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Yeah I kinda get that feel as well. The thing that makes me suspicious is the amount of unnecessary and obfuscating jargon that gets thrown about. They've even

by spangry 10y ago

Yeah I kinda get that feel as well. The thing that makes me suspicious is the amount of unnecessary and obfuscating jargon that gets thrown about. They've even invented new jargon to replace perfectly confusing old jargon (e.g. your model's "error residual" is now your "function cost"). I've spent the past couple of weeks doing a bit of ML vision stuff. Most of the terminology was lost on me, at least until today when I discovered "Machine Learning is Fun": https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471 https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec... I think I learned more in an hour than I did in the past week, thanks to this series. The author actually bothers to explain concepts (that turn out to be fairly simple btw) like 'gradient descent'. Highly recommended read if you have the time and interest. Just to whet your appetite: ...current machine learning algorithms aren’t that good yet — they only work when focused a very specific, limited problem. Maybe a better definition for “learning” in this case is “figuring out an equation to solve a specific problem based on some example data”. Unfortunately “Machine Figuring out an equation to solve a specific problem based on some example data” isn’t really a great name. So we ended up with “Machine Learning” instead.

nerdponx 10y ago
First of all, errors and residuals are different things. Second, "cost" is not new jargon. What is relatively new is thinking about probabilistic modeling in terms of abstracted cost functions, but only relatively. There are dozens of tutorials, courses, etc that are clear and don't introduce unnecessary jargon. Nobody is trying to keep you out of data science. As for the term "machine learning," it's because what we today call ML gree out of actual AI research. It so happened that a lot of progress was made very quickly by the ML researchers, so the ML-oriented terms became popular as some older statistics terms were subsumed.
didibus 10y ago
This is true of most domain. The jargon often gives the impression things are really complicated, but often time it's just that you don't know the domain language. Even with math, 60% of the challenge is remembering the notation and substituting it in your head with the real construct. Now, as you get into advanced topics of any field, you'll start to find difficult concepts, but 90% of the time, the foundation just appears hard because of the language you don't know. Also, keep in mind Machine Learning is not "Machine Figuring out an equation to solve a specific problem based on some example data”. Machine Learning is a subset of AI which focuses on teaching the computer how to perform a task without explicitly programming the task execution logic. Currently, the known practical technique of doing so can be described as "Machine Figuring out an equation to solve a specific problem based on some example data”. This might not be true in the future, as better ML techniques are researched and discovered. Also, it's good to keep in mind ML is a software engineer discipline, and data scientist just benefit from modern software, the same way Excel created jobs, this technique of ML created jobs. In the future, different ML techniques might create more jobs or replace the current ones. This puts the data science field at the mercy of ML research. Already I think it's been hard to keep up as a data scientist, since ML research is being financed greatly and advanced quickly.

by spangry 10y ago

nerdponx 10y ago

First of all, errors and residuals are different things. Second, "cost" is not new jargon. What is relatively new is thinking about probabilistic modeling in terms of abstracted cost functions, but only relatively. There are dozens of tutorials, courses, etc that are clear and don't introduce unnecessary jargon. Nobody is trying to keep you out of data science. As for the term "machine learning," it's because what we today call ML gree out of actual AI research. It so happened that a lot of progress was made very quickly by the ML researchers, so the ML-oriented terms became popular as some older statistics terms were subsumed.

didibus 10y ago

This is true of most domain. The jargon often gives the impression things are really complicated, but often time it's just that you don't know the domain language. Even with math, 60% of the challenge is remembering the notation and substituting it in your head with the real construct. Now, as you get into advanced topics of any field, you'll start to find difficult concepts, but 90% of the time, the foundation just appears hard because of the language you don't know. Also, keep in mind Machine Learning is not "Machine Figuring out an equation to solve a specific problem based on some example data”. Machine Learning is a subset of AI which focuses on teaching the computer how to perform a task without explicitly programming the task execution logic. Currently, the known practical technique of doing so can be described as "Machine Figuring out an equation to solve a specific problem based on some example data”. This might not be true in the future, as better ML techniques are researched and discovered. Also, it's good to keep in mind ML is a software engineer discipline, and data scientist just benefit from modern software, the same way Excel created jobs, this technique of ML created jobs. In the future, different ML techniques might create more jobs or replace the current ones. This puts the data science field at the mercy of ML research. Already I think it's been hard to keep up as a data scientist, since ML research is being financed greatly and advanced quickly.