Feels like most of these except the kidney are fairly easily faked or not that relevant. Community engagement and working for your own non-profit are about the first thing anyone seeking to grift will do, as well as people who are sincere (including as others have pointed out, the people who are sincerely wrong)
And Sam Bankman Fried was a vegan who apparently started off with a completely sincere desire to work for animal nonprofits. Treating these as signals and the people complaining about his behaviour and some of the things he said as noise was a bad move.
And apart from the modal return being probably ~0 (which might not be a problem if funding lots of useless stuff guarantees sufficiently useful projects get funded), it is also possible for longtermist work to be actively counterproductive, with significant uncertainty around the direction as well as the magnitude of the impact of many longtermist activities[1]
Indeed many longtermist arguments strongly imply that work of other longtermists is counterproductive (it is hard to see how Anthropic and Pause AI can both be right about frontier models)
And even you're certain you've picked the correct side of a particular risk-mitigation debate, political donations and campaigning are particularly prone to provoke responses from opponents. This is true of a lot of campaigning, but "AI is very dangerous" is more easily inadvertently motivating accelerationists to act to militarise it first than "this suffering could be stopped" is pivoted into an argument for more cages or fewer bednets
there are probably exceptions to this; it's difficult to imagine that an asteroid early warning system will hurt us, even if it turns out to be useless
As a non-vegan, if people tell me they're vegan I won't necessarily feel judged but can ensure they get a reasonable choice of food they will feel comfortable eating
Whereas if they describe themselves as a "pro-animal person" it sounds like someone who likes puppies (or if activism is implied, potentially more extreme than vegans, though of course that very much depends on how much they elaborate!). Alternatives like "I prefer plant-based food" sound like a taste preference or fad diet.
Individual people don't tend to brag about how as they've read everything that everyone else wrote, they've learned better than everybody else and can replace everybody else with better outputs at lower cost.
If they did this, I suspect they would not be popular
(Also, if we're humouring AI companies' claims that their products should be treated just like humans when it comes to "learning", we should probably question the double standard where both corporations and computer programs evade any accountability for AI generated outputs which would be considered unethical, malicious or negligent if they were the work by human employees...)
Interesting numbers (although with it being AI, I wonder if it's derived from substantive research others might have done or if they're purely arbitrary figures hallucinated to fill the gap).
A single factory making 2% of global burgers sounds like an implausibly large factory, but I don't see why it would actually need to be that big to achieve cost parity. There isn't a massive R&D cost (cf cultured meats and some alternative proteins) and most of the ingredients I'm aware of are available in bulk at relatively low cost. Of course there is also a wide disparity between burgers to achieve cost parity with and wholesale and retail prices. Being cheaper than the cheapest brand probably isn't necessary, being cheaper than the most expensive burgers brands which market themselves based on meat quality (which I think has already been achieved) probably isn't sufficient
I'm not sure naive total utility maximization [in a static framework] is the best framework to be thinking about dealing with existential risk over time.[1]
Assuming the number of risks and error bars are not trivially small, the universal outcome of concentrating all your risk mitigations on one is that most risks continue to be a high as they could possibly be. The modal outcome is that the risks ignored includes at least one risk greater than the one all efforts are concentrated on mitigating. Some reasonable assumptions in the article above show this can hold even where the actual biggest risk is orders of magnitude greater than the one targeted. In the diversified approach, less money are devoted to reducing the perceived biggest risk, but the rest is apportioned to reducing other risks. This seems more robust to conventional assumptions like uncertainty and some risks being easier to mitigate than others.
And tbh I'm not even seeing an average utility boost from concentrating on the single largest risk as opposed to mitigating lots of risks without ancillary assumptions like increasing returns to risk reduction expenditure or the actual value of many risks under consideration being 0.
It would be interested to see a more detailed and systematicreport on the activity and findings so far.
In some respects, it seems like a strange thing for GiveDirectly to be piloting. On the one hand, GiveDirectly has expertise in systematic studies of behavioural change in LDCs , and the chatbot possibly also performed programmatic functions in a cost effective manner. On the other hand it involves a charity known for its "let local people decide how to use money spent on their behalf, Western aid agencies doing it can be disempowering and often wrong" ethos asking "which parameters should we use to fine tune this [adaptation of a commercial] product we've designed to give them the most suitable answers before scaling up its deployment"... which seems like a very different ethos and approach.[1]
The conclusions highlighted from the research so far - both that if you give poor Rwandans access to ChatGPT they have a similar range of interaction to other humans[2] and that responses generated by an LLM with no meaningful local training dataset were often inadequate - seem unsurprising. I am sympathetic to arguments that people make better decisions with access to information, but I am also sympathetic to arguments a ChatGPT derivative is not the most valuable information Rwandans could receive (and may have minimal or even negative value)
I'm not actually sure what the costs of acquiring relevant local data and training a chatbot to achieve greater fluency in spoken Kinyarwada dialects and safeguarding against advice that is very bad in a local context are,[3] but they seem like a pretty relevant benchmark, since they might actually be considerable on a per user basis and the alternative for critical information like "what is the nearest health centre" might be something like signing people up to email lists, or a small number of human agents in Kigali costing surprisingly little.[4] I guess there's also the "who's paying?" question, especially when the current implementation appears to involve providing training data for one of the world's most valuable companies (and obscure languages may or may not add value to their model).
I feel one relevant benchmark for GiveDirectly specifically might be "what is the estimated cost per per person reached to improve it: would locals rather have a better chatbot or the cash?". It's possible the insights they're getting are extremely valuable particularly in the context of limited/no of web access, but it's possible they're not...
the relevant comparator might be the One Laptop Per Child project. Well intentioned, theory of change centred on the idea that people in LEDCs can be empowered by interacting with modern technology and better information too, but perhaps actual educational benefits didn't really stack up with the costs and the participants would have chosen to have something other than a computer
I must admit, I am curious about the extent to which Rwandans engaged in "witty banter" or attempts to manipulate the chatbot into saying something silly...
I don't know how bad the speaking and dataset is, and whether an adequate "solution" looks like a finetuning prompt with some info or developing a corpus of services data and synthetic idiosyncratic Kinyarwada to fix the model, but the latter option could be very expensive compared with the people it would actually reach...
yeah, agree there are some military subsystems that would go down (and alternative PNT options have their own drawbacks). But it's not a particularly decisive advantage: to take the topical example if Russia took out global GNSS it wouldn't seriously harm the defensive posture of Ukranian front line troops that have been experiencing local GNSS jamming and finding alternative ways to get drones to work for years now, and would disrupt a bunch of Russian based logistics and civilian systems further from the front line nearly as much as it did to Kyiv's. So it wouldn't even significantly help them advance in Ukraine, even before the likely response from NATO and China was considered...
I think the conclusion that diversification is a good strategy follows trivially from the optimizers' curse: if you focus all your efforts on the apparent biggest threat, you've probably just focused on the cause with the largest risk assessment error and entirely neglected the actual biggest threat. A more diverse allocation is more likely to address the actual biggest threat. If there are diminishing returns to resources allocated to mitigate particular risk areas that makes diversification look better (complex nonlinear returns complicate it). As does the possibility that larger errors in risk assessment for a particular type of risk are inversely correlated with ability to invest in the best mitigation strategy for that type of risk.[1]
But your point about adverse selection is a good one too. Metrics are gameable, and there are stronger incentives to do so when funding is "winner takes all" rather than "we disburse funds to a wide selection of causes and value rigour and disclosure of uncertainties"
I think there are probably exceptions to this, but I think it's generally true. Good understanding of celestial mechanics and early warning systems, for example, are absolutely essential to potentially preventing hypothetical large space rocks colliding with earth, but also mean that we are less likely to overestimate the imminence of destruction by a rogue asteroid than we are for more unpredictable phenomenon.
A few comments, some of which you may be intending to cover in updates
First of all we actually have a pretty decent idea of what happens in GNSS-denied environments because localised GNSS jamming is a thing,[1] It's especially a thing in combat zones, which means that people and infrastructure affected typically have other problems[2]
Because GNSS denial is a thing, militaries have alternative PNT systems to aid them in combat. So actively disabling GNSS satellites is a pretty extreme measure that mostly hurts civilians, including in about 250 countries not currently at war with you. And if it involves use of anti-satellite weapons or EMPs, probably takes out a whole bunch of other space infrastructure too[3]
As it's a pretty extreme measure that annoys everyone worldwide without even offering you a decisive advantage in a local conflict, it's most likely to happen during escalation of a great power conflict. Great power conflicts mean that sectors like maritime would be experiencing COVID level downturns already. The "solar storm" is more interesting because it might be largely unexpected (and would also likely impair a lot of non-GNSS comms stuff)
But costs of nuisance level GNSS jamming in borderlands between states not actually at war (say, the Baltic...) has a scaled down version of this impact which I guess might be underestimated...
current generation GNSS satellites are in fairly empty medium earth orbits and can be disabled without kinetic weapons, but this doesn't rule out collateral damage and likely won't be the case for future GNSS (in part because they want more redundancy even though the system(s) just work.
Feels like most of these except the kidney are fairly easily faked or not that relevant. Community engagement and working for your own non-profit are about the first thing anyone seeking to grift will do, as well as people who are sincere (including as others have pointed out, the people who are sincerely wrong)
And Sam Bankman Fried was a vegan who apparently started off with a completely sincere desire to work for animal nonprofits. Treating these as signals and the people complaining about his behaviour and some of the things he said as noise was a bad move.
And apart from the modal return being probably ~0 (which might not be a problem if funding lots of useless stuff guarantees sufficiently useful projects get funded), it is also possible for longtermist work to be actively counterproductive, with significant uncertainty around the direction as well as the magnitude of the impact of many longtermist activities[1]
Indeed many longtermist arguments strongly imply that work of other longtermists is counterproductive (it is hard to see how Anthropic and Pause AI can both be right about frontier models)
And even you're certain you've picked the correct side of a particular risk-mitigation debate, political donations and campaigning are particularly prone to provoke responses from opponents. This is true of a lot of campaigning, but "AI is very dangerous" is more easily inadvertently motivating accelerationists to act to militarise it first than "this suffering could be stopped" is pivoted into an argument for more cages or fewer bednets
there are probably exceptions to this; it's difficult to imagine that an asteroid early warning system will hurt us, even if it turns out to be useless
As a non-vegan, if people tell me they're vegan I won't necessarily feel judged but can ensure they get a reasonable choice of food they will feel comfortable eating
Whereas if they describe themselves as a "pro-animal person" it sounds like someone who likes puppies (or if activism is implied, potentially more extreme than vegans, though of course that very much depends on how much they elaborate!). Alternatives like "I prefer plant-based food" sound like a taste preference or fad diet.
Individual people don't tend to brag about how as they've read everything that everyone else wrote, they've learned better than everybody else and can replace everybody else with better outputs at lower cost.
If they did this, I suspect they would not be popular
(Also, if we're humouring AI companies' claims that their products should be treated just like humans when it comes to "learning", we should probably question the double standard where both corporations and computer programs evade any accountability for AI generated outputs which would be considered unethical, malicious or negligent if they were the work by human employees...)
Interesting numbers (although with it being AI, I wonder if it's derived from substantive research others might have done or if they're purely arbitrary figures hallucinated to fill the gap).
A single factory making 2% of global burgers sounds like an implausibly large factory, but I don't see why it would actually need to be that big to achieve cost parity. There isn't a massive R&D cost (cf cultured meats and some alternative proteins) and most of the ingredients I'm aware of are available in bulk at relatively low cost. Of course there is also a wide disparity between burgers to achieve cost parity with and wholesale and retail prices. Being cheaper than the cheapest brand probably isn't necessary, being cheaper than the most expensive burgers brands which market themselves based on meat quality (which I think has already been achieved) probably isn't sufficient
I'm not sure naive total utility maximization [in a static framework] is the best framework to be thinking about dealing with existential risk over time.[1]
Assuming the number of risks and error bars are not trivially small, the universal outcome of concentrating all your risk mitigations on one is that most risks continue to be a high as they could possibly be. The modal outcome is that the risks ignored includes at least one risk greater than the one all efforts are concentrated on mitigating. Some reasonable assumptions in the article above show this can hold even where the actual biggest risk is orders of magnitude greater than the one targeted. In the diversified approach, less money are devoted to reducing the perceived biggest risk, but the rest is apportioned to reducing other risks. This seems more robust to conventional assumptions like uncertainty and some risks being easier to mitigate than others.
And tbh I'm not even seeing an average utility boost from concentrating on the single largest risk as opposed to mitigating lots of risks without ancillary assumptions like increasing returns to risk reduction expenditure or the actual value of many risks under consideration being 0.
It would be interested to see a more detailed and systematic report on the activity and findings so far.
In some respects, it seems like a strange thing for GiveDirectly to be piloting. On the one hand, GiveDirectly has expertise in systematic studies of behavioural change in LDCs , and the chatbot possibly also performed programmatic functions in a cost effective manner. On the other hand it involves a charity known for its "let local people decide how to use money spent on their behalf, Western aid agencies doing it can be disempowering and often wrong" ethos asking "which parameters should we use to fine tune this [adaptation of a commercial] product we've designed to give them the most suitable answers before scaling up its deployment"... which seems like a very different ethos and approach.[1]
The conclusions highlighted from the research so far - both that if you give poor Rwandans access to ChatGPT they have a similar range of interaction to other humans[2] and that responses generated by an LLM with no meaningful local training dataset were often inadequate - seem unsurprising. I am sympathetic to arguments that people make better decisions with access to information, but I am also sympathetic to arguments a ChatGPT derivative is not the most valuable information Rwandans could receive (and may have minimal or even negative value)
I'm not actually sure what the costs of acquiring relevant local data and training a chatbot to achieve greater fluency in spoken Kinyarwada dialects and safeguarding against advice that is very bad in a local context are,[3] but they seem like a pretty relevant benchmark, since they might actually be considerable on a per user basis and the alternative for critical information like "what is the nearest health centre" might be something like signing people up to email lists, or a small number of human agents in Kigali costing surprisingly little.[4] I guess there's also the "who's paying?" question, especially when the current implementation appears to involve providing training data for one of the world's most valuable companies (and obscure languages may or may not add value to their model).
I feel one relevant benchmark for GiveDirectly specifically might be "what is the estimated cost per per person reached to improve it: would locals rather have a better chatbot or the cash?". It's possible the insights they're getting are extremely valuable particularly in the context of limited/no of web access, but it's possible they're not...
the relevant comparator might be the One Laptop Per Child project. Well intentioned, theory of change centred on the idea that people in LEDCs can be empowered by interacting with modern technology and better information too, but perhaps actual educational benefits didn't really stack up with the costs and the participants would have chosen to have something other than a computer
I must admit, I am curious about the extent to which Rwandans engaged in "witty banter" or attempts to manipulate the chatbot into saying something silly...
I don't know how bad the speaking and dataset is, and whether an adequate "solution" looks like a finetuning prompt with some info or developing a corpus of services data and synthetic idiosyncratic Kinyarwada to fix the model, but the latter option could be very expensive compared with the people it would actually reach...
I suspect you get many person years of Rwandan human call centre time for a month or two of a mid-level AI engineer's time...
yeah, agree there are some military subsystems that would go down (and alternative PNT options have their own drawbacks). But it's not a particularly decisive advantage: to take the topical example if Russia took out global GNSS it wouldn't seriously harm the defensive posture of Ukranian front line troops that have been experiencing local GNSS jamming and finding alternative ways to get drones to work for years now, and would disrupt a bunch of Russian based logistics and civilian systems further from the front line nearly as much as it did to Kyiv's. So it wouldn't even significantly help them advance in Ukraine, even before the likely response from NATO and China was considered...
I think the conclusion that diversification is a good strategy follows trivially from the optimizers' curse: if you focus all your efforts on the apparent biggest threat, you've probably just focused on the cause with the largest risk assessment error and entirely neglected the actual biggest threat. A more diverse allocation is more likely to address the actual biggest threat. If there are diminishing returns to resources allocated to mitigate particular risk areas that makes diversification look better (complex nonlinear returns complicate it). As does the possibility that larger errors in risk assessment for a particular type of risk are inversely correlated with ability to invest in the best mitigation strategy for that type of risk.[1]
But your point about adverse selection is a good one too. Metrics are gameable, and there are stronger incentives to do so when funding is "winner takes all" rather than "we disburse funds to a wide selection of causes and value rigour and disclosure of uncertainties"
I think there are probably exceptions to this, but I think it's generally true. Good understanding of celestial mechanics and early warning systems, for example, are absolutely essential to potentially preventing hypothetical large space rocks colliding with earth, but also mean that we are less likely to overestimate the imminence of destruction by a rogue asteroid than we are for more unpredictable phenomenon.
A few comments, some of which you may be intending to cover in updates
Interested to see the followup
Similarly, people working with navigation systems prone to spoofed location results (maritime navigation) have to find workarounds
though electronic warfare can affect neutral neighbours and overflying aircraft too...
current generation GNSS satellites are in fairly empty medium earth orbits and can be disabled without kinetic weapons, but this doesn't rule out collateral damage and likely won't be the case for future GNSS (in part because they want more redundancy even though the system(s) just work.