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: What failure looks like, published by...
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: What failure looks like, published by Paul Christiano on the AI Alignment Forum.
The stereotyped image of AI catastrophe is a powerful, malicious AI system that takes its creators by surprise and quickly achieves a decisive advantage over the rest of humanity.
I think this is probably not what failure will look like, and I want to try to paint a more realistic picture. I’ll tell the story in two parts:
Part I: machine learning will increase our ability to “get what we can measure,” which could cause a slow-rolling catastrophe. ("Going out with a whimper.")
Part II: ML training, like competitive economies or natural ecosystems, can give rise to “greedy” patterns that try to expand their own influence. Such patterns can ultimately dominate the behavior of a system and cause sudden breakdowns. ("Going out with a bang," an instance of optimization daemons.)
I think these are the most important problems if we fail to solve intent alignment.
In practice these problems will interact with each other, and with other disruptions/instability caused by rapid progress. These problems are worse in worlds where progress is relatively fast, and fast takeoff can be a key risk factor, but I’m scared even if we have several years.
With fast enough takeoff, my expectations start to look more like the caricature---this post envisions reasonably broad deployment of AI, which becomes less and less likely as things get faster. I think the basic problems are still essentially the same though, just occurring within an AI lab rather than across the world.
(None of the concerns in this post are novel.)
Part I: You get what you measure
If I want to convince Bob to vote for Alice, I can experiment with many different persuasion strategies and see which ones work. Or I can build good predictive models of Bob’s behavior and then search for actions that will lead him to vote for Alice. These are powerful techniques for achieving any goal that can be easily measured over short time periods.
But if I want to help Bob figure out whether he should vote for Alice---whether voting for Alice would ultimately help create the kind of society he wants---that can’t be done by trial and error. To solve such tasks we need to understand what we are doing and why it will yield good outcomes. We still need to use data in order to improve over time, but we need to understand how to update on new data in order to improve.
Some examples of easy-to-measure vs. hard-to-measure goals:
Persuading me, vs. helping me figure out what’s true. (Thanks to Wei Dai for making this example crisp.)
Reducing my feeling of uncertainty, vs. increasing my knowledge about the world.
Improving my reported life satisfaction, vs. actually helping me live a good life.
Reducing reported crimes, vs. actually preventing crime.
Increasing my wealth on paper, vs. increasing my effective control over resources.
It’s already much easier to pursue easy-to-measure goals, but machine learning will widen the gap by letting us try a huge number of possible strategies and search over massive spaces of possible actions. That force will combine with and amplify existing institutional and social dynamics that already favor easily-measured goals.
Right now humans thinking and talking about the future they want to create are a powerful force that is able to steer our trajectory. But over time human reasoning will become weaker and weaker compared to new forms of reasoning honed by trial-and-error. Eventually our society’s trajectory will be determined by powerful optimization with easily-measurable goals rather than by human intentions about the future.
We will try to harness this power by constructing proxies for what we care about, but over time those proxies will come apart:
Corporations will deliver value to consumers as measured by profit. Eventually th...
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