Readit NewsWhy would a language model do anything other than "hallucinate" (i.e. generate words without any care about truthiness) ? These aren't expert systems dealing in facts, they are statistical word generators dealing in word statistics.
The useful thing of course is that LLMs often do generate "correct" continuations/replies, specifically when that's predicted by the training data, but it's not like they have a choice of not answering or saying "I don't know" in other cases. They are just statistical word generators - sometimes that's useful, and sometimes it's not, but it's just what they are.
What would happen to T1 or T2 diabetics if we would stop eating all sources of sugars and carbs? So no fruit, no rice, no potatoes and so on?
Would it be possible to survive and live comfortably in a state of Ketosis? Or is a 100% ketogenic diet simply not possible on diabetes?
I’m asking because my true question is: what if insulin becomes too expensive? Then what? Do we die? Or is there some form of diet that we could live on??
I was diagnosed as pre-diabetic/T2. I started wearing a cgm and watching how various foods affected my blood sugar. I eliminated foods that caused spikes, and started cooking my own meals so I could control what went into them. I wound up with a very low carb diet of meat and vegetables, and a very stable blood sugar with NO spikes ever. According to my blood work and checkups I cured my NAFLD, cured my hypertension (including getting off drugs for that), and "cured" my pre-diabetes. I lost a lot of weight, but still have a lot more to lose.
I put cured in quotes because I don't think this diet can cure you once you're bad enough to need treatment. I think it can only put your disease into remission so that you don't suffer any health effects from it. Some of us just can't overeat carbs or we develop this disease, and the only effective treatment is to stop eating the carbs.
Fat adaptation is about shifting your hormonal balance and response to retrain your body to maintain a lower level of glucose, and to retrain your cravings and hunger.
Just today, I spent an hour documenting a function that performs a set of complex scientific simulations. Defined the function input structure, the outputs, and put a bunch of references in the body to function calls it would use.
I then spent 15 minutes explaining to the free version of ChatGPT what the function needs to do both in scientific terms and in computer architecture terms (e.g. what needed to be separated out for unit tests). Then it asked me to answer ~15 questions it had (most were yes/no, it took about 5 min), then it output around 700 lines of code.
It took me about 5 minutes to get it working, since it had a few typos. It ran.
Then I spent another 15 minutes laying out all the categories of unit tests and sanity tests I wanted it to write. It produced ~1500 lines of tests. It took me half an hour to read through them all, adjusting some edge cases that didn't make sense to me and adjusting the code accordingly. And a couple cases where it was testing the right part of the code, but had made valiant but wrong guesses as to what the scientifically correct answer would be. All the tests then passed.
All in all, a little over two hours. And it ran perfectly. In contrast, writing the code and tests myself entirely by hand would have taken at least a couple of entire days.
So when you say they're good for those simple things you list and "that's about it", I couldn't disagree more. In fact, I find myself relying on them more and more for the hardest scientific and algorithmic programming, when I provide the design and the code is relatively self-contained and tests can ensure correctness. I do the thinking, it does the coding.
So that's... math. A very well defined problem, defined very well. Any decent programmer should be able to produce working software from that, and it's great that ChatGPT was able to help you get it done much faster than you could have done it yourself. That's also the kind of project that's very well suited for unit testing, because again: math. Functions with well defined inputs, outputs, and no side-effects.
Only a tiny subset of software development projects are like that though.