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Comment by euske | Hacker News Reader

Applying machine learning to any industry.

Here's a checklist that I just came up with:

  Step 1. Pick an industry. Any industry.
  Step 2. Find a problem that can be formulated as a function.
  Step 3. Is that function non-trivial? If not, go back to Step 1.
  Step 4. List all the input parameters for that function.
  Step 5. Is any of them accurately observable? If not, focus on that parameter and go back to Step 2.
  Step 6. Apply some ML to it. (Choice of the tech wouldn't matter that much.)
  Step 7. Had some improvement?

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It seems like they're following the even more basic/old school formula first and then adding on the ML:

    Step 1. Pick a domain with loads of trivial, low-value paperwork
    Step 2. Convert the paperwork into online forms 
    Step 3. Plug your forms into existing player's tech
    Step 4. Battle to get adoption

  Step 5. Add some blockchain technology to it or call it "disrupting".
  Step 6. Collect venture millions

Step 7. Get Bored.

Step 8. Create Space Rocket Company.

Step 9. Go to Mars.

Edit: Missed a vital step :-

Step 6.5 Sell Company for Billions

If the problem is painful enough, it doesn't matter if the function is trivial.

Now write a startup AI and raise your millions

To be fair, that seems a pretty accurate list. You will find yourself stuck in step two quite often.

Wrong. What about an industry where data is either hard or unethical to gather such as the medical industry?

Isn't that step 5? I took that to be: "Do you have all of the data necessary to formulate an answer? If not, focus on solving that problem or eliminate the need for the data you can't get by going back to step two and picking a formula that doesn't need the data you can't get."

I'm not sure if I just read a post detailing just that: https://backchannel.com/you-too-can-become-a-machine-learnin...

Step 5. Is any of them accurately observable? If not, implement a Kalman filter.

This is missing the fundamental step which is collecting data.

I think you missed step 5.

Step 8. Tell everyone how innovative you are Step 9. Constant Blog post on your "Big Data" problems

My super basic understanding of deep learning is that it lets you skip step 5. Is that true?

No. Garbage in, garbage out is true for deep learning as well.

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