trentmkelly/LessWrong-43k huggingface dataset:
Learning as closing feedback loops
by ambigram — 2022-04-17T09:50:57.804Z

Epistemic status: Just a pattern I've observed from my own learning experiences and advice I've encountered about learning. I am not qualified and the essay is not properly justified - it's meant to be more like a source of ideas on things to try[1]. I would be happy to work on this further if there is interest.

When I think about my own learning experiences and the various studying techniques, advice on how to learn better, or methods to improve faster at skills, I observe some commonalities - it seems like most advice can be interpreted as ways to improve our feedback loops in learning[2].

In this essay, I will be describing the key steps in a learning feedback loop and the ideal conditions and common challenges for each step. I will then list the different ways of improving learning that are suggested by this framework, and provide examples of how to apply them. Finally, I will end with some rambly questions and thoughts.

Framework

I like to think of learning[3] as closing feedback loops, because it helps contextualize the different advice and techniques, and also helps me identify bottlenecks in my learning.

Here's the learning feedback loop:

Make an attempt: try something based on a goal (e.g. practice a song, with the goal of playing it well)
Observe the feedback: observe how your experience and the outcomes differ from what you desired, to see if you are getting better or worse (e.g. compare your playing with an expert's rendition of the song, or listen to your teacher's response to your playing to see how well you did)
Reflect & hypothesize: analyze your experience and generate hypotheses to explain why your attempt was better or worse, and what you can do differently next time (e.g. you observe that the expert's rendition uses a wider range of dynamics, and conclude that varying the loudness/softness would make your version more interesting)
Go back to Step 1, making a new attempt that tests your hypotheses

Examples of not-learning

Mindlessly playing through a song 100 times, because your teacher told you to practice
Reading through practice questions, answering them in your head briefly in vague terms (e.g. to answer this question, I will apply concept X), then reading the answers immediately, concluding that you have understood the topic because the provided answer (which is much more detailed) matches your answer
Observing tha

...
Alignment and Deep Learning
by Aiyen — 2022-04-17T00:02:10.206Z

Between the recent AI breakthroughs and Eliezer's open admission of how bleak the chances of alignment are, everyone is speaking up and contributing what they can. It seems to me that there's a route that very few people are talking about that stands a better chance of successful alignment than conventional approaches, and if there's ever a time to talk about such things, this is it.

We all know the basics of the alignment question-how can we figure out and designate human values well enough to define an AI's goals, despite the fact that our values are complex, fragile, and understood on an intuitive level much more than a rigorous one?

Ten years ago, AI researchers were working on a goal that was complex, fragile and almost purely intuitive, one that resisted both brute force and all attempts to define clever strategies to the point that many experts claimed it was literally unsolvable. I am talking, of course, about the game of go.

While chess masters will sometimes talk about recognizing patterns of checkmate that can be reused from game to game[1], go is incredibly dependent on intuition. Not only are there vastly more possible go games than particles in the known universe, but it's chaotic in the sense of Chaos Theory: incredible sensitivity to initial conditions. While two pictures that differ by a pixel are effectively the same image, two games differing by a single stone can have opposite outcomes. This is not a domain where one can simply run a Monte Carlo Tree Search and call it a day[2]!

No one ever made the MIRI approach work on go: explicit rules in a rigorous system that would encompass exactly what we want to do on a go board[3]. And if Friendly AI and the potential fate of the human race depended on doing so before anyone developed and deployed AGI, it's fair to say that we would be out of options. Yet by 15 March 2016, AlphaGo had defeated Lee Se-dol soundly, and soon attained such a dominance that the Korean master retired, unwilling to pursue a domain where even if he defeated every human opponent, machine intelligence was unchallengeable. Could a similar approach work on AI alignment?

The conventional wisdom here has been that alignment research is something that ought to be undertaken without help from AI: a stupid program cannot meaningfully help you and a...
Modect Englich Cpelling Reformc
by Gordon Seidoh Worley — 2022-04-16T23:38:50.212Z

Given the voting on this post suggests it's pretty controversial, I should add a note that this is mostly just for fun. I would have posted this during Good Heart Week but forgot about it, so you're getting it late. This post is more lazy afternoon pipedream than anything else.

It's no secret that English spelling is a mess. And we know the main reason: English spelling was standardized in the middle of a vowel shift, so it reflect an intermediate state of the language rather than the comparatively stable state the language is in today (yes, vowel positions vary across dialects, but these follow patterns that would mostly permit a standardized spelling if one was attempted today).

But not all of the problems are due to the vowel shift. Some of them are because English is written using the Latin alphabet which doesn't quite line up with its sound system. And so we have letters that simultaneously do too much work and not enough.

Real spelling reform for English is nearly impossible at present. It would require the coordination of a billion people across dozens of nations to have a real shot of sticking. Also, there's no mechanism by which to carry a spelling reform out since there's no standards body everyone agrees with (English dictionaries are descriptive rather than prescriptive, Anglo school systems are decentralized, etc.). Publishers and writers could try to unilaterally change spelling, but it's going to be hard to get enough people on board with learning a new spelling system to read their work. Maybe the next J.K. Rowling could pull off a spelling reform if it gradually showed up in a highly popular children's book, but that's tall order.

The most realistic option is to aim for modest spelling reforms and introduce them gradually. Get influential publishers to make small changes to their standards, let people get used to them, and then turn the crank on more reforms generation after generation until the spelling is better.

Here's an idea for the first such modest spelling reform we could make.

Let's fix the letter "c".

"C" makes two sounds, /s/ and /k/. By extension this means "s" and "c" are two letters doing the same job, and in the same way "c", "k", and to some extent "q" and "x" are also used to represent the same sounds.

Let's clean this up. Let's drop 3 letters out of these 5 and keep just 2.

I'm going to suggest we keep the letters "c" and "x". Why t...
Do universities have an incentive to ignore data ethics as more small sample bias is brought to light? 
by casualphysicsenjoyer — 2022-04-16T20:32:38.819Z

Many older pieces of once recognised scientific literature are now being deemed unreliable due to small sample bias. An increasing amount of meta-analyses are identifying unreliable fields of study, citing lack of reproducibility in larger sample sizes. It seems likely that we will face increasingly difficult ethical questions about large data collection as academicians attempt to check or re-verify their past claims with larger samples. Do we expect academic institutions to be put under pressure to ignore data-ethics in the future in an effort to correct for small sample bias?
Fiction: My alternate earth story.
by Donald Hobson — 2022-04-16T19:06:18.798Z

Stories of the alternate worlds people are from are getting pretty popular here.

The first deviation between this reality and the world I am from, is as far as I can tell, early robots in science fiction. Or rather my worlds total lack thereof. I don't know if Isaac Asimov never existed in that world, or if he existed and never got inspired to think about robots, and so wrote something else. Science fiction contained, as far as I know, no mention whatsoever of human created artificial intelligence until around 1970.

Turing wrote about Turing machines, but never about a Turing test. I J good never published their speculations on the intelligence explosion. There was some research on computer vision and handwriting recognition, but no concept of maybe one day making a smart generally intelligent machine.

The next difference seems to be nuclear power. In my world Chernobyl never happened. Probably something to do with the USSR never really being a big thing. There wasn't a cold war. There was some anti-nuclear sentiment, especially from coal minors forming a small and radically anti nuclear lobby. Nuclear reactors got built anyway. The anti-nuclear crowd mostly saw that their predictions of doom had failed, and left, leaving only a small number of rabid anti-nuclear activists. The economy grew on cheap nuclear energy. A general societal convention of ignoring the Luddites was strengthened.

The first time the idea of generally smart AI was introduced, it was in the bad scifi of the 1970's. You may think this world's scifi is bad. My worlds scifi was worse. In the first ever movie to feature an AGI, the AGI went rouge due to its computer chips containing too many 1's and not enough 0's. The solution to this was to run around taking a photo of anything that contained a 0 or looked like a 0. Street signs, door numbers, doughnuts, whatever. Load all that data onto a hard drive and chuck it at the AI. As the hard disk shatters against the metal surface of a glowing red eyed humanoid robot, the 0's magically flow into the circuits and the AI is fixed.

Or like how kissing a computer chip seemed to magically fix any AI with the power of love. Whether or not the AI had any way of knowing the chip was being kissed.

No really, the scifi was that bad. In this period, if someone went up to a top computer science expert and said they ...
Consciousness: A Compression-Based Approach
by interstice — 2022-04-16T16:40:11.168Z

TL;DR: To your brain, "explaining things" means compressing them in terms of some smaller/already-known other thing. So the seemingly inexplicable nature of consciousness/qualia arises because qualia are primitive data elements which can't be compressed. The feeling of there nonetheless being a "problem" arises from a meta-learned heuristic that thinks everything should be compressible.

What's up with consciousness? This question has haunted philosophers and scientists for centuries, and has also been a frequent topic of discussion on this forum. A solution to this problem may have moral relevance soon, if we are going to create artificial agents which may or may not have consciousness. Thus, attempting to derive a satisfying theory seems highly desirable.

One popular approach is to abandon the goal of directly explaining consciousness and instead try to solve the 'meta-problem' of consciousness -- explaining why it is that we think there is a hard problem in the first place. This is the tactic I will take.

There have been a few attempts to solve the meta-problem, including some previously reviewed on LessWrong. However, to me, none of them have felt quite satisfying because they lacked analysis of what I think of as the central thing to be explained by a meta-theory of consciousness -- the seeming inexplicability of consciousness. That is, they will explain why it is that we might have an internal model of our awareness, but they won't explain why aspects of that model feel inexplicable in terms of a physical mechanism. After all, there are other non-strictly-physical parts of our world model, such as the existence of countries, plans, or daydreams, which don't generate the same feeling of inexplicability. So, whence inexplicability? To answer this, we first need to have a model of what "explaining things" means to our brain in the first place.

Explanations as Compression

What is an explanation? On my (very rough) model of the brain[1], there are various internal representations of things -- concepts, memories, plans, sense-data. These representations can be combined together, or transformed into each other. A representation X explains representation(s) Y, if Y can be produced from X under a transformation. The brain is constantly trying to explain complex representations in terms of simpler ones, thus producing an overall compression of its internal data. Thi...
Pop Culture Alignment Research and Taxes
by Jan — 2022-04-16T15:45:26.831Z
Link: https://universalprior.substack.com/p/pop-culture-alignment-research-and?s=w

TL;DR: A quick recap of all the AI progress published recently, a shortcoming of the alignment tax definition, and a dynamical systems model of AI progress.

Previously in this series:

Cognitive Biases in Large Language Models

,

Drug addicts and deceptively aligned agents - a comparative analysis

,

Inferring utility functions from locally non-transitive preferences

.

All Bad Ends All

(click for soundtrack)

It's been a couple of exciting weeks.

Google DeepMind

published a paper

showing we've been training our language models in a suboptimal way. Their new model (continuing the rodent-themed naming scheme),

Chinchilla

, fixes that issue and manages to compete with/beat the much larger

Gopher

.
Google Brain didn't get that message in time (also not the message about the rodent-themed naming scheme)[1] and

published a paper

where they train a model that's even larger than Gopher, but with the old, suboptimal training scheme. That model can now solve logic puzzles that I could not solve in a thousand years[2].
The new kid on the block, Anthropic, is not dragging its feet and

published a paper

where they show that reinforcement learning from human values meshes well with their "Helpful, Harmless, and Honest"

approach

[3]. This paper contains the first plot I've seen with a language model score better than a human expert on a relevant metric[4] - but that doesn't mean it didn't happen before. I don't pay super close attention to the metrics.
Finally, OpenAI

published a paper

where they trained a new type of text-to-image model based on their previously released

CLIP

. The result is... mind-blowing, to the point where people on Twitter

announce the end of traditional visual art forms

[5].

Through a series of wacky coincidences (TBA), yours truly got access to the beta version of OpenAI’s new image generation technology and has been updating the thumbnails of his Substack (see

here

for a disclosure).

Three images created by me with OpenAI’s DALL-E 2. The prompts are: "A painting of Leonardo Da Vinci holding a slice of pizza.", "A painting of a chinchilla and a gopher dancing.", "A painting of a painting of a painting of a painting."

Not coincidentally, one of the founding figures of AI Safety published an April Fool's post announcing that their new strategy is

just giving up

, accompanied by a fair

amount

of

despair

in the

community

. More moderate voices are

willing to bet mon

...
Did OpenAI let GPT out of the box?
by ChristianKl — 2022-04-16T14:56:34.710Z

At WebGPT: Improving the Factual Accuracy
of Language Models through Web Browsing, OpenAI describes their model:

We’ve fine-tuned GPT-3 to more accurately answer open-ended questions using a text-based web browser. Our prototype copies how humans research answers to questions online—it submits search queries, follows links, and scrolls up and down web pages.

To me that sounds like they decided to let GPT-3 interact with the open internet which is the common standard for what it means to be left out of the box. If my assessement correct? If so, what do we learn from this?
Introducing Mnosis
by  — 2022-04-16T14:35:33.154Z
Link: https://guildoftherose.org/articles/introducing-mnosis?utm_campaign=lwMnosis

Introducing Mnosis

The Guild of the ROSE is proud to announce the beta launch of a new self-improvement tool: Mnosis.

mne-, root. Mind, memory.

Gnosis. Knowledge.

What is Mnosis? In a nutshell -- a magic deck of flashcards. Think of it as Anki for predefined subjects. Instead of manually creating a deck of cards by hand, Mnosis comes preloaded with all the cards needed to train your skills in a particular area.

As you click through cards, Mnosis adjusts itself to fit your mental profile. Mnosis is designed to use your time optimally, showing you the exact card you need to see to advance your mastery without boring or overwhelming you. It also tracks a complex web of dependencies between cards. If you get one card wrong, Mnosis will show you cards covering related concepts. This mimics the process of real learning, where a student comes at a topic from multiple angles until they understand it.

Mnosis currently has the following modules:

Math. Master mental arithmetic from 1+1=2 through 254*29=7336
Organic Chemistry. Effortlessly translate the IUPAC names for organic molecules and ace the OChem college weed-out courses.
Chemistry. Memorize the atomic symbols and numbers.
Probability. Master the basic definitions and concepts of probability theory.

The long-term goal of Mnosis is to solve learning, reducing mastery of complex subjects to a simple (for the user) process. As a result, the underlying algorithms undergo constant improvement and each module is continually tweaked and extended.

Additional Modules

A fifth module -- music -- is on the roadmap. Stay tuned for more information.

Mnosis was designed from the ground up to be highly extensible. New modules are easy to create. If you would like to see a specific module added to Mnosis, contact Matt Freeman: moridinamael#1693 on Discord.

Access

Mnosis is currently available with a free Guild account. After registering, you'll be able to access the Mnosis page.
What recent academic areas of study have a sample size that is 'too small'? 
by casualphysicsenjoyer — 2022-04-16T13:52:56.257Z

In an article in Nature magazine, Scott Marek et al. [1] asserts that current studies that link behaviour to brain imaging have datasets which are too small to be reliable. In taking a larger dataset and reproducing previously established results on different subsets of data, it is found that few of these studies are reproducible.

Marek and his colleagues show that even large brain-imaging studies, such as his, are still too small to reliably detect most links between brain function and behaviour.

I was wondering if the community has a prior on what other areas of recent academic interest have fallen to a similar trap?

References
[1] https://www.nature.com/articles/d41586-022-00767-3
Clem's Memo
by abstractapplic — 2022-04-16T11:59:55.704Z

Declassified document sourced from Cabinets and The Bomb by Peter Hennessy; reproduced verbatim.

TOP SECRET

GEN 75/1

28th August, 1945

THE ATOMIC BOMB

Memorandum by the Prime Minister[1]

A decision on major policy with regard to the atomic bomb is imperative. Until this is taken civil and military departments are unable to plan. It must be recognised that the emergence of this weapon has rendered much of our post-war planning out of date.
For instance a redistribution of industry planned on account of the experience of bombing attacks during the war is quite futile in the face of the atomic bomb. Nothing can alter the fact that the geographical situation of Britain offers to a Continental Power such targets as London and the other great cities. Dispersal of munition works and airfields cannot alter the facts of geography.
Again it would appear that the provision of bomb proof basements in factories and offices and the retention of A.R.P.[2] and Fire Services is just futile waste.
All considerations of strategic bases in the Mediterranean or the East Indies are obsolete. The vulnerability of the heart of the Empire is the one fact that matters. Unless its safety can be secured, it is no use bothering about things on the periphery. It is difficult for people to adjust their minds to an entirely new situation. I noticed at Potsdam that people still talked of the line of the Western Neisse although rivers as strategic frontiers have been obsolete since the advent of Air Power. It is infinitely harder for people to realise that even the modern conception of war to which in my lifetime we have become accustomed is now completely out of date.
We recognise or some of us did before this war that bombing could only be answered by counter bombing. We were right. Berlin and Magdeburg were the answer to London and Coventry. Both derive from Guernica. The answer to an atomic bomb on London is an atomic bomb on another great city.
Duelling with swords and inefficient pistols was bearable. Duelling had to go with the advent of weapons of precision. What is to be done about the atomic bomb? It has been suggested that by a Geneva Convention all nations might agree to abstain from its use. This method is bound to fail as it has failed in the past. Gas was forbidden but used in the first world war. It was not used in World War 2, but its belligerents were armed with it. We should hav

...
Contra Alexander on the Virtue of Silence
by benjaminalt — 2022-04-16T08:15:58.684Z

At my local LW meetup, we recently discussed Scott Alexander's post on the Virtue of Silence. For reasons I could not immediately discern, I intuitively disagreed with his argument. The following is my attempt to find out what exactly I disagreed with, and why.

The Virtue of Silence: Scott Alexander's original example

Consider a patient who is telling their doctor that they sent an innocent person to prison, and that this causes them stress which may be relevant to a medical treatment. The doctor is, of course, bound by medical confidentiality, but also faces an ethical dilemma: Should they uphold medical confidentiality, or should they report this to the authorities, which could possibly free the wrongfully convicted?

Leah Libresco argues that the doctor should not violate medical confidentiality, because "violating medical confidentiality creates an expectation that medical confidentiality will be violated in the future, thus dooming patients who are too afraid to talk about drug use or gay sex or other potentially embarrassing but important medical risk factors."

Scott Alexander then raises the stakes: He proposes that the decision of a patient to tell their doctor about some medical risk factor is not influenced by whether or not a doctor violated medical confidentiality in the past, but rather by the patient's hearing about it. He rightly argues that if such a violation were published in a high-volume outlet such as the New York Times, the patient would be much more likely to hear about it than, say, via the proceedings of the resulting court case.

Alexander also argues that a New York Times article about a doctor considering a violation of medical confidentiality would have a similar effect on the patient than an article about an actual violation.

I don't disagree with Alexander's reasoning up to this point. I disagree, however, with the conclusion he then draws: That "whether the doctor actually keeps her promise or not in this particular case is of miniscule importance compared to the damage that the column has already done", and that the New York Times ought not have published the column containing the doctor's ethical qualms.

Unbreakable Rules

To disentangle exactly where my disagreement lies, let us examine the assertion that a New York Times article about a doctor considering a violation of medical confidentiality would have a similar effect on the...