Vanessa Kosoy

• Vanessa Kosoy 6 Jul 2025 12:56 UTC

  3 points

  0

  in reply to: KycoK_Ov4arku’s comment on: Derivative

  Fixed!

• Vanessa Kosoy 30 Jun 2025 11:49 UTC

  LW: 20 AF: 10

  5

  AF

  in reply to: johnswentworth’s comment on: john­swent­worth’s Shortform

  I found LLMs to be very useful for literature research. They can find relevant prior work that you can’t find with a search engine because you don’t know the right keywords. This can be a significant force multiplier.

  They also seem potentially useful for quickly producing code for numerical tests of conjectures, but I only started experimenting with that.

  Other use cases where I found LLMs beneficial:

  • Taking a photo of a menu in French (or providing a link to it) and asking it which dishes are vegan.

  • Recommending movies (I am a little wary of some kind of meme poisoning, but I don’t watch movies very often, so seems ok).

  That said, I do agree that early adopters seem like they’re overeager and maybe even harming themselves in some way.

• Vanessa Kosoy 27 Jun 2025 6:02 UTC

  10 points

  0

  in reply to: the gears to ascension’s comment on: New Paper: Am­bigu­ous On­line Learning

  I did link the relevant section of my agenda post:

  This work is my first rigorous foray into compositional learning theory.

  A brief and simplified summary:

  • In order to have powerful learning algorithms with safety guarantees, we first need learning algorithms with powerful generalization guarantees that we know how to rigorously formulate (otherwise how do you know the algorithm will correctly infer the intended goal/​behavior from the training data?).

  • Additionally, in order to formally specify “aligned to human values”, we need to formally specify “human values”, and it seems likely that the specification of “X’s values” should be something akin to “the utility function w.r.t. which X has [specific type of powerful performance guarantees]”. These powerful performance guarantees are probably a form/​extension of powerful generalization guarantees.

  • Both reasons require us to understand the kind of natural powerful generalization guarantees that efficient learning algorithms can satisfy. Moreover, such understanding would likely be applicable to deep learning as well, as it seems likely deep learning algorithms satisfy such guarantees, but we currently don’t know how to formulate them.

  • I conjecture that a key missing ingredient in deriving efficient learning algorithms with powerful guarantees (more powerful than anything we already understand in computational learning theory), is understanding the role of compositionality in learning. This is because compositionality is a ubiquitous feature of our thinking about the world and, intuitively, particular forms of compositionality are strong candidates for properties that are both very general and strong enough to enable efficient learning. This line of thinking led me to some success in the context of control theory, which is a necessary ingredient of the kind of guarantees we will ultimately need.

  • I identified sequence prediction /​ online learning in the deterministic realizable case as a relatively easy (but already highly non-trivial) starting point for investigating compositional learning.

  • For the reasons stated in the OP, this led me to ambiguous online learning.

  I’m open to chatting on Discord.

• Vanessa Kosoy 11 Jun 2025 6:29 UTC

  12 points

  3

  in reply to: johnswentworth’s comment on: john­swent­worth’s Shortform

  I never did quite that thing successfully. I did have one time when I dropped progressively unsubtle hints on a guy, who remained stubbornly oblivious for a long time until he finally got the message and reciprocated.

• Vanessa Kosoy 19 May 2025 12:52 UTC

  LW: 5 AF: 3

  0

  AF

  in reply to: Vanessa Kosoy’s comment on: Model­ing ver­sus Implementation

  Btw, what are some ways we can incorporate heuristics into our algorithm while staying on level 1-2?

  1. We don’t know how the prove to required desiderata about the heuristic, but we can still reasonably conjecture them and support the conjectures with empirical tests.

  2. We can’t prove or even conjecture anything useful-in-itself about the heuristic, but the way the heuristic is incorporated into the overall algorithm makes it safe. For example, maybe the heuristic produces suggestions together with formal certificates of their validity. More generally, we can imagine an oracle-machine (where the heuristic is slotted into the oracle) about which we cannot necessarily prove something like a regret bound w.r.t. the optimal policy, but we can prove (or at least conjecture) a regret bound w.r.t. some fixed simple reference policy. That is, the safety guarantee shows that no matter what the oracle does, the overall system is not worse than “doing nothing”. Maybe, modulo weak provable assumptions about the oracle, e.g. that it satisfies a particular computational complexity bound.

  3. [Epistemic status: very fresh idea, quite speculative but intriguing.] We can’t find even a guarantee like a above for a worst-case computationally bounded oracle. However, we can prove (or at least conjecture) some kind of an “average-case” guarantee. For example, maybe we have high probability of safety for a random oracle. However, assuming a uniformly random oracle is quite weak. More optimistically, maybe we can prove safety even for any oracle that is pseudorandom against some complexity class $C_1$ (where we want $C_1$ to be as small as possible). Even better, maybe we can prove safety for any oracle in some complexity class $C_2$ (where we want $C_2$ to be as large as possible) that has access to another oracle which is pseudorandom against $C_1$. If our heuristic is not actually in this category (in particular, $C_2$ is smaller than $P$ and our heuristic doesn’t lie in $C_2$), this doesn’t formally guarantee anything, but it does provide some evidence for the “robustness” of our high-level scheme.

• Vanessa Kosoy 19 May 2025 11:42 UTC

  15 points

  0

  in reply to: Cole Wyeth’s comment on: Model­ing ver­sus Implementation

  Hold my beer ;)

• Vanessa Kosoy 19 May 2025 7:19 UTC

  LW: 21 AF: 7

  7

  AF

  on: Model­ing ver­sus Implementation

  I see modeling vs. implementation as a spectrum more than a dichotomy. Something like:

  1. On the “implementation” extreme you prove theorems about the exact algorithm you implement in your AI, s.t. you can even use formal verification to prove these theorems about the actual code you wrote.

  2. Marginally closer to “modeling”, you prove (or at least conjecture) theorems about some algorithm which is vaguely feasible in theory. Some civilization might have used that exact algorithm to build AI, but in our world it’s impractical, e.g. because it’s uncompetitive with other AI designs. However, your actual code is conceptually very close to the idealized algorithm, and you have good arguments why the differences don’t invalidate the safety properties of the idealized model.

  3. Further along the spectrum, your actual algorithm is about as similar to the idealized algorithm as DQN is similar to vanilla Q-learning. Which is to say, it was “inspired” by the idealized algorithm but there’s a lot of heavy lifting done by heuristics. Nevertheless, there is some reason to hope the heuristic aspects don’t change the safety properties.

  4. On the “modeling” extreme, your idealized model is something like AIXI: completely infeasible and bears little direct resemblance to the actual algorithm in your AI. However, there is still some reason to believe real AIs will have similar properties to the idealized model.

  More precisely, rather than a 1-dimensional spectrum, there are at least two parameters involved:

  • How close is the object you make formal statements about to the actual code of your AI, where “closeness” is measured by the strength of the arguments you have for the analogy, on a scale from “they are literally the same” to solid theoretical and/​or empirical evidence to pure hand-waving/​intuition

  • How much evidence you have for the formal statements, on a scale from “I proved it within some widely accepted mathematical foundation (e.g. PA)” to “I proved vaguely related things, tried very hard but failed to disprove the thing and/​or accumulated some empirical evidence”.

  [EDIT: And a 3rd parameter is, how justified/​testable the assumptions of your model is. Ideally, you want these assumptions to be grounded in science. Some will likely be philosophical assumptions which cannot be tested empirically, but at least they should fit into a coherent holistic philosophical view. At the very least, you want to make sure you’re not assuming away the core parts of the problem.]

  For the purposes of safety, you want to be as close to the implementation end of the spectrum as you can get. However, the model side of the spectrum is still useful as:

  • A backup plan which is better than nothing, more so if there is some combination of theoretical and empirical justification for the analogizing

  • A way to demonstrate threat models, as the OP suggests

  • An intermediate product that helps checking that your theory is heading in the right direction, comparing different research agendas, and maybe even making empirical tests.

• Vanessa Kosoy 15 May 2025 3:03 UTC

  LW: 5 AF: 2

  0

  AF

  in reply to: James Payor’s comment on: Work­ing through a small tiling result

  Sorry, I was wrong. By Lob’s theorem, all versions of ${\text{good}}_{\text{new}}$ are provably equivalent, so they will trust each other.

• Vanessa Kosoy 14 May 2025 18:33 UTC

  LW: 3 AF: 1

  1

  AF

  on: Work­ing through a small tiling result

  IIUC, fixed point equations like that typically have infinitely many solution. So, you defined not one ${\text{good}}_{\text{new}}$ predicate, but an infinite family of them. Therefore, your agent will trust a copy of itself, but usually won’t trust variants of itself with other choices of fixed point. In this sense, this proposal is similar to proposals based on quining (as quining has many fixed points as well).

• Vanessa Kosoy 30 Apr 2025 8:16 UTC

  6 points

  2

  on: Dat­ing Roundup #4: An App for That

  I don’t know what’s so bad about the “human male” bio. I might have swiped right on that one. (Especially if the profile had additional info that makes him sound interesting.)

• Vanessa Kosoy 27 Apr 2025 8:06 UTC

  LW: 13 AF: 8

  0

  AF

  in reply to: Vanessa Kosoy’s comment on: Vanessa Kosoy’s Shortform

  TLDR: A new proposal for a prescriptive theory of multi-agent rationality, based on generalizing superrationality using local symmetries.

  Hofstadter’s “superrationality” postulates that a rational agent should behave as if other rational agents are similar to itself. In a symmetric game, this implies the selection of a Pareto efficient outcome. However, it’s not clear how to apply this principle to asymmetric games. I propose to solve this by suggesting a notion of “local” symmetry that is much more ubiquitous than global (ordinary) symmetry.

  We will focus here entirely on finite deterministic (pure strategy) games. Generalizing this to mixed strategies and infinite games more generally is an important question left for the future.

  Quotient Games

  Consider a game $\Gamma$ with a set $P$ of players, for each player $i\in P$ a non-empty set $S_i$ or pure strategies and a utility function $u_i:S_{\ast }\to R$, where $S_{\ast }:={\prod }_{j\in P}{S}_j$. What does it mean for the game to be “symmetric”? We propose a “soft” notion of symmetry that is only sensitive to the ordinal ordering between payoffs. While the cardinal value of the payoff will be important in the stochastic context, we plan to treat the latter by explicitly expanding the strategy space.

  Given two games ${\Gamma }^{\left(1\right)}$, ${\Gamma }^{\left(2\right)}$, a premorphism from ${\Gamma }^{\left(1\right)}$ to ${\Gamma }^{\left(2\right)}$ is defined to consist of

  • A mapping $q:P^{\left(1\right)}\to P^{\left(2\right)}$

  • For each $i\in P^{\left(1\right)}$, a mapping ${\overline{q}}_i:S_{q\left(i\right)}^{\left(2\right)}\to S_i^{\left(1\right)}$

  Notice that these mappings induce a mapping $\hat{q}:S_{\ast }^{\left(2\right)}\to S_{\ast }^{\left(1\right)}$ via

  $$\hat{q}(x{)}_i:={\overline{q}}_i\left(x_{q\left(i\right)}\right)$$

  A premorphism is said to be a homomorpism if $q$ and $\overline{q}$ are s.t. that for any $i\in P^{\left(1\right)}$ and $x,y\in S_{\ast }^{\left(2\right)}$, if $u_{q\left(i\right)}\left(x\right)\le u_{q\left(i\right)}\left(y\right)$ then $u_i\left(\hat{q}\right(x\left)\right)\le u_i\left(\hat{q}\right(y\left)\right)$. Homomorphisms makes games into a category in a natural way.

  An automorphism of $\Gamma$ is a homomorphism from $\Gamma$ to $\Gamma$ that has an inverse homomorphism. An automorphism $q$ of $\Gamma$ is said to be flat when for any $i\in P$, if $q\left(i\right)=i$ then ${\overline{q}}_i$ is the identity mapping^[1]. A flat symmetry of $\Gamma$ is a group $G$ together with a group homomorphism $\phi :G\to Aut\left(\Gamma \right)$ s.t.$\phi \left(g\right)$ is flat for all $g\in G$.

  Given a flat symmetry $(G,\phi )$, we can apply the superrationality principle to reduce $\Gamma$ to the “smaller” quotient game $\Gamma /G$. The players are $P/G$ i.e. orbits in the original player set. Given $\alpha \in P/G$, we define the set of strategies for player $\alpha$ in the game $\Gamma /G$ to be

  $$(S/G{)}_{\alpha }:=\{\sigma \in \prod _{i\in \alpha }{S}_{\alpha }|\forall i\in \alpha ,g\in G:\sigma \left(i\right)={\overline{\phi \left(g\right)}}_i\sigma \left(\phi \right(g\left)i\right)\}$$

  This is non-empty thanks to flatness.

  Observe that there is a natural mapping $\gamma :(S/G{)}_{\ast }\to S_{\ast }$ given by

  $$\gamma (x{)}_i:=x_{Gi}(i)$$

  Finally, the utility function of $\alpha$ is defined by

  $$u_{\alpha }\left(x\right):=\frac{1}{\left|\alpha \right|}\sum _{i\in \alpha }{u}_i\left(\gamma \right(x\left)\right)$$

  It is easy to see that the construction of $\Gamma /G$ is functorial w.r.t. the category structure on games that we defined.

  We can generalize this further by replacing game homomorphisms with game quasimorphisms: premorphisms satisfying the following relaxed condition on $q$ and $\overline{q}$:

  • For any $i\in P^{\left(2\right)}$ and $x,y\in S_{\ast }^{\left(1\right)}$, if $u_{q\left(i\right)}\left(x\right)<u_{q\left(i\right)}\left(y\right)$ then $u_i\left(\hat{q}\right(x\left)\right)\le u_i\left(\hat{q}\right(y\left)\right)$.

  This is no longer closed under composition, so no longer defines a category structure^[2]. However, we can still define flat quasisymmetries and the associated quotient games, and this construction is still functorial w.r.t. the original (not “quasi”) category structure. Moreover, there is a canonical homomorphism (not just a quasimorphism) from $\Gamma$ to $\Gamma /G$, even when $G$ is a mere quasisymmetry.

  The significance of quasisymmetries can be understood as follows.

  The set of all games on a fixed set of players $P$ with fixed strategy sets $\{S_i{\}}_{i\in P}$ naturally forms a topological space^[3] $X$. Given a group $G$ acting on the game via invertible premorphisms, the subset of $X$ where $G$ is a symmetry is not closed, in general. However, the subset of $X$ where $G$ is a quasisymmetry is closed. I believe this will be important when generalizing the formalism to infinite games.

  Coarse Graining

  What if a game doesn’t have even quasisymmetries? Then, we can look for a coarse graining of the game which does have them.

  Consider a game $\Gamma$. For each $i\in P$, let $S_i^{\prime }$ be some set and $f_i:S_i\to S_i^{\prime }$ a surjection. Denote $S_{\ast }^{\prime }:={\prod }_{i\in P}{S}_i^{\prime }$. Given any $x\in S_{\ast }^{\prime }$, we have the game ${\Gamma }^x$ in which:

  • The set of players is $P$.

  • For any $i\in P$, their set of strategies is $S_i^x:=f_i^{-1}\left(x_i\right)$. In particular, it implies that the set of outcomes $S_{\ast }^x$ satisfies $S_{\ast }^x\subseteq S_{\ast }$.

  • For any $i\in P$, their utility function is $u_i^x:=u_i{|}_{S_{\ast }^x}$.

  If for any $x\in S_{\ast }^{\prime }$ we choose some ${\alpha }_x\in S_{\ast }^x$ this allows us to define the coarse-grained game ${\Gamma }^{\left(\alpha \right)}$ in which:

  • The set of players is $P$.

  • For any $i\in P$, the set of strategies is $S_i^{\prime }$.

  • For any $i\in P$ and $x\in S_{\ast }^{\prime }$, the utility function is $u_i^{\left(\alpha \right)}\left(x\right):=u_i\left({\alpha }_x\right)$.

  Essentially, we rephrased $\Gamma$ as an extensive form game in which first the game ${\Gamma }^{\left(\alpha \right)}$ is played and then, if the outcome was $x$, the game ${\Gamma }^x$ is played. This, assuming the expected outcome of game ${\Gamma }^x$ is ${\alpha }_x$.

  It is also possible to generalize this construction by allowing multivalued $f$.

  Local Symmetry Solutions

  Given a game $\Gamma$, a local symmetry presolution (LSP) is recursively defined to be one of the following:

  • Assume there is $i\in P$ s.t. for any $j\in P\setminus i$, $|S_j|=1$. Then, any $x^{\ast }\in \arg {max}_{x\in S_{\ast }}{u}_i\left(x\right)$ is an LSP of $\Gamma$. [Effectively single player game]

  • Let $\{f_i:S_i\to S_i^{\prime }{\}}_{i\in P}$ be a coarse-graining of $\Gamma$ and for any $x\in S_{\ast }^{\prime }$, let ${\alpha }_x$ be an LSP of ${\Gamma }^x$. Let $\beta$ be an LSP of ${\Gamma }^{\left(\alpha \right)}$. Define $x^{\ast }\in S_{\ast }$ by $x_i^{\ast }:={\alpha }_{{\beta }_i}$. Then, $x^{\ast }$ is an LSP of $\Gamma$.

  • Let $G$ be a quasisymmetry of $\Gamma$ and let $y^{\ast }$ be an LSP of $\Gamma /G$. Then, $\gamma \left(y^{\ast }\right)$ is an LSP of $\Gamma$.

  It’s easy to see that an LSP always exists, because for any game we can choose a sequence of coarse-grainings whose effect is making the players choose their strategies one by one (with full knowledge of choices by previous players). However, not all LSP are “born equal”. It seems appealing to prefer LSP which use “more” symmetry. This can be formalized as follows.

  The way an LSP is constructed defines the set of coherent outcomes $A\subseteq S_{\ast }$, which is the set of outcomes compatible with the local symmetries^[4]. We define it recursively as follows:

  • For the LSP of an effectively single-player game, we set $A:=S_{\ast }$.

  • For a coarse-graining LSP, for any $x\in S_{\ast }^{\prime }$, let $A_x$ be the set of coherent outcomes of ${\Gamma }_x$ and let $B$ be the set of coherent outcomes of ${\Gamma }^{\left(\alpha \right)}$. We then define $A:=\{x\in S_{\ast }|f(x)\in B\text{and}x\in A_{f\left(x\right)}\}$. Here, $f:S_{\ast }\to S_{\ast }^{\prime }$ is defined by $f(x{)}_i:=f_i(x_i)$.

  • For a quasisymmetry LSP, let $B$ be the set of coherent outcomes of $\Gamma /G$. We then define $A:=\gamma \left(B\right)$.

  We define a local symmetry solution (LSS) to be an LSP whose set of coherent outcomes is minimal w.r.t. set inclusion.

  Examples

  • In the Prisoner’s Dilemma, the only LSS is Cooperate-Cooperate.

  • In Stag Hunt, the only LSS is Stag-Stag.

  • In Chicken, the only LSS is Swerve-Swerve. If we add a finite number of randomized strategies, it opens more possibilities.

  • In Battle of the Sexes, assuming a small payoff from going to your preferred place alone, the only LSS is: each player goes to their own preferred place. If we give each player a “coinflip” strategy, then I think the only LSS is both players using the coinflip.

  • Consider a pure cooperation game where both players have strategies $\{a,b\}$ and the payoff is 1 if they choose the same strategy and 0 if they choose different strategies. Then, any outcome is an LSS. I think that, if we add a “fair coinflip” strategy, any LSS has payoff at least $\frac{1}{4}$. I believe the LSS payoff will approach optimum if we (i) allow randomization (ii) make the game repeated with time long horizon and (iii) add some constraints on the symmetries, but I’ll leave this for a future post.

  • Consider a pure cooperation game where both players have strategies $\{a,b\}$, the payoff is 2 if both choose $a$, 1 if both choose $b$ and 0 if they choose different strategies. Then, the only LSS is $aa$.

  • In any game, an LSS guarantees each player at least their deterministic-maximin payoff. In particular, in a two-player zero-sum game in which a pure Nash equilibrium exists, any LSS is a Nash equilibrium.

  1. ^

     Note that flatness is in general not preserved under composition.

  2. ^

     I believe this can be interpreted as a category enriched over the category of sets and partial functions, with the monoidal structure given by Cartesian product of sets.

  3. ^

     An affine space, even.

  4. ^

     Maybe we can interpret the set of coherent outcomes as a sharp infradistirbution corresponding to the players’ joint belief about the outcome of the game.

• Vanessa Kosoy 27 Apr 2025 8:06 UTC

  LW: 2 AF: 2

  0

  AF

  on: Vanessa Kosoy’s Shortform

  Master post for multi-agency. See also.

• Vanessa Kosoy 15 Apr 2025 19:10 UTC

  LW: 11 AF: 5

  0

  AF

  in reply to: Richard_Ngo’s comment on: ricraz’s Shortform

  In (non-monotonic) infra-Bayesian physicalism, there is a vaguely similar asymmetry even though it’s formalized via a loss function. Roughly speaking, the loss function expresses preferences over “which computations are running”. This means that you can have a “positive” preference for a particular computation to run or a “negative” preference for a particular computation not to run^[1].

  1. ^

     There are also more complicated possibilities, such as “if P runs then I want Q to run but if P doesn’t run then I rather that Q also doesn’t run” or even preferences that are only expressible in terms of entanglement between computations.

• Vanessa Kosoy 13 Apr 2025 17:41 UTC

  4 points

  0

  in reply to: faul_sname’s comment on: New Paper: In­fra-Bayesian De­ci­sion-Es­ti­ma­tion Theory

  It’s roughly on the right track, but here are some inaccuracies in your description that stood out to me:

  • There is no requirement that the “hidden state space” is finite. It is perfectly fine to consider a credal set which is not a polytope (i.e. not a convex hull of a finite set of distributions).

  • The point of how market prices are computed, missing from your description, is that they prevent any bettor from making unbounded earnings (essentially, by making them bet against each other). This is the same principle as Garrabrant induction. In particular, this implies that if any of our models is true then the market predictions will converge to lying inside the corresponding credal set.

  • The market predictions do not somehow assume that “the parts of the universe we don’t observe are out to get us”. Thanks to the pessimistic better, they do satisfy the “not too optimistic condition”, but that’s “not too optimistic” relatively to the true environment.

  • Your entire description only talks about the “estimation” part, not about the “decision” part.

• Vanessa Kosoy 12 Apr 2025 15:03 UTC

  LW: 4 AF: 3

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  in reply to: Lorxus’s comment on: Some Rules for an Alge­bra of Bayes Nets

  I think that there are two different questions which might be getting mixed up here:

  Question 1: Can we fully classify all rules for which sets of Bayes nets imply other Bayes nets over the same variables? Naturally, this is not a fully rigorous question, since “classify” is not a well-defined notion. One possible operationalization of this question is: What is the computational complexity of determining whether a given Bayes net follows from a set of other Bayes nets? For example, if there is a set of basic moves that generate all such inferences then the problem is probably in NP (at least if the number of required moved can also be bounded).

  Question 2: What if we replace “Bayes nets” by something like “string diagrams in a Markov category”? Then there might be less rules (because maybe some rules hold for Bayes nets but not in the more abstract setting).

• Vanessa Kosoy 10 Apr 2025 9:33 UTC

  4 points

  0

  in reply to: Alexander Gietelink Oldenziel’s comment on: New Paper: In­fra-Bayesian De­ci­sion-Es­ti­ma­tion Theory

  Thank you <3

  Any chance of more exposition for those of us less cognitively-inclined? =)

  Read the paper! :)

  It might seem long at first glance, but all the results are explained in the first 13 pages, the rest is just proofs. If you don’t care about the examples, you can stop on page 11. Naturally, I welcome any feedback on the exposition there.

• Vanessa Kosoy 9 Apr 2025 6:40 UTC

  LW: 6 AF: 4

  −3

  AF

  in reply to: Cole Wyeth’s comment on: abramdem­ski’s Shortform

  I think that in 2 years we’re unlikely to accomplish anything that leaves a dent in P(DOOM), with any method, but I also think it’s more likely than not that we actually have >15 years.

  As to “completing” the theory of agents, I used the phrase (perhaps perversely) in the same sense that e.g. we “completed” the theory of information: the latter exists and can actually be used for its intended applications (communication systems). Or at least in the sense we “completed” the theory of computational complexity: even though a lot of key conjectures are still unproven, we do have a rigorous understanding of what computational complexity is and know how to determine it for many (even if far from all) problems of interest.

  I probably should have said “create” rather than “complete”.

• Vanessa Kosoy 8 Apr 2025 17:49 UTC

  LW: 12 AF: 3

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  in reply to: Cole Wyeth’s comment on: abramdem­ski’s Shortform

  (Summoned by @Alexander Gietelink Oldenziel)

  I don’t understand this comment. I usually don’t think of “building a safer LLM agent” as a viable route to aligned AI. My current best guess about how to create aligned AI is Physicalist Superimitation. We can imagine other approaches, e.g. Quantilized Debate, but I am less optimistic there. More importantly, I believe that we need to complete the theory of agents first, before we can have strong confidence about which approaches are more promising.

  As to heuristic implementations of infra-Bayesianism, this is something I don’t want to speculate about in public, it seems exfohazardous.

• Vanessa Kosoy 8 Apr 2025 15:16 UTC

  LW: 2 AF: 2

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  in reply to: Lorxus’s comment on: Some Rules for an Alge­bra of Bayes Nets

  Somehow being able to derive all relevant string diagram rewriting rules for latential string diagrams, starting with some fixed set of equivalences?

  What are “latential” string diagrams?

  What does it it mean that you can’t derive them all from a “fixed” set? Do you imagine some strong claim e.g. that the set of rewriting rules being undecidable, or something else?

  Two Bayes nets are of the same Markov equivalence class when they have precisely the same set of conditionality relations holding on them (and by extension, precisely the same undirected skeleton).

  Okay, so this is not what you care about? Maybe you are saying the following: Given two diagrams X,Y, we want to ask whether any distribution compatible with X is compatible with Y. We don’t ask whether the converse also holds. This is a certain asymmetric relation, rather than an equivalence.

• Vanessa Kosoy 8 Apr 2025 11:45 UTC

  LW: 2 AF: 2

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  in reply to: Lorxus’s comment on: Some Rules for an Alge­bra of Bayes Nets

  I found the above comment difficult to parse.

  that’s not a thing that really happens?

  What is the thing that doesn’t happen? Reading the rest of the paragraph only left me more confused.

  we don’t quite care about Markov equivalence class

  What do you mean by “Markov equivalence class”?