This weekly roundup thread is intended for all culture war posts. 'Culture war' is vaguely defined, but it basically means controversial issues that fall along set tribal lines. Arguments over culture war issues generate a lot of heat and little light, and few deeply entrenched people ever change their minds. This thread is for voicing opinions and analyzing the state of the discussion while trying to optimize for light over heat.
Optimistically, we think that engaging with people you disagree with is worth your time, and so is being nice! Pessimistically, there are many dynamics that can lead discussions on Culture War topics to become unproductive. There's a human tendency to divide along tribal lines, praising your ingroup and vilifying your outgroup - and if you think you find it easy to criticize your ingroup, then it may be that your outgroup is not who you think it is. Extremists with opposing positions can feed off each other, highlighting each other's worst points to justify their own angry rhetoric, which becomes in turn a new example of bad behavior for the other side to highlight.
We would like to avoid these negative dynamics. Accordingly, we ask that you do not use this thread for waging the Culture War. Examples of waging the Culture War:
-
Shaming.
-
Attempting to 'build consensus' or enforce ideological conformity.
-
Making sweeping generalizations to vilify a group you dislike.
-
Recruiting for a cause.
-
Posting links that could be summarized as 'Boo outgroup!' Basically, if your content is 'Can you believe what Those People did this week?' then you should either refrain from posting, or do some very patient work to contextualize and/or steel-man the relevant viewpoint.
In general, you should argue to understand, not to win. This thread is not territory to be claimed by one group or another; indeed, the aim is to have many different viewpoints represented here. Thus, we also ask that you follow some guidelines:
-
Speak plainly. Avoid sarcasm and mockery. When disagreeing with someone, state your objections explicitly.
-
Be as precise and charitable as you can. Don't paraphrase unflatteringly.
-
Don't imply that someone said something they did not say, even if you think it follows from what they said.
-
Write like everyone is reading and you want them to be included in the discussion.
On an ad hoc basis, the mods will try to compile a list of the best posts/comments from the previous week, posted in Quality Contribution threads and archived at /r/TheThread. You may nominate a comment for this list by clicking on 'report' at the bottom of the post and typing 'Actually a quality contribution' as the report reason.
Jump in the discussion.
No email address required.
Notes -
On the plausibility of Mars Bases vs that of AI
Responding to @FeepingCreature from last week:
That was a continuation of this discussion in which I say of AI 2027:
As to Mars:
Most of what I know here comes from reading Zach Wiener-Smith (of SMBC)'s A City on Mars. It was wildly pessimistic. For a taste, see Gemini chapter summaries and an answer to:
"Given an enormous budget (10% of global GDP) and current tech, how realistic is a 1 year duration mars base? an indefinite one? what about with highly plausible 2035 tech?"
I agree with the basic take there, both as a summary of the book and as a reflection of my broader (but poorly researched) understanding/intuition of the area: Mars is not practical. We could probably do the 1 year base if we don't mind serious risk of killing the astronauts (which, politically, probably rules it out. Maybe Musk will offer it as a Voluntary Exit Program for soon-to-be-ex X SWEs?)
My main interesting/controversial (?) take: there is an important sense in which Mars bases are much less of baseless scifi nonsense than AI 2027.
Mars is a question of logistics: on the one hand, building a self-contained, O2 recycling, radiation hardened, etc, base requires tech we may (?) not quite have yet. On the other hand, it strikes me as closer to refinements of existing tech than to entirely new concepts. Note that "enormous budget" is doing a lot of work in here. I am not saying it is practical to expect we will pay to ship all of this to Mars, or risk the lives, just that there is good reason to believe we could.
AI is a question of fundamental possibility: by contrast, with AI, there is no good reason to think we can create AI sufficient to replace OpenAI-grade researchers with forseeable timelines/tech. Junior SWEs, maybe, but it's not even clear they're on average positive-value beyond the investment in their future (see my previous rant about firing one of ours).
I don't understand how anyone can in good faith believe that even with an arbitrary amount of effort and funding, AGI, let alone ASI, is coming in the next few years. Any projection out decades is almost definitionally in the realm of speculative science-fiction here. Even mundane tech can't be predicted decades out, and AI has higher ceilings/variance than most things.
And yet, I am sensitive to my use of the phrase "I don't understand." People often unwittingly use it intending to mean "I am sure I understand." For example: "I don't understand how $OTHER_PARTY can think $THING." This is intended to convey "$OTHER_PARTY thinks $THING because they are evil/nazis/stupid/brainwashed." But, the truth of their cognitive state is closer to the literal usage: they do not understand.
So, in largely the literal sense of the phrase: I do not understand the belief in and fear of AI progress I see around me, in people I largely respect on both politics and engineering.
We know that systems capable of acting like smart humans are possible (after all, there are smart humans). Will LLMs get us there? It's unclear. Could they, in the arid sense that there is some unknown collection of weights that would be capable of outputting tokens that simulate an OpenAI researcher working on novel tasks? Absolutely. (As to how to actually learn those weights, that's left as an exercise to the reader.)
I think the dynamism of the research program is relevant, though. Right now, you can, as an individual, decide to spend a quarter and a couple thousands in compute to research a particular area of LLMs and have a reasonable expectation of finding something interesting, and sometimes it's actually useful. This isn't merely hypothetical but is something happening every single day. There is a lot of low hanging fruit. Might there be some collection of a dozen different improvements on the horizon which, when taken collectively, would get us to AGI? Maybe. It's plausible, at least, while it's not plausible that a dozen different innovations are on the horizon that would enable a cheap base on Mars.
Why so confident? A 10 dimensional best fit line obviously won't work, nor will a vast fully connected neural net - so why should an LLM be capable?
I mean it in the sense that LLMs are capable of creating a token stream that is identical to an AI researcher. This is mathematically proven--see various universality theorems--but has the critical drawback that it doesn't really give you any information on how to find that optimal set of weights.
A MLP absolutely could also do this, or even some absurd polynomial best fit (not, however, a ten or quadrillion dimensional linear model). What MLPs offer over polynomials and transformers offer over MLPs is increased training efficiency and stability for actually finding those weights.
The universality theorems don't say that it's possible with any remotely practical number of weights, even aside from training time. But yes, I do grant that they say it is possible in theory.
To even achieve GPT2 performance with a basic, non-recurrent neural net, I would not be surprised if you need > # of atoms in the universe weights, which clearly isn't physically possible. (Ok, you can maybe theoretically have > 1 weight per atom, but s/atom/gluon, or just don't take me super literally on "atom".)
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link