Ben Smith

Karma: 309

Ben Smith 9 Oct 2019 0:05 UTC
8 points
on: Can we really prevent all warming for less than 10B$ with the mostly side-effect free geoengineering technique of Marine Cloud Brightening?
I have a few technical quibbles here.
1. It’s not quite accurate to imply Philosophical Transactions of the Royal Society B itself has made a claim about the robustness of MCB. Generally only an editorial endorsed by the editorial board should be taken as a statement about the journal’s position in particular. Generally academic journals provide a forum for academic debate. Only the authors of an article are really standing behind the position. The journal only publishes work of a certain standard, and publishing a paper is somewhat an endorsement of the quality of work in the paper, but not of the finding itself.
2. Generally I understand it is not a given that MCB will work. Only the sulphur dioxide solution is really proven, and “sulphur” makes me nervous (I don’t know if there are good grounds for that). More research is needed on MCB, which a point in favour that the research should be funded and carried out as soon as possible.
3. It may be that researchers could do research into MCB without it being seen as an endorsement by the entire field that we don’t need other solutions. MCB can and should be presented as important experimental work that needs to be done, as a last resort. When it is actually proven, I think that’s when we have the dilemma about what to tell the public. But at that point, looking at the status quo, it may be our only option left.

Ben Smith 9 Feb 2020 5:41 UTC
1 point
on: Welcome to Auckland EA/SSC Meetup Group [Edit With Your Details]
For everyone interested in using evidence to find more effective ways to give and to have a career that makes an impact addressing the world’s most pressing problems.

We meet regularly every second Tuesday at 6PM. we maintain full details about events on our meetup page at https://www.meetup.com/Effective-Altruism-Auckland/

We also catch up regularly on our FB Messenger group which you are welcome to join at https://m.me/join/Abaero_uwyCzE_9m

We are building a community of people committed doing as much good as possible in life. We consider scale, neglectedness, and tractability to look for underrated opportunities to do good and then go over those opportunities. We support and care for one another to plan for the long term: building, not burnout.

Ben Smith 25 Nov 2020 2:49 UTC
3 points
on: Propinquity Cities So Far
Hi Mako,
First, a quick empirical quibble:
Cost-efficacy, beyond a point, is not rewarded with any increase in market share. The unfit are not selected out.
Not true, I don’t think. I have personally noticed rental rates decline in Auckland City Centre during the covid-19 period because of the abnormally low demand here, and in a Downtown LA housing boom several years ago, it was common for landlords to give away free months’ rent. If the amount of supply exceeds demand by a sufficient amount, you will see market forces work. Why wouldn’t you?
I think I have two key objections, not necessarily an exhaustive list but two fairly key objections that come to mind quickly:
(1) Is there any mechanism here for actually increasing the sum total amount of high-demand property? If not, there are still going to be a large amount of people who didn’t get to be located in high-demand areas. If so, is that mechanism intrinsic to the Propinquity City model or could it be implemented just as well within our current system of property ownership? For instance, I imagine this system would include rules about land use that would legally permit density where many people want to live. But there is no technical reason we couldn’t implement the same rules within our current system. As one can observe in areas of cities where density is in demand and is allowed, over time, density is actually developed by the market according to demand. And that’s a hell of a lot easier than developing an entirely new economic system, particularly if it also involves switching to a modular housing system.
(2) And largely as a result of my previous comment, I suspect that the increase in well-being through the implementation of this system would not be particularly large compared to more limited increases that could be obtained with more incremental land use liberalisation plans. That would limit its potential as an “effective cause area” because it would be much less tractable than other reform proposals that accomplish most of the same value as this system.
As you said, it would be very difficult to implement this in existing cities so this is limited to designing in new cities. Initially I thought this was a limitation, but then I reconsidered: in the next few decades there will be billions of people across the developing world moving into new urban areas, so there is plenty of scope for implementation if developers wanted to implement it.
It would be worth doing an analysis. I recommend, if you proceed to further develop the idea, you do an impact estimate via spreadsheet or other software. Consider the number of people likely to be impacted, the increase in well-being that would result (using QALYs or other empirical measure; be sure to consider anxiety arising from the lack of stability that people may be asked to move at any time), and the amount of political energy that would be need to be invested to achieve the calculated impact. And calculate this relative to the next best solution (maybe a classical Georgist Land Value Tax combined with liberalisation of land use would be a good baseline).

Ben Smith 25 Nov 2020 4:04 UTC
1 point
in reply to: Viliam’s comment on: Propinquity Cities So Far
But just this point is a rabbit hole of questions in itself.
If we equipped every store with tracking devices that measured the amount of time spent by people visiting (!!!!), that might incentivize making products really hard to find in the store, or making really long lines, so people spend more time there.
If it’s the raw number of times people spend visiting the store, I am sure there are ways to game that too (“visit us 10 times this week for 10% off your next purchase!”). There could be laws against that, but..

Ben Smith 25 Nov 2020 4:08 UTC
2 points
in reply to: MikkW’s comment on: Propinquity Cities So Far
in practice, similar proposals (that have actually been implemented, both in communist and nominally capitalist countries) have vastly underestimated the difficulty of this problem, leading to large problems that have made life harder for many people
Singapore and Hong Kong are two generally-capitalist cities that have employed largely government housing development of very dense, tall housing.
It worked REALLY well in capitalist, uber-wealthy Singapore (GDP per capita substantially higher than the USA). ~78% of Singaporeans live in housing developed by the Singapore Government’s Housing and Development Board (https://en.wikipedia.org/wiki/Housing_and_Development_Board). It works a bit less well in Hong Kong, but still remarkably well considering how many people are housed in the very small area available.

Ben Smith 25 Nov 2020 4:13 UTC
3 points
in reply to: mako yass’s comment on: Propinquity Cities So Far
Two examples come to mind:
- fascinating, but you wouldn’t want to emulate it: https://en.wikipedia.org/wiki/Kowloon_Walled_City
- “Tokyo may have found the solution to soaring housing costs”—this one we probably should absolutely study closely: https://www.vox.com/2016/8/8/12390048/san-francisco-housing-costs-tokyo

Ben Smith 4 May 2021 17:16 UTC
1 point
on: Conservatism in neocortex-like AGIs
As you’re aware, I’m very much exploring this approach using a multi-objective decision-making approach, with conservativism through only acting when an action is non-negative on the whole set of objective functions that an actor regards.
The alternative, Bayesian AGI approach is also worth thinking about too. A conservative Bayesian AGI might not need multiple objectives. For each action, it just needs a single probability distribution of outcomes. If there are multiple theories of how to translate consequences of its actions into its single utility function, each of those theories might be given some weight, and then they’d be combined into the probability distribution. Then a conservative Bayesian AGI only acts if an action’s utility function doesn’t exceed below zero. Or maybe there’s always some remote possibility of going below zero, and programming this sort of behavior would be absolutely paralyising. In that case maybe we just make it loss-averse rather than strictly avoiding any possibility of a negative outcome.

Ben Smith 7 Jul 2021 8:27 UTC
3 points
AF
on: Supplement to “Big picture of phasic dopamine”
Interesting. Is it fair to say that Mollick’s system is relatively more “serial” with fewer parallelisms at the subcortical level, whereas you’re proposing a system that’s much more “parallel” because there are separate systems doing analogous things at each level? I think that parallel arrangement is probably the thing I’ve learned most personally from reading your work. Maybe I just hadn’t thought about it because I focus too much on valuation and PFC decision-making stuff and don’t look broadly enough at movement and other systems.
Apropos of nothing, is there any role for the visual cortex within your system?
I too am puzzled about why some people talk about “mPFC” and others talk about “vmPFC”. I focus on “vmPFC”, mostly because that’s what people in my field talk about. “vmPFC” focuses much more on valuation systems. Theoretically I guess “mPFC” would also include the dorsomedial prefrontal cortex, which includes the anterior cingulate cortex, I guess some systems related to executive control, perhaps response inhibition (although that’s usually quite lateral), perhaps abstract processing. Tends to be a bit of a decision-making homunculous of sorts :/ And then there’s the ACC, whose role in various things is fairly well defined.
So maybe authors who talk about the mPFC aren’t as concerned about distinguishing value processing from all those other things.

Ben Smith 30 Aug 2021 1:48 UTC
7 points
in reply to: Capybasilisk’s comment on: Signaling Virtuous Victimhood as Indicators of Dark Triad Personalities
I know it’s a touchy topic. In my defense, the research is solid, published in social psychology’s top journal. I suppose the study deals with rhetoric in a political context. This community has a long history of drawing on social and cognitive psychological research to understand fallacies of thought and rhetoric (HPMOR), and I posted in that tradition. Apologies if I have strayed a little too far into a politicized area.
One needn’t see this study as a shot at any particular political side—I can imagine people engaging ‘virtuous-victimhood-signalling’ within a wide range of different politicized narratives, as well as in completely apolitical contexts.
It also shouldn’t be read to delegitimize victims from speaking out about their perspective! But perhaps it does provide evidence that sympathy can be weaponized in rhetorical conflict. We can all recognize this in political opponents and be blind to it amongst political allies.

Ben Smith 18 Sep 2021 2:45 UTC
1 point
on: Why I am not currently working on the AAMLS agenda
Interesting comments, thanks. Currently exploring an agenda of my own and this is food for thought.

Ben Smith 12 Oct 2021 0:29 UTC
LW: 3 AF: 2
AF
in reply to: Steven Byrnes’s comment on: A brief review of the reasons multi-objective RL could be important in AI Safety Research
That’s right. What I mainly have in mind is a vector of Q-learned values V and a scalarization function that combines them in some (probably non-linear) way. Note that in our technical work, the combination occurs during action selection, not during reward assignment and learning.
I guess whether one calls this “multi-objective RL” is semantic. Because objectives are combined during action selection, not during learning itself, I would not call it “single objective RL with a complicated objective”. If you combined objectives during reward, then I could call it that.
re: your example of real-time control during hunger, I think yours is a pretty reasonable model. I haven’t thought about homeostatic processes in this project (my upcoming paper is all about them!). Definitely am not suggesting that our particular implementation of “MORL” (if we can call it that) is the only or even the best sort of MORL. I’m just trying to get started on understanding it! I really like the way you put it. It makes me think that perhaps the brain is a sort of multi-objective decision-making system with no single combinatory mechanism at all except for the emergent winner of whatever kind of output happens in a particular context—that could plausibly be different depending on whether an action is moving limbs, talking, or mentally setting an intention for a long term plan.

Ben Smith 12 Oct 2021 0:37 UTC
1 point
in reply to: Koen.Holtman’s comment on: A brief review of the reasons multi-objective RL could be important in AI Safety Research
The only resource I’d recommend, beyond MODEM, when that’s back up, and our upcoming JAMAAS special issue, is to check out Elicit, Ought’s GPT-3-based AI lit search engine (yes, they’re teaching GPT-3 about how to create a superintelligent AI. hmm). It’s in beta, but if they waitlist you and don’t accept you in, email me and I’ll suggest they add you. I wouldn’t say it’ll necessarily show you research you’re not aware of, but I found it very useful for getting into the AI Alignment literature for the first time myself.
https://elicit.org/

Ben Smith 3 Mar 2022 3:17 UTC
5 points
in reply to: mako yass’s comment on: Are our community grouphouses typically rented, or owned?
Owning a house doesn’t give you fewer ongoing costs. It tends to give you less costs overall, but that’s heavily contingent on rental and mortgage rates. And it’s actually more administrative hassle, because you have to spend money on rates (local property taxes), repairs, and so on. The main thing owning a house gives you is it gives you is stability in terms of predicting future price changes.

Ben Smith 9 Jun 2022 15:23 UTC
11 points
2
on: AGI Safety FAQ / all-dumb-questions-allowed thread
It seems like even amongst proponents of a “fast takeoff”, we will probably have a few months of time between when we’ve built a superintelligence that appears to have unaligned values and when it is too late to stop it.

At that point, isn’t stopping it a simple matter of building an equivalently powerful superintelligence given the sole goal of destroying the first one?

That almost implies a simple plan for preparation: for every AGI built, researchers agree together to also build a parallel AGI with the sole goal of defeating the first one. perhaps it would remain dormant until its operators indicate it should act. It would have an instrumental goal of protecting users’ ability to come to it and request the first one be shut down..

Ben Smith 30 Jun 2022 21:50 UTC
1 point
on: Open Problems in Negative Side Effect Minimization
One approach to low-impact AI might be to pair an AGI system with a human supervisor who gives it explicit instructions where it is permitted to continue. I have proposed a kind of “decision paralysis” where, given multiple conflicting goals, a multi-objective agent would simply choose not to act (I’m not the first or only one to describe this kind of conservativism, but I don’t recall the framing others have used). In this case, the multi-objectives might be the primary objective and then your low-impact objective.
This might be a way forward to deal with your “High-Impact Interference” problem. Perhaps preventing an agent to engage in high-impact interference is a necessary part of safe AI. When fulfillment of the primary objective seems to require engaging in high-impact interference, a safe AI might report to a human supervisor that it cannot proceed because of a particular side effect. The human supervisor could then decide whether the system should proceed or not. If the human supervisor makes the judgement the system should proceed, then they can re-specify the objective to permit the potential side effect, by specifying it as part of the primary objective itself.

Ben Smith 1 Jul 2022 0:55 UTC
1 point
on: A descriptive, not prescriptive, overview of current AI Alignment Research
Very very helpful! The clustering is obviously a function of the corpus. From your narrative, it seems like you only added the missing arx.iv files after clustering. Is it possible the clusters would look different with those in?

Ben Smith 1 Jul 2022 0:57 UTC
1 point
on: A descriptive, not prescriptive, overview of current AI Alignment Research
I would very much like to see your dataset, as a zotero database or some other format, in order to better orient myself to the space. Are you able to make this available somehow?

Ben Smith 2 Jul 2022 21:06 UTC
6 points
0
on: Looking back on my alignment PhD
That was an inspiring and enjoyable read!

Can you say why you think AUP is “pointless” for Alignment? It seems to me attaining cautious behavior out of a reward learner might turn out to be helpful. Overall my intuition is it could turn out to be an essential piece of the puzzle.

I can think of one or two reasons myself, but I barely grasp the finer points of AUP as it is, so speculation on my part here might be counterproductive.

Ben Smith 3 Jul 2022 1:26 UTC
3 points
0
in reply to: TurnTrout’s comment on: Looking back on my alignment PhD
Not sure what I was thinking about, but probably just that my understanding is that “safe AGI via AUP” would have to penalize the agent for learning to achieve anything not directly related to the end goal, and that might make it too difficult to actually achieve the end goal when e.g. it turns out to need tangentially related behavior.
Your “social dynamics” section encouraged me to be bolder sharing my own ideas on this forum, and I wrote up some stuff today that I’ll post soon, so thank you for that!

Ben Smith 4 Jul 2022 15:54 UTC
11 points
9
in reply to: Davidmanheim’s comment on: We Need a Consolidated List of Bad AI Alignment Solutions
A consolidated list of bad or incomplete solutions could have considerable didactic value—it could keep people learn more about the various challenges involved.