The Nonlinear Library LW - Now THIS is forecasting: understanding Epoch's Direct Approach by Elliot Mckernon
May 4, 2024
34:03
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: Now THIS is forecasting: understanding Epoch's Direct Approach, published by Elliot Mckernon on May 4, 2024 on LessWrong.
Happy May the 4th from Convergence Analysis! Cross-posted on the EA Forum.
As part of Convergence Analysis's scenario research, we've been looking into how AI organisations, experts, and forecasters make predictions about the future of AI. In February 2023, the AI research institute Epoch published a report in which its authors use neural scaling laws to make quantitative predictions about when AI will reach human-level performance and become transformative. The report has a corresponding blog post, an interactive model, and a Python notebook.
We found this approach really interesting, but also hard to understand intuitively. While trying to follow how the authors derive a forecast from their assumptions, we wrote a breakdown that may be useful to others thinking about AI timelines and forecasting.
In what follows, we set out our interpretation of Epoch's 'Direct Approach' to forecasting the arrival of transformative AI (TAI). We're eager to see how closely our understanding of this matches others'. We've also fiddled with Epoch's interactive model and include some findings on its sensitivity to plausible changes in parameters.
The Epoch team recently attempted to replicate DeepMind's influential Chinchilla scaling law, an important quantitative input to Epoch's forecasting model, but found inconsistencies in DeepMind's presented data. We'll summarise these findings and explore how an improved model might affect Epoch's forecasting results.
This is where the fun begins (the assumptions)
The goal of Epoch's Direct Approach is to quantitatively predict the progress of AI capabilities.
The approach is 'direct' in the sense that it uses observed scaling laws and empirical measurements to directly predict performance improvements as computing power increases. This stands in contrast to indirect techniques, which instead seek to estimate a proxy for performance. A notable example is Ajeya Cotra's Biological Anchors model, which approximates AI performance improvements by appealing to analogies between AIs and human brains.
Both of these approaches are discussed and compared, along with expert surveys and other forecasting models, in Zershaaneh Qureshi's recent post, Timelines to Transformative AI: an investigation.
In their blog post, Epoch summarises the Direct Approach as follows:
The Direct Approach is our name for the idea of forecasting AI timelines by directly extrapolating and interpreting the loss of machine learning models as described by scaling laws.
Let's start with scaling laws. Generally, these are just numerical relationships between two quantities, but in machine learning they specifically refer to the various relationships between a model's size, the amount of data it was trained with, its cost of training, and its performance.
These relationships seem to fit simple mathematical trends, and so we can use them to make predictions: if we make the model twice as big - give it twice as much 'compute' - how much will its performance improve? Does the answer change if we use less training data? And so on.
If we combine these relationships with projections of how much compute AI developers will have access to at certain times in the future, we can build a model which predicts when AI will cross certain performance thresholds. Epoch, like Convergence, is interested in when we'll see the emergence of transformative AI (TAI): AI powerful enough to revolutionise our society at a scale comparable to the agricultural and industrial revolutions.
To understand why Convergence is especially interested in that milestone, see our recent post 'Transformative AI and Scenario Planning for AI X-risk'.
Specifically, Epoch uses an empirically measured scaling ...
Happy May the 4th from Convergence Analysis! Cross-posted on the EA Forum.
As part of Convergence Analysis's scenario research, we've been looking into how AI organisations, experts, and forecasters make predictions about the future of AI. In February 2023, the AI research institute Epoch published a report in which its authors use neural scaling laws to make quantitative predictions about when AI will reach human-level performance and become transformative. The report has a corresponding blog post, an interactive model, and a Python notebook.
We found this approach really interesting, but also hard to understand intuitively. While trying to follow how the authors derive a forecast from their assumptions, we wrote a breakdown that may be useful to others thinking about AI timelines and forecasting.
In what follows, we set out our interpretation of Epoch's 'Direct Approach' to forecasting the arrival of transformative AI (TAI). We're eager to see how closely our understanding of this matches others'. We've also fiddled with Epoch's interactive model and include some findings on its sensitivity to plausible changes in parameters.
The Epoch team recently attempted to replicate DeepMind's influential Chinchilla scaling law, an important quantitative input to Epoch's forecasting model, but found inconsistencies in DeepMind's presented data. We'll summarise these findings and explore how an improved model might affect Epoch's forecasting results.
This is where the fun begins (the assumptions)
The goal of Epoch's Direct Approach is to quantitatively predict the progress of AI capabilities.
The approach is 'direct' in the sense that it uses observed scaling laws and empirical measurements to directly predict performance improvements as computing power increases. This stands in contrast to indirect techniques, which instead seek to estimate a proxy for performance. A notable example is Ajeya Cotra's Biological Anchors model, which approximates AI performance improvements by appealing to analogies between AIs and human brains.
Both of these approaches are discussed and compared, along with expert surveys and other forecasting models, in Zershaaneh Qureshi's recent post, Timelines to Transformative AI: an investigation.
In their blog post, Epoch summarises the Direct Approach as follows:
The Direct Approach is our name for the idea of forecasting AI timelines by directly extrapolating and interpreting the loss of machine learning models as described by scaling laws.
Let's start with scaling laws. Generally, these are just numerical relationships between two quantities, but in machine learning they specifically refer to the various relationships between a model's size, the amount of data it was trained with, its cost of training, and its performance.
These relationships seem to fit simple mathematical trends, and so we can use them to make predictions: if we make the model twice as big - give it twice as much 'compute' - how much will its performance improve? Does the answer change if we use less training data? And so on.
If we combine these relationships with projections of how much compute AI developers will have access to at certain times in the future, we can build a model which predicts when AI will cross certain performance thresholds. Epoch, like Convergence, is interested in when we'll see the emergence of transformative AI (TAI): AI powerful enough to revolutionise our society at a scale comparable to the agricultural and industrial revolutions.
To understand why Convergence is especially interested in that milestone, see our recent post 'Transformative AI and Scenario Planning for AI X-risk'.
Specifically, Epoch uses an empirically measured scaling ...