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Miles Brundage

@Miles_Brundage

Dec 26

Some meta-level thoughts on the ChatGPT-induced alarm among teachers/professors about LM-enabled cheating (substantive thoughts to come next year):

- like many LM things, it is not coming out of nowhere. People warned about it (+ some started cheating) no later than Feb. 2019.

- similarly to other LM things, the severity of the issue (and the prospective benefits for education via legitimate uses of LMs) scales with model capabilities, so was nascent before, and will be more severe in the future. Also scales with wide access + usability of interface.

- similarly to other LM things, it requires a bunch of stakeholders to manage. Even if API providers and first party LM product providers prevented cheating entirely, open source models would only be a generation or do behind. So educational institutions will need to adapt.

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Miles Brundage

@Miles_Brundage

Oct 30

Like everyone else, it seems, I have hot takes on Elon/Twitter stuff. I will try to differentiate mine by making them falsifiable.

Let’s call three scenarios for how this plays out “Exit Left,” “Mission Accomplished,” and “Golden Age.”
🧵

Exit Left (~40% chance): Mass exodus after overly aggressive relaxing of rules in a more politically conservative direction + general anti-Elon animus on the left leads …

… to Twitter being widely seen on the left (and to some extent also among centrists) as inhospitable. Elon realizes he overcorrected and that actually rampant harassment, hate speech, crush videos etc. drive away ppl and the people he fired were doing a pretty good job.

Read 17 tweets

Miles Brundage

@Miles_Brundage

Jul 24, 2021

I think some are reticent to be impressed by AI progress partly because they associate that with views they don't like--e.g. that tech co's are great or tech ppl are brilliant.

But these are not nec related. It'd be better if views on AI were less correlated with other stuff.🧵

(The premise is admittedly speculative--I am confident there's a correlation but less so re: causality. Best theory I have but will be curious for reactions. There are of course other reasons to be unimpressed such as things really being unimpressive, fear of seeming naive, etc.)

To be more precise, I think there is a strong correlation in AI ethics/policy world between (A) thinking that present/past AI achievements are overhyped and future progress is likely to be slow, and (B) left leaning political views and skepticism re: big tech companies/elites.

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Miles Brundage

@Miles_Brundage

Jul 8, 2021

Excited to finally share a paper on what a huge chunk of OpenAI has been working on lately: building a series of code generation models and assessing their capabilities and societal implications. 🧵

arxiv.org/abs/2107.03374

First, just want to emphasize how collaborative the effort has been. Assessing the societal implications of any technology—especially pre-widespread deployment—is inherently hard and requires drawing on lots of disciplinary perspectives. See long authors list + acknowledgements!

I’ll have more to say on the societal implication stuff than the capabilities + eval thereof, but will briefly note the many fascinating and hard-won results including the effectiveness of sampling many times + how this relates to temperature, novel eval dataset + framework, etc.

Read 23 tweets

Miles Brundage

@Miles_Brundage

Mar 1, 2021

What's going on here besides people optimizing for different things, or not bothering to do their homework? One answer is that AI policy researchers are falling victim to Collingridge's dilemma (en.wikipedia.org/wiki/Collingri…).

That is, by default, people miss the sweet spot between when AI outcomes are not yet foreseeable in detail, and when a lot of the key decisions have been made. That time period is short (small number of months/years), e.g. I think language models are in the sweet spot right now.

In order to hit that sweet spot, you may need to be in orgs making key decisions, or be in touch with them, or specifically *try* to do work in the sweet spot, e.g. picking up on trends in the literature and jumping on them quickly. Many researchers aren't in these categories.

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Miles Brundage

@Miles_Brundage

Mar 1, 2021

There's been a ton of growth in publications on AI ethics/policy over the past few years. I think most observers would agree that only a small fraction of that output is "impactful," though. A thread of preliminary thoughts on what that means/why it's happening:🧵

[A note on terminology: AI ethics/policy/safety/governance etc. can be distinguished in various ways. I use "AI policy" as a catch-all for all these here because I think my points are pretty general, but I'm interested in feedback on that among other points made here!]

By any reasonable measure, there are vastly more publications on AI policy than there was, say, 5 years ago. This has roughly paralleled the growth in AI capabilities research, applications, and regulation over that time period.

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