5 ms·

The entire product I built over the last year can be reduced to basic statistics (e.g. ratios, probabilities) but because of the hype train we build "models" an

by sidlls 10y ago

The entire product I built over the last year can be reduced to basic statistics (e.g. ratios, probabilities) but because of the hype train we build "models" and "predict" certain outcomes over a data set. One of the products the company I work for sells more or less attempts to find duplicate entries in a large, unclean data set with "machine learning." The value added isn't in the use of ML techniques itself, it's in the hype train that fills the Valley these days: our customers see "Data Science product" and don't get that it's really basic predictive analytics under the hood. I'm not sure the product would actually sell as well as it does without that labeling. To clarify: the company I work for actually uses ML. I actually work on the data science team at my company. My opinion is that we don't actually need to do these things, as our products are possible to create without the sophistication of even the basic techniques, but that battle was lost before I joined.

AndrewKemendo 10y ago
The value added isn't in the use of ML techniques itself, it's in the hype train that fills the Valley these days: our customers see "Data Science product" and don't get that it's really basic predictive analytics under the hood. I'm not sure the product would actually sell as well as it does without that labeling. So you are misleading your customers through omission? This is the kind of thing that makes people question anyone stating they are using ML. Those of us actually implementing ML techniques (aka training neural nets and automating processes with data) are met with unnecessary skepticism as a result. edit: OP clarified his position since this post so take that into account when reading.
- sidlls 10y ago
  No, we actually use ML. We just don't need to, in my opinion, because the problems our products solve are more or less solvable without these techniques. My point was that using ML, even though we don't need to, "adds value" by virtue of the hype train. We need ML to sell products, not to create them. I do agree that this sort of arrangement lends itself to supporting skepticism around AI and ML. On the other hand I don't think that's a bad thing.
  - AndrewKemendo 10y ago
    Got it. Thanks for the clarification. It is true that people are using ML where other, simpler options are available, but I wouldn't immediate discount the value of using nets for your problem. I don't know enough about your problem/implementation to speak to it really.
    - sidlls 10y ago
      Yes, thanks for highlighting the deficiency in my original post. I can see it is easily interpretated as you did. I added a clarification (or what I hope is one).
      - lbhnact 10y ago
        I know a few companies riding the ml/nn train. Any chance you are based in NYC?
        sidlls 10y ago
        No, in the bay area. The hype is strong here.
  - deleted 10y ago
    [deleted]
Bartweiss 10y ago
It's interesting to me that with all the ML hype, it's still not clear what constitutes ML. A basic k-means or naive Bayes approach will show up in ML textbooks, but those aren't clearly different from "use some statistics to make a prediction". There's an interesting group of marginal approaches that have existed as-is for years, but have increasingly focused their branding on machine learning as its profile has risen.
- sidlls 10y ago
  This mirrors my own thoughts on the matter. Especially as regards the "branding" issue.
- jorgemf 10y ago
  > but those aren't clearly different from "use some statistics to make a prediction" You can reduce 90% of ML to this. Even neural networks are based on statistics. If I have to draw a line between statistics and ML is that ML learns, it means it can predict things, however statistics only gives you information about the data you have. But for sure statistics and ML overlap a lot.
  - Bartweiss 10y ago
    Even that doesn't seem like a clear distinction? If you ask me for the most likely new value for a dataset, I won't know. But if I graph a few things and then write a function to spit back the current mean or median, is that machine learning? I'm not trying to be snarky there, I agree that the bulk of ML tools are fundamentally just statistical tricks with some layer of abstraction. As a result, I have a lot of trouble knowing how much abstraction justifies the ML title. I see some people using "statistics to produce unintuitive solutions" as a standard, but that just begs that we ask unintuitive to who?
    - BickNowstrom 10y ago
      I feel like it is foremost a matter of attitude of the practitioner. An applied statistician and a machine learning engineer may deliver exactly the same end product, just the reasoning and assumptions differ. Machine learning uses little to no assumptions, where statisticians do. I also feel that machine learning engineers have a bit less fear of building black boxes. Caruana showed the cartoon of the difference between a statistician and a machine learning practitioner by showing a cliff. The statistician carefully inches to the edge, stomping her feet to see if the ground is still stable, then 10 meters before the edge she stops and draws her conclusions. The machine learning practitioner dives headfirst from the cliff, with a parachute that reads "cross-validation". See also: http://norvig.com/chomsky.html http://norvig.com/chomsky.html On Chomsky and the Two Cultures of Statistical Learning. And http://projecteuclid.org/euclid.ss/1009213726 http://projecteuclid.org/euclid.ss/1009213726 Statistical Modeling: The Two Cultures by Leo Breiman. and this joke: > Norvig teamed up with a Stanford statistician to prove that statisticians, data scientists and mathematicians think the same way. They hypothesized that, if they all received the same dataset, worked on it, and came back together, they’d find they all independently used the same techniques. So, they got a very large dataset and shared it between them. > Norvig used the whole dataset and built a complex predictive model. The statistician took a 1% sample of the dataset, discarded the rest, and showed that the data met certain assumptions. > The mathematician, believe it or not, didn’t even look at the dataset. Rather, he proved the characteristics of various formulas that could (in theory) be applied to the data.

by sidlls 10y ago

AndrewKemendo 10y ago

The value added isn't in the use of ML techniques itself, it's in the hype train that fills the Valley these days: our customers see "Data Science product" and don't get that it's really basic predictive analytics under the hood. I'm not sure the product would actually sell as well as it does without that labeling. So you are misleading your customers through omission? This is the kind of thing that makes people question anyone stating they are using ML. Those of us actually implementing ML techniques (aka training neural nets and automating processes with data) are met with unnecessary skepticism as a result. edit: OP clarified his position since this post so take that into account when reading.

sidlls 10y ago
No, we actually use ML. We just don't need to, in my opinion, because the problems our products solve are more or less solvable without these techniques. My point was that using ML, even though we don't need to, "adds value" by virtue of the hype train. We need ML to sell products, not to create them. I do agree that this sort of arrangement lends itself to supporting skepticism around AI and ML. On the other hand I don't think that's a bad thing.
- AndrewKemendo 10y ago
  Got it. Thanks for the clarification. It is true that people are using ML where other, simpler options are available, but I wouldn't immediate discount the value of using nets for your problem. I don't know enough about your problem/implementation to speak to it really.
  - sidlls 10y ago
    Yes, thanks for highlighting the deficiency in my original post. I can see it is easily interpretated as you did. I added a clarification (or what I hope is one).
    - lbhnact 10y ago
      I know a few companies riding the ml/nn train. Any chance you are based in NYC?
      - sidlls 10y ago
        No, in the bay area. The hype is strong here.
- deleted 10y ago
  [deleted]

Bartweiss 10y ago

It's interesting to me that with all the ML hype, it's still not clear what constitutes ML. A basic k-means or naive Bayes approach will show up in ML textbooks, but those aren't clearly different from "use some statistics to make a prediction". There's an interesting group of marginal approaches that have existed as-is for years, but have increasingly focused their branding on machine learning as its profile has risen.

sidlls 10y ago
This mirrors my own thoughts on the matter. Especially as regards the "branding" issue.
jorgemf 10y ago
> but those aren't clearly different from "use some statistics to make a prediction" You can reduce 90% of ML to this. Even neural networks are based on statistics. If I have to draw a line between statistics and ML is that ML learns, it means it can predict things, however statistics only gives you information about the data you have. But for sure statistics and ML overlap a lot.
- Bartweiss 10y ago
  Even that doesn't seem like a clear distinction? If you ask me for the most likely new value for a dataset, I won't know. But if I graph a few things and then write a function to spit back the current mean or median, is that machine learning? I'm not trying to be snarky there, I agree that the bulk of ML tools are fundamentally just statistical tricks with some layer of abstraction. As a result, I have a lot of trouble knowing how much abstraction justifies the ML title. I see some people using "statistics to produce unintuitive solutions" as a standard, but that just begs that we ask unintuitive to who?
  - BickNowstrom 10y ago
    I feel like it is foremost a matter of attitude of the practitioner. An applied statistician and a machine learning engineer may deliver exactly the same end product, just the reasoning and assumptions differ. Machine learning uses little to no assumptions, where statisticians do. I also feel that machine learning engineers have a bit less fear of building black boxes. Caruana showed the cartoon of the difference between a statistician and a machine learning practitioner by showing a cliff. The statistician carefully inches to the edge, stomping her feet to see if the ground is still stable, then 10 meters before the edge she stops and draws her conclusions. The machine learning practitioner dives headfirst from the cliff, with a parachute that reads "cross-validation". See also: http://norvig.com/chomsky.html http://norvig.com/chomsky.html On Chomsky and the Two Cultures of Statistical Learning. And http://projecteuclid.org/euclid.ss/1009213726 http://projecteuclid.org/euclid.ss/1009213726 Statistical Modeling: The Two Cultures by Leo Breiman. and this joke: > Norvig teamed up with a Stanford statistician to prove that statisticians, data scientists and mathematicians think the same way. They hypothesized that, if they all received the same dataset, worked on it, and came back together, they’d find they all independently used the same techniques. So, they got a very large dataset and shared it between them. > Norvig used the whole dataset and built a complex predictive model. The statistician took a 1% sample of the dataset, discarded the rest, and showed that the data met certain assumptions. > The mathematician, believe it or not, didn’t even look at the dataset. Rather, he proved the characteristics of various formulas that could (in theory) be applied to the data.