But the world is not deterministic, inherently so. We know it's probabilistic at least at small enough scales. Most hidden variable theories have been disproven, and to the best of our current understanding the laws of the physical universe are probabsilitic in nature (i.e the Standard Model). So while we can probably come up with a very good probabilistic model of things that can happen, there is no perfect prediction, or rather, there cannot be
Dummit and Foote is the classic abstract Algebra textbook to learn about how to precisely define these. Its treatment of ring theory is very well motivated and easy to grasp
I don't think anyone is advocating for incentivizing forced/child labour.
Given that the ILAB link you posted itself is maintained by EO 13126 signed by the Clinton Administration, I think there can be nuance in the discussion around whether or not the blanket application of certain foreign policy instruments is the right way to induce a change in the domestic policy of another country to solve the problem of bad labour practices.
We can do this without it becoming an argument about whether trade is "good" or "bad" depending on what "side" you are on.
This difference in emotional reaction is because of the effort involved in the process. Functionally, we see YouTube video creation as a fundamentally difficult exercise (to do well) and results in a singular product (one video). Any additional content would need an ongoing investment of time and money from the creator. The LLMs though would not require an ongoing investment beyond the first training run, that is probably why you have a problem with it, they're an extremely high leverage way of taking advantage of content.
The difference is though there isn't a whole lot of "whole cloth novel solutions" being written in software today so much as a "write me this CRUD app to do ABC" which current generations are exceedingly good at.
There are probably 10% of truly novel problems out there, the rest are just already solved problems with slightly different constraints of resources ($), quality (read: reliability) and time. If LLMs get good enough at generating a field of solutions that minimize those three for any given problem, it will naturally tend to change the nature of most software being written today.
I think there's a gap of problems between CRUD and novel. I imagine novel to be very difficult, unsolved problems that would take some of the best in the industry to figure out. CRUD problems are really basic reading/writing data to a database with occasional business logic.
But there's also bespoke problems. They aren't quite novel, yet are complicated and require a lot of inside knowledge on business edge cases that aren't possible to sum up in a word document. Having worked with a lot of companies, I can tell you most businesses literally cannot sum up their requirements, and I'm usually teaching them how their business works. These bespoke problems also have big implications on how the app is deployed and run, which is a whole different thing.
Then you have LLMs, which seem allergic to requirements. If you tell an LLM "make this app, but don't do these 4 things," it's very different from saying "don't do these 12 things." It's more likely to hallucinate, and when you tell it to please remember requirement #3, it forgets requirement #7.
Well, my job is doing things with lots of restraints. And until I can get AI to read those things without hallucinating, it won't be helpful to me.
I'd like to see the diets in the study that are specified as the "calorie-reduced diets". (Can't seem to find the paper). If it's the same as the Standard American Diet, this muscle loss is quite explainable. I think the mitigation is relatively easy though, if you want to shift the p-ratio, recommending a daily high protein shake would do a lot to stave off muscle loss (and even more if resistance training is applied of course). The exercise addition is probably the hardest to adhere to.
I'd suggest investing your time in the product first and converting the waitlist to real users before trying to take on external investment. It will either make it much easier or you won't need to raise depending on what happens when your product goes out to the 1000 user list.
If you train one of the larger models on these specific problems (i.e DM for D&D problems) it probably will surprise you. The larger models are great at generic text production but when fine-tuned for specific people/task emulation they're quite surprisingly good.
Are there models that haven't been RLHF'd to the point of sycophancy that are good for this? I find that the models are so keen to affirm, they'll generally write a continuation where any plan the PCs propose works out somehow, no matter what it is.
Doesn't seem impossible to fix either way. You could have like a preliminary step where a conventional algorithm decides if a proposal will work at random, with the probability depending on some variable, before handing it out to the DM AI. "The player says they want to do this: <proposed course of action>. This will not work. Explain why."
I'm a bit skeptical to give up conservation of energy in a system with friction. Isn't it more accurate to say that if we were to calculate every specific interaction we'd still end up having conservation of energy. Now whether or not we're dealing with a closed system etc becomes important but if we were to able to truly model the entire physical system with friction, we'd still adhere to our conservation laws.
So they are not approximations, but are just terribly difficult calculations, no?
Maybe I'm misunderstanding your point, but this should be true regardless of our philosophy of physics correct?
It is an analogy stating that dissipative systems do not have a Lagrangian, Noether's work applies to Lagrangian systems
Conservation laws in particular are measurable properties of an isolated physical system do not change as the system evolves over time.
It is important to remember that Physics is about finding useful models that make useful predictions about a system. So it is important to not confuse the map for the territory.
Gibbs free energy and Helmholtz free energy are not conserved.
As thermodynamics, entropy, and entropy are difficult topics due to didactic half-truths, here is a paper that shows that the nbody problem becomes invariant and may be undecidable due to what is a similar issue (in a contrived fashion)
While Noether's principle often allows you to see things that can often be simplified in an equation, often it allows you to not just simplify 'terribly difficult calculations' but to actually find computationally possible calculations.
