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I don't believe in "everyone should work on machine learning". I worked on several deep learning models but I don't really like it. It is a very different job

by arbre 10y ago

I don't believe in "everyone should work on machine learning". I worked on several deep learning models but I don't really like it. It is a very different job than software engineering in my opinion. ML is more about gathering data and tuning the models as opposed to building stuff. I have spent months working on models and barely wrote any code. It is more efficient to have ML experts focus on the modeling and software engineers use the model. I do believe however that some experience is needed to understand what is possible and best benefit from existing tools or to be able to communicate with machine learning engineers about your needs.

personjerry 10y ago
Hi arbre, would you mind explaining what is possible and what benefits from existing tools in machine learning at the moment? I am clueless and find ML rather frustrating to get into.
- arbre 10y ago
  I meant that learning about ML and getting some field experience helps you figuring out when to use ML and how. For how to get into, there are a lot of resources and state of the art algorithms/papers/implementations are freely available. For me working on ML projects at my job and talking to some experts was ideal, but I am sure it is possible to learn on one's own with enough motivation. Good luck!
  - personjerry 10y ago
    Ah, yes, I understood what you meant (and thank you for pointing to where I should look next!). I was hoping, too, that you might share your ML knowledge in layman's terms.
zmj 10y ago
This is how software eats your job.
giardini 10y ago
I concur. ML isn't programming per se; it is experimental problem-solving with a particular dataset and algorithm. Your result may/not work well, may/not generalise, and will almost undoubtedly not contribute anything new to any discipline, even to ML. When all ML work is done we'll have great pattern recognizers but nothing remotely akin to thought. And we won't understand how they work or the best way to build the next one. It isn't AI, although it is a part of AI, just as the visual system is part of AI. I was reading Domingos' "The Master Algorithm" several days ago and a mathematician inquired about the book. He knew a group of ML developers. His opinion was that "ML doesn't look very interesting: all you do is play with the parameters, turn the knobs, and/or change the model until something works. There's no real progress there; nothing substantial." Rather than sending a batallion of bright developers into the ML swamp where they will largely be frustrated, learn little and contribute less, I'd be tempted to guide them into other fields.
- visarga 10y ago
  > And we won't understand how they work Is this a critique of the human mind or a praise of AI? > When all ML work is done we'll have great pattern recognizers but nothing remotely akin to thought Maybe our brains too are nothing but pattern recognizers. Maybe they are nothing but chemical reactions, or energy fields. But being reductionist about AI won't help us understand it either.
- zby 10y ago
  What we need is models that are more retrospectable - so that you can find the rules that it learned. Most of the time they will be too complex for any human understanding - but from time to time we'll find something interesting, something that we can build other things upon. I have never used neural nets etc - but with simplified bayes spam filters this was possible and quite useful. I used to check which words were pushing a text into one or other category and which did not (when they should).
- ioeu 10y ago
  What fields would you guide them to instead? I may be one of the developers you speak of (with academic aspirations), presently considering my path forward. I'm sceptical if going down the ML swamp is the best way forward.
dgacmu 10y ago
Absolutely. In general, ML needs a collaboration between ML expertise and application domain expertise. It's very helpful if there's someone who can help bridge those two - enough app experience to understand the domain deeply, and enough ML experience to know what questions to ask of the ML gurus and what pitfalls to expect. As I see it, that's one of the goals of the ML ninja program.
etangent 10y ago
I don't know. I know some engineers who have spent months going back-and-forth over communication protocols while barely writing any code, yet somehow their job is considered to be quite core to software engineering. I don't really see how fine-tuning communication protocols is fundamentally different from fine-tuning machine learning models. But overall, I agree with your sentiment: different things are different and appropriate for different people.
- serge2k 10y ago
  Wouldn't that be more akin to the design of the models?
srtjstjsj 10y ago
> "everyone should work on machine learning" > software engineers use the model. You aren't disagreeing.
uola 10y ago
"Moving data around" is what a lot of software engineering is these days. Facebook, Google etc. are more data companies than software companies (and probably close to media than communcations companies).
lunchTime42 10y ago
Sounds like a job machines could do..

by arbre 10y ago

personjerry 10y ago

Hi arbre, would you mind explaining what is possible and what benefits from existing tools in machine learning at the moment? I am clueless and find ML rather frustrating to get into.

arbre 10y ago
I meant that learning about ML and getting some field experience helps you figuring out when to use ML and how. For how to get into, there are a lot of resources and state of the art algorithms/papers/implementations are freely available. For me working on ML projects at my job and talking to some experts was ideal, but I am sure it is possible to learn on one's own with enough motivation. Good luck!
- personjerry 10y ago
  Ah, yes, I understood what you meant (and thank you for pointing to where I should look next!). I was hoping, too, that you might share your ML knowledge in layman's terms.

zmj 10y ago

This is how software eats your job.

giardini 10y ago

I concur. ML isn't programming per se; it is experimental problem-solving with a particular dataset and algorithm. Your result may/not work well, may/not generalise, and will almost undoubtedly not contribute anything new to any discipline, even to ML. When all ML work is done we'll have great pattern recognizers but nothing remotely akin to thought. And we won't understand how they work or the best way to build the next one. It isn't AI, although it is a part of AI, just as the visual system is part of AI. I was reading Domingos' "The Master Algorithm" several days ago and a mathematician inquired about the book. He knew a group of ML developers. His opinion was that "ML doesn't look very interesting: all you do is play with the parameters, turn the knobs, and/or change the model until something works. There's no real progress there; nothing substantial." Rather than sending a batallion of bright developers into the ML swamp where they will largely be frustrated, learn little and contribute less, I'd be tempted to guide them into other fields.

visarga 10y ago
> And we won't understand how they work Is this a critique of the human mind or a praise of AI? > When all ML work is done we'll have great pattern recognizers but nothing remotely akin to thought Maybe our brains too are nothing but pattern recognizers. Maybe they are nothing but chemical reactions, or energy fields. But being reductionist about AI won't help us understand it either.
zby 10y ago
What we need is models that are more retrospectable - so that you can find the rules that it learned. Most of the time they will be too complex for any human understanding - but from time to time we'll find something interesting, something that we can build other things upon. I have never used neural nets etc - but with simplified bayes spam filters this was possible and quite useful. I used to check which words were pushing a text into one or other category and which did not (when they should).
ioeu 10y ago
What fields would you guide them to instead? I may be one of the developers you speak of (with academic aspirations), presently considering my path forward. I'm sceptical if going down the ML swamp is the best way forward.

dgacmu 10y ago

Absolutely. In general, ML needs a collaboration between ML expertise and application domain expertise. It's very helpful if there's someone who can help bridge those two - enough app experience to understand the domain deeply, and enough ML experience to know what questions to ask of the ML gurus and what pitfalls to expect. As I see it, that's one of the goals of the ML ninja program.

etangent 10y ago

I don't know. I know some engineers who have spent months going back-and-forth over communication protocols while barely writing any code, yet somehow their job is considered to be quite core to software engineering. I don't really see how fine-tuning communication protocols is fundamentally different from fine-tuning machine learning models. But overall, I agree with your sentiment: different things are different and appropriate for different people.

serge2k 10y ago
Wouldn't that be more akin to the design of the models?

srtjstjsj 10y ago

> "everyone should work on machine learning" > software engineers use the model. You aren't disagreeing.

uola 10y ago

"Moving data around" is what a lot of software engineering is these days. Facebook, Google etc. are more data companies than software companies (and probably close to media than communcations companies).

lunchTime42 10y ago

Sounds like a job machines could do..