trentmkelly/LessWrong-43k huggingface dataset:
Research snap-shot: question about Global Workspace Theory
by Hazard — 2020-06-15T21:00:28.749Z

Cross-posted on my roam blog. Part of ongoing research on consciousness.

I wrote an intro to some of my thoughts on consciousness here, which was more conceptual and less neurosciency. This post is a snap-shot of some of the current technical questions that are on my mind. Please chime in if you know anything relevant to any of them. This is a pretty high context snap-shot and might not be that useful without familiarity with many of the ideas and research.

Q.1: Is the global neuronal workspace a bottleneck for motor control?

(Kaj's GNW intro, GNW wikipedia page)

Some observations to help build up context for the question and my confusion around it (it ends up being less a question and more a hypothesis I'm asserting).

Observation 1: People have trouble multitasking in dual-task style experiments, but training can improve their performance.

Corollary of 1: Some tasks requires attention and you can't do multiple things that require attention. But if you practice something a lot, you can do it "unconsciously", and you can do several "unconscious" tasks at the same time.

Observation 2: The "conscious bottleneck" seems to come into play during decision making / action-selection when in a novel or uncertain setting (i.e performing an unpracticed and unfamiliar task)

Corollary of 2: The "conscious bottleneck" is a conflict resolution mechanism for when competing subsystems have different ideas on how to drive the body.

I think these are all basically true, but I now think that the implicit picture I was drawing based off of them is wrong. Here's what I used to think: the conscious bottleneck is basically the GNW. This serial, bottlenecked, conflict resolution mechanism really only is used when things go wrong. When two subsystems try to send conflicting commands to the body, or you get sense data that wildly violates your priors. The brain can basically "go about it's business" and do things in a parallel way, only having to deal with the conflict resolution process of the GNW if there's error.

Easy tasks can route around the global workspace, hard ones or ones that produce error have to go through it. That's the previous idea. Now, this paper has begun to shift my thinking. For a specific set of tasks, it claims to show that training doesn't shift activity away from a bottleneck location, but instead makes the processing at the point of the bottleneck more efficient.

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By what metric do you judge a reference class?
by B Jacobs — 2020-06-15T18:34:18.262Z

The reference class problem is when you have a singular phenomena (e.g your friend Josh) and to extrapolate data and make predictions about this singular phenomena, you have to put it in a reference class of similar phenomena. The question becomes how you quantify similarity. Everything has an indefinite number of proporties that could be used as the basis for selecting a reference class (Josh is male, likes jazz, is an animal, is born in Germany, has a freckle on his toe etc). You can almost always select a reference class in such a way that you get the results you want to see. So how do you judge a reference class?

EDIT: Put up a $100 bounty for anyone who can solve it before 2022
Causality Adds Up to Normality
by johnswentworth — 2020-06-15T17:19:58.333Z

For definitions and notational conventions used here, see What is Abstraction?.

The world turns out to run on quantum mechanics, rather than classical. But classical mechanics still predicts the world exactly as well as it did all along. In particular, it still works in all the situations where we already knew it worked before figuring out quantum, i.e. high-level (“macroscopic”) objects. It all adds up to normality.

Formalizing this idea somewhat in the context of abstraction suggests an interesting class of questions.

We learn some high-level model MH by looking only at high-level data - e.g. we learned the laws of classical physics by looking at the behavior of macroscopic objects. Eventually, we learn a lower-level model ML of the same system. What properties of MH must carry over to ML? When we learn high-level properties of a system, what does that tell us about the low-level properties?

This post is about one particular class of properties: causal structure, i.e. arrows in graphical models. Basic results:

Conditional independence relations between far-apart high-level components must also hold between the corresponding low-level components
Interventional dependence relations between far-apart high-level components must also hold between the corresponding low-level components

Together, these imply that the causal structure of far-apart low-level components is compatible with the causal structure of the corresponding high-level components, assuming that both levels follow a causal DAG at all. Visually:

Conditional Independence

We’ll start with unconditional independence, as an easy warmup. We’ll assume that far-apart high-level variables XHi and XHj are independent:

P[XHi,XHj]=P[XHi]P[XHj]

Working directly from the systems view of abstraction, we then have:

P[(XLi,XHi),(XLj,XHj)]=P[XHi,XHj]P[XLi|XHi]P[XLj|XHj]

=P[XHi]P[XHj]P[XLi|XHi]P[XLj|XHj] (by independence assumption)

We can then integrate out the high-level variables to obtain:

P[XLi,XLj]=P[XLi]P[XLj]

This is fairly obvious from a visual representation of the abstraction condition:

Abstraction validity means that far-apart low-level X_i’s are independent given their corresponding high-level summaries f_i(X_i). In other words, we can swap each low-level variable with its summary, and just keep track of dependence relations between the summaries rather than

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If the reproduction number is socially "controlled" to its inflection point 1, what are the ethical and predictive implications?
by Tim Liptrot — 2020-06-15T16:01:34.185Z

Zvi wrote that he beleives the closeness of virus reproduction to one is not a coincidence, but a result of people responding to high reproduction by forbidding actions and low reproduction by going outside. I have copied his entire comment at the bottom of this post.

Assuming that Zvi is right, how would you update your positions? Would some actions previously seen as unethical now be ethical to you, and vice-versa?

When things are ‘getting worse’ we take ‘action’ by forbidding and forcibly stopping actions, and privately taking a mix of arbitrary and more sensible precautions, until we plausibly have things under control and cases shrinking. Anything beyond that, people won’t support.

https://www.lesswrong.com/posts/P7crAscAzftdE7ffv/covid-19-my-current-model
Simulacra Levels and their Interactions
by Zvi — 2020-06-15T13:10:00.717Z

Previously: Covid-19: My Current Model, On Negative Feedback and Simulacra

This post aims to unpack and explain simulacra levels of action using the threat of covid-19 as its central example. My intention is for future posts to then apply this model to many covid-related dynamics.

In Elizabeth’s Negative Feedback and Simulacra, she examined several example situations on which information was being processed on multiple simulacra levels at once. On Negative Feedback and Simulacra was my take on those examples.

To re-familiarize ourselves with the simulacra levels, here’s the introduction Elizabeth offered to them in her post:

My friend Ben Hoffman talks about simulacra a lot, with this rough definition:

First, words were used to maintain shared accounting. We described reality intersubjectively in order to build shared maps, the better to navigate our environment. I say that the food source is over there, so that our band can move towards or away from it when situationally appropriate, or so people can make other inferences based on this knowledge.

2. The breakdown of naive intersubjectivity – people start taking the shared map as an object to be manipulated, rather than part of their own subjectivity. For instance, I might say there’s a lion over somewhere where I know there’s food, in order to hoard access to that resource for idiosyncratic advantage. Thus, the map drifts from reality, and we start dissociating from the maps we make.

3. When maps drift far enough from reality, in some cases people aren’t even parsing it as though it had a literal specific objective meaning that grounds out in some verifiable external test outside of social reality. Instead, the map becomes a sort of command language for coordinating actions and feelings. “There’s food over there” is perhaps construed as a bid to move in that direction, and evaluated as though it were that call to action. Any argument for or against the implied call to action is conflated with an argument for or against the proposition literally asserted. This is how arguments become soldiers. Any attempt to simply investigate the literal truth of the proposition is considered at best naive and at worst politically irresponsible.

But since this usage is parasitic on the old map structure that was meant to describe something outside the system of describers, language is still structured in terms of reification and ob

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Achieving AI alignment through deliberate uncertainty in multiagent systems
by Florian Dietz — 2020-06-15T12:19:07.472Z

Epistemic status: A crazy idea I had that probably won't work. But: It's a very unusual and creative approach to AI alignment, and I suspect this will inspire new ideas in other researchers.

I outline a general approach to achieve this goal that counterintuitively relies on confusing the AI on purpose.

Basic observations

This approach relies on a number of basic observations about the nature of Artificial Intelligence.

An AI is different from a human in multiple ways. This is part of what makes AI alignment such a difficult problem, because our intuitions for how people act often do not apply to AI's. However, several of these differences between AI and humans actually work in our favor, and are essential for the approach I want to outline:

An AI can be copied. It is trivially easy to create an exactly identical copy of an AI.
An AI can be reset to an earlier state. It is easily possible to test what an AI will do in any given situation, then reset it to wipe its memory of the test.
It is possible to compose an AI out of a system of many independent AI agents.
Through game theory, it is possible to incentivize independent AI agents to take on adversarial or supervisory roles towards other AI agents.
This results in an evolutionary system. Not in the sense of genetic programming, since there is no need for an explicit algorithm for modifying the AI agents, but in the sense that only AI agents that follow the rules layed out for them get copied and thereby reproduce.
It is possible to inspect and modify every aspect of an AI while it is running.
At least in theory it is possible to understand every aspect of an AI's thought process, and there is no way for the AI to hide anything about itself.
This does not mean that it is easy to understand what an AI is thinking in practice. It only means that we can read an AI's mental state in theory, not that it will be easy.
In particular, it is possible to train one AI to take on a supervisory role over another AI. The supervisor can read and alter everything about the supervised AI, and can be trained to understand how to enforce desired behaviors on the supervised AI.
It is possible for a supervising AI to make a supervised AI think anything it wants.
The supervised AI may be tricked into believing that unusual situations have occurred by directly modifying its mental state at runtime.
It is not possible for

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The Economic Consequences of Noise Traders 
by FactorialCode — 2020-06-14T17:14:59.343Z
Link: https://www.nber.org/papers/w2395.pdf

Credits to quanticle for bringing this paper to my attention.

From the abstract:

The claim that financial markets are efficient is backed by an implicit argument that misinformed "noise traders" can have little influence on asset prices in equilibrium. If noise traders' beliefs are sufficiently different from those of rational agents to significantly affect prices, then noise traders will buy high and sell low. They will then lose money relative to rational investors and eventually be eliminated from the market. We present a simple overlapping-generations model of the stock market in which noise traders with erroneous and stochastic beliefs (a) significantly affect prices and (b) earn higher returns than do rational investors. Noise traders earn high returns because they bear a large amount of the market risk which the presence of noise traders creates in the assets that they hold: their presence raises expected returns because sophisticated investors dislike bearing the risk that noise traders may be irrationally pessimistic and push asset prices down in the future. The model we present has many properties that correspond to the "Keynesian" view of financial markets. (i) Stock prices are more volatile than can be justified on the basis of news about underlying fundamentals. (ii) A rational investor concerned about the short run may be better off guessing the guesses of others than choosing an appropriate portfolio. (iii) Asset prices diverge frequently but not permanently from average values, giving rise to patterns of mean reversion in stock and bond prices similar to those found directly by Fama and French (1987) for the stock market and to the failures of the expectations hypothesis of the term structure. (iv) Since investors in
assets bear not only fundamental but also noise trader risk, the average prices of assets will be below fundamental values; one striking example of substantial divergence between market and fundamental values is the persistent discount on closed-end mutual funds, and a second example is Mehra and Prescott's (1986) finding that American equities sell for much less than the consumption capital asset pricing model would predict. (v) The more the market is dominated by short-term traders as opposed to long-term investors, the poorer is its performance as a social capital allocation mechanism. (vi) Dividend policy and capital structure

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How alienated should you be?
by Vaniver — 2020-06-14T15:55:24.043Z

Epistemic status: a post about values, where I’m confident the question is interesting but not confident my answer is correct. Factual statements are intended to be correct summaries, but I won’t be careful about citations. This post was written in January, and edited and published today.

The “correct” level and type of alienation, like a “balanced” posture, is a dynamic function of the individual and environment. Nevertheless, we can say useful things about it, as we can about posture and balance.

We often talk on LessWrong about the importance of human intelligence. Often, we mean individual human intelligence, instead of collective human intelligence, and for many topics the difference is immaterial. But when we talk about alienation, or the separation between the individual and the collective, the distinction is paramount.

For the strength of the pack is the wolf, and the strength of the wolf is the pack. --Kipling, The Law For Wolves

The evolutionary history of humans is inexorably tied up with their existence in bands. The drive to imitate allows for cultural accumulation, and the drive to teach further accelerated that growth. Language gave structure to thoughts, and more importantly allowed easier transmission. Trade allowed for specialization and fragmentation of knowledge.

We see some evidence that improving collective ability was worth individual costs. Much has been made out of how early farmers had clear signs of malnutrition compared to hunter gatherers, like their shorter skeletons. Note that malnutrition is bad for individual intelligence, and farming benefits collective capability primarily through the increase in population carrying capacity. (It also allows for saving of wealth in a way that means society can be arranged differently, and more reliable trade because people are more stationary, but I think those effects are small for our purposes here.)

To live with an immense and proud composure: always beyond. - To have and not have one's feelings, one's for and against, voluntarily, to condescend to them for hours, to sit on them, as if on a horse, often as if on a donkey: - for one needs to know how to use their stupidity as well as their fire. To preserve one's three hundred foregrounds, as well as one's dark glasses: for there are occasions when no one should be allowed to look i

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List of public predictions of what GPT-X can or can't do?
by Daniel Kokotajlo — 2020-06-14T14:25:17.839Z

Watching this video, (Edit: around 25:50) I was pleased to see a specific prediction made by the person claiming that GPT-3 couldn't do reasoning: He claimed that GPT-3's ability to unscramble a word was not reasoning but mere pattern-matching and that he would be very surprised if GPT-3 could scramble an arbitrary word. He said that if it could do that, that would be evidence that it really was reasoning.

Anyone know of other prominent (controversial, important) public predictions of what GPT-3 can or can't do? What about GPT-4? Or GPT-2? It would be interesting to compile a list and see if any patterns jump out, e.g. people consistently over or under-estimating it.
Second Wave?
by jefftk — 2020-06-14T14:00:02.786Z

I've seen several people sharing things like

this
NPR piece: "Is the U.S. at the start of a second wave? Have states
reopened too soon? And have the recent widespread demonstrations
against racial injustice inadvertently added fuel to the fire? The
short, unpleasant answer to the first question is that the U.S. has
not even gotten through the current first wave of infections":

If you look at state-by-state data, however, you can see some
states falling dramatically, while others are rising. For example, this
NPR visualization shows NY has peaked while CA has not:

The national curve is the sum of the curves for all the individual
states, and when states are on such different trajectories looking at
national numbers hides important information. I looked through the
per-state numbers and saw some rough patterns:

First Wave: Some states have peaked, and are now trending
down. This includes the Northeast
megapolis and several other states: Colorado, Hawaii, Illinois,
Indiana, Iowa, Kansas, Louisiana, Michigan, Minnesota, Montana,
Nebraska, and Vermont.

Second Wave: Some states are still rising, without much
indication of slowing down. This includes Alabama, Arizona, Arkansas,
California, Georgia, Mississippi, New Mexico, North Carolina, South
Carolina, Tennessee, Texas, Utah, and Virginia.

Other: Some states seem to have more complicated things going
on, or have low enough case numbers that it's hard to tell. Alaska,
Florida, Idaho, Kentucky, Maine, Missouri, Nevada, New Hampshire,
North Dakota, Ohio, Oklahoma, Oregon, South Dakota, Washington, West
Virginia, Wisconsin, and Wyoming.

Here's what confirmed cases look like for the three groups:

It's two waves superimposed: one that peaked in April, and one that
hasn't peaked yet. Both of those waves are composed of many smaller
waves, one for each state:

'State' likely isn't the right level of analysis; I expect there are
parts of some states, for example, that followed more of a "first wave"
pattern, even as the rest of the state is on the slower trajectory.
I'm pulling these from the convenient JHU CSSE GitHub
repo, which has county level data if anyone wants to play with it.

Speculating now, it looks to me like there's a pattern where people
take precautions more seriously once people they know start dying.
I don't think the second-wave states have hi...
What are some Civilizational Sanity Interventions?
by Eli Tyre — 2020-06-14T01:38:44.980Z

Lately, I've been thinking about the class of things that I'm calling "Civilizational Sanity Interventions." With that term I'm meaning to refer to technologies, institutions, projects, or norms that, if implemented, would improve the quality of high level decision making about important issues.

Which things if they existed in the world, would make our society, collectively, saner?

Some examples (with which I expect most people around here to be familiar):

Prediction markets

Prediction markets are a clever way to aggregate all the available information to make accurate predictions.

Robin Hanson posits that the reason why there isn't wider adoption of prediction markets is because they are a threat to the authority of existing executives.

If we lived in a world where the use of prediction markets were commonplace standard practice, eventually, decision makers would face flack for acting against the predictions of the market, and pundits would have a lot less leeway to make inaccurate, politically-motivated predictions.

Hanson, in a recent interview,

I’d say if you look at the example of cost accounting, you can imagine a world where nobody does cost accounting. You say of your organization, “Let’s do cost accounting here.”

That’s a problem because you’d be heard as saying, “Somebody around here is stealing and we need to find out who.” So that might be discouraged.

In a world where everybody else does cost accounting, you say, “Let’s not do cost accounting here.” That will be heard as saying, “Could we steal and just not talk about it?” which will also seem negative.

Similarly, with prediction markets, you could imagine a world like ours where nobody does them, and then your proposing to do it will send a bad signal. You’re basically saying, “People are bullshitting around here. We need to find out who and get to the truth.”

But in a world where everybody was doing it, it would be similarly hard not to do it. If every project with a deadline had a betting market and you say, “Let’s not have a betting market on our project deadline,” you’d be basically saying, “We’re not going to make the deadline, folks. Can we just set that aside and not even talk about it?”

So pushi...