trentmkelly/LessWrong-43k huggingface dataset:
Experimentation with AI-generated images (VQGAN+CLIP) | Solarpunk airships fleeing a dragon
by Kaj_Sotala — 2021-07-15T11:00:05.099Z

A few days ago I found the Twitter account @images_ai, which posts AI-generated images and links to these instructions for generating your own. I started playing around with it; some of my choice picks:

The first image I generated, “sci-fi heroes fighting fantasy heroes”

“cute catboys having a party”

Someone had figured out that if you add words like “unreal engine” to your prompt, you get more realistic graphics.

so here’s “sci-fi heroes fighting fantasy heroes trending on artstation | unreal engine”

“young sorcerer heiress of a cyberpunk corporation, with dragons on her side | unreal engine”

“the cat is over the moon|unreal engine”

“Ghosts of Saltmarsh”

wikiart16384 model, “Fully Automated Luxury Gay Space Communism:2 | unreal engine:1 | logo:-1”

At this point I read a tweet suggesting that a 16:9 image ratio generally produces better results, so I switched to using that.

“solarpunk forest village | studio ghibli | trending on artstation”

“mountain expedition encounters stargate | full moon | matte painting”

“xcom fighting sectoids | matte painting”

I decided to do some systematic experimentation: picking a starting prompt (“solarpunk airships fleeing a dragon”), fixing the random seed, and varying parts of the prompt to see how they changed.

In retrospect the initial prompt wasn’t great, since I’m not sure if any of the pictures really incorporated the “fleeing” aspect, so I could have picked a more fully meaningful prompt. In general, this AI seems to be better at understanding individual words than it is at understanding sentences. Oh well.

All of these use seed 13039289688260605078 (no special significance; it’s what got randomly rolled for the first one so I kept using it), image size 640*360.

I don’t have anything interesting conclude besides what you see from the images themselves. As I was posting these on Facebook, Marcello Herreshoff kindly joined in on the experimentation, coming up with additional prompts and generating pictures with them; I’ve credited his images where they appear. [EDIT: Marcello let me know that he hadn't remembered to fix the seed for his pictures, but they got similar enough to my pictures anyway that I never even suspected!]

“solarpunk airships fleeing a dragon”

“solarpunk airships fleeing a dragon | Dr. Seuss” (Marcello)”

“solarpunk airships fleeing a dragon | Dr. Seuss | text:-1” (Marcello)

“solarpunk airships fleeing a dragon | paul ...
A closer look at chess scalings (into the past)
by hippke — 2021-07-15T08:13:34.756Z

Introduction

I had explored measuring AI or hardware overhang in August 2020 using chess. Hardware overhang is when sufficient compute is available, but the algorithms are suboptimal. I examined the strongest chess engine of 2020, Stockfish 8, performing at 3,400 ELO under tournament conditions. When reducing compute to 1997 levels (equivalent to a Pentium-II 300 MHz), its ELO score was still ~3,000. That is an important year: In 1997, the IBM supercomputer "Deep Blue" defeated the world chess champion Gary Kasparov. With Stockfish, no supercomputer would have been required. I estimated that SF8 drops to Kasparov level on a 486-DX4 100 MHz, available already in 1994. To sum it up, the hardware overhang in chess is about 10 years, or 2-3 orders of magnitude in compute.

About a year later, in July 2021, Paul Christiano asked similar questions: How much compute would the old engine need to match the current engines? What is the influence of RAM (size and speed), opening books, endgame tables, pondering? Also, my old post gave some insights, but it can be improved by sharing the sources and making it reproducible. That's the aim of the current post (the other questions will be adressed in a later post).

Reproducing chess scaling from 2020

History of PC Programs (ELO by year)

As a baseline of engine performance over the years, we plot the winner from the yearly rating list of the Swedish Chess Computer Association. Run on contemporary hardware,

The list begins in 1984 when the program "Novag Super Constellation" reached 1631 ELO running on a 6502 CPU at 4 MHz.
By 2005, Shredder 9 surpassed human levels on an AMD Athlon 1200 MHz.
Today (2020), the leading engine is Stockfish 12 running on an AMD 1800X at 3.6 GHz.

Human grandmasters

To compare human grandmasters, we take the ELO over time for Kasparov and Carlsen. Carlsen's rating between 2003 and 2011 (age 13 to 21) grew from 2000 ELO to grandmaster strength, faster than any engine :-) [Thanks to User Bucky for the correction]

Deep Blue

The marker for "Deep Blue" in the year 1997 is a bit arbitrarily set to 2900 ELO. At the time, Kasparov had 2860 ELO, Deep Blue won, although close.

Stockfish 8 experiment

The main part is the Stockfish 8 experiment. How well does SF8 perform on slower PCs?

As a baseline, we need to establish its ELO at a defined speed.

To obtain the speed baseline, we find that SF8 makes 721 kNodes/s on an

...
Re: Competent Elites
by lsusr — 2021-07-15T05:16:41.718Z

One of the major surprises I received when I moved out of childhood into the real world, was the degree to which the world is stratified by genuine competence.

⋮

[T]hese people of the Power Elite were visibly much smarter than average mortals. In conversation they spoke quickly, sensibly, and by and large intelligently. When talk turned to deep and difficult topics, they understood faster, made fewer mistakes, were readier to adopt others' suggestions.

No, even worse than that, much worse than that: these CEOs and CTOs and hedge-fund traders, these folk of the mid-level power elite, seemed happier and more alive.

―Competent Elites by Eliezer Yudkowsky

This is 100% true.

The first time I felt like I was talking to cognitive equals was at the Y-Combinator interview pool when I met a pair of Nigerians (this is not a joke) who were attempting to monopolize the entire African financial system.

The second time was when I met someone on Less Wrong who runs his own hedge fund.

―Advice for High School #2 by me

Differences between geniuses are hard for non-geniuses to measure. You can understand everything that's going through the head of someone dumber than you. When someone is smarter than you, you cannot tell if they're one level above you or fifty because you literally cannot comprehend their reasoning.

General intelligence, measured relatively against your age cohort, tends to be stable over a person's lifetime. I had a teacher take me aside and tell me "I'd cure AIDs or something" when I was nine. It was that obvious.

Isn't this a contradiction? I just said you can't tell if someone is smarter than you. Then I said that my teacher (who, while smart, was not a genius) could tell that I was off the charts.

Note the qualifier "relative to your age cohort". My 4th grade teacher could tell how smart I was because I could be much smarter than a class of 4th graders while still being dumber than him.

I have observed this phenomenon in a wide variety of contexts, especially job interviews. People dumber than me can quickly tell within two minutes of conversation I'm smarter than them but it is hard for them to figure out how wide the gap is.

I once hired a technician to solder circuits. He knew my brother and I were smart because we had started a consumer hardware company in our basement. The shock came when he heard us playing an impromptu puzzle game based on our knowledge ...
Is social theory our doom?
by pchvykov — 2021-07-15T03:31:16.192Z

Complexity science, and especially the dream to deeply understand social systems and build concrete predictive models (such as Asimov’s “

psychohistory

”), raise some slippery ethical questions. If we do develop this area of science and technology, allowing us to predict, and consequently also to purposefully leverage and manipulate our society, who gets to be at the controls? Democracy can no longer work, even conceptually, because mass opinions become merely a reflection of these control algorithms. Though individual and oligarchical leadership becomes similarly subject to such controls — though perhaps with larger uncertainty, being less predictable as per law of large numbers. Whoever is at the controls is also being controlled by the very same system — creating a feedback loop, which is not so different from how our society is already functioning.

Let’s unpack this a bit. First let’s admit that we already are, and always have been, strongly influenced by external factors. Our opinions are some fusion of the cultural trends, mass-media influencers, our family upbringing, all filtered through some inherent (genetic?) predisposition to care more about some issues than others. If we really try to dig deep, it becomes quite hard to find anything that can really be identified as “my true individual opinion.” This is similarly true of the president’s opinions, as well as that of any dictator. In this sense, there can never be some “external independent free will that controls or influences the society” — such a will is always a product of that same society, thus somehow reflecting its needs and values.

One could argue that historically, this process of formation of individuals’ opinions, and the subsequence exercise of the formed will, happens somehow “naturally” and is not consciously manipulated by anyone. Developing a predictive science that allows to engineer social behaviors would force us to make conscious choices as to how we use it. This basically amounts to someone having to take responsibility for such choices, which are presently left up to chance. And of course, with responsibility come all sorts of moral and ethical questions of how such choices “should be” made (cf. “with great power comes great responsibility”).

One specific point that bothers me is that all such reasoning comes from within our current paradigm of viewing the world: that of competiti...
Lafayette: traffic vessels
by KatjaGrace — 2021-07-15T01:00:15.658Z

This week I’m in Lafayette, a town merely twenty-three minutes further from my San Franciscan office than my usual San Franciscan home, thanks to light rail. There are deer in the street and woods on the walk from the train to town.

On this occasion at least, Lafayette doesn’t feel properly like a small town to me. I think it’s the main road. A lot of the town is spread along this single road, but the road itself doesn’t feel like its main deal is being Lafayette’s main road. It feels more focused on being an energetic transitway between somewhere and somewhere else, neither in Lafayette. Which probably isn’t even that true, since there is a perfectly giant highway also spanning Lafayette just North of it. Maybe the problem is that it’s too wide, so that the town feels like it’s tenuously accumulated on the sides of a road like plaque, rather than the road being an organic vessel of the town. Or, you know, I’m imagining things.

I seem to imagine things a lot regarding some kind of road Feng Shui (note: I know nothing about actual Feng Shui). My mind natively reads roads as conduits of some kind of ‘energy’, and tries to apply appropriate intuitive physics. For instance, if you have big flows in and out of a place, relative to the place itself, it won’t feel like its own place. It will feel like a section of a larger place. For instance, the typical random intersection in a big American city can’t be a place with its own local vibe, where you might feel like staying, because it can’t be very separate from the surrounding city that its energy (traffic?) is constantly being exchanged with. It’s just going to feel like a section of various energetic routes.

This intuitive physics is sort of like the physics of streams with leaves and debris in them. For a place to be placelike, and appealing to settle down in, it needs to have enough nooks or ponds or complications for the fast flowing streams in and out to eddy around in and slow down and let the debris be still. And this main street is a big stream running through a small place.

This is all contradicted by the frequency with which people stand in narrow thoroughfares at parties even in the face of literal physical streams of partygoers pressing against them. (My intuition on this case is that the pressure of the partygoer liquid is so high that it somehow makes sense to be stuck in the doorway, but I don’t explici...
A cognitive algorithm for "free will."
by DirectedEvolution — 2021-07-14T21:33:11.400Z

So here's your homework problem: What kind of cognitive algorithm, as felt from the inside, would generate the observed debate about "free will"? - Dissolving the Question

Edit: In the linked post by Eliezer, he shifts the debate around free will and determinism. Instead of asking which is true, or trying to resolve or dissolve the philosophical question, he asks a different but related question about human psychology. Why does it feel like it makes sense to have this philosophical debate in the first place? What is it about our psychology that makes positions vis-a-vis free will and determinism seem so compelling? It is this question that I have attempted to answer here.

Put a glass of water in front of yourself. Now, make a decision: take a drink, or do not take a drink. Or at least imagine doing so.

When you've made your decision and acted on it, get very clear on which option you chose. Perhaps you should write it down, or at least make a mental note. Do not proceed until you've come to this point.

Now, you have a memory, one in which you seem to have made a decision of your own free will. Why does it feel that way? And why might somebody else argue that, no, in fact, you were fated to make that decision?

Let's consider the one or two main choices you made for this exercise. The first choice was whether or not to comply with the instructions. If you did, the second choice was whether or not to take a drink. Of course, there were other choices: how exactly to carry this out (in your imagination or in real life? with water, or coffee?). But we'll ignore them for now.

The process of making these major decisions took place slowly enough that you have some felt memory of the psychological operations you experienced in the course of making them. You read the instructions. Then, you may have split the future into two imagined alternatives. Some mysterious further operations that you'd have a very hard time putting into words took place. Your subconscious mind was playing a role too, but that's beyond your comprehension. But you clearly remember taking the physical actions to bring about one world state or the other.

Some aspects of this process of thought and action took place slowly and consciously enough to leave an accessible memory. Others were too fast, or two unconscious, to leave a trace.

The words of the instructions, the sense of splitting the worl...
Analyzing Punishment as Preventation
by Yair Halberstadt — 2021-07-14T15:12:09.071Z

Continued from https://www.lesswrong.com/posts/NvNmDWEpr8FSicgYN/reasons-for-punishment

In the previous post we listed a number of reasons given for punishing people. The first was as preventation.

In some cases punishment can prevent the person being able to commit the crime in the future. For example it's much more difficult to commit certain crimes in prison. This is relevant if you believe people who commit such crimes once are more likely to do it again.

In this post I would like to analyze this from a game theoretic perspective.

Preventative Policies

There are actions that we do not want people to do (crimes), and there are policies we can take to reduce the chance of anyone being able to commit those crimes.

For example we don't want people to murder other people. In order to reduce the chance of murder, we could ban guns, and we could also ban all sharp implements.

However such policies have costs. If you ban guns people will find protecting themselves from bears harder. If you ban sharp implements people will find cooking a real challenge.

So in order to weigh up whether or not to impose some policy to reduce a crime, we have to consider (possible units in brackets):

The prior likelihood of the crime (average cases per person per year).
The damage caused by the crime if it occurs (per instance).
The amount the policy will reduce the likelihood of the crime (average cases per person per year).
The cost of the policy (per person per year).

If (cost of the policy) < (reduction in likelihood of crime) * (damage caused by crime) then the policy is worth implementing.

For a given crime different policies will come out differently from this calculation. Hence why some countries ban guns, but none that I know of ban sharp implements.

This assumes we can only apply the policy on a blanket level. But these factors are different for every individual. Some people are more likely to commit crimes, some people will suffer more from the policy, and so on.

Hence why every country which bans guns allows soldiers to have them - the cost of banning guns is much higher for soldiers. And many polities ban children from having sharp implements - the chance of them hurting someone with them is much higher.

By applying a customized policy to each individual you can get better total utility than you can by having a single policy that applies to all people. The cost of this is compl...
Model-based RL, Desires, Brains, Wireheading
by Steven Byrnes — 2021-07-14T15:11:13.090Z

Summary

I’m very interested in

model-based RL as a framework for how people might build AGI

, in large part because

I think that human intelligence is built from scratch by within-lifetime learning in a model-based RL framework

. (Meanwhile I give Evolution credit for developing the learning & inference algorithms, neural network architecture, reward functions, hyperparameters, various non-”intelligence”-related activities, etc. etc.)

But a lot of people find it more natural to think in terms of “agents with desires” rather than thinking in terms of “model-based RL algorithms”.

So this is my attempt at a pedagogical discussion of how the two paradigms relate: I’ll argue that a model-based RL algorithm is kinda like a system consisting of

an agent with desires / preferences / goals / etc., and
a little demon that manipulates the desires of that agent.

This then leads into a discussion of corrigibility and wireheading. For example, I’ll argue:

If you have a model-based RL agent that does foresighted planning, it’s generally “trying to do something”, but that thing it’s trying to do is generally

not

“maximize the reward signal”.

This parallels the by-now-familiar discourse on

inner alignment

, but it's not identical because most of the existing discourse is talking about model-free RL instead.

I’ll also talk about some aspects of how this seems to work in humans, which requires going beyond that simple picture, including a system for real-time-editable desires, and the possibility / inevitability of non-self-consistent desires.

Let’s dive in!

Terminology

What do I mean by “model-based RL”?

In

My AGI Threat Model: Misaligned Model-Based RL

, I offered something like this (somewhat oversimplified) diagram of what I'm thinking about:

“Model-based RL” is a big category with blurry boundaries, I guess. (I don't keep up with the RL literature.) But when I talk about a model-based RL agent, I mean the following:

The agent builds a predictive model of itself and its world based on experience of actions and observations. (“If I drop this ball it’s gonna bounce”.)
The agent also builds a predictive model of both its immediate rewards and its longer-term reward expectations (a.k.a. value function), by combining the above model with a lifetime of experience of rewards. (“I hate stubbing my toe”.)

…And in particular, the agent can not submit arbitrary hypothetical queries to the groun

...
Essentialness of Data
by jefftk — 2021-07-14T03:40:03.795Z

In discussing online privacy, people will sometimes say things like
"

if the data
isn't required to operate, you don't need it." If I can turn on
tracking protection, stop sending data, and nothing breaks, then
clearly it wasn't needed, right? But consider these three cases:

Learning from implicit feedback: dictation software can operate
without learning what corrections people make, or a search engine can
operate without learning what links people click on, but the overall
quality will be lower. Each individual piece of information isn't
required, but the feedback loop allows building a substantially better
product.

Incremental rollouts: when you make changes to software that
operates in complex environments it can be very difficult to ensure
that it operates correctly through testing alone. Incremental
rollouts, with telemetry to verify that there are no regressions or
that relevant bugs have been fixed, produces better software. Even
Firefox collects
telemetry by default.

Ads: most websites are able to offer their writing for free,
without a paywall, because they can get paid for showing ads.
Collecting more data makes ads more efficient, which makes them more
profitable for the sites, which translates into more competition to
provide users with things to read. (more)

Instead of pushing for "don't collect data", I think it would make a
lot more sense for advocates to push for "only collect data privately"
and work to make that easier (carrot) or mandatory (stick). None of
these uses require individual level data, they're just easiest to
implement by sending all of the data back to a central server and
processing it there.

(What does "private" mean? Ideally it means that no one reviewing the
data can tell what your, or any other individual's, contribution was.
This is formalized as differential
privacy, and is typically implemented by adding noise proportional
to the maximum contribution any individual could have. In some cases
k-anonymity
may also provide good protection, but it's trickier. And this is only the beginning; privacy
researchers and engineers have been putting a lot of work into this
space.)

Comment via: facebook