This is an experiment in short-form content on LW2.0. I'll be using the comment section of this post as a repository of short, sometimes-half-baked posts that either:
I ask people not to create top-level comments here, but feel free to reply to comments like you would a FB post.
In my experience, constant-sum games are considered to provide "maximally unaligned" incentives, and common-payoff games are considered to provide "maximally aligned" incentives. How do we quantitatively interpolate between these two extremes? That is, given an arbitrary payoff table representing a two-player normal-form game (like Prisoner's Dilemma), what extra information do we need in order to produce a real number quantifying agent alignment?
If this question is ill-posed, why is it ill-posed? And if it's not, we should probably understand how to quantify such a basic aspect of multi-agent interactions, if we want to reason about complicated multi-agent situations whose outcomes determine the value of humanity's future. (I started considering this question with Jacob Stavrianos over the last few months, while supervising his SERI project.)
You’re a deity, tasked with designing a bird brain. You want the bird to get good at singing, as judged by a black-box hardcoded song-assessing algorithm that you already built into the brain last week. The bird chooses actions based in part on within-lifetime reinforcement learning involving dopamine. What reward signal do you use?
Well, we want to train the bird to sing the song correctly. So it’s easy: the bird practices singing, and it listens to its own song using the song-assessing black box, and it does RL using the rule:
The better the song sounds, the higher the reward.
Oh wait. The bird is also deciding how much time to spend practicing singing, versus foraging...
Tracey Davis went to the second-floor girls' lavatory for her regular practice. Tracey drew her Gibson Flying V2 from of its case. Tracey inserted her Gibson Flying V2 back in its case. What was the point?
If you looked through the bars of the wrought iron cage by Luna Lovegood's bed you wouldn't see anything inside. The cage's door was sealed with a heavy padlock. Luna didn't remember locking it.
Tracey liked that there was a staircase from the Slytherin dormitories to Ravenclaw. It meant she didn't have to answer the raven's riddles. Tracey could answer the raven's riddles. If she wanted to. (Not that she had ever tried.) It was just demeaning.
The Ravenclaw common room was brightly-lit by the tall windows all around the circular Common Room. A...
"Your fingers are all wrong. So is your posture. And how you hold it," said Myrtle.
Tracey tried again. Her fingers hurt.
"Still wrong. Sit like this," Myrtle demonstrated.
Tracey mirrored Myrtle.
"No. Sit literally right here where I'm sitting," said Myrtle.
Tracey reached into Myrtle. It felt like ice water. Myrtle sat still. Tracey gritted her teeth, took a deep breath and superimposed herself. Tracey's skin rippled grey where bits of Myrtle protruded.
"You feel cold," said Tracey.
"Ghosts can't feel temperature. We can't smell. We can't taste. We see in shades of grey. But we can hear," said Myrtle.
Myrtle moved her hands into position. Tracey followed.
Nearly Headless Nick's deathday anniversary was October 31st. All the Hogwarts ghosts had attended. Many wore formal white sheets.
Myrtle was already on stage. Tracey had transfigured herself...
A friend of mine has been bootstrapping a business-to-business software-as-a-service startup that's seeing serious growth. It needs someone who can put dedicated effort into scaling it, but my friend is near the end of their career and looking to retire. What do people do in this situation?
More details: they were running a traditional labor-limited small business and they automated some of the work. This automation was a huge improvement and they realized it could be useful to other companies. In early 2019 they had a web app ready and started taking external customers. In late 2020 they started to see serious growth, which has continued. They let me share some numbers:
This is a run rate of ~$340k/y, up from ~$100k/y a quarter ago, ~$52k a quarter before that, ~$12k/y a quarter before...
As the world knows, the FDA approved Biogen’s anti-amyloid antibody today, surely the first marketed drug whose Phase III trial was stopped for futility. I think this is one of the worst FDA decisions I have ever seen, because – like the advisory committee that reviewed the application, and like the FDA’s own statisticians – I don’t believe that Biogen really demonstrated efficacy. No problem apparently. The agency seems to have approved it based on its demonstrated ability to clear beta-amyloid, and is asking Biogen to run a confirmatory trial to show efficacy.
So the FDA has, for expediency’s sake, bought into the amyloid hypothesis although every single attempt to translate that into a beneficial clinical effect has failed. I really, really don’t like the precedent that this
(originally posted at Secretum Secretorum)
I think there is something fascinating and useful about many of the observations, adages, and aphorisms that we (often sarcastically) designate as eponymous laws, effects, or principles in the same way we might for a scientific law. They are often funny (and memorable because of it), but many of them do speak to very fundamental aspects of human psychology and the human condition more generally. Murphy’s Law is probably the most well known example.
Murphy’s Law – “Anything that can go wrong will go wrong”
There are also a few lesser-known corollaries.
Murphy's Second Law – “Nothing is as easy as it looks”
Murphy's Third Law – “Everything takes longer than you think it will (even when you account for Murphy’s Third Law).1
Murphy's Fourth Law – “If there is...