Lukas_Gloor

Karma: 2,469

Lukas_Gloor 29 Mar 2023 16:36 UTC
2 points
0
in reply to: CarlShulman’s comment on: Updating my AI timelines
“Effective compute” is the combination of hardware growth and algorithmic progress? If those are multiplicative rather than additive, slowing one of the factors may only accomplish little on its own, but maybe it could pave the way for more significant changes when you slow both at the same time?

Unfortunately, it seems hard to significantly slow algorithmic progress. I can think of changes to publishing behaviors (and improving security) and pausing research on scary models (for instance via safety evals). Maybe things like handicapping talent pools via changes to immigration policy, or encouraging capability researchers to do other work. But that’s about it.
Still, combining different measures could be promising if the effects are multiplicative rather than additive.

Edit: Ah, but I guess your point is that even a 100% tax on compute wouldn’t really change the slope of the compute growth curve – it would only move the curve rightward and delay a little. So we don’t get a multiplicative effect, unfortunately. We’d need to find an intervention that changes the steepness of the curve.

Lukas_Gloor 20 Mar 2023 16:12 UTC
3 points
1
in reply to: Said Achmiz’s comment on: Tabooing “Frame Control”
At first maybe you try to argue with them about it. But over time, a) you find yourself not bothering to argue with them
>Whose fault is that, exactly…?
b) even when you do argue with them, they’re the ones choosing the terms of the argument.
>Ditto.
If they think X is important, you find yourself focused on argue whether-or-not X is true, and ignoring all the different Ys and Zs that maybe you should have been thinking about.
>Ditto.

---
I agree that nothing about the examples you quote is unacceptably bad – all these things are “socially permissible.”
At the same time, your “Whose fault is that, exactly...?” makes it seem like there’s nothing the guru in question could be doing differently. That’s false.
Sure, some people are okay with seeing all social interactions as something where everyone is in it for themselves. However, in close(r) relationship contexts (e.g. friendships, romantic relationships, probably also spiritual mentoring from a guru?), many operate on the assumption that people care about each other and want to preserve each other’s agency and help each other flourish. In that context, it’s perfectly okay to have an expectation that others will (1) help me notice and speak up if something doesn’t quite feel right to me (as opposed to keeping quiet) and (2) help me arrive at informed/balanced views after carefully considering alternatives, as opposed to only presenting me their terms of the argument.

If the guru never says “I care about you as a person,” he’s fine to operate as he does. But once he starts to reassure his followers that he always has their best interest in mind – that’s when he crosses the line into immoral, exploitative behavior.
You can’t have it both ways. If your answer to people getting hurt is always “well, whose fault was that?”
Then don’t ever fucking reassure them that you care about them!
In reality, I’m pretty sure “gurus” almost always go to great lengths convincing their followers that they care more about them than almost anyone else. That’s where things become indefensible.

Lukas_Gloor 20 Mar 2023 15:47 UTC
2 points
−1
on: Tabooing “Frame Control”
Compare the two:

(1) The difference between “bad frame control” and “good frame control” is that, in the latter, the frame matches physical reality and social reality.

Here, I use “social reality” in the sense of “insights about what types of actions or norms help people flourish.”

(2) The difference between lying and telling the truth is that, when someone doesn’t lie, what they say matches physical reality and social reality.

I feel like there’s a sense in which (1) is true, but it’s missing the point if someone thinks that this is the only difference. If you lie a lot around some subject matter, or if you manipulate someone with what aella calls frame control, there’s always an amount of friction around the subject matter or around the frame that you introduce. This friction wouldn’t be there if you’re going with the truth. The original post points out how frame controllers try to hide that sort of friction or bring down your defenses against it. Those subtleties are what’s bad about the bad kind of frame control. Noticing these subtleties is what it’s all about.
Someone might object as follows:

“Friction” can mean many things. If you try to push people to accomplish extraordinary feats with your weird-seeming startup, you have to motivate them and push against various types of “friction” – motivate your co-workers, make them okay with being seen as weird as long as your idea hasn’t succeeded, etc.
I agree with all that. Good leaders have to craft motivating frames and inspire others with their vision. But I still feel like that’s not the same thing as what happens in (the bad kind of) frame control. The word “control” is a clue about where the difference lies. It’s hard to pin down the exact difference. Maybe it’s something like this:
Good leadership is about offering frames to your followers that create win-win situations (for them and for the world!) by appealing to virtues that they already endorse, deliberately drawing attention to all the places where there’s friction from social conventions or inertia/laziness, but presenting a convincing vision about why it’s worth it to push against that friction.
By contrast, frame control (the bad, sneaky/coercive kind) is about guilting people into thinking it’s their fault if they struggle because of the friction, or trying to not have them notice that there are alternative frames for them.

Lukas_Gloor 20 Mar 2023 11:53 UTC
2 points
in reply to: Going Durden’s comment on: Frame Control
Hm, maybe. I can see that frame control comes in handy when you’re a general in a war, or a CEO of a startup (and probably at least some generals or CEOs are good people with good effects on the world). However, in wartime, it feels like a necessary evil to have to convince your soldiers to march to the their death. And in startups – I don’t know, cultishness can have its advantages, but I feel like the best leadership is NOT turning your underlings into people who look cultish to outsiders. So, I think the good version of frame control is generally weaker than the bad version, for instance because good leaders don’t have anything to fear in terms of their followers becoming better at passing Ideological Turing tests for opposing views. But I guess that’s just expressing your point in different words: we can say that, if our frame is aligned with physical reality and avoids negative social outcomes, it shouldn’t look like the people who buy into it are cultists.

I also think it’s informative to think about the context of a romantic relationship. In that context, I’m not sure there’s a version of “good frame control” that’s necessary. Except maybe for frames like “good communication is important” – if one person so far struggled to express their needs because they weren’t taken seriously in their past life, it can be good for both individuals if the more securely attached person pushes that kind of frame. However, the way you would do that isn’t by repeating “good communication is important” as a mantra or weapon to shame the other person for not communicating the way you want! Instead, you try showing them the benefits of good communication, convincing them through evidence of how nice it feels when it works. That’s very different from the bad type of frame control in relationships. Also, let’s say you have two people who already understand that good communication is important. Then no one is exerting any frame control – you simply have two happy people who live in the same healthy frame. And insofar as they craft features of their personal “relationship frame,” it’s a mutual sort of thing, so no one is exactly exerting any sort of control.
These examples, and the fact that you can have relationships (not just romantic ones) where something feels mutual rather than “control exerted by one party,” makes me think that there’s more to it than “good frame control differs from bad frame control merely in terms of correspondence to physical reality (and social reality).” I guess it depends what we mean by “social reality.” I think bad frame control is primarily about a lack of empathy, and that happens to leave a very distinct pattern, which you simply can’t compare to “good leadership.”
Edit: I saw another commenter making a good point in reply to your comment. What you call “good frame control” is done out in the open. The merits of good frames are often self-evident or at least verifiable. By contrast, the OP discusses (bad) frame control as a type of sneak attack. It tries to overcome your epistemic defenses.

Lukas_Gloor 16 Mar 2023 11:07 UTC
4 points
2
in reply to: Chris_Leong’s comment on: Wittgenstein’s Language Games and the Critique of the Natural Abstraction Hypothesis
Yeah, what I meant was the belief that there’s no incorrect way to set up a language game.

Lukas_Gloor 16 Mar 2023 9:52 UTC
4 points
1
in reply to: LVSN’s comment on: Wittgenstein’s Language Games and the Critique of the Natural Abstraction Hypothesis
and some audiences have measurably better calibration.
It’s not straightforward in all contexts to establish what counts as good calibration. It’s straightforward for empirical forecasting, but if we were to come up with a notion like “good calibration for ethical judgments,” we’d have to make some pretty subjective judgment calls. Similarly, something like “good calibration for coming up with helpful abstractions for language games” (which we might call “doing philosophy” or a subskill of it) also seems (at least somewhat) subjective.

That doesn’t mean “anything goes,” but I don’t yet see how your point about dialogue trees applies to “maybe a society of AIs would build abstractions we don’t yet understand, so there’d be a translation problem between their language games and ours.”

Lukas_Gloor 16 Mar 2023 9:39 UTC
4 points
1
in reply to: LVSN’s comment on: Wittgenstein’s Language Games and the Critique of the Natural Abstraction Hypothesis
There are correct and incorrect ways to play language games.
That’s the crux. Wittgenstein himself believed otherwise and spent the most part of the book arguing against it. I think he makes good points.

At one point, he argues that there’s no single correct interpretation for “What comes next in the sequence: ‘2, 4, 6, 8, 10, 12, …?’”

Maybe this goes a bit too far. :) I think he’s right in some nitpicky sense, but for practical purposes, sane people will say “14″ every time and that works well for us.

We can see this as version of realism vs anti-realism debates: realism vs anti-realism about natural abstractions. As I argue in the linked post, anti-realism is probably the right way of looking at most or even all of these, but that doesn’t mean “anything goes.” Sometimes there’s ambiguity about our interpretations of things, but reality does have structure, and “ambiguity” isn’t the same as “you can just make random stuff up and expect it to be useful.”

Lukas_Gloor 15 Mar 2023 1:04 UTC
10 points
3
on: Shutting Down the Lightcone Offices
Thanks for sharing your reasoning, that was very interesting to read! I kind of agree with the worldview outlined in the quoted messages from the “Closing-Office-Reasoning” channel. Something like “unless you go to extreme lengths to cultivate integrity and your ability to reason in truth-tracking ways, you’ll become a part of the incentive-gradient landscape around you, which kills all your impact.”

Seems like a tough decision to have to decide whether an ecosystem has failed vs. whether it’s still better than starting from scratch despite its flaws. (I could imagine that there’s an instinct to just not think about it.)
Sometimes we also just get unlucky, though. (I don’t think FTX was just bad luck, but e.g., with some of the ways AI stuff played out, I find it hard to tell. Of course, just because I find it hard to tell doesn’t mean it’s objectively hard to tell. Maybe some things really were stupid also when they happened, not just in hindsight.)

I’m curious if you think there are “good EA orgs” where you think the leadership satisfies the threshold needed to predictably be a force of good in the world (my view is yes!). If yes, do you think that this isn’t necessarily enough for “building the EA movement” to be net positive? E.g., maybe you think it boosts the not-so-good orgs just as much as the good ones, and “burns the brand” in the process?

I’d say that, if there are some “good EA orgs,” that’s a reason for optimism. We can emulate what’s good about them and their culture. (It could still make sense to be against further growth if you believe the ratio has become too skewed.) Whereas, if there aren’t any, then we’re already in trouble, so there’s a bit of a wager against it.

Lukas_Gloor 14 Mar 2023 21:56 UTC
4 points
0
on: Success without dignity: a nearcasting story of avoiding catastrophe by luck
I think “Luck could be enough” should be the strong default on priors,² so in some sense I don’t think I owe tons of argumentation here (I think the burden is on the other side).
I agree with this being the default and the burden being on the other side. At the same time, I don’t think of it as a strong default.

Here’s a frame that I have that already gets me to a more pessimistic (updated) prior:

It has almost never happened that people who developed and introduced a revolutionary new technology displayed a lot of foresight about its long-term consequences. For instance, there were comparatively few efforts at major social media companies to address ways in which social media might change society for the worse. The same goes for the food industry and the obesity epidemic or online dating and its effects on single parenthood rates. When people invent cool new technology, it makes the world better on some metrics but creates new problems on its own. The whole thing is accelerating and feels out of control.

It feels out of control because even if we get cool new things from tech progress, we don’t seem to be getting any better at fixing the messiness that comes with it (misaligned incentives/goodhearting, other Molochian forces, world-destroying tech becoming ever more accessible). Your post says “a [] story of avoiding catastrophe by luck.” This framing makes it sound like things would be fine by default if it isn’t for some catastrophe happening. However, humans have never seemed particularly “in control” over technological progress. For things to go well, we need the opposite of a catastrophe – a radical change towards the upside. We have to solve massive coordination problems and hope for a technology that gives us god-like power, finally putting sane and compassionate forces in control over the future. It so happens that we can tell a coherent story about how AI might do this for us. But to say that it might go right just by luck – I don’t know, that seems far-fetched!

All of that said, I don’t think we can get very far arguing from priors. What carries by far the most weight are arguments about alignment difficulty, takeoff speeds, etc. And I think it’s a reasonable view to say that it’s very unlikely that any researchers currently know enough to make highly confident statements about these variables. (Edit: So, I’m not sure we disagree too much – I think I’m more pessimistic about the future than you are, but I’m probably not as pessimistic as the position you’re arguing against in this post. I mostly wanted to make the point that I think the “right” priors support at least moderate pessimism, which is a perspective I find oddly rare among EAs.)
FWIW, it’s not obvious to me that slow takeoff is best. Fast takeoff at least gives you god-like abilities early on, which are useful from a perspective of “we were never particularly in control over history; lots of underlying problems need fixing before we pass a point of no return.” By contrast, with slow takeoff, coordination problems seem more difficult because (at least by default) there will be more actors using AIs in some ways or other and it’s not obvious that the AIs in a slow-takeoff scenario will be all that helpful at facilitating coordination.

Lukas_Gloor 1 Mar 2023 20:49 UTC
4 points
0
in reply to: Zach Stein-Perlman’s comment on: Taboo “compute overhang”
I don’t understand this sentence:
Oh, yeah, I butchered that entire description.
It’s the gap between the training compute of ‘the first AGI’ and what?
What I had in mind was something like the gap between how much “intelligence” humans get from the compute they first build AGI with vs. how much “intelligence” AGI will get out of the same compute available, once it optimizes software progress for a few iterations.
So, the “gap” is a gap of intelligence rather than compute, but it’s “intelligence per specified quantity of compute.” (And that specified quantity is how much compute we used to build AGI in the first place.)

Lukas_Gloor 1 Mar 2023 20:41 UTC
4 points
2
on: Taboo “compute overhang”
And I asked some friends what “hardware overhang” means and they had different responses (a plurality said it means sufficient hardware for human-level AI already exists, which is not a useful concept).
It’s not a useful concept if we can’t talk about the probability of “finding” particularly efficient AGI architectures through new insights. However, it seems intelligible and strategically important to talk about something like “the possibility that we’re one/a few easy-to-find insight(s) away from suddenly being able to build AGI with a much smaller compute budget than the largest training runs to date.” That’s a contender for the concept of “compute overhang.” (See also my other comment.)
Maybe what you don’t like about this definition is that it’s inherently fuzzy: even if we knew everything about all possible AGI architectures, we’d still have uncertainty about how long it’ll take AI researchers to come up with the respective insights. I agree that this makes the concept harder to reason about (and arguably less helpful).

Lukas_Gloor 1 Mar 2023 20:18 UTC
7 points
3
on: Taboo “compute overhang”
I agree that it seems best for people to define the concept whenever they use it.
Instead of tabooing it, we could declare a canonical definition. I think the best candidate is something like: there is a compute overhang to the extent that the largest training runs could quickly be scaled up.
This proposal has little to do with hard vs. soft takeoff, which (IIRC) was the context in which Bostrom used “hardware overhang” in Superintelligence.
One thing that made the discussion confusing is that Bostrom originally discussed hard vs. soft takeoff as having relevance only after we build AGI, whereas Paul Christiano’s view on soft takeoff introduced the idea that “takeoff” already starts before AGI.

This made me think that it could be useful to distinguish between “post-AGI” and “pre-AGI” compute overhangs. It could go as follows:
Pre-AGI compute overhang:

There’s a pre-AGI compute overhang to the degree that the following could happen: we invent an algorithm that will get us to AGI before we scale up training runs to the biggest attainable sizes (on some short timescale).

So, on this definition, there are two ways in which we might already be in a pre-AGI compute overhang:

(1) Timelines are very short and we could get AGI with “current algorithms” (not necessarily GPT_n with zero tweaks, but obvious things to try that require no special insight) with less scaling effort than a Manhattan project.

(2) We couldn’t get AGI with current algorithms via any less-than-maximal scaling effort (and maybe not even with a maximal one – that part isn’t relevant for the claim), but there are highly significant algorithmic insights waiting for us (that we have a realistic chance of discovering). Once we incorporate these insights, we’ll be in the same situation as described in (1).

Post-AGI compute overhang:

Once we build AGI with realistic means, using far-from-optimal algorithms, how much room is there for it to improve its algorithms during “takeoff”/intelligence explosion? “Post-AGI compute overhang” here describes the gap between compute used to build AGI in the first place vs. more efficient designs that AI-aided progress could quickly discover.
[Edit: Correction: “Post-AGI compute overhang” here describes the gap in “intelligence” of the first AGI vs. the “intelligence” of a more efficient design (using the same amount of training compute as that first AGI) that AI-aided progress could quickly discover.]
On that definition, it’s actually quite straightforward that shorter timelines imply a smaller compute overhang (so maybe that’s what Sam Altman meant here).

Lukas_Gloor 1 Mar 2023 17:52 UTC
9 points
3
in reply to: Vladimir_Nesov’s comment on: Enemies vs Malefactors
My stance is “the more we promote awareness of the psychological landscape around destructive patterns of behavior, the better.” This isn’t necessarily at odds with what you’re saying because “the psychological landscape” is a descriptive thing, whereas your objection to Nate’s proposal is that it seeks to be “immediately-decision-relevant,” i.e., that it’s normative (or comes with direct normative implications).
So, maybe I’d agree that “maleficient” might be slightly too simplistic of a classification (because we may want to draw action-relevant boundaries in different places depending on the context – e.g., different situations call for different degrees of risk tolerance of false positives vs. false negatives).
That said, I think there’s an important message in Nate’s post and (if I had to choose one or the other) I’m more concerned about people not internalizing that message than about it potentially feeding ammunition to witch hunts. (After all, someone who internalizes Nate’s message will probably become more concerned about the possibility of witch hunts – if only explicitly-badly-intentioned people instigated witch hunts or added fuel to the fires, history would look very different.)

Lukas_Gloor 1 Mar 2023 16:48 UTC
6 points
4
in reply to: Slimepriestess’s comment on: Enemies vs Malefactors
If there’s a legitimate belief that they’re trying to do better and act in good faith, then what can be done to actually empower them to change in a positive direction? That’s of course if they actually want to change, if they’re keeping themselves in a state that causes harm because it benefits them while insisting its fine, well, to steal a sith’s turn of phrase: airlocked
I agree that it’s important to give people constructive feedback to help them change. However, I see some caveats around this (I think I’m expanding on the points in your comment rather than disagreeing with it). Sometimes it’s easier said than done. If part of a person’s “destructive pattern” is that they react with utter contempt when you give them well-meant and (reasonably-)well-presented feedback, it’s understandable if you don’t want to put yourself in the crossfire. In that case, you can always try to avoid contact with someone. Then, if others ask you why you’re doing this, you can say something that conveys your honest impressions while making clear that you haven’t given this other person much of a chance.

Just like it’s important to help people change, I think it’s also important to seriously consider the hypothesis that some people are so stuck in their destructive patterns that giving constructive feedback is no longer justifiable in terms of social opportunity costs. (E.g., why invest 100s of hours helping someone become slightly less destructive if you can promote social harmony 50x better by putting your energy into pretty much anyone else.)
Someone might object as follows. “If someone is ‘well-intentioned,’ isn’t there a series of words you* can kindly say to them so that they’ll gain insight into their situation and they’ll be able to change?”
I think the answer here is “no” and I think that’s one of the saddest things about life. Even if the answer was, “yes, BUT, …”, I think that wouldn’t change too much and would still be sad.
*(Edit) Instead of “you can kindly say to them,” the objection seems stronger if this said “someone can kindly say to them.” Therapists are well-positioned to help people because they start with a clean history. Accepting feedback from someone you have a messy history with (or feel competitive with, or all kinds of other complications) is going to be much more difficult than the ideal scenario.
One data point that seems relevant here is success probabilities for evidence-based treatments of personality disorders. I don’t think personality disorders capture everything about “destructive patterns” (for instance, one obvious thing that they miss is “person behaves destructively due to an addiction”), nor do I think that personality disorders perfectly carve reality at its joints (most traits seem to come on a spectrum!). Still, it seems informative that the treatment success for narcissistic personality disorder seems comparatively very low (but not zero!) for people who are diagnosed with it, in addition to it being vastly under-diagnosed since people with pathological narcissism are less likely to seek therapy voluntarily. (Note that this isn’t the case for all personality disorders – e.g., I think I read that BPD without narcissism as a comorbidity has something like 80% chance of improvement with evidence-based therapy.) These stats are some indication that there are differences in people’s brain wiring or conditioned patterns that are deep enough that they can’t easily be changed with lots of well-intentioned and well-informed communication (e.g., trying to change beliefs about oneself and others).
So, I think it’s a trap to assume that being ‘well-intentioned’ means that a person is always likely to improve with feedback. Even if, from the outside, it looks as though someone would change if only they could let go of a particular mindset or set of beliefs that seems to be the cause behind their “destructive patterns,” consider the possibility that this is more of a symptom rather than the cause (and that the underlying cause is really hard to address).

Lukas_Gloor 1 Mar 2023 13:02 UTC
2 points
0
in reply to: Lukas_Gloor’s comment on: Sam Altman: “Planning for AGI and beyond”
I guess another way to use the concept is the following:

Once we build AGI with realistic means, using far-from-optimal algorithms, how much room is there for it to improve its algorithms during “takeoff”/intelligence explosion? “Compute overhang” here describes the gap between compute used to build AGI in the first place vs. more efficient designs that AI-aided progress could quickly discover.
On that definition, it’s actually quite straightforward that shorter timelines imply less compute overhang.
Also, this definition arguably matches the context from Bostrom’s Superintelligence more closely, where I first came across the concept of a “hardware overhang.” Bostrom introduced the concept when he was discussing hard takeoff vs. soft takeoff.
(To complicate matters, there’s been a shift in takeoff speeds discussions where many people are now talking about pre-TAI/pre-AGI speeds of progress, whereas Bostrom was originally focusing on claims about post-AGI speeds of progress.)

Lukas_Gloor 1 Mar 2023 12:44 UTC
2 points
0
in reply to: Lukas_Gloor’s comment on: Sam Altman: “Planning for AGI and beyond”
It was brought to my attention that not everyone might use the concept of a “compute overhang” the same way.
In my terminology, there’s a (probabilistic) compute overhang to the degree that the following could happen: we invent an algorithm that will get us to TAI before we even max out compute scaling as much as we currently could.

So, on my definition, there are two ways in which we might already be in a compute overhang:

(1) Timelines are very short and we could get TAI with “current algorithms” (not necessarily GPT_n with zero tweaks, but obvious things to try that require no special insight) with less scaling effort than a Manhattan project.

(2) We couldn’t get TAI with current algorithms via any less-than-maximal scaling effort (and maybe not even with a maximal one – that part isn’t relevant for the claim), but there are highly significant algorithmic insights waiting for us (that we have a realistic chance of discovering). Once we incorporate these insights, we’ll be in the same situation as described in (1).
I would’ve guessed that Sam Altman was using it the same way, but now I’m not sure anymore.

Lukas_Gloor 25 Feb 2023 10:35 UTC
2 points
0
in reply to: Seth Herd’s comment on: Sam Altman: “Planning for AGI and beyond”
Okay, I’m also not sure if I agree with the conclusion, but the argument makes sense that way. I just feel like it’s a confusing use of terminology.
I think it would be clearer to phrase it slightly differently to distinguish “(a) we keep working on TAI and it takes ~10 years to build” from “(b) we stop research for 10 years and then build AGI almost immediately, which also takes ~10 years.” Both of those are “10 year timelines,” but (a) makes a claim about the dangers of not pushing forward as much as possible and (a) has higher “2020 training compute requirements” (the notion from Ajeya’s framework to estimate timelines given the assumption of continued research) than (b) because it involves more algorithmic progress.

Lukas_Gloor 24 Feb 2023 22:04 UTC
4 points
3
on: Sam Altman: “Planning for AGI and beyond”
Many of us think the safest quadrant in this two-by-two matrix is short timelines and slow takeoff speeds; shorter timelines seem more amenable to coordination and more likely to lead to a slower takeoff due to less of a compute overhang, and a slower takeoff gives us more time to figure out empirically how to solve the safety problem and how to adapt.
I don’t understand the part of “less of a compute overhang.” There’s still room to ramp up compute use in the next few years, so if timelines are very short, that means transformative AI happens when we’re not yet pushing the limits of compute. That seems to me where the compute overhang is uncontroversially quite large?

Conceivably, there could also be a large compute overhang in other scenarios (where actors are pushing the competitive limits of compute use). However, wouldn’t that depend on the nature of algorithmic progress? If you think present-day algorithms combined with “small” improvements can’t get us to transformative AI but some a single (or small number of) game-changing algorithmic insight(s) will get us there, then I agree that “the longer it takes us to find out the algorithmic insight(s), the bigger the compute overhang.” Is that the view here?

If so, that would be good to know because I thought many people were somewhat confident that algorithmic progress is unlikely to be “jumpy” in that way? (Admittedly, that never seemed like a rock-solid assumption to me.) If not, does anyone know how this statement about short timelines implying less of a compute overhang was meant?
What links here?
- Lukas_Gloor's comment on Taboo “compute overhang” by Zach Stein-Perlman (1 Mar 2023 20:18 UTC; 7 points)

Lukas_Gloor 24 Feb 2023 16:00 UTC
2 points
4
on: Consent Isn’t Always Enough
And let’s further imagine that [...] both have a sophisticated understanding of power dynamics, great communication, solid introspection, strong self-confidence, and the best of intentions.
Somewhat of a separate point: People may tend to overestimate these factors. A friend brought up the argument that it seems to them that people sleeping with each other often makes things awkward or creates a mess of some sort. If true, that point by itself, independently of any further reasoning about whether anyone is morally at fault and independently of questions about bad incentives around power dynamics, is an (impact-driven) argument to not do it in your professional community.

Lukas_Gloor 22 Feb 2023 14:02 UTC
2 points
0
in reply to: Daniel Paleka’s comment on: The shallow reality of ‘deep learning theory’
What if the arguments are more philosophy than math? In that case, I’d say there’s still some incentive to talk to the experts who are most familiar with the math, but a bit less so?