trentmkelly/LessWrong-43k huggingface dataset:
The AI Driver's Licence - A Policy Proposal
by Joshua W — 2024-07-21T20:38:07.093Z

TL;DR: In response to the escalating capabilities and associated risks of advanced AI systems, we advocate for the implementation of an “AI Driver’s Licence” policy. Our proposal is informed by existing licencing frameworks and existing AI legislation. This initiative mandates that users of advanced AI systems must obtain a licence, ensuring they have undergone minimal technical and ethical training The licence requirements would be defined by an international regulatory body, such as the ISO, to maintain consistent and up-to-date standards globally. Independent organisations would issue the licences, while local governments would enforce compliance through audits and penalties. By focusing on the usage stage of the AI lifecycle, this policy aims to mitigate misuse risks , contributing to a safer AI landscape. Our proposal complements existing regulations and emphasises the need for international cooperation to effectively manage the deployment and usage of advanced AI technologies.

Current governance approaches regulate developer and deployers of AI systems. Our policy proposal of users being required to have an AI driver's licence also affects the user side.

Introduction

Sam Altman, the CEO of OpenAI, claims we need “a new agency that licences any effort above a certain scale of capabilities and could take that licence away and ensure compliance with safety standards” (Wheeler, 2023).

Licencing has a long history, with key success stories in protecting the safety of the public by controlling access to potentially harmful activities and items. However, in some cases it has also resulted in adverse effects, driving large parts of industries to the black market, an entirely unregulated space.

Today, we face an uncertain and rapidly changing AI landscape, with AI tools and models capable of increasingly broad and self-determined actions. Many have written about the catastrophic scenarios that may unfold if AI is not appropriately regulated, and experts continue to ascribe higher chances of seeing these scenarios play out.

A safe AI landscape necessarily involves various legally enforceable regulations, applied at all stages of the AI life cycle. We can think of this body of cooperating regulations as a Swiss cheese model. This model suggests that multiple layers of regulations acting on various different aspects of the issue at hand, although individually imperfect,...
Demography and Destiny
by Zero Contradictions — 2024-07-21T20:34:07.176Z
Link: https://thewaywardaxolotl.blogspot.com/2017/01/demography-and-destiny.html

The PDF version can be read here.

And the video version can be watched here:

In this essay, I will make the case that demographic transition theory is wrong.

Demographic transition theory (DTT) proposes that people go through a transition from high fertility to low fertility as their societies modernize. Supposedly, this will lead to a stable or declining world population at some point in the future. This assumption is built into UN population projections.

DTT fits the evidence of recent history. Over the last 100 years, fertility rates have fallen dramatically as modern civilization spread around the world. Today, most parts of the world have low or declining fertility.

What caused this change in human behavior?

The generally accepted view is that poverty causes high fertility, and thus alleviating poverty causes lower fertility. According to this view, poor people choose to have more children either to help on the farm, or to care for them in old age. They also have extra children to replace those who die young. If poverty is alleviated and childhood mortality is lowered, people will choose to have fewer children.

This view is rather strange. It assumes that people have children based on rational economic calculations, and that those choices are mostly based on concern for their own welfare, as if children were a means to an economic end, rather than vice versa. It does not make sense biologically, psychologically or economically. It also doesn’t fit the evidence of history.

It isn’t biologically plausible, because life forms are shaped by evolution to reproduce. Thus, abundant food should cause population growth, not population stability or decline. And that is what we observe in nature. An expansion of the food supply causes population growth for every other species. There is no theoretical reason why humans should be exempt from this general principle. Also, there have been human population explosions in the past, when food production increased.

It isn’t psychologically plausible, because parents don’t view their children as economic assets. We evolved to reproduce, not to accumulate wealth for its own sake. Parents invest much more labor in having and raising children than they could ever get back. Likewise, it is implausible that parents have extra children to compensate for child mortality. Would you have more children if you believed that they were likely to...
The $100B plan with "70% risk of killing us all" w Stephen Fry [video]
by Oleg Trott — 2024-07-21T20:06:39.615Z
Link: https://www.youtube.com/watch?v=-H7e4XlMgg0

A high production value 16-minute video that summarizes the popular safety concerns, featuring Hinton, Russell and Claude 3.5.
Raising Welfare for Lab Rodents
by xanderbalwit — 2024-07-21T19:18:41.131Z
Link: https://press.asimov.com/articles/rodent-welfare

My article offering concrete suggestions to improve the welfare of rodents used in scientific research is out today in Asimov Press.
What is in here for the decerning rationalist you might wonder? Well, lots. I know you as people who like to think critically about how science proceeds and ask questions like: Why this stubborn attachment to animal experimentation? How large of a problem is this anyway? Have researchers just been socialized into thinking it is necessary? Does animal research even translate to meaningful human benefit? What if we just gene-edited animals to be less pain-sensitive?
I touch on all this while offering thoughtfully reasoned suggestions as to how we can improve translational research while mitigating the suffering of animals used as experimental subjects.
Please enjoy and share it with those for whom it might be of interest!
https://twitter.com/AsimovPress/status/1815050448736551101
A simple model of math skill
by Alex_Altair — 2024-07-21T18:57:33.697Z

I've noticed that when trying to understand a math paper, there are a few different ways my skill level can be the blocker. Some of these ways line up with some typical levels of organization in math papers:

Definitions: a formalization of the kind of objects we're even talking about.
Theorems: propositions on what properties are true of these objects.
Proofs: demonstrations that the theorems are true of the objects, using known and accepted previous theorems and methods of inference.

Understanding a piece of math will require understanding each of these things in order. It can be very useful to identify which of type of thing I'm stuck on, because the different types can require totally different strategies.

Beyond reading papers, I'm also trying to produce new and useful mathematics. Each of these three levels has another associated skill of generating them. But it seems to me that the generating skills go in the opposite order.

This feels like an elegant mnemonic to me, although of course it's a very simplified model. Treat every statement below as a description of the model, and not a claim about the totality of doing mathematics.

Understanding

Understanding these more or less has to go in the above order, because proofs are of theorems, and theorems are about defined objects. Let's look at each level.

Definitions

You might think that definitions are relatively easy to understand. That's usually true in natural languages; you often already have the concept, and you just don't happen to know that there's already a word for that.

Math definitions are sometimes immediately understandable. Everyone knows what a natural number is, and even the concept of a prime number isn't very hard to understand. I get the impression that in number theory, the proofs are often the hard part, where you have to come up with some very clever techniques to prove theorems that high schoolers can understand (Fermat's last theorem, the Collatz conjecture, the twin primes conjecture).

In contrast, in category theory, the definitions are often hard to understand. (Not because they're complicated per se, but because they're abstract.) Once you understand the definitions, then understanding proofs and theorems can be relatively immediate in category theory.

Sometimes the definitions have an immediate intuitive understanding, and the hard part is understanding exactly how the formal definition is ...
Using an LLM perplexity filter to detect weight exfiltration
by Adam Karvonen — 2024-07-21T18:18:05.612Z

A recent area of focus has been securing AI model weights. If the weights are located in a data center and an adversary wants to obtain model weights, the weights have to leave physically (such as a hard drive going out the front door) or through the data center's internet connection. If the facility has perfect physical security, then the weights have to leave through the internet connection. Recently, there has been discussion on how to make model weight exfiltration more difficult, such as Ryan Greenblatt's proposal for upload limits.

A key factor enabling this is that the critical data we want to protect (model weights) are very large files. Current models can have trillions of parameters, which translates to terabytes of data. Ryan calculated that the total amount of outgoing data from a data center during a model's lifetime could be within a few orders of magnitude of the size of the model itself. This post proposes an additional layer of security that could significantly enhance the effectiveness of upload limits: perplexity filtering using a small language model.

Perplexity, in the context of language models, is a measure of how surprised a model is by a given sequence of text. More formally, it's the exponential of the cross-entropy of the text under the model. In simpler terms, if a piece of text has low perplexity, it means the model finds it very predictable or natural; high perplexity indicates that the text is unexpected or unnatural to the model. By using perplexity as a filter, we can potentially distinguish between normal model outputs (which should have relatively low perplexity) and attempts to exfiltrate model weights encoded as text (which would likely have high perplexity).

The core idea is to use a small language model as a perplexity filter on all outgoing data from the inference servers. Here's how it would work:

All outgoing data is treated as language tokens. A small language model (much smaller than the main model being protected) is used to calculate the perplexity of these tokens. This filtering can potentially be performed offline to avoid adding latency to the system's responses. Data with high perplexity (i.e., data that doesn't look like natural language to the small model) is blocked or flagged.

Why This Could Work

Scale Advantage: The weights of large models are on the order of terabytes. Encoding this much data in a way that ap

...
Would a scope-insensitive AGI be less likely to incapacitate humanity?
by Jim Buhler — 2024-07-21T14:15:27.934Z

I was listening to Anders Sandberg talk about "humble futures" (i.e., futures that may be considered good in a sober non-"let's tile the universe with X" way), and started wondering whether training (not yet proven safe) AIs to have such "humble" scope-insensitive-ish goals -- which seems more tractable than (complete) value alignment -- might disincentivize the AI from incapacitating humans?
Why would it disincentivize it this way? I have some ideas but I thought I wouldn't flesh them out here to make sure people don't anchor on the particular scenarios I have in mind.

Here's an AI-generated image of a scope-insensitive AI chilling with a cup of tea to help you think:
Holomorphic surjection theorem (Picard's little theorem)
by dkl9 — 2024-07-21T13:24:18.300Z
Link: https://dkl9.net/essays/little_picard.html

Consider an entire function (complex-differentiable everywhere) f(z). I will intuitively prove that certain lemmas hold on any such f. If f is a polynomial, I can combine those lemmas with another one, showing that the holomorphic surjection theorem (more commonly, Picard's little theorem) applies. Any entire function is the limit of a sequence of polynomials, so a theorem holding for all polynomials is a compelling hint that it may also hold for all entire functions.

Pick an input point a. Suppose ε is a small positive real number, and b is a unit-modulus complex number. Incrementing a to a+εb takes f(a) to f(a+εb)≈f(a)+εbf′(a). That approximation holds sith f is entire, and is closer to exact for smaller ε.

From any complex f(a) — except 0 — exactly two directions of increment keep its modulus constant, which are those directions along the circle in output space centred at 0. b is arbitrary, so the direction of the increment εbf′(a) is arbitrary. Hence, for any input a, there are two directions to increment it, along which to develop a contour of constant modulus. We can repeat this operation at the ends of the contour, so every such contour is either closed or infinite. The exceptions are f(a)=0, which form degenerate "contours" of a single point.

Every point in the input space (complex numbers) lies on some contour, either degenerate, closed, or infinite. The contours we care about are defined by the modulus of the outputs along them, which precludes intersections. So any closed contour encloses some area, within which all points are on contours either closed — fitting entirely within the enclosing contour — or degenerate.

Pick a closed contour C, as described above. Suppose the smallest-area contour within it is D, enclosing area exceeding zero. But D is a closed contour, holding points within it belonging to other contours, which must enclose smaller areas. Contradiction. The smallest-area contour within C has area zero.

A zero-area contour is either open or degenerate. An open contour within a closed contour must be finite. Our earlier contour construction forbade finite open contours. The smallest-area contour within C is degenerate, i.e. a single point, which only arises when 0...
aimless ace analyzes active amateur: a micro-aaaaalignment proposal
by lemonhope — 2024-07-21T12:37:39.925Z

This idea is so simple that I'm sure it's been had by someone somewhere.

Suppose we have some method to make really smart honest AIs that do not have goals. Let's say it's a yes/no oracle. Our aimless ace.

But we want to accomplish stuff! AIcorp wants the printmoneynow.py. I'm worried that if I train the ace to fold fitted sheets and unkink the garden hose then it will go overboard and fold my femur bone and unkink the Yellowstone super volcano.

So let's use a bit-less-smart model to do tasks. The active amateur.

We'll ask the ace if the amateur is plotting something. If yes, we shut down the amateur. Every day we also ask "is today the last day I can shut down the amateur?"

(We do not generate code with the ace!)

So we get super-smart supervision of a decent task AI. The temptation (ie competitive pressure) to agentify the oracle is there still, but no longer overwhelming.

Maybe we can even have the amateur write ace_v2, have ace_v1 check ace_v2, switch to ace_v2, have amateur_v1 write amateur_v2, check it with ace_v2, then switch to amateur_v2.

What goes wrong? Which assumption fails?
Pivotal Acts are easier than Alignment?
by Michael Soareverix — 2024-07-21T12:15:12.818Z

The prevailing notion in AI safety circles is that a pivotal act—an action that decisively alters the trajectory of artificial intelligence development—requires superhuman AGI, which itself poses extreme risks. I challenge this assumption.

Consider a pivotal act like "disable all GPUs globally." This could potentially be achieved through less advanced means, such as a sophisticated computer virus akin to Stuxnet. Such a virus could be designed to replicate widely and render GPUs inoperable, without possessing the capabilities to create more dangerous weapons like bioweapons.

I've observed a lack of discussion around these "easier" pivotal acts in the AI safety community. Given the possibility that AI alignment might prove intractable, shouldn't we be exploring alternative strategies to prevent the emergence of superhuman AI?

I propose that this avenue deserves significantly more attention. If AI alignment is indeed unsolvable, a pivotal act to halt or significantly delay superhuman AI development could be our most crucial safeguard.

I'm curious to hear the community's thoughts on this perspective. Are there compelling reasons why such approaches are not more prominently discussed in AI safety circles?
Ball Sq Pathways
by jefftk — 2024-07-21T02:20:06.607Z

With the Red Line shut down north of Kendall

for
repairs I've been walking to the Green Line at Ball Sq. It's
definitely a slower commute, but it's not bad. Except for 150 painful
feet.

Here's how I walk to the station (240ft):

And here's what I want to do (90ft):

They've put up enough of a fence to keep a desire path from
forming, but it still bugs me.

Looking at the plans
from 2019 you can see how it ended up this way:

Without the crosswalks marked it's much less obvious that you're
making a bunch of people walk around three sides of a square. And
this crosswalk didn't even exist before the station:

This does actually affect a good number of people. Here's my estimate
for the houses where this adds 150ft when walking to the platform:

Unfortunately I don't think the cost of adding a second, slightly
shorter, path would be worth
it now that we've already built it out differently, but this is
something to keep in mind for grouping with future renovations.

Comment via: facebook, mastodon
Freedom and Privacy of Thought Architectures
by SebastianG  — 2024-07-20T21:43:11.419Z

I don't work in cyber security, so others will have to teach me.

I'm interested in the question of how AI systems can become private. How to make communications with an AI system as protected as the confessional. Some AI capabilities are throttled not for public interest reasons but because if those private conversations became public, the company would suffer reputational damage.

I'm not libertarian enough to mind that AI companies don't allow certain unsavory conversations to occur, but I do think they could be more permissive if there were less risk of blowback.

A lot of high value uses of a eyes is impossible without data security of the inputs and outputs. Sensitive financial information, State secrets, health data: this isn't information you can just hand over to an AI company no matter the promise of security.

Similarly a lot of individuals are going to want to cordon off certain parts of their life, including their own mental health.

The obvious answer is to have locally hosted AI. However even vast improvements in data cleaning and algorithmic learning are unlikely to get us acceptably high performance.

You could start out with your local Host, send an encrypted file, and receive an encrypted file from a huge Network hosted model. But I don't see how that model could interact with that encrypted file not being trained on that type of thing as an input. There's no point in sending the key along with it.

Or is there?

If there is an encryption and decryption layer in the AI system for the inputs and the outputs, an AI service could probably use zero knowledge proofs (or something else) to help create trust that they do not have method to read your messages. At the very least this would help with blocking out third parties.

But I don't know enough about software architecture for creating an audit that would show the AI company did not have access to the unencrypted input or output.
Introduction to Modern Dating: Strategic Dating Advice for beginners 
by Jesper Lindholm — 2024-07-20T15:45:25.705Z

Heads up: This is not really a post about rationality. That said, there are few times in your life when you are more prone to biases and emotional overrides than when you engage in dating.

We are emotional creatures above all. Knowing principles of rational thinking and tactics for overcoming biased thinking will not protect you from emotional triggers. That's why we structure our lives strategically to begin with, instead of just improvising.

Practical > Romantic.

As a rationalist, you want to taboo the word romantic altogether.

--

Welcome! This is a unisex guide. Where there is gender specific advice, it is mentioned explicitly.

--

One of my goals in life is to have my existence provide positive net value to humanity - or at least to selected subsets of humanity. Here I wanted to write about something that I have significant expertise in, which is also a vastly underestimated and overlooked topic in the context of human life in general. According to me at least. Yes you got it right from the headline: this is about dating.

This is a beginner-friendly post. Although comprehensive enough, the post is really more of a primer to get you to reflect over your dating life and understand why this is worth your time, rather than a fully fleshed out introduction to all relevant concepts you may wish to consider while optimizing dating.

The primary aim of this post is to make you think rationally about dating and why it matters. The secondary aim is to give you some hints on what you may want to be doing, if your end goal is a committed relationship that makes you more happy and/or more efficient in your life pursuits. The third aim is to make you understand some core principles related to the topic.

Dating matters

Dating matters a lot, both to us personally and to society as a whole. We are social animals. If we are good at dating, then our happiness increases at least while we are occupied with dating, which is in itself valuable. Our life expectancy and general health also increases.

I strongly recommend that you have a goal to be happy; if nothing else, it helps with dating too. So, there's a nice feedback loop. For more on why happiness matters, see How to be happy and Pain is not the unit of effort.

Behind the fun of it there is also the point of it. If we excel at dating, then our chances to establish healthy, meaningful relationships...
Why Georgism Lost Its Popularity
by Zero Contradictions — 2024-07-20T15:08:41.469Z
Link: https://zerocontradictions.net/civilization/why-georgism-lost-popularity

Henry George’s 1879 book Progress & Poverty was the second best-selling book in the entire world during the 1880s and 1890s, outsold by only the Bible. Nobody knows exactly how many copies it sold during those two decades since nobody was keeping track, but it definitely sold at least several millions of copies for sure. The Progressive Era is literally named after the book itself.

Georgism used to have millions of followers, and many of them were very famous people. When Henry George died in 1897 (just a few days before the election for New York City mayor), an estimated 100,000 people attended this funeral.

The mid-20th century labor economist and journalist George Soule wrote that George was “By far the most famous American economic writer,” and “author of a book which probably had a larger world-wide circulation than any other work on economics ever written.”

Few people know it, but the board game Monopoly and its predecessor The Landlord’s Game were actually created to promote the economic theories of Henry George, as noted in the second introduction paragraph of the Wikipedia article on board game Monopoly. The board games intend to show that economies that eliminate rent-seeking are better than ones that don’t.

So if Georgism used to have millions of supporters and solid economic reasoning, why did it never catch on and how did it lose its popularity over the past century?

(see the rest of the post in the link)