The Nonlinear Library EA - Timelines to Transformative AI: an investigation by Zershaaneh Qureshi
Mar 26, 2024
01:38:20
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Timelines to Transformative AI: an investigation, published by Zershaaneh Qureshi on March 26, 2024 on The Effective Altruism Forum.
This post is part of a series by Convergence Analysis' AI Clarity team.
Justin Bullock and Elliot Mckernon have recently motivated AI Clarity's focus on the notion of transformative AI (TAI). In an earlier post, Corin Katzke introduced a framework for applying scenario planning methods to AI safety, including a discussion of strategic parameters involved in AI existential risk. In this post, I focus on a specific parameter: the timeline to TAI. Subsequent posts will explore 'short' timelines to transformative AI in more detail.
Feedback and discussion are welcome.
Summary
In this post, I gather, compare, and investigate a range of notable recent predictions of the timeline to transformative AI (TAI).
Over the first three sections, I map out a bird's eye view of the current landscape of predictions, highlight common assumptions about scaling which influence many of the surveyed views, then zoom in closer to examine two specific examples of quantitative forecast models for the arrival of TAI (from Ajeya Cotra and Epoch).
Over the final three sections, I find that:
A majority of recent median predictions for the arrival of TAI fall within the next 10-40 years. This is a notable result given the vast possible space of timelines, but rough similarities between forecasts should be treated with some epistemic caution in light of phenomena such as Platt's Law and information cascades.
In the last few years, people generally seem to be updating their beliefs in the direction of shorter timelines to TAI. There are important questions over how the significance of this very recent trend should be interpreted within the wider historical context of AI timeline predictions, which have been quite variable over time and across sources.
Despite difficulties in obtaining a clean overall picture here, each individual example of belief updates still has some evidentiary weight in its own right.
There is also some conceptual support in favour of TAI timelines which fall on the shorter end of the spectrum. This comes partly in the form of the plausible assumption that the scaling hypothesis will continue to hold. However, there are several possible flaws in reasoning which may underlie prevalent beliefs about TAI timelines, and we should therefore take care to avoid being overconfident in our predictions.
Weighing these points up against potential objections, the evidence still appears sufficient to warrant (1) conducting serious further research into short timeline scenarios and (2) affording real importance to these scenarios in our strategic preparation efforts.
Introduction
The timeline for the arrival of advanced AI is a key consideration for AI safety and governance. It is a critical determinant of the threat models we are likely to face, the magnitude of those threats, and the appropriate strategies for mitigating them.
Recent years have seen growing discourse around the question of what AI timelines we should expect and prepare for. At a glance, the dialogue is filled with contention: some anticipate rapid progression towards advanced AI, and therefore advocate for urgent action; others are highly sceptical that we'll see significant progress in our lifetimes; many views fall somewhere in between these poles, with unclear strategic implications.
The dialogue is also evolving, as AI research and development progresses in new and sometimes unexpected ways. Overall, the body of evidence this constitutes is in need of clarification and interpretation.
This article is an effort to navigate the rough terrain of AI timeline predictions. Specifically:
Section I collects and loosely compares a range of notable, recent predictions on AI timelines (taken from su...
This post is part of a series by Convergence Analysis' AI Clarity team.
Justin Bullock and Elliot Mckernon have recently motivated AI Clarity's focus on the notion of transformative AI (TAI). In an earlier post, Corin Katzke introduced a framework for applying scenario planning methods to AI safety, including a discussion of strategic parameters involved in AI existential risk. In this post, I focus on a specific parameter: the timeline to TAI. Subsequent posts will explore 'short' timelines to transformative AI in more detail.
Feedback and discussion are welcome.
Summary
In this post, I gather, compare, and investigate a range of notable recent predictions of the timeline to transformative AI (TAI).
Over the first three sections, I map out a bird's eye view of the current landscape of predictions, highlight common assumptions about scaling which influence many of the surveyed views, then zoom in closer to examine two specific examples of quantitative forecast models for the arrival of TAI (from Ajeya Cotra and Epoch).
Over the final three sections, I find that:
A majority of recent median predictions for the arrival of TAI fall within the next 10-40 years. This is a notable result given the vast possible space of timelines, but rough similarities between forecasts should be treated with some epistemic caution in light of phenomena such as Platt's Law and information cascades.
In the last few years, people generally seem to be updating their beliefs in the direction of shorter timelines to TAI. There are important questions over how the significance of this very recent trend should be interpreted within the wider historical context of AI timeline predictions, which have been quite variable over time and across sources.
Despite difficulties in obtaining a clean overall picture here, each individual example of belief updates still has some evidentiary weight in its own right.
There is also some conceptual support in favour of TAI timelines which fall on the shorter end of the spectrum. This comes partly in the form of the plausible assumption that the scaling hypothesis will continue to hold. However, there are several possible flaws in reasoning which may underlie prevalent beliefs about TAI timelines, and we should therefore take care to avoid being overconfident in our predictions.
Weighing these points up against potential objections, the evidence still appears sufficient to warrant (1) conducting serious further research into short timeline scenarios and (2) affording real importance to these scenarios in our strategic preparation efforts.
Introduction
The timeline for the arrival of advanced AI is a key consideration for AI safety and governance. It is a critical determinant of the threat models we are likely to face, the magnitude of those threats, and the appropriate strategies for mitigating them.
Recent years have seen growing discourse around the question of what AI timelines we should expect and prepare for. At a glance, the dialogue is filled with contention: some anticipate rapid progression towards advanced AI, and therefore advocate for urgent action; others are highly sceptical that we'll see significant progress in our lifetimes; many views fall somewhere in between these poles, with unclear strategic implications.
The dialogue is also evolving, as AI research and development progresses in new and sometimes unexpected ways. Overall, the body of evidence this constitutes is in need of clarification and interpretation.
This article is an effort to navigate the rough terrain of AI timeline predictions. Specifically:
Section I collects and loosely compares a range of notable, recent predictions on AI timelines (taken from su...