For our November meetup, the Chicago Rationality group is focusing on predictions markets, and more generally on the practice of betting on beliefs. As part of this, we have a "homework assignment" for the meetup: each of us is trying to make at least one bet before the meetup happens....
I recently came across this post by Kevin Lacker about narrow AI risks. TLDR: There are reasonable routes to apocalyptic AI far before we achieve AGI. My default position has been extremely skeptical on the risks of AGI. I am generally in agreement with the Andrew Ng quote that "I...
Here are some attributes I've noticed among people who self-identify as rationalists. They are:
For our November meetup, the Chicago Rationality group is focusing on predictions markets, and more generally on the practice of betting on beliefs. As part of this, we have a "homework assignment" for the meetup: each of us is trying to make at least one bet before the meetup happens. This turns out to be kind of hard! I've challenged a handful of people on some online forums to bets and found no takers. Unsurprisingly, when people make absolute statements and you ask them to put money behind them, they become much more circumspect (which is kind of the point).
We're looking for people who want to make direct bets, individual to individual.... (read 383 more words →)
Is this like "have the hackathon participants do manual neural architecture search and train with L1 loss"?
Ah, I misinterpreted your question. I thought you were looking for ideas for your team that was participating in the hackation, not as the organizer of the hackation.
In my experience, most hackathons are judged qualitatively, so I wouldn't worry about ideas (mine or others') without a strong metric
Do a literature survey for the latest techniques on detecting if a image/prose text/piece of code is computer-generated or human-generated. Apply it to a new medium (i.e. if it's an article about text, borrow techniques to apply it to images, or vice-versa).
Alternatively, take the opposite approach and show AI safety risks. Can you train a system that looks very accurate, but gives incorrect output on specific examples that you choose during training? Just as one idea, some companies use face recognition as a key part of their security system. Imagine a face recognition system that labels 50 "employees" that are images of faces you pull from the internet, including images of Jeff Bezos. Train that system to correctly classify all the images, but also label anyone wearing a Guy Fawkes mask as Jeff Bezos. Think about how you would audit something like this if a malicious employee handed you a new set of weights and you were put in charge of determining if they should be deployed or not.
>75% confidence: No consistent strong play in simple game of imperfect information (e.g. battleship) for which it has not been specifically trained.
>50% confidence: No consistent "correct" play in a simple game of imperfect information (e.g. battleship) for which it has not been specifically train. Correct here means making only valid moves, and no useless moves. For example, in battleship a useless move would be attacking the same grid coordinate twice.
>60% confidence: Bad long-term sequence memory, particularly when combined with non-memorization tasks. For example, suppose A=1, B=2, etc. What is the sum of the characters in a given page of text (~500 words)?
Above 99% certainty:
Run inference in reasonable latency (e.g. < 1 second for text completion) on a typical home gaming computer (i.e. one with a single high-powered GPU).
Didn't this basically happen with LTCM? They had losses of $4B on $5B in assets and a borrow of $120B. The US government had to force coordination of the major banks to avoid blowing up the financial markets, but meltdown was avoided.
Edit: Don't pyramid schemes do this all the time, unintentionally? Like, Madoff basically did this and then suddenly (unintentionally) defaulted.
But if I had to use the billion dollars on evil AI specifically, I'd use the billion dollars to start an AI-powered hedge fund and then deliberately engineer a global liquidity crisis.
How exactly would you do this? Lots of places market "AI powered" hedge funds, but (as someone in the finance industry) I haven't heard much about AI beyond things like regularized regression actually giving significant benefit.
Even if you eventually grew your assets to $10B, how would you engineer a global liquidity crisis?
I recently came across this post by Kevin Lacker about narrow AI risks. TLDR: There are reasonable routes to apocalyptic AI far before we achieve AGI.
My default position has been extremely skeptical on the risks of AGI. I am generally in agreement with the Andrew Ng quote that "I don’t work on preventing AI from turning evil for the same reason that I don’t work on the problem of overpopulation on the planet Mars." I am still very skeptical of even the Kevin Lacker scenarios, but somewhat less so.
A lot of the AI risk discussion I've seen focuses on hypotheticals, or theories of alignment, or distant future low-probability scenarios.
I'd like to ask... (read more)
+1, CLion is vastly superior to VsCode or emacs/vi for capabilities and ease of setup, particularly for C++ and Rust
Despite being a webcomic, I think this is a funny, legitimate, and scathing critique of the philosophic life and to some extent the philosophy of rationality
https://www.smbc-comics.com/comic/think
This is not a great No bet at current odds even if you are certain the event will not happen. The market resolves Dec 31, which means that you have to lock up your cash for about 9 months for about a 3% rate of return. The best CDs are currently paying around 4-4.5% for 6mo-1y terms. So even for people who bought No at 96% it seems like a bad trade, since you're getting less than the effective risk-free rate, and you're not getting compensated for the additional idiosyncratic risk (e.g. Polymarket resolves to yes because shenanigans, polymarket gets hacked, etc).