5 ms·

"Obfuscation" and "delusions of grandeur" are practically synonyms for ML and Data "Science" in this industry. I've been around for a while and I've never quite

by sidlls 7y ago

"Obfuscation" and "delusions of grandeur" are practically synonyms for ML and Data "Science" in this industry. I've been around for a while and I've never quite seen something as over-hyped and hyper-glamorized as these two specializations.

danielmg 7y ago
Does "blockchain" get an honourable mention?
- sidlls 7y ago
  Definitely.
codesushi42 7y ago
Really? Were you around during the dotcom era? Although I'm not old enough, I've heard that OR in the 80s was the same crap.
- mr_toad 7y ago
  Nobody talks about operations research today. But techniques that fell under that umbrella, like ARIMA and linear programming are still widely used, and aren’t going anywhere. (And it’s not without some irony that automated bulk time series forecasting is now sold as AI).
- lliamander 7y ago
  It's funny but at my last company, one of our systems used some linear programming to generate a model of physical processes. The problem could have been tackled with greater accuracy using machine learning, but it would have taken a long time for the system to generate enough data points for a sound model and would have required more storage space. This was also complicated by the fact that the model had to be regenerated whenever the physical system being modeled was changed. The linear programming solution was a lot cheaper and was "close enough" to serve as a useful approximation.
- SenHeng 7y ago
  What's OR?
  - goatinaboat 7y ago
    What's OR? https://en.wikipedia.org/wiki/Operations_research https://en.wikipedia.org/wiki/Operations_research Basically a mathematical approach to problems of logistics and scheduling developed first in WW2. Very powerful in the domains for which it was developed but less generally applicable than enthusiasts hoped, leading to the usual “hype cycle”. If you have a problem OR could solve or just want to fool around with it PuLP is very easy to use https://pythonhosted.org/PuLP/ https://pythonhosted.org/PuLP/ Of course the ease of use means that it is a commodity skill now.
    - codesushi42 7y ago
      There is also Google OR tools. https://developers.google.com/optimization https://developers.google.com/optimization
- dekhn 7y ago
  Linear and quadratic programming are amazing and totally underappreciated. Often they are the fastest way to get useful answers for problems (the solvers got really good over the past few decades).
bitL 7y ago
Heh, given that I am starting to see more and more companies that offer ML engineers $2-6k/month (before tax), it's starting to resemble gaming industry in all its negative characteristics instead.
- hcknwscommenter 7y ago
  I cannot tell from your comment whether 2-6k/month before tax should be considered a lot or a little. I think in the major tech centers that 2-6k/month is quite low for anyone with significant experience (>5 yrs). Do you disagree?
  - sjtindell 7y ago
    They used the word negative.
  - joaogui1 7y ago
    Since he compared to gaming I think he's saying it's low
  - deleted 7y ago
    [deleted]
dchichkov 7y ago
Calm down. Machine learning is a part of software engineering. Like multiprocessing, computer graphics or network protocols. It is here to stay. It is a part of a pallete of algorithms with which one can build software.
- dtech 7y ago
  Yes! It's one tool in the software engineering toolbox! It's a great tool for some problems! Due to the hype it becomes a goal in some organizations however. "We need to do machine learning because we have big data" or some such. Doesn't matter if the problem could've been solved in 5% of the time and cost with 20 lines of code, thou shalt use machine learning. It doesn't help that data scientists (creating and training the ML model) and software developers (creating and maintaining the software) usually come from different backgrounds, requiring a "data engineer" as an additional intermediary. It always a problem with hype, blockchain (or merkle trees) has the same problem but worse, because the problems it solves well are rarer and more narrow.
  - dchichkov 7y ago
    To me, it seem to be larger than one tool. I think of it as a color in a pallete, with which one can paint software. Octarine. To put this statement into context, I'm speaking as someone who had been writing code in C, from the era of PC XT. Perhaps NIPS 2010 was a rite of passage to ML for me. There is a screen, full of industry grade C++ and PyTorch in front of me, right now...
- crispyambulance 7y ago
  ML can be useful, but it is getting too much attention. Far more hype than the value it actually provides in many domains, IMHO. Yes, I know that there are folks that deal with vast amounts of data with inscrutable relationships where you need fancy algorithms to make progress. But seriously, most problems just don't need it, and many folks would be better off with mastering basic statistics and data analysis. It's fascinating how far you can get with basic stuff. My favorite? Statistics for Experimenters, by George E. Box. It's like a secret weapon! https://www.amazon.com/Statistics-Experimenters-Design-Innovation-Discovery/dp/0471718130 https://www.amazon.com/Statistics-Experimenters-Design-Innov...

by sidlls 7y ago

danielmg 7y ago

Does "blockchain" get an honourable mention?

sidlls 7y ago
Definitely.

codesushi42 7y ago

Really? Were you around during the dotcom era? Although I'm not old enough, I've heard that OR in the 80s was the same crap.

mr_toad 7y ago
Nobody talks about operations research today. But techniques that fell under that umbrella, like ARIMA and linear programming are still widely used, and aren’t going anywhere. (And it’s not without some irony that automated bulk time series forecasting is now sold as AI).
lliamander 7y ago
It's funny but at my last company, one of our systems used some linear programming to generate a model of physical processes. The problem could have been tackled with greater accuracy using machine learning, but it would have taken a long time for the system to generate enough data points for a sound model and would have required more storage space. This was also complicated by the fact that the model had to be regenerated whenever the physical system being modeled was changed. The linear programming solution was a lot cheaper and was "close enough" to serve as a useful approximation.
SenHeng 7y ago
What's OR?
- goatinaboat 7y ago
  What's OR? https://en.wikipedia.org/wiki/Operations_research https://en.wikipedia.org/wiki/Operations_research Basically a mathematical approach to problems of logistics and scheduling developed first in WW2. Very powerful in the domains for which it was developed but less generally applicable than enthusiasts hoped, leading to the usual “hype cycle”. If you have a problem OR could solve or just want to fool around with it PuLP is very easy to use https://pythonhosted.org/PuLP/ https://pythonhosted.org/PuLP/ Of course the ease of use means that it is a commodity skill now.
  - codesushi42 7y ago
    There is also Google OR tools. https://developers.google.com/optimization https://developers.google.com/optimization
dekhn 7y ago
Linear and quadratic programming are amazing and totally underappreciated. Often they are the fastest way to get useful answers for problems (the solvers got really good over the past few decades).

bitL 7y ago

Heh, given that I am starting to see more and more companies that offer ML engineers $2-6k/month (before tax), it's starting to resemble gaming industry in all its negative characteristics instead.

hcknwscommenter 7y ago
I cannot tell from your comment whether 2-6k/month before tax should be considered a lot or a little. I think in the major tech centers that 2-6k/month is quite low for anyone with significant experience (>5 yrs). Do you disagree?
- sjtindell 7y ago
  They used the word negative.
- joaogui1 7y ago
  Since he compared to gaming I think he's saying it's low
- deleted 7y ago
  [deleted]

dchichkov 7y ago

Calm down. Machine learning is a part of software engineering. Like multiprocessing, computer graphics or network protocols. It is here to stay. It is a part of a pallete of algorithms with which one can build software.

dtech 7y ago
Yes! It's one tool in the software engineering toolbox! It's a great tool for some problems! Due to the hype it becomes a goal in some organizations however. "We need to do machine learning because we have big data" or some such. Doesn't matter if the problem could've been solved in 5% of the time and cost with 20 lines of code, thou shalt use machine learning. It doesn't help that data scientists (creating and training the ML model) and software developers (creating and maintaining the software) usually come from different backgrounds, requiring a "data engineer" as an additional intermediary. It always a problem with hype, blockchain (or merkle trees) has the same problem but worse, because the problems it solves well are rarer and more narrow.
- dchichkov 7y ago
  To me, it seem to be larger than one tool. I think of it as a color in a pallete, with which one can paint software. Octarine. To put this statement into context, I'm speaking as someone who had been writing code in C, from the era of PC XT. Perhaps NIPS 2010 was a rite of passage to ML for me. There is a screen, full of industry grade C++ and PyTorch in front of me, right now...
crispyambulance 7y ago
ML can be useful, but it is getting too much attention. Far more hype than the value it actually provides in many domains, IMHO. Yes, I know that there are folks that deal with vast amounts of data with inscrutable relationships where you need fancy algorithms to make progress. But seriously, most problems just don't need it, and many folks would be better off with mastering basic statistics and data analysis. It's fascinating how far you can get with basic stuff. My favorite? Statistics for Experimenters, by George E. Box. It's like a secret weapon! https://www.amazon.com/Statistics-Experimenters-Design-Innovation-Discovery/dp/0471718130 https://www.amazon.com/Statistics-Experimenters-Design-Innov...