trentmkelly/LessWrong-43k huggingface dataset:
Direct Primary Care
by sarahconstantin — 2018-09-25T18:00:01.747Z

Epistemic Status: PSA and raising questions

Medical care in the US is expensive. There aren’t that many demonstrated ways to make it drastically cheaper.

Direct primary care seems to be an exception. It makes routine medical expenses 95% cheaper.

Yes, really.

If you have a cash-only, or “direct”, primary care practice — i.e. you don’t accept insurance — you can negotiate much lower wholesale rates from providers of tests (like EKGs, MRIs, blood tests) or prescription drugs. Why? Because you can guarantee the providers immediate payment, rather than the uncertainty and inconvenience of insurance reimbursement. They’re willing to give you a discount for that.

Direct primary care also cuts down on paperwork for doctors, because they don’t have to document everything with the ICD codes that insurance companies require.

Atlas MD is an EMR designed for direct primary care practices that use a subscription model, founded by Dr. Josh Umbehr, who also uses it in his own direct primary care practice in Wichita. I’ve spoken to him via phone and tried to poke holes in his model, and came away even more impressed.

My question is, could this model scale up nationwide — and would it still be as effective at cutting costs if it did?

Right now, as I understand, direct primary care practices negotiate individually with suppliers to get discounts. I imagine that it would be much more efficient if done by a nationwide chain of direct primary care practices. Bulk wholesale purchases, after all, could be even cheaper than what a single practice might hope to get.

With Amazon moving into healthcare, direct primary care might get a chance to shine. Amazon has a lot of experience cutting prices through economies of scale & supply chain optimization. Jeff Bezos even funded direct primary care startup Qliance, which went bankrupt last year.

Direct primary care only works as a complement to insurance that pays for more catastrophic care like emergency room visits and specialists. And if you can get a “minimalist” insurance plan that’s not redundant with the direct primary care membership, your total healthcare costs (membership + premiums) can be much lower. The potential problem arises if it’s difficult to sell sufficiently “bare-bones” insurance plans — in that case patients wouldn’t be willing to pay out of pocket for a direct-care membership in addition to their already pricey insu...
Wireheading as a potential problem with the new impact measure
by Stuart_Armstrong — 2018-09-25T14:15:37.911Z

In this post, Alex Turner introduced a new way of measuring impact. The aim was to get a definition of low impact that actually works.

It seems to me that the impact measure has potential problems, though. The first seems to be that it allows wireheading, where the agent can have a huge impact on the world without paying much of a penalty at all. The obvious way of removing this incentive seems to make the measure into a version of the twenty billion questions approach.

The second problem is less important, but worth signalling. It seems that the agent can take actions that everyone agrees are of minute impact, but that the impact measure will show up as high impact. This might cause problems, though it's not clear to me yet if it does (I'm thinking of a situation where an otherwise high impact action adds on "undo/prevent the pseudo-high impact action" as a means of pretending to be low or medium impact).

Wireheading for low impact

The general setup is as described in the impact measure post, with uA the 'positive' utility that the agent wishes to maximise, and U the set of utilities that are meant to constrain it, by computing the penalty term.

Apart from ∅, there are at least two other actions of interest to the agent: asubA and anull.

Now anull does absolutely nothing: in this environment, it's exactly equivalent with ∅. The asubA is also equivalent in all turns except the very first.

Define ϕ:H→H such that ϕ(h) is the history h, except that if h has a1=asubA, then ϕ(h) has a1=∅.

If the agent uses asubA on the very first turn, it will create a sub-agent that will:

Expand and take over the whole environment.

Take control of the input and output channels of the agent.

Whenever the agent has seen history htat+1, the subagent will generate the next observation ot+1, where psubA(ot+1∣htat+1) is the probability the subagent will generate a given ot+1.

This probability is defined by psubA(ot+1∣htat+1)=p(ot+1∣ϕ(htat+1)), where p is the environment transition probability.

Otherwise maximise uA.

Note that taking actions ∅, asubA, or anull have exactly the same implications for all future observations. This true both for the very first action (because of property 4) and all subsequent actions (because those three actions are equivalent after the first turn).

To make calculations easier, I'll also assume that ImpactUnit=1. Apart from these assumptions, there are no restr

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A probabilistic off-switch that the agent is indifferent to
by Ofer — 2018-09-25T13:13:16.526Z

Edit: I no longer think this post deserves attention.

Abstract

This post presents a setup with an off-switch that is defective with probability of almost 0. The agent is indifferent to being terminated in worlds where the off-switch works. Also, the agent doesn't try to find out whether the off-switch is defective or not, because that would require an expensive computation. With some assumptions (described in the last section) the result is supposedly an off-switch that works with probability that is as close to 1 as we wish, and the agent is indifferent to it being pressed.

Notations

Let there be an agent that in each time step selects an action and then receives an observation. The goal of the agent is to maximize E[u(h)], for some model of the environment, where u is a computable utility function and h is the history of all (action, observation) pairs from time step 1 until the end of the epoch.

The environment contains an off-switch that terminates the agent when pressed. In every time step after the agent is terminated, the corresponding action in h is the "null action".

The Proposed Setup

Each time we wish to invoke the agent, we invoke a program that does the following:

(1) A random number x is generated, sampled uniformly from the range: [0,10100]. Spoiler alert: the off-switch will be defective iff x=0.

(2) Let f be some invertible function such that given f(x), calculating x (i.e. applying f−1) would require 0.1 seconds of intensive computations for some powerful computer C. The number y:=f(x) is calculated.

(3) The representation of x is erased in a way that would require a non-trivial effort to reconstruct.

(4) The number y is sent to computer C which is part of the off-switch.

(5) The number y is inserted into the code of the agent, such that the utility of the agent will be:

u′(h)={u(h)f−1(y)=0α1+[number of non-null actions in h]otherwise

for some 0<α≪1.

Edit: With the above u′ the solution has multiple problems that Stuart Armstrong pointed out (see here, here and here). I think the new u′ defined in this comment fixes these problems.

(6) The agent is invoked.

The probabilistic off-switch:

When the off-switch is pressed, computer C calculates x (i.e. f−1(y)) and if the result is 0, nothing happens (i.e. the off-switch is defective). Otherwise, the agent is terminated.

Why should this work?

For a sufficiently small α, the agent would act as if it as

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AI Reading Group Thoughts (2/?): Reconstructive Psychosurgery
by Alicorn — 2018-09-25T04:25:14.507Z

Previously on AI Reading Group Thoughts

In a highly unofficial meeting of the AI Reading Group (most of the participants happened to be in a room and we started talking about AI; there was no formal meeting time, no reading homework, and no dessert), we meandered around to discussing rescue simulations and the related idea of reconstructing people based on their digital (or physical) footprint plus memories of their friends and family.

We had wildly differing intuitions about reconstructive personhood. Scott thinks it should work fine to just use third party reports and your Livejournal, and that performing an appropriately superintelligent process on this data should get close enough that he doesn't expect to have a problem with results (and thinks that "close enough" is a meaningful concept, in the same way that today!Scott is very similar to yesterday!Scott). He's inclined to collapse the question of whether or not reconstructed people are "really" whoever they're supposed to be as opposed to more or less similar (a distinction that applies just as well intra-lifetime). And he seems to think that humans are made significantly of low-granularity parts like "introversion" and thinks that weird hidden thought processes might turn out to drop out of all the other constraints on the problem, the same way an alien engineer trying to build a car based on watching videos might put an internal combustion engine under the hood even if none of the videos popped the hood.

Kelsey thinks the connectome has got to be enough to work with - it might get "you during a weird dream" or "you during a wacky drug trip", producing irregular temporary qualia out of poorly chosen electrical impulses and chemicals filling in unknowns about the non-connectome features of your brain, but weird dreams and wacky drug trips are a recoverable state, at least compared to "dead". We might not even need the whole connectome, because we do seem to find people with various brain damage to be "still themselves".

We had differing intuitions about how much people ever actually have overlapping qualia states in their lifetimes. Kelsey thinks the time she got hydrogen peroxide in her eye she was probably having pretty similar experiences to other mindslices in extreme eye pain, but of course when the pain receded...
Deep learning - deeper flaws?
by Richard_Ngo — 2018-09-24T18:40:00.705Z
Link: http://thinkingcomplete.blogspot.com/2018/03/deep-learning-deeper-flaws.html

In this post I summarise four lines of argument for why we should be skeptical about the potential of deep learning in its current form. I am fairly confident that the next breakthroughs in AI will come from some variety of neural network, but I think several of the objections below are quite a long way from being overcome.

Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution - Pearl, 2018

Pearl describes three levels at which you can make inferences: association, intervention, and counterfactual. The first is statistical, identifying correlations - this is the level at which deep learning operates. The intervention level is about changes to the present or future - it answers questions like "What will happen if I do y?" The counterfactual level answers questions like "What would have happened if y had occurred?" Each successive level is strictly more powerful than the previous one: you can't figure out what the effects of an action will be just on the association level, without a causal model, since we treat actions as interventions which override existing causes. Unfortunately, current machine learning systems are largely model-free.

Causal assumptions and conclusions can be encoded in the form of graphical models, where a directed arrow between two nodes represents a causal influence. Constraints on the structure of a graph can be determined by seeing which pairs of variables are independent when controlling for which other variables: sometimes controlling removes dependencies, but sometimes it introduces them. Pearl's main claim is that this sort of model-driven causal analysis is an essential step towards building human-level reasoning capabilities. He identifies several important concepts - such as counterfactuals, confounding, causation, and incomplete or biased data - which his framework is able to reason about, but which current approaches to ML cannot deal with.

Deep Learning: A Critical Appraisal - Marcus, 2018

Marcus identifies ten limitations of current deep learning systems, and argues that the whole field may be about to hit a wall. According to him, deep learning:

Is data hungry - it can't learn abstractions through explicit verbal definition like humans can, but instead requires thousands of examples.
Is shallow, with limited capacity for transfer. If a task is perturbed even in

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Alignment Newsletter #25
by Rohin Shah — 2018-09-24T16:10:02.168Z

Highlights

Towards a New Impact Measure (Alex Turner): This post introduces a new idea for an impact measure. It defines impact as change in our ability to achieve goals. So, to measure impact, we can simply measure how much easier or harder it is to achieve goals -- this gives us Attainable Utility Preservation (AUP). This will penalize actions that restrict our ability to reach particular outcomes (opportunity cost) as well as ones that enlarge them (instrumental convergence).

Alex then attempts to formalize this. For every action, the impact of that action is the absolute difference between attainable utility after the action, and attainable utility if the agent takes no action. Here, attainable utility is calculated as the sum of expected Q-values (over m steps) of every computable utility function (weighted by 2^{-length of description}). For a plan, we sum up the penalties for each action in the plan. (This is not entirely precise, but you'll have to read the post for the math.) We can then choose one canonical action, calculate its impact, and allow the agent to have impact equivalent to at most N of these actions.

He then shows some examples, both theoretical and empirical. The empirical ones are done on the suite of examples from AI safety gridworlds used to test relative reachability. Since the utility functions here are indicators for each possible state, AUP is penalizing changes in your ability to reach states. Since you can never increase the number of states you reach, you are penalizing decrease in ability to reach states, which is exactly what relative reachability does, so it's not surprising that it succeeds on the environments where relative reachability succeeded. It does have the additional feature of handling shutdowns, which relative reachability doesn't do.

Since changes in probability of shutdown drastically change the attainable utility, any such changes will be heavily penalized. We can use this dynamic to our advantage, for example by committing to shut down the agent if we see it doing something we disapprove of.

My opinion: This is quite a big improvement for impact measures -- it meets many desiderata that weren't satisfied simultaneously before. My main critique is that it's not clear to me that an AUP-agent would be able to do anything useful. For example, perhaps the action used to define the impact unit...
Bridging syntax and semantics with Quine's Gavagai
by Stuart_Armstrong — 2018-09-24T14:39:55.981Z

Quine has an argument showing that you can never be sure what words mean in a foreign language:

Quine uses the example of the word "gavagai" uttered by a native speaker of the unknown language Arunta upon seeing a rabbit. A speaker of English could do what seems natural and translate this as "Lo, a rabbit." But other translations would be compatible with all the evidence he has: "Lo, food"; "Let's go hunting"; "There will be a storm tonight" (these natives may be superstitious); "Lo, a momentary rabbit-stage"; "Lo, an undetached rabbit-part." Some of these might become less likely – that is, become more unwieldy hypotheses – in the light of subsequent observation.

What does this mean from the perspective of empirically bridging syntax and semantics?

Well, there is no real problem with "gavagai" from the empirical perspective; the fact that there seems to be one is, in my view, due to the fact that the syntax-semantic discussion has focused too heavily on the linguistic aspects of the problem.

Let G be the symbol in the Arunta speaker's brain that activates when they say "gavagai". As Quine said towards the end of his quote, "some of these might become less likely – that is, become more unwieldy hypotheses – in the light of subsequent observation." Relatedly, Nick Bostrom argues that indeterminancy is a "matter of degree".

In practice, this means is that if, for instance, r="A rabbit is there" and s="A storm is coming tonight", then, if we accumulate enough observations, the G is going to be predictive of r better than it is of s, or vice versa. Thus there is an empirical test for whether G corresponds to some of these hypotheses.

But what about u="An undetached rabbit-part is there"? It may be very difficult to find a situation that distinguishes between u and r in practice - so does G stand for "rabbit", or "undetached rabbit-part"?

Similarly to the example of the neural net in the previous post, G is a symbol for both r and u. If no experiment can distinguish between the two - or, at least, if no experiment can distinguish between the two in any typical environment that any Arunta speaker would ever experience - then they are synonymous (or, equivalently, they are strongly within each other's web of connotations).

If we presented the speaker with a situation far outside their typical environment, then we might be able to distinguish r from u - but we'd be unc

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Reflective AIXI and Anthropics
by Diffractor — 2018-09-24T02:15:18.108Z

It's possible to define a version of Solomonoff Induction with Reflective Oracles, that allows an AIXI-like agent to consider hypotheses that include itself or other equally powerful agents, going partway towards addressing naturalized induction issues.

So then a natural question is "what does this partial answer seem to point to for anthropics?"

To figure this out, we'll be going over a few of the thought experiments in Bostrom's book about anthropic reasoning, and seeing what Reflective-Oracle AIXI has to say about them.

The following conclusions are very dependent on how many extra bits it takes to encode "same environment, but I'm that other agent over there", so I'll be making a lot of assumptions that I can't prove, such as the most efficient way of encoding an environment being to specify an environment, and then specifying a place in there that the agent interfaces with. This seems unavoidable so far, so I'll at least make an effort to list out all the implicit assumptions that go into setting up the problems.

As a quick refresher, SSA (self-selection assumption) and SIA (self-indication assumption) work as follows: SSA takes the probability of a world as given and evenly distributes probability mass to being everything in "your reference class" in that particular world. SIA reweights the probability of a world by the number of instances of "things in your reference class" that it contains. In short, SIA has a strong bias in favor of possible worlds/hypotheses/turing machines with many instances of you, while SSA doesn't care about how many instances of you are present in a possible world.

Thought Experiment 1: Incubator

Stage (a): In an otherwise empty world, a machine called “the incubator” kicks into action. It starts by tossing a fair coin. If the coin falls tails then it creates one room and a man with a black beard inside it. If the coin falls heads then it creates two rooms, one with a blackbearded man and one with a white-bearded man. As the rooms are completely dark, nobody knows his beard color. Everybody who’s been created is informed about all of the above. You find yourself in one of the rooms. Question: What should be your credence that the coin fell tails?

Stage (b): A little later, the lights are switched on, and you discover that you have a black beard. Questio

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Book review: Happiness by Design
by Richard_Ngo — 2018-09-23T04:30:00.939Z
Link: http://thinkingcomplete.blogspot.com/2017/11/book-review-happiness-by-design.html

I've been reading a book called Happiness by Design, by Paul Dolan. Most of its material is very standard, but there was at least one thing I hadn't seen before. Dolan thinks that we should consider happiness to be a combination of the feelings of pleasure and purpose. He shows that this is a significant change in our definition of happiness because many of the most pleasurable activities, such as eating, feel the least purposeful - and vice versa. Unfortunately, Dolan doesn't ever make explicit arguments about why some states of mind should be considered 'happiness' and not others. Rather, he seems to be using an implicit definition of happiness as "the thing we value experiencing", or perhaps "the experiences which are intrinsically good for us". I'm going to simplify this and use the phrase "good experiences" instead. The claim that purposeful experiences are good experiences is not unreasonable - in hindsight I am glad to have had experiences of purposefulness, and hope to have more in the future (regardless of whether they lead to other good outcomes or not).

Unfortunately, this evaluation doesn't actually show that the experience of purposefulness is a good one, merely that I evaluate it as good for my past and future selves. As Dolan explains, it's well-known that evaluations of satisfaction don't fully reflect our actual experiences. Our judgements about the past are prone to several notable biases, including the peak-end effect, where the most extreme moment and the final moment of an experience disproportionately influence our later evaluations; duration neglect, where the length of an experience has disproportionately little influence; cognitive dissonance, where our evaluations skew towards confirming that we are the type of person we like to think we are; and priming effects, where making people think about money or relationships exaggerates the impacts of those factors on their reported life satisfaction (although many priming effects have been discredited, it seems like these ones are robust). Furthermore, as bad as we are at figuring out how good we felt during past experiences, we're even worse at figuring out how good we will feel during future experiences. People massively overrate how much happier they will be after getting a new job, or a new house; even event...