Setting the stage

Being a polyglot is a problem of definition first. Who can be described as a polyglot? At what level do you actually “speak” the given language? Some sources cite that polyglot means speaking more than 4 languages, others 6. My take is it doesn’t matter. I am more interested in the definition of when you speak the language. If you can greet and order a coffee in 20 languages do you actually speak them? I don’t think so. Do you need to present a scientific document or write a newspaper worthy article to be considered? That's too much. I think the best definition would be that you can go out with a group of native speakers, understand what they are saying and participate in the discussion that would range from everyday stuff to maybe work related stuff and not switching too often to English nor using [...]

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In March I posted a very short description of my PhD thesis, Algorithmic Bayesian Epistemology, on LessWrong. I've now written a more in-depth summary for my blog, Unexpected Values. Here's the full post:

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In January, I defended my PhD thesis. My thesis is called Algorithmic Bayesian Epistemology, and it's about predicting the future.

In many ways, the last five years of my life have been unpredictable. I did not predict that a novel bat virus would ravage the world, causing me to leave New York for a year. I did not predict that, within months of coming back, I would leave for another year — this time of my own free will, to figure out what I wanted to do after graduating. And I did not predict that I would rush to graduate in just seven semesters so I could go work [...]

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Outline:

(03:48) Chapter 0: Preface

(04:17) Chapter 1: Introduction

(07:39) Chapter 2: Preliminaries

(09:33) Chapter 3: Incentivizing precise forecasts

(11:00) Chapter 4: Arbitrage-free contract functions

(14:10) \* Chapter 5: Quasi-arithmetic pooling

(21:11) Chapter 6: Learning weights for logarithmic pooling

(23:01) \* Chapter 7: Robust aggregation of substitutable signals

(30:01) Chapter 8: When does agreement imply accuracy?

(36:09) \* Chapter 9: Deductive circuit estimation

(46:34) Epilogue

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Predicting the future is hard, so it's no surprise that we occasionally miss important developments.

However, several times recently, in the contexts of Covid forecasting and AI progress, I noticed that I missed some crucial feature of a development I was interested in getting right, and it felt to me like I could’ve seen it coming if only I had tried a little harder. (Some others probably did better, but I could imagine that I wasn't the only one who got things wrong.)

Maybe this is hindsight bias, but if there's something to it, I want to distill the nature of the mistake.

First, here are the examples that prompted me to take notice:

Predicting the course of the Covid pandemic:

• I didn’t foresee the contribution from sociological factors (e.g., “people not wanting to get hospitalized” – Zvi called it “the control system”).
  • As a result, I overpredicted [...]

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It was a remarkably quiet announcement. We now have Alpha Fold 3, it does a much improved job predicting all of life's molecules and their interactions. It feels like everyone including me then shrugged and went back to thinking about other things. No cool new toy for most of us to personally play with, no existential risk impact, no big trades to make, ho hum.

But yes, when we look back at this week, I expect what we remember will be Alpha Fold 3.

Unless it turns out that it is Sophon, a Chinese technique to potentially make it harder to fine tune an open model in ways the developer wants to prevent. I do not expect this to get the job done that needs doing, but it is an intriguing proposal.

We also have 95 theses to evaluate in a distinct post, OpenAI sharing the [...]

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Outline:

(01:14) Language Models Offer Mundane Utility

(05:41) Language Models Don’t Offer Mundane Utility

(10:24) GPT-2 Soon to Tell

(13:49) Fun with Image Generation

(14:03) Deepfaketown and Botpocalypse Soon

(18:05) Automation Illustrated

(23:15) They Took Our Jobs

(25:56) Apple of Technically Not AI

(28:09) Get Involved

(28:46) Introducing

(31:11) In Other AI News

(32:47) Quiet Speculations

(37:35) The Quest for Sane Regulations

(39:51) The Week in Audio

(40:38) Rhetorical Innovation

(45:58) Open Weights Are Unsafe and Nothing Can Fix This

(51:34) The Lighter Side

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Suppose Alice and Bob are two Bayesian agents in the same environment. They both basically understand how their environment works, so they generally agree on predictions about any specific directly-observable thing in the world - e.g. whenever they try to operationalize a bet, they find that their odds are roughly the same. However, their two world models might have totally different internal structure, different “latent” structures which Alice and Bob model as generating the observable world around them. As a simple toy example: maybe Alice models a bunch of numbers as having been generated by independent rolls of the same biased die, and Bob models the same numbers using some big complicated neural net.

Now suppose Alice goes poking around inside of her world model, and somewhere in there she finds a latent variable _Lambda_A_ with two properties (the Natural Latent properties):

• _Lambda_A_ approximately mediates between two different [...]

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Outline:

(04:23) So What Could We Do With That?

(04:35) Interpretability

(06:29) Value Learning and The Pointers Problem

(08:03) Where We’re Currently Headed With This

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The curious tale of how I mistook my dyslexia for stupidity - and talked, sang, and drew my way out of it.

Sometimes I tell people I’m dyslexic and they don’t believe me. I love to read, I can mostly write without error, and I’m fluent in more than one language.

Also, I don’t actually technically know if I’m dyslectic cause I was never diagnosed. Instead I thought I was pretty dumb but if I worked really hard no one would notice. Later I felt inordinately angry about why anyone could possibly care about the exact order of letters when the gist is perfectly clear even if if if I right liike tis.

Thanks for reading Think Feel Play! Subscribe for free to receive new posts and support my work.

I mean, clear to me anyway.

I was 25 before it dawned on me that all the tricks [...]

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Outline:

(02:29) Read Out Loud

(04:42) Draw the Letters

(07:46) Sing it

(09:40) Substituting brain processes

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I mostly wrote this for facebook, but it ended up being a whole-ass post so I figured I'd put it here too.

I'm helping run "LessOnline: A Festival of Writers Who Are Wrong On the Internet (But Striving To Be Less So)".

I'm incentivized to say nice things about the event. So, grain of salt and all. But, some thoughts, which roughly breakdown into:

– The vibe: preserving cozy/spaciousness of a small retreat at a larger festival

– The audience: "Reunion for the The Extended Family Blogosphere, both readers and writers."

– Manifest, and Summer Camp

...

I. The Vibe

I've been trying to explain the vibe I expect and it's tricksy. I think the vibe will be something like "CFAR Reunion meets Manifest."

But a lot of people haven't been to a CFAR Reunion or to Manifest.

I might also describe it like "the thing the [...]

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The first speculated on why you’re still single. We failed to settle the issue. A lot of you were indeed still single. So the debate continues.

The second gave more potential reasons, starting with the suspicion that you are not even trying, and also many ways you are likely trying wrong.

The definition of insanity is trying the same thing over again expecting different results. Another definition of insanity is dating in 2024. Can’t quit now.

You’re Single Because Dating Apps Keep Getting Worse

A guide to taking the perfect dating app photo. This area of your life is important, so if you intend to take dating apps seriously then you should take photo optimization seriously, and of course you can then also use the photos for other things.

I love the ‘possibly’ evil here.

Misha Gurevich: possibly evil idea: Dating app that [...]

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Outline:

(00:37) You’re Single Because Dating Apps Keep Getting Worse

(05:38) You’re Single Because Dating Apps Keep Getting Worse

(07:24) You’re Single Because Everyone is Too Superficial

(09:48) You’re Single Because You Refuse to Shamefully Falsify Your Politics

(16:12) You Are Single Because You Do Not Employ Good Strategy

(18:45) You Are Single Because You Don’t Know How to Flirt

(22:43) You Are Single Because You Don’t Date Your Married Boss

(26:12) You Are Single Because You Are Afraid to Fail

(27:02) You Are Single Because No One Likes You On Dates

(29:39) You’re Single Because You Are Bad at Sex

(30:51) You’re Single Because You’re Not Hot

(31:39) You’re Single Because You Don’t Know What People Care About

(33:10) You’re Single Because You Are Inappropriate

(34:11) You’re Single Because of Your Pet

(35:23) You’re Single Because You Won’t Spend Money

(40:05) You’re Single Because You’re Not Over Your Ex

(41:27) You’re Single Because You Thought You Could Do 25% Better

(47:27) Polyamory

(53:29) You’re Single Because You Don’t Know What You Want

(01:00:43) You’re Single Because You’re Too Busy Writing Comments

(01:07:27) You’re Single and Not Getting Properly Compensated

(01:08:34) You’re Not Single and You’re an Inspiration

(01:09:49) Your Moment of Zen

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If you’ve ever been to Amsterdam, you’ve probably visited, or at least heard about the famous cookie store that sells only one cookie. I mean, not a piece, but a single flavor.

I’m talking about Van Stapele Koekmakerij of course—where you can get one of the world's most delicious chocolate chip cookies. If not arriving at opening hour, it's likely to find a long queue extending from the store's doorstep through the street it resides. When I visited the city a few years ago, I watched the sensation myself: a nervous crowd awaited as the rumor of ‘out of stock’ cookies spreaded across the line.

The store, despite becoming a landmark for tourists, stands for an idea that seems to be forgotten in our culture: crafting for a single purpose.

In the tech scene where [...]

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Outline:

(02:35) Going back to basics as a way to find purpose

(06:34) The bloated culture

(12:33) In more recent times

(13:58) Drinking beers and building businesses in Japan

(17:20) What might come next?

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Most people avoid saying literally false things, especially if those could be audited, like making up facts or credentials. The reasons for this are both moral and pragmatic — being caught out looks really bad, and sustaining lies is quite hard, especially over time. Let's call the habit of not saying things you know to be false ‘shallow honesty’[1].

Often when people are shallowly honest, they still choose what true things they say in a kind of locally act-consequentialist way, to try to bring about some outcome. Maybe something they want for themselves (e.g. convincing their friends to see a particular movie), or something they truly believe is good (e.g. causing their friend to vote for the candidate they think will be better for the country).

Either way, if you think someone is being merely shallowly honest, you can only shallowly trust them: you might be confident that [...]

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Outline:

(01:43) Examples of shallow (versus deep) honesty

(03:45) Why deep honesty?

(06:47) What deep honesty is not

(06:59) It is not a universal stance

(07:14) It is not independent of the listener

(07:29) It is not telling people everything

(07:54) It does not relieve you of a responsibility to be kind

(08:35) It is not incompatible with consequentialism

(10:00) Challenging cases for deep honesty

(10:29) Large inferential gaps

(11:51) Audiences you don’t want to cooperate with

(12:09) Multiple audiences

(13:09) What being deeply honest might look like

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