Destroying your own property is legally allowed. Doing it at scale sucks, but is still allowed. As long as Anthropic et al don't distribute their digital copies then there's nothing legally wrong about what they're doing.

I've been using Gemini Deep Research to replace my Google search since it does a web search and provides links you can check yourself to any citation that the resulting report uses.

It really is sad how much data has been captured and monetized of the average person. It seems like we're only continuing to turn up the heat as we continue to 'boil the frog'.

pair this with each district, state, and constitute company/nonprofit/whatever looking for a piece of the pie creates an utter mess of non-stop spending

https://www.jdpower.com/business/press-releases/2024-us-init...

> I think it only appears that way if you take small quotations like this out of context.

Can you help me understand this. Like the purpose of using logic to deduce decision making is because there's a fundamental structure in assuring the result from those deductions is accurate (or enough to continue manipulation of the problem data). When you try to predict market reaction to a decision and ascribe harm or success based on the decision you're creating causation out of correlation, often incorrectly. Again, not an expert in economics, but when i view theories of free market forces and how they're part of the logic of business decisions i can't help but think the people using this information are assuming their knowledge and logic aren't fundamentally flawed and as a result are essentially just guessing without thinking they are.

> “...consider a situation in which there are grocery stores serving a neighborhood inhabited by people who have a strong aversion to being waited on by Negro clerks. Suppose one of the grocery stores has a vacancy for a clerk and the first applicant qualified in other respects happens to be a Negro. Let us suppose that as a result of the law the store is required to hire him. The effect of this action will be to reduce the business done by this store and to impose losses on the owner. If the preference of the community is strong enough, it may even cause the store to close. When the owner of the store hires white clerks in preference to Negroes in the absence of the law, he may not be expressing any preference or prejudice, or taste of his own. He may simply be transmitting the tastes of the community. He is, as it were, producing the services for the consumers that the consumers are willing to pay for. Nonetheless, he is harmed, and indeed may be the only one harmed appreciably, by a law which prohibits him from engaging in this activity, that is, prohibits him from pandering to the tastes of the community for having a white rather than a Negro clerk. The consumers, whose preferences the law is intended to curb, will be affected substantially only to the extent that the number of stores is limited and hence they must pay higher prices because one store has gone out of business.”

In my view, the scenario and reasoning by Friedman in how he applies market forces applying to business decisions is a view where you have an assumption of 'knowing the result' of any decision and using that 'knowledge' as justification for reasoning. When in reality, you don't know the result, you can estimate, but that's about it. So when an exec is applying Friedman principles they're trying to 'know the market' and that's a fundamental error in my mind due to the chaos of the world how it can manifest across all avenues of life.

> To ensure that we did not overfit PaperQA2 to achieve high performance on LitQA2, we generated a new set of 101 LitQA2 questions after making most of the engineering changes to PaperQA2. The accuracy of PaperQA2 on the original set of 147 questions did not differ significantly from its accuracy on the latter set of 101 questions, indicating that our optimizations in the first stage generalized well to new and unseen LitQA2 questions (Table 2).

> To compare PaperQA2 performance to human performance on the same task, human annotators who either possessed a PhD in biology or a related science, or who were enrolled in a PhD program (see Section 8.2.1), were each provided a subset of LitQA2 questions and a performance-related financial incentive of $3-12 per question to answer as many questions correctly as possible within approximately one week, using any online tools and paper access provided by their institutions. Under these conditions, human annotators achieved 73.8% ± 9.6% (mean ± SD, n = 9) precision on LitQA2 and 67.7% ± 11.9% (mean ± SD, n = 9) accuracy (Figure 2A, green line). PaperQA2 thus achieved superhuman precision on this task (t(8.6) = 3.49, p = 0.0036) and did not differ significantly from humans in accuracy (t(8.5) = −0.42, p = 0.66).