trentmkelly/LessWrong-43k huggingface dataset:
Personal experience of coffee as nootropic
by ryan wong — 2020-06-23T12:00:54.843Z

My life took a turn in 2015. That was when I first started drinking coffee on a regular basis. Not Americanos or Lattes, but 'Kopi' brewed with higher-caffeine Robusta beans using a 'sock' immersed in water close to boiling temperature, extracting massive amounts of caffeine along with strong bitter notes that are neutralized by sweet, sugary condensed milk.

My rate of learning in sports shot up seemingly overnight. My field of vision and attention increased tremendously. I gained more stamina and muscle control. It wasn't just a subjective feeling. The results showed in the speed and accuracy of the pitches I threw. The first training session I had with coffee was so productive, I almost swore that I would never train without it again.

Seeing its benefits, I began to use it for my studies as well. The hit of dopamine I got from starting problem sets with a cup of kopi was nothing like I'd experienced before. While in the past I had to start slow and enter 'flow' gradually, this was a shortcut to the flow state. Sure, learning didn't happen instantly, and I still had to struggle, but I enjoyed the process. I felt unnaturally productive; my absorption of information shot up, and I could go at it for progressively longer hours. This was my NZT pill.

Gradually, the more I was able to accomplish, the greater the aims I set for myself. I couldn't believe it. As a result of drinking coffee, my 'ambition' increased because my self-judgement on learning ability and productivity increased. I had other strong reasons for working hard, but the biochemical changes that occurred in my brain and body after ingesting coffee brought an emotional multiplier to those reasons.

However, this is where complications came in. I slowly began to build up a heavy tolerance to it. When before it raised my energy and loosened my social inhibitions, I started "zoning out" with friends, craving for that focused mode where I could work on my studies. Initially, I attributed the gradual shelling up to the long hours of study and mental stress caused by the major exam at the end of the year. Only after abstaining from coffee for a period of time, did I think of linking that never-before-experienced social discomfort to coffee.

On hindsight, I realised that I might have been experiencing a mild but non-negligible form of anx...
SlateStarCodex deleted because NYT wants to dox Scott
by Rudi C — 2020-06-23T07:51:30.859Z

NYT Is Threatening My Safety By Revealing My Real Name, So I Am Deleting The Blog

PS: One suggestion I have is to allow anonymous posts on Lesswrong that show the author’s anonymized karma. This is far from a good or complete solution, but I imagine it would at least come in handy in situations like this.
Slate Star Codex Deleted
by Jeremy_Nixon — 2020-06-23T07:21:09.733Z

Scott's Justification:

https://slatestarcodex.com/?fbclid=IwAR0sgQbdV7QlDx8VnVejpQmyCVypFnVJt0HbMGCy6mN5ae2bA6UZFIb0SWI

SSC Reddit Discussion:

https://www.reddit.com/r/slatestarcodex/comments/he95ak/blog_deleted_due_to_nyt_threatening_doxxing_of/
Prediction = Compression [Transcript]
by alkjash — 2020-06-22T23:54:22.170Z

(Talk given on Sunday 21st June, over a zoom call with 40 attendees. Alkjash is responsible for the talk, Ben Pace is responsible for the transcription.)

Ben Pace: Our next speaker is someone you'll all know as Alkjash on LessWrong, who has written an awesome number of posts. Babble and Prune, Hammertime Final Exam – which is one of my favorite names of a curated post on LessWrong. Alkjash, go for it.

Prediction = Compression Talk

Alkjash: I will be talking about a bit of mathematics today. It's funny that this audience is bigger than any I've gotten in an actual maths talk. It's a bit depressing. Kind of makes me question my life choices...

Alkjash: Hopefully this mathematics is new to some of you. I'm sure that the machine learning people know this already. The principle is that prediction is the same thing as compression. And what that means is that whenever you have a prediction algorithm, you can also get a correspondingly good compression algorithm for data you already have, and vice versa.

Alkjash: Let me dive straight into some examples. Take this sentence and try to fill in the vowels.

Ppl bcm wh thy r mnt t b, Mss Grngr, by dng wht s rght.

Translation:

People become who they are meant to be, Miss Granger, by doing what is right.

Alkjash: You'll notice that it's actually quite easy, hopefully, to read the sentence, even though I've taken out all the vowels. The human brain is very good at reconstructing certain kinds of missing data, and in some sense, that's a prediction algorithm, but immediately it also gives us a compression algorithm. If I take this sentence and I remove all the vowels, it's transmitting the same amount of data to a human brain because they can predict what's missing.

Alkjash: This is a very simple example where you can predict with certainty what's missing. In general, we don't always have prediction algorithms that can predict the future with certainty. But anytime we can do better than chance, we can also get some compression improvements.

Alkjash: Here's a slightly more sophisticated example. Suppose I have a four character alphabet and I encode some language with a four character alphabet in this obvious default way. Each character is encoded in two bits, then the following string will be encoded in this fifth string.

Alkjash: I've highlighte...
Locality of goals
by adamShimi — 2020-06-22T21:56:01.428Z

Introduction

Studying goal-directedness produces two kinds of questions: questions about goals, and questions about being directed towards a goal. Most of my previous posts focused on the second kind; this one shifts to the first kind.

Assume some goal-directed system with a known goal. The nature of this goal will influence which issues of safety the system might have. If the goal focuses on the input, the system might wirehead itself and/or game its specification. On the other hand, if the goal lies firmly in the environment, the system might have convergent instrumental subgoals and/or destroy any unspecified value.

Locality aims at capturing this distinction.

Intuitively, the locality of the system's goal captures how far away from the system one must look to check the accomplishment of the goal.

Let's give some examples:

The goal of "My sensor reaches the number 23" is very local, probably maximally local.
The goal of "Maintain the temperature of the room at 23 °C" is less local, but still focused on a close neighborhood of the system.
The goal of "No death from cancer in the whole world" is even less local.

Locality isn't about how the system extract a model of the world from its input, but about whether and how much it cares about the world beyond it.

Starting points

This intuition about locality came from the collision of two different classification of goals: the first from from Daniel Dennett and the second from Evan Hubinger.

Thermostats and Goals

In "The Intentional Stance", Dennett explains, extends and defends... the intentional stance. One point he discusses is his liberalism: he is completely comfortable with admitting ridiculously simple systems like thermostats in the club of intentional systems -- to give them meaningful mental states about beliefs, desires and goals.

Lest we readers feel insulted at the comparison, Dennett nonetheless admits that the goals of a thermostat differ from ours.

Going along with the gag, we might agree to grant [the thermostat] the capacity for about half a dozen different beliefs and fewer desires—it can believe the room is too cold or too hot, that the boiler is on or off, and that if it wants the room warmer it should turn on the boiler, and so forth. But surely this is imputing too much to the thermostat; it has no concept of heat or of a boiler, for inst

...
Question on GPT-3 Excel Demo
by Zhitao Hou — 2020-06-22T20:31:12.494Z

In the Excel demo of OpenAI https://openai.com/blog/openai-api/ which using GPT-3 model based API to generate a data table based on the topics, headers input.

Is the generated table data all from the GPT-3 model? Or there is a separate database like Wikidata used in the demo?
News ⊂ Advertising
by lsusr — 2020-06-22T19:19:29.948Z

For-profit news outlets are financial incentivized to write about things that are easy to write about. The easiest articles to write are the subsidized ones. Public relations firms subsidize news by writing press releases. Then news outlets republish the press releases as news. That's why so much news is corporate and political advertising.

Here are the top stories on Ars Technica at the time of writing[1].

"NordVPN users’ passwords exposed in mass credential-stuffing attacks"

"AT&T’s priciest “unlimited” plan now allows 100GB+ of un-throttled data"

"Researchers unearth malware that siphoned SMS texts out of telco’s network"

"The count of managed service providers getting hit with ransomware mounts"

"Facebook deletes the accounts of NSO Group workers"

Having only skimmed the articles, I suspect they were put there by the following companies.

Have I Been Pwned (breach notification service)

AT&T

FireEye (security firm)

Armor (global cloud security provider)

Facebook

The first article lets slip who wrote it in the following line.

Readers who are NordVPN users should visit Have I Been Pwned[2] and check to see if their email address is contained in any of the lists.

Can you spot how this sentence attempts to influence reader behavior?

Different organizations write articles for different news outlets. Ars Technica is unusual in its disproportionate publishing of articles written by cybersecurity firms and its relatively low density of political propaganda compared to more traditional news outlets like The Economist. The Art of Manliness Podcast interviewees usually discuss the books they're selling.[3]

News is advertising. Ad-supported news is ad-supported advertising. Subscription-supported news is subscription-supported advertising. Advertising can't directly control what you believe. Advertisers can control what you think about. The more advertising I expose myself to, the more I think about the things advertisers want me to.

Here is what advertisers want me to think about.

Products I haven't bought

National politics[4]

More ad-supported media, such as celebrities and free-to-play videogames

Here is what I want to think about.

Things I can make and do myself

My local community

My friends, my family and me

My personal happiness is inversely related to how much news I expose myself to. It's not just a subjective feeling. I behave more healthily. I'

...
The Indexing Problem
by johnswentworth — 2020-06-22T19:11:53.626Z

Meta: this project is wrapping up for now. This is the first of probably several posts dumping my thought-state as of this week.

Suppose we have a simple causal model of a system:

We hire someone to study the system, and eventually they come back with some data: “X=2”, they tell us. Apparently they are using a different naming convention than we are; what is this variable X? Does it correspond to a? To b? To c? To something else entirely?

I’ll call this the indexing problem: “index into” a model to specify where some data “came from”. (If someone knows of a better existing name, let me know.)

In practice, we usually “solve” the indexing problem by coordinating on names of things. I use descriptive variable names like “counterweight mass” or “customer age” in my model, and then when someone gives me data, it comes with object names like “counterweight” or “customer” and field names like “mass” or “age”. Of course, this has a major failure mode: names can be deceiving. A poorly documented API may have a field called “date”, and I might mistake it for the date on which some document was signed when really it was the date on which the document was delivered. Or a social scientist may have data on “empathy” or “status” constructed from a weighted sum of questionnaire responses, which may or may not correspond to anybody else’ notion/measurement of “empathy” or “status”.

The expensive way around this is to explicitly model the data-generation process. If I look at the actual procedure followed by the social scientist, or the actual code behind the poorly-documented API, then I can ignore the names and figure out for myself what the variables tell me about the world.

Usually this is where we’d start talking about social norms or something like that, but the indexing problem still needs to be solved even in the absence of any other agents. Even if I were the last person on a postapocalyptic Earth, I might find a photograph and think “where was this photograph taken?” - that would be an indexing problem. I might get lost and end up in front of a distinctive building, and wonder “where is this? Where am I?” - that’s an indexing problem. A premodern natural philosopher might stare through a telescope and wonder where that image is really coming from, and if it can really be trusted - that’s an indexing problem. I might smell oranges but not see any fruit, or I might he...
AI Benefits Post 1: Introducing “AI Benefits”
by Cullen — 2020-06-22T16:59:22.605Z

This is a post in a series on "AI Benefits." It is cross-posted from my personal blog. For other entries in this series, navigate to the AI Benefits Blog Series Index page.

This post is also discussed on the Effective Altruism Forum. Links to those cross-posts are available on the Index page.

For comments on this series, I am thankful to Katya Klinova, Max Ghenis, Avital Balwit, Joel Becker, Anton Korinek, and others. Errors are my own.

If you are an expert in a relevant area and would like to help me further explore this topic, please contact me.

Introducing “AI Benefits”

Since I started working on the Windfall Clause report in summer 2018, I have become increasingly focused on how advanced AI systems might benefit humanity. My present role at OpenAI has increased my attention to this question, given OpenAI’s mission of “ensur[ing] artificial general intelligence (AGI) . . . benefits all of humanity.”

This post is the first in a series on “AI Benefits.” The series will be divided into two parts. In the first part, I will explain the current state of my thinking on AI Benefits. In the second part, I will highlight some of the questions on which I have substantial uncertainty.

My hope is that this series will generate useful discussion—and particularly criticism—that can improve my thinking. I am particularly interested in the assistance of subject-matter experts in the fields listed at the end of who can offer feedback on how to approach these questions. Although I do raise some questions explicitly, I also have uncertainty surrounding many of the framing assumptions made in this series. Thus, commentary on any part of this series is welcome and encouraged.

In the first few posts, starting with this one, I want to briefly explain what I mean by “AI Benefits” for the purposes of this series. The essential idea of AI Benefits is simple: AI Benefits means AI applications that are good for humanity. However, the concept has some wrinkles that need further explaining.

AI Benefits is Distinct from Default Market Outcomes

One important clarification is the distinction between AI Benefits as I will be using the term and other benefits from AI that arise through market mechanisms.

I expect AI to create and distribute many benefits through market systems, and in no way wish to reject markets as important mechanisms for generating and distributing social benefits. However, I

...
The Queen of the Damned
by Slimepriestess — 2020-06-22T04:50:02.730Z

A short story set in the EVE universe. Read other short stories by me and by other members of the Alexylva Paradox at Alexylvaparadox.wordpress.com/chronicles

“They played you like a fiddle Metz.” The woman was everything Metz wasn’t. He was tall and lanky, rough, ragged on the edges, with a normally relaxed and carefree demeanor, dust worn and weather beaten. He was scruffy, with eyes that made him look older than he actually was, those dark eyes had seen a thousand little tragedies. Murders, execution, torture, and worse.

In contrast, Endorsei Edlrif looked like a paragon of the world of professional business. She was polished and sharpened, from her perfectly pressed suit to her immaculately brushed hair, the tasteful but understated jewelry she wore and the smooth and confident manner she carried herself with. She radiated an almost innocent poise, and with a face had hid her years, she would have fit in anywhere from a boardroom to a college campus.

She absolutely terrified Metz. The crimes, the inhumanities, the deaths, they weighed on Metz like a heavy stone around his neck, he felt the weight of his deeds with every step he took. He saw the faces of the dead when he closed his eyes. He wasn’t a good person, but he felt the cost of his sins, the barbs embedded in his soul. In contrast, Endorsei had an almost insane lightness to her. She was happy, cheerful, even chipper. He’d seen her squeal with glee and clap like a little girl when a group of traitors was strung up in front of her, none of the horror seemed to phase her in the slightest, on the contrary, she seemed to revel in it.

Metz Jerindold was a fixer, he made problems go away, but he was also a leader, he took responsibility for his actions, he might be a murderous bastard, but Skarkon was his system and he was still very protective of it. He was an Angel first, but he was a Skarkon native second. He was a matari, even if many in the republic would be loath to admit he was one of them.

Endorsei Edlrif on the other hand, was a monster. Metz had trouble believing she was actually human at times, much less Matari. The slaver’s fangs voluval appearing beneath her lips marked her as an outcast even among outcasts. She was the Cartel’s razor, gleaming in the darkness, the Queen of the Damned.

“I had to do something,” he said, nervously taking a drag of his cigarette, “ we couldn’t just let the Krullefor ...
Neural Basis for Global Workspace Theory
by Hazard — 2020-06-22T04:19:15.947Z

Epistemic Status: Intense amateur neuroscience here. Hoping to leverage Cunningham's Law to reach enlightenment.

Kaj Sotala has a great sequence on Multiagent Models of the Mind, a sequence that's lead to a lot of fun developments in how I think about minds in general. It also introduced me to Global Workspace Theory, one of the current mainstream theories of consciousness.

When studying the mind, you can attack from different levels of abstraction. From the lowest level of studying the anatomy of neurons, all the way up to postulating abstract cognitive algorithms that people might use in their thought. Kaj's sequence lives mostly on a mid-tier level; "imagine a system that works like this", and only dips into the neuroscience enough to give you a sense that someone has in fact looked into the neural plausibility of the idea. I think this was a good decision, as most of the interesting ideas come at the mid-tier functional level ("If you think of your mind as composed of subagents communicating through a global workspace, you'd expect ABC behavior").

This post details some of the lower level brain anatomy I've been investigating in an attempt to clear up some confusions I've had from thinking a lot of about Global Workspace Theory and how it relates to consciousness and Predictive Processing. Specifically, it looks at the neural basis for attention mechanisms, and the neural basis for the Global Workspace. Most of the details come from this paper, and this paper.

The Tangential Intracortical Network (TIN) (i.e The Global Neuronal Workplace?)

People like Jeff Hawkins assert that every part of the neocortex is running the same algorithm. Even people that don't go as far as him note that there's a lot of uniformity in the neocortex (it looks like it's mostly composed of tons of cortical columns).

Most of the connections in the cortex go from one part of a column to another part of the same column. There's also plenty of cortex to [non cortex part of the brain] connections. Most of the intracortical connections are part of a big web of mid-range fibers that span the whole neocortex, called the Tangential Intracortical Network (TIN) by Baars and Newman. They point to it as a plausible physical basis for the GNW. We'll run with that.

Functionally, the global workspace is an area that disparate parts of the cortex can all compete to put a value on. This compe

...
Fight the Power
by Jacob Falkovich — 2020-06-22T02:19:39.042Z

On power grabs, slogans, and how the collapse of authority leads to anxiety which leads to anger which leads to submission which leads to whatever is happening right now.

Cross-posted from Putanumonit.

Gurri’s World

…the elites that ran our institutions had the authority to provide information, frame it and explain the world. That’s completely gone, and with it there’s been a bleeding away of expert authority, and a public has been created that’s essentially very angry…

–

Martin Gurri

, author of “The Revolt of the Public and the Crisis of Authority”

Martin Gurri wrote

his seminal book in 2014

, to explain the public revolts against authority in the early part of the 21st century, such as the Arab Spring. But as the century wears on, the book becomes even more timely than when it was written, the events proceeding exactly as he described them. Trump, Brexit, the fall of old media, culture war in academia — all are revolts by a newly informed public against established authorities and gatekeepers of information.

In Martin Gurri’s world, it is no surprise that we are seeing protests and revolts on a global scale right on the heels of the first wave of a global pandemic. COVID exposed what few authorities made claims to credibility beforehand. The government,

the media

, acronym bureaucracies like the CDC and FDA — they all abdicated their responsibilities, lied at every turn, and failed to do much of anything about a disease that has been killing 1,000 Americans a day for months.

The collapse of authority, even for those who yet cling to one of those I mentioned in the previous paragraph, provokes anxiety and anger. They intuitively seek epistemic authority, to tell them what is true and false. They seek moral authority, to tell them what is good and evil. And they want someone to blame, someone to take their anxiety and anger out on. It can’t be the virus. It has to be a person, and they have to be within reach of a fist.

The Grab

Humans did not evolve to tell very well what is true or false, or to think clearly about good and evil. But we evolved to be good at tribalism, to divide into “us” and “them”, to be on the winning side when “us” gain an edge in power and jump on “them” to take their stuff, their status, or their lives. We do this subconsciously,

even as our brains confirm our moral righteousness to ourselves

.

Coordinated grabs for status, wealth, and power need

...