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40 minute read
Throughout my studies in alignment and AI-related existential risks, I’ve found it helpful to build a mental map of the field and how its various questions and considerations interrelate, so that when I read a new paper, a post on the Alignment Forum, or similar material, I have some idea of how it might contribute to the overall goal of making our deployment of AI technology go as well as possible for humanity. I’m writing this post to communicate what I’ve learned through this process, in order to help others trying to build their own mental maps and provide them with links to relevant resources for further, more detailed information. This post was largely inspired by (and would not be possible without) two talks by Paul Christiano and Rohin Shah, respectively, that give very similar overviews of the field,1 as well as a few posts on the Alignment Forum that will be discussed below. This post is not intended to replace these talks but is instead an attempt to coherently integrate their ideas with ideas from other sources attempting to clarify various aspects of the field. You should nonetheless watch these presentations and read some of the resources provided below if you’re trying to build your mental map as completely as possible.
4 minute read
I’ll be spending the next month getting some hands-on experience with deep reinforcement learning via OpenAI’s Spinning Up in Deep RL, which includes both an overview of key concepts in deep reinforcement learning and a well-documented repository of implementations of key algorithms that are designed to be 1) “as simple as possible while still being reasonably good,” and 2) “highly-consistent with each other to expose fundamental similarities between algorithms.” I’ll be posting here about this endeavor in order to document the process and share the lessons I learn along the way for those who are also looking to “spin up” in deep RL.
25 minute read
Recently, I listened to a podcast1 from the Future of Life Institute in which Andrew Critch (from the Center for Human Compatible AI at Berkeley) discussed his and David Krueger’s recent paper, “AI Research Considerations for Human Existential Safety (ARCHES)”2. Throughout the episode, I found myself impressed by the clarity and the strength of many of the points Critch made. In particular, I’m thinking about how Critch distinguishes “existential safety” from “safety” more generally, “delegation” from “alignment,” and “prepotent AI” from “generally intelligent AI” or “superintelligent AI” as concepts that can help give us more traction in analyzing the potential existential risks posed by artificial intelligences. So, I decided it would be worthwhile to write this post on one of my key takeaways from the episode: the community working on AI-related existential risks needs to adopt better, more precise terminology.
Future of Life Institute, Andrew Critch on AI Research Considerations for Human Existential Safety ↩
Andrew Critch and David Krueger, AI Research Considerations for Human Existential Safety (ARCHES) ↩
18 minute read
In my last post, I showed how adding ELMo features to a seq2seq model improved performance on semantic parsing tasks. Recently, I have been experimenting with adding OpenAI GPT and BERT to the model in order to compare their performance against ELMo’s. All the data, configuration files, and scripts needed to reproduce my experiments have been pushed to the GitHub repository. I’m excited to share my results!