5 ms·

Mostly just arguing that it's special and not 'statistics' because it's statistics being calculated at a much larger scale in automated fashions. Seems like unn

Mostly just arguing that it's special and not 'statistics' because it's statistics being calculated at a much larger scale in automated fashions. Seems like unnecessary gatekeeping just for the purpose of inflating egos.

basch 8y ago
"I havent learned classical statistics, but I did take a machine learning course, so what I did must not have been that thing I dont know much about." >Throughout the class, my fellow students and I successfully trained models On top of that, arguing that training models is some sort of contribution to the field? In High School Statistics, we were taught how to do everything by hand, with arithmatic. Then after we demonstrated we understood the formula, how it worked, and why, the teacher would show us that the graphing calculator had a function to do it automatically all along. Rinse and Repeat over the duration of the course. From then on, we could use the prewritten function. Training a model is skipping to executing the function, without first learning how to write it. Is training a model even a feat worth bragging about? >Yet, if you had asked me, or most of the students in that class, how to calculate the variance of a population, or to define marginal probability, you likely would have gotten blank stares. But if I showed you the automatic function in the calculator or excel, and you understood how to input the paramaters correctly, I would be disappointed if you COULDNT get it to work. >Information theory, in general, requires a strong understanding of data and probability Probability theory is the underlying foundation of statistics. If you are going to argue that ML isnt Statistics, youre still agreeing that they are both subsets of probability theory. >How could someone with virtually no background in stats develop a deep understanding of cutting-edge ML concepts? Because you either a) learned statistics under a different name or b) learned how to input data into someone elses black box formulas and take credit for the output. Overall, this was a pretty painful read. The writer should check out a Philosophy of Science or Logic course. And are you really claiming a deep understanding of the concepts, or are we back to you can input data into someone elses function, and think the output is novel. > as if to distinguish themselves from the regular, less statistical kind. Furthermore, most of the hype-fueling innovation in machine learning in recent years has been in the domain of neural networks, so the point is irrelevant. .....what is an example of this "less statistical kind" of Machine learning? Am I taking crazy pills, neural networks are still probabilistic models. Giving such significance to the difference between AI vs ML vs deep learning vs neural networks, probably demonstrates more than anything, not knowing the unifying and underlying probability theory that well. >Batch normalization? ... (probably because they are not statistical techniques at all). Are you really arguing that Batch normalization isnt an application of NORMALIZATION, you know, from statistics??
- kgwgk 8y ago
  Or c) you didn't "develop a deep understanding of cutting-edge ML concepts."
  - basch 8y ago
    Don't get me wrong, there is immense value to being an index of what tools are available to solve a job, which works best for what, what is on the horizon, how to put the pieces together in a way they compliment each other, when to wait for the next big thing vs jumping into the legacy way of solving a problem. Just call a spade a spade, and admit youre more of a journalist/strategist/architect/historian than a scientist. I dont understand what is to gain by trying to paint ML as a new field, vs being Applied Statistics, unless the end goal is to CREATE confusion and mysticism, which all in all seems very unscientific.
- autokad 8y ago
  > "Training a model is skipping to executing the function, without first learning how to write it. Is training a model even a feat worth bragging about?" do you really think its easy? go do kaggle competitions. if its really easy you should just be able to run a few models and quit your day job. ML and statistics are very much the same and just as different. maybe its true about ML people as well, but statisticians have a dogma. they think there is a right model, and they came up with it. ML practitioners generally think they have a more useful model than what was previously done. I had a PHD in statistics try to do deep learning on images. they never got far because they could not get over the idea of having more parameters than observations. I had stats people ask me what is the r^2 on the results of an xgboost model, which the best answer I could give them was that the mean was not a good representation of the data therefore any r^2 number could not be trusted, to which they hated.
  - basch 8y ago
    it was a bit of hyperbole.
stonogo 8y ago
His bio indicates he does 'ML @ Harvard'. I would imagine there's more than just ego riding on this.
- tempodox 8y ago
  Indeed, he should have attached ©, ®, and ™ to that phrase.
- typomatic 8y ago
  Your suggestion is that someone who did a degree specifically in machine learning at Harvard would be less likely to be defending their discipline out of a sense of ego?
  - stonogo 8y ago
    Not merely a suggestion; I'm stating outright that when someone's career (both the value of an acquired scholarly degree and the earned income within the field) depends on a field being regarded as a valid specialization, that person is going to put effort into reinforcing that perceived validity.
    - typomatic 8y ago
      Oh okay! I thought you were suggesting the opposite (which is nonsense). Thanks!

basch 8y ago

"I havent learned classical statistics, but I did take a machine learning course, so what I did must not have been that thing I dont know much about." >Throughout the class, my fellow students and I successfully trained models On top of that, arguing that training models is some sort of contribution to the field? In High School Statistics, we were taught how to do everything by hand, with arithmatic. Then after we demonstrated we understood the formula, how it worked, and why, the teacher would show us that the graphing calculator had a function to do it automatically all along. Rinse and Repeat over the duration of the course. From then on, we could use the prewritten function. Training a model is skipping to executing the function, without first learning how to write it. Is training a model even a feat worth bragging about? >Yet, if you had asked me, or most of the students in that class, how to calculate the variance of a population, or to define marginal probability, you likely would have gotten blank stares. But if I showed you the automatic function in the calculator or excel, and you understood how to input the paramaters correctly, I would be disappointed if you COULDNT get it to work. >Information theory, in general, requires a strong understanding of data and probability Probability theory is the underlying foundation of statistics. If you are going to argue that ML isnt Statistics, youre still agreeing that they are both subsets of probability theory. >How could someone with virtually no background in stats develop a deep understanding of cutting-edge ML concepts? Because you either a) learned statistics under a different name or b) learned how to input data into someone elses black box formulas and take credit for the output. Overall, this was a pretty painful read. The writer should check out a Philosophy of Science or Logic course. And are you really claiming a deep understanding of the concepts, or are we back to you can input data into someone elses function, and think the output is novel. > as if to distinguish themselves from the regular, less statistical kind. Furthermore, most of the hype-fueling innovation in machine learning in recent years has been in the domain of neural networks, so the point is irrelevant. .....what is an example of this "less statistical kind" of Machine learning? Am I taking crazy pills, neural networks are still probabilistic models. Giving such significance to the difference between AI vs ML vs deep learning vs neural networks, probably demonstrates more than anything, not knowing the unifying and underlying probability theory that well. >Batch normalization? ... (probably because they are not statistical techniques at all). Are you really arguing that Batch normalization isnt an application of NORMALIZATION, you know, from statistics??

kgwgk 8y ago
Or c) you didn't "develop a deep understanding of cutting-edge ML concepts."
- basch 8y ago
  Don't get me wrong, there is immense value to being an index of what tools are available to solve a job, which works best for what, what is on the horizon, how to put the pieces together in a way they compliment each other, when to wait for the next big thing vs jumping into the legacy way of solving a problem. Just call a spade a spade, and admit youre more of a journalist/strategist/architect/historian than a scientist. I dont understand what is to gain by trying to paint ML as a new field, vs being Applied Statistics, unless the end goal is to CREATE confusion and mysticism, which all in all seems very unscientific.
autokad 8y ago
> "Training a model is skipping to executing the function, without first learning how to write it. Is training a model even a feat worth bragging about?" do you really think its easy? go do kaggle competitions. if its really easy you should just be able to run a few models and quit your day job. ML and statistics are very much the same and just as different. maybe its true about ML people as well, but statisticians have a dogma. they think there is a right model, and they came up with it. ML practitioners generally think they have a more useful model than what was previously done. I had a PHD in statistics try to do deep learning on images. they never got far because they could not get over the idea of having more parameters than observations. I had stats people ask me what is the r^2 on the results of an xgboost model, which the best answer I could give them was that the mean was not a good representation of the data therefore any r^2 number could not be trusted, to which they hated.
- basch 8y ago
  it was a bit of hyperbole.

stonogo 8y ago

His bio indicates he does 'ML @ Harvard'. I would imagine there's more than just ego riding on this.

typomatic 8y ago
Your suggestion is that someone who did a degree specifically in machine learning at Harvard would be less likely to be defending their discipline out of a sense of ego?
- stonogo 8y ago
  Not merely a suggestion; I'm stating outright that when someone's career (both the value of an acquired scholarly degree and the earned income within the field) depends on a field being regarded as a valid specialization, that person is going to put effort into reinforcing that perceived validity.
  - typomatic 8y ago
    Oh okay! I thought you were suggesting the opposite (which is nonsense). Thanks!