Agreeing on building safe, trustworthy, and human-centric AI is akin to making an open call for a DIY definitions for different regulatory environments.
People in EA end up optimizing for EA credentials so they can virtue signal to grantmakers, but grantmakers would probably like people to scope out non-EA opportunities because that allows us to introduce unknown people to the concerns we have
Good question, this can look very different for different people. My advice(and this might be not applicable to all) is do things that interest/excite you. The AI safety and governance field is very broad (especially with AI popping up in every field), so there's tons of subfields to explore.
There might be issues that are more local/regional that are important to you, maybe there are things that worry you or your demographic and so on.
For policy recommendations, put forth things that actually build on or move the status quo.
For example, recommending a "National Youth Council" without a mandate can be a uniquely bad idea- instead of ignoring your (usually inactive) youth org, now, policymakers will ignore the Council of (usually inactive)youth orgs all while you(the actually proactive person), walk away with the false notion of a job well done.
Nice breakdown, I can see myself visiting this sometime in the future(hopefully not too soon)
My take on this is way too many people optimize for impact in the early rungs, which is bad. I think way too much of the messaging is impact-centric, which leads to people optimizing for the wrong end-goals when, in reality, hardly anyone will read/care about your shiny new fellowship paper.
For the past ~3 quarters, I have been optimizing for fun, and this gives me the right amount of kick to keep me going.
Additionally, for fields like policy, the lack of objective results is made up for by the higher requirements of social clout, which involves building a network that probably takes a lot of time(this is one of those pesky chicken and egg problems).
The problem with AI safety policy is that if we don't specify and attempt to answer the technical concerns then someone else will and safety wash the concerns away.
CSOs need to understand what they themselves mean when they say "explainable" and "algorithmic transparency."
(Comments from skimming the piece and general thoughts from the current state of AI legislation)
->If there is agreement, there should be a pause, building international trust for a pause is crucial- Current verification mechanisms are rather weak. -> Current policy discourse rarely includes X-risks (coming from legislative drafts, frameworks, and National strategies countries are releasing). A very small minority of people in the broader CSO space seem concerned about X-risks. The recent UN AI Advisory Body report on AI also doesn't really hone in on x-risks. -> There might be strange observer effects wherein proposing the idea of a pause makes that party look weak and makes the tech seem even more important. -> Personally, I am not sure if there is a well-defined end-point to the alignment problem. Any argument for a pause should come with what the "resume" conditions are going to be. In the current paradigm, there seems to be no good definition of acceptable/aligned behavior accepted across stakeholders.
Now, -> Pausing is a really bad look for people in office. Without much precedent, they would be treading straight into the path of innovation while also angering the tech lobby. They need a good reason to show their constituents why they want to take a really extreme step, such as pausing progress/innovation in a hot area(this is why trigger events are a thing). This sets bad precedents and spooks other sectors as well(especially in the US where this is going to be painted as a Big Government move). Remember, policymakers have a much broader portfolio than just AI, and they do not necessarily think this is the most pressing problem. -> Pausing hurts countries that stand to gain(or think that they do) the most from it (this tends to be Global South, AI For Good/SDGs folk). -> Any arguments for pause will also have to consider the opportunity cost of delaying more capable AI. -> Personally, I don't update much on the US public being surveyed because of potential framing biases, little downside cost of agreeing, etc. I also don't think the broader public understands the alignment problem well.
Agreeing on building safe, trustworthy, and human-centric AI is akin to making an open call for a DIY definitions for different regulatory environments.
People in EA end up optimizing for EA credentials so they can virtue signal to grantmakers, but grantmakers would probably like people to scope out non-EA opportunities because that allows us to introduce unknown people to the concerns we have
At some point, one has to ask "Am I in the cause area because I am an EA or am in EA because I am in the cause area?"
Reminds me of keeping your identity small by Paul Graham
At this point, we need an 80k page on "What to do after leaving Open AI"
Good question, this can look very different for different people. My advice(and this might be not applicable to all) is do things that interest/excite you. The AI safety and governance field is very broad (especially with AI popping up in every field), so there's tons of subfields to explore.
There might be issues that are more local/regional that are important to you, maybe there are things that worry you or your demographic and so on.
For policy recommendations, put forth things that actually build on or move the status quo.
For example, recommending a "National Youth Council" without a mandate can be a uniquely bad idea- instead of ignoring your (usually inactive) youth org, now, policymakers will ignore the Council of (usually inactive)youth orgs all while you(the actually proactive person), walk away with the false notion of a job well done.
Nice breakdown, I can see myself visiting this sometime in the future(hopefully not too soon)
My take on this is way too many people optimize for impact in the early rungs, which is bad. I think way too much of the messaging is impact-centric, which leads to people optimizing for the wrong end-goals when, in reality, hardly anyone will read/care about your shiny new fellowship paper.
For the past ~3 quarters, I have been optimizing for fun, and this gives me the right amount of kick to keep me going.
Additionally, for fields like policy, the lack of objective results is made up for by the higher requirements of social clout, which involves building a network that probably takes a lot of time(this is one of those pesky chicken and egg problems).
The problem with AI safety policy is that if we don't specify and attempt to answer the technical concerns then someone else will and safety wash the concerns away.
CSOs need to understand what they themselves mean when they say "explainable" and "algorithmic transparency."
(Comments from skimming the piece and general thoughts from the current state of AI legislation)
->If there is agreement, there should be a pause, building international trust for a pause is crucial- Current verification mechanisms are rather weak.
-> Current policy discourse rarely includes X-risks (coming from legislative drafts, frameworks, and National strategies countries are releasing). A very small minority of people in the broader CSO space seem concerned about X-risks. The recent UN AI Advisory Body report on AI also doesn't really hone in on x-risks.
-> There might be strange observer effects wherein proposing the idea of a pause makes that party look weak and makes the tech seem even more important.
-> Personally, I am not sure if there is a well-defined end-point to the alignment problem. Any argument for a pause should come with what the "resume" conditions are going to be. In the current paradigm, there seems to be no good definition of acceptable/aligned behavior accepted across stakeholders.
Now,
-> Pausing is a really bad look for people in office. Without much precedent, they would be treading straight into the path of innovation while also angering the tech lobby. They need a good reason to show their constituents why they want to take a really extreme step, such as pausing progress/innovation in a hot area(this is why trigger events are a thing). This sets bad precedents and spooks other sectors as well(especially in the US where this is going to be painted as a Big Government move). Remember, policymakers have a much broader portfolio than just AI, and they do not necessarily think this is the most pressing problem.
-> Pausing hurts countries that stand to gain(or think that they do) the most from it (this tends to be Global South, AI For Good/SDGs folk).
-> Any arguments for pause will also have to consider the opportunity cost of delaying more capable AI.
-> Personally, I don't update much on the US public being surveyed because of potential framing biases, little downside cost of agreeing, etc. I also don't think the broader public understands the alignment problem well.