In this post, I proclaim/endorse forum participation (aka commenting) as a productive research strategy that I've managed to stumble upon, and recommend it to others (at least to try). Note that this is different from saying that forum/blog posts are a good way for a research community to communicate. It's about individually doing better as researchers.
Hi, I’d like to share my paper that proposes a novel approach for building white box neural networks. It introduces a concept of „semantic feature” and builds a simple white box PoC.
As an independent researcher I’d be grateful for your feedback!
Thank you! I wanted to keep the original post concise but I might have overshot in terms of brevity, this is my first post on this forum. Do you think I should edit the original post to include the quote you mentioned? I think it is on point.
This is my personal opinion, and in particular, does not represent anything like a MIRI consensus; I've gotten push-back from almost everyone I've spoken with about this, although in most cases I believe I eventually convinced them of the narrow terminological point I'm making.
In the AI x-risk community, I think there is a tendency to ask people to estimate "time to AGI" when what is meant is really something more like "time to doom" (or, better, point-of-no-return). For about a year, I've been answering this question "zero" when asked.
This strikes some people as absurd or at best misleading. I disagree.
The term "Artificial General Intelligence" (AGI) was coined in the early 00s, to contrast with the prevalent paradigm of Narrow AI. I was getting my undergraduate computer science...
Oh, by "as qualitatively smart as humans" I meant "as qualitatively smart as the best human experts".
I think that is more comparable to saying "as smart as humanity." No individual human is as smart as humanity in general.
On 16 March 2024, I sat down to chat with New York Times technology reporter Cade Metz! In part of our conversation, transcribed below, we discussed his February 2021 article "Silicon Valley's Safe Space", covering Scott Alexander's Slate Star Codex blog and the surrounding community.
The transcript has been significantly edited for clarity. (It turns out that real-time conversation transcribed completely verbatim is full of filler words, false starts, crosstalk, "uh huh"s, "yeah"s, pauses while one party picks up their coffee order, &c. that do not seem particularly substantive.)
ZMD: I actually have some questions for you.
CM: Great, let's start with that.
ZMD: They're critical questions, but one of the secret-lore-of-rationality things is that a lot of people think criticism is bad, because if someone criticizes you, it hurts your...
I was actually looking for specific examples, precisely so that we could test our intuitions, rather than just stating our intuitions. Do you happen to have any particular ones in mind?
This is the ninth post in my series on Anthropics. The previous one is The Solution to Sleeping Beauty.
There are some quite pervasive misconceptions about betting in regards to the Sleeping Beauty problem.
One is that you need to switch between halfer and thirder stances based on the betting scheme proposed. As if learning about a betting scheme is supposed to affect your credence in an event.
Another is that halfers should bet at thirders odds and, therefore, thirdism is vindicated on the grounds of betting. What do halfers even mean by probability of Heads being 1/2 if they bet as if it's 1/3?
In this post we are going to correct them. We will understand how to arrive to correct betting odds from both thirdist and halfist positions, and...
And the answer is no, you shouldn’t. But probability space for Technicolor Sleeping beauty is not talking about probabilities of events happening in this awakening, because most of them are illdefined for reasons explained in the previous post.
So probability theory can't possibly answer whether I should take free money, got it.
And even if "Blue" is "Blue happens during experiment", you wouldn't accept worse odds than 1:1 for Blue, even when you see Blue?
There's a particular kind of widespread human behavior that is kind on the surface, but upon closer inspection reveals quite the opposite. This post is about four such patterns.
One of the most useful ideas I got out of Algorithms to Live By is that of computational kindness. I was quite surprised to only find a single mention of the term on lesswrong. So now there's two.
Computational kindness is the antidote to a common situation: imagine a friend from a different country is visiting and will stay with you for a while. You're exchanging some text messages beforehand in order to figure out how to spend your time together. You want to show your friend the city, and you want to be very accommodating and make sure...
Forget where I read it, but this Idea seems similar. When responding to a request, being upfront about your boundaries or constraints feels intense but can be helpful for both parties. If Bob asks Alice to help him move, and Alice responds "sure thing" that leaves the interaction open to miscommunication. But if instead Alice says, " yeah! I am available 1pm to 5pm and my neck has been bothering me so no heavy lifting for me!" Although that's seems like less of a kind response Bob now doesn't have to guess at Alice's constraints and can comfortably move forward without feeling the need to tiptoe around how long and to what degree Alice can help.
Welcome, new readers!
This is my weekly AI post, where I cover everything that is happening in the world of AI, from what it can do for you today (‘mundane utility’) to what it can promise to do for us tomorrow, and the potentially existential dangers future AI might pose for humanity, along with covering the discourse on what we should do about all of that.
You can of course Read the Whole Thing, and I encourage that if you have the time and interest, but these posts are long, so they also designed to also let you pick the sections that you find most interesting. Each week, I pick the sections I feel are the most important, and put them in bold in the table of contents.
Not everything...
Moreover, legal texts are not super strict (much is left to interpretation) and we are often selective about "whether it makes sense to apply this law in this context" for reasons not very different from religious people being very selective about following the laws of their holy books.
Today a trend broke in Formula One. Max Verstappen didn't win a Grand Prix. Of the last 35 Formula One Grand Prix, Max Verstappen has won all but 5. Last season he had something like 86% dominance.
For context I believe that I am overall pessimistic when asked to give a probability range about something "working out". And since sports tend to vary in results if using a sport like Formula One would be a good source of data to make and compare predictions against?
Everything from estimating the range a pole position time, or the difference between pole and the last qualifier, from to a fastest lap in a race or what lap a driver will pit for fresh tyres.
What is the best way of doing it?
Tracking your predictions and improving your calibration over time is good. So is practicing making outside-view estimates based on related numerical data. But I think diversity is good.
If you start going back through historical F1 data as prediction exercises, I expect the main thing that will happen is you'll learn a lot about the history of F1. Secondarily, you'll get better at avoiding your own biases, but in a way that's concentrated on your biases relevant to F1 predictions.
If you already want to learn more about the history of F1, then go for it, it...