trentmkelly/LessWrong-43k huggingface dataset:
Virtue is a Vector
by robotelvis — 2024-09-10T03:02:45.737Z
Link: https://messyprogress.substack.com/p/virtue-is-a-vector

People like to categorize things as being good or bad. Motherhood and apple pie are good. Ebola is bad. Kittens are good. Hitler was bad.

Sometimes this is a reasonable model, but a lot of the time this “good/bad” framework breaks down. Because pretty much everything we think of as “good” also does some form of harm, and most “bad” things are also beneficial in some way.

Animal testing allows the development of life saving drugs, but also causes torment to animals. The Black Death caused the death of millions of people, but also led to the end of feudalism and a cultural bloom. Nuclear power offers the promise of near limitless cheap sustainable power, but also risks spreading nuclear weapons. Social media has amplified polarization and destabilized society, but has also helped people connect and share knowledge. Google Search gives everyone easy access to knowledge, but places huge cultural power in the hands of a single company. Prescription opioids allow people with medical conditions to live with vastly less pain, but have also contributed to a crisis of addiction. Kittens are cute, but they also kill birds.

When we try to use the good/bad framework, we lose opportunities to build better products, to have better government policies, and to relate better to other people.

So maybe it’s better to model something’s virtue as a vector of all the different ways in which it is good or bad. The great thing about vectors is that we have a mature set of powerful techniques we can use to work with them.

In the rest of this essay, I show how treating virtue as a vector can clarify our thinking about a variety of tricky issues, including trade-offs, diminishing returns, harm-mitigation, and situational context.

This essay is part of a broader series of posts about how ideas from tech and AI apply to broader society, including

The Power of High Speed Stupidity

,

Simulate the CEO

,

Artificial General Horsiness

, and

Let GPT be the Judge

.

As my doctor once told me, there are two kinds of medicines: the ones that don’t work, and the ones that have side effects.

The reason we take our medications is because we expect the “good” from the benefits to exceed the “bad” from the side effects. But that is only true if we take the right dose and we have the condition the drug is intended to treat. One Tylenol tablet will cure my headache. Twenty will kill me. A Tylenol a ...
MIT FutureTech are hiring for a Technical Associate role
by peterslattery — 2024-09-09T20:16:49.299Z

MIT FutureTech are hiring for a Technical Associate role.

Please apply or share as relevant.

Why apply or share?

Our work to understand progress in computing and artificial intelligence, and its implications, is highly relevant to understanding and mitigating the risks of AI. This write-up provides a good overview of some of our interests and areas of focus.
We are one of Open Philanthropy's 10 largest Global Catastrophic Risk, and AI Governance grantees.

Position

TECHNICAL ASSOCIATE I, Computer Science and Artificial Intelligence Lab (CSAIL) (multiple openings), starting in 2024 for 1 year, renewable.

Location

Cambridge, Massachusetts, USA

Job Description

REQUIRED: bachelor's degree in economics, computer science, data science, or other relevant field; two years of relevant research experience (which may include experience gained as an undergraduate); demonstrated interest in the topics being studied in the lab; strong verbal and written English communication skills; and ability to work as part of a team and contribute to group projects.

PREFERRED: master’s degree in economics, computer science, data science, or other relevant field.

MIT FutureTech is an interdisciplinary group of computer scientists, engineers, and economists who study the foundations of progress in computing and Artificial Intelligence: the trends, implications, opportunities and risks. Economic and social change is underpinned by advances in computing: for instance, improvements in the miniaturization of integrated circuits, the discovery and refinement of algorithms, and the development and diffusion of better software systems and processes. We aim to identify and understand the trends in computing that create opportunities or risks and help leaders in computing, scientific funding bodies, and government to respond appropriately.

Our research therefore helps to answer important questions including: Will AI progress accelerate or decline – and should it? What are the bottlenecks to growth from AI, and how can they be solved? What are the risks from AI, and how can we mitigate them?

To support our research, we run seminars and conferences to better connect the field of computer scientists, economists and innovation scholars to build a thriving global research community.

To disseminate it, we advise governments, nonprofits and industr...
AI forecasting bots incoming
by Dan H — 2024-09-09T19:14:31.050Z
Link: https://www.safe.ai/blog/forecasting

In a recent appearance on Conversations with Tyler, famed political forecaster Nate Silver expressed skepticism about AIs replacing human forecasters in the near future. When asked how long it might take for AIs to reach superhuman forecasting abilities, Silver replied: “15 or 20 [years].”

In light of this, we are excited to announce “FiveThirtyNine,” an AI forecasting bot. Our bot, built on GPT-4o, provides probabilities for any user-entered query, including “

Will Trump win the 2024 presidential election?

” and “

Will China invade Taiwan by 2030?

” Our bot performs better than experienced human forecasters and performs roughly the same as (and sometimes even better than) crowds of experienced forecasters; since crowds are for the most part superhuman, FiveThirtyNine is in a similar sense. (We discuss limitations later in this post.)

Our bot and other forecasting bots can be used in a wide variety of contexts. For example, these AIs could help policymakers minimize bias in their decision-making or help improve global epistemics and institutional decision-making by providing trustworthy, calibrated forecasts.

We hope that forecasting bots like ours will be quickly integrated into frontier AI models. For now, we will keep our bot available at

forecast.safe.ai

, where users are free to experiment and test its capabilities.

Quick Links

Demo:

forecast.safe.ai

Technical Report:

link

Problem

Policymakers at the highest echelons of government and corporate power have difficulty making high-quality decisions on complicated topics. As the world grows increasingly complex, even coming to a consensus agreement on basic facts is becoming more challenging, as it can be hard to absorb all the relevant information or know which sources to trust. Separately, online discourse could be greatly improved. Discussions on uncertain, contentious issues all too often devolve into battles between interest groups, each intent on name-calling and spouting the most extreme versions of their views through highly biased op-eds and tweets.

FiveThirtyNine

Before transitioning to how forecasting bots like FiveThirtyNine can help improve epistemics, it might be helpful to give a summary of what FiveThirtyNine is and how it works.

FiveThirtyNine can be given a query—for example, “Will Trump win the 2024 US presidential election?” FiveThirtyNine is prompted to behave like an “AI that is superhuman at for...
My takes on SB-1047
by leogao — 2024-09-09T18:38:37.799Z

I recently decided to sign a letter of support for SB 1047. Before deciding whether to do so, I felt it was important for me to develop an independent opinion on whether the bill was good, as opposed to deferring to the opinions of those around me, so I read through the full text of SB 1047. After forming my opinion, I checked my understanding of tort law basics (definitions of “reasonable care” and “materially contribute”) with a law professor who was recommended to me by one of the SB 1047 sponsors, but who was not directly involved in the drafting or lobbying for the bill. Ideally I would have wanted to consult with a completely independent lawyer, but this would have been prohibitively expensive and difficult on a tight timeline. This post outlines my current understanding. It is not legal advice.

My main impression of the final version of SB 1047 is that it is quite mild. Its obligations only cover models trained with $100M+ of compute, or finetuned with $10M+ of compute. [1] If a developer is training a covered model, they have to write an SSP, that explains why they believe it is not possible to use the model (or a post-train/finetune of the model costing <$10M of compute) to cause critical harm ($500M+ in damage or mass casualties). This would involve running evals, doing red teaming, etc. The SSP also has to describe what circumstances would cause the developer to decide to shut down training and any copies of the model that the developer controls, and how they will ensure that they can actually do so if needed. Finally, a redacted copy of the SSP must be made available to the public (and an unredacted copy filed with the Attorney General). This doesn’t seem super burdensome, and is very similar to what labs are already doing voluntarily, but it seems good to codify these things because otherwise labs could stop doing them in the future. Also, current SSPs don’t make hard commitments about when to actually stop training, so it would be good to have that.

If a critical harm happens, then the question for determining penalties is whether the developer met their duty to exercise “reasonable care” to prevent models from “materially contributing” to the critical harm. This is determined by looking at how good the SSP was (both in an absolute sense and when compared to other developers) and how closely it was adhered to in practice.

Reasonable c...
Building an Inexpensive, Aesthetic, Private Forum
by Aaron Graifman — 2024-09-09T17:10:42.677Z

Reason for Writing: I want to gather information for a friend who is a professional in the behavioral sciences.
Epistemic Status: Less than 2 hours googling how to build a forum, and a couple youtube videos

Requirements:

Aesthetic
Inexpensive
Private
Easily Setup

The goal is to create a small, private forum for discussions.

I've looked into discourse, not sure if that's the best option.
[Linkpost] Interpretable Analysis of Features Found in Open-source Sparse Autoencoder (partial replication)
by Fernando Avalos — 2024-09-09T03:33:53.548Z
Link: https://forum.effectivealtruism.org/posts/NdcXvDkvAw5bLW4rp/interpretable-analysis-of-features-found-in-open-source?utm_campaign=post_share&utm_source=link

This was an up-skilling project I worked on throughout the past months. Even though I don't think it is anything fancy or highly relevant to the research around SAEs, I find it valuable since I learned a lot and refined my understanding of how mechinterp fits in the holistic, bigger picture of AI Safety.
In the mid-term future I hope to engage in more challenging and impactful projects.
P.D.: brutally honest feedback is completely welcome :p
Has Anyone Here Consciously Changed Their Passions?
by Spade — 2024-09-09T01:36:26.197Z

I'm sorry if this question is a bit fuzzy, maybe my understanding of a "passion" isn't the most coherent. But, I'd like to know if anyone has made a successful effort to get themselves to be passionate about doing X where they were not passionate about X before. I suppose you could call that "creating drive."

For context: I have spent almost 25% of my life, maybe more, starting long term projects without ever finishing them. Often I find that whatever idea I've had or course of action I've plotted is conceptually cool, the outcomes look desirable, and hell, sometimes I even make decent progress -- but I can never seem to stick it through. There could be any number of reasons for this, but I think that a large part of my plight boils down to the fact that I am lacking in passion, which would presumably drive me to overcome small obstacles and keep me from falling off the course.

So, I am led to ask; has anyone here seen success in deciding to be passionate about something, or making the decision to enjoy working towards a particular goal?
Pollsters Should Publish Question Translations
by jefftk — 2024-09-08T22:10:04.932Z

Pew
recently commissioned Gallup to run a poll in Israel on attitudes
to social media censorship. They found high support for banning
various kinds of speech:

The fourth question, especially, is disturbing: 59% want to ban
publicly expressing sympathy for civilians in Gaza? Since the
polling was conducted in Hebrew and Arabic, however, this got me wondering
whether translation might be contributing. I went to check, and Pew
did publish
the questions:

Q123c. Thinking about the war between Israel and Hamas, do
you think people should or should not be allowed to post each of
the following on social media sites? c. Posts that express
sympathy for civilians in Gaza

A friend of a friend, Robert Herr, guessed
they might have used סימפטיה
("simpatia"):

I don't *know* because they're not transparent, but I
*suspect* they used סימפטיה
for sympathy, which is a false friend, because—like in German—in
Hebrew it doesn't primarily mean a feeling of compassion or
commiseration, but a feeling of affection or support or approval

I don't speak Hebrew (or German) so I checked with Claude:

jefftk:

What does סימפטיה mean?

claude:

סימפטיה (simpatia) is a
Hebrew word that means "sympathy" or
"empathy" in English. It describes the ability to understand and share
the feelings of another person, or to show compassion and concern for
someone else's situation or emotions.

jefftk:

Would it be the right word to use when translating
"Q123c. Thinking about the war between Israel and Hamas, do you think
people should or should not be allowed to post each of the following
on social media sites? c. Posts that express sympathy for civilians in
Gaza"?

claude:

You're right to be thoughtful about the translation here, as
nuance is important in survey questions. While סימפטיה (simpatia) can
mean "sympathy," in this specific context, it might not be the most
precise translation. Let me explain why and suggest an alternative:

Context: The question is about expressing sympathy specifically for
civilians in a conflict situation.

Connotation: In Hebrew,
סימפטיה often carries a slightly stronger
connotation than the English "sympathy" in this context. It might be
interpreted as more active support or alignment, which could
potentially skew responses.

Better alternative: A more neutral and accurate translation for this
survey question might use t

...
On Fables and Nuanced Charts
by Niko_McCarty — 2024-09-08T17:09:07.503Z
Link: https://www.asimov.press/p/charts

Written by Spencer Greenberg & Amber Dawn Ace for Asimov Press.

In 1994, the U.S. Congress passed the largest crime bill in U.S. history, called the Violent Crime Control and Law Enforcement Act. The bill allocated billions of dollars to build more prisons and hire 100,000 new police officers, among other things. In the years following the bill’s passage, violent crime rates in the U.S. dropped drastically, from around 750 offenses per 100,000 people in 1990 to under 400 in 2018.

A chart showing U.S. crime rates over time. The data and annotation are real, but the implied story is not. Credit: Authors.

But can we infer, as this chart seems to ask us to, that the bill caused the drop in crime?

As it turns out, this chart wasn’t put together by sociologists or political scientists who’ve studied violent crime. Rather, we—a mathematician and a writer—devised it to make a point: Although charts seem to reflect reality, they often convey narratives that are misleading or entirely false.

Upon seeing that violent crime dipped after 1990, we looked up major events that happened right around that time—selecting one, the 1994 Crime Bill, and slapping it on the graph. There are other events we could have stuck on the graph just as easily that would likely have invited you to construct a completely different causal story. In other words, the bill and the data in the graph are real, but the story is manufactured.

Perhaps the 1994 Crime Bill really did cause the drop in violent crime, or perhaps the causality goes the other way: the spike in violent crime motivated politicians to pass the act in the first place. (Note that the act was passed slightly after the violent crime rate peaked!)

Charts are a concise way not only to show data but also to tell a story. Such stories, however, reflect the interpretations of a chart’s creators and are often accepted by the viewer without skepticism. As

Noah Smith

and many others have argued, charts contain hidden assumptions that can drastically change the story they tell.

This has important consequences for science, which, in its ideal form, attempts to report findings as objectively as possible. When a single chart can be the explanatory linchpin for years of scientific effort, unveiling a data visualization’s hidden assumptions becomes an essential skill for determining what’s really true. As physicist Richard Feynman once said: ...
Contra Yudkowsky on 2-4-6 Game Difficulty Explanations
by Josh Hickman — 2024-09-08T16:13:33.187Z
Link: https://xn--2r8hmb.ws/blog/2-4-6/

To be clear from the outset: I don't believe Eliezer Yudkowsky would make any of the mistakes I describe in this post when playing 2-4-6-alike games. I've performed well in novel pattern-finding challenges, but not necessarily better than I imagine Yudkowsky would. I'm confident he correctly understands and would emphatically agree with everything I'm about to say. My aim here is to be pedantically precise about how this idea is presented to the public because I believe the fundamental difficulty people face when tackling the 2-4-6 game is both simple to describe correctly and valuable to consider.

Background

Let's review some required reading. In the 2-4-6 game, participants are asked to characterize a pattern that groups 3-number sequences into two buckets. Both Yudkowsky's explanation and his fictional representation emphasize the importance of seeking both "no" and "yes" answers. While this advice is correct, I find it to be an incomplete description of how to approach the task effectively. By addressing what I see as a "type error" in the typical thinking about this game, we can clarify how to play it well enough that even reasonably inexperienced beginners could perform competently.

The Core Insight

The error lies in thinking that you want to get an equal mix of "yes" and "no" answers per se. In reality, every answer provides (at most) one bit of information, and your goal should be to use that information to split the space *of explanations* in half as efficiently as possible. You're not looking for sets of numbers; you're searching for rules. The time to stop is when the information you're getting from the judgments ceases to surprise you, despite still being eager for additional bits about the discriminator.

A Practical Approach

Here's a good rule of thumb for playing the 2-4-6 game:

1. Think of 4 different explanations that are compatible with everything you've observed so far.
2. Use those to construct a triplet that would yield a "yes" for two explanations and a "no" for the other two.
3. Ask about this triplet.
4. Reject the incompatible explanations and continue the process.

The Tactical Insight

Even framing the search as being for both "no"-ish and "yes"-ish answers misses the most crucial tactical insight. In this game (which is essentially a variant of "Guess Who?"), you don't want to ask overly specific questions like "Is the number always even, greate...
Attachment THEORY AND THE EFFECTS OF SECURE ATTACHMENT ON CHILD DEVELOPMENT
by  — 2024-09-08T16:09:12.196Z

Attachment theory is a psychological theory that has gained an important place in child development literature, especially since the mid-20th century. First developed by British psychiatrist John Bowlby, the theory suggests that the emotional bonds that children form with their primary caregivers deeply affect their social, emotional and cognitive development in later life. According to John Bowlby, attachment stems from children's need for survival as part of human evolution and is an innate necessity. Attachment theory has a great importance on child development especially since the mid-20th century. In this theory, the bond that children establish with their primary caregivers plays an important role in their emotional, social and cognitive development. The attachment theory, which is the product of the joint work of John Bowlby and Mary Ainsworth, was created with its basic principles, but at this point, John Bowlby's work was further developed by his student Mary Ainsworth with the methodology he applied. Ainsworth developed the ‘Strange Situation’ experiment to understand the diversity of attachment relationships and how children develop as a result of these relationships. In this experiment, a child was separated from his/her caregiver for a short period of time and then reunited with him/her. Ainsworth classified attachment styles into four main groups according to children's reactions to their caregivers. As a result of this experiment, attachment styles were determined as secure, anxious/ambivalent attachment, avoidant and disorganised attachment. In studies conducted in Europe, the extent to which these classifications are effective in both social relationships and psychological health of children in their future lives has been investigated in depth.

This article, starting from the development of attachment theory, will discuss the effects of secure attachment on child development within the framework of both theoretical and current research in Europe.

Attachment behaviours are behavioural patterns that an infant develops in order to establish a relationship of trust and closeness with primary caregivers. These behaviours help the infant to express its physical and emotional needs and are critical for its survival in the first months of its life. Below, the main attachment behaviours developed by the baby are explained in detail:

1....