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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.
Yes and no. For vision, we can use gan for image data augmentation and get bigger dataset because there are patterns and vision is predictable. How constantly evolving persons think in uncertain situations, not so much. The issue is lack of understanding human decision making
The more we understand the more ways we find to do same thing with more accuracy and efficiency and this is how tech develops ... I guess that's the very point of post as well Developing better tech to solve a problem with smaller datasets !
Sometime you need a paradigm shift within the same discipline. For instance it's sometime impossible to collect enough perfect pairs of data to train a model. But approach this with GAN or VAE, and a large unpaired dataset will solve your problem.
Are generative datasets infinite??
Can we dream to the stars?
We could teach a child to read, but they will need to live at least 4-6 years. Seems like a sensible thing to say to me!
They won't abandon the golden hammer. I like thathttps://twitter.com/trylks/status/1084165440656551936 …
If the value created by X is (maybe) superior to the cost of the dataset, then it is a good slide for a startup :)
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