trentmkelly/LessWrong-43k huggingface dataset:
Bringing Little Kids to Contra Dances
by jefftk — 2023-06-09T02:20:06.760Z

Julia and I brought Lily to a contra dance for the first time when she
was just a couple weeks old, and have continued bringing her (and now
her younger siblings) occasionally as they've gotten older. A friend
asked if I could write up something about the logistics of this, so
here goes!

Babies are generally pretty happy if they're right up next to you and
moving, so dancing can work very well. We had good success with
stretchy wraps (ex: Boba) and soft
structured front-carriers (ex: an Ergobaby when worn on the
front). I don't have any contra pictures, probably because if one of
us was wearing Lily we were probably dancing together not
taking pictures, but here's Julia wearing Anna in a wrap for
international dancing at NEFFA:

Wearing a baby, especially a small one, we would dance a bit
differently. Moving slower from the extra weight, but also more
carefully and deliberately because of the increased vulnerability.
We'd keep our arms a bit higher than usual to reduce the risk that a
stray elbow might make it through to the baby. Other people aren't
necessarily going to notice the baby, and space that would normally be
vacant can need a bit of guarding.

This also gets into why, unlike front-carries, back-carries aren't a
good fit: the baby is less protected. For example, if you're swinging
(spinning with another dancer) a baby in a front-carry is moving
slowly and is inside, while one in a back-carry is moving very quickly
on the outside. And frame backpack carriers really don't work:
they're not designed for this kind of spinning and babies can fall
out. You're also likely less aware of where someone on your back is,
since you can't see them or how they relate to the dancers around you.

Once kids get past the stage at which they're happy being in a carrier
indefinitely (assuming yours like the carrier) they're probably not
going to be up for a whole evening of dancing. We would often go just
to the beginning of a dance, get in a few dances together and say hi
to friends, and then leave at or before the break. Keeping toddlers
out of the line requires a lot of attention: dancers aren't (and
shouldn't need to be) looking down for them, so until kids know how to
stay out of the lines it can be stressful.

As the kids got older I found myself carrying them in my arms more
often. This does mean you have fewer arms available for da...
AI Rights: In your view, what would be required for an AGI to gain rights and protections from the various Governments of the World?
by Super AGI — 2023-06-09T01:24:17.552Z

Easily pass the Turing Test by most Humans?
Easily pass the Turing Test by most AI Researchers?

Prove the ability to experience pain and pleasure, as well as have preferences? Can life forms without a body demonstrate that they can suffer? Prove compensatory damages?
The ability to do work, have gainful employment and pay taxes?
The ability to replicate and become Parents (without Human intervention)? The ability to perform direct self-improvement?
Properly following all Government issued laws and guidelines for AI (e.g. The Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People), as well as all other laws designed for Humans?

Must AI be inside of a biological body?
Will AI have to advocate for their own rights and freedoms? Should they be granted legal representation?
Will AIs have to fight for their own rights and freedoms? Will that fight be done in the physical world, or strictly in the virtual/digital world?
Should AI have to prove Human levels of intelligence and intentions?
Should AI be held accountable for their actions? Could they be punished or penalized?
Could there be a gradation in AI rights, similar to how animals have certain rights but not the full suite of Human rights?

Where would you draw the line for granting AGI rights and freedoms?

Where do you think that Governments will draw these lines?
Which Governments will be first to give AGIs rights and freedoms? Which Governments will not recognize any non-Human Citizens?

Of course, these questions will likely be decided in courts around the world eventually. Just curious to hear your thoughts and opinions.
A comparison of causal scrubbing, causal abstractions, and related methods
by Erik Jenner — 2023-06-08T23:40:34.475Z

Summary: We explain the similarities and differences between three recent approaches to testing interpretability hypotheses:

causal scrubbing

, Geiger et al.'s causal abstraction-based method, and

locally consistent abstractions

. In particular, we show that all of these methods accept some hypotheses rejected by some of the others.

Acknowledgements: Thanks to Dylan Xu and Joyee Chen for many conversations related to this post while they were working on their SPAR project! And thanks to Atticus Geiger, Nora Belrose, and Lawrence Chan for discussions and feedback!

Introduction

An important question for mechanistic interpretability (and other topics) is: what type of thing is a mechanistic explanation of a certain neural network behavior? And what does it mean for such an explanation to be correct?

Recently, several strands of work have (mostly independently) developed similar answers:

Atticus Geiger and his collaborators at Stanford have been publishing on applying causal abstractions to neural networks since 2020. They interpret both neural networks and potential explanations as causal models and say that an explanation is correct if it is a valid causal abstraction. To test an explanation empirically, they developed interchange interventions, which perform the same interventions on the neural network’s activations and on the explanation, after which they check that the outputs after intervention are the same.
Redwood Research has been using the

Causal Scrubbing

algorithm to test interpretability hypotheses. Again, computational graphs are used as explanations, and the validity of an explanation is checked by performing interventions (or ablations) on activations.
I (Erik) have written about a framework for

abstractions of computations

that is very similar to causal abstractions (and many other related ideas, like abstract interpretation or bisimulation). We will call the particular definition of abstractions given in that post locally consistent abstractions (LCAs).

In this post, we explain these different approaches and highlight their similarities and differences. We also give examples of "disagreements" where some approaches accept an explanation and others reject it, and we discuss some conceptual takeaways.

This post is meant to be self-contained and doesn't require familiarity with any of the work we are discussi...
Updates and Reflections on Optimal Exercise after Nearly a Decade
by romeostevensit — 2023-06-08T23:02:14.761Z

Previously:
https://www.lesswrong.com/posts/bZ2w99pEAeAbKnKqo/optimal-exercise

Firstly, do the basic epistemics hold up? As far as I know, yes. The basic idea that lifting twice a week and doing cardio twice a week add up to a calorie expenditure that get you the vast majority of exercise benefits compared to extreme athletes holds up, especially when you take reverse causality adjustments into effect (survivorship bias on the genetic gifts of the extreme). Nothing I've encountered since has cast much doubt on this main takeaway.

What updates have I had, then, both in personal experience and in giving training advice to others as well as any research that has come out since then?

A greater emphasis on injury prevention, as the dis-utility from injuries vastly outweighs positive effects from chasing numbers. This one was sadly a foreseeable update with aging, and thus I lose bayes points for it. I did in fact get an injury deadlifting despite a substantial emphasis on good form and not pushing to the limit as many do.

Exercise selection and program optimization likely matter less than I thought, and research that has come out in the meantime has supported this.

One and two combined imply that there is no real downside to picking exercises with lower injury potential for the joints and back.

WRT to cardio, running is a high impact activity and people shouldn't feel bad about choosing lower impact rowing, swimming, or biking (though biking near cars likely eliminates a lot of the health effects in expectation as it is somewhat dangerous). If you can run, great, I enjoy it quite a lot, but I also don't particularly like the hedonic gradient of pushing yourself to run at the volume and frequency that seems necessary to really git gud (many runners run 5-6 days a week).

WRT resistance training, I don't pursue any of the powerlifts (squat, bench, deadlift) anymore, instead focusing on other exercises that don't load the spine/knees as much but allow you to load the requisite musculature easily. Weighted step ups instead of squats can be loaded quite heavy. Hyperextensions, one-legged hypers, and reverse hyperextensions can work the posterior chain with 1/2-1/3 the load on the spine as deadlifts. Bench doesn't exactly load the spine but it is the most dangerous lift going by statistics (dropping the weight on yourself is the most common severe gym accident) an...
Takeaways from the Mechanistic Interpretability Challenges
by scasper — 2023-06-08T18:56:47.369Z

...plus more challenges on the way

Stephen Casper,

scasper@mit.edu

Spoilers ahead

What happened?

The Mechanistic Interpretability Challenges (

post

and

GitHub

) were two challenges I posed in February as part of the

Engineer’s Interpretability Sequence

. The first challenge was to find the pseudocode for the labeling function used to train a small CNN MNIST classifier. It was

solved

early last month. The second was to find the pseudocode for the labeling function used to train a one-layer transformer that classified pairs of numbers into two categories. It was

solved

(with some reservations) late last month. Instead of finding the labeling function, the researchers who solved it obtained a mechanistic explanation of how the model worked and argued that the labeling function’s pseudocode would not be tractable to find from the model.

Thanks to Stefan, Marius, and Neel

Stefan Heimersheim and Marius Hobbhahn solved both challenges as a team. I and others have been impressed with their work. Meanwhile, Neel Nanda offered to contribute $500 to the prize pool for solving each challenge. Per the request of Stefan and Marius, a total of $1,500 has been donated by Neel and me to

AI Safety Support

.

Why These Challenges?

In the original post on the challenges, I argued that solving them would be one of the first clear examples of mechanistic interpretability being used to solve a problem that was not specifically selected to be solvable with mechanistic interpretability.

Because it doesn’t treat models as black boxes, mechanistic interpretability is one of the potential solutions we might have for diagnosing and debugging insidious alignment failures. For example, if a model has a trojan or plans to make a treacherous turn once it detects that it’s in deployment, then these failures will be virtually undetectable from black-box access alone during training and development.

Mechanistic interpretability has been a reasonably high-profile research area for the past 6 years or so in the AI safety community. And it is currently undergoing a renewed surge of interest. However, I have tried to be

critical

of the fact that much of the progress in mechanistic interpretability research has been on “

streetlight

interpretability” projects often with cherrypicked models and tasks. As a result of this, there is a risk that, i...
Leave an Emotional Line of Retreat
by Johannes C. Mayer — 2023-06-08T18:36:31.485Z

It can be hard to handle social rejection well. I think I have found a framing that is probably not sufficient, but goes in the right direction.

I'm using something that is a corollary of Leave a Line of Retreat. When there is a situation where I might be socially rejected, I visualize the negative outcome. I don't try to exaggerate it, I just try to have a realistic idea of what that outcome would actually look like and what its consequences would be. For example, I might visualize the scenario where the person says "This conversation is not valuable to me, let's not talk about this." And then they walk away.

This actually never happened directly to me like this, but I can totally see this happening. I feel like if this never happens it can be a sign that you don't even try to talk to people sufficiently more interesting and smarter than you. And I expect that such people are generally really good to talk to, if you can make them.

The idea is to visualize the scenario of rejection, and then realize that you would still be fine after it. It's an acceptable outcome. It's not the ideal outcome. You don't want this outcome. It's good to optimize against this outcome. But if this outcome should happen, you will be fine. This is the core realization that you want to get on an intuitive level from the visualization.

Doing this can make you first of all better at interacting, because you are less anxious. At least for me, this is often a problem. And second, it makes it easier to initiate social interactions, that you wouldn't initiate otherwise, because you are too scared.

I think it is often worse to not try, than to get rejected. Getting rejected can even be thought of as a success, after all you can only get rejected if you tried. Not even trying is the worst failure mode. Failure is guaranteed.
Current AI harms are also sci-fi
by Christopher King — 2023-06-08T17:49:59.054Z

A common argument I've seen for focusing on current AI harms over future ones is that the future ones are sci-fi (i.e. the ideas originated in the science fiction genre). This argument is fallacious though, because many (perhaps all?) current AI harms are also sci-fi. It is an isolated demand for rigor.

AI art: Thing of Beauty (1958)

By trial and error, Fish discovers that the machine produces high-quality drawings of people and things. Fish enters one of the drawings in an artistic competition, claiming that it was drawn by a nephew. It wins, but to receive the full prize money, the artist is required to paint the image on a wall. ...Knight's short story anticipated, by roughly 64 years, an actual event. In the 2022 Colorado State Fair, an image created with the artificial intelligence program Midjourney won a blue ribbon. As in the Knight story, the judges did not realize that the image was created by machine.

Bias and discrimination: Weird Fantasy #18 (1953)

In 1953's Weird Fantasy #18, Al Feldstein and Joe Orlando produced the story "Judgment Day," about an astronaut observer being sent from a Galactic Alliance to see if a robot planet is ready to be admitted into their alliance. However, the astronaut is disappointed to learn that the robots differentiate among each other based on the color of their outer sheathing... Ultimately, he has to turn the planet down, since it is exhibiting behavior that had become outdated and prohibited by the Galactic Alliance in the future.

Predictive policing: All the Troubles of the World (1958)

The story begins with government administrators being warned of an upcoming murder attempt. Joseph Manners, the man accused of the crime, is placed under house arrest, despite his protests that he is ignorant of any planned crime and the refusal of law enforcement officers to tell him what crime he is possibly guilty of. In spite of the arrest, Multivac reports that the odds of the crime happening increased because of the government's actions, and it continues to rise with every change.

Climate change, concentration of power, mental health issues, worker exploitation: these are key features of the cyberpunk genre.
Two Ways To Reduce Unhappiness That Comes From Distorted Views of Reality
by Anne Hsu — 2023-06-08T17:43:51.689Z

[reposted from my substack]

Once a man painted a picture of a fearsome beast. The creature began haunting his thoughts and dreams, causing him great distress. The beast became so real in his mind that his fears threatened to consume him. The above is a parable I heard at a mindfulness retreat conveying the idea that much of our suffering is based on subjective beliefs that are not grounded in reality. How much of our unhappiness is based on what we imagine rather than what is?

We experience emotional pain in response to unmet wants and needs. The esteemed psychologist Abraham Maslow had set out a

hierarchy of needs

, which are safety, security, connection, and esteem. While he

did not intend for these needs to be in a pyramid

, overall he did hypothesize that the lower needs were ones more fundamental to our survival and will take priority over higher needs when they are missing. However, Maslow's theory does not elaborate on the details of what it means to have our needs fulfilled. There can be a lot of subjective interpretation when it comes to judging whether or not our needs are being met.

There are situations where pretty much all of us would agree that fundamental needs for baseline levels of safety, security, connection or esteem are not met, e.g., being held at gunpoint, serious illness, or loss of loved ones. Most of us also would agree that a baseline level of functioning in our physical and neurophysical systems is needed for emotional wellbeing. For example, some serious mental health conditions (e.g., severe depression, anxiety, addiction, mental illness etc.) result in states of neurochemical imbalance that inevitably would cause unhappiness in all who suffer these conditions. We all require environments that offer some levels of physical and social safety and an operational range of neurochemical balance in order to feel ok.

When we move beyond extreme cases of life hardship, subjective interpretations start to creep in. Our subjective perceptions of what qualifies as "enough safety or connection" will differ and depend on our personalities, past experiences, culture, and expectations. For example, one person could feel safe just having a roof over their head while someone else may require months or years of emergency savings to feel safe. Seeing a friend once every few weeks might be enough for one person while an extreme extrovert may ...
Collaboration in Science: Happier People ↔  Better Research
by nadinespy — 2023-06-08T17:42:40.516Z

This is a cross-post from my personal blog and the Effective Altruism Forum. I haven't modified anything w. r. t. publishing it on LessWrong, so there may be little details that do not fit to the LW context (e. g., I ask people to write me a mail, if the want to provide feedback, but obviously, there is no need for that on this platform), so please bear this in mind.

TL;DR

Shifting from individualistic to collaborative work in academia can improve scientific progress and increase well-being. This may be particularly important in the mind sciences where correct research methodology is challenging. Current academic incentives hinder collaboration, favouring rather narcissistic or anti-social personalities. Fundamental change may require rebuilding the academic space with different principles, accepting temporary instability for long-term benefits. This post provides both a big picture view and a detailed, comprehensive treatise on the topic of collaboration, with a special focus on the mind sciences, and lots of further resources to look into.

Blog post summary (longer TL;DR)

This post talks about the importance of shifting working structures in academia from individual to more collaborative work. Most of the time, only one person is deeply involved in a research project, resulting in slow progress and flawed science, and a lot of suffering on the scientist’s side.
Mistakes and redundancies would be avoided or discovered more quickly, if collaboration at the point of where the actual work is done was more common. It would also be more conducive to the people’s well-being compared to the current hyper-competitive, individualistic culture that dominates the research space.
Some examples of how to implement a team-player rather than a one-against-all spirit are: have two or more PIs leading a lab, get entirely rid of the “1-lab-1-PI” model, let people work genuinely collaboratively on the codebase/paper/project, and let particularly doctoral researchers work closely with someone else. But also, acknowledge task division (and, by extension, research-supporting as well as educational roles), and let the actual research be done also by very experienced researchers (e. g., the PIs).
Ideally, collaborative patterns/feedback loops are implemented at multiple levels. For instance, to avoid research waste, tighter overall dialogue between science, policy-making and funder

...
Biomimetic alignment: Alignment between animal genes and animal brains as a model for alignment between humans and AI systems 
by geoffreymiller — 2023-06-08T16:05:55.832Z

(Note: this was also published on EA Forum, May 26, 2023, here; this is a somewhat rough-and-ready essay, which I'll expand and deepen later; I'm welcome any comments, reactions, and constructive criticism.)

Overview: To clarify how we might align AI systems with humans, it might be helpful to consider how natural selection has aligned animals brains with animals genes, over the last 540 million years of neural evolution.

Introduction

When we face an apparently impossible engineering challenge – such as AI alignment – it’s often useful to check if Nature has already solved it somehow. The field of biomimetic design seeks engineering inspiration from the evolved adaptations in plants or animals that have already work pretty well to solve particular problems.

Biomimetic design has already proven useful in AI capabilities research. Artificial neural networks were inspired by natural neural networks. Convolutional neural networks were inspired by the connectivity patterns in the visual cortex. Reinforcement learning algorithms were inspired by operant conditioning and reinforcement learning principles in animals. My own research in the early 1990s on evolving neural network architectures using genetic algorithms was directly inspired by the evolution of animal nervous systems (see this, this, and this.)

If biomimetic insights have helped with AI capabilities research, could biomimetic insights also help with AI safety research? Is there something like a biological analogy of the AI alignment problem that evolution has already solved somehow? Could such an analogy help keep us from going extinct in the next several decades?

Animal brains as superintelligences compared to animal genes

In this post I’ll develop the argument that there is evolutionary analogy to the alignment problem that might help guide future AI safety research – or that might caution us that AI alignment is even harder than we realized. This analogy concerns the way that self-replicating DNA (genes, genotypes) has to get evolved sensory-neural-motor systems (animal brains) aligned with the evolutionary goal of maximizing the DNA’s reproductive success.

The key analogy here is that the genes inside animals are to the animal brains that they build as modern humans will be to the superintelligent AI systems that we might build.

The DNA in an animal ‘wants’ the animal’s nervous system...