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You might not need machine learning

nullprogram.com

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Re: You might not need machine learning

#111

Post by username90 »

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…

Could a person with ML experience come up with this solution? Yes! Would his ML experience help him come up with this solution compared to someone who just learned numerical methods and automatic control theory? No. This isn't an ML solution.

Just because something is taught in an ML course doesn't mean that it is ML. It is pretty common for physics classes to teach maths and for chemistry classes to teach physics for example.

So if something is taught in ML class but also in statistics class then it is statistics and not ML. If something is taught in ML class but also in a numerical methods class then it is numerical methods and not ML.

Re: You might not need machine learning

#112

Post by disgruntledphd2 »

wokwokwok wrote: post #42

I feel like there’s something more interesting going on here than the author is giving credit to. Most tools you can start by solving simple problems with, and gradually work up the complexity of the problem you’re addressing until it does “something useful”. This is a good way to learn what you can, can’t and should use a tool for. Deep learning is problematic though; solving trivial tasks is actually quite difficul…

> and we have like 20 years of proof that using hand crafted models has been proven not to scale effectively.

What do you mean by this? In what context?

Like, the twenty year old models are still running in credit risk and insurance, so I'm confused if you mean in ML/statistical modelling.

Re: You might not need machine learning

#113

Post by disgruntledphd2 »

mlthoughts2018 wrote: post #78
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…

It depends.

Rule based systems are great if you have people with deep domain understanding developing the rules.

Unfortunately, those people are rare, so most rule-based approaches fail to perform well.

However, most recommendation systems suck unless you get someone who knows what they are doing to build them.

In terms of business value, I would be very hesitant to make strogn statements like the above (in both cases, actually).

Re: You might not need machine learning

#114

Post by disgruntledphd2 »

username90 wrote: post #97
mjlee wrote: post #18

Earlier quoted context omitted.

I think Arthur Samuel would agree. This approach has a loss function, parameters, and inputs that feed in to a model to optimise the parameters. The big difference between this and the other approach mentioned in the article is the model is a simple one that's easy to understand instead of a many layered neural network which is rather opaque. I think the article may be better titled "You might not need neural network…

Machine learning is a set of techniques developed to attack modeling problems that traditional algorithms couldn't solve. So if the algorithm was developed and used before computers it definitely isn't machine learning. Everything done in this article was known and used before computers existed hence not machine learning. Doing automatically controlled systems was still possible before computers just that it required…

> Machine learning is a set of techniques developed to attack modeling problems that traditional algorithms couldn't solve. So if the algorithm was developed and used before computers it definitely isn't machine learning

I think that it's important to note here that this was the set of problems that computer science researchers didn't know how to solve.

In the early days, they mostly ended up re-inventing statistical methods.

And to be fair, a neural net is just a bunch of linear models joined by a non-linearity. In that case, it's essentially stacked logistic regression, which was invented before computers.

Re: You might not need machine learning

#115

Post by tel »

It's always a disappointing state of affairs when people skip simple models. If nothing else, a nice baseline like the one described in this article should be constructed before using ML techniques to serve as a comparison point.

Re: You might not need machine learning

#116

Post by disgruntledphd2 »

username90 wrote: post #106
mlthoughts2018 wrote: post #81

Earlier quoted context omitted.

That’s a very unfair distinction, almost like a No True Scotsmam fallacy to say machine learning is only bad and other stuff is only good (in terms of transparency). But machine learning has predated neural networks by hundreds of years. The core mathematical basis of all machine learning coursework linear regression and decision trees. Other models like SVMs, Bayesian models, nearest neighbor indexes, TFIDF text sea…

Saying that linear regression is machine learning is like saying that newtons laws is chemistry. There was no machine learning before computers, just regular old optimization algorithms.

Yes, there was. It was just called statistical modelling.

Re: You might not need machine learning

#117

Post by offtop5 »

I wasted roughly a month trying to get machine learning working for a game.

Unless you seriously need it, and have a massive budget to match, basic boolean logic will probably get you where you want to be faster.

I wouldn't advise any solo developer to use machine learning unless that's the entire product. It's very easy to make an insanely difficult game without it

Re: You might not need machine learning

#118

Post by cupofcoffee »

mlthoughts2018 wrote: post #83
dhruvdh wrote: post #13

Earlier quoted context omitted.

Isn't that just searching a space? Machine learning generally refers to a fancy way of searching a space. Here the guy searched randomly so I'd say not machine learning. But perhaps using a cost function or a loss function is enough to call it machine learning. A machine just used an algorithm to learn another algorithm after all.

Stochastic gradient descent and other things like genetic algorithms and simulated annealing are random search techniques specifically created and taught in the context of machine learning.

Lol. Are all optimization problems specifically created and taught in the context of machine learning? Do you know any other context than ML?

Re: You might not need machine learning

#119

Post by finnthehuman »

mumblemumble wrote: post #108
blackbrokkoli wrote: post #80

Earlier quoted context omitted.

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 imple…

Video recommendation is the quintessential machine learning killjoy. YouTube and Netflix were a lot more interesting before they achieved algorithmic homogeneity.

They kill exposure to anything fresh, you teach it a couple things you like and then it keeps you swimming in the same pool.

Rather than discovering something new, everyone just watches The Office and Parks and Rec., again and again. Now those theme songs make my skin fucking crawl.

Re: You might not need machine learning

#120

Post by mathattack »

blackbrokkoli wrote: post #80
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 imple…

You may be overrating what they do. I suspect that 90% of the recommendation weight is based on what other people clicked after watching the same video.
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