trentmkelly/LessWrong-43k huggingface dataset:
Bad Imitation Instruments
by jefftk — 2023-08-03T02:30:02.937Z

It's common to look down on modern instruments as cheap imitations:
the digital piano as an inferior piano,

foot drums as an
inferior drum kit. While this isn't wrong, as far is it goes, it's a
very limiting way to look at these instruments. Instead of writing
them off for their faults, it's a lot better to figure out what
they're good at and what new musical possibilities they open up.

Some historical perspective might be useful: there's a long line of
classic instruments that were created as bad imitations of other
instruments, that we now appreciate for themselves!

Consider the Hammond organ:

It was invented and originally marketed as a cheaper alternative to
the pipe organ, and even lost an enforcement action from the FTC after
advertising that it was an equivalent instrument. Which makes sense:
if you evaluate it as a pipe organ, it's really not a very good one.
And yet, appreciated for itself, it's an incredible versatile
instrument, well worth playing in its own right.

Or the Rhodes piano:

It's a similar story: what began as a cheap portable substitute for the
piano in military hospitals is now a valued for its own complex sound.

Or the DX7
keyboard:

Many of its presets are pretty lacking imitations of the instruments
they're modeled after, but the DX7, played by people who wanted it for the
sounds it did make, was tremendously influential in the mid-80s.

Others include the Mellotron/Orchestron, Harmonium,
and even the electric
guitar.

So I'd encourage you to keep an open mind when listening to new
instruments, and focus on what they have to offer instead of whether
they represent a strict improvement over more established options.
Kolmogorov's theory of Algorithmic Probability
by Aidan Rocke — 2023-08-03T00:58:08.395Z
Link: https://keplerlounge.com/posts/entropy/

Motivation:

In 1970, Andrey Kolmogorov presented a remarkable vision for the combinatorial foundations of probability theory at the International Congress of Mathematicians in Nice which anticipates the modern development of Artificial Intelligence:

Using his brain, as given by the Lord, a mathematician may not be interested in the combinatorial basis of his work. But the artificial intellect of machines must be created by man, and man has to plunge into the indispensable combinatorial mathematics. - Kolmogorov

The full text was eventually published in 1983 [1] and Kolmogorov's papers on the subject, where he formulated the fundamental notion of Kolmogorov Complexity, represent a partial fulfilment of his original vision. However, it would be his PhD student Leonid Levin that would complete Kolmogorov's theory of Algorithmic Probability via Levin's Universal Distribution and Levin's Coding theorem which together emphasize the fundamental importance of prefix-free encodings.

As Marvin Minsky, Marcus Hutter and Shane Legg have fully recognised, the implications of Kolmogorov's theory for Artificial Intelligence can't be overstated as it defines the fundamental limits of Machine Learning. In particular, Kolmogorov's theory allows us to reason about Black Swans in AI Safety as Black Swans represent the epistemic limits of a particular ontology.

I wrote this synthesis thanks to the encouragement of Igor Rivin and Alex Kolpakov(aka Sasha), who both recognise that outside a small number of elite institutions such as DeepMind and OpenAI...the current state of the scientific community's understanding of Algorithmic Probability is limited.

Fundamental theorems of Algorithmic Probability:

In this synthesis, I carefully introduce and prove the four fundamental theorems of Algorithmic Probability which lead to Kolmogorov's formulation of Entropy as Expected Kolmogorov Complexity. In summary, these are:

Kolmogorov's Invariance theorem
Levin's Universal Distribution
Levin's Coding theorem
Expected Kolmogorov Complexity equals Shannon Entropy

As an application, the last fundamental theorem is then used to derive the Erdős-Euclid theorem which demonstrates that the information content of finitely many primes is insufficient to generate all the integers. There is also a discussion section where I briefly consider the implications of Markus ...
Work culture creep
by CrimsonChin — 2023-08-03T00:38:13.876Z

epistemic status: This is not my field of study. I am working through some ideas here that I think might resonate with the community.

I am a fun, happy, goofy dad. I can play music and perform magic tricks. I sing silly songs and make stupid jokes.

However, for 8 out of the 16 hours that I am awake, I sit at a desk, type, occasionally take phone calls, and talk about workflow. That's what I do 50% of my weekdays.

I am on a journey, trying to figure out what can be done to prevent these work habits from leaking into my personal life.

this post will be three major sections:

First, I'll argue that work habits are bad for personal life.
After that, I'll contend that they are impossible to completely isolate from home life.
Finally, I'll get into my thoughts on mitigating them.

This post is me working through some ideas. I'll do a separate post later on the efficacy of these.

work behaviors don't generally port into personal life

Why are work-life and home life-different?

I try to optimize my decisions and behavior so that me and my family are happy and healthy. But when I work, I am a piece of a company that has different priorities.

Some companies optimize on profits, some non-profits optimize on effect of some type, but no company optimizes on the happiness and well-being of their employees. Some companies care, but it's never the overarching goal. This isn't an anti-work post, companies have to do this or they wouldn't be a company. That's okay by me.

My goal at home is to keep my family happy and healthy, my goal at work is to accomplish tasks for the company. Because part of my job is to help my company accomplish their goal, some of my behavior at work is completely unaligned with behavior outside work. I am not saying this behavior is bad at work, I'm just saying it would not usually be behavioral choices I would make outside work.

Work-behaviors have to be different from home-behaviors because all the incentives are different. Birds are different from fish because they both adapted in different environments, just as work behavior and home behavior are different because they both have different incentive structures.

How are work-life and home-life different?

Just about every work behavior is an example of bad home behavior but I will go through a lot of examples because I feel like this takes a big shift in perspective.

Closed door meetings are normal wh...
Boxing
by Zach Stein-Perlman — 2023-08-02T23:38:36.119Z

Labs should do "boxing," I hear. I'm confused about what kinds-of-systems this applies to and what labs should do to improve their "boxing."

Kinds-of-systems: my impression is that there's little direct threat from LMs simply undergoing gradient descent. But there's some risk from models deployed internally, especially if they're in scaffolding that lets them make recursive calls and use tools. But I suspect the previous sentence is confused or missing a lot.

Boxing tactics: I hear of tactics like automatically monitoring for unusual behavior, limiting data upload speed, and using honeypots. What should labs do; if you were in charge of boxing for a lab, what would you do? What should I read to learn more?
External rationality vs. internal rationality
by metachirality — 2023-08-02T23:29:59.368Z

Internal rationality is using truth-preserving thought processes that output good hypotheses given good data. Examples include:

Identifying inconsistencies in hypotheses.
Having detailed models/inside views.
Not doing motivated reasoning.
Evaluating one's thought processes.

External rationality is discerning good data from bad data. Examples include:

Identifying the selection effects and incentives behind what data one sees.
Identifying whether some data is "adversarial": data that strongly suggests a hypothesis just because there will inevitably be one source of data that strongly suggests a given hypothesis just because there are so many sources of data.
Knowing when to use an outside view.
Knowing what experts to trust on a topic one knows nothing about.

External rationality and internal rationality intersect and build upon one another.

I like using these words instead of "inside view" and "outside view" because "outside view" tends to exclude model-based reasoning when you can have detailed models of what data is good or bad. Also "outside view" specifically refers to reference class forecasting and I don't want to overload the term. I also feel like "external rationality" and "internal rationality" are better at communicating the fuzzy intuition I'm getting at here.
When performing a dimensionality reduction on tensors, the trace is often zero.
by Joseph Van Name — 2023-08-02T21:06:55.423Z

In this post, we shall define my new dimensionality reduction for tensors in V⊗n where n≥3, and we shall make an empirical observation about the structure of the dimensionality reduction. There are various simple ways of adapting this dimensionality reduction algorithm to tensors in V1⊗⋯⊗Vn and even mixed quantum states (mixed states are just positive semidefinite matrices in V1⊗⋯⊗Vn which trace 1), but that will be a topic for another post.

This dimensionality reduction shall represent tensors in V⊗n as tuples of matrices A1,…,Ar. Computer experiments indicate that, in many cases, we have Tr(Ai1…Aim)=0 whenever m≠0modn.

If X is a matrix, then the spectral radius of X is the value

ρ(X)=max{|λ|:λ is an eigenvalue of X}=limn→∞∥An∥1/n.

If X is a matrix, then define the conjugate matrix ¯¯¯¯¯X=(X∗)T=(XT)∗; this is the matrix obtained from X by replacing each entry with its complex conjugate.

If (X1,…,Xr) is a tuple of real or complex matrices, then define the L2-spectral radius by setting

ρ2(X1,…,Xr)=ρ(X1⊗¯¯¯¯¯¯¯X1+⋯+Xr⊗¯¯¯¯¯¯¯Xr)1/2.

Suppose that K is either the field of real numbers or the field of complex numbers. Suppose that p(x1,…,xr) is a non-commutative homogeneous of degree n polynomial with coefficients in K (it is easier to define the dimensionality reduction in terms of homogeneous non-commutative polynomials than tensors).

Then define a fitness function Mp:Md(K)r→[0,∞) by setting

Mp(A1,…,Ar)=ρ(p(A1,…,Ar))1/nρ2(A1,…,Ar).

This function Mp is bounded, and it has a maximum value, but to prove that it attains its maximum value, we need to use quantum channels.

We shall call a tuple (A1,…,Ar) where Mp(A1,…,Ar) is maximized an

L2,d-spectral radius dimensionality reduction (LSRDR) of the non-commutative polynomial p(x1,…,xr). The motivation behind the notion of an LSRDR is that it is easier to represent the variables x1,…,xr as the matrices A1,…,Ar than it is to work with the non-commutative polynomial p(x1,…,xr). The d×d-matrices A1,…,Ar have d2⋅r parameters while the non-commutative polynomial could have up to rn parameters where n is the degree of the polynomial&n...
Progress links digest, 2023-08-02: Superconductor edition
by jasoncrawford — 2023-08-02T20:27:29.676Z
Link: https://rootsofprogress.org/links-digest-2023-08-02

Opportunities

Stripe Press is hiring a creative producer for video, audio, & special projects (via @_TamaraWinter)
Astera’s new Science Entrepreneur-in-Residence program will support new tools for scientific publishing (via @mattsclancy)

Announcements

The Frontier Model Forum is an industry body co-founded by OpenAI, Anthropic, Google/DeepMind, and Microsoft “focused on ensuring safe development of future hyperscale AI models” (via @OpenAI)
Impetus Grants has closed $10M for more longevity projects (via @LNuzhna)
Emergent Ventures winners, 27th cohort (via @tylercowen)
The Studies Show is a new science podcast by Tom Chivers and Stuart Ritchie, also on Spotify (via @TomChivers, @s8mb)
The Lean FRO will work on “scalability, usability, and proof automation in the Lean proof assistant” (via @leanprover)
Ed Yong is leaving The Atlantic. Subscribe to his newsletter to keep up with him (via @edyong209)
Works in Progress is now available on Apple News (via @WorksInProgMag)

Superconductors

Lots of chatter about LK-99, the supposed room-temperature superconductor. I have been holding off on speculating or sharing too many links until it is more solid
Derek Lowe wrote an explainer and gave an update Aug 1 in which he is “guardedly optimistic”
Andrew McCalip and some of his co-workers at Varda have been trying to replicate. Here is a list of all the replication attempts
At least one paper suggested a theoretical basis for superconductivity in this material, but a physics prof who studies materials says it doesn’t matter, and gives a history of disappointments in the space
Alex Kaplan shares a lot of news and is a good follow (even though IMO he got too excited too quickly)
Andrew Cote has a long, technical thread on the implications if this is real
Manifold Markets currently at about a 1-in-3 chance. Kalshi market coming soon

Video

Where is my flying RV? The Helihome was “a fully furnished flying home based on the body of a surplus Sikorsky helicopter,” and it was actually built and sold

Other links

NASA plans to launch a nuclear-powered rocket engine into space as early as 2027 (via @isabelleboemeke)
Max Roser: The limits of our personal experience and the value of statistics
How phosphorus was discovered by alchemists trying to make gold, via Molecule of the Month
Candle clock, via @Rainmaker1973, who points out: “To set an alarm, you pushed a nail into the desired point and the na

...
What works for ADHD and/or related things?
by TeaTieAndHat — 2023-08-02T18:37:18.216Z

It may be that I have ADHD, and I’d like to know if you have good tips and bits of advice on how to manage it. Specifically, there are tips to be found in every listicle out there on the Internet, but I’m curious which of those actually work? And which actually work but no one usually mentions? I expected there’d be a post like that here, but I didn’t see it.

To be a little more specific, I mostly have issues with my attention flinching to every fun thing I’ve come across, and it’s pretty easy to come across a zillion fun ways to waste time when one is procrastinating by reading LW posts and looking at memes. Also, some lack of a felt sense of progress, apart from the sense of progress I’m easily spoon-fed by articles and games? Also, rejection sensitivity, which seems to come with some fear of doing new stuff when it’s not a clear net intake of dopamine?

It may also be that I don’t actually have ADHD at all, or that I have other issues, even though I’m really more interested in ADHD here. Chief suspects would be: depression (In fact, I did have a depression until a few months ago, but it may have originated in the feeling that I wasn’t getting anywhere because of being constantly flinching and getting bored of everything after barely trying. Which sounds like ADHD) ; ASD (I most likely do have ASD) ; anxiety disorders ; schizoid disorder (looks a lot like ASD, but the differences sound uncannily familiar), etc., etc. So, if you can also give me the sort of advice on ADHD that could also help me ‘screen out’ other things, or general other advice on how to get a good idea whether it’s ADHD or not, I’d be grateful.
The Roots of Progress Blog-Building Intensive: advice for applicants, request for support
by jasoncrawford — 2023-08-02T15:37:56.375Z
Link: https://rootsofprogress.org/blog-building-intensive-update

We’ve gotten over 250 applications to The Roots of Progress Blog-Building Intensive! And the quality level is surprisingly high. I’m glad to see so many talented writers interested in progress.

If you want to apply

Do it now! Applications are open until August 11, but don’t wait. We’re reviewing everything on a rolling basis, and by the end there will only be one or two slots left.

If you want to support this program

We’ve gotten so many great applications that we want to expand it from a max of 15 up to potentially 20 participants.

To do this, we’re raising an additional $30,000. The funds cover writing instruction during the eight-week program, a three-day in-person closing event, and post-program support.

The applicants range from college students to industry experts to academics. Many of them are experienced writers, some from relevant think tanks, and some who have already been published in mainstream media. They’re writing on a wide range of topics, from specific cause areas like housing, energy, space exploration, robotics, and AI; to metascience and the philosophy of progress. I’m excited to see what they’ll produce next, how we can help them, and how they will help each other.

If you’re excited too, then donate today to help us expand this program. We’re a 501(c)(3), and we take donations via PayPal, Patreon, check, wire, or DAF. Donation links, address, EIN, and other details here.
3 levels of threat obfuscation
by HoldenKarnofsky — 2023-08-02T14:58:32.506Z

One of the biggest reasons alignment might be hard is what I’ll call threat obfuscation: various dynamics that might make it hard to measure/notice cases where an AI system has problematic misalignment (even when the AI system is in a controlled environment and one is looking for signs of misalignment). I write more about this general fear in AI Safety Seems Hard to Measure.

I think it’s very important to understand threat obfuscation dynamics; to try to design ways of assessing danger that are designed with threat obfuscation dynamics in mind; and to avoid interpreting reassuring-seeming evidence too optimistically, by keeping in mind the various threat obfuscation dynamics that could affect it.

I’ve seen three noticeably different threat obfuscation dynamics discussed, in ways that often leave me confused about exactly what dynamic someone is talking about, and when. This post aims to clear that up, based on a conversation with Paul Christiano.

The three dynamics are:

Simple “training game” dynamics: an AI system appears to behave helpfully, rather than attempting deception/manipulation/disempowerment of humans, because (a) it is good at predicting whether such attempts will be caught; (b) it is aiming for good in-episode performance (in terms of loss or reward); (c) such attempts would result in poor in-episode performance if caught. But if an AI system had a chance to do deception/manipulation/disempowerment that would not be detected, and would result in “good performance” within an episode, it would do so (this could include large-scale world takeover).

These dynamics are dangerous because we might observe dangerous behavior, train AIs to behave less dangerously, then observe less dangerous behavior, and conclude that we’ve become more safe (when in fact we’ve trained the AIs to be more effective at deception).

But we can still detect dangerous behavior in cases where it would not result in poor in-episode performance. For example, we might decide that when an AI system in the wild hacks its way to good in-episode performance, and is caught after the fact, we will at that point act as if the performance was actually good/intended (for example, no gradient descent against the behavior). Or we might deliberately conduct tests in which AI systems have opportunities to take over digital environments, obtaining high reward (no catch, not a trap

...
ChatGPT for translation

by Varshul Gupta — 2023-08-02T11:57:21.099Z
Link: https://dubverseblack.substack.com/p/chatgpt-for-translation

This post is picking up from some of the points mentioned in our

Q2’23 work post

(and continuing our

experiments with ChatGPT

). One big hurdle we are currently facing is translations not bing contextual / not so vernacular.

(If you just want to jump on to the results:

here is the ChatGPT translation based video

VS

our pre-ChatGPT translation video

)

Current translation setup

Under the hood, we use google translate, and for one of the instance it converted the word sleek to `चिकना` in a tech video, and without human supervision and/or context, maybe that’s not enough to convey the meaning. If only the API had the option to pass the previous relevant texts, while also being able to pick up some context and tonality etc, it could have the potential to resolve some of these errors (awaiting availability of PaLM 2). Following is an example of translation for a text from a Tech Conference video:

Sample phrase generated from

Whisper

for

Meta Connect ‘22 highlights

and translated to Hindi using Google Translate.

So, we looked the other way to see if ChatGPT had some any solutions up its sleeve.

ChatGPT iteration 1

Asking ChatGPT to give the translation for the same text in a more colloquial Hindi (~Hinglish) format provides:

जब हम प्रकाश को घुमाते हैं, तो आप मेरे बालों के साथ कैसे इंटरैक्ट करता है, यह मेरी त्वचा पर छिपकर दिखता है और आप तकनीक से मेरी आंखों में भी इसे देख सकते हैं।

The output isn’t great, and actually loses the context in the first sentence itself. So, ChatGPT’s translation out-of-the-box doesn’t seem to be of a good enough quality.

ChatGPT iteration 2

Next up, changing the prompt a bit, some context and some translation grounding yields the following:

जब हम लाइट को इधर-उधर घुमाते हैं, तो आप देख सकते हैं कि यह मेरे बालों के साथ कैसे इंटरैक्ट करता है, यह मेरी स्किन पर रिफ्लेक्ट होता है, और आप इसे मेरी आंखों में भी देख सकते हैं।

As one can see, the words ‘reflects’, ‘light’ and ‘skin’ are kept as is, just transcribed. This is a useful output for dubbing, as the output speech modality is the one that comes into play and not the text (an important metric). ChatGPT’s translation seems to do a lot better when Google translate’s high quality output is provided as grounding.

But why ChatGPT?

The advantage of ChatGPT is the ability to configure/control the output via text interface (prompting) instead of re-visiting/training the translation algorithm. So to achie...