Researching Alignment Research: Unsupervised Analysis
Paper • 2206.02841 • Published
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id string | title string | url string | source string | source_type string | text string | date_published timestamp[us] | authors sequence | summaries sequence | filename string |
|---|---|---|---|---|---|---|---|---|---|
025ec6c77a59b3363162576fa55a4fd7 | Modeling Agents with Probabilistic Programs | https://agentmodels.org/chapters/3-agents-as-programs.html | agentmodels | markdown | ---
layout: chapter
title: "Agents as probabilistic programs"
description: "One-shot decision problems, expected utility, softmax choice and Monty Hall."
is_section: true
---
## Introduction
Our goal is to implement agents that compute rational *policies*. Policies are *plans* for achieving good outcomes in environm... | 2018-06-21T16:25:20 | [
"Owain Evans",
"Andreas Stuhlmüller",
"John Salvatier",
"Daniel Filan"
] | [] | 3-agents-as-programs.md |
ea441710b479ba9d9171a1b2273a2aca | Modeling Agents with Probabilistic Programs | https://agentmodels.org/chapters/5-biases-intro.html | agentmodels | markdown | ---
layout: chapter
title: "Cognitive biases and bounded rationality"
description: Soft-max noise, limited memory, heuristics and biases, motivation from intractability of POMDPs.
is_section: true
---
### Optimality and modeling human actions
We've mentioned two uses for models of sequential decision making:
Use (1... | 2017-03-19T18:46:48 | [
"Owain Evans",
"Andreas Stuhlmüller",
"John Salvatier",
"Daniel Filan"
] | [] | 5-biases-intro.md |
de7fd0b4b054937b7a2d8856f11ee694 | Modeling Agents with Probabilistic Programs | https://agentmodels.org/chapters/7-multi-agent.html | agentmodels | markdown | ---
layout: chapter
title: Multi-agent models
description: Schelling coordination games, tic-tac-toe, and a simple natural-language example.
is_section: true
---
In this chapter, we will look at models that involve multiple agents reasoning about each other.
This chapter is based on reft:stuhlmueller2013reasoning.
##... | 2016-12-04T11:26:34 | [
"Owain Evans",
"Andreas Stuhlmüller",
"John Salvatier",
"Daniel Filan"
] | [] | 7-multi-agent.md |
32ca55d04d57aa1d34e0db0fa171a0c9 | Modeling Agents with Probabilistic Programs | https://agentmodels.org/chapters/5d-joint-inference.html | agentmodels | markdown | ---
layout: chapter
title: Joint inference of biases and preferences I
description: Assuming agent optimality leads to mistakes in inference. Procrastination and Bandit Examples.
---
### Introduction
Techniques for inferring human preferences and beliefs from their behavior on a task usually assume that humans solve... | 2019-08-29T10:20:19 | [
"Owain Evans",
"Andreas Stuhlmüller",
"John Salvatier",
"Daniel Filan"
] | [] | 5d-joint-inference.md |
a894af445e54ec10a745213ccd2d14e3 | Modeling Agents with Probabilistic Programs | https://agentmodels.org/chapters/3b-mdp-gridworld.html | agentmodels | markdown | ---
layout: chapter
title: "MDPs and Gridworld in WebPPL"
description: Noisy actions (softmax), stochastic transitions, policies, Q-values.
---
This chapter explores some key features of MDPs: stochastic dynamics, stochastic policies, and value functions.
### Hiking in Gridworld
We begin by introducing a new gridwor... | 2016-12-13T14:21:09 | [
"Owain Evans",
"Andreas Stuhlmüller",
"John Salvatier",
"Daniel Filan"
] | [] | 3b-mdp-gridworld.md |
255b3dc4b0cda7173da19999092818bb | Modeling Agents with Probabilistic Programs | https://agentmodels.org/chapters/5c-myopic.html | agentmodels | markdown | ---
layout: chapter
title: Bounded Agents-- Myopia for rewards and updates
description: Heuristic POMDP algorithms that assume a short horizon.
---
### Introduction
The previous chapter extended the MDP agent model to include exponential and hyperbolic discounting. The goal was to produce models of human behavior th... | 2017-03-19T18:54:16 | [
"Owain Evans",
"Andreas Stuhlmüller",
"John Salvatier",
"Daniel Filan"
] | [] | 5c-myopic.md |
f9925fa4aa8c50448d99bfdb6889ffa9 | Modeling Agents with Probabilistic Programs | https://agentmodels.org/chapters/3a-mdp.html | agentmodels | markdown | ---
layout: chapter
title: "Sequential decision problems: MDPs"
description: Markov Decision Processes, efficient planning with dynamic programming.
---
## Introduction
The [previous chapter](/chapters/3-agents-as-programs.html) introduced agent models for solving simple, one-shot decision problems. The next few sect... | 2017-06-16T23:10:13 | [
"Owain Evans",
"Andreas Stuhlmüller",
"John Salvatier",
"Daniel Filan"
] | [] | 3a-mdp.md |
44d01d6d6c9feba874f40b861e6cc502 | Modeling Agents with Probabilistic Programs | https://agentmodels.org/chapters/6a-inference-dp.html | agentmodels | markdown | ---
layout: chapter
title: Dynamic programming
description: Exact enumeration of generative model computations + caching.
status: stub
is_section: false
hidden: true
---
| 2016-03-09T21:34:03 | [
"Owain Evans",
"Andreas Stuhlmüller",
"John Salvatier",
"Daniel Filan"
] | [] | 6a-inference-dp.md |
d5e89ed22367f9ddf80e6ecc23bc7b71 | Modeling Agents with Probabilistic Programs | https://agentmodels.org/chapters/3d-reinforcement-learning.html | agentmodels | markdown | ---
layout: chapter
title: "Reinforcement Learning to Learn MDPs"
description: RL for Bandits, Thomson Sampling for learning MDPs.
---
## Introduction
Previous chapters assumed that the agent already knew the structure of the environment. In MDPs, the agent knows everything about the environment and just needs to co... | 2018-06-24T18:01:20 | [
"Owain Evans",
"Andreas Stuhlmüller",
"John Salvatier",
"Daniel Filan"
] | [] | 3d-reinforcement-learning.md |
25ae0e08efcf1ce08fbf7423fd4fdb65 | Modeling Agents with Probabilistic Programs | https://agentmodels.org/chapters/5a-time-inconsistency.html | agentmodels | markdown | ---
layout: chapter
title: "Time inconsistency I"
description: Exponential vs. hyperbolic discounting, Naive vs. Sophisticated planning.
---
### Introduction
Time inconsistency is part of everyday human experience. In the night you wish to rise early; in the morning you prefer to sleep in. There is an inconsistency ... | 2019-08-24T14:52:08 | [
"Owain Evans",
"Andreas Stuhlmüller",
"John Salvatier",
"Daniel Filan"
] | [] | 5a-time-inconsistency.md |
8adf0ba4ce94372feb4380f99a96c790 | Modeling Agents with Probabilistic Programs | https://agentmodels.org/chapters/6c-inference-rl.html | agentmodels | markdown | ---
layout: chapter
title: Reinforcement learning techniques
description: Max-margin and linear programming methods for IRL.
status: stub
is_section: false
hidden: true
---
- Could have appendix discussing Apprenticeship Learning ideas in Abbeel and Ng in more detail.
| 2016-03-09T21:34:01 | [
"Owain Evans",
"Andreas Stuhlmüller",
"John Salvatier",
"Daniel Filan"
] | [] | 6c-inference-rl.md |
End of preview.
AI Alignment Research Dataset
The AI Alignment Research Dataset is a collection of documents related to AI Alignment and Safety from various books, research papers, and alignment related blog posts. This is a work in progress. Components are still undergoing a cleaning process to be updated more regularly.
Sources
Here are the list of sources along with sample contents:
agisf - recommended readings from AGI Safety Fundamentals
aisafety.info - Stampy's FAQ
arxiv - relevant research papers
blogs - entire websites automatically scraped
- AI Impacts
- AI Safety Camp
- carado.moe
- Cold Takes
- DeepMind technical blogs
- DeepMind AI Safety Research
- EleutherAI
- generative.ink
- Gwern Branwen's blog
- Jack Clark's Import AI
- MIRI
- Jacob Steinhardt's blog
- ML Safety Newsletter
- Transformer Circuits Thread
- Open AI Research
- Victoria Krakovna's blog
- Eliezer Yudkowsky's blog
eaforum - selected posts
lesswrong - selected posts
special_docs - individual documents curated from various resources
- Make a suggestion for sources not already in the dataset
youtube - playlists & channels
Keys
All entries contain the following keys:
id- string of unique identifiersource- string of data source listed abovetitle- string of document title of documentauthors- list of stringstext- full text of document contenturl- string of valid link to text contentdate_published- in UTC format
Additional keys may be available depending on the source document.
Usage
Execute the following code to download and parse the files:
from datasets import load_dataset
data = load_dataset('StampyAI/alignment-research-dataset')
To only get the data for a specific source, pass it in as the second argument, e.g.:
from datasets import load_dataset
data = load_dataset('StampyAI/alignment-research-dataset', 'lesswrong')
Limitations and Bias
LessWrong posts have overweighted content on doom and existential risk, so please beware in training or finetuning generative language models on the dataset.
Contributing
The scraper to generate this dataset is open-sourced on GitHub and currently maintained by volunteers at StampyAI / AI Safety Info. Learn more or join us on Discord.
Rebuilding info
This README contains info about the number of rows and their features which should be rebuilt each time datasets get changed. To do so, run:
datasets-cli test ./alignment-research-dataset --save_info --all_configs
Citing the Dataset
For more information, here is the paper and LessWrong post. Please use the following citation when using the dataset:
Kirchner, J. H., Smith, L., Thibodeau, J., McDonnell, K., and Reynolds, L. "Understanding AI alignment research: A Systematic Analysis." arXiv preprint arXiv:2022.4338861 (2022).
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