TL;DR: Someone should probably write a grant to produce a spreadsheet/dataset of
past instances of where people claimed a new technology would lead to societal
catastrophe, with variables such as “multiple people working on the tech
believed it was dangerous.”
Slightly longer TL;DR: Some AI risk skeptics are mocking people who believe AI
could threaten humanity’s existence, saying that many people in the past
predicted doom from some new tech. There is seemingly no dataset which lists and
evaluates such past instances of “tech doomers.” It seems somewhat ridiculous*
to me that nobody has grant-funded a researcher to put together a dataset with
variables such as “multiple people working on the technology thought it could be
very bad for society.”
*Low confidence: could totally change my mind
I have asked multiple people in the AI safety space if they were aware of any
kind of "dataset for past predictions of doom (from new technology)"? There have
been some articles and arguments floating around recently such as "Tech Panics,
Generative AI, and the Need for Regulatory Caution
in which skeptics say we shouldn't worry about AI x-risk because there are many
past cases where people in society made overblown claims that some new
technology (e.g., bicycles, electricity) would be disastrous for society.
While I think it's right to consider the "outside view" on these kinds of
things, I think that most of these claims 1) ignore examples of where there were
legitimate reasons to fear the technology (e.g., nuclear weapons, maybe
synthetic biology?), and 2) imply the current worries about AI are about as
baseless as claims like "electricity will destroy society," whereas I would
argue that the claim "AI x-risk is >1%" stands up quite well against most
(These claims also ignore the anthropic argument/survivor bias—that if they ever
were right about doom we wouldn
Protesting at leading AI labs may be significantly more effective than most
protests, even ignoring the object-level arguments for the importance of AI
safety as a cause area. The impact per protester is likely unusually big, since
early protests involve only a handful of people and impact probably scales
sublinearly with size. And very early protests are unprecedented and hence more
likely (for their size) to attract attention, shape future protests, and have
other effects that boost their impact.
Hey - I’d be really keen to hear peoples' thoughts on the following
career/education decision I'm considering (esp. people who think about AI a
* I’m about to start my undergrad studying PPE at Oxford.
* I’m wondering whether re-applying this year to study CS & philosophy at
Oxford (while doing my PPE degree) is a good idea.
* This doesn’t mean I have to quit PPE or anything.
* I’d also have to start CS & philosophy from scratch the following year.
* My current thinking is that I shouldn’t do this - I think it’s unlikely that
I’ll be sufficiently good to, say, get into a top 10 ML PhD or anything, so
the technical knowledge that I’d need for the AI-related paths I’m
considering (policy, research, journalism, maybe software engineering) is
either pretty limited (the first three options) or much easier to self-teach
and less reliant on credentials (software engineering).
* I should also add that I’m currently okay at programming anyway, and plan
to develop this alongside my degree regardless of what I do - it seems like
a broadly useful skill that’ll also give me more optionality.
* I do have a suspicion that I’m being self-limiting re the PhD thing - if
everyone else is starting from a (relatively) blank slate, maybe I’d be on
* That said, I also have my suspicions that the PhD route is actually my
highest-impact option: I’m stuck between 1) deferring to 80K here, and 2)
my other feeling that enacting policy/doing policy research might be
* They’re also obviously super competitive, and seem to only be getting
* One major uncertainty I have is whether, for things like policy, a PPE degree
(or anything politics-y/economics-y) really matters. I’m a UK citizen, and
given the record of UK politicians who did PPE at Oxford, it seems like it
What mistakes am I making here/am I being too self-limiting? I s
I thought the recent Hear This Idea podcast episode with Ben Garfinkel
[https://hearthisidea.com/episodes/garfinkel] was excellent. If you are at all
interested in AI governance (or AI safety generally), you probably want to check
I'm thinking about the matching problem of "people with AI safety questions" and
"people with AI safety answers". Snoop Dogg hears Geoff Hinton on CNN (or
wherever), asks "what the fuck?"
[https://twitter.com/pkedrosky/status/1653955254181068801], and then tries to
find someone who can tell him what the fuck.
I think normally people trust their local expertise landscape--if they think the
CDC is the authority on masks they adopt the CDC's position, if they think their
mom group on Facebook is the authority on masks they adopt the mom group's
position--but AI risk is weird because it's mostly unclaimed territory in their
local expertise landscape. (Snoop also asks "is we in a movie right now?"
because movies are basically the only part of the local expertise landscape that
has had any opinion on AI so far, for lots of people.) So maybe there's an
opportunity here to claim that territory (after all, we've thought about it a
I think we have some 'top experts' who are available for, like, mass-media
things (podcasts, blog posts, etc.) and 1-1 conversations with people they're
excited to talk to, but are otherwise busy / not interested in fielding ten
thousand interview requests. Then I think we have tens (hundreds?) of people who
are expert enough to field ten thousand interview requests, given that the
standard is "better opinions than whoever they would talk to by default" instead
of "speaking to the whole world" or w/e. But just like connecting people who
want to pay to learn calculus and people who know calculus and will teach it for
money, there's significant gains from trade from having some sort of
clearinghouse / place where people can easily meet. Does this already exist? Is
anyone trying to make it? (Do you want to make it and need support of some
Why aren't we engaging in direct action (including civil disobedience) to pause
Here's the problem:
researchers steeped in these issues, including myself, expect that the most
likely result of building a superhumanly smart AI, under anything remotely like
the current circumstances, is that literally everyone on Earth will die."
Here's one solution:
FLI Open Letter
[https://futureoflife.org/open-letter/pause-giant-ai-experiments/]: "all AI
labs...immediately pause for at least 6 months the training of AI systems more
powerful than GPT-4. This pause should be public and verifiable, and include all
key actors. If such a pause cannot be enacted quickly, governments should step
in and institute a moratorium."
Here's what direct action in the pursuit of that solution could look like (most
examples are from the UK climate movement):
Picketing AI offices
[https://twitter.com/Radlib4/status/1653135998501662722?s=20] (this already
seems to be happening!)
Mass non-disruptive protest
(by AI developers/researchers/academics)
[https://www.bbc.co.uk/news/uk-england-gloucestershire-64193016] of AI offices
Performative vandalism of art
Sabotage of AI computing infrastructure (on the model of ecotage
I wonder if anyone has moved from longtermist cause areas to neartermist cause
areas. I was prompted by reading the recent Carlsmith piece and Julia Wise's
Messy personal stuff that affected my cause prioritization.
Load more (8/32)
I'd like to try my hand at summarizing / paraphrasing Matthew Barnett's
interesting twitter thread on the FLI letter
The tl;dr is that trying to ban AI progress will increase the hardware overhang,
and risk the ban getting lifted all of a sudden in a way that causes a dangerous
jump in capabilities.
Background reading: this summary will rely on an understanding of hardware
overhangs [https://aiimpacts.org/hardware-overhang/] (second link
[https://www.lesswrong.com/tag/computing-overhang]), which is a somewhat
slippery concept, and I myself wish I understood at a deeper level.
BARNETT AGAINST MODEL SCALING BANS
Effectiveness of regulation and the counterfactual
It is hard to prevent AI progress. There's a large monetary incentive to make
progress in AI, and companies can make algorithmic progress on smaller models.
"Larger experiments don't appear vastly more informative than medium sized
experiments." The current proposals on the table on ban the largest runs.
Your only other option is draconian regulation, which will be hard to do well
and will unpredictable and bad effects.
Conversely, by default, Matthew is optimistic about companies putting lots of
effort into alignment. It's economically incentivized. And we can see this
happening: OpenAI has put more effort into aligning its models over time, and
GPT-4 seems more aligned than GPT-2.
But maybe some delay on the margin will have good effects anyway? Not
Matthew's arguments above about algorithmic progress still occurring imply that
AI progress will occur during a ban. Given that, the amount of AI power that
can be wrung out humanity's hardware stock will be higher at the end of the ban
than at the start. What are these consequences of that? Nothing good, says
First, we need to account for the sudden jump in capabilities when the ban is
relaxed. Companies will suddenly train up to the economicall