trentmkelly/LessWrong-43k huggingface dataset:
Titan (the Wealthfront of active stock picking) - What's the catch?
by Liron — 2020-08-06T01:06:04.599Z

Titan is a Y Combinator startup that launched in 2018 and aims to do for active investing what Wealthfront, Betterment and Vanguard have done for passive investing.

They pick a basket of 20 companies with $10B+ market cap which they believe are above-average long-term-focused investments relative to the whole S&P 500.

Originally, their stock picking was done via a deterministic process of copying what a group of top hedge funds were reporting that they were doing. I'm not sure if that's still the case.

Their 2018-2020 performance has been 16.8%/yr (net of fees) compared to 10.0% for the S&P 500, and a higher Sharpe ratio (.77 vs .51).

My question is, what's the catch?

Here's my guess: They're buying high-quality companies at high prices. That's how they can expect to have steady market-beating returns for a few years, until momentum reverses and/or once-in-a-few-years risks play out, at which point their P/E multiples will shrink and they'll plunge all the way down to cumulative market-matching returns, and worse after subtracting their fees.

Their "process" page claims they look for a Warren Buffet style "Margin of safety":

Valuation is important. We seek companies that are trading at a meaningful discount to our estimate of their long-term intrinsic value, with little to no risk of permanent capital impairment.

But I'm not convinced there's much substance to their use of this term.

More (vague) info about how they pick stocks here.
What should an Einstein-like figure in Machine Learning do?
by Razied — 2020-08-05T23:52:14.539Z

Suppose you are an extremely talented but still unknown machine learning researcher (like Einstein of patent-clerk days), and you discover something very important, maybe a few tricks that allow you to train neural nets 4 orders of magnitude faster than anyone else. You can train something of GPT-3 level on your personal rig in a few weeks, those tricks also generalize to larger networks. What should you do with this information to benefit humanity?

Publishing seems out of the question, as it would probably mean that AGI is within reach of a large project. But you personally cannot yet produce it, you can produce GPT-3 like performance, but nothing more. You could do nothing at all, keeping silent and hoping that your discoveries are not replicated by the main players. Or maybe you could try to covertly help OpenAI, which would then have a massive advantage, and enough lead time to properly treat safety. What would you do in this scenario?
Diagramming "Replacing Guilt," Part 1
by armatures — 2020-08-05T23:36:42.277Z

I am interested communicating ideas visually. In general, I'm able to remember images for a longer time than I remember language, and the act of refining an idea into a drawing involves wrestling with the idea a little more deeply than I would with a simple summary in text. This is my first post here, and it's unconventional, so I'd love to get feedback about what works & doesn't work about these drawings.
I have been enjoying Nate Soares' sequence of blog posts titled Replacing Guilt, and I decided this would be good material to experiment on. This post consists of drawings I've made corresponding to the first seven posts: each post is listed here by title followed by the image I've drawn and an explanation of the image. These are not intended as a substitute for the original work, and I expect that browsing them without referencing the original posts would be a lackluster experience. If you've already read that sequence, you might find these drawings of interest.

Preliminaries

Half-assing it with everything you've got

Remember what you're fighting for: you may follow the paved conventional path for a time before diverging from the triers.
From the post:

If you're trying to pass the class, then pass it with minimum effort. Anything else is wasted motion.
If you're trying to ace the class, then ace it with minimum effort. Anything else is wasted motion.
If you're trying to learn the material to the fullest, then mine the assignment for all its knowledge, and don't fret about your grade. Anything else is wasted motion.

Failing with abandon

You can fail gracefully instead of giving up once you've missed your deadline.

1. Fighting for something

Replacing guilt

Guilt-based motivation cannot be sustained long-term, but your current pursuits may not be sustainable with anything else. If you are not doing something you genuinely care about, you may not have any intrinsic motivation to continue doing it.

The stamp collector

When you think you care about the world, it's not just selfish motivated reasoning dressed up as altruism. You haven't deceived yourself into telling stories that make you look good: you can be genuine.

You're allowed to fight for something

There's a failure mode where you sneer at people trying to change the world, because what they're really changing is thei...
Measuring hardware overhang
by hippke — 2020-08-05T19:59:00.003Z

Measuring hardware overhang

Summary

How can we measure a potential AI or hardware overhang? For the problem of chess, modern algorithms gained two orders of magnitude in compute (or ten years in time) compared to older versions. While it took the supercomputer "Deep Blue" to win over world champion Gary Kasparov in 1997, today's Stockfish program achieves the same ELO level on a 486-DX4-100 MHz from 1994. In contrast, the scaling of neural network chess algorithms to slower hardware is worse (and more difficult to implement) compared to classical algorithms. Similarly, future algorithms will likely be able to better leverage today's hardware by 2-3 orders of magnitude. I would be interested in extending this scaling relation to AI problems other than chess to check its universality.

Introduction

Hardware overhang is a situation where sufficient compute is available, but the algorithms are suboptimal. It is relevant if we build AGI with large initial build, but cheaper run costs. Once built, the AGI might run on many comparably slow machines. That's a hardware overhang with a risk of exponential speed-up. This asymmetry exists for current neural networks: Creating them requires orders of magnitude more compute than running them. On the other hand, in The Bitter Lesson by Rich Sutton it is argued that the increase in computation is much more important (orders of magnitude) than clever algorithms (factor of two or less). In the following, I will examine the current state of the algorithm-art using chess as an example.

The example of chess

One of the most well-researched AI topics is chess. It has a long history of algorithms going back to a program on the 1956 MANIAC. It is comparatively easy to measure the quality of a player by its ELO score. As an instructive example, we examine the most symbolic event in computer chess. In 1997, the IBM supercomputer "Deep Blue" defeated the reigning world chess champion under tournament conditions. The win was taken as a sign that artificial intelligence was catching up to human intelligence.

By today's standards, Deep Blue used simple algorithms. Its strength came from computing power. It was a RS/6000-based system with 30 nodes, each with a 120 MHz CPU plus 480 special purpose VLSI chess chips. For comparison, a common computer at the time was the Intel Pentium II at 300 MHz.

Method: An experiment using a 2020 chess engin

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Which COVID-19 serology (antibody) test is best?
by knite — 2020-08-05T18:54:41.583Z

https://www.fda.gov/medical-devices/coronavirus-disease-2019-covid-19-emergency-use-authorizations-medical-devices/eua-authorized-serology-test-performance

The FDA lists two dozen tests with different sensitivity and specificity characteristics, different sample sizes, and different 95% CIs. Some of these tests are clear losers (lower bound on sensitivity CI ~75%!), but are there any clear winners?
[AN #111]: The Circuits hypotheses for deep learning
by Rohin Shah — 2020-08-05T17:40:22.576Z

Alignment Newsletter is a weekly publication with recent content relevant to AI alignment around the world. Find all Alignment Newsletter resources here. In particular, you can look through this spreadsheet of all summaries that have ever been in the newsletter.

Audio version here (may not be up yet).

HIGHLIGHTS

Thread: Circuits (Chris Olah et al) (summarized by Nicholas): The (currently incomplete) Circuits thread of articles builds a case around 3 main claims:

1. Neural network features - the activation values of hidden layers - are understandable.

2. Circuits - the weights connecting these features - are also understandable.

3. Universality - when training different models on different tasks, you will get analogous features.

Zoom In provides an overview of the argument. The next two articles go into detail on particular sets of layers or neurons.

Claim 1: Neural Network Features - the activation values of hidden layers - are understandable.

They make seven arguments for this claim in Zoom In which are expanded upon in subsequent articles.

1. Feature Visualization: By optimizing the input to maximize the activation of a particular neuron, they can obtain an image of what that neuron reacts most strongly to. They create and analyze these for all 1056 neurons in the first five layers of the InceptionV1 image classification model. While some of them were difficult to understand, they were able to classify and understand the purpose of most of the neurons. A simple example is that curve detecting neurons produce feature visualizations of curves of a particular orientation. A more complex example is neurons detecting boundaries between high and low frequency, which often are helpful for separating foreground and background.

2. Dataset examples: They also look at the examples in the dataset that maximize a particular neuron. These align with the feature visualizations. Neurons with a particular curve in the feature visualization also fire strongest on dataset examples exhibiting that curve.

3. Synthetic Examples: They also create synthetic examples and find that neurons fire on the expected synthetically generated examples. For example, they generate synthetic curves with a wide range of orientations and curvatures. Curve detectors respond most strongly to a particular orientation and curvature that matches the feature visualizations and highest activation data...
Can Social Dynamics Explain Conjunction Fallacy Experimental Results?
by curi — 2020-08-05T08:50:05.855Z

Is there any conjunction fallacy research which addresses the alternative hypothesis that the observed results are mainly due to social dynamics?

Most people spend most of their time thinking in terms of gaining or losing social status, not in terms of reason. They care more about their place in social status hierarchies than about logic. They have strategies for dealing with communication that have more to do with getting along with people than with getting questions technically right. They look for the social meaning in communications. E.g. people normally try to give – and expect to receive – useful, relevant, reasonable info that is safe to make socially normal assumptions about.

Suppose you knew Linda in college. A decade later, you run into another college friend, John, who still knows Linda. You ask what she’s up to. John says Linda is a bank teller, doesn’t give additional info, and changes the subject. You take this to mean that there isn’t more positive info. You and John both see activism positively and know that activism was one of the main ways Linda stood out. This conversation suggests to you that she stopped doing activism. Omitting info isn’t neutral in real world conversations. People mentally model the people they speak with and consider why the person said and omitted things.

In Bayesian terms, you got two pieces of info from John’s statement. Roughly: 1) Linda is a bank teller. 2) John thinks that Linda being a bank teller is key info to provide and chose not to provide other info. That second piece of info can affect people’s answers in psychology research.

So, is there any research which rules out social dynamics explanations for conjunction fallacy experimental results?
Tools for keeping focused
by benkuhn — 2020-08-05T02:10:08.707Z

Once I realized that my attention was even scarcer than my time, I became an anti-distraction fanatic. During my weekly reviews I methodically went through my past week, figured out what had been distracting me, and tried to eliminate it or replace it with something less distracting.

Over time, this has led me to find lots of tools (and ways of using my tools) that help me stay more focused. Here are some of the things I’ve started doing:

Anxious yet?

I aggressively disable notifications and badges so that I don’t mindlessly open distracting apps. If you’re into eliminating distractions you’ve probably already done this. But if you haven’t, it’s by far the most important thing you can do to improve your focus, so I’m putting it first anyway.

I have a zero-tolerance notification policy: if an app interrupts me, I ask myself whether the interruption was valuable, and if not, the app doesn’t get to notify me anymore. This has weeded out pretty much everything except for inboxes (phone, texts, reminders) and apps with a human on the other end (ride sharing, delivery).

A special dishonorable mention goes to Slack, which can easily suck away a quarter of your time without you noticing. If you use Slack and you haven’t disabled the “unread messages” badge, stop reading this post and do it now. (Consider setting a two-minute timer to remind you to quit in case you get distracted by checking your unreads, like I did while taking the screenshot above.)

On the subject of Slack, I try to keep it closed as much as possible and check it in batches a few times a day. Not all workplace Slack cultures allow this, but if yours does, I highly, highly recommend it. Doing this led to my biggest single quantifiable productivity improvement.✻✻
If your workplace culture doesn’t allow you to keep Slack closed, because it requires quick Slack responses, this is a bad sign.

I only check my email once per day.

Gmail filters send important email to a label called “Temp Inbox” and the rest to a label called “Unimportant.” I check Temp Inbox each evening and Unimportant once a week. Since incoming email doesn’t go to the inbox, I can open Gmail to compose or search messages without getting distracted by unread messages.

(I use a piece of Google Apps Script for this, but I think the Gmail UI has improved recently so that you can now do something similar with filters and it’ll have decen

...
The Golden Age of Data
by Collisteru — 2020-08-04T21:35:37.899Z

The more recent something is, the more data we have about it.

This is true in all fields where collecting information over time is important, including astronomy, archaeology, geology, paleontology, the internet, and even our own memories. Why is this so universal? It's not as if 100-year-old rocks are any more complicated than 300-year-old rocks.

There are three factors that create this effect. The first one is the fact that information becomes less accessible over time. It degrades. In geology, old rocks are worn away by water or covered in deep sand. In astronomy, starlight shifts into undetectable wavelengths as it travels through space. In the brain, memories are reevaluated, modified, and deleted. The same volume of data is generated at all points in time, but older data has had more time to degrade, so there is less of it.

There are some fields where data generation isn't constant, and then the situation is more complicated. History is the most important example. Historical data is produced by humans, and thus the rate of historic data production follows an exponential curve that roughly correlates with the global population. As the population increases, there are more people to produce records, so more records are produced. This is the second factor.

The third factor is innovation. Humans have invented increasingly efficient methods of storing information throughout history. Papyrus is cheaper than clay tablets, and books are more compact than scrolls. These innovations rarely happen, but when they do, they are revolutionary: Historians today see a spike in information from the mid-15th century not because of the growing dark ages population, but because of the printing press.

The pace of data innovation was very slow before the Information Age, but it has since rocketed forward. Over the past few decades, hard disk drives and SSDs have become more efficient at an astounding rate. We can fit all the English books ever published on a nice hard drive. In fact, now we don't even need to carry storage media; we can store information in servers thousands of miles away. These servers will also continue to become more efficient as bandwidth and upload speeds increase. The acceleration of the Information Age cannot be overstated: for most of history, humanity only stored information in a handful of ways. Today, a new method is created eve...
My paper was signalling the whole time - Robin Hanson wins again
by Tim Liptrot — 2020-08-04T21:13:16.016Z

Robin Hanson Meme

I recently wrote a paper on how the Jordanian monarchy decided who to give water to and who to take water away from. As I near completion, I am realizing that signalling theory from Robin Hanson gives a pretty compelling explanation of my results, probably better than my explanation. I will give a brief summary.

So in the 1990s and 2000's poor neighbourhoods of East Amman periodically had water shortages. Whole neighbourhoods would go without pumped water for a month and people would riot. The Government of Jordan (GoJ) does not like riots and were motivated to stop this.

Jordan has two relevant water sources they could use to make up the shortfall of 100-150 million cubic meters MCM. The Northern Highlands are close to the capital Amman and have a few profitable farms and a lot of smaller, unprofitable "prestige" farms owned by Jordanians. The southern desert is a good 600 km away and has four large profitable farms. The farms are owned by rich, politically connected Jordanian families and operated almost entirely by Egyptian migrant laborers.

The World Bank for twenty years suggested taxing the farms in the Northern Highlands to close the unprofitable ones then redirecting that water from the capital. Since Amman sits on the Northern Highlands the costs of transporting the water are trivial.

Instead the Jordanians paid about a billion dollars to build a massive pipeline to the southern farms, then shut them down instead. They don't publish the data I could use to compare how much more expensive the Disi pipeline solution was, but capital costs were about a billion USD and the energy costs are likely double the cost of other sources (of order 1 dollar per cubic meter). The water sectors cost recovery ration dropped by 30% the year they finished the project, financed by public debt until a 2018 fee increase forced by the WB.

The Jordanians have justified their decision for two reasons. The first is that closing the farms in the north would require negotiating with hundreds of farmers with diverse motives and finances, which was beyond the governments capacity. In the south they had only to negotiate with a small number of elites. This argument is strong.

The second argument is that closing the northern farms would have created unemployment which would create instability. They worried that farmers would lose their jobs and head to Amman to burden t...
Boston/Cambridge LW Hangout, Sunday 8/16/20
by ItsGiusto — 2020-08-04T20:43:38.090Z

Come hang out outside at Powderhouse Park near Davis Square: https://goo.gl/maps/7DMM5oJ7nJUn6s5U7

This will be a bring-your-own-food event. There are plenty of takeout restaurants nearby.

Let me know if you have any questions!
Infinite Data/Compute Arguments in Alignment
by johnswentworth — 2020-08-04T20:21:37.310Z

This is a reference post. It explains a fairly standard class of arguments, and is intended to be the opposite of novel; I just want a standard explanation to link to when invoking these arguments.

When planning or problem-solving, we focus on the hard subproblems. If I’m planning a road trip from New York City to Los Angeles, I’m mostly going to worry about which roads are fastest or prettiest, not about finding gas stations. Gas stations are abundant, so that subproblem is easy and I don’t worry about it until harder parts of the plan are worked out. On the other hand, if I were driving an electric car, then the locations of charging stations would be much more central to my trip-planning. In general, the hard subproblems have the most influence on the high-level shape of our solution, because solving them eats up the most degrees of freedom.

In the context of AI alignment, which subproblems are hard and which are easy?

Here’s one class of arguments: compute capacity and data capacity are both growing rapidly over time, so it makes sense to treat those as “cheap” - i.e. anything which can be solved by throwing more compute/data at it is easy. The hard subproblems, then, are those which are still hard even with arbitrarily large amounts of compute and data.

In particular, with arbitrary compute and data, we basically know how to get best-possible predictive power on a given data set: Bayesian updates on low-level physics models or, more generally, approximations of Solomonoff induction. So we’ll also assume predictive power is “cheap” - i.e. anything which can be solved by more predictive power is easy.

This is also reasonable in machine learning practice - once a problem is reduced to predictive power on some dataset, we can throw algorithms at it until it’s solved. The hard part - as many data scientists will attest - is reducing our real objective to a prediction problem and collecting the necessary data. It’s rare to find a client with a problem where all we need is predictive power and the necessary data is just sitting there.

(We could also view this as an

interface argument

: “predictive problems” are a standard interface, with libraries, tools, algorithms, theory and specialists all set up to handle them. As in many other areas, setting up our actual problem to fit that interface while still consistently doing what we want is the hard/expensive part.)

The u...