trentmkelly/LessWrong-43k huggingface dataset:
How accurate are standard Dark Triad personality scales?
by jamesbill — 2023-11-12T08:21:10.582Z

Dark Triad, the group of three traits—Machiavellianism, narcissism, and psychopathy—has been studied by well-known researchers Delroy L. Paulhus and Kevin M. Williams, and they published their study in a research paper in 2002. The research suggests that every individual exhibits socially unstable traits, mainly focusing on Machiavellianism, narcissism, and psychopathy.

Machiavellianism was named after the Italian diplomat, author, philosopher, and historian Niccolò Machiavelli. In his book "The Prince," he suggests and justifies that leaders use manipulation and deceit to achieve personal goals.

Narcissism is characterized by a grandiose sense of self-importance, beliefs of being unique, and a lack of empathy.

Psychopathy, also known as antisocial personality, involves a lack of empathy and a willingness to engage in impulsive behaviors. Psychopaths use manipulative tactics to achieve personal goals."

The research paper gained a lot of popularity among psychologists and attracted more research and researchers started developing screening tools. There are only a few screening tools available that have been validated. The first validated test is the Short Dark Triad (SD3) A Brief Measure of Dark Personality Traits and Deenz dark triad personality scale.

Short Dark Triad (SD3)

We tried to find out whether the dark triad test and Deenz dark triad personality scale are accurate in measuring the dark side of personality. We started by asking participants to provide feedback through a private forum and a survey. Finally, 209 participants provided feedback on SD3 and DDTPS-33.

Among these 209 participants, 88 provided negative feedback and marked the SD3 as inaccurate.

Twenty-one participants were not completely sure about the results, and they skipped some items as they were irrelevant to them. Seventy participants said that the SD3 is accurate but needs improvements. Thirty participants said the SD3 is very accurate at assessing the traits.

Deenz Dark Triad Personality Scale

We found the research paper on ResearchSquire on the validation and reliability of DDTPS-33 with 0.85 internal consistency. Our 209 participants provided mixed feedback about the test. Ten participants said that the test needs improvements and item reduction; they did not mention whether the test was accurate or not. Seventy-five participants said the test was modestly accurate but needs improvem...
What ML gears do you like?
by Ulisse Mini — 2023-11-11T19:10:11.964Z

In John's recent post he mentions many people in ML not having good gears level models of what's going on.

To wit; what gears-level models do you know for ML? How much support is there for them? Are there "settled science" kind models that have tons of empirical support?

What gears-level models informed the people who made major AI advancements? Is there a list, or writing about this somewhere?
Smart Sessions - Finally a (kinda) window-centric session manager
by Eli Tyre — 2023-11-11T18:54:16.073Z

This is a short post about some software functionality that I’ve long wanted, and a browser extension that does it well enough. I'm sharing in case anyone else has also been wanting the same thing.

The dream

There’s a simple piece of software that I’ve wanted for several years. Few months, I go on a binge of trying to find something that do what I’m looking for.

Basically: a session manager that allows you to group windows together somehow, so that you can close and save them all with one click, and then reopen them all with one click.

I make heavy use of the OSX feature “desktops”, which allows multiple separate workspaces in parallel. I’ll typically have a desktop for my logging and tracking, one for chats and coms, one with open blog posts, one with writing projects, one with an open coding project, etc. Each of these are separate contexts that I can switch between for doing different kinds of work.

What I want is to be able to easily save each of those contexts, and easily re-open them later.

But since I’ll often have multiple sessions open at the same time, across multiple desktops, I don’t want the session-saver app to save all my windows. Just the ones that are part of a given workspace context.

The best way to do this is if the software could tell which windows were open on which desktops and use that as the discriminator. But some sort of manual drag and drop for adding a (representation of) a window (on a list of windows) to a group would work too.

The situation

This seems to me like something that…there should be a lot of demand for? I think lots of people have many windows, related to different projects that they want to keep separate, open on their computer at the same time.

But, as near as I can tell there’s almost nothing like this.

There are a lot of session managers, browser extensions that allow you to save your tabs for the future (I’ve mostly used OneTab, but there are dozens). However, they’re virtually all tab-centric. A “session” typically refers to a single window, with multiple tabs, not to multiple windows, with multiple tabs each, which means that to reopen a session (in my “multiple windows” sense of the word), I need to mentally keep track of which windows were included and open all of them one by one, instead of clicking one button to get the context back.

There are a few session managers that save multiple windows in a session (I’m th...
AISC project: SatisfIA – AI that satisfies without overdoing it 
by Jobst Heitzig — 2023-11-11T18:22:57.050Z
Link: https://docs.google.com/document/d/1JhmK31IwYGcwqX0nKmxKsbmTh_DX3o1OoW7NJmhVbIw/edit?usp=sharing)

This project is part of the upcoming round of AI Safety Camp.

SatisfIA – AI that satisfies without overdoing it

Summary

This project will make a contribution to some fundamental design aspects of AI systems. We will explore novel designs for generic AI agents – AI systems that can be trained to act autonomously in a variety of environments – and their implementation in software.

Our designs deviate from most existing designs in that they are not based on the idea that the agent should aim to maximize some kind of objective function (which I argue is inherently unsafe if the agent is powerful enough and one cannot be absolutely sure to have found the exactly right objective function). Rather than aiming to maximize some objective function, our agents will aim to fulfill goals that are specified via constraints called “aspirations” (which I argue implies a much lower probability of taking “extreme” actions and therefore is likely much safer).

For example, I might want my AI butler to prepare 100–150 ml of tea, having a temperature of 70–80°C, taking for this at most 10 minutes, spending at most $1 worth of resources, and succeeding in this with at least 95% probability (rather than: prepare as much tea as fast and cheap as possible with the largest possible probability).

For a lightweight introduction into this way of thinking, you can watch this interview.

We will study several versions of such “non-maximizing” agent designs and corresponding learning algorithms (mostly variants of Reinforcement Learning in Markov Decision Problems).

This involves designing agents and algorithms in theory, implementing them in software (mostly Python), simulating their behavior in selected test environments (e.g., in the AI safety gridworlds), formulating hypotheses about that behavior, especially about its safety-relevant consequences, then trying to prove or disprove these hypotheses formally and/or provide numerical evidence that supports them, and writing-up these results in blog posts and an academic paper.

Details

For details, please consult the full proposal

Applying

To apply working on this project, please visit the AI Safety Camp webpage and apply for project #21
Control Symmetry: why we might want to start investigating asymmetric alignment interventions
by domenicrosati — 2023-11-11T17:27:10.636Z

[This is a post summarizing the motivation for an AISC 2024 project: If you are interested in participating you can apply here:

https://aisafety.camp/

(project 25:

Asymmetric control in LLMs: model editing and steering that resists control for unalignment

)]

Tl;dr: Techniques for AI alignment could equally be used to create misaligned models in many cases. We should attempt to develop methods that might only work for alignment and not misalignment to reduce the risk of externalities related to technical alignment research.

The problem

A recent paper led by the Center for AI Safety entitled “

Representation Engineering: A Top-Down Approach to AI Transparency

” demonstrates a number of comprehensive control techniques in the domain of machine ethics and AI safety. Notably, an impressive result on reducing power seeking and immorality on the

MACHIAVELLI benchmark

using these techniques is shown. However, they equally demonstrate the same technique can be used to increase power seeking and immorality! Several other settings in the paper demonstrate the same type of control being effective for alignment and misalignment. I am calling this property control symmetry: the degree to which a given method for controlling an agent could be equally used for control towards one end and control towards the opposite end.

Why is this a problem? It is true, if we can demonstrate comprehensive control then we have made a lot of progress in AI safety since we can use these techniques to prevent harmful and catastrophic behavior. The concern about control symmetry is externalities, that is the degree to which safety research can end up being used towards harmful ends. It is preferred if we develop control techniques that could not be used by bad actors to create misaligned agents.

Control symmetry also provides a novel framework for asking specific questions about control and safety. For instance, is there an orthogonality of control where the level of intelligence of an agent correlates to the degree of control symmetry? Are there some properties under which control is more symmetric than others? Factuality for instance seems unlikely to be symmetric since the world itself is factually consistent. Controlling in the direction of fictionality or misinformation would lead to more and more inconsistencies that could break general behavior and cap...
Game Theory without Argmax [Part 2]
by Cleo Nardo — 2023-11-11T16:02:41.836Z

Written during the SERI MATS program under the joint mentorship of John Wentworth, Nicholas Kees, and Janus.

Motivation

I am surrounded, incessantly, by other people, possessing a range of different capabilities, striving for goals both common and opposing — and, what is more, they are incessantly surrounded by me. Fangs pierce the cartesian boundary. Outsideness leaks in. Insideness leaks out. It is the source of all joy and misery in my life.

Anyway. It's the interaction of n≥2 agents that characterises game theory.[1] Classical game theory says that when two utility-maximisers interact in a shared environment, they will choose options in nash equilibrium, where a profile of options is in nash equilibrium if no player can increase their utility function by unilaterally changing their option.

What does higher-order game theory (which dispenses with utility functions and maximisation) say about the interaction between agents? Would a satisficer cooperate with a quantiliser in prisoner's dilemma?

Let's find out.

Preliminaries

All you need to know is Definition 3 from [Part 1], and some basic classical game theory.

Definition 3: Let X be any set of options and R be any set of payoffs. An optimiser is any functional ψ:(X→R)→P(X). A ψ-task is any function u:X→R. An option x∈X is ψ-optimal for a task u:X→R if and only if x∈ψ(u).

Let's review the textbook definition of classical simultaneous games.

Definition 5.

Let {Xi}i∈I be a family of sets indexed by a set I of players.
A classical simultaneous game over {Xi}i∈I is a function g:∏i∈IXi→RI.

The option-profiles of g are the elements of the product X=∏i∈IXi.
The i-th deviation map Ui:X→(Xi→X) is given by Ui(x)(α)j={αif i=jxjotherwise for all x∈X, i∈I, α∈Xi, and j∈I.
In the finite case, where I={1,…,n}, then Ui(x1,…,xn):Xi→X,α↦(x1,…,xi−1,α,xi,…,xn).
The deviation map Ui:X→(Xi→X) describes how the ith player can change an option-profile x by unilaterally changing their own option. Note that the deviation maps only depend on the option spaces, not the game itself.

The best-response function of g is the the function B:X→P(X) defined byB:x↦∏i∈IargmaxXi(πi∘g∘Ui(x)) where&n

...
Game Theory without Argmax [Part 1]
by Cleo Nardo — 2023-11-11T15:59:47.486Z

Written during the SERI MATS program under the joint mentorship of John Wentworth, Nicholas Kees, and Janus.

Preface

In classical game theory, we characterise agents by a utility function and assume that agents choose options which cause maximal utility. This is a pretty good model, but it has some conceptual and empirical limitations which are particularly troublesome for AI safety.

Higher-order game theory (HOGT) is an attempt to rebuild game theory without appealing to either utility functions or maximisation. I think Higher-Order Game Theory is cool so I'm writing a sequence on it.

[Part 1]
[Part 2]
[Part 3]
...
[Part n]

I'll try to summarise the relevant bits of the literature, present my own minor extensions, and apply HOGT to problems in AI safety

You're reading the first post! Let's get into it.

The role of argmax

For each set X, let argmaxX:(X→R)→P(X) be the familiar function which receives a function u:X→R and produces the set of element which maximise u. A function like argmaxX is sometimes called a higher-order function or functional, because it receives another function as input.

Explicitly, argmaxX=λu:X→R.{x∈X|∀x′∈X,u(x)≥u(x′)}.[1]

As you all surely know, argmax plays a central role in classical game theory. Typically we interpret the set X as the agent's options,[2] and the function u:X→R as the agent's task, which assigns a payoff u(x)∈R to each option x∈X. We say an option x∈X is optimal to the agent for the task u:X→R whenever x∈argmaxX(u). Classical game theory is governed by the assumption that agents choose optimal options in whatever task they face, where optimality strictly means utility-maximisation.

Definition 1 (provisional): Let X be any set of options. A task is any function u:X→R. An option x∈X is optimal for a task u:X→R if and only if x∈argmaxX(u).

Due to the presence of the powerset operator P in argmaxX:(X→R)→P(X), this model of the agent is possibilistic — for each task u:X→R, our model says which options are possibly chosen by agent. The model doesn't say which options are probably chosen by the agent — for that we'd need a function (X→R)→Δ(X). Nor does the model say which options are actually chosen by the agent — for that we'd need a fu...
It's OK to be biased towards humans
by dr_s — 2023-11-11T11:59:16.568Z

Let's talk about art.

In the wake of AI art generators being released, it's become pretty clear this will have a seismic effect on the art industry all across - from illustrators, to comic artists, to animators, many categories see their livelihood threatened, with no obvious "higher level" opened by this wave of automation for them to move to. On top of this, the AI generators seem to have mostly been trained with material whose copyright status is... dubious, at the very least. Images have been scraped from the internet, frames have been taken from movies, and in general lots of stuff that would usually count as "pirated" if you or I just downloaded it for our private use has been thrown by the terabyte inside diffusion models that can now churn out endless variations on the styles and models they fitted over them.

On top of being a legal quandary, this issues border into the philosophical. Broadly speaking, one tends to see two interpretations:

the AI enthusiasts and companies tend to portray this process as "learning". AIs aren't really plagiarizing, they're merely using all that data to infer patterns, such as "what is an apple" or "what does Michelangelo's style look like". They can then apply those patterns to produce new works, but these are merely transformative remixes of the originals, akin to what any human artist does when drawing from their own creative inspirations and experiences. After all, "good artists copy, great artists steal", as Picasso said;
the artists on the other hand respond that the AI is not learning in any way resembling what humans do, but is merely regurgitating minor variations on its training set materials, and as such it is not "creative" in any meaningful sense of the world - merely a way for corporations to whitewash mass-plagiarism and resell illegally acquired materials.

Now, both these arguments have their good points and their glaring flaws. If I was hard pressed to say what is it that I think AI models are really doing I would probably end up answering "neither of these two, but a secret third thing". They probably don't learn the way humans do. They probably do learn in some meaningful sense of the word, they seem too good at generalizing stuff for the idea of them being mere plagiarizers to be a defensible position. I am similarly conflicted in matters of copyright. I am not a fan of our current copyright laws, w...
The Top AI Safety Bets for 2023: GiveWiki’s Latest Recommendations
by Dawn Drescher — 2023-11-11T09:04:27.585Z

Summary: The AI Safety GiveWiki (formerly Impact Markets) has completed its third round of retroactive impact evaluations – just in time to provide updated recommendations for the giving season!

Here is a reminder of how the platform works.

Want to donate? Open up the page of our

top project/s

, double-check that they are still fundraising, and ka-ching!

Interested in regranting? Check out our post on the (now) $700,000 that want to be allocated.

Top Projects

Our top projects stand out by virtue of their high support scores. There are a lot of ties between these top projects, so we’ve categorized them into tiers.

Note that we determine the top projects according to their support. Further down we’ll cover how our latest evaluation round worked out. But the support scores are two hops removed from those results: (1) Projects receive support in the form of donations as a function of donation size, earliness, and the score of the donor; (2) donors get their scores as a function of size and earliness of their donations and the scores of the beneficiary projects; (3) projects receive their credits from our evaluators:

Project credits → donor scores → project support.

This mimics the price discovery process of a for-profit impact market. Hence it’s also likely that the scores are slightly different by the time you read this article because someone may have entered fresh donation data into the platform.

We have tried to find and reach out to every notable AI safety project, but some may yet be missing from our list because (1) they haven’t heard of us after all, (2) they’re not fundraising from the public, (3) they prefer to keep a low profile, (4) etc. But at the time of writing, we have 106 projects on the platform that are publicly visible and fundraising.

Ties for Tier 1

These are the projects at the very top!

FAR AI

and the

Simon Institute

with a support score of (at the time of writing) 213.

Ties for Tier 2

Alignment Research Center

AI Safety Support

Rational Animations

Ought

AI Impacts

They all have a support score of 212. Such small differences in support are probably quite uninformative. New data or tweaks to our algorithm could easily change their rank.

Ties for Tier 3

Center for AI Safety

Alignment Jams (of Apart Research)

Other projects with > 200 support

Pour Demain

Center for Reducing Suffering

Center on Long-Term Risk

Future of

...
Artificial General Horsiness
by robotelvis — 2023-11-11T05:15:03.107Z
Link: https://messyprogress.substack.com/p/artificial-general-horsiness

A lot of attention has been paid to the question of Artificial General Intelligence (AGI). Will artificial intelligence ever reach the level at which it can accomplish every intellectual task a human can do, and if so, how soon will that happen?

I think that this is the wrong question.

Cars are a long way from achieving Artificial General Horsiness. Even today, 100 years after the invention of the car, there are many things a horse can do vastly better than a car, such as jumping over fences, navigating by itself, swimming through water, dealing with mud, navigating through tight passageways, fueling themselves from vegetation, and providing social companionship.

And yet cars have still managed to replace horses for pretty much all tasks that people previously used horses for. This is because it turned out that, while cars can’t do everything horses can do, their speed and cost advantages were big enough that it was worth adapting the rest of the world (e.g. building roads) to compensate for their limitations.

There is a long history of people claiming that a task is a true mark of intelligence right until a computer becomes good at it, at which people people decide that that task doesn’t require true intelligence. So far that list includes logical reasoning, playing chess, translating between languages, driving cars, writing poetry, creating art, playing video games, and passing academic exams.

I expect that people will still be claiming that computers aren’t “really” intelligent long after the human race has been subjugated by its AI overlords and humans no longer have any economic purpose.

In 2011 Marc Andreessen said that

software was eating the world

. What he meant by this was that computer technology had reached a level of maturity where it had the potential to transform pretty much every area of industry. All that needed to happen was for software engineers to do the often-mundane bits of software engineering needed to connect computers to the problems we wanted them to solve.

And Marc Andreessen was mostly right. In the years since he wrote that post, a large fraction of our economy has been transformed by technology, with companies either being transformed by computers, or replaced by tech-driven companies. We can see this in the way Uber replaced Taxis, Amazon took over shopping, AirBnb changed the hotel market, and streaming companies took over TV and Mu...