Alex Flint

The ground of optimization

This work was supported by OAK, a monastic community in the Berkeley hills. This document could not have been written without the daily love of living in this beautiful community. The work involved in writing this cannot be separated from the sitting, chanting, cooking, cleaning, crying, correcting, fundraising, listening, laughing, and teaching of the whole community. What is optimization? What is the relationship between a computational optimization process — say, a computer program solving an optimization problem — and a physical optimization process — say, a team of humans building a house? We propose the concept of an optimizing system as a physically closed system containing both that which is being optimized and that which is doing the optimizing, and defined by a tendency to evolve from a broad basin of attraction towards a small set of target configurations despite perturbations to the system. We compare our definition to that proposed by Yudkowsky, and place our work in the context of work by Demski and Garrabrant’s Embedded Agency, and Drexler’s Comprehensive AI Services. We show that our definition resolves difficult cases proposed by Daniel Filan. We work through numerous examples of biological, computational, and simple physical systems showing how our definition relates to each. Introduction In the field of computer science, an optimization algorithm is a computer program that outputs the solution, or an approximation thereof, to an optimization problem. An optimization problem consists of an objective function to be maximized or minimized, and a feasible region within which to search for a solution. For example we might take the objective function (x2−2)2 as a minimization problem and the whole real number line as the feasible region. The solution then would be x=√2 and a working optimization algorithm for this problem is one that outputs a close approximation to this value. In the field of operations re

254Jun 20, 2020

Alex Flint

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Independent AI alignment researcher

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Announcement: Learning Theory Online Course

The application deadline for the course has now passed. We received a very promising number of submissions! Feel free to continue discussion in the comments below. Hey everyone! Gergely and Kōshin here from ALTER and Monastic Academy, respectively. We are excited to announce a 6-week, 4-hours-per-week mathematics course designed to...

Jan 20, 202563

ChatGPT struggles to respond to the real world

I wanted to test whether ChatGPT could respond to situations as they arise in the real world, so I asked it to guide me through making a turmeric latte. It had no problem giving me a nicely formatted recipe but it really struggled to guide me through the process of...

Jan 12, 202331

Coherent extrapolated dreaming

This work was supported by the Monastic Academy for the Preservation of Life on Earth. You can support my work here. I will give a short presentation of this work followed by discussion on Wednesday Dec 28 at 12pm Pacific / 3pm Eastern. RSVP here. Outline * I have four...

Dec 26, 202238

Response to Holden’s alignment plan

This work was supported by the Monastic Academy for the Preservation of Life on Earth. You can support my work directly here. I will give a short presentation of this work followed by discussion on Saturday (Dec 24) at 9am Pacific / 12pm Eastern. RSVP here. Outline * This is...

Dec 22, 202236

Notes on OpenAI’s alignment plan

This work was supported by the Monastic Academy for the Preservation of Life on Earth. OpenAI has published a plan for producing aligned AI systems. Very roughly, their plan is to build language models capable of producing human-level alignment research. The output of these models might be natural language essays...

Dec 8, 202240

Logical induction for software engineers

This work was supported by the Monastic Academy for the Preservation of Life on Earth and the Long Term Future Fund. Outline * This post is an explanation of the theory of logical induction developed by Garrabrant et al * I formulate the theory in a way that should make...

Dec 3, 2022163

Implications of automated ontology identification

Financial status: supported by individual donors and a grant from LTFF. Epistemic status: early-stage technical work. This write-up benefited from conversations with John Wentworth. Outline * This write-up is a response to ARC’s request for feedback on ontology identification, described in the ELK technical report. * We suppose that a...

Feb 18, 202269

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Alex Flint

Alex Flint

Alex Flint

The ground of optimization

Logical induction for software engineers

Agency in Conway’s Game of Life

Our take on CHAI’s research agenda in under 1500 words

Alex Flint

Announcement: Learning Theory Online Course

ChatGPT struggles to respond to the real world

Coherent extrapolated dreaming

Response to Holden’s alignment plan

Notes on OpenAI’s alignment plan

Logical induction for software engineers

Implications of automated ontology identification

The ground of optimization

Logical induction for software engineers

Agency in Conway’s Game of Life

Our take on CHAI’s research agenda in under 1500 words

Announcement: Learning Theory Online Course

ChatGPT struggles to respond to the real world

Coherent extrapolated dreaming

Response to Holden’s alignment plan

Notes on OpenAI’s alignment plan

Logical induction for software engineers

Implications of automated ontology identification