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In our last major updates—our 2017 strategic update and fundraiser posts—we said that our current focus is on technical research and executing our biggest-ever hiring push. Our supporters responded with an incredible show of support at the end of the year, putting us in an excellent position to execute on our most ambitious growth plans.

In this post, I’d like to provide some updates on our recruiting efforts and successes, announce some major donations and grants that we’ve received, and provide some other miscellaneous updates.

In brief, our major announcements are:

1. We have two new full-time research staff hires to announce.
2. We’ve received $1.7 million in major donations and grants,$1 million of which came through a tax-advantaged fund for Canadian MIRI supporters.

For more details, see below.

# New paper: “Forecasting using incomplete models”

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MIRI 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.

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# Challenges to Christiano’s capability amplification proposal

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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.