~[~agent ~foundations]
I endorse and operate by Crocker’s rules.
Mateusz Bagiński
Karma: 881
Samo said that he would bet that AGI is coming perhaps in the next 20-50 years, but in the next 5.
I haven’t listened to the pod yet but I guess you meant “but not in the next 5”.
FWIW Oliver’s presentation of (some fragment of) his work at ILIAD was my favorite of all the talks I attended at the conference.
I am not totally sure why he considers discrete models to be unable to describe initial states or state-transition programming.
AFAIU, he considers them inadequate because they rely on an external interpreter, whereas the model of reality should be self-interpreting because there is nothing outside of reality to interpret it.
Wheeler suggests some principles for constructing a satisfactory explanation. The first is that “The boundary of a boundary is zero”: this is an algebraic topology theorem showing that, when taking a 3d shape, and then taking its 2d boundary, the boundary of the 2d boundary is nothing, when constructing the boundaries in a consistent fashion that produces cancellation; this may somehow be a metaphor for ex nihilo creation (but I’m not sure how).
See this as an operation that takes a shape and produces its boundary. It goes 3D shape → 2D shape → nothing. If you reverse the arrows you get nothing → 2D shape → 3D. (Of course, it’s not quite right because (IIUC) all 2D shapes have boundary zero but I guess it’s just meant as a rough analogy.)
He notes a close relationship between logic, cognition, and perception: for example, “X | !X” when applied to perception states that something and its absence can’t both be perceived at once
This usage of logical operators is confusing. In the context of perception, he seems to want to talk about NAND: you never perceive both something and its absence but you may also not perceive either.
(note that “X | !X” is equivalent to ”!(X & !X)” in classical but not intuitionistic logic)
Intuitionistic logic doesn’t allow either.[1] It allows .
Langan contrasts between spatial duality principles (“one transposing spatial relations and objects” and temporal duality principles (“one transposing objects or spatial relations with mappings, functions, operations or processes”). This is now beyond my own understanding.
It’s probably something like: if you have a spatial relationship between two objects X and Y, you can view it as an object with X and Y as endpoints. Temporally, if X causes Y, then you can see it as a function/process that, upon taking X produces Y.
The most confusing/unsatisfying thing for me about CTMU (to the extent that I’ve engaged with it so far) is that it doesn’t clarify what “language” is. It points ostensively at examples: formal languages, natural languages, science, perception/cognition, which apparently share some similarities but what are those similarities?
- ^
Though paraconsistent logic does.
- ^
Did they name it after the strawberry problem!?
Here are some axes along which I think there’s some group membership signaling in philosophy (IDK about the extent and it’s hard to disentangle it from other stuff):
Math: platonism/intuitionism/computationalism (i.e. what is math?), interpretations of probability, foundations of math (set theory vs univalent foundations)
Mind: externalism/internalism (about whatever), consciousness (de-facto-dualisms (e.g. Chalmers) vs reductive realism vs illusionism), language of thought vs 4E cognition, determinism vs compatibilism vs voluntarism
Metaphysics/ontology: are chairs, minds, and galaxies real? (this is somewhat value-laden for many people)
Biology: gene’s-eye-view/modern synthesis vs extended evolutionary synthesis
Moreover, I don’t think that some extra/different planning machinery was required for language itself, beyond the existing abstraction and model-based RL capabilities that many other animals share.
I would expect to see sophisticated ape/early-hominid-lvl culture in many more species if that was the case. For some reason humans went on the culture RSI trajectory whereas other animals didn’t. Plausibly there was some seed cognitive ability (plus some other contextual enablers) that allowed a gene-culture “coevolution” cycle to start.
My feedback is that I absolutely love it. My favorite feature released since reactions or audio for all posts (whichever was later).
In other words, there’s a question about how to think about truth in a way that honors perspectivalism, while also not devolving into relativism. And the way Jordan and I were thinking about this, was to have each filter bubble—with their own standards of judgment for what’s true and what’s good—to be fed the best content from the other filter bubbles by the standards from within each filter bubble, rather than the worst content, which is more like what we see with social media today.
Seems like Monica Anderson was trying to do something like that with BubbleCity. (pdf, podcast)
This is not quite an answer to your question but some recommendations in the comments to this post may be relevant: https://www.lesswrong.com/posts/SCs4KpcShb23hcTni/ideal-governance-for-companies-countries-and-more
It does for me
Sometimes I look up a tag/concept to ensure that I’m not spouting nonsense about it.
But most often I use them to find the posts related to a topic I’m interested in.
Possible problems with this approach
One failure mode you seem to have missed (which I’m surprised by) is that the SOO metric may be good-heartable. It might be the case (for all we know) that by getting the model to maximize SOO (subject to constraints of sufficiently preserving performance etc), you incentivize it to encode the self-other distinction in some convoluted way that is not adequately captured by the SOO metric, but is sufficient for deception.
Radical probabilist
Paperclip minimizer
Child of LDT
Dragon logician
Embedded agent
Hufflepuff cynic
Logical inductor
Bayesian tyrant
Asexual species universally seem to have come into being very recently. They likely go extinct due to lack of genetic diversity and attendant mutational load catastrophe and/or losing arms races with parasites.
Bdelloidea are an interesting counterexample: they evolved obligate parthenogenesis ~25 mya.
There’s a famous prediction market about whether AI will get gold from the International Mathematical Olympiad by 2025.
correction: it’s by the end of 2025
Also, they failed to provide the promised fraction of compute to the Superalignment team (and not because it was needed for non-Superalignment safety stuff).
Well, past events—before some time t—kind of obviously can’t be included in the Markov blanket at time t.
As far as I understand it, the MB formalism captures only momentary causal interactions between “Inside” and “Outside” but doesn’t capture a kind of synchronicity/fine-tuning-ish statistical dependency that doesn’t manifest in the current causal interactions (across the Markov blanket) but is caused by past interactions.
For example, if you learned a perfect weather forecast for the next month and then went into a completely isolated bunker but kept track of what day it was, your beliefs and the actual weather would be very dependent even though there’s no causal interaction (after you entered the bunker) between your beliefs and the weather. This is therefore omitted by MBs and CBs want to capture that.
(Continuity.) If , then there exists such that a gamble assigning probability to and to satisfies .
Should be ” to ”
Actually, it might be it, thanks!
I meant this strawberry problem.