trentmkelly/LessWrong-43k huggingface dataset:
How I Make Diagrams
by jefftk — 2022-10-01T21:10:01.215Z

Someone asked me how I make diagrams like the ones in my

two loft

bed posts, so here's how. Note,
though, that these aren't perspective drawings: lines that are
parallel in reality remain parallel on the page. This is a lot
simpler to draw, and looks good enough.

I start with a rough pencil sketch:

The goal is to make sure I know what all the lines are and about where
things fit together. Sometimes I need to do a few rounds of these,
but they're fast enough this isn't much a problem. Lines that are
vertical in real life go up and down, lines that are horizontal and
perpendicular to me go across the page, and lines that would be coming
out of the page instead go down and to the left.

Once I'm happy I start over with a ruler and a pencil. At this stage
you can make it a scale diagram if you want by picking a ratio between
real-life distances and on-page distances, like 1' = 1/2". For lines
coming out of the page you can keep the same ratio, or use a smaller
one like 1' = 1/4". Most of the time I just eyeball this and it's
good enough for what I'm doing. I draw any lines that make things
easier to see how parts fit together, even ones that would be occluded
behind solid things:

If any of it looks wrong I erase and re-draw.

Then I come back with a pen or marker and, again with the
straight-edge, ink the lines:

You'll notice I often inked lines in slightly different places than
I'd penciled them in. When inking I am a lot more careful about
getting angles correct: I pick a reference line for each axis and
match everything I draw to that. I could do this at the pencil-ruler
stage, but this feels a bit easier to me.

Lines that aren't parallel to any of the three axes are harder. I
didn't have any in this diagram, but the basic idea is you figure out
where the two ends go and then draw a straight line connecting them.
If I had something with a ton of these, or with a lot of curves I
would probably choose another method to represent them.

The last step is to come back and erase the pencil marks. A dedicated
eraser generally works better than the ones on the ends of pencils,
and you want one that hasn't gone hard with age:

When I'm making things for my own use I usually only do the first
step, or sometimes the first two. Not much reason to ink it unless I
want it to look nice. The extra steps beyond the ...
Are c-sections underrated? 
by braces — 2022-10-01T20:32:19.127Z

Summary

I think so. Correlations tend to show worse outcomes for c-sections, but the guidelines take this evidence too literally. Careful causal evidence finds that it’s more of a mixed bag. Moderate negative effects on subsequent births seem more robust. An elective c-section might be the defensible choice for someone’s final pregnancy.

Disclaimer: Not a doctor.

The official guidelines

C-sections cost about

twice as much

as vaginal delivery and are associated with worse health outcomes for infants and moms. These facts are concerning enough to the US Medicaid system that one of their “Improvement Initiatives” is

reducing low-risk cesarean delivery

. They write: “Cesarean delivery poses a greater risk of maternal morbidity and mortality for low-risk pregnancies when compared to vaginal births, a risk that ideally should be avoided.”

They lead seminars with slides like this, highlighting that everything bad is correlated with c-sections:

Hospitals have been falling in line. UCSF

boasts

: “Our view of labor and childbirth as a natural process has helped keep our overall Cesarean rate at 20 percent, among the lowest rates in California…Our threshold for making the decision to recommend a C-section is a lot higher than in other places.”

The American College of Obstetricians and Gynecologists (ACOG) has similar advice, and to their credit they spell out the

evidence and their reasoning

. The main paper they cite is this

Canadian study

of 2.3m vaginal and 46k c-section deliveries. “A large population-based study from Canada found that the risk of severe maternal morbidities…was increased threefold for cesarean delivery as compared with vaginal delivery.”

But what really should we make of the association between c-sections and bad outcomes? The observed correlation is ripe for reverse causality, for the same reason that going to the hospital is correlated with dying: c-sections are often performed because of some dangerous condition (e.g., preeclampsia). In the Canadian study, the treatment group was mothers who had scheduled a c-section due to breech position, and breech is correlated with other negative outcomes. In other studies, the c-sections could happen for reasons that are never recorded in hospital data, so even lots of control variables should make you worried about selection effects.

The causal e...
Do anthropic considerations undercut the evolution anchor from the Bio Anchors report?
by Ege Erdil — 2022-10-01T20:02:48.464Z

The Bio Anchors report by Ajeya Cotra tries to forecast AGI timelines by getting estimates of how much compute might be needed to build or train a model that has general intelligence capabilities. This estimate is based on various biological comparisons, and for the purposes of this question I want to focus on the most conservative estimate from the report, that coming from how much computation was done by evolution in the history of life.

It turns out we can bound this in several different ways by a number on the order of 1045 FLOP. One argument could be this: the Landauer limit bound on how much compute can be done on Earth given how much energy the Earth is able to radiate is something like 1047 bit erasures/year, and we can cut this down by many orders of magnitude due to various considerations: brains operate far from the Landauer limit at the bit level (though maybe not if you use a larger scale Landauer limit that conditions on irreversible information transmission via electrons, see this post), evolution is only responsible for a small fraction of the Earth's radiation, et cetera. Overall I think something on the order of 1045 FLOP for total compute done by evolution seems sufficiently big to be safe. The Bio Anchors report uses a different method but arrives at a number that's not very different from this one.

There is, however, a potential problem with this: we can imagine that evolution is an optimization process that's running on many different planets, and only in a select few number of them does evolution end up producing general intelligence. If this is true, producing general intelligence can be a much harder problem than this calculation gives it credit for, because anthropic considerations mean we would only be asking the question of how difficult general intelligence is to produce in worlds where general intelligence was actually produced. We should therefore multiply our compute upper bound by the reciprocal of the probability that this amount of compute is actually sufficient to produce general intelligence, and anthropic considerations make it very difficult to put any nontrivial bounds on this probability, threatening to make the entire estimate worthless.

One argument I see on the other side is from Robin Hanson's grabby aliens model. He points out that if evolution happens in a sequence of steps and some of these steps are "hard" in the...
AGI by 2050 probability less than 1%
by fumin — 2022-10-01T19:45:27.564Z
Link: https://docs.google.com/document/d/e/2PACX-1vTZLp5ZKdIV1c6rQR8fa-btfgiyuunliwmD2gYvvSlS7GpJUhahlwxuEr_PCIqCsDgD5SfYAfMubaDE/pub

There are three serious gaps in our current scientific understanding of AI:

We know nothing about working memory
Our knowledge save for feed forward networks with gradient descent is invalid
We know nothing about AGI, except for an uncomputable definition

Due to these basic deficiencies in our science, the probability of it turning into applied engineering in any near future is nil. Besides, history provides ample lessons on the follies of current popular ways of estimating time to AGI. Last but not least, a down to earth review of our societal and economic environment cautions excessive exuberance embraced by the AI community.

We know nothing about working memory

Working memory here refers to the ability of recalling past observations in the near future. This is an essential feature of any definition of intelligence. In the context of current mainstream research, the concept of working memory can be made more concrete by defining it as the ability of a neural network to

recall past observations beyond a single backpropagation step

Unfortunately, despite the glut of research papers containing the word memory in their titles, an AI that demonstrates the above ability remains out of reach. In truth, what models of famous papers claim to do and what they actually can do are far apart:

Long short-term memory (LSTM)

, or recurrent neural networks (RNN) in general, suggest that they differ from traditional feed forward neural networks in their unique ability to recall past frames in a sequence. However, this is false at the fundamental level, in the sense that a feed forward network processing an image can be viewed as recalling previous observations in a stream of pixels, too. Within a single backpropagation step, the information flow of a LSTM as shown below is indeed different from feed forward network.

However, this information flow does not go beyond a single backpropagation step, thus it remains void of working memory according to the above definition. Now, some claim that even though information flow is confined to a single backpropagation step, that step nonetheless consists of multiple frames in an episode, hence it may still be fair to claim those networks as possessing

episodic memory

. Such play of words, while common in psychology and neuroscience academia, bears no weight to the discussion whose conclusion can be summarised as backpropagation-thro...
What sorts of preparations ought I do in case of further escalation in Ukraine?
by tailcalled — 2022-10-01T16:44:58.046Z

I guess the Russian invasion of Ukraine can go in many ways. It might just fizzle out, or continue to be a fairly conventional war with extent limited to Ukraine, or it might escalate with a Russian nuclear strike against Ukraine followed by NATO counterattacks against Russia, and it doesn't seem implausible to me that it could spin totally out of control into a WW3 scenario. Or various other paths.

The more extreme end of the range with these scenarios seem worth preparing for, but I'm not completely sure what preparations would be the most relevant. I thought LessWrong might have some good ideas.

If relevant, I live in Denmark, a NATO country close to the core of the EU, not quite at the border of Russia but also not quite buffered away from Russia (especially if counting Kaliningrad). Though preparation ideas that are more relevant to people in other countries than Denmark are welcome too as they might be useful to someone else.
Interesting predictions on manifold.markets
by jmh — 2022-10-01T16:09:46.005Z

I noticed two separate predictions on manifold.markets related to Xi and Xi & Putin remaining at the top of their governments.

Xi and Putin at 93% and Xi at 95%.

What's interesting, and I wonder if it makes sense, is that together these suggest people think Putin is more secure in his future than Xi.

Curious about other's take on this.
What's the actual evidence that AI marketing tools are changing preferences in a way that makes them easier to predict?
by Emrik — 2022-10-01T15:21:13.883Z

I've encountered this claim multiple times over the years (most recently on this AXRP episode), but I can't trace its origins (it doesn't seem to be on Wikipedia). Quoting Evan from the episode:

And so if you think about, for example, an online learning setup, maybe you’re imagining something like a recommendation system. So it’s trying to recommend you YouTube videos or something. One of the things that can happen in this sort of a setup is that, well, it can try to change the distribution to make its task easier in the future. You know, if it tries to give you videos which will change your views in a particular way such that it’s easier to satisfy your views in the future, that’s a sort of non-myopia that could be incentivized just by the fact that you’re doing this online learning over many steps.
And if you think about something, especially what can happen in this sort of situation is, let’s say I have a - Or another situation this can happen is let’s say I’m just trying to train the model to satisfy humans’ preferences or whatever. It can try to modify the humans’ preferences to be easier to satisfy.

Furthermore, there's a world a difference between deliberately optimising for modifying preferences in order to make them easier to predict, vs preferences changing as a byproduct of the AI getting better at predicting them and thus converging on what to advertise. This matters for what predicted features of strategies an AI is likely to pick out of strategy space when new options are introduced.
Google could build a conscious AI in three months
by derek shiller — 2022-10-01T13:24:10.752Z

Summary: Theories of consciousness do not present significant technical
hurdles to building conscious AI systems. Recent advances in AI relate to
capacities that aren't obviously relevant to consciousness. Satisfying major
theories with current technology has been and will remain quite
possible. The potentially short time lines to plausible digital consciousness mean that issues relating to digital minds are more pressing than they might at first seem. Key ideas are bolded. You can get the main points just by skimming these.

Viability

Claim 1: Google could build a conscious AI in as little as three months
if it wanted to.

Claim 2: Microsoft[1] could have done the same in 1990.

I'm skeptical of both of these claims, but I think something in the
ballpark is true. Google could assemble a small team of engineers to
quickly prototype a system that existing theories, straightforwardly
applied, would predict is conscious. The same is true for just about
any tech company, today or in 1990.[2]

Philosophers and neuroscientists have little to say about why a digital
system that implemented fairly simple patterns of information processing
would not be conscious. Even if individual theorists might have a story
to tell about what was missing, they would probably not agree on what
that story was.

The most prominent theories of consciousness lay out relatively vague
requirements for mental states to be conscious. The requirements for
consciousness (at least for the more plausible theories[3]) generally
have to do with patterns of information storage, access, and
processing. Theorists typically want to accommodate our uncertainty
about the low-level functioning of the human brain and also allow for
consciousness in species with brains rather different from ours. This
means that their theories involve big picture brain architectures, not
specific cellular structures.

Take the Global Workspace Theory: roughly put, conscious experiences
result from stored representations in a centralized cognitive workspace.
That workspace is characterized by its ability to broadcast its contents
to a variety of (partially) modularized subsystems, which can in turn
submit future contents to the workspace. According to the theory, any
system that uses such an architecture to route information is conscious.

A software system that included a global workspace would be easy to
build. All you have to do i...
Announcing the AI Safety Nudge Competition to Help Beat Procrastination
by Marc Carauleanu — 2022-10-01T01:49:23.005Z

Have you achieved your goal? Fill in

this form

to be added to the prize draw!

Have you been postponing doing alignment work? Is your learning progressing slower than you'd like? We've had these experiences too, so we've created the AI Safety Nudge competition to help.

You have until the end of October to register your goal and complete it. Then you have until the 7th of November to register completion. We will only accept registrations up to this date.

Pre-register your goal here before you start work.

The AI Safety Nudge Competition aims to encourage people to do things related to AI Safety today instead of procrastinating it into the future by allowing them to enter into a draw if they complete the goal that they set for themselves.

Participants will define a specific goal for themselves, examples include:

• Finish reading Superintelligence

• Finish writing up a relevant blog post

• Organize a local dinner for people interested in AI Safety

If you complete the goal you set out for yourself you will be entered into the draw:

• ten prizes of $100

• two prizes of $100 specifically for Australia and New Zealand

Unfortunately, we cannot include participants from the EU due to local laws.

We are relying on an honor system for the completion of projects. We would prefer if you registered your goal before you started working on it so that we can best learn from this experiment.

We encourage you to pick something for which you need motivation rather than something where it won't make a difference.

To prevent downside risks, certain projects would be excluded including:

• Outreach to famous people, politicians, the media, children, high-net-worth individuals, and top AI researchers

• Projects that could be controversial, come with significant down-side risks, or could produce negative PR

• AI Safety research with high capabilities externalities

For the above kinds of projects, we believe that they should only be pursued after careful consideration rather than under a deadline. Please message chris@aisafetysupport.org if you are uncertain.

Dates

Applications start the 1st of October
Participants will have to finish the goal they set out for themselves by the 31st of October, but will have until the 7th Novermber to register a successful completion.
Winners will be announced by the 14th of Nove

...
A Few Terrifying Facts About The Russo-Ukrainian War
by Pee Doom — 2022-09-30T21:34:10.194Z

Epistemic status: trying to summarize the news and predict, post is under revision, too lazy to citation everything

I wanted to collect a few observations I've made, as best I understand them. This PBS article does a good job of explaining much of it.

Vladimir Putin has announced the annexation of four Ukrainian territories.

This makes them Russian territory from Russia's perspective.
“People living in Donetsk, Luhansk, Zaporizhzhia and Kherson are becoming our citizens forever” - Putin

The West does not acknowledge this annexation, describing it as illegal. Ukraine does not acknowledge this annexation and says it plans to take the territories back.

"By attempting to annex Ukraine's Donetsk, Luhansk, Zaporizhzhia and Kherson regions, (Russian President Vladimir) Putin tries to grab territories he doesn't even physically control on the ground. Nothing changes for Ukraine: we continue liberating our land and our people, restoring our territorial integrity," Ukraine's Foreign Minister Dmytro Kuleba said on social media.

Russian military doctrine allows the usage of nuclear weapons to defend Russian territory.
Putin has a track record of escalating apparently (this needs more data) and Russia seems to be planning for escalation until the war is won.

https://www.themoscowtimes.com/2022/09/29/putin-always-chooses-escalation-a78923

"All of our sources in the elite — who all spoke on the condition of anonymity — said the military conflict will only escalate in the coming months."

Putin has clearly stated that they will defend this territory, including with tactical nukes if need be.

He said they would use "any means available" to defend it
He has mentioned usage of nukes some number of times (a nice-to-have: a list of all the times he has said this)
Medyedev has stated the West would not retaliate if nuclear weapons are used.
"Under Russia’s amended constitution, no Kremlin leader can cede territories once they are annexed." - someone on Twitter

Putin has stated he is not bluffing.

The U.S. Secretary of State says it is "loose talk".

Putin has called for a ceasefire.

Ukraine and U.S. does not want to do this.

The U.S. has said there will be "catastrophic consequences" if nuclear weapons are used.

They are keeping the consequences vague for strategic flexibility.

Concerning escalatory developments that aren't directly related to nuclear brinksmanship:

The Nordstream natural gas pipes were

...
What Is the Idea Behind (Un-)Supervised Learning and Reinforcement Learning?
by Morpheus — 2022-09-30T16:48:06.523Z

Through the authority of my ml class and Wikipedia[1] I "learned" that ml algorithms can be roughly put into 3 clusters: unsupervised, supervised, and reinforcement learning.

When learning about machine learning, one term I stumbled over is (un)supervised learning. While at this point I might be able to guess the teacher's password when someone asked me what (un)supervised learning means[1:1], I don't understand why one would carve up reality this way. Most resources I found through googling didn't really help but suggested that for some reason, reinforcement learning would also be thought of as another separate category.

So instead, I felt forced to think about this for myself. The rough abstract picture in my head after thinking for a few minutes:

How do they relate to carving up thing space/feature space?

Unsupervised learning: Computing "summary statistics" for a set of data points.

Supervised learning: Extrapolating missing features for a set of data points.

Reinforcement learning: Special case of supervised learning, learning some function (action, states) -> "values" (something humans care about).

Why are these categories useful?

Machine learning is about getting rid of humans for getting things done. Here is how different algorithms contribute:

Unsupervised learning: Computes summary statistics informing human decisions

Supervised learning: Making predictions for humans and implicitly making decisions for humans.

Reinforcement learning: Explicitly making decisions on behalf of humans.

How do they relate to each other?

It seems like any supervised learning algorithm would need to contain some unsupervised algorithm doing the compression. The unsupervised algorithm could be turned into a "supervised" one, by using the "summary statistics" to predict features we don't know about a data point through its other features. In the end, the whole reason we are doing anything is that we care about it: reinforcement learning is a special kind of supervised algorithm, designed such that we can exceed humans by explicitly encoding their judgement to "act" on surfaced insights.

The unsupervised/supervised distinction still seems a bit "unnatural"/useless to me. Feel free to leave a comment if you have more insight into why one might or might not carve up reality this way.

Definition from Wikipedia: "Unsupervised learning is a type of algorithm that learns pa

...