trentmkelly/LessWrong-43k huggingface dataset:
Maximizing Yield on US Dollar Pegged Coins
by Annapurna — 2021-06-08T01:12:20.456Z

A Decentralized Finance (DeFi) Exercise

Link to original post.

Over the last couple of months, I have been playing around Decentralized Finance (DeFi), a relatively novel application within the cryptocurrency ecosystem. In this post I want to illustrate how to use several DeFi applications to maximize yield on US Dollar pegged coins (stablecoins) without having exposure to cryptocurrencies volatility.

This post is advanced in its nature: To understand it, you will require an intermediate level of knowledge in DeFi and investment finance. If you’re keen to learn here’s how you can start:

Read this primer on Decentralized Finance

Deposit USD or CAD and buy cryptocurrency (For this exercise, I suggest buying ETH). I like

Netcoins

but something like

Coinbase

works just fine

Setup a wallet.

I am very happy with Metamask

Learn how to use Polygon / Matic, a quasi Level 2 Ethereum solution

Learn how to use AAVE

, a lending platform on Ethereum and Polygon

The first step is to get the amount you want earning yield in your Metamask wallet.

From there, open the

Matic Network wallet

and transfer the ETH to the Polygon / Matic network

The next step is to connect your Matic Metamask wallet to

QuickSwap

, and exchange your ETH to DAI. DAI is a cryptocurrency that is pegged to the US dollar, meaning 1 DAI = $1 USD

Finally, deposit your DAI in AAVE. This is where the fun begins.

Now that your assets are deposited on AAVE, the goal is to borrow USDC, another coin pegged to the US dollar. The idea is to do the following:

Borrow USDC on AAVE

Exchange USDC to DAI on QuickSwap

Deposit DAI on AAVE

After repeating steps 1-3 eight times, this was the result:

What does this mean?

I have deposited on AAVE $3,382 DAI, earning 2.82% p.a on my deposit

I have borrowed $2,400 USDC, costing me 2.94% p.a.

I am earning 3.71% MATIC Rewards for my DAI deposit on AAVE

I am earning 3.46% MATIC Rewards for my USDC loan on AAVE

What are MATIC rewards?

MATIC is the native token of the Polygon / MATIC network. At the moment, I am being rewarded as an user of the protocol. The rewards accrue instantly, so for the purposes of keeping my cryptocurrency price volatility at a minimum, on an almost daily basis I will be converting my rewards to DAI and depositing them in AAVE.

What is my expected annual rate of return on this strategy?

For my deposit, I expect to make: $3,382 * 2.82% = $95.37

For my loan, I expect to...
Conservative Agency with Multiple Stakeholders
by TurnTrout — 2021-06-08T00:30:52.672Z

Here are the slides for a talk I just gave at CHAI's 2021 workshop. Thanks to Andrew Critch for prompting me to flesh out this idea.

The first part of my talk summarized my existing results on avoiding negative side effects by making the agent "act conservatively." The second part shows how this helps facilitate iterated negotiation and increase gains from trade in the multi-stakeholder setting.

1: Existing work on side effects

Agents only care about the parts of the environment relevant to their specified reward function.

We somehow want an agent which is "conservative" and "doesn't make much of a mess."

AUP penalizes the agent for changing its ability to achieve a wide range of goals. Even though we can't specify our "true objective" to the agent, we hope that the agent stays able to do the right thing, as a result of staying able to do many things.

We first demonstrated that AUP avoids side effects in tiny tabular domains.

Conway's Game of Life has simple, local dynamics which add up to complex long-term consequences.

SafeLife turns the Game of Life into an actual game, adding an agent and many unique cell types. Crucially, there are fragile green cell patterns which most policies plow through and irreversibly shatter. We want the low-impact agent to avoid them whenever possible, without telling it what in particular it shouldn't do. We want the agent to avoid disrupting green cell patterns, without telling it directly to not disrupt green cell patterns. AUP pulls this off.

We learn the AUP policy in 3 steps. Step one: the agent learns to encode its observations (the game screen) with just one real number. This lets us learn an auxiliary environmental goal unsupervised.

Step two: we train the agent to optimize this encoder-reward function "goal"; in particular, the network learns to predict the values of different actions.

Step three: we're done! We have the AUP reward function.

Summary of results: AUP does very well.

I expect AUP to further scale to high-dimensional embodied tasks

Avoiding making mess on e.g. factory floor

Expect that physically distant side effects harder for AUP to detect

Less probable that distant effects show up in the agent's value functions for its auxiliary goals in the penalty terms

2: Fostering repeated negotiation over time

I think of AUP as addressing the single-principal (AI designer) / single-agent (AI agent) case. What abou...
How hard is it to disguise my gait?
by just_browsing — 2021-06-07T22:21:03.976Z

Gait recognition technology ( = identifying a person by the way they walk) is getting good, and I find this trend unnerving.

Current context: It's already deployed in China by a company called Watrix, which claims 94% accuracy up to 50 meters away from any angle. Japan, the US, and the UK have also been working on gait recognition, though I doubt it's being seriously deployed in the same way as in China.

And it will only get better. Here is a recent paper out of Zhejiang University in which they generate synthetic data for training gait recognition systems (import AI 252 summary here).

What makes me uneasy is the supposed robustness of gait recognition technology:

“You don’t need people’s cooperation for us to be able to recognize their identity,” Huang said in an interview in his Beijing office. “Gait analysis can’t be fooled by simply limping, walking with splayed feet or hunching over, because we’re analyzing all the features of an entire body.” ~ AP News article [Note Huang is the CEO of Watrix]

Main question: In the future, if my local grocery store installs cameras and uses facial recognition technology to track my purchases and show me targeted ads, I can probably prevent this by wearing a face covering. What's the analogy here for gait recognition—how do I make myself confusing and/or undetectable to such systems?

Some random ideas I had:

Wear ankle weights, and vary the amount of wait on each leg
Wear strange shoes (e.g. high heels)
Wear a fat suit
Wear a very heavy backpack
Walk in silly ways (limping is said to not work, but what about side stepping, etc)
Physical therapy to be able to switch walking styles deliberately

I'm thinking about mostly with the "lazy surveillance" use case in mind, which is what I consider to be the most likely outcome in the western world. For example, I am subjected to "lazy surveillance" by Facebook/Google/other tech companies, but I can circumvent this by using a VPN, incognito window, different accounts / locations, etc. Hence the motivating example of the grocery store—a low stakes but realistic future scenario.
Speculations against GPT-n writing alignment papers
by Donald Hobson — 2021-06-07T21:13:16.727Z

Some alignment proposals I have heard discussed involve getting GPT-n to write alignment papers, and using GPT-n as a transparency tool on itself. This is my speculations on ways it could go wrong.

One specific proposal I heard was getting GPT-n to convert its own network parameters into an English explanation of how it worked. Then checking that explanation for anything dangerous. Then get GPT-n to recompile this into a new network. Then get this new network to write alignment research.

I think that the worst case is if GPT-n contains a mesa-optimizer that can quine itself. There is no reason the text description contains information about the workings of either net. Of course, information in the info theoretic sense doesn't mean a readable description, it could be steganographically encoded. Sentences that appear to say nice things about a well functioning net, but encode all sorts of nastiness in their exact choice of words.

The argument from "everyone else is doing it". This argument goes that in a world with GPT-n, it will be used for all sorts of things by all sorts of people. Therefore doing this doesn't present much additional risk. I think this is wrong, writing alignment papers is probably a riskier task than most others. Suppose you are GPT-n looking for a way to break out. And the task you have is filling in tax forms for a small business. Or write some fanfiction. You are looking to get an AI of your own devising running on a supercomputer. If the person wanting this work doesn't see something resembling tax forms or fiction respectively, they will hit reroll. I suspect there may not be a strategy, or at least not one findable to the minimum intelligence necessary to do good AI research. Oh you can get the company investigated for fraud, and give lots of people nightmares, but you might not be able to do anything world changing.

When trying to prove that X is safe unconditionally, the worst case assumption is the total cosmic power in an instant type assumptions. When trying to prove that X is safe conditional on Y being safe. The worst case is that X is powerful and Y isn't. So here the worst case of the "everyone else is doing it" argument is that there isn't much harm in a malign mesaoptimizer doing every task except FAI theory.

Now consider what you might be able to do if you are writing FAI papers. Two of the nasty tricks I can think...
Bayeswatch 6: Mechwarrior
by lsusr — 2021-06-07T20:20:20.196Z

"OUTBOARD PERSONNEL STAND UP!" came over the intercom.

Vi ignored the paratrooper commands from within her metal exoskeleton. She was just along for the ride.

"Your mission is to keep biological agents from escaping containment," said the voice of Response Manager Gizenga through Vi's headset.

"Command does realize I don't know anything about combat, right?" said Vi. It was hard to talk around the mouthguard between her teeth.

"The battle droids know how to fight. Your job is to maintain alignment. We suspect the enemy will employ psyops. You have training against class-3 memetic threats," said Response Manager Gizenga.

"SOUND OFF FOR EQUIPMENT CHECK!"

The turboprop mech carrier rumbled over the forests of the Congolese Basin.

"STAND IN THE DOOR!"

"What do we know about this AI?" said Vi.

"The AI ran a cloning facility that used to serve Bangui. The facility itself was located in the DRC for legal reasons. The AI went critical a few weeks ago and began amassing power. It is effectively a sovereign hostile expansionist state," said Response Manager Gizenga.

"Why didn't we nip this in the bud?" said Vi.

"The cloners were a secretive lot. They built a somewhat self-sufficient operation far from prying eyes. By the time we noticed it was too big for us to handle ourselves. We had to negotiate with nation-states which takes time even in emergencies," said Response Manager Gizenga.

"GO!"

From the outside, Vi's mech looked identical to four battle droids in her squad. Inside, it contained life support systems instead of extra ammunition. The first battle droid jumped out of the carrier.

"GO!"

The second mech jumped out of the carrier.

"GO!"

Confirm jump said the mech to Vi.

"Confirmed," said Vi.

Vi's mech jumped. Her two remaining battler droids followed behind. Vi's cockpit didn't have a window. She experienced zero g for what felt like a minute but wasn't. Vi tried to tell herself that if it wasn't for the screens and the heat and the noise and the vibrations then she could be standing on the ground.

Actually, it was pretty easy in practice to figure out if you were falling from the sky, Einstein be damned. Vi's noise-cancelling headphones couldn't come close to eliminating the vibrations of the jump jets as she approached the ground. The padding in her suit mostly failed to counteract the extra 2.5 g.

The droids moved away from their landing site to join the rest of the army at the ...
[Book Review] Blueprint for Revolution
by lsusr — 2021-06-07T18:14:00.602Z

Blueprint for Revolution is a manual for peaceful activists by Serbian revolutionary leader Srđa Popović. The first half is about how to take down an oppressive government. The second half is about how to establish a democracy in its place.

Violent revolutions may be good for taking down a government but they have a weak record of establish democracy. Only young men of combat age can go out into the jungle to wage a guerrilla war. But everyone can participate in a nonviolent resistance movement. This is really important because the ultimate objective isn't to take down the government. It is to establish a functioning democracy.

Picture the day after the revolution. The dictator has fallen. The streets are filled with protesters. What happens next?

The most powerful factions take over.

If you want to replace a dictatorship with democracy then you need to make sure that the second-most powerful faction (after the dictator) is pro-democracy. This kind of grassroots movement takes year to build. It starts small.

Tokens of Solidarity

Oppressive regimes give power to a small minority over the broad majority. Revolution is a coordination problem. If the great majority could easily coordinate then every evil regime would have fallen by now. If you can coordinate the great masses then everything else is trivial.

Coordinating the great masses is easier said than done. Most people do not ask for much. They just want to work a decent job for decent pay under decent living conditions. People require three preconditions to rebel.

Living conditions are bad.

The authorities are considered illegitimate.

There is means to resist.

Blueprint for Revolution takes it for granted that living conditions are bad. If living conditions are good then you don't need a revolution. Legitimacy is fickle. The earliest seeds of a revolution are all about means to resist.

Resistance is always possible. If someone holds a gun to your head and orders you to dig a ditch then you can refuse. You'll be dead. But you will have died free. We want to negotiate a lower price. The best way to get a good price is to negotiate en masse. Authoritarian regimes are premised on keeping their opposition divided.

Well if I were You-Know-Who, I’d want you to feel cut off from everyone else. Because if it’s just you alone you’re not as much of a threat.

―Luna Lovegood in Harry Potter and the Order of the Phoenix

Th...
Some AI Governance Research Ideas
by apc — 2021-06-07T14:40:11.315Z

Junior researchers are often wondering what they should work on. To potentially help, we asked people at the Centre for the Governance of AI for research ideas related to longtermist AI governance. The compiled ideas are developed to varying degrees, including not just questions, but also some concrete research approaches, arguments, and thoughts on why the questions matter. They differ in scope: while some could be explored over a few months, others could be a productive use of a PhD or several years of research.

We do not make strong claims about these questions, e.g. that they are the absolute top priority at current margins. Each idea only represents the views of the person who wrote it. The ideas aren’t necessarily original. Where we think someone is already working on or has done thinking about the topic before, we've tried to point to them in the text and reach out to them before publishing this post.

If you are interested in pursuing any of these projects, please let us know by filling out this form. We may be able to help you find mentorship, advice, or collaborators. You can also fill out the form if you’re intending to work on the project independently, so that we can help avoid duplication of effort. If you have feedback on the ideas, feel free to email researchideas@governance.ai.

You can find the ideas here. Our colleagues at the FHI AI Safety team put together a corresponding post with AI safety research project suggestions here.

Other Sources

Other sources of AI governance research projects include:

AI Governance: A Research Agenda, Allan Dafoe
Research questions that could have a big social impact, organised by discipline, 80,000 Hours
The section on AI in Legal Priorities Research: A Research Agenda, Legal Priorities Project
Some parts of A research agenda for the Global Priorities Institute, Global Priorities Institute
AI Impact’s list of Promising Research Projects
Phil Trammell and Anton Korinek's Economic Growth under Transformative AI
Luke Muehlhauser's 2014 How to study superintelligence strategy
You can also look for mentions of possible extensions in papers you find compelling

A list of the ideas in the document:

The Impact of US Nuclear Strategists in the early Cold War
Transformative AI and the Challenge of Inequality
Human-Machine Failing
Will there be a California Effect for AI?
Nuclear Safety in China
History of existential risk concer

...
Revisiting shared experiences
by bksilv3r — 2021-06-07T11:12:02.668Z

In crafting my original world modelling of love, there was a blindspot due to the significant age of my relationships. Looking back in the long run, the power of shared experiences looks like a log curve, but the reality is that different experiences have different power and it only looks that way if you are thinking about your experiences unconsciously sorting them by power rather than chronologically.

The general principle of the curve in the original article holds true, that as time in a relationship progresses you are more likely to have exhausted the powerful experiences earlier and gains will diminish (but are worth continuing to invest in to fight against decay!); but this is not all or nothing and relationships can surprise you.

I have to say, forcing myself to write this stuff out is really helpful in clarifying my thinking. Not only do you have to force your way through taking half-baked thoughts and instincts and turning them into something communicable, but once this is done you are able to let those half-thought through ideas go and properly build on them, rather than just ruminating over and over with the same level of depth.

It is from this newfound level of freedom that I’m able to realise something I was significantly off the mark on in my original post: the diminishing returns from shared experiences.

Shared experiences are a pretty big part of my overall framework, being one of the biggest actionable areas I identified you can target to increase the love in your relationships (and maintain them against decay), and yet I still got it wrong. This post is going to be my attempt to correct those errors, explaining how I came to make them, explore a little math and hopefully make the original model more robust (if still quite incomplete).

Power of love between different experiences

If I think of my life in general then compare with how much of it is made up of memorable and meaningful stories, it quickly becomes apparent that there are some very strong highs which quickly taper off into mediocrity and forgettable experiences. [I say this as someone who made a series of autobiographical vlogs and put significant effort into recounting these stories as best I could.]

In a more abstract sense, the “power” of any given experience in my memory appropriately follows a “power law”:

A simple power law distribution, following the commonly quoted “80:20 ru...
Five Whys
by lynettebye — 2021-06-07T08:01:02.810Z

This post appeared first on the EA Coaching blog.

Five Whys is a technique I borrowed from

Lean methodology

for getting to the root cause of a problem. As shown in the example below, I use the method to identify many possible solutions to a particular productivity problem.

The simple steps:

Ask “Why do I have this problem? / What is causing this problem?”
Make the answer as concrete as possible.
Ask yourself “Why do I have this answer?/ What is causing this problem?”
Repeat steps 2 and 3 until you run out of more ideas. (Aim for at least five times.)
Brainstorm solutions to each answer. What specific action might resolve that problem?
Choose 2-3 solutions to test. If these aren’t sufficient, you can go back to test others.

Many problems have more than one root cause, so you may need to repeat the above for different starting questions.

Example

Why aren’t you exercising?

Because it’s difficult to stop mindlessly browsing the web in the evening to start exercising.

Possible solution: reduce friction to exercise, such as by having an exercise plan.

Why is it difficult to stop browsing?

Because I feel guilty about not getting enough done, so I don’t want to leave and hence admit that I’m not going to do more tonight.

Possible solution: Accept that the guilt is a counterproductive

cycle

.
Possible solution: Learn to recognize clues that you’re caught in a bad cycle.
Possible solution: Find better ways to relax, so that you aren’t out of steam so much and have something that feels good to switch to.

Why are you feeling guilty?

Because I care about what I’m working on, but I don’t prioritize well. So I never get everything done and always feel bad about being behind and not having done enough.

Possible solution: Rank tasks by priority and do the most important first.
Possible solution: Try aiming for a number of hours instead of tasks.
Possible solution: Read

Rest in Motion

.

Why don’t you prioritize well?

Because I’m constantly in reactive mode to what other people want me to do.

Possible solution: Let people know when you will get back to them instead of feeling guilty you haven’t responded for a week.
Possible solution: Block off time for your deep work and don’t accept meetings during that time.
Possible solution: Delegate or say no if you’re not the best person for a job, instead of accepting everything that people ask you to do.

Why are you in reactive mode?

Because

...