trentmkelly/LessWrong-43k huggingface dataset:
Yamaha P-Series Overview
by jefftk — 2023-12-12T20:30:09.428Z

Yamaha has made many

p-series
("portable") keyboards over the years, and it's pretty confusing. I
play a p-85 at home, which I'm unreasonably fond of, but when I travel
I'm often playing other people's keyboards. When someone says they
have a p-NNN available what does that mean?

Within the p-series there have been tiers, and within each tier Yamaha
had many years when they'd make new versions that were very minor
changes. Currently, the p-series tiers are:

Low: p-143

Mid: p-125a

High: p-525

Higher tiers cost more, but generally give you more or better
features: physical response ("action") closer to a real piano, more
voices, more pedals, more realistic voices, better interface for
choosing voices, more simultaneous notes (polyphony) etc. Here's my
attempt to make sense of the historical lineup. I've marked them with
their release year, and in cases where they have a fancier action than
GHS I've marked that as well.

Entry

p-35 (2012)

p-45; p-71 when sold by Amazon (2015)

p-143; p-145 internationally (2023)

Low-mid

p-60 (2002, GH)

p-70 (2005)

p-85 (2007)

p-95 (2010)

p-105 (2012, MIDI is now USB)

p-115 (2015)

p-125 (2018)

p-121 (2019, a p-125 with 73 keys)

p-125a (2023, a p-125 without USB audio)

High-mid

p-80 (1999, GH)

p-120 (2001, GH)

p-90 (2003, GH, a p-120 no speakers)

p-140 (2005, GH)

p-155 (2009, GH)

p-255 (2014, GH)

High

p-250 (2003, GH)

[gap where their higher-end portable digital pianos were
in the CP-series, starting with the CP-300]

p-515 (2018, NWX)

p-525 (2023, GT-S)

Go back to the 90s and there's also the p-100, p-150, p-200,
p-300, and p-500, which are all old enough that it's harder to get information on
them, and you're much less likely to run into them.

Personally I'm happy with any of these; the features distinguishing
the fancier models aren't ones I need. I like the built-in piano
sounds (Piano 2 and Electric Piano 2 on my p-85) and at least from the
p-70 onwards, use external midi for other voices, don't use the
pedals, don't need more polyphony than I have fingers, use external
midi for most voices, can handle either USB or 5-pin MIDI, and I like
how light the GHS action is.

Comment via: facebook, mastodon
Balsa Update and General Thank You
by Zvi — 2023-12-12T20:30:03.980Z

Wow, what a year it has been. Things keep getting crazier.

Thank you for taking this journey with me. I hope I have helped you keep pace, and that you have been able to discern for yourself the parts of this avalanche of words and events that were helpful. I hope to have helped things make somewhat more sense.

And I hope many of you have taken that information, and used it not only to be able to check Twitter less, but also to make better decisions, and, hopefully, to help make the world a better place—one in which humanity is more likely to survive.

Recently, my coverage of the Biden administration executive order and the events at OpenAI have been received very positively. I’d like to do more in that mold: more focused, shorter pieces that pull the story together, hopefully de-emphasizing more ephemeral weekly posts over time. I am also happy that this work has potentially opened doors that might grant me larger platforms and other ways to make a difference.

If you feel it would make the world better to do so, please help spread the word to others who would find my work useful.

Thank you especially to both my long-time and recent paid subscribers and my Patreon supporters. It is important to me that all my content remain freely accessible— so please do not subscribe if it would be a hardship—but subscriptions and other contributions are highly motivating and allow me to increase my budget.

You can also help by contributing to my 501c(3), Balsa Research.

The rest of this post is an update on what is happening there.

Balsa First Targets the Jones Act

Even with the craziness that is AI, it is important not to lose sight of everything else going on and to seek out opportunities to create a better, saner world. That means building a world that’s better equipped to handle AI’s challenges and one that knows it can do sensible things.

Previously I shared both an initial announcement and Balsa FAQ. Since then, we’ve focused on identifying particularly low-hanging fruit where key bridging work is not being done. I’ve hired Jennifer Chen, who has set up the necessary legal and logistical infrastructure for us to begin work. We’ve had a lot of conversations with volunteers, considered many options and game plans, and are ready to begin work in earnest.

As our first major project, we’ve decided that Balsa will work to repeal the Jones Act. That is a big sw...
Towards an Ethics Calculator for Use by an AGI
by sweenesm — 2023-12-12T18:37:47.407Z

If Artificial General Intelligence (AGI) is achieved without a highly consistent way of determining what’s the most ethical decision for it to make, there’s a very good chance it’ll do things that many humans won’t like. One way to give an AGI the ability to consistently make ethical decisions could be to provide it with a straightforward mathematical framework to calculate the ethics of a situation based on approximated parameters. This would also likely enable some level of explainability for the AGI’s decisions. I’ve been pursuing such a framework and have come up with a preliminary system that appears to calculate the “ethics” of some idealized decisions in a manner that’s consistent with my values and ethical intuitions, meaning it hasn’t produced any wildly counterintuitive results for the admittedly very limited number of ethical decision scenarios I’ve looked at so far. I don’t put forward my values and ethical intuitions as the “right” ones, but believe they're reasonably consistent so should provide a decent foundation to build a proof-of-concept ethics calculation system around.

For determining the "most ethical” decision an AGI could make in a given situation, the criterion I’ve chosen is that the decision should maximize expected long-term value in the world. I define value to be how useful something ultimately is in supporting and promoting life and net “positive” experiences, where “positive” can contain significant subjectivity. This is basically a utilitarian philosophical approach, although I include the expected value of upholding rights as well.

Setting Up and Applying a Mathematical Framework

Here’s an outline of the steps I’ve used to devise and apply this system:

Come up with a “minimal set” of value destructions that could occur over a wide range of situations.
Come up with parameters to calculate approximate weights for each value destruction in the minimal set.
Come up with a “minimal set” of possible value builds that could occur over a wide range of situations.
Come up with parameters to calculate approximate weights for each value build.
For each side of the decision to be made in a given situation, list relevant value destructions and value builds and estimate the parameters to calculate weights for each one.
Take the net value builds and destructions, and subtract out any value builds or destructions that individuals who’d experience th

...
Why Psychologists Are Wrong About The Illusion Of Explanatory Depth
by moses onyedikachukwu — 2023-12-12T18:32:33.893Z

The Khan Academy did a video where they asked random people in the streets what the internet is, a question to which almost all gave some ridiculous answers —confidently. A young girl said it is the WiFi because, according to her, "no WiFi, no internet", and a grown-up said, "It is like plumbing, it’s always moving".

The phenomenon this tries to prove is that we most times possess untested knowledge about things, which may turn out to be incomplete or outrightly wrong. This is called The Illusion Of Explanatory Depth (IOED for short).

Yet, the reality of tacit knowledge proves that it is human to know and not be able to explain in great detail, or in any details for that matter.

The Illusion Of Explanatory Depth

The term IOED was coined by Leonid Rozenblit and Frank Keil in 2002, stating: "Most people feel they understand the world with far greater detail, coherence, and depth when they really don’t".

The two were able to prove this oddity through multiple multiphase studies. In the first phase, Rozenblit and Keil asked participants to rank how well they understood fabrications such as a cell phone, crossbow, or a sowing machine; and in the second phase asked the participants to put down a well-detailed explanation of how each fabrication works. Afterward, the participants were finally asked to re-rank how well they understood each one.

Repeatedly, the outcome of the study showed that ratings of self-knowledge plummeted dramatically from phase one to phase two, after participants were faced with their inability to explain how the fabrications operate.

Beyond fabricated objects, the IOED extends to more abstract things like politics, tax laws, human rights, and many other things. It seems we don’t know as much as we think we do, and we sometimes possess a misunderstanding in the place of an understanding.

Explanations So Far

In part, the reason for this may be because what we think we know about a thing is usually dominated by how we feel about it.

Prof. Steven Sloman, a cognitive psychologist at Brown University explained: “If you’re forced to give an explanation, you have to really understand, and you have to confront the fact that you might not understand. Whereas when you give reasons, then you do what people do around the Thanksgiving dinner table. They talk about their feelings about it, what they like, what they don’t like.”

Also, it may well be as a result ...
A design concept for superintelligent machines (and Popper's critique of induction)
by tiplur-bilrex — 2023-12-12T18:31:15.757Z
Link: https://tiplur-bilrex.tlon.network/applied-fallibilism

This is my first post on LessWrong and I apologize for the length. I thought it would be possible that someone here is interested in reading or critiquing it.
The blog post is my attempt to explain why we do not yet have AGI and a possible short path for getting there. The ideas are extrapolated from an interpretation of Karl Popper and David Miller's critique of inductive probability. My view is that a world model composed of formal statements (theories) can only be constrained by observation (including observations in the form of induction); theories cannot be supported by evidence, they can only be consistent or not.

I try to clearly define two categories of knowledge with their unique properties, derive some principles for creating an explanatory world model, and share a toy example of how an LLM may be used to generate a formal explanatory world model (which I believe will be the foundation for AGI).

I am writing from the perspective of a physician with some background in philosophy and physics, not a software engineer. I will respond to any serious feedback. The text is a draft and I do intend to fix the typos.
Significantly Enhancing Adult Intelligence With Gene Editing May Be Possible
by GeneSmith — 2023-12-12T18:14:51.438Z

TL;DR version

In the course of my life, there have been a handful of times I discovered an idea that changed the way I thought about where our species is headed. The first occurred when I picked up Nick Bostrom’s book “superintelligence” and realized that AI would utterly transform the world. The second was when I learned about

embryo selection

and how it could change future generations. And the third happened a few months ago when I read a message from a friend of mine on Discord about editing the genome of a living person.

We’ve had gene therapy to treat cancer and single gene disorders for decades. But the process involved in making such changes to the cells of a living person is excruciating and extremely expensive.

CAR T-cell therapy

, a treatment for certain types of cancer, requires the removal of white blood cells via IV, genetic modification of those cells outside the body, culturing of the modified cells, chemotherapy to kill off most of the remaining unmodified cells in the body, and reinjection of the genetically engineered ones. The price is

$500,000 to $1,000,000

.

And it only adds a single gene.

This is a big problem if you care about anything besides monogenic diseases. Most traits, like personality, diabetes risk, and intelligence are controlled by hundreds to tens of thousands of genes, each of which exerts a tiny influence. If you want to significantly affect someone’s risk of developing lung cancer, you probably need to make hundreds of edits.

If changing one gene to treat cancer requires the involved process described above, what kind of tortuous process would be required to modify hundreds?

It seemed impossible. So after first thinking of the idea well over a year ago, I shelved it without much consideration. My attention turned to what seemed like more practical ideas such as embryo selection.

Months later, when I read a message from my friend Kman on discord talking about gene editing adults in adult brains, I was fairly skeptical it was worth my time.

How could we get editing agents into all 200 billion brain cells? Wouldn’t it cause major issues if some cells received edits and others didn’t? What if the gene editing tool targeted the wrong gene? What if it provoked some kind of immune response?

But recent progress in AI had made me think we might not have much time left before AGI, so given that adult gene ed...
Why No Automated Plagerism Detection For Past Papers?
by Lao Mein — 2023-12-12T17:24:31.544Z

Automated plagerism detection software is common. But cases like the recent incident with Harvard administrator Gay have shown that egregious cases of plagerism are still being uncovered. Why would this be the case? Is it really so hard to run plagerism checks for every paper on Sci-hub? Has anyone tried?

I am curious since I am currently upskilling for the purposes of technical alignment research and it seems like an interesting project to pursue.
OpenAI: Leaks Confirm the Story
by Zvi — 2023-12-12T14:00:04.812Z

Previously: OpenAI: Altman Returns, OpenAI: The Battle of the Board, OpenAI: Facts from a Weekend, additional coverage in AI#41.

We have new stories from The New York Times, from Time, from the Washington Post and from Business Insider.

All paint a picture consistent with the central story told in OpenAI: The Battle of the Board. They confirm key facts, especially Altman’s attempted removal of Toner from the board via deception. We also confirm that Altman promised to help with the transition when he was first fired, so we have at least one very clear cut case of Altman saying that which was not.

Much uncertainty remains, especially about the future, but past events are increasingly clear.

The stories also provide additional color and key details. This post is for those who want that, and to figure out what to think in light of the new details.

The most important new details are that NYT says that the board proposed and was gung ho on Brett Taylor, and says D’Angelo suggested Summers and grilled Summers together with Altman before they both agreed to him as the third board member. And that the new board is remaining quiet while it investigates, echoing the old board, and in defiance of the Altman camp and its wish to quickly clear his name.

The New York Times Covers Events

The New York Times finally gives its take on what happened, by Tripp Mickle, Mike Isaac, Karen Weise and the infamous Cade Metz (so treat all claims accordingly).

As with other mainstream news stories, the framing is that Sam Altman won, and this shows the tech elite and big money are ultimately in charge. I do not see that as an accurate description what happened or its implications, yet both the tech elite and its media opponents want it to be true and are trying to make it true through the magician’s trick of saying that it is true, because often power resides where people believe it resides.

I know that at least one author did read my explanations of events, and also I talked to a Times reporter not on the byline to help make everything clear, so they don’t have the excuse that no one told them. Didn’t ultimately matter.

Paul Graham is quoted as saying Altman is drawn to power more than money, as an explanation for why Altman would work on something that does not make him richer. I believe Graham on this, but also I think there are at least three damn good other reasons to do it, mak...
Navigating the Attackspace
by Jonas Kgomo — 2023-12-12T13:59:14.738Z

As Artificial Intelligence (AI) continues its rapid ascent, we are witnessing increasing evidence of well-crafted methods to undermine and exploit AI responses. Carefully crafted inputs can exploit vulnerabilities and lead to harmful or undesired results.

In recent times, we have seen numerous individuals independently uncover failure modes within AI systems, particularly through targeted attacks on language models.

tipping a language model with 'money' to get longer context answers
using Zulu to get harmful responses

By crafting clever prompts and inputs, these individuals have exposed the models' vulnerabilities, causing them to generate offensive content, reveal sensitive information, or behave in ways that deviate from their intended purpose.

To address these concerns and gain a deeper understanding of this evolving threat landscape, we are undertaking two key initiatives:

1. Attack Space

AttackSpace is an open-source curated comprehensive list of LLM security methods and safeguarding techniques.

Located at https://github.com/equiano-institute/attackspace, this open-source repository is dedicated to collecting and documenting known attacks on language models. This comprehensive resource serves as a vital platform for researchers, developers, and the general public to access information on these vulnerabilities. By fostering a collaborative space for information sharing, we aim to accelerate research and development efforts focused on improving the robustness and security of language models. This collection contains work by Viktoria Krakovna. The goal is to have a structured view and characterisation of the latent attack space. We want to model satisfiability of AI attacks. This concept, similar to the P-NP problem, involves determining whether a given set of conditions can be met to successfully launch an attack against a language model. By analyzing the features and conditions of successful attacks, we can develop efficient algorithms for identifying and preventing future attacks.

2. Project Haystack

A suite of red teaming and evaluation frameworks for language models

This project aims to develop Haystack, an open-source platform for red teaming and human feedback on LLMs with crowd-sourcing and automated methods

Goals

build a frontend for Pythia model evaluation

https://github.com/EleutherAI/pythia

develop a model evalu

...
Nonlinear’s Evidence: Debunking False and Misleading Claims
by KatWoods — 2023-12-12T13:16:12.008Z

Recently, Ben Pace wrote a well-intentioned blog post mostly based on complaints from 2 (of 21) Nonlinear employees who 1) wanted more money, 2) felt socially isolated, and 3) felt persecuted/oppressed.

Of relevance, one has accused the majority of her previous employers, and 28 people of abuse

- that we know of.

She has accused multiple people of threatening to kill her and literally accused an ex-employer of murder. Within three weeks of joining us, she had accused five separate people of abuse: not paying her what was promised, controlling her romantic life, hiring stalkers, and other forms of persecution.

We have empathy for her. Initially, we believed her too.

We spent weeks helping her get her “nefarious employer to finally pay her” and commiserated with her over how badly they mistreated her.

Then she started accusing us of strange things.

You’ve seen Ben’s evidence, which is largely the word of two people and a few misleadingly cropped screenshots. Below, we provide extensive evidence (contracts, recordings, screenshots, etc) demonstrating that the post’s claims are false, misleading, or are catastrophizing normal things. This post is a summary; we also include a ~200 page appendix of additional evidence. We also present a hypothesis for how Ben got so much wrong.

Two ways you can read this: 1) stop whenever you’re convinced because you’ve seen enough falsehoods that you no longer think their remaining claims are likely to be true, or 2) jump to the specific claims that are most important to you, and look at the evidence we provide for them. You can see summary tables of the key claims and evidence here, here, and here.

Our request as you read on: consider this new evidence you haven’t seen yet with a scout mindset, and reflect on how to update on the accuracy of the original claims.

It’s messy, sorry. Given the length, we’re sure we’ve made mistakes - please do let us know. We’re very happy to receive good faith criticism - this is what makes EA amazing.

Finally, we want to note that we have a lot of empathy for Alice and Chloe. We believe them when they say they felt bad, and we present a hypothesis for what caused their negative emotions.

Short summary overview table

Claim	What actually happened
Alice claimed: they asked me to travel with illegal drugs.

- False. It was legal medicine - from a pharmacy.

- Ben knew this and

...
Funding case: AI Safety Camp 10
by Remmelt — 2023-12-12T09:08:18.911Z
Link: https://manifund.org/projects/10th-edition-of-ai-safety-camp

Project summary

AI Safety Camp is a program with a 5-year track record of enabling people to find careers in AI Safety.

We support up-and-coming researchers outside the Bay Area and London hubs.
We are out of funding. To make the 10th edition happen, fund our stipends and salaries.

What are this project's goals and how will you achieve them?

AI Safety Camp is a program for inquiring how to work on ensuring future AI is

safe

, and try concretely working on that in a team.

For the 9th edition of AI Safety Camp we opened applications for

29 projects

.
We are first to host a special area to support “Pause AI” work. With funding, we can scale from 4 projects for restricting corporate-AI development to 15 projects next edition.
We are excited about our new research lead

format

, since it combines:

Hands-on guidance: We guide research leads (RLs) to carefully consider and scope their project. Research leads in turn onboard teammates and guide their teammates through the process of doing new research.
Streamlined applications: Team applications were the most time-intensive portion of running AI Safety Camp. Reviewers were often unsure how to evaluate an applicant’s fit for a project that required specific skills and understandings. RLs usually have a clear sense of who they would want to work with for three months. So we instead guide RLs to prepare project-specific questions and interview their potential teammates.
Resource-efficiency: We are not competing with other programs for scarce mentor time. Instead, we prospect for thoughtful research leads who at some point could become well-recognized researchers. The virtual format also cuts on overhead – instead of sinking funds into venues and plane tickets, the money goes directly to funding people to focus on their work in AI safety.
Flexible hours: Participants can work remotely from their timezone alongside their degree or day job – to test their fit for an AI Safety career.

How will this funding be used?

We are fundraising to pay for:

Salaries for the organisers for the current AISC
Funding future camps (see budget section)

Whether we run the tenth edition, or put AISC indefinitely on hold depends on your donation.

Last June, we had to freeze a year's worth of salary for three staff. Our ops coordinator had to leave, and Linda and Remmelt decided to run one more edition as volunteers.

AI...