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> * manually classifying 10,000 images or documents * Ah, the 50's era sweatshop mentality! Most excellent. I would fire a person who wasted days doing such a

> * manually classifying 10,000 images or documents * Ah, the 50's era sweatshop mentality! Most excellent. I would fire a person who wasted days doing such a stupid operation. Take 10 minutes, do some research, find a better way. Spend some company money to get it done today. If you can't figure a good way, start asking up the chain.

MauranKilom 5y ago
Very curious how you think ML without data is going to work. 10 minutes of research will neither give you a solution to that nor will it procure the data needed. In the end, ground truth data is almost always going to originate from humans putting in man-hours to create it, one after the other. Whether those humans need to be ones that are paid ML Engineer salaries for this is an important business question, but someone will have to do the work.
- PaulHoule 5y ago
  Yup. People complained about the cost of getting an expert to cough up secrets about their thought process in the 1970s. The essential problem is not different in magnitude with ML because you need to design the classification to be solvable and then get the right answers for many hard cases. From an academic perspective ML is advantaged in that multiple people can train algos on the same data, which leads to a certain kind of progress through competition. When it comes to most projects where you need the model you have to supply your own training set. Sometimes there is a download or a weird trick, but that conversation that ‘we can’t afford you to make the training set’ leads to bad outcomes: * you never get a training set * you download the wrong data from Kaggle * you get an unusable training set * you wind up spending more time of expensive people herding inexpensive people, when…. one ‘expensive’ person who could get over the stigma of doing what they think is menial, be 4x as productive as an imagined ‘cheap’ person and then the team can move on to other tasks that are necessary and unglamorous. The team can still win the game by default in 2021 because your competitor thinks it is too good to do those things.

MauranKilom 5y ago

Very curious how you think ML without data is going to work. 10 minutes of research will neither give you a solution to that nor will it procure the data needed. In the end, ground truth data is almost always going to originate from humans putting in man-hours to create it, one after the other. Whether those humans need to be ones that are paid ML Engineer salaries for this is an important business question, but someone will have to do the work.

PaulHoule 5y ago
Yup. People complained about the cost of getting an expert to cough up secrets about their thought process in the 1970s. The essential problem is not different in magnitude with ML because you need to design the classification to be solvable and then get the right answers for many hard cases. From an academic perspective ML is advantaged in that multiple people can train algos on the same data, which leads to a certain kind of progress through competition. When it comes to most projects where you need the model you have to supply your own training set. Sometimes there is a download or a weird trick, but that conversation that ‘we can’t afford you to make the training set’ leads to bad outcomes: * you never get a training set * you download the wrong data from Kaggle * you get an unusable training set * you wind up spending more time of expensive people herding inexpensive people, when…. one ‘expensive’ person who could get over the stigma of doing what they think is menial, be 4x as productive as an imagined ‘cheap’ person and then the team can move on to other tasks that are necessary and unglamorous. The team can still win the game by default in 2021 because your competitor thinks it is too good to do those things.