One completely realistic example of an agent is given in the appendix (an agent that recommends actions to improve soil health or carbon sequestration). Some more examples are given in this comment:
An info agent that recommends me info resources (news, papers, posts, op-eds, books, videos) to consume, based on my current preferences and demands (and info from other others, such as those listed below, or this agent that predicts the personalised information value of the comments on the web)
Scaling to the group/coordination: optimise informational intake of a team, an organisation, or a family
Learning agent that recommends materials based on the previous learning trajectory and preferences, a-la liirn.space
Scaling to the group/coordination: coordinate learning experiences and lessons between individual learning agents based on who is on what learning level, availailiity, etc.
Financial agent that recommends spending based on my mid- and long-term goals
Equivalent of this for an organisation: “business development agent”, recommends an org to optimise strategic investments based on the current market situation, goals of the company, other domain-specific models provided (i.e., in the limit, communicated by other Gaia agents responsible for these models), etc.
Investment agent recommends investment strategy based on my current financial situation, financial goals, and other investment goals (such as ESG)
Scaling to the group/coordination: optimise joint investment strategy for income and investment pools a-la pandopooling.com
Energy agent: decides when to accumulate energy and when to spend it based on the spot energy prices, weather forecast for renewables, and the current and future predicted demands for power
Scale this to microgrids, industrial/manufacturing sites, etc.
The Gaia network does not currently exist AFAIK, yet it is already possible to build all of those things right now, right? Can you elaborate on what would be different in a Gaia network version compared to my building those tools right now without ever having heard of Gaia?
Right now, if the Gaia Network already existed, but there were little models and agents on it, there would be no or little advantages (e.g., leveraging the tooling/infra built for the Gaia Network) in joining the network.
This is why I personally think that the bottom-up approach: building these apps and scaling them (thus building up QRFs) first is somewhat more promising path than the top-down approach, the ultimate version of which is the OAA itself, and the research agenda of building Gaia Network is a somewhat milder version, but still top-down-ish. That’s why in the comment that I already linked to above, the implication is that these disparate apps/models/”agents” are built first completely independently (mostly as startups), without conforming to any shared protocol (like the Gaia protocol), and only once they grow large and sharing information across the domains becomes evidently valuable to these startups, then the conversation about a shared protocol will find more traction.
Then, why a shared protocol, still? Two reasons:
Practical: it will reduce transaction costs for all the models across the domains to start communicating to improve the predictions of each other. Without a shared protocol, this requires ad-hoc any prospective direction of information sharing. This is the practicality of any platforms, from the Internet itself to Airbnb to SWIFT (bank wires), and Gaia Network should be of this kind, too.
AI and catastrophic risk safety: to ensure some safety against rogue actors (AI or hybrid human-AI teams or whatever) through transparency and built-in mechanisms, we would want as much economic activity to be “on the network” as possible.
You may say that this is would be a tough political challenge to convince everybody to conform to the network in the name of some AI safety, but surely this would still be a smaller challenge than to abolish much of the current economic system altogether as (apparently) implied by the “vanilla” Davidad’s OAA, and as we discuss throughout the article. In fact, this is one of the core points of the article.
Then, even though I advocate for a bottom-up approach above, there is still a room, and even a need for a parallel top-down activity (given the AGI timelines), so these two streams of activity meet each other somewhere in the middle. This is why we are debating all these blue-sky AI safety plans on LessWrong at all; this is why OAA was proposed, and this is why we are now proposing the Gaia Network.
My friendly suggestion would be to have one or more anecdotes of a specific concrete (made-up) person trying to build a specific concrete AI thing, and they find themselves facing one or more very specific concrete transaction costs (in the absence of Gaia), and then Gaia enters the scene, and then you specifically explain how those specific transaction costs thereby become lower or zero, thanks to specific affordances of Gaia.
I’ve tried to read everything you’ve linked, but am currently unable to generate even one made-up anecdote like that.
(I am not the target audience here, this isn’t really my wheelhouse, instead I’m just offering unsolicited advice for when you’re pitching other people in the future.)
(Bonus points if you have talked to an actual human who has built (or tried to build) such an AI tool and they say “oh yeah, that transaction cost you mentioned, it really exists, and in fact it’s one of our central challenges, and yes I see how the Gaia thing you describe would substantially help mitigate that.”)
Hey Steven, I’ll answer your question/suggestion below. One upfront request: please let us know if this helps. We’ll write a follow-up post on LW explaining this.
As mentioned in the appendix, most of what we wrote up is generalized from concrete people (not made-up, my IRL company Digital Gaia) trying to build a specific concrete AI thing (software to help farmers and leaders of regeneration projects maximize their positive environmental impact and generate more revenue by being able to transparently validate their impact to donors or carbon credit buyers). We talked extensively to people in the ag, climate and nature industries, and came to the conclusion that the lack of transparent, unbiased impact measurement and validation—ie, exactly the transaction costs you mention—is the reason why humanity is massively underinvested in conservation and regeneration. There are gazillions of “climate AI” solutions that purport to measure and validate impact, but they are all fundamentally closed and centralized, and thus can’t eliminate those transaction costs. In simple terms, none of the available systems, no matter how much money they spent on data or compute, can give a trustworthy, verifiable, privacy-preserving rationale for either scientific parameters (“why did you assume the soil carbon captured this year in this hectare was X tons?”) or counterfactuals (“why did you recommend planting soybeans with an alfalfa rotation instead of a maize monoculture?”). We built the specific affordances that we did—enabling local decision-support systems to connect to each other forming a distributed hierarchical causal model that can perform federated partial pooling—as a solution to exactly that problem:
The first adopters (farmers) already get day-1 benefits (a model-based rationale that is verifiable and privacy-preserving), using models and parameters bootstrapped from the state of the art of open transaction-cost-reduction: published scientific literature, anecdotal field reports on the Web, etc.
The parameter posteriors contributed by the first adopters drive the flywheel. As more adopters join, network effects kick in and transaction RoI increases: both parameter posteriors become increasingly truthful and easier to verify (posterior estimates from multiple sources mostly corroborate each other, confidence bands get narrower).
Any remaining uncertainty, in turn, drives incentives for scientists and domain experts to refine models and perform experiments, which will benefit all adopters by making their local impact rationales and recommendations more refined.
As an open network, models and parameters can be leveraged in adjacent domains, which then generate their own adjacencies, eventually covering the entire spectrum of science and engineering. For instance, we have indoor farms and greenhouses interested in our solution; they would need to incorporate not only agronomic models but also energy consumption and efficiency models. This then opens the door to industrial and manufacturing use cases, and so on and so forth...
We validated the first two steps of this theory in a pilot; it worked so well that our pilot users keep ringing us back saying they need us to turn it into production-ready software...
Disclaimer: We did not fully implement or validate two important pieces of the architecture that are alluded to in the post: free energy-based economics and trust models. These are not crucial for a small-scale, controlled pilot, but would be relevant for use at scale in the wild.
An actual anecdote may look something like this: “We are a startup that creates nutrition assistant and family menu helper app. We collect anonymised data from the users and ensure differential privacy, yada-yada. We want to sell this data to hedge funds that trade food company stocks (so that we can offer the app for free for to our users), but we need to negotiate the terms of these agreements in an ad-hoc way with each hedge fund individually, and we don’t have a principled way to come up with a fair price for the data. We would benefit from something like a ‘platform’ on which we can just publish the API spec of our data and then the platform (i.e., the Gaia Network) takes care of finding buyers for our data and paying us a fair price for it, etc.”
BTW, this particular example sounds just like Numer.ai Signals, but Gaia Network is supposed to be more general and not to revolve around the stock market alone. E.g., the same nutritional data could be bought by food companies themselves, logistics companies, public health agencies, etc.
One completely realistic example of an agent is given in the appendix (an agent that recommends actions to improve soil health or carbon sequestration). Some more examples are given in this comment:
An info agent that recommends me info resources (news, papers, posts, op-eds, books, videos) to consume, based on my current preferences and demands (and info from other others, such as those listed below, or this agent that predicts the personalised information value of the comments on the web)
Scaling to the group/coordination: optimise informational intake of a team, an organisation, or a family
Learning agent that recommends materials based on the previous learning trajectory and preferences, a-la liirn.space
Scaling to the group/coordination: coordinate learning experiences and lessons between individual learning agents based on who is on what learning level, availailiity, etc.
Financial agent that recommends spending based on my mid- and long-term goals
Equivalent of this for an organisation: “business development agent”, recommends an org to optimise strategic investments based on the current market situation, goals of the company, other domain-specific models provided (i.e., in the limit, communicated by other Gaia agents responsible for these models), etc.
Investment agent recommends investment strategy based on my current financial situation, financial goals, and other investment goals (such as ESG)
Scaling to the group/coordination: optimise joint investment strategy for income and investment pools a-la pandopooling.com
Energy agent: decides when to accumulate energy and when to spend it based on the spot energy prices, weather forecast for renewables, and the current and future predicted demands for power
Scale this to microgrids, industrial/manufacturing sites, etc.
The Gaia network does not currently exist AFAIK, yet it is already possible to build all of those things right now, right? Can you elaborate on what would be different in a Gaia network version compared to my building those tools right now without ever having heard of Gaia?
Right now, if the Gaia Network already existed, but there were little models and agents on it, there would be no or little advantages (e.g., leveraging the tooling/infra built for the Gaia Network) in joining the network.
This is why I personally think that the bottom-up approach: building these apps and scaling them (thus building up QRFs) first is somewhat more promising path than the top-down approach, the ultimate version of which is the OAA itself, and the research agenda of building Gaia Network is a somewhat milder version, but still top-down-ish. That’s why in the comment that I already linked to above, the implication is that these disparate apps/models/”agents” are built first completely independently (mostly as startups), without conforming to any shared protocol (like the Gaia protocol), and only once they grow large and sharing information across the domains becomes evidently valuable to these startups, then the conversation about a shared protocol will find more traction.
Then, why a shared protocol, still? Two reasons:
Practical: it will reduce transaction costs for all the models across the domains to start communicating to improve the predictions of each other. Without a shared protocol, this requires ad-hoc any prospective direction of information sharing. This is the practicality of any platforms, from the Internet itself to Airbnb to SWIFT (bank wires), and Gaia Network should be of this kind, too.
AI and catastrophic risk safety: to ensure some safety against rogue actors (AI or hybrid human-AI teams or whatever) through transparency and built-in mechanisms, we would want as much economic activity to be “on the network” as possible.
You may say that this is would be a tough political challenge to convince everybody to conform to the network in the name of some AI safety, but surely this would still be a smaller challenge than to abolish much of the current economic system altogether as (apparently) implied by the “vanilla” Davidad’s OAA, and as we discuss throughout the article. In fact, this is one of the core points of the article.
Then, even though I advocate for a bottom-up approach above, there is still a room, and even a need for a parallel top-down activity (given the AGI timelines), so these two streams of activity meet each other somewhere in the middle. This is why we are debating all these blue-sky AI safety plans on LessWrong at all; this is why OAA was proposed, and this is why we are now proposing the Gaia Network.
My friendly suggestion would be to have one or more anecdotes of a specific concrete (made-up) person trying to build a specific concrete AI thing, and they find themselves facing one or more very specific concrete transaction costs (in the absence of Gaia), and then Gaia enters the scene, and then you specifically explain how those specific transaction costs thereby become lower or zero, thanks to specific affordances of Gaia.
I’ve tried to read everything you’ve linked, but am currently unable to generate even one made-up anecdote like that.
(I am not the target audience here, this isn’t really my wheelhouse, instead I’m just offering unsolicited advice for when you’re pitching other people in the future.)
(Bonus points if you have talked to an actual human who has built (or tried to build) such an AI tool and they say “oh yeah, that transaction cost you mentioned, it really exists, and in fact it’s one of our central challenges, and yes I see how the Gaia thing you describe would substantially help mitigate that.”)
Hey Steven, I’ll answer your question/suggestion below. One upfront request: please let us know if this helps. We’ll write a follow-up post on LW explaining this.
As mentioned in the appendix, most of what we wrote up is generalized from concrete people (not made-up, my IRL company Digital Gaia) trying to build a specific concrete AI thing (software to help farmers and leaders of regeneration projects maximize their positive environmental impact and generate more revenue by being able to transparently validate their impact to donors or carbon credit buyers). We talked extensively to people in the ag, climate and nature industries, and came to the conclusion that the lack of transparent, unbiased impact measurement and validation—ie, exactly the transaction costs you mention—is the reason why humanity is massively underinvested in conservation and regeneration. There are gazillions of “climate AI” solutions that purport to measure and validate impact, but they are all fundamentally closed and centralized, and thus can’t eliminate those transaction costs. In simple terms, none of the available systems, no matter how much money they spent on data or compute, can give a trustworthy, verifiable, privacy-preserving rationale for either scientific parameters (“why did you assume the soil carbon captured this year in this hectare was X tons?”) or counterfactuals (“why did you recommend planting soybeans with an alfalfa rotation instead of a maize monoculture?”). We built the specific affordances that we did—enabling local decision-support systems to connect to each other forming a distributed hierarchical causal model that can perform federated partial pooling—as a solution to exactly that problem:
The first adopters (farmers) already get day-1 benefits (a model-based rationale that is verifiable and privacy-preserving), using models and parameters bootstrapped from the state of the art of open transaction-cost-reduction: published scientific literature, anecdotal field reports on the Web, etc.
The parameter posteriors contributed by the first adopters drive the flywheel. As more adopters join, network effects kick in and transaction RoI increases: both parameter posteriors become increasingly truthful and easier to verify (posterior estimates from multiple sources mostly corroborate each other, confidence bands get narrower).
Any remaining uncertainty, in turn, drives incentives for scientists and domain experts to refine models and perform experiments, which will benefit all adopters by making their local impact rationales and recommendations more refined.
As an open network, models and parameters can be leveraged in adjacent domains, which then generate their own adjacencies, eventually covering the entire spectrum of science and engineering. For instance, we have indoor farms and greenhouses interested in our solution; they would need to incorporate not only agronomic models but also energy consumption and efficiency models. This then opens the door to industrial and manufacturing use cases, and so on and so forth...
We validated the first two steps of this theory in a pilot; it worked so well that our pilot users keep ringing us back saying they need us to turn it into production-ready software...
Disclaimer: We did not fully implement or validate two important pieces of the architecture that are alluded to in the post: free energy-based economics and trust models. These are not crucial for a small-scale, controlled pilot, but would be relevant for use at scale in the wild.
Thanks for suggestions.
An actual anecdote may look something like this: “We are a startup that creates nutrition assistant and family menu helper app. We collect anonymised data from the users and ensure differential privacy, yada-yada. We want to sell this data to hedge funds that trade food company stocks (so that we can offer the app for free for to our users), but we need to negotiate the terms of these agreements in an ad-hoc way with each hedge fund individually, and we don’t have a principled way to come up with a fair price for the data. We would benefit from something like a ‘platform’ on which we can just publish the API spec of our data and then the platform (i.e., the Gaia Network) takes care of finding buyers for our data and paying us a fair price for it, etc.”
BTW, this particular example sounds just like Numer.ai Signals, but Gaia Network is supposed to be more general and not to revolve around the stock market alone. E.g., the same nutritional data could be bought by food companies themselves, logistics companies, public health agencies, etc.