trentmkelly/LessWrong-43k huggingface dataset:
Collection of GPT-3 results
by Kaj_Sotala — 2020-07-18T20:04:50.027Z
Link: https://twitter.com/xuenay/status/1283312640199196673

I kept seeing all kinds of crazy reports about people's experiences with GPT-3, so I figured that I'd start collecting them.

first gwern's crazy collection of all kinds of prompts, with GPT-3 generating poetry, summarizing stories, rewriting things in different styles, and much much more. (previous discussion)
Automatic code generation from natural language descriptions. "Give me a page with a table showing the GDP of different nations, and a red button."
Building a functioning React app by just describing it to GPT-3.
Taking a brief technical tweet about GPT-3 and expanding it to an essay which the author of the original tweet mostly endorses.
Acting as a more intense therapist than ELIZA ever was. [1, 2]
On the other hand, you can trick GPT-3 into saying nonsense. On the other hand, you can just prompt it to point out the nonsense.
Redditor shares an "AI Dungeon" game played with the new GPT-3 -based "Dragon Model", involving a cohesive story generated in response to their actions, with only a little manual editing.

The official Dragon Model announcement.
I was a little skeptical about some of these GPT-3 results until I tried the Dragon Model myself, and had it generate cohesive space opera with almost no editing.

Another example of automatically generated code, this time giving GPT-3 a bit of React code defining a component called "ThreeButtonComponent" or "HeaderComponent", and letting it write the rest.
From a brief description of a medical issue, GPT-3 correctly generates an explanation indicating that it's a case of asthma, mentions a drug that's used to treat asthma, the type of receptor the drug works on, and which multiple-choice quiz question this indicates.
GPT-3 tries to get a software job, and comes close to passing a phone screen.
Translating natural language descriptions into shell commands, and vice versa.
Given a prompt with a few lines of dialogue, GPT-3 continues the story, incorporating details such as having a character make 1800s references after it was briefly mentioned that she's a nineteenth-century noblewoman.
Turning natural language into lawyerese.
Using GPT-3 to help you with gratitude journaling.
Source is an anonymous image board poster so could be fake, but: if you give an AI Dungeon character fake wolf ears and then ask her to explain formal logic to

...
Calibrate words, not just probabilities
by MikkW — 2020-07-18T05:56:11.120Z

In Phil Tetlock's book Superforecasters, much emphasis is put on making sure that a forecaster's predictions are well calibrated- if a forecaster gives a 70% chance to 100 different events, we should expect that 70 of those events will have happened, and 30 didn't happen. If only 50 events actually happened when the forecaster said 70%, then the forecaster may want to improve their calibration (especially if they are the betting sort), and anybody listening to them would be well advised to take their poor calibration into account when hearing a prediction.

Tetlock laments that television pundits almost never give probabilities for their predictions, so they basically can't be calibrated, furthermore, the events they describe are so vague that it can't even be readily agreed whether or not their predictions were correct even in retrospect. All of these things give a headache to someone who actually wants to hold pundits accountable, and to have a meaningful conversation with people who are influenced by such pundits.

Now consider the phrase "I predict event X will happen by November 31st with 80% probability"- the function of such an utterance is that upon hearing these sounds, an idea will be formed inside my mind, that if we saved the state of the simulator we live in, and ran the simulator from that point 100 times, I should expect to see event X happen in 80 of these histories by November 31st". When a pundit utters the phrase "Zombies will certainly roam the Earth if we implement policy Y", a similar idea is formed in my mind. But where the first statement allowed me to form an idea with a clear timeframe, and a precise level of certainty, listening to the pundit, I have to infer these for myself.

To help me get the best possible understanding from the pundit's words, I can calibrate his words just the same as I can calibrate probabilities. After all, if Freddy Forecaster says "70% probability" for events that happen only 60% of the time, I know to correct, in mind, Freddy's forecast- when he says 70%, I know to anticipate that it will actually happen only 60% of the time, and would bet accordingly. So if Peter Pundit says something "certainly" will happen 100 times, and we see 55 of these events actually happen, the next time he says something "certainly" will happen, I would be willing to bet based on his words suggesting a 55% probability. I can likewise...
Erving Goffman’s ‘paper’
by Saffron — 2020-07-18T01:12:25.587Z

I’ve never posted a question here, as to be honest, I’m a little intimidated. On page 128 of Schelling’s ‘Strategy of Conflict’, he refers to Erving Goffman. In the footnote, he refers to ‘Goffman’s paper’, but nothing more. Does anyone know the full reference, because it’s killing me that I can’t find it. Thank you very much in advance :)
Lessons on AI Takeover from the conquistadors
by Daniel Kokotajlo — 2020-07-17T22:35:32.265Z

(Talk given at an event on Sunday 28th of June. Daniel Kokotajlo is responsible for the talk, David Lambert edited the transcript.

If you're a curated author and interested in giving a 5-min talk, which will then be transcribed and edited, sign up here.)

Daniel Kokotajlo: I'm basically going to recap

my conquistadors posts

. I'm interested to hear what people think about it. I know a lot more about the situation now because I've been reading some history books. But my overall opinion hasn't changed.

I wrote this post because in talking to various people and reading some things, there was what seemed to me to be a simplistic model of how military conflicts work, or how a military takeover would work. Here's the simple model.

If we're imagining a scenario where an AI appears, it will likely be in some lab somewhere. So it will start off with very few resources, very small amounts of money, for example. So you would have to have some sort of god-like intelligence or a technological advantage in order to have more strength than the rest of the world combined.

I think the conquistadors are counterexamples to this. So for a brief overview, I picked out three conquistadors. I had already known about Cortes and Pizarro, which were probably the most extreme examples. But I predicted that there would be a general trend here, so I did some Googling and after 20 minutes I found Alfonso, who is another example of this trend.

Alfonso was a Portuguese explorer general. Here is a map of his exploration.

Ben Pace: Can I ask what period of history this was?

Daniel Kokotajlo: This is all around the early 1500s. Alfonso was 1506 to 1513, and then Cortes was 1519 and Pizarro was 1530.

If you look at this map, basically, Columbus went across the Atlantic in 1492, and the Portuguese were, in the same time, exploring down the coast of Africa. They were all trying to get to China and India’s lucrative trade routes. Basically, within 30 to 50 years, the Spanish had turned this entire region into a Spanish lake, controlling the main ports and only allowing Spanish ships.

The Portuguese did the same thing. Alfonso was responsible for the blue region. It only took them six years or so, and involved defeating the Ottoman Empire in several naval battles, conquering various port cities around the edge, defeating some Indian empires, and so fo...
Can an agent use interactive proofs to check the alignment of succesors?
by PabloAMC — 2020-07-17T19:07:54.072Z

Some time ago I read an article on the difficulties of agents building succesors. The argument was something like this:
Suppose we have solved the ultimate objective of goodness and built an agent A who does only things that he can prove are good (according to such definition). Can agent A build a more powerful successor B? The reasoning then goes that it cannot since A cannot prove that anything B does will be good, since B is more powerful than A. If he could prove that, one may consider them equally intelligent.
However, I think the argument above does not consider the power of interactive proofs. With interactive proofs one can go all the way up the complexity classes in terms of convincing an sceptical verifier of the truth of some statement by an all-powerful prover (L. Babai, L. Fortnow, and C. Lund. Nondeterministic exponential time has two-prover interactive protocols, Computational Complexity 1:3-40, 1991.; Z. Ji, A. Natarajan, T. Vidick, J. Wright, H. Yuen. MIP* = RE, arXiv:2001.04383, 2020.). My question is: Am I just reinventing the wheel with such idea? (My secondary question is: is that even an expression in English? :) )
The way to proceed would be: A builds B who proposes actions and then plays an interactive proof game with A to prove it that whatever he is proposing is good. So, agent A can check that the proposals of B are sound even if it is much weaker in terms of intellectual power.
Anthropomorphizing Humans
by johnswentworth — 2020-07-17T17:49:37.086Z

Sometimes, we assign desires and emotions to cars. We say that the car wants to drive, or we say that a strange vibration is the car expressing its displeasure at a too-long gap between oil changes. That’s anthropomorphization: we imagine that non-human objects have desires and emotions driving their behavior. We model the car as having desires (i.e. oil changes), emotions in response to those desires being met or not met, and observable behavior corresponding to those emotions. Of course, the actual cause of the car’s behavior is much more mechanical - low oil or coolant, built-up sludge, etc.

Now, consider

hangriness

.

I don’t notice that I’m hungry. I notice that the trash is overflowing and hasn’t been taken out. I feel angry about the trash, so I model myself as angry because of the trash. If someone says “why are you angry?”, I talk about how I want a clean house, and how annoying it is that the trash has not been taken out. But the actual cause is simply low blood sugar, or something like that.

This is anthropomorphization of myself: I imagine that my behavior is driven by some desire (i.e. I want a clean house) and the frustration of not having that desire met. Yet the actual cause is much more mechanical, and unrelated to the supposed desire.

Likewise, we often anthropomorphize other humans. If someone else is hangry, I might notice their anger and ask them what they’re angry about, without realizing that they just haven’t eaten in a while. In general, if I ask someone why they think X or why they decided Y, they’ll come up with a whole explanation for why X or Y makes sense, which may or may not have anything at all to do with the actual causes of their belief/decision - i.e. they rationalize post-hoc. Mistaking that post-hoc justification for the actual cause of the belief/action would be anthropomorphization.

Empathic Reasoning

Empathic reasoning is especially prone to the anthropomorphization failure mode in general, and to anthropomorphization of humans in particular.

Empathic reasoning is all about putting yourself in someone else’ shoes, asking “What do I want? What do I feel?”, and explaining behavior in terms of those wants and feelings. Essentially, empathic reasoning assumes the anthropomorphic hypothesis - it assumes that behavior is a result of desires and emotions - and tries to back out those desires and emotions by simulating oneself in the ...
Telling more rational stories
by DirectedEvolution — 2020-07-17T17:47:31.831Z

My composition teacher in college told me that in some pottery schools, the teacher holds up your pot, examines it, comments on it, and then smashes it on the floor. They do this for your first 100 pots.

In that spirit, this post's epistemic status is SMASH THIS POT.

Eliezer’s fundamental genius is that he is a profoundly original storyteller. From fables to fanfic, childhood recollections to water-cooler anecdotes, he has a keen eye for the firings and misfirings of the brain and how they operate in groups, conversations, and within himself. He gestures at scientific literature, references game theory, but narrative is the motor of his argument. His posts are replete with his ideals, visions, fears, and sheer force of personality, imbuing the world with his indelible stamp.

Stories are the motor of Eliezer's rationality, and they are at the same time the root cause of our insanity. You wouldn't cut off your right arm to cure your clumsiness, and so you shoudn't try to put your own story-telling capacity in a straight jacket. The only way to learn its virtues and capacity to lead you astray is to practice it.

I'm no master storyteller, but I motivate some of my music students by telling them tales in which they can go on adventures and earn rewards in exchange for doing what I ask of them. The locations, the events, the moral of the story is all for me. Through practice, I learn how to shape the story in ways that they will respond to. It comes more easily than you might think, but you need feedback and a willingness to take risks.

Somewhere in the effort to systematize rationality, I think the deeply personal nature of this effort got lost. We know self-help is idiosyncratic, and yet...

Maybe your rationality has a different motor that Eliezer's. And perhaps we as a community have moved on. So much of LessWrong's early writings are steeped in scientific findings that died in replication. I read Thinking Fast and Slow on a family vacation in Italy, in between visits to cathedrals in the capitals of ancient superpowers. I was already full of the mix of frustration and idealism that predisposes one to Effective Altruism long before I ever heard the term. Exposure to these ideas can be a catalyst when the reactants are already present, but what keeps the reaction self-sustaining?

To be honest, I never liked Eliezer's writings. The Biblical language, the cutesie fabl...
Solving Math Problems by Relay
by Ben Goldhaber — 2020-07-17T15:32:00.985Z

From September to November 2018 we ran an experiment where people did programming in relay. Each player spent ten minutes on a programming problem before passing on their code to a new player who had not seen any of the previous work. We found that people were able to solve some problems using the relay approach, but that the approach was less efficient than having a single person work on their own. This project explored hypotheses around Factored Cognition, testing whether people can solve problems by decomposing them into self-contained sub-problems.

Since this was an "explorative" experiment it wasn't a priority to write up, though we are excited to have gotten around to it now and hope this both informs and inspires other experiments.

Introduction

Factored cognition research investigates ways of accomplishing complex tasks by decomposing them into smaller sub-tasks. Task decomposition is not a new idea: it’s widely recognized as fundamental to modern economies. People have worked out ways to decompose complex tasks (e.g. create an electric car) into smaller sub-tasks (create an engine and battery, design steering controls, test the car for safety) which in turn are broken down into yet smaller tasks, and so on. The smallest sub-tasks are carried out by individual humans, who may have a limited understanding of how their work relates to the original task.

The focus is on the decomposition of cognitive tasks, where the goal is to provide information or to answer a question. Cognitive tasks include solving a mathematics problem, analyzing a dataset, or summarizing a story.

Factored Cognition research explores whether complex cognitive tasks can be solved through recursive decomposition into self-contained, interpretable sub-tasks that can be solved more easily than the originally task.[1]

Sub-tasks are "self-contained" if they are solvable without knowledge about the broader context of the task. If the task is a hard physics problem, then self-contained sub-tasks would be solvable for someone who hasn’t seen the physics problem (and need not know the task is about physics). This differs from most real-world examples of collaborative problem solving, where everyone in the team knows what task is being solved.

Testing the Factored Cognition Hypothesis involves the following steps:

Finding cognitive tasks that seem costly or dif

...
What are the best tools you have seen to keep track of knowledge around testable statements?
by migueltorrescosta — 2020-07-17T15:02:06.490Z

Hey all, I have been working on a way to keep track of testable statements, do you know of any such tools?

The one I have focused on is this one: https://questpowered.com , but I would be curious to know of other ones.
Environments as a bottleneck in AGI development
by Richard_Ngo — 2020-07-17T05:02:56.843Z

Given a training environment or dataset, a training algorithm, an optimiser, and a model class capable of implementing an AGI (with the right parameters), there are two interesting questions we might ask about how conducive that environment is for training an AGI. The first is: how much do AGIs from that model class outperform non-AGIs? The second is: how straightforward is the path to reaching an AGI? We can visualise these questions in terms of the loss landscape of those models when evaluated on the training environment. The first asks how low the set of AGIs is, compared with the rest of the landscape. The second asks how favourable the paths through that loss landscape to get to AGIs are - that is, do the local gradients usually point in the right direction, and how deep are the local minima?

Some people believe that there are many environments in which AGIs can be reached via favourable paths in the loss landscape and dramatically outperform non-AGIs; let’s call this the easy paths hypothesis. By contrast, the hard paths hypothesis is that it’s rare for environments (even complex meta-environments consisting of many separate tasks) to straightforwardly incentivise the development of general intelligence. This would suggest that specific environmental features will be necessary to prevent most models from getting stuck in local minima where they only possess narrow, specialised cognitive skills. There has been a range of speculation on what such features might be - perhaps multi-agent autocurricula, or realistic simulations, or specific types of human feedback. I’ll discuss some of these possibilities later in the post.

This spectrum is complicated by its dependence on the model class, training algorithm, and choice of optimiser. If we had a perfect optimiser, then the hilliness of the loss landscape wouldn’t matter. For now, I'm imagining using optimisers fairly similar to current stochastic gradient descent. Meanwhile, I’m assuming in this post that (in accordance with

Rich Sutton’s bitter lesson

) our models and training algorithms won’t contain very strong inductive biases. In other words: we’ll develop powerful function approximators, but which functions they approximate will primarily be determined by their training environments (and possibly also regularisation, as I’ll discuss late...
My Dating Plan ala Geoffrey Miller
by snog toddgrass — 2020-07-17T04:52:29.612Z

How should a rationalist approach dating? - I have just finished Geoffrey Miller's excellent book Mate, a reductionist, signalling-heavy guide to how to attracting women for men. This post lays my "dating plan" a la Miller. I post for constructive comments and to give an example of applying LessWrong style rationality. The plan is for me, a heterosexual man, but I appreciate input from all gender/sexual identities.

***EDIT*** A NOTE ON MOTIVATION AND ETHICS

I'm writing this because I am sad that so many are alone and don't know why. I was lonely for much of my life and lacked tools to understand or change my situation. Talking to friends and family taught me that our society fails to equip lonely people with useful tools to become more attractive (behaviorally and physically).

The conventional advice is terrible; "be yourself" and "be honest, tell her how you feel" are so easily misinterpreted that they make things worse. Meanwhile pick up artist forums have an uneven epistemology, a weak evidence base, are poorly explained, and are often unethical. A third way is possible. I wrote this to show people it exists, that they don't have to be lonely and confused forever.

My dating ethics

1. Preferences cannot be immoral. It doesn't create utility to judge a woman for preferring physically attractive, high status men. It doesn't create utility to judge me for preferring physically attractive, ambitious women. The conscious part of your brain does not get to override the part that chooses when to be horny (imo, not a psychologist).

2. Honestly describe your dating goals. I make sure people know my intentions as early as reasonable (expressing them in a non-awkward way). That is why I start the dating plan with my intentions.

3. General transhumanist utilitarian ethics I apply to other areas.

***END OF EDIT***

1. Clarify Your Dating Goal

My dating goal is to experiment with short term dating in the American context, then find a long-term mate. I expect to find short-term dating unsatisfying, and switch after a few months. For a long-term partner I like women who are intelligent, good problem solvers, assertive, attractive. open-minded and a little aspie.

2. Highlight Your Attractive Traits and Work on Your Weak Ones

Strengths - I am intelligent, extroverted, fairly handsome, fairly athletic, experienced, well-traveled...