Birds, Brains, Planes, and AI: Against Appeals to the Complexity / Mysteriousness / Efficiency of the Brain

[Epistemic status: Strong opinions lightly held, this time with a cool graph.] I argue that an entire class of common arguments against short timelines is bogus, and provide weak evidence that anchoring to the human-brain-human-lifetime milestone is reasonable. In a sentence, my argument is that the complexity and mysteriousness and efficiency of the human brain (compared to artificial neural nets) is almost zero evidence that building TAI will be difficult, because evolution typically makes things complex and mysterious and efficient, even when there are simple, easily understood, inefficient designs that work almost as well (or even better!) for human purposes. In slogan form: If all we had to do to get TAI was make a simple neural net 10x the […]

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Against GDP as a metric for AI timelines and takeoff speeds

Or: Why AI Takeover Might Happen Before GDP Accelerates, and Other Thoughts On What Matters for Timelines and Takeoff Speeds I think world GDP (and economic growth more generally) is overrated as a metric for AI timelines and takeoff speeds. Here are some uses of GDP that I disagree with, or at least think should be accompanied by cautionary notes: Timelines: Ajeya Cotra thinks of transformative AI as “software which causes a tenfold acceleration in the rate of growth of the world economy (assuming that it is used everywhere that it would be economically profitable to use it).” I don’t mean to single her out in particular; this seems like the standard definition now. Takeoff Speeds: Paul Christiano argues for […]

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Incentivizing forecasting via social media

Summary Most people will probably never participate on existing forecasting platforms which limits their effects on mainstream institutions and public discourse. Changes to the user interface and recommendation algorithms of social media platforms might incentivize forecasting and lead to its more widespread adoption. Broadly, we envision i) automatically suggesting questions of likely interest to the user—e.g., questions related to the user’s current post or trending topics—and ii) rewarding users with higher than average forecasting accuracy with increased visibility. In a best case scenario, such forecasting-incentivizing features might have various positive consequences such as increasing society’s shared sense of reality and the quality of public discourse, while reducing polarization and the spread of misinformation. Facebook’s Forecast could be seen as one […]

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Persuasion Tools: AI takeover without takeoff or agency?

[epistemic status: speculation] I'm envisioning that in the future there will also be systems where you can input any conclusion that you want to argue (including moral conclusions) and the target audience, and the system will give you the most convincing arguments for it. At that point people won't be able to participate in any online (or offline for that matter) discussions without risking their object-level values being hijacked. --Wei Dai What if most people already live in that world? A world in which taking arguments at face value is not a capacity-enhancing tool, but a security vulnerability? Without trusted filters, would they not dismiss highfalutin arguments out of hand, and focus on whether the person making the argument seems […]

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How Roodman's GWP model translates to TAI timelines

How does David Roodman’s world GDP model translate to TAI timelines? Now, before I go any further, let me be the first to say that I don’t think we should use this model to predict TAI. This model takes a very broad outside view and is thus inferior to models like Ajeya Cotra’s which make use of more relevant information. (However, it is still useful for rebutting claims that TAI is unprecedented, inconsistent with historical trends, low-prior, etc.) Nevertheless, out of curiosity I thought I’d calculate what the model implies for TAI timelines. Here is the projection made by Roodman’s model. The red line is real historic GWP data; the splay of grey shades that continues it is the splay […]

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The date of AI Takeover is not the day the AI takes over

Instead, it’s the point of no return—the day we AI risk reducers lose the ability to significantly reduce AI risk. This might happen years before classic milestones like “World GWP doubles in four years” and “Superhuman AGI is deployed." The rest of this post explains, justifies, and expands on this obvious but underappreciated idea. (Toby Ord appreciates it; see quote below). I found myself explaining it repeatedly, so I wrote this post as a reference. AI timelines often come up in career planning conversations. Insofar as AI timelines are short, career plans which take a long time to pay off are a bad idea, because by the time you reap the benefits of the plans it may already be too […]

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