New paper: “Forecasting using incomplete models”

 |   |  Papers

Forecasting Using Incomplete ModelsMIRI Research Associate Vadim Kosoy has a paper out on issues in naturalized induction: “Forecasting using incomplete models”. Abstract:

We consider the task of forecasting an infinite sequence of future observations based on some number of past observations, where the probability measure generating the observations is “suspected” to satisfy one or more of a set of incomplete models, i.e., convex sets in the space of probability measures.

This setting is in some sense intermediate between the realizable setting where the probability measure comes from some known set of probability measures (which can be addressed using e.g. Bayesian inference) and the unrealizable setting where the probability measure is completely arbitrary.

We demonstrate a method of forecasting which guarantees that, whenever the true probability measure satisfies an incomplete model in a given countable set, the forecast converges to the same incomplete model in the (appropriately normalized) Kantorovich-Rubinstein metric. This is analogous to merging of opinions for Bayesian inference, except that convergence in the Kantorovich-Rubinstein metric is weaker than convergence in total variation.

Kosoy’s work builds on logical inductors to create a cleaner (purely learning-theoretic) formalism for modeling complex environments, showing that the methods developed in “Logical induction” are useful for applications in classical sequence prediction unrelated to logic.

“Forecasting using incomplete models” also shows that the intuitive concept of an “incomplete” or “partial” model has an elegant and useful formalization related to Knightian uncertainty. Additionally, Kosoy shows that using incomplete models to generalize Bayesian inference allows an agent to make predictions about environments that can be as complex as the agent itself, or more complex — as contrasted with classical Bayesian inference.

For more of Kosoy’s research, see “Optimal polynomial-time estimators” and the Intelligent Agent Foundations Forum.
 

Sign up to get updates on new MIRI technical results

Get notified every time a new technical paper is published.

 

June 2018 Newsletter

 |   |  Newsletters

May 2018 Newsletter

 |   |  Newsletters

Challenges to Christiano’s capability amplification proposal

 |   |  Analysis

The following is a basically unedited summary I wrote up on March 16 of my take on Paul Christiano’s AGI alignment approach (described in “ALBA” and “Iterated Distillation and Amplification”). Where Paul had comments and replies, I’ve included them below.


I see a lot of free variables with respect to what exactly Paul might have in mind. I've sometimes tried presenting Paul with my objections and then he replies in a way that locally answers some of my question but I think would make other difficulties worse. My global objection is thus something like, "I don't see any concrete setup and consistent simultaneous setting of the variables where this whole scheme works." These difficulties are not minor or technical; they appear to me quite severe. I try to walk through the details below.

It should be understood at all times that I do not claim to be able to pass Paul’s ITT for Paul’s view and that this is me criticizing my own, potentially straw misunderstanding of what I imagine Paul might be advocating.

Read more »

April 2018 Newsletter

 |   |  Newsletters

2018 research plans and predictions

 |   |  MIRI Strategy

Scott Garrabrant is taking over Nate Soares’ job of making predictions about how much progress we’ll make in different research areas this year. Scott divides MIRI’s alignment research into five categories:


naturalized world-models — Problems related to modeling large, complex physical environments that lack a sharp agent/environment boundary. Central examples of problems in this category include logical uncertainty, naturalized induction, multi-level world models, and ontological crises.

Introductory resources: “Formalizing Two Problems of Realistic World-Models,” “Questions of Reasoning Under Logical Uncertainty,” “Logical Induction,” “Reflective Oracles

Examples of recent work: “Hyperreal Brouwer,” “An Untrollable Mathematician,” “Further Progress on a Bayesian Version of Logical Uncertainty


decision theory — Problems related to modeling the consequences of different (actual and counterfactual) decision outputs, so that the decision-maker can choose the output with the best consequences. Central problems include counterfactuals, updatelessness, coordination, extortion, and reflective stability.

Introductory resources: “Cheating Death in Damascus,” “Decisions Are For Making Bad Outcomes Inconsistent,” Functional Decision Theory

Examples of recent work: Cooperative Oracles,” “Smoking Lesion Steelman” (1, 2), “The Happy Dance Problem,” “Reflective Oracles as a Solution to the Converse Lawvere Problem


robust delegation — Problems related to building highly capable agents that can be trusted to carry out some task on one’s behalf. Central problems include corrigibility, value learning, informed oversight, and Vingean reflection.

Introductory resources: The Value Learning Problem,” “Corrigibility,” “Problem of Fully Updated Deference,” “Vingean Reflection,” “Using Machine Learning to Address AI Risk

Examples of recent work: “Categorizing Variants of Goodhart’s Law,” “Stable Pointers to Value


subsystem alignment — Problems related to ensuring that an AI system’s subsystems are not working at cross purposes, and in particular that the system avoids creating internal subprocesses that optimize for unintended goals. Central problems include benign induction.

Introductory resources: What Does the Universal Prior Actually Look Like?”, “Optimization Daemons,” “Modeling Distant Superintelligences

Examples of recent work: Some Problems with Making Induction Benign


other — Alignment research that doesn’t fall into the above categories. If we make progress on the open problems described in Alignment for Advanced ML Systems,” and the progress is less connected to our agent foundations work and more ML-oriented, then we’ll likely classify it here.


Read more »

New paper: “Categorizing variants of Goodhart’s Law”

 |   |  Papers

Categorizing Variants of Goodhart's LawGoodhart’s Law states that “any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.” However, this is not a single phenomenon. In Goodhart Taxonomy, I proposed that there are (at least) four different mechanisms through which proxy measures break when you optimize for them: Regressional, Extremal, Causal, and Adversarial.

David Manheim has now helped write up my taxonomy as a paper going into more detail on the these mechanisms: “Categorizing variants of Goodhart’s Law.” From the conclusion:

This paper represents an attempt to categorize a class of simple statistical misalignments that occur both in any algorithmic system used for optimization, and in many human systems that rely on metrics for optimization. The dynamics highlighted are hopefully useful to explain many situations of interest in policy design, in machine learning, and in specific questions about AI alignment.

In policy, these dynamics are commonly encountered but too-rarely discussed clearly. In machine learning, these errors include extremal Goodhart effects due to using limited data and choosing overly parsimonious models, errors that occur due to myopic consideration of goals, and mistakes that occur when ignoring causality in a system. Finally, in AI alignment, these issues are fundamental to both aligning systems towards a goal, and assuring that the system’s metrics do not have perverse effects once the system begins optimizing for them.

Let V refer to the true goal, while U refers to a proxy for that goal which was observed to correlate with V and which is being optimized in some way. Then the four subtypes of Goodhart’s Law are as follows:


Regressional Goodhart — When selecting for a proxy measure, you select not only for the true goal, but also for the difference between the proxy and the goal.

  • Model: When U is equal to V + X, where X is some noise, a point with a large U value will likely have a large V value, but also a large X value. Thus, when U is large, you can expect V to be predictably smaller than U.
  • Example: Height is correlated with basketball ability, and does actually directly help, but the best player is only 6’3″, and a random 7′ person in their 20s would probably not be as good.

Extremal Goodhart — Worlds in which the proxy takes an extreme value may be very different from the ordinary worlds in which the correlation between the proxy and the goal was observed.

  • Model: Patterns tend to break at simple joints. One simple subset of worlds is those worlds in which U is very large. Thus, a strong correlation between U and V observed for naturally occuring U values may not transfer to worlds in which U is very large. Further, since there may be relatively few naturally occuring worlds in which U is very large, extremely large U may coincide with small V values without breaking the statistical correlation.
  • Example: The tallest person on record, Robert Wadlow, was 8’11” (2.72m). He grew to that height because of a pituitary disorder; he would have struggled to play basketball because he “required leg braces to walk and had little feeling in his legs and feet.”

Causal Goodhart — When there is a non-causal correlation between the proxy and the goal, intervening on the proxy may fail to intervene on the goal.

  • Model: If V causes U (or if V and U are both caused by some third thing), then a correlation between V and U may be observed. However, when you intervene to increase U through some mechanism that does not involve V, you will fail to also increase V.
  • Example: Someone who wishes to be taller might observe that height is correlated with basketball skill and decide to start practicing basketball.

Adversarial Goodhart — When you optimize for a proxy, you provide an incentive for adversaries to correlate their goal with your proxy, thus destroying the correlation with your goal.

  • Model: Consider an agent A with some different goal W. Since they depend on common resources, W and V are naturally opposed. If you optimize U as a proxy for V, and A knows this, A is incentivized to make large U values coincide with large W values, thus stopping them from coinciding with large V values.
  • Example: Aspiring NBA players might just lie about their height.

For more on this topic, see Eliezer Yudkowsky’s write-up, Goodhart’s Curse.
 

Sign up to get updates on new MIRI technical results

Get notified every time a new technical paper is published.

 

March 2018 Newsletter

 |   |  Newsletters