From someone with a science background: There are a lot of expenses with that type of clinical trial.
In particular, if you’re going to assign someone to a group that is known to be unhealthy (brushing once a month) you need to pay for any dental or medical problems that arise from them not brushing.
It would be a problem if he was brought on to talk about COVID being a hoax. It’s not really a problem if he’s talking about something entirely different and not problematic
Probably not. Just make a new account and fly under the radar for a while. If you keep getting banned on different accounts they will IP ban you.
Technically it’s against the rules to make a new account to avoid a ban, but like I said, they probably won’t look into it unless you give them a reason to
I think the problem is that a lot of shows become overhyped once they reach a certain level of popularity. While you may have enjoyed it if you came across it naturally, the hype you’ve encountered around it makes you expect more. Avatar and Parks and Rec are great shows, but they’re definitely not the best shows of all time (although I do really love Parks and Rec) and you’d probably expect them to be these amazing shows if all you know about them is the hype from superfans.
Two cuties*
Always get cats in pairs. They tire themselves out instead of constantly needing attention from you
Oh, it’s definitely likely to be more than just saturated fats at play. My post was a) a gross oversimplification, and b) probably missing some nuance that I’m not informed on. My doctorate is in cognitive psychology, not nutrition science :)
The thing that tastes “creamy” is the saturated fat, the same type of fat in butter and cheese. Peanuts are around 6.3% saturated fat by mass, and cashews are 7.9%.
So you’re right on: cashews are creamier.
Always good to recognize that while there are many things most people should realize by the time they are adults, there are also about 10,000 people becoming adults every day.
Aldi has the best store-brand snacks
Their chips, though, are legitimately better than any other brand I’ve had including name brand
Sync has a monthly fee to get rid of ads, Eternity is always ad-free
Sounds horrible. Quality of life must be terrible there!
The work is not reproduced in its entirety. Simply using the work in its entirety is not a violation of copyright law, just as reading a book or watching a movie (even if pirated) is not a violation. The reproduction of that work is the violation, and LLMs simply do not store the works in their entirety nor are they capable of reproducing them.
The argument is less that an LLM is a human and more that it is not a copyright violation to use a material to train the LLM. By current legal definitions, it is fair use unless the material is able to be reproduced in its entirety (or at least, in some meaningful way).
It’s only black box because nobody has the time (likely years to decades) to wade through the layers of a finished model to check every node and weight.
This is exactly correct, except you’re also not accounting for the insane amount of computational power that would be necessary to backtrack a single output of a single model. This is why it is a black box. It simply is not possible on a meaningful level.
So if math and computer science isn’t an exact science, what is?
Things that are reproducible with known inputs and outputs, allowing for all components to be studied and explained. As an example from my field: if you damage the dorsolateral prefrontal cortex in a fully grown adult, they will have the impulse control of a three-year old. We know this because we have observed damage to this area in multiple individuals, and can measure the effects based on the severity of that damage.
In contrast, if you provide the same billion-parameter neural network identical inputs, you will not receive identical outputs.
Look, I understand why you think this. I thought this too when I was first beginning to learn machine learning and data science. But I’ve now been working with machine learning models including neural networks for nearly a decade, and the truth is that is nearly impossible to track the path of an input to a given output in machine learning models other than regression-based models and decision tree-based models.
There is an entire field of data science devoted to explaining how these models arrive at their conclusions. It’s called “explainable AI” or “xAI”, and I have a few papers that I’ve published in exploring the utility of them. The basic explanation for how they work is that we run hundreds of thousands of different models and then do statistical analysis to estimate why the models arrived at their conclusion. It isn’t an exact science, however.
You really don’t understand how these models work and you should learn about them before you make statements about them.
Machine learning models are, almost by definition, non-deterministic.
Neither citation nor compensation are necessary for fair use, which is what occurs when an original work is used for its concepts but not reproduced.
I agree. But that isn’t what AI is doing, because it doesn’t store the actual book and it isn’t possible to reproduce any part in a format that is recognizable as the original work.
LOL
We understand less about how LLMs generate a single output than we do about the human brain. You clearly have no experience developing models.
Not in any way, no. Our brains can’t predict what we will be interested in or good at.
But we do have predispositions. If you are able to focus intently on things, or are creative, or are more athletic, which are reflected in neuronal connectivity, you might be more interested in more detail-oriented or creative or athletic hobbies.