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> Good CS expert says: Most firms that think they want advanced AI/ML really just need linear regression on cleaned-up data. Cleaning up data is very expensive

by chime 10y ago

> Good CS expert says: Most firms that think they want advanced AI/ML really just need linear regression on cleaned-up data. Cleaning up data is very expensive. And without that, the analysis is good for nothing. AI helps provide good analysis without having to cleaning up data manually. I don't see how that is going away.

LeanderK 10y ago
i would say that this is one of the main advantages of ML. You can even use it to process unstructured data.
tommynicholas 10y ago
Not only is cleaned up data expensive, dirty data is expensive! Clean data pipes are now and will be increasingly really interesting businesses IMO.
shadowmint 10y ago
how so? Practically speaking isn't unsupervised learning one of the areas that hasn't really shown much practical application? I mean, if I'm wrong point me to some examples, but I was under the impression cleaning up data wasn't realky a ML strength at this point...?
kristjankalm 10y ago
> AI helps provide good analysis without having to cleaning up data manually. I don't even know where to start. I suppose you don't really think that what separates ML/AI (whatever that means) from your standard OLS regression is that denoising is not done manually? > Cleaning up data is very expensive. And without that, the analysis is good for nothing. I hope you are not saying here that linear regression cannot handle noise. In the end, as this blog post also points out, ML/AI is just a vague blanket term. What you want is a statistical method that captures the signal as fast and efficiently as possible and often the gain from going beyond simple linear models might be marginal.
joe_the_user 10y ago
I agree that using AI to get something that sort-of works out of dirty data is one of the appeals and is going to get people using it. If this works at all, the problem is that what you wind-up with is a system that's been heuristically taught to clean-up data for a single snap-shot of your data - and the teaching is expensive and requires experts who are going to move on. Less expensive than clean your data but still a cost. So when you wind-up with a different pattern of dirty data after a year's time, the system winds crappier than previously and no one will be able to agree how to fix it. Eventually AI is going to get an evil reputation and that may kill its appeal.
cardine 10y ago
> AI helps provide good analysis without having to cleaning up data manually. My own experience has shown that dirty data impacts advanced AI just as much as it impacts far more basic ML techniques. Even for the most advanced AI we work on, we spend just as much time worrying about clean data as we do anything else.
- throw_away_777 10y ago
  When you say "clean data", what exactly do you mean? I've often seen this claim that cleaning data takes a lot of time, but it seems like an ill-defined term.
  - bonoboTP 10y ago
    It can mean different things. In general: duplicate data, missing fields, different formats for different parts of the data, inconsistent naming schemes For text: character encodings, special symbols, escape characters, punctuation, extra or missing spaces and newlines, capitalization For images: different sizes, rotations, crops, blurry images For numbers: inconsistent decimal point/comma, outliers with obviously nonsense values or zeros, values in different units of measurement etc.
    - nostrademons 10y ago
      For user behavior: bots, clickfraud/clickjacking, bored teenagers, competitors who are sussing out your product, people who got confused by your user interface, users who have Javascript disabled and so never trigger your clicktracking, users who are on really old browsers who don't have Javascript to begin with. And then there's bugs in your data pipeline: browser (particularly IE) bugs, logging bugs, didn't understand your distributed databases's conflict resolution policy bugs, failed attempts at cleaning all the previous categories, incorrect assumptions about the "shape" of your data, self-DOS attacks (no joke - Google almost brought down itself by having an img with an empty src tag, which forces the browser to make a duplicate request on every page) which result in extra duplicate requests, incorrectly filtering requests so you count /favicon.ico as a pageview, etc.
garysieling 10y ago
AI techniques can help with cleanup too. I've been using entity recognition to find speaker names to use in faceted search of lectures for https://www.findlectures.com https://www.findlectures.com. The NER library just finds low hanging fruit that I check manually.
BatFastard 10y ago
Garbage in, garbage out. I have always been a stickler for making sure the data going in is good. Takes a bit longer, but makes life much much easier in the long run.
mcguire 10y ago
Garbage in, gospel out.

by chime 10y ago

LeanderK 10y ago

i would say that this is one of the main advantages of ML. You can even use it to process unstructured data.

tommynicholas 10y ago

Not only is cleaned up data expensive, dirty data is expensive! Clean data pipes are now and will be increasingly really interesting businesses IMO.

shadowmint 10y ago

how so? Practically speaking isn't unsupervised learning one of the areas that hasn't really shown much practical application? I mean, if I'm wrong point me to some examples, but I was under the impression cleaning up data wasn't realky a ML strength at this point...?

kristjankalm 10y ago

> AI helps provide good analysis without having to cleaning up data manually. I don't even know where to start. I suppose you don't really think that what separates ML/AI (whatever that means) from your standard OLS regression is that denoising is not done manually? > Cleaning up data is very expensive. And without that, the analysis is good for nothing. I hope you are not saying here that linear regression cannot handle noise. In the end, as this blog post also points out, ML/AI is just a vague blanket term. What you want is a statistical method that captures the signal as fast and efficiently as possible and often the gain from going beyond simple linear models might be marginal.

joe_the_user 10y ago

I agree that using AI to get something that sort-of works out of dirty data is one of the appeals and is going to get people using it. If this works at all, the problem is that what you wind-up with is a system that's been heuristically taught to clean-up data for a single snap-shot of your data - and the teaching is expensive and requires experts who are going to move on. Less expensive than clean your data but still a cost. So when you wind-up with a different pattern of dirty data after a year's time, the system winds crappier than previously and no one will be able to agree how to fix it. Eventually AI is going to get an evil reputation and that may kill its appeal.

cardine 10y ago

> AI helps provide good analysis without having to cleaning up data manually. My own experience has shown that dirty data impacts advanced AI just as much as it impacts far more basic ML techniques. Even for the most advanced AI we work on, we spend just as much time worrying about clean data as we do anything else.

throw_away_777 10y ago
When you say "clean data", what exactly do you mean? I've often seen this claim that cleaning data takes a lot of time, but it seems like an ill-defined term.
- bonoboTP 10y ago
  It can mean different things. In general: duplicate data, missing fields, different formats for different parts of the data, inconsistent naming schemes For text: character encodings, special symbols, escape characters, punctuation, extra or missing spaces and newlines, capitalization For images: different sizes, rotations, crops, blurry images For numbers: inconsistent decimal point/comma, outliers with obviously nonsense values or zeros, values in different units of measurement etc.
  - nostrademons 10y ago
    For user behavior: bots, clickfraud/clickjacking, bored teenagers, competitors who are sussing out your product, people who got confused by your user interface, users who have Javascript disabled and so never trigger your clicktracking, users who are on really old browsers who don't have Javascript to begin with. And then there's bugs in your data pipeline: browser (particularly IE) bugs, logging bugs, didn't understand your distributed databases's conflict resolution policy bugs, failed attempts at cleaning all the previous categories, incorrect assumptions about the "shape" of your data, self-DOS attacks (no joke - Google almost brought down itself by having an img with an empty src tag, which forces the browser to make a duplicate request on every page) which result in extra duplicate requests, incorrectly filtering requests so you count /favicon.ico as a pageview, etc.

garysieling 10y ago

AI techniques can help with cleanup too. I've been using entity recognition to find speaker names to use in faceted search of lectures for https://www.findlectures.com https://www.findlectures.com. The NER library just finds low hanging fruit that I check manually.

BatFastard 10y ago

Garbage in, garbage out. I have always been a stickler for making sure the data going in is good. Takes a bit longer, but makes life much much easier in the long run.

mcguire 10y ago

Garbage in, gospel out.