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You might not need machine learning

nullprogram.com

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Re: You might not need machine learning

#71

Post by ccortes »

spratzt wrote: post #51
ccortes wrote: post #23

As a former data scientist I feel we try solve problems the hard way because one of two reasons (maybe both): 1. To feel smart 2. To justify our paycheck Most of the time simple solutions like the one in the link will be more than enough but we just can't resist the urge to implement this new paper we just found. I remember thinking about using a NN for a problem we had, after looking it closely for two days all I ne…

Genuinely curious as to what you moved into after working as a data scientist. I'm a data scientist and desperate to get out.

I founded my own company after I had a problem that solved from myself and thought I could actually scale it. Just starting tho.

Re: You might not need machine learning

#72

Post by LoSboccacc »

well yeah anything you can do with NN you can do chaining enough functions

what NN allows is to test millions of permutations of thousands these functions to find the one that works

if your problem is intractably hard to figure out functions for, NN provide a larger hammer to brute-force an approximate solution.

Re: You might not need machine learning

#74

Post by lopmotr »

maweki wrote: post #36
rorykoehler wrote: post #33

Earlier quoted context omitted.

> Cynically, neural networks are easier as you don't really have to think about your model. Give some examples with some classes and you're done. Or give examples of one class and let the neural net generate new ones. Doing away with the abstraction beforehand is an enticing prospect. If you're trying to solve a well understood business problem sure but my issue with this is that you pigeonhole yourself and your solu…

Oh, I was not defending neural networks. This was the cynical sales pitch for the case where you don't want to employ mathematicians or computer scientists, but just throw code and computational resources at the problem.

But isn't that an important part of the value of neural networks? Mathematicians are expensive so we'd like a computer to make a model for us, just like drivers are expensive so we want self-driving cars.

Re: You might not need machine learning

#76

Post by westwing »

rorykoehler wrote: post #33
maweki wrote: post #27

Earlier quoted context omitted.

The machine learning course at my university starts out with polynomial regression and estimators, statistics of classification, etc.. Neural networks are only one tool in a large toolbox. But they are all the rage and it is no surprise that a lot of people want to play with them. Cynically, neural networks are easier as you don't really have to think about your model. Give some examples with some classes and you're…

> Cynically, neural networks are easier as you don't really have to think about your model. Give some examples with some classes and you're done. Or give examples of one class and let the neural net generate new ones. Doing away with the abstraction beforehand is an enticing prospect. If you're trying to solve a well understood business problem sure but my issue with this is that you pigeonhole yourself and your solu…

> It might be a good short term solution but long term it certainly isn't.

It is only a temporary solution - unless it works.

https://www.youtube.com/watch?v=pY7nx5Z6Kzo

Re: You might not need machine learning

#77

Post by mlthoughts2018 »

This is a farcical idea if the author actually is serious. The polynomial method is deep, dangerous overfitting. It’s the same reason why you don’t make a hand-made decision tree: you don’t know which features of the data are important or not. Presuming you do know, like basing the car’s steering on a polynomial fit of the three directional probes, will be a disaster in non-toy problems.

This example shows why you _do_ need machine learning. The toy steering problem is a place for you to work out how the car can learn to drive with as little structure or assumption baked in as possible. Putting less a priori structure on it directly means you need models with higher capacity to figure it out.

To put this all more succinctly, “never use machine learning when business logic would do” is a statement about how commonly needed machine learning is, not business logic.

Re: You might not need machine learning

#78

Post by mlthoughts2018 »

sdoering wrote: post #16

It is a nice, albeit problematic example. Nonetheless I find many such examples in my daily work as a Data Analyst. Regarding recommender systems I see many companies trying neural nets and so many other fancy ML stuff for things that - in AB-tests are always outperformed by basic rules. I understand the fun it is to build stuff and to use the new hot stuff. And at least for many analysts and marketing people as well…

> “ Regarding recommender systems I see many companies trying neural nets and so many other fancy ML stuff for things that - in AB-tests are always outperformed by basic rules.”

I work on large scale recommender systems for an ecommerce company and in my career I’ve seen only the exact opposite.

Don’t get me wrong, sometimes simpler ML models, like clustering LSA vectors or nearest neighbors, work better than complex models like neural nets.

But I have never seen plain rule systems work better for any problem even remotely at scale. Rule systems give an illusory sense of control and understanding, yet are rife with complex interaction effects and edge cases that typically make them intractable to change.

Re: You might not need machine learning

#79

Post by mjburgess »

maweki wrote: post #27
karolkozub wrote: post #20

I love the article, but I don't agree with the premise that machine learning equals neural nets. In my understanding machine learning is a very broad term that just as well could be applied to the polynomial model if the constants were optimized algorithmically. I feel like the presented argument is more for transparent vs opaque models rather than machine learning vs something else. Also one could argue that the pol…

The machine learning course at my university starts out with polynomial regression and estimators, statistics of classification, etc.. Neural networks are only one tool in a large toolbox. But they are all the rage and it is no surprise that a lot of people want to play with them. Cynically, neural networks are easier as you don't really have to think about your model. Give some examples with some classes and you're…

That's an excellent approach -- and how I try to introduce people to NNs.

NNs are just polynomial regression with polynomial activations; and piece-wise linear regression with relu activations (etc.).

A NN is just a highly parameterized regression model -- for better, or worse.

Re: You might not need machine learning

#80

Post by blackbrokkoli »

stfwn wrote: post #31

> A key feature of neural networks is that the outputs are a nonlinear function of the inputs. However, steering a 2D car is simple enough that a linear function is more than sufficient, and neural networks are unnecessary. This depends entirely on the definition of 'steering a 2D car'. In the model used, throttle is simply proportional to the distance to the nearest wall in front of the car. This means the agent wil…

I see the article more as a broader metaphor for the AI hype. Take, I dunno, video recommendation.

Sure, YouTube itself proably built insane stuff in their engine you could never replicate with classic methods (ignoring whether the YT algo is any good).

However, if we are just talking about the Vlog of your real estate company, you should probably A/B-test whether your viewers prefer order by time or clicks and implement a decent title search bar. And kick the consultant hyping you up about ML out, now.

So my takeaway is that is not about whether AI is never useful or about 2D steering, but about using the right tool for the right job.

And building on that, I have to give the author probs to demonstrating an alternative solution to I problem wich I would have definitely solved via AI.

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