Frustrated by claims that "enlightenment" and similar meditative/introspective practices can't be explained and that you only understand if you experience them, Kaj set out to write his own detailed gears-level, non-mysterious, non-"woo" explanation of how meditation, etc., work in the same way you might explain the operation of an internal combustion engine.
Here we briefly summarize the results so far from our U.S. nationally representative survey on Artificial Intelligence, Morality, and Sentience (AIMS), conducted in 2021 and 2023. The full reports are available on Sentience Institute’s website for the AIMS 2023 Supplemental Survey, AIMS 2023 Main Survey, and AIMS 2021 Main Survey. The raw data is available on Mendeley.
tl;dr: Results show that, from 2021 to 2023, there were increases in expectations of AI harm, moral concern for AIs, and mind perception of AIs. U.S. adults expect sentient AI to be developed sooner, now only in five years (median), and they strongly support AI regulation and slowdown.
Americans are significantly more concerned about AI in 2023 than they were in 2021 before ChatGPT. Only 23% of U.S. adults trust AI companies to put safety over...
A few days ago I wrote about my experience with MathML, and despite being somewhat positive on it in that post I've decided to stop using it for now. The problem is, it doesn't display for people who follow my blog through RSS on (I'm guessing) most popular RSS system.
Here's a screenshot from my most recent MathML-containing post, on my website:
And here's the same portion of that post running in the web version of Feedly on the same browser:
Poking at developer tools, here's what Feedly is sending over the network to my browser:
<p> It definitely does look nicer: </p> <p> </p> <p> On the other hand
This shows that they're removing the MathML on the server, instead of there being some issue once it gets to the client.
This also explains why it doesn't work in other...
Epistemic status: model which I find sometimes useful, and which emphasizes some true things about many parts of the world which common alternative models overlook. Probably not correct in full generality.
Consider Yoshua Bengio, one of the people who won a Turing Award for deep learning research. Looking at his work, he clearly “knows what he’s doing”. He doesn’t know what the answers will be in advance, but he has some models of what the key questions are, what the key barriers are, and at least some hand-wavy pseudo-models of how things work.
For instance, Bengio et al’s “Unitary Evolution Recurrent Neural Networks”. This is the sort of thing which one naturally ends up investigating, when thinking about how to better avoid gradient explosion/death in e.g. recurrent nets, while...
In government, it’s not just a matter of having the best policy; it’s about getting enough votes. This creates a problem when the self-interests of individual voters don’t match the best interests of the country.
For instance, voting researchers widely consider the presidential voting system in America to be inferior to many alternatives. But if you want to change it, you require consent from Democrats and Republicans—i.e. the very people who benefit from the status quo.
Or consider the land-value tax. This tax is considered among economists to be uniquely efficient (i.e. it causes zero reduction in the good being taxed). When implemented correctly, it can address edge cases, such as new property developments, and can even prevent reductions in new land production, like the creation of artificial islands....
Epistemic Status: self-reported musings
Mental health is a complicated topic, and “sanity” can be a loaded word, so I’ll offer a few definitions to make sure we’re all on the same page.
The colloquial definition of sanity, if such a thing exists, is formed in contrast to insanity. Someone is sane who is not insane.
So what’s insanity?
The colloquial definition of insanity is repeating the same act over and over again and expecting different results.
The dramatic definition of insanity generally involves seeing and/or hearing things that aren’t there, laughing hysterically at nothing, and getting punched in the face by Batman.
My personal definition of insanity is not getting out of bed for two days, not answering your family’s and/or friends’ calls because you’re irrationally terrified of talking to other people,...
Today, we’re announcing that Amazon will invest up to $4 billion in Anthropic. The agreement is part of a broader collaboration to develop reliable and high-performing foundation models.
(Thread continues from there with more details -- seems like a notable major development!)
Long ago, there was a mighty king who had everything in the world that he wanted, except trust. Who could he trust, when anyone around him might scheme for his throne? So he resolved to study the nature of trust, that he might figure out how to gain it. He asked his subjects to bring him the most trustworthy thing in the kingdom, promising great riches if they succeeded.
Soon, the first of them arrived at his palace to try. A teacher brought her book of lessons. “We cannot know the future,” she said, “But we know mathematics and chemistry and history; those we can trust.” A farmer brought his plow. “I know it like the back of my hand; how it rolls, and how it turns, and...
When transit gets better the land around it becomes more valuable: many people would like to live next to a subway station. This means that there are a lot of public transit expansions that would make us better off, building space for people to live and work. And yet, at least in the US, we don't do very much of this. Part of it is that the benefits mostly go to whoever happens to own the land around the stations.
A different model, which you see with historical subway construction or Hong Kong's MTR, uses the increase in land value to fund transit construction. The idea is, the public transit company buys property, makes it much more valuable by building service to it, and then sells it.
While I would be pretty positive on US...
This is a linkpost for Sparse Autoencoders Find Highly Interpretable Directions in Language Models
We use a scalable and unsupervised method called Sparse Autoencoders to find interpretable, monosemantic features in real LLMs (Pythia-70M/410M) for both residual stream and MLPs. We showcase monosemantic features, feature replacement for Indirect Object Identification (IOI), and use OpenAI's automatic interpretation protocol to demonstrate a significant improvement in interpretability.
To reverse engineer a neural network, we'd like to first break it down into smaller units (features) that can be analysed in isolation. Using individual neurons as these units can be useful but neurons are often polysemantic, activating for several unrelated types of feature so just looking at neurons is insufficient. Also, for some types of network activations, like the residual stream...