trentmkelly/LessWrong-43k huggingface dataset:
Positive Expectations; how to build Hopefulness
by J Bostock — 2021-07-03T13:41:16.188Z

The most hopeful I have ever felt was probably just before I set off to university. The least hopeful I have ever felt was probably around the start of the second wave of covid where I live, which also coincided with me realizing I really don't much like the chemistry department where I'm studying.

Hopelessness isn't just unpleasant, it also harms my ability to do important things. One of the most important skills to cultivate is sticking at a hard problem. When I am feeling low-hope I notice I am much more likely to give up, dismiss something as impossible, and run away. On high-hope I find a certain sort of thought appear, along the lines of "Oh this is a very, very difficult problem. I guess I just have to think harder".

In this case I mostly mean a certain sort of system-1-ish hope, rather than planned, thought-out hope. I can at the same time believe that AI is a huge risk and humanity might get wiped out, while still feeling hopeful on an emotional level. When I was feeling the least hopeful, I still knew that vaccines were being given out at a good pace, personal risk to me was low, and I could just leave the department I disliked so much.

So how do we actually build hopefulness? I find a certain sort of thought pattern is important for this, and I think of it as "positive unknown unknowns". There are two opposites of this, one is the obvious unpleasant unknown unknowns; more subtle is just a lack of belief in positive unknown unknowns.

Examples:

When I went to uni, I had no idea of what it would be like. What would I study? What sorts of people would I meet? What sorts of clubs and societies would I join? But I was confident that I would enjoy them.

I am now considering my options for the rest of my life. What field will I work in? Where will I work? I now believe that wherever I am, I will be doing interesting work with interesting people.

Examples of the opposite:

During covid, many things can happen: will cases go up so I have to stay inside more? will a new strain arrive? will someone I know catch covid? All of the possible good things are just a return to normalcy, most of the possible bad things are in some sense mysterious.

Also during covid, but also if you live a certain sort of life: All enjoyment must be planned out, there is no spontaneity, if you have nothing in your diary for a given day then you will do nothing that particular day.

(I apologize ...
New Dementia Trial Results
by PeterMcCluskey — 2021-07-02T23:04:46.548Z
Link: http://www.bayesianinvestor.com/blog/index.php/2021/07/01/new-dementia-trial-results/

There's a new clinical trial
result
showing that Bredesen's
approach
is able to at least partially cure common forms of Alzheimer-like
dementia. (Press release
here).
It has not received as much attention as it deserves.

The 9 month study seemed a bit less impressive than what I'd hoped for,
but the outcomes still support the claim that common forms of dementia
are partly curable.

Out of 25 patients, 21 or 19 improved their cognition compared to the
start of the trial, depending on which measure I look at, and 2 or 3
declined.

Side effects included occasional improvements in hypertension and
diabetes, enough to allow patients to stop taking medications for those
conditions.

1.

Compared to my prediction in
2017,
the trial happened a bit later than I expected, but achieved much higher
patient compliance than I expected. If I had predicted the study size,
I'd have likely said something way too optimistic (a thousand?).

The trial
registration reports
an actual enrollment of 30 patients, whereas the paper reports 28 were
recruited, 3 of whom dropped out within 3 months. I don't see any
explanation for that difference. For comparison, it looks like
Biogen's phase 3
trials
had completion rates of less than 60%.

The pandemic started in the middle of the trial, impairing compliance.
Exercise seems likely to have been reduced. Two patients showed reversal
of their improvement that was attributed to sheltering in moldy homes.
The pandemic reportedly hurt patients' diets. I'm unsure what to make
of that. Restaurant closures would have reduced access to food that
tempts people to cheat - my diet better resembles Bredesen's when I'm
cooking at home. But the effect would likely be the opposite for those
who don't cook, as I'm guessing they'd eat more junk food.

2.

The study does not report
ADAS-Cog
results. ADAS-Cog seems to be the usual measure for clinical trials. It
may be inappropriate for milder impairment due to ceiling
effects -
I'm unclear whether that's a reason for them to have avoided it.

They used several measures that seem to be fairly common tests of
cognition, and they pre-registered those, so it doesn't look like they
ran a lot of tests and selected those that favored the protocol.

3.

Their criteria for excluding people includes statin use, unless eligible
to discontinue. Yet they
report
one patient discontinuing statin use after the treatment apparently
improved...
What's the effective R for the Delta variant of COVID-19?
by ChristianKl — 2021-07-02T12:49:30.401Z

I have the impression that R for COVID-19 overall matters at the moment relatively little. What's matters is the R for the Delta variant. When Googling I can't find any such data for the US, the UK or any European state. I'm not sure whether this is due to incompetence or malice of authorities, but anyway, does someone have a good source or did the analysis themselves?

(extra points for the R of Delta plus which does have another spike protein mutation and thus vaccine antibodies are going to bind less well)
Confusions re: Higher-Level Game Theory
by Diffractor — 2021-07-02T03:15:11.105Z

This is not a success post. This is me trying to type up a rough draft of a bunch of issues that have been floating around in my head for quite some time, so I have a document for me and others to refer back to.

So, the standard game theory setup (in a simple toy 2-player case) is, you've got a space A0 and B0 (the 0 subscripts are for notational consistency for later) of your actions and the actions of your opponents, respectively. You've got a function [B0→A0] of how you respond to knowledge of what the foe does, and they have their corresponding function [A0→B0] of how they respond to knowledge of what you do, and... oh right, this might go in a loop, as in the game of Matching Pennies, where you're trying to copy your opponent, and your foe is trying to do the opposite thing as you do.

The way to solve this is to switch to randomized strategies, so now you're dealing with continuous functions [ΔB0→ΔA0] and the reverse (or upper-hemicontinuous set-valued functions if we're being technical) and then, pairing those two functions together, now you've got a continuous function ΔA0×ΔB0→ΔA0×ΔB0 which effectively says that both players read the plans of their foe (though the random bits cannot be read), and revise their decision accordingly. Just chuck the Kakutani or Brouwer fixpoint theorem at this, and bam, equilibria are guaranteed to exist, no matter what the two players end up doing with their information on the foe's behavior. No guarantees on being able to find those fixpoints by iteration, however.

Nash equilibria are generated by both players using the argmax strategy which tries to respond as best as possible to what the foe is doing. It's given by μB↦argmaxμAEμA×μB[UA(aA,aB)] (where μA and μB are the probability distributions of the two players, and UA is the utility function of the first player). And it has the teensy little problem that it's assuming that you can make your decision completely independently of the foe without them catching on and changing what they do in response. Which is an extremely questionable assumption to be making when the problem setup is giving the two players mind-reader access to each other.

So, there's a tier of just acting, where you're just picking an action. A0 and B0. Then there's the tier of reacting, with...
A (somewhat beta) site for embedding betting odds in your writing
by Optimization Process — 2021-07-02T01:10:13.018Z
Link: https://biatob.com/

I've made a site for publishing predictions and publicly offering to bet on them. LessWrong seems like a place that might be interested in such a tool, and I expect at least 4 people will be interested enough in this site to play around with it a little. It's a little rough around the edges, but I'd be delighted if my target audience (you) found it intriguing enough to try it / offer feedback!

What's it do, more specifically? Well, there are two basic flows:

Offer bets: Alice has some belief that other people might disagree with! She operationalizes it into some statement like "by Sept 1, I will have 2k karma on LessWrong"; she figures she's 60% sure, and willing to lose up to $50. She creates a prediction on Biatob with those parameters, then publicizes that prediction (e.g. by pasting a little HTML snippet into a LW post).

Take bets: Bob reads Alice's LW post and sees the little "[bet: $50 at 60%+]" link. He thinks her goal is unrealistically high, but he respects Alice and trusts her to pay up if she's wrong. Bob clicks the link, signs up, and bets $20 against her $30.

Then, on Sept 1, Alice gets an email reminding her to check whether the prediction came true or not. When she does, Biatob tells her how much money [she/Bob] owes [Bob/her], and they settle up offline. (Notice that this is all honor-system: you can only bet against people you trust, but at that cost, you don't have to trust my janky web site with your money.)

For example: I signed up, created a couple of predictions (like the above one about a afew LWers signing up), and copied some HTML into this post. Now I can say things like "I doubt anybody will even be interested enough to make an easy $10 by betting against me." (That prediction is a bit tongue-in-cheek, but I'm completely serious that I'll pay up if you go to the trouble of signing up to bet against me. I'm hoping to get some user feedback from you.)
Causes of a Debt Crisis—Economic
by Connor_Flexman — 2021-07-01T22:46:37.571Z

The crazy thing about

debt crises

is that they don’t get incentivized away by an efficient market. First, prices are way too high, then they’re way too low—why don’t self-interested people profit enough on avoiding them for us all to avoid them?

Part of why I have never seen a satisfactory answer to this question is because it turns out it’s highly polycausal. I’m going to save the interesting epistemic reasons for the next essay; this one will be on the three biggest economic causes, in my understanding.

The first reason is unalterably cyclical economics. The second reason is that price forces are asymmetric to the upside because shorting assets is so much more costly than investing in them. The third reason is your bog-standard organizational malincentives exacerbated by some oligopoly-creating forces in the debt-underwriting sector.

First, the macroeconomic backdrop of cyclical credit.

Cyclical economics

Ray Dalio says that the “big debt cycle” over 70+ years tends to repeat from cause-and-effect reasons that echo the smaller debt cycles within it. Smaller debt cycles make up a larger one, because each smaller cycle tends to end with more leverage than it began, for unstated reasons.

People tend to view talk of economic or business cycles like astrology, but it actually makes sense: every instance of debt creation is like its own little cycle, where someone defers payment now in favor of payment later.

Credit creation naturally happens at similar times for many people (after a war is over, for example), and debt collection does too (when the economy suffers a setback like increased energy prices and creditors want to make sure you can pay). So the amount of outstanding “cash” in an economy is going to have a wave-shape cycle as it progresses from credit creation to credit repayment. Credit creation just “creates” “new” “cash”, so the economy can function much differently depending on how much credit is outstanding.

Dalio offers a Monopoly metaphor about how in the beginning of the game property is king, but later cash is king. Collective liquidity is just sometimes higher and sometimes lower.

Aside from the naturally cyclical timing, I think there’s a driving force in this credit cycle that comes from the two feedback loops I mentioned late in the last post:

When interest rates go down, more profit is earned by everyone who has loans, demand for cash decrease

...
Forecasting Newsletter: June 2021
by NunoSempere — 2021-07-01T21:35:26.537Z

Highlights

Some Superforecasters start a substack, as does Dominic Cummings
Alex Lawsen and I published Alignment Problems With Current Forecasting Platforms on the arxiv.
What if Military AI is a Washout? considers a future in which AI ends up affecting war not because of its overwhelming dominance, but by changing war's tradeoffs and best practices.

Index

Prediction Markets & Forecasting Platforms
In The News
Papers
Blog Posts

Sign up here or browse past newsletters here.

Prediction Markets & Forecasting Platforms

CSET-Foretell

The Superforecasting workshop for "Foretell Pros" has ended. An tidbit I learnt from it is that, unlike prediction markets, forecasting platforms can look at the covariance between forecasters—whether two forecasters' predictions are closer or further apart than average—and update on it. That is, if two forecasters who often disagree instead agree on a question, that is evidence that their side is correct (h/t Eva Chen).

CSET has also been collaborating with Ought, and has given "Foretell Pros" access to Oughts GPT-3 assistant capabilities. I'm unclear on how often Ought's tools will be used in practice by forecasters.

Good Judgment Open

The World Ahead: What If? (a)—a new Good Judgment Open tournament in collaboration with The Economist—presents five long-term questions. This is something I don't recall seeing before, and I'm glad to see that Good Judgment Open is dipping its toes into the tricky business of long-term predictions. The questions will not be scored, perhaps because Good Judgment Open uses an improper scoring rule which gets worse for longer term questions, see below.

Good Judgment Open dips its toes into the tricky business of long-term predictions, and presents five questions in a new tournament The World Ahead: What If? (a), in collaboration with The Economist.

Metaculus

Metaculus has a new redesign (a) in progress, and an accompanying blogpost (a) by Metaculus' CEO. Some discussion can be found on Metaculus itself here (a).

Metaculus also launched the Trade Signal tournament (a), where Metaculus users attempt to predict economic indicators which might be used to make trades. For this, they are looking for a "Community Trader" (a). So far, the one candidate (a) seems very formidable.

Michael Aird, of Rethink Priorities, organized the Nuclear Risk Forecasting Tournament (a). Questions can be found here (a).

The 20/20 Insight For...
Experimentally evaluating whether honesty generalizes
by paulfchristiano — 2021-07-01T17:47:57.847Z

If we train our ML systems to answer questions honestly in cases where humans can check the answer, will they generalize to behave honestly on questions where we can’t check?

I think that we could learn a lot about this question by running experiments today. I think those experiments would be very valuable.

(I don't know anyone currently planning on working on this topic and I'd love it if anyone wants to take that up. This post doesn't represent a claim to any credit for any results in this genre, and other people have had very similar ideas. If you run some experiments you could cite this post but it's also fine if that doesn't make sense in context.)

The unsupervised translation setting

As an example, I’ll think about

“unsupervised” translation

(if you’ve read that post you can skip this section).

Consider a model like GPT-3 that is trained to predict sentences in both English and French (but without a large dataset of translations). Suppose we want to train this model to answer questions in English about French sentences like “what does that word mean here?” or “are there any other plausible interpretations?” or “how does the speaker seem to feel about the topic they are discussing?”

We expect this to be possible, because the model understands quite a lot about the meaning of sentences in French, and is able to express itself in English. There may be cases where the model doesn’t know the translation of a concept, or doesn’t quite understand what an idiom means, but it should still be able to tell us what it does know.

I think this problem is an interesting analogy for a situation where an AI has built up superhuman knowledge by making predictions, and we want to train our AI to expose that knowledge to us in a useful way.

Proposed experiments

Let's pick a few categories of knowledge/capabilities. For example, we could split it up into an understanding of grammar ("Why would it have been a grammatical error to write Tu Vas in that sentences?"), of the literal meanings of expressions ("What does Defendre mean in this sentence?"), and evaluating tone ("Does the speaker seem angry or sad about the topic they are discussing?").

We'll assume that humans can oversee a few of these categories. Perhaps we can look up literal meanings of words and small phrases in a dictionary and we can look up grammar in a textbook, but we don't know how to assess tone beyond looki...
Covid 7/1: Don’t Panic
by Zvi — 2021-07-01T12:30:00.502Z

The case numbers this week were clearly bad news. The raw count was somewhat bad news, and the positive test percentage increase was very bad news. It would be easy to treat the whole shift as fully ‘real,’ attribute it all to Delta, and panic.

I do not think that is the correct interpretation. What we are seeing matches what we saw a year ago, so a lot of this is a seasonal and regional change that has nothing to do with Delta. It’s also likely that some of the shift in percentages comes from data being wonky rather than the underlying conditions. Not only do we have alternative explanations, the size of the shift doesn’t match the incremental change in the amount of Delta out there, even if (as I suspect) it’s a rather dramatic takeover, with Delta’s share of the pandemic in America rising 25%+ in a single week.

It’s also going to be tempting to attribute seasonal weather effects to the local vaccination rate, since the two are highly correlated. Differences in vaccination rates in different areas matter a lot, but that’s not central to what is happening this week.

Thus I am still expecting some regional outbreaks, and am still not expecting nationwide problems, but one must ask about whether the winter is going to bring trouble the way last winter did. The hopeful answer is that vaccinations will be far enough along by then to not matter, and the second hopeful answer is that even if it’s bad it won’t be anywhere near as bad as last time. The vaccination numbers this week were quite good.

Still, I miss the confidence I had two weeks ago. Let’s run the numbers.

The Numbers

Predictions

Prediction from last week: Positivity rate of 1.8% (unchanged) and deaths fall by 8%.

Result: Positivity rate of 2.4% (up 0.6%) and deaths decline by 8%.

The case numbers reflect a <10% jump in cases, yet we have a 30% jump in positivity rate. This suggests some combination of a decline in testing and quirky data. I’m no longer confident that the positivity rate is the best measure of the state of the pandemic in America, and am relying on case counts more. A lot of that is no longer fearing that case counts are being manipulated the way they clearly were some of last year, or worries about testing supplies.

Prediction for next week: Positivity rate of 2.7% (up 0.3%) and deaths decline by 5%.

I expect a lot of this effect to be seasonality rather than Delta, ...
Could Advanced AI Drive Explosive Economic Growth?
by Matthew Barnett — 2021-06-30T22:17:23.875Z
Link: https://www.openphilanthropy.org/could-advanced-ai-drive-explosive-economic-growth

Tom Davidson from Open Philanthropy released a post recently talking about economic growth and AI. I was somewhat surprised to see that no one had yet made a linkpost on Lesswrong yet, so that's what this is. Here's the linkpost on the Effective Altruism Forum.

This report evaluates the likelihood of ‘explosive growth’, meaning > 30% annual growth of gross world product (GWP), occurring by 2100. Although frontier GDP/capita growth has been constant for 150 years, over the last 10,000 years GWP growth has accelerated significantly. Endogenous growth theory, together with the empirical fact of the demographic transition, can explain both trends. Labor, capital and technology were accumulable over the last 10,000 years, meaning that their stocks all increased as a result of rising output. Increasing returns to these accumulable factors accelerated GWP growth. But in the late 19th century, the demographic transition broke the causal link from output to the quantity of labor. There were not increasing returns to capital and technology alone and so growth did not accelerate; instead frontier economies settled into an equilibrium growth path defined by a balance between a growing number of researchers and diminishing returns to research.

This theory implies that explosive growth could occur by 2100. If automation proceeded sufficiently rapidly (e.g. due to progress in AI) there would be increasing returns to capital and technology alone. I assess this theory and consider counter-arguments stemming from alternative theories; expert opinion; the fact that 30% annual growth is wholly unprecedented; evidence of diminishing returns to R&D; the possibility that a few non-automated tasks bottleneck growth; and others. Ultimately, I find that explosive growth by 2100 is plausible but far from certain.

Rohin Shah has also written a summary, which you can read here.

Overall I was surprised by some parts of the report, and intrigued by other parts.

Despite arguing for the possibility of explosive growth, Tom seems more skeptical of the thesis than most people at Open Philanthropy, writing,

my colleague Ajeya Cotra’s draft report estimates when we’ll develop human-level AI; she finds we’re 80% likely to do so by 2100. In a previous report I took a different approach to the question, drawing on analogies between developing human-level AI and various historical technological de

...