The Nonlinear Library LW - Bayesian updating in real life is mostly about understanding your hypotheses by Max H
Jan 1, 2024
18:29
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Bayesian updating in real life is mostly about understanding your hypotheses, published by Max H on January 1, 2024 on LessWrong.
My sense is that an increasingly common viewpoint around here is that the last ~20 years of AI development and AI x-risk discourse are well-described by the following narrative:
Eliezer Yudkowsky (and various others who were at least initially heavily influenced by his ideas) developed detailed models of key issues likely to be inherent in the process of developing smarter-than-human AI.
These models were somewhere between "maybe plausible" and "quite compelling" at the time that they were put forth, but recent developments in AI (e.g. behavioral characteristics of language models, smoothness / gradualness of scaling) have shown that reality just isn't panning out in quite the way Eliezer's models predicted.
These developments haven't entirely falsified Eliezer's models and key predictions, but there are now plenty of alternative models and theories. Some or all of these competing models either are or claim to:
have a better recent track record of predicting near-term AI developments
better retrodict past developments[1]
be backed by empirical results in machine learning and / or neuroscience
feel more intuitively plausible and evidence-backed to people with different backgrounds and areas of expertise
Therefore, even if we can't entirely discount Eliezer's models, there's clearly a directional Bayesian update which any good Bayesian (including Eliezer himself) should be able to make by observing recent developments and considering alternate theories which they support. Even if the precise degree of the overall update (and the final landing place of the posterior) remains highly uncertain and debatable, the basic direction is clear.
Without getting into the object-level too much, or even whether the narrative as a whole reflects the actual views of particular real people, I want to make some remarks on the concept of belief updating as typically used in narratives like this.
Note, there's a sense in which any (valid) change in one's beliefs can be modeled as a Bayesian update of some kind, but here I am specifically referring to the popular rationalist practice of thinking and communicating explicitly in terms of the language of probabilities and likelihood ratios.
There are some questionable assumptions embedded in (what I suspect are) common views of (a) how the updating process is supposed to work in general and (b) how to apply the process validly to the particular case of updating one's models of AI development and x-risk.
When such views are expressed implicitly in the context of a sentiment that "updating" is broadly virtuous / desirable / correct, I find that there tends to be a lot of gloss over important caveats and prerequisites that keep the underlying mental motion tethered to reality - that is, ensure it remains a systematic (if rough and approximate) method for valid reasoning under uncertainty.
The rest of this post is a review of some of the key concepts and requirements for Bayesian updating to work as intended, with some examples and non-examples of how these requirements can fail to be met in practice.
My conclusion is not that the practice of explicit Bayesian updating is inherently flawed, but that it must be applied with attention to the preconditions and assumptions firmly in mind at all times. Local validity at each step must be tracked strictly and adhered to closely enough to ensure that the process as a whole actually holds together as a method for systematically minimizing expected predictive error.
Further, I think that most of the utility of explicit reasoning and communication in Bayesian terms derives not from the end result (whether that end result is a precise numerical posterior probability or just a rou...
My sense is that an increasingly common viewpoint around here is that the last ~20 years of AI development and AI x-risk discourse are well-described by the following narrative:
Eliezer Yudkowsky (and various others who were at least initially heavily influenced by his ideas) developed detailed models of key issues likely to be inherent in the process of developing smarter-than-human AI.
These models were somewhere between "maybe plausible" and "quite compelling" at the time that they were put forth, but recent developments in AI (e.g. behavioral characteristics of language models, smoothness / gradualness of scaling) have shown that reality just isn't panning out in quite the way Eliezer's models predicted.
These developments haven't entirely falsified Eliezer's models and key predictions, but there are now plenty of alternative models and theories. Some or all of these competing models either are or claim to:
have a better recent track record of predicting near-term AI developments
better retrodict past developments[1]
be backed by empirical results in machine learning and / or neuroscience
feel more intuitively plausible and evidence-backed to people with different backgrounds and areas of expertise
Therefore, even if we can't entirely discount Eliezer's models, there's clearly a directional Bayesian update which any good Bayesian (including Eliezer himself) should be able to make by observing recent developments and considering alternate theories which they support. Even if the precise degree of the overall update (and the final landing place of the posterior) remains highly uncertain and debatable, the basic direction is clear.
Without getting into the object-level too much, or even whether the narrative as a whole reflects the actual views of particular real people, I want to make some remarks on the concept of belief updating as typically used in narratives like this.
Note, there's a sense in which any (valid) change in one's beliefs can be modeled as a Bayesian update of some kind, but here I am specifically referring to the popular rationalist practice of thinking and communicating explicitly in terms of the language of probabilities and likelihood ratios.
There are some questionable assumptions embedded in (what I suspect are) common views of (a) how the updating process is supposed to work in general and (b) how to apply the process validly to the particular case of updating one's models of AI development and x-risk.
When such views are expressed implicitly in the context of a sentiment that "updating" is broadly virtuous / desirable / correct, I find that there tends to be a lot of gloss over important caveats and prerequisites that keep the underlying mental motion tethered to reality - that is, ensure it remains a systematic (if rough and approximate) method for valid reasoning under uncertainty.
The rest of this post is a review of some of the key concepts and requirements for Bayesian updating to work as intended, with some examples and non-examples of how these requirements can fail to be met in practice.
My conclusion is not that the practice of explicit Bayesian updating is inherently flawed, but that it must be applied with attention to the preconditions and assumptions firmly in mind at all times. Local validity at each step must be tracked strictly and adhered to closely enough to ensure that the process as a whole actually holds together as a method for systematically minimizing expected predictive error.
Further, I think that most of the utility of explicit reasoning and communication in Bayesian terms derives not from the end result (whether that end result is a precise numerical posterior probability or just a rou...