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Sometimes people say, "X is technologically feasible (with deep learning), but it would require a dataset much larger than what we can collect". A more accurate take is we don't have the technology to do X given a realistically-sized dataset.
It's a rather bizarre perspective, like a bicycle rental shop saying "it's feasible to go from Paris to Beijing on our bicycles, but it would take very long". You just need a faster vehicle. If you can't do something with a small dataset, that's a technology problem.
Every problem is solvable with k-NN. You "just" need enough data.
A model with sufficient (accurate) simplifying assumptions can do a lot with a small amount of data.
Yes but making assumptions compromises the ethical applicability of model
It makes me think at this: are there attempts to use topological results in order to (if you can) produce specifics data. You can imagine to do it so to have some topological constants. It has been done to avoid holes in detector coverage without knowing location.
Good thing we never have to say this. We symthesize data from physics simulation. This changes the problem and introduces other problems, but feasibility is in sight. It requires not only sim2real but also a loop closure with real2sim.
Does this really generalize? Scale of dataset and complexity of problem are deeply intertwined. Unless you're an oracle that can always pick the perfect priors, at some point you're going to run into problems where you need more data to appropriately define the distribution.
Pioneers like Amelia Earheart may disagree. Pushing and probing leads to exploration and discovery. Not bizarre, but not for everybody.
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