Aims

In this post I summarize my discoveries from a semester of AI readings and discussions drawn primarily from the 2022 AGI Safety Fundamentals Alignment Curriculum. I am grateful to Professor Anton Korinek for his generous advising in my independent study which lead to this writeup. I am also grateful to my fellow AI reading group discussants at UVA (and especially our discussion leader Ryan Bloom) for their thoughtful contributions to our group which informed my thinking for this post. While this post was primarily written as a means to clarify my own thinking as I learned more about AI Safety, I also hope to use it as a reference point for facilitating my university’s AI discussion group. I also aim to write a Part 2, covering the second half of the 2022 AGI Safety Fundamentals Alignment Curriculum and discuss any updates to my understanding as I begin facilitating.

Technical Background: What is Artificial Intelligence?

Definitions

Artificial Intelligence (AI) is both the study of intelligent algorithms and the intelligent algorithms themselves.^[1] For the purposes of this post, we’ll hold that intelligence measures one’s ability to achieve one’s goals across a wide range of environments.^[2] An algorithm is a “step-by-step procedure” for solving a problem, typically written on a computer.^[3] In turn, intelligent algorithms are computational procedures that can achieve their goals across many environments.

A very brief history of AI

Beginning in the 1950s, “Good Old Fashioned AI” (GOFAI), also known as symbolic AI, used search and logic to solve high-level mathematical equations. In 1997 Deep Blue beat chess Grandmaster Garry Kasparaov by using GOFAI—Deep Blue searched over millions of positions to find the optimal play.^[4] In the 1960s, a series of criticisms argued GOFAI could never handle the complexities of the real world. This burst the building AI hype and led to an AI winter, in which funders pulled out and AI progress slowed.^[5] In the 1990s, AI research made a comeback as researchers shifted from symbolic AI to machine learning, which remains the dominant paradigm today. ^[6]

Machine Learning basics

Unlike symbolic AI, Machine Learning (ML) is adept at addressing real world complexity. Rather than searching through every possible configuration to find the optimal algorithm, ML relies on using statistical te...

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