Do Apple Watches sufficiently track sleep apnea? Or is going in for a sleep study the only way to get a decent signal of if one is suffering from sleep apnea?
It seems the case. I used to think weed was for enlightened individuals given how it was portrayed as a natural alternative to pharmaceuticals (e.g., for pain or psychological reasons). Many forms of media would discuss it positively. But it is still smoking and how many people really have medical justification for the usage?
Mushrooms have a similar portrayal I would say as some god-given superfood.
Alternatively, you win the highest prize in your field and the only path you see forward is solving the most impossible problem in the field, which of course you will fail to do.
Since I am hitting the reply depth: You “solve” a dataset or task when you translate some model into actual real world problems by creating a model that actually “works” (not just high accuracy). What is otherwise the point of training the model other than writing blog posts? Second to that, you can train a model that performs well on the dataset but is less useful in the real world.
This is a health dataset, there are many inputs and outputs to health (e.g., cell level, protein level, tumors, organs, etc.). In this case, it is mRNA focused, which is a broad category that translates to potentially immune responses like vaccines (exactly what kind of therapy, I’m not sure other than “25 species”). Once the model is trained, you can use it to solve real problems, perhaps to develop a therapy that makes its way to clinical trials and eventually actually treats some disease. The model by itself is useless without the ability to have that impact.
So for other examples, take any disease (e.g., Covid19), create a dataset to mirror that problem using some technique (e.g., Covid19 mRNA prediction of some sort), and solve it to create a treatment (e.g., get a safe and effective vaccine). Obviously, you can say the vaccine can be improved so it is not “solved”, but most people would be quite happy with a “almost cure for cancer” even if it wasn’t literally optimal (we don’t even know if a cure for cancer is possible).
My suggestion and question to the author is to outline what is the implications of the work rather than focusing on accuracy statistics that are meaningless without such context.
If you want to be literal with language, then do you ever really “solve” anything? Even tying your shoes is not solved. One day you may tie them better, but for practical purposes we can say it is solved.
Likewise, you can spend 55 hours of GPU time to produce very different things. Can those 55 hours cure cancer? Definitely not. Can it pick up correlations with a small subset of proteins that are perhaps not representative of practical problems? Probably. Can it learn a pattern to tie your shoes, given all your life experiences tying them? Sure.
I asked the question to determine what is the impact of the task and dataset. Curing cancer is huge, tying shoes is not. What are the strengths and limitations?
MNIST (the number classification task) has been “solved” a billion times and it is hard to imagine any subsequent advances there as scores using a variety of methods have hit the saturation point of accuracy. Any further improvements are likely overfitting to noise. Therefore, we know that it is easy to detect handwritten numbers. However, we may not know how to detect other things as well, like reading an MRI. Those datasets/tasks are clearly different and require different techniques. Training an LLM is likewise different.
You solve a dataset when you learn what there is to learn about the phenomenon of interest. The limit of such phenomenon is “cure all disease”, and clearly this is not solving that.