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The most interesting applications are what you don’t see. For instance, you can do a lot of your engineering and even data science work without ever touching r
by lettergram 7y ago
The most interesting applications are what you don’t see.
For instance, you can do a lot of your engineering and even data science work without ever touching real data (due to ML):
https://medium.com/capital-one-tech/why-you-dont-necessarily-need-data-for-data-science-48d7bf503074 https://medium.com/capital-one-tech/why-you-dont-necessarily...
Not saying it’s not over hyped, just that good AI would augment your life in ways subtle, but hugely impactful.
Edit: Another thought - how are funds in the market allocated? Partially by humans, largely augmented (if not explicitly allocated) by AI... meaning AI determines which companies get funding and thus what you can buy / access is determined by these algorithms
- gran_colombia 7y agoIf you work primarily on synthetic data as a data scientist, you deserve to get fired. The link is pretty cool though.
- lettergram 7y agoThe argument is you can design a model, check that it runs, then submit the job to train / evaluate the model on real data. The data scientist doesn't necessarily need to touch the real data ever (especially if ML is used).
- tomrod 7y agoThis is not a great setup if one requires a model that can extrapolate.
- Mathnerd314 7y agoThat article reinforces the view that you need big datasets to do ML. Generating a synthetic dataset still requires a real dataset to work from, and all the useful datasets are controlled by the big tech companies.