The main problem of nutritional research is that it’s hard to get people to eat controlled diets. I don’t think the key problem is about sourcing ingredients.
ChristianKl
Karma: 34,860
Egyptians felling all their trees and turning their environment into a desert feels quite similar to fossil fuels.
A rationalist interjects: “You should make public predictions about this stuff!” Idk, should I? What should I make predictions about? About whether individual cases succeed, or some broader trends? I’m not sure if it’s worth my time. I really like $ as a metric, not sure what the predictions add. Very open to being convinced here!
Predictions about individual cases would be great. Whenever you take a deposit write down the condition for the bounty being paid out, the amount of the bounty, and your self-assessed likelihood of the person paying the bounty in the following twelve months to you into a public Google Sheet. Maybe, add another row for “time-spent with the person”.
The exercise about thinking beforehand about how likely you will solve the issue for the person is useful for you to understand your method better. It also help informing potential customers well about what they can expect from your service.
Finally, it would be great to have a one-year follow-up after a bounty is paid and that information also added to the Google Sheet.
Anthropic should have a clear policy about exceptions they make to their terms of use that includes them publically releasing a list of each expectation they make for their terms of use.
The should have mechanisms to catch API users who try to use Antrophics models in a violation of the terms of use. This includes having contracts that allow them to make sure that classified programs don’t violate the agreed upon terms of use for the models.
Which one’s do you see as the top ones?
That sounds like it’s relatively easy to game by the company who chooses the investigators.
[Question] What’s the best metric for measuring quality of life?
Exploitation is using a superior negotiating position to inflict great costs on someone else, at small cost to yourself.
I think the word exploitation as it’s generally used, is about one party getting a benefit at the expense of another party. It’s not about one party getting nothing/pays a small cost while the other party suffers a lot.
Promoting an alternative definition of what it means to exploit is likely going to make reasoning harder. Google suggests as definition for exploit “make use of (a situation) in a way considered unfair or underhand”.
Wage theft is a clear example of exploitation. For many jobs, there’s information asymmetry where the person seeking the job does not get informed fully about how his job will be before they accept the job, that’s also clearly exploitation. Multiple-level marketing companies like Amway are exploitative because they mislead people about the likely results of working for them.
In general, there’s value created through trade. If one party captures nearly all of the surplus value of the trade, many people consider that unfair and thus exploitative.
A key aspect of your examples is further that total utility might not be maximized and because one party has little power, utility maximizing trades don’t happen. That’s a different issue from how the trade surplus is distributed.
If people complain about Amazon, to my knowledge most of the people complain that while Amazon runs very efficient and is run to maximize total utility, they capture most of the generated value and don´t pay their employees very much.
Maybe, economists do have a term for the case where one party being powerless leads to utility not being maximized?
I think it would be good to automate the moderation process. Current LLM should be able to make the decision about whether a post is containing the kind of profanity that would lead to account bans.
Annoying civilisational inadequacy:
USB-C cables differ a lot. Some only allow power delivery and no data, while others support different levels of data transfer. Power delivery capabilities also differ.
Most cables do have an E-Marker chips that contain the relevant information. However, Android does not provide that information to the user when they plug into an USB-C cable.
Edit: After looking more into it, it seems while some cables do have E-Marker chips, most don’t :(
Is answer assumes that you either have a fully chat based version or one that operates fully autonomous.
You could build something in the middle where every step of the agent gets presented to a human who can press next or correct the agent. An agent might even propose multiple ways forward and let the human decide. That then produces the training data for the agent to get better in the future.
[Question] Why don’t we currently have AI agents?
You could say that Wikipedia falls into the category but given the way it’s discourse goes right now it tries to represent the mainstream view.
For specific claims, https://skeptics.stackexchange.com/ is great.
https://www.rootclaim.com/ is another project worth checking out.
You see that each of the project has their own governing philosophy, that gives the investigation a structure.
Yet discourse about these topics more than anything else fundamentally combats propaganda and misinformation.
The phrase “combat” is interesting here. Julia Galef speaks about the soldier mindset and the scout mindset. Combating anything is essentially about the soldier mindset. On the other hand you need the scout mindset to think well and come to correct conclusions.
By in large the movement that bills itself as “combating misinformation” is about defending the hegemonic Western elite discourse. It’s not about truthseeking.
When reading posts about AI development I get the impression that many people follow a model where the important variables are the data that, out there in the world, the available compute for model training and the available training algorithm.
I think this underrated the importance of synthetic training data generation.
AlphaStar trained entirely on synthetic data to become much better than humans.
There’s an observation that you can’t improve a standard LLM much by retraining it by just feeding it random pieces of it’s own output.
I think there’s a good chance that training on the output on models that can reason like o1 and o3 does allow for improvement.
Just like AlphaStar could make up the necessary training data to become superhuman on its own, it’s possible that this is true for the kind of models like o3 simply by throwing compute at them.
Why do you care about how effectively the iron in iron supplements gets absorbed? The iron that’s not absorbed just gets flashed out. Can’t you just supplement more to get what you need?
It’s worth noting that the Californian choice isn’t free. Californian like residential solar to allow homeowners to feel good about themselves and use net metering to incentives residential solar. Grid electricity in California are double of what residential customers in Texas pay.
Why do you think it would require a central planner to implement agrivoltaics but the profit seeking market isn’t doing it on their own?
Your first post is about optimal policy. The optimal response to usually-bogus-but-impactful objections is permitting reform.
How do you know that if you would get rid of net metering subventions which are about letting other energy produces pay for residential solar and other subventions for residential solar, it would still be economical to build residential solar in the US over specialized installations?
You need to pay anyway for the gas plant if you want to have electricity even on days where the sun isn’t shining.
You can optimize for different goals. If you want you could optimize for a minimum of new land use. That would however be stupid economic policy as there’s enough land and cheaper energy is more valuable.
Using central planing to enforce more expensive energy production because agrivoltaics are cool and reduce land use is not good policy.
If it would be only true in the case of calorie restriction, why don’t we have better studies about the effects of salt?
People like to eat together with other people. They go together to restaurants to eat shared meals. They have family dinners.