One note from my experience at @stripe — surprisingly, the credit card processing industry is primarily industry-regulated. The card networks like Visa make rules, which get pushed to their member banks; those banks make rules for the processors who run on top of them. (1/8)
We're working with others in the nascent industry of creating & deploying language models in order to produce safety best practices.
Ultimate goal is to have a practice for cooperating on increasingly high-stakes safety problems as AI progresses.
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The fact that card processing is industry-regulated is very beneficial for consumers, since the rules can adapt when new paradigms appear (e.g. to allow crowdfunding—great but risky innovation, since card networks might have pay back consumers if the maker doesn't ship). (2/8)
Jun 2, 2022 · 6:14 PM UTC
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It didn't have to be this way. Many other financial sectors are primarily government-regulated, which makes innovation much harder & rules often end up not matching the needs of the present day. This results in more consumer protection, at the cost of less useful products. (3/8)
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Credit card processing ended up being primarily industry-regulated for historical reasons — the networks established rules with a strong eye towards consumer protection early on, and so there was not a strong public need for government to upend the industry. (4/8)
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The best practices we developed, with cooperation & support from others in the industry, are the most concrete step so far towards building industry regulations for AI. We'll welcome thoughtful government regulation, but can't delay for it given how fast the tech is moving. (5/8)
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We ultimately hope to grow cooperation like this into an approach to mitigate ex-risk from race conditions & a multi-polar world around AGI (something covered in our charter, and which everyone agrees is good in theory, but to date no approach has found traction). (6/8)
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What makes our approach different is that we're starting with a practical, useful foundation that companies have an incentive to opt into — these best practices are grounded in hard-won experience with how these models are used & abused in practice. (7/8)
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