Isaac Poulton


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I Really Don't Understand Eliezer Yudkowsky's Position on Consciousness

I don't agree with Eliezer here. I don't think we have a deep enough understanding of consciousness to make confident predictions about what is and isn't conscious beyond "most humans are probably conscious sometimes".

The hypothesis that consciousness is an emergent property of certain algorithms is plausible, but only that.

If that turns out to be the case, then whether or not humans, GPT-3, or sufficiently large books are capable of consciousness depends on the details of the requirements of the algorithm.

I Really Don't Understand Eliezer Yudkowsky's Position on Consciousness

If I'm not mistaken, that book is behaviourally equivalent to the original algorithm but is not the same algorithm. From an outside view, they have different computational complexity. There are a number of different ways of defining program equivalence, but equivalence is different from identity. A is equivalent to B doesn't mean A is B.

See also: Chinese Room Problem

Dating profiles from first principles: heterosexual male profile design

While it's important to bear in mind the possibility that you're not as below average as you think, I don't know your case so I will assume you're correct in your assessment.

Perhaps give up on online dating. "Offline" dating is significantly more forgiving than online.

Truthful AI: Developing and governing AI that does not lie

I think this touches on the issue of the definition of "truth". A society designates something to be "true" when the majority of people in that society believe something to be true.

Using the techniques outlined in this paper, we could regulate AIs so that they only tell us things we define as "true". At the same time, a 16th century society using these same techniques would end up with an AI that tells them to use leeches to cure their fevers.

What is actually being regulated isn't "truthfulness", but "accepted by the majority-ness".

This works well for things we're very confident about (mathematical truths, basic observations), but begins to fall apart once we reach even slightly controversial topics. This is exasperated by the fact that even seemingly simple issues are often actually quite controversial (astrology, flat earth, etc.).

This is where the "multiple regulatory bodies" part comes in. If we have a regulatory body that says "X, Y, and Z are true" and the AI passes their test, you know the AI will give you answers in line with that regulatory body's beliefs.

There could be regulatory bodies covering the whole spectrum of human beliefs, giving you a precise measure of where any particular AI falls within that spectrum.

Cup-Stacking Skills (or, Reflexive Involuntary Mental Motions)

I wonder if this makes any testable predictions. It seems to be a plausible explanation for how some people are extremely good at some reflexive mental actions, but not the only one. It's also plausible that some people are "wired" that way from birth, or that a single or small number of developmental events lead to them being that way (rather than years of involuntary practice).

I suppose if the hypothesis laid out in this post is true, we'd expect people to exhibit get significantly better at some of these "cup-stacking" skills within a few years of being in an environment that builds them. Perhaps it could be tested by seeing if people get significantly better at the "soft skills" required to succeed in an office after a few years working in one.

How much slower is remote work?

Specialising days like that seems like a good idea at first glance, but I get the feeling I'd burn out on meetings pretty quick if all my week's meetings were scheduled on one day. Being able to use a meeting as a break from concrete thinking to switch to more abstract thinking for a while is very refreshing.

The Towel Census: A Methodology for Identifying Orphaned Objects in Your Home

IMO, this is pretty necessary in any shared space. My company does this twice a year for for the office umbrella rack, fridge, and cupboard.

What's going on with this failure of Bayes to converge?

This highlights an interesting case where pure Bayesian reasoning fails. While the chance of it occurring randomly is very low (but may rise when you consider how many chances it has to occur), it is trivial to construct. Furthermore, it potentially applies in any case where we have two possibilities, one of which continually becomes more probable while the other shrinks, but persistently doesn't become disappear.

Suppose you are a police detective investigating a murder. There are two suspects: A and B. A doesn't have an alibi, while B has a strong one (time stamped receipts from a shop on the other side of town). A belonging of A's was found at the crime scene (which he claims was stolen). A has a motive: he had a grudge against the victim, while B was only an acquaintance.

A naive Bayesian (in both senses) would, with each observation, assign higher and higher probabilities to A being the culprit. In the end, though, it turns out that B commited the crime to frame A. He chose someone B had a grudge against, planted the belonging of A's, and forged the receipts.

It's worth noting that, assuming your priors are accurate, given enough evidence you *will* converge on the correct probabilities. Actually acquiring that much evidence in practice isn't anywhere near guaranteed, however.

What I talk about when I talk about AI x-risk: 3 core claims I want machine learning researchers to address.

IMO, this is a better way of splitting up the argument that we should be funding AI safety research than the one presented in the OP. My only gripe is in point 2. Many would argue that it wouldn't be really bad for a variety of reasons, such as there are likely to be other 'superintelligent AIs' working in our favour. Alternatively, if the decision making were only marginally better than a human's it wouldn't be any worse than a small group of people working against humanity.