You might not need machine learning • Hacker News Board

Re: You might not need machine learning

#161

Post by noobermin » Tue, Nov 24, 2020, 7:48 PM UTC

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…

> 1. To feel smart 2. To justify our paycheck

Finally someone on HN is being honest. It's also a good touch that this is downvoted somewhat while replies getting mad at essentially choice of words (of the title, nonetheless) are on top.

Doing the elegant solution was the first thing I learned in grad school, it was literally a comment a professor made. In undergraduate, you feel smart for working out a long calculation. In grad school and beyond, you should feel smart by doing the least math possible and using intuition. It's not that long calculations aren't necessary, it's just that thinking a little first is important rather than just turning the hard work crank.

Re: You might not need machine learning

#162

Post by noobermin » Tue, Nov 24, 2020, 7:57 PM UTC

cupofcoffee wrote: post #55

I simply do not understand the ML hype. It's absolutely ridiculous. On top of that you have Elon Musk thinking we are a few months away from SkyNet even though we are decades and decades away from AGI. Each and every time I see neural networks on some "techies" blog, I wanna vomit.

Were you around when every company hawked a blockchain of a type? It's a marketing fling right now, that's mostly what it is for.

Re: You might not need machine learning

#163

Post by edgyquant » Tue, Nov 24, 2020, 8:12 PM UTC

curiousllama wrote: post #96

The best time series prediction model is often “Use the previous day (or week’s) value”. The best classification model is often “pretend everything is the same.” The best regression model is often “assume it’s the mean”. This should not be surprising: a core premise of data science (empiricism in general? All logic and human knowledge?) is that intuition is often wrong. Initial hypotheses/problem definitions are just…

This is incorrect and I’m not sure the point you’re trying to make.

Re: You might not need machine learning

#164

Post by thotsBgone » Tue, Nov 24, 2020, 8:27 PM UTC

martamorena9182 wrote: post #152

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…

> Sure, YouTube itself proably built insane stuff in their engine you could never replicate with classic methods Probably. But pretty much anything they recommend is junk, so... That's where the author may have a point. If you don't understand your AI algorithm anymore, it's hard to improve it or even realize how wrong it is. AI is generally good at steering the masses into a couple of "averaged" directions. At the i…

What's good for you is not the same as what's good for Youtube. Youtube wants to maximize watch time at all costs. Their algorithms is probably very good at that.

Re: You might not need machine learning

#165

Post by thotsBgone » Tue, Nov 24, 2020, 8:29 PM UTC

blackbear_ wrote: post #145

finnthehuman wrote: post #119

Earlier quoted context omitted.

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.

> ... and then it keeps you swimming in the same pool. This is a consequence of the metrics that are being optimized, it's not a fault of the algorithm per se.

It's not a fault at all. If you're going to spend more time watching videos if you're recommended stuff Youtube knows you already like, that's what it's going to do. Youtube just wants you to watch more videos. They don't care whether you are exposed to a variety of content.

Re: You might not need machine learning

#166

Post by didibus » Tue, Nov 24, 2020, 8:33 PM UTC

> If I wanted to generate a good driver for a course, I’d run a few thousand of these and pick the coefficients that complete the course in the shortest time.

I might be pedantic here, but wouldn't this then be a machine learning algorithm? Since the machine is learning the most appropriate coefficient based on some heuristic (best of X random).

Wouldn't it be better (and more honest) to say then that, simpler ML models and learning techniques can often yield good enough result that don't justify going to more advanced model and learning techniques?

Re: You might not need machine learning

#167

Post by adontz » Tue, Nov 24, 2020, 8:35 PM UTC

I like how these cars on the last animation move like a school of fish.

Re: You might not need machine learning

#168

Post by dr_dshiv » Tue, Nov 24, 2020, 8:40 PM UTC

fractionalhare wrote: post #129

dr_dshiv wrote: post #92

Earlier quoted context omitted.

As soon as we recognize plain old regression as machine learning, then we start to see "averages" as models of systems and how practically useful could that be?

I think you're being facetious, but on the off-chance you're not, and for the benefit of others: averages are incredibly practically useful for modeling systems. Parameter estimation (which generalizes averages and applies to other distribution features like variance) is a foundational modeling methodology. It's useful for both understanding and forecasting data. Measures of central tendency are nearly always good (i…

Thank you for your fabulous response. I hope my provocative comment wasn't in bad humor, disrespectful or trolling. I too love averages and regressions. Thank you for proudly defending these marvelously simple and powerful tools.

Re: You might not need machine learning

#169

Post by bastawhiz » Tue, Nov 24, 2020, 8:45 PM UTC

didibus wrote: post #166

> If I wanted to generate a good driver for a course, I’d run a few thousand of these and pick the coefficients that complete the course in the shortest time. I might be pedantic here, but wouldn't this then be a machine learning algorithm? Since the machine is learning the most appropriate coefficient based on some heuristic (best of X random). Wouldn't it be better (and more honest) to say then that, simpler ML mod…

> I might be pedantic here, but wouldn't this then be a machine learning algorithm?

I would argue no. Simply trying a bunch of inputs and choosing the most effective ones based on the output is like running a single round of training on a machine learning model. It's hardly machine learning by any stretch—there's no feedback loop, the machine doesn't "learn" anything.

If I build a compiler and test a thousand constants for default configuration options and choose the ones that perform the best, or would be a very bold claim to call my compiler "powered by machine learning".

Re: You might not need machine learning

#170

Post by SomeoneFromCA » Tue, Nov 24, 2020, 9:07 PM UTC

0-_-0 wrote: post #64

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…

> I think the article may be better titled "You might not need neural networks." Since a linear model is essentially a single layer neural network with linear activation, we can't even say that . The athor was using a neural network without realising it :)

I'd argue that a NN cannot have linear activation. I mean, if the activation function is linear, it is not an NN anymore.