they believe to be quite good was "poorly received" on the site, while still having a positive and significant number of upvotes. Now, none of the articles I'm thinking of had what I would call a high number of upvotes, but it was enough that at least several people had read the article, and a majority of them had upvoted the post. As a proportion of the people who read the article, it tells us a very significant minority, typically between 30-45%, disagreed with or disliked it enough to downvote it.

So, they're articles which are not very well-received. Unless I'm missing something, an article having a positive number of upvotes should be interpreted as one's article being at least somewhat well-received by the readers. If someone thinks that an article on the EA Forum or LW has received too many downvotes, or not enough upvotes, because there is something wrong with the general culture of the respective membership bases of these sites, that is one argument. Yet that is a different one than the arguments I see typically made by those who complain about the proportion of upvotes:downvotes they receive on the EA Forum or LessWrong. They just say that based on the appearance of not receiving strong, consistent, vocal support for their articles that they would like to have seen, that the reception to their article was overwhelmingly and uniformly negative. This is in spite of the fact they received at least a higher proportion of positive:negative feedback, even if only on the measure of karma. In other words, it's my observation a lot of people who complain as if they've gotten uniformly, overwhelmingly, and inappropriately negative feedback on their articles have false impressions of how their articles were received, and are wrong.

The common tendency I see in articles like this is that there is often a high proportion of comments that disagree with or criticize the OP, and that these comments often receive more upvotes than the OP. So, it seems like what people are really upset about when their articles on the EA Forum or LW receive a merely a lukewarm reception, as opposed to an overwhelmingly negative one, is that, while there are at least a majority of the community who at least weakly supports them, there is a significant minority of the community who is more willing to strongly and vocally disagree with, criticize, or oppose them.

It seems to me one solution, to move to a better equilibrium of discussion, would be for users who agree with an original article, but are able to make the arguments for its thesis better than the original author, to write their own comments and articles that do so. There is a fear of politics among some rationalists, so that stigmatizes discussions that might appear to heighten tensions between different groups of people within effective altruism, and/or the rationality community. It's my impression in these communities there is also a culture of letting an individuals words and ideas stand on their own, and not associating the arguments from just one individual on one side of a debate to everyone on that side of the debate. So, it strikes me as unlikely the vast majority of any effective altruists or LessWrongers would care enough to join an interpersonal/intercernine online disagreement to the point the community at large should have to concern ourselves about whether we should quell it.

Of course, one of the reasons LW and the EA Forum use karma scores as they do is so for the discourse on these fora to be shaped in a way satisfying to most users, without us all having to get bogged down in endless debates about the state of the discourse itself. At least that is what I've taken the karma systems of LW and the EA Forum in large part to be about, and why I have what I believe is an appropriate level of respect for them. They're certainly not everything one should take into account for evaluating the quality of an article on LW or the EA Forum. Yet I don't think they should definitely count for little, or even nothing, which is sometimes the reaction I see from EA Forum or LW members who aren't satisfied with the proportion and kind of positive feedback their article receives, especially relative to the proportion and kind of negative feedback received.

These are my impressions as a long-time user of both the EA Forum and LessWrong. I was just wondering what other people's thoughts on the subject are; whether they're similar to, or different than, mine; and why.




Discuss

Should I wear wrist-weights while playing Beat Saber?

21 июля, 2019 - 22:56
Published on July 21, 2019 7:56 PM UTC

I recently started playing the VR game Beat Saber as a form of exercise. It involves waving your arms around a bunch.

I *also* got 1.5 pound wrist weights to use while playing, to increase the degree of workout.

Since then, someone made a vague claim about this potentially being damaging for joints, or something. I'm curious if anyone has a clear sense of whether and why wrist-weights would be beneficial or harmful.



Discuss

Cross-Validation vs Bayesian Model Comparison

21 июля, 2019 - 21:14
Published on July 21, 2019 6:14 PM UTC

Suppose we need to predict the outcomes of simulated dice rolls as a project for a machine learning class. We have two models: an "unbiased" model, which assigns equal probability to all outcomes, and a "biased" model, which learns each outcome frequency from the data.

In a machine learning class, how would we compare the performance of these two models?

We'd probably use a procedure like this:

  • Train both models on the first 80% of the data (although training is trivial for the first model).
  • Run both models on the remaining 20%, and keep whichever one performs better.

This method is called cross-validation (along with its generalizations). It's simple, it's intuitive, and it's widely-used.

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How do cross-validation and Bayesian model comparison differ?

Biased/Unbiased Die Simulation

Let's run a simulation. We'll roll a 100-sided die N times, using both a biased die and an unbiased die. We'll apply both cross-validation and Bayesian model comparison to the data, and see which model each one picks. Specifics:

  • We'll use N-fold cross-validation with log likelihood loss: for each data point xi, we learn the maximum-likelihood parameters p∗i based on all data except xi. To get the a final metric, we then sum log likelihood over all points: ∑ilnP[xi|p∗i]
  • For Bayesian model comparison, we'll compute lnP[model|data] for an unbiased model, and a model with uniform prior on the biases, just like we did for Wolf's Dice
  • The simulated biased die has probability 1/200 on half the faces and 3/200 on the other half

We'll plot the difference in score/evidence/whatever-you-want-to-call-it assigned to each model by each method, as the number of data points N ranges from 1 up to 10000.

Here are the results from one run:

First and most important: both cross-validation and Bayesian model comparison assign more evidence to the biased model (i.e. line above zero) when the die is biased, and more evidence to the unbiased model (i.e. line below zero) when the die is unbiased.

The most striking difference between the methods is in the case of an unbiased die: Bayesian model comparison assigns lower and lower probability to the biased model, whereas cross-validation is basically flat. In theory, as N→∞, the cross-validation metric will be random with roughly zero mean.

Why the difference? Because cross-validation and Bayesian model comparison answer different questions.

Different Questions

Compare:

  • Cross-validation: how accurately will this model predict future data gathered the same way?
  • Bayesian: How likely is this model given the data? Or equivalently, via Bayes' rule: how well does this model predict the data we've seen?

So one is asking how well the model can predict future data, while the other is asking how well the model predicted past data. To see the difference, think about the interesting case from the simulation: biased vs unbiased model running on data from an unbiased die. As N→∞, the biased model learns the true (unbiased) frequencies, so the two models will make the same predictions going forward. With the same predictions, cross-validation is indifferent.

For cross-validation purposes, a model which gave wrong answers early but eventually learned the correct answers is just as good as a model which gave correct answers from the start.

If all we care about is predicting future data, then we don't really care whether a model made correct predictions from the start or took a while to learn. In that case, cross-validation works great (and it's certainly much computationally easier than the Bayesian method). On the other hand, if one model made correct predictions from the start and the other took a long time to learn, then that's Bayesian evidence in favor of the model which was correct from the start.

That difference becomes important in cases like Wolf's Dice II, where we wanted to deduce the physical asymmetries of a die. In that case, the fully-general model and our final model both make the same predictions about future rolls once they have enough data. But they differ on predictions about what the world looks like aside from the data itself - for instance, they make different predictions about what we would find if we took out some calipers and actually measured the dimensions of Wolf's white die.

Prediction vs Understanding

Years ago, while working in the lab of a computational biologist, he and I got into an argument about the objective of "understanding". I argued that, once some data can be predicted, there is nothing else left to understand about it. Whether it's being predicted by a detailed physical simulation or a simple abstract model or a neural network is not relevant.

Today, I no longer believe that.

Wolf's Dice II is an excellent counter-example which highlights the problem. If two models always make the same predictions about everything, then sure, there's no important difference between them. But don't confuse "make the same predictions about everything" with "make the same predictions about the data" or "make the same predictions about future data of this form". Even if two models eventually come to the exact same conclusion about the outcome distribution from rolls of a particular die, they can still make different predictions about the physical properties of the die itself.

If two models make different predictions about something out in the world, then it can be useful to evaluate the probabilities of the two models - even if they make the same predictions about future data of the same form as the training data.

Physical properties of a die are one example, but we can extend this to e.g. generalization problems. If we have models which make similar predictions about future data from the training distribution, but make different predictions more generally, then we can apply Bayesian model comparison to (hopefully) avoid generalization error. Of course, Bayesian model comparison is not a guarantee against generalization problems - even in principle it can only work if there's any generalization-relevant evidence in the data at all. But it should work in almost any case where cross-validation is sufficient, and many other cases as well. (I'm hedging a bit with "almost any"; it is possible for cross-validation to "get lucky" and outperform sometimes, but that should be rare as long as our priors are reasonably accurate.)

Conclusion?

In summary:

  • Cross-validation tells us how well a model will predict future data of the same form as the training data. If that's all you need to know, then use cross-validation; it's much easier computationally than Bayesian model comparison.
  • Bayesian model comparison tells us how well a model predicted past data, and thus the probability of the model given the data. If want to evaluate models which make different predictions about the world even if they converge to similar predictions about future data, then use Bayesian model comparison.

One final word of caution unrelated to the main point of this post. One practical danger of cross-validation is that it will overfit if we try to compare too many different models. As an extreme example, imagine using one model for every possible bias of a coin - a whole continuum of models. Bayesian model comparison, in that case, would simply yield the posterior distribution of the bias; the maximum-posterior model would likely be overfit (depending on the prior), but the full distribution would be correct. This is an inherent danger of maximization as an epistemic technique: it's always an approximation to the posterior, so it will fail whenever the posterior isn't dominated by the maximal point.



Discuss

Learning to Learn and Navigating Moods

21 июля, 2019 - 18:53
Published on July 21, 2019 3:53 PM UTC

Moods are important to learning. Clearly, if you’re upset, it will be harder to learn JavaScript, how to cook a fine pesto, or surf. If you’re curious and filled with wonder, it will be much easier. Because your mood can be either promote or hamper your learning success, learning how to navigate moods usefully is an important metaskill for skill acquisition. Knowing about the importance of moods is useful for teaching, students or colleagues, in addition to learning.

Those are the main claims of Olivia Flores’ Learning to Learn and the Navigation of Moods: The Meta-Skill for the Acquisition of Skills. In this post I’ll summarize the book. I’ve also created an audio for an exercise that she suggests using AWS Polly that you can access here.

What are the components of this meta-skill?

  • First, a learner knows about the varieties of different moods and whether they are useful or not.
  • Second, a learner knows which judgements give rise to their specific moods.
  • Third, a learner knows which moods tend to arise at which learning stage. They know what to watch out for and what to promote.
  • Finally, a learner knows how to interact with these judgements to nudge their mood into a different direction.
The Varieties of Moods

A quick definition, what are moods? In Flores’ words:

“Moods are like the coloring of how we encounter the world around us, what it says to us or how it appears to us” (21-22)

I think of moods as been made up of two components:

  • dispositional: dispositions to act or think in a particular way
  • experiential: a mood has an experiential quality, one is disposed to feeling a particular way in addition to acting or thinking a particular way

If someone is in a blue mood, they may skip a party they would have otherwise gone to and enjoyed. If someone is annoyed, they will be more likely to lash out at others. If someone is feeling joyous, they’ll be far less likely to lash out and more likely to play with and help others. Each of these moods have a particular experiential quality to them, there’s something it’s like to have a blue, annoyed, or joyous mood.

Flores provides a number of different moods that may productively impact our learning. Here are a list of them with associated thoughts and feelings:

  • Wonder
    • I don’t know what’s going on and that’s exciting! I want to know!
  • Perplexity
    • I am totally confused, but I’ve got to know what’s going on. I’m going to persevere until I get to the bottom of this.
  • Serenity
    • I accept the past and present and am open to the future. I’ve been right and made mistakes in the past, I will be right and make mistakes in the future.
  • Patience
    • I accept that sometimes things move slowly. I am plodding along, perhaps happily, calmly, or with resolve.
  • Ambition
    • I want to win. I will experience setbacks as challenges to master, not evidence of what is or isn’t possible.
  • Resolve
    • I see opportunities here and I’m committed to taking action right now
  • Confidence
    • I have had successful results in this area. I will have more successful results in the future.
  • Trust
    • I am learning from people who I trust. Because I have confidence in them and don’t feel judged, I feel likely to succeed. (29)

Some of these moods may hamper our learning progress. For example, we can be overconfident and over trusting or too patient. Perhaps there are some things we just shouldn’t be serene about. However, by and large, these moods are very useful for learning.

On the unproductive side of things we have these moods:

  • Confusion
    • I don’t know what’s going on and I don’t like it. This is bad. I want to escape this situation!
  • Resignation
    • I am too x to learn this. I will ever be able to do this, no matter how hard I try, so what’s the point?
  • Frustration
    • I’ve tried to do this before but failed. I should have succeeded already. I’m not moving as efficiently as I should.
  • Arrogance
    • There is nothing new for me to learn here. This is beneath me.
  • Impatience
    • There is nothing new for me to learn here. This is a waste of my time.
  • Boredom
    • There is nothing of value for me here. I am not interested in anything here.
  • Anxiety
    • I don’t know how to do this. I will make mistakes and mistakes are bad. I want to quit because that would be better than making a mistake.
  • Overwhelm
    • There is too much going on! There is so much I don’t know or can’t do.
  • Lack of confidence
    • I am not competent enough to learn this. I have always been bad at x. I’m not good enough to be here. Everyone else is so much better than I am.
  • Distrust
    • I don’t trust that the people I’m learning from will be helpful. This process may work for some people, but I don’t think it will work for me. (25)

Sometimes, these moods may be appropriate for our circumstances: for example, boredom and distrust may sometimes be useful. However, typically these moods hold us back.

These moods are likely familiar to you. I’ve experienced all of them.

The Causes of Moods

Flores has a similar model of moods as CBT types do for emotions. Moods are produced by particular judgements and events in the world (Flores calls what I call judgements “assessments”, I prefer the term “judgement”). Judgements are evaluative and reference norms, values, and our desires. Such as: I like this. This is good. This is bad. This is right/wrong. This is appropriate/inappropriate. These judgments are made automatically and habitually.

While attempting to solve a bug while working on a web application, one may feel frustrated. One may think, “I should have solved this bug already” or “why am I even having a bug like this while writing a simple program” or “If I were a 10x engineer I wouldn’t be experiencing this bug.” These judgements and events may bring about (or may reflect) a rather frustrated mood. In this way events and moods can interact and bring about a negative feedback loop.

Alternatively, after solving a bug, one may feel elated and confident. One may think, “Yes, I solved this hard bug fast, I’m great” or “I am glad I fixed that in the time I did” or “I am a 10x engineer give me more bugs.” This may bring out a general mood of confidence in addition to a success spirals.

As with CBT, one of the first steps with navigating moods while learning is observing moods. Take our programmer. How do they feel when they encounter a bug? How do they feel when they solve one? What thoughts surround these events? What judgments do these thoughts express, if any?

The causes of many unproductive moods can be tied to judgements concerning the following:

  • Competence
    • It is important to know the right answer and make sure that others know that you know.
    • If you don’t know what to do, you are incompetent. Which is, just to be clear, bad.
    • Making mistakes are bad.
  • Efficiency
    • Learning has to happen fast.
    • One must get thing right right away.
    • One must not waste time.
  • Independence
    • One mustn't depend on others.
    • If one doesn’t know something, one should figure it out on one’s own.
    • Don’t ask for help.
  • Usefulness
    • One must contribute useful work right away.
    • If one isn’t useful, one won’t be accepted.
    • If one isn’t useful, one should resign.
  • Preparation
    • One must be prepared at all times.
    • Before you try something, you must be certain that it will work.
    • If one doesn’t know what to do, it’s better to do nothing. (14-15)

These judgements and the associated thought patterns can get in the way of learning. They are behind many of the unproductive moods above.

Moods During the Learning Lifecycle

Flores discusses Staurt Dreyfus and Hubert Dreyfus’s (Yes, that Dreyfus) learning model and maps different moods to the different stages. I found this useful, since the stages are largely recognizable and the relevant moods map nicely enough to my experience and others. Nonetheless, this learning model is too low resolution for me and I’d like to see something more detailed developed.

According to the Dreyfus brothers there are six stages of learning:

  • Beginner
    • The beginner wants to learn something new. They are starting out and don’t know what to do.
    • Next level: The beginner must gain experience. They must cultivate moods that are conducive to the discomfort they will experience as they continue to practice. Confidence and trust in the learning process are important.
  • Advanced Beginner
    • The advanced beginner has been practicing for a bit and know the basic moves. They are comfortable and at risk of becoming too comfortable
    • Next level: an advanced beginner must become more involved, they must take more risks. There is a risk of becoming bored or relying on shortcuts here and not advancing.
  • Competent
    • The competent are responsible for producing results, but they don’t always know what to do or how to do it. It can be overwhelming.
    • Next level: The Dreyfus brothers state that the learner must experience both success and failure. Ambition and resolve are important moods to experience here so that the failure can be experienced as part of the process as opposed to reasons for quitting.
  • Proficient
    • The proficient generally knows what needs to get done, but I don’t always know how to do it.
    • Next level: A proficient person needs more experience before they can react automatically. They must be motivated in order to become an expert.
  • Expert
    • The expert generally knows what needs to get done and how to do it. In teamwork situations, this person is a leader.
    • Next level: An expert must be willing to override the perspective that as an expert performer they intuitively experience. They need to violate norms and risk regression in performance for the sake of trying new, less obvious approaches. Arrogance is a risk here.
  • Master
    • The master is able to perform intuitively in their domain of expertise. A master is committed to do more and sees possibilities for innovation and new contributions to their field. (52-53)

The trajectory of these categories seem right to me and though it doesn’t exactly cut reality at the joints, it’s a workable model.

Flores helpfully maps out what moods are associated with what stage here:

We can run through this life cycle with our programmer.

Beginner: Our programmer begins their journey at a bootcamp...

  • Productive: starts with wonder, confidence, and trusts the learning process (but moves on if things aren’t useful).
  • Unproductive: bugs are evidence that one will never become a programmer, insecure, won’t ask for help, confusion seems insurmountable.

Advanced beginner: Our programmer has managed to last in the bootcamp for a month or two and may or may not be having a lot of fun

  • Productive: Our programmer is happy to have made it so far. They’re committed to finishing, confident that they can finish, and believe that they’re being adequately prepared to get a job after the bootcamp.
  • Unproductive: The tests are too easy. The bootcamp staff doesn’t seem that useful though and our programmer is becoming a bit too disagreeable. They don’t know if they’re being taught the right stuff for the job. They pass the unit tests, but don’t dive deeper.

Competent: Our programmer lands their first job...

  • Productive: The responsibility feels good. There are so many new things to learn and many of them quite interesting. It’s sometimes difficult to understand what’s going on the first time something is explained, but that’s ok. The team is filled with strong engineers who could be useful resources -- at least their code will be useful to read.
  • Unproductive: The responsibility feels overwhelming. There are too many new things and many of them are completely mysterious. It’s difficult to understand what’s going on the first time and that is really disheartening -- especially after performing so well at the bootcamp. Your teammates are good, but some of them come from fancy schools and actually studied something relevant at college which makes our programmer feel out of place.

Proficient: Our programmer is plodding along at the job, squashing bugs and shipping features. Sometimes people ask them for help!

  • Productive: Our programmer is committed to continuing to improve. They’re not disheartened by the fact that there’s so much to learn. They feel comfortable not knowing the ins and outs of many libraries. They occasionally make mistakes, but that’s expected.
  • Unproductive: Our programmer is impatient. They feel like they should know more by now. They are frustrated when they encounter things they don’t know or make mistakes.

Expert: Our programmer has become a lead engineer -- they are manage others, play a crucial role in design decisions, but still manage to program!

  • Productive: Our programmer is excited and committed into becoming a master. They are able to learn from masters around them. They lead others with patience and instill trust.
  • Unproductive: Our programmer is often impatient when leading others. They alternate between arrogance and insecurity in the presence of masters, both hinder moving forward.

Master: Our programmer may or may not be the mythical beast known as a 10x engineer, either way, they’ve mastered their craft.

  • Productive: Wonder helps our programmer catch important insights. It keeps boredom at bay. The inevitable decline in skills is accepted. Perpetual ambition helps the master further their craft.
  • Unproductive: Arrogance occasionally causes our programmer to miss out on important insights. Boredom creeps in. Decline in skills causes resignation. The sense that one has made it blinds the master from moving even further.

Again, this system is hardly gospel. Many of these stages manage similar moods. Ambition and wonder are useful for nearly all of them. But there are useful and practical upshots. For example:

  • At the initial learning stages, confidence and trust in the learning process are necessary.
  • Boredom can hinder learning as soon as one becomes good enough at a given thing. This pattern appears at various stages, such as at the advanced beginner and expert stage.
  • As one becomes better, there will be periods where one is more susceptible to arrogance and frustration (“I should have solved this already!”).
  • As one becomes better, the cost of failure sometimes seems to loom larger.
  • Cultivating wonder and ambition have seriously large rewards.
  • Though ambition is nearly always useful, it can become unproductive when paired with insecurity.
  • Observing one’s moods and knowing how to move in and out of them is likely useful. A concrete way I’ve applied this insight is by adding a section to track my mood in between work cycles.
Navigating Moods

There are a plethora of techniques for navigating moods. From CBT, mindfulness, focusing, and many more. I’ll summarize a specific exercise that Flores recommends and uses during her workshops. I’ve created an audio version of this exercise with Polly that you can listen to here.

The exercise is as follows:

  1. Reflect on one’s learning objective
  2. Identify and explore the unproductive mood
  3. Identify moods that would be more conducive to reaching your learning objectives
  4. Speculate about what action you could take to shift the unproductive moods into moods that will be more conducive to your learning
  5. Take action (32-36)

We can call this REISA: reflect, explore, identify, speculate, action

First reflect, ask questions like the following:

  • What future are you committed to bringing about?
  • Why are you learning this in the first place?
  • If you follow through, what would you be able to do that you can’t now?

The key idea is to be explicit about why you are doing what you are doing.

Then explore the unproductive mood. You can do this mindfully in a nonconceptual way and/or do this in an explicit way by uncovering what judgements and events have brought about the mood. You can ask:

  • What mood am I in? Can I conceptualize it? Is it one of the above moods?
  • What judgements are you making about yourself in this situation?
  • What kind of expectations or normative claims gives rise to these judgements?
  • Are these expectations or normative claims relevant? Do they help?

For example, you may note that the judgement is: “I am incompetent.” Is this judgement true? What gave rise to it? It seems very similar to a cognitive distortion. Likely, you’ll find that the judgement is not well grounded and not helpful.

Next identify what mood may be conducive for learning. If you’re feeling insecure, confidence would be a natural pairing. If you’re feeling resigned, ambition would be useful. Consider what kind of judgements one makes in the opposing mood. Consider whether there are quick actions that one can take to move into the productive mood.

Often useful actions will reveal themselves quickly. If one is say playing world of warcraft with a team and one is feeling resigned (because you’re holding back the team) you may find, while reflecting, that you are not asking for help. You are not asking for help because you judge that you must appear competent to your teammates -- and a competent person never asks for help. This judgement is off base. The salient action is simply ask for help. More specifically, you may ask for help at the next available opportunity.

Occasionally quick actions like the above will not be available. Merely considering the judgements associated with productive moods can only do so much. What one wants to do is create a system such that one can identify as the sort of person who is confident and ambitious. There are ways to do this, not discussed here. Instead, come up with a concerted plan to move into that mood.

Finally, take action.

The rest of Flores’ book is full of case studies from workshops that she has run. In these workshops, professionals would learn how to play WoW together. There are fruitful discussions of how real participants fell into unproductive moods and how they moved out of those moods. I’d recommend leafing through them if you’d like stories to grok the above content.

TLDR: moods are important for learning. Different moods appear at different stages of the learning process. It’s important to recognize the patterns in one’s moods and be able to navigate through them well. There are a variety of ways to do this.



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Bayes' Theorem in three pictures

21 июля, 2019 - 10:01
Published on July 21, 2019 7:01 AM UTC

Epistemic status: Just another visualization of Bayes' Theorem (and some light introductory text).

Let's say you have a belief — I'll call it "B" for "belief" — that you would assign 2:1 odds to. In other words, you consider it twice as likely that B is true than that B is false. If you were to sort all possible worlds into piles based on whether B is true, there would be twice as many where it's true than not.

Then you make an observation O, which you estimate to have had different probabilities depending on whether B is true. In fact:

  • If B is true, then O had a 1/8 chance of occurring.
  • If B is false, then O had a 3/4 chance of occurring.

In other words:

  • O occurs in 1/8 of the worlds where B is true.
  • O occurs in 3/4 of the worlds where B is false.

We know you're in a world where O occurred — you just saw it happen! So let's go ahead and fade out all the worlds where O didn't occur, since we know you're not in one of them. This will slash the "B is true" pile of worlds down to 1/8 of its original size and the "B is false" pile of worlds down to 3/4 of its original size.

Why did I write Evidence: 1:6 at the top? Let's ignore that for a second. For now, it's safe to throw away the faded-out worlds, because, again, we now know they aren't true.

I'm going to make a bold claim: having made observation O, you have no choice but to re-asses your belief B as having 1:3 odds (three times as likely to be false than true).

How can I say such a thing? Well, initially you assigned it 2:1 odds, meaning that you thought there were twice as many possible worlds where it was true than where it was false. Having made observation O, you must eliminate all the worlds where O doesn't happen. As for the other worlds, if you considered them possible before, and O doesn't contradict them, then you should still consider them possible now. So there is no wiggle room: an observation tells you exactly which worlds to keep and which to throw away, and therefore determines how the ratio of worlds changes — or, how the odds change.

What about "Evidence: 1:6"?

Well, first notice that it doesn't really matter how many worlds you're considering total — what matters is the ratio of worlds in one pile to the other. 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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')} 21 . The numerator represents the "B is true" worlds and the denominator represents the "B is false" worlds. Then, when you see the evidence, the numerator gets cut down to 1/8 of its initial size, and the denominator gets cut down to 3/4 of its initial size.

We can write this as an equation:

21∗1/83/4=13

Now notice something else: it also doesn't matter what the actual likelihood of observation O is in each type of world. All that matters is the ratio of likelihoods of O between one type of world and the other. O could just as well have had these probabilities instead:

21∗1/803/40=13

And our final odds come out the same.


That's why we write the evidence as 1:6 — because only the ratio matters.

This — the fact that each new observation uniquely determines how your beliefs should change, and the fact that this unique change is to multiply by the ratio of likelihoods of the observation — is Bayes' Theorem. If it all seems obvious, good: clear thinking renders important theorems obvious.

For your viewing pleasure, here's a combined picture of the three important diagrams. Link.

And here's a version with no numbers on it and here's a version where the bars aren't labeled.



Discuss

What questions about the future would influence people’s actions today if they were informed by a prediction market?

21 июля, 2019 - 08:26
Published on July 21, 2019 5:26 AM UTC

I'm looking for questions that would be useful to many individuals and would influence their decisions today (not questions for those working on shaping the long term future of Earth originating life).

I would also like if you specified how those questions might influence people's actions.



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