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ML is not that good at predicting consumers' choices

statmodeling.stat.columbia.edu

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Re: ML is not that good at predicting consumers' choices

#131

Post by treesprite82 »

ninkendo wrote: post #113
treesprite82 wrote: post #112

Earlier quoted context omitted.

> I mean basically that is what ML is, trying to predict the future If being so reductive, that's also the scientific method. Form a model on some existing data, with the goal of it being predictive on new unseen data. Key is in favoring the more predictive models. > they called it magic, we call it math, but both seem to have about the same outcome Find me some sheep entrails that can do this: https://imagen.researc…

One of the oft overlooked, yet critically important aspects of the scientific method is the hypothesis. You don’t design an experiment having absolutely no idea what to expect. You have an educated guess in mind (the hypothesis), and you design the experiment in such a way that says “this result will rule out my hypothesis, while this other result might confirm it.” Just trying two things at random and picking the on…

Note that I'm being intentionally reductive to argue that the net buscoquadnary threw around ML models and future-telling with sheep entrails also includes the scientific method.

I do also think that ML as a field progresses through the scientific method ("I theorise that this network with residual connections will converge faster, lets see if there's a significant difference") - but maybe not to the full extent it could.

> Just trying two things at random and picking the one that makes some arbitrary metric go up, is not the scientific method. It’s gradient descent.

I'd say that's closer to evolutionary algorithms. GD finds (locally) the direction to tweak the weights to improve predictions on a given batch.

Re: ML is not that good at predicting consumers' choices

#132

Post by Melatonic »

tomcam wrote: post #29

I can personally vouch that Amazon, Twitter, and YouTube all do horrible horrible jobs predicting my taste. And they have got worse over the years, not better

If you go deep into all the settings you can turn all of amazons predictive jibber jabber off and turn off a ton of tracking. It has been awhile but I swear there were settings hidden everywhere. I just went back to amazon and it still seems to suggest some products based on what I just viewed so now I am wondering what the hell I even turned off.....it is actually fairly accurate now though (I looked at a timex watch and it is suggesting a very similar timex watch)

edit: My bad - it was just suggesting products I had recently previously viewed

Re: ML is not that good at predicting consumers' choices

#134

Post by saurik »

ape4 wrote: post #22

You just bought a washing machine... could I interest you in a washing machine?

College Humor--may it rest in peace :( :( :(--did a great bit about this a few years ago. https://www.youtube.com/watch?v=KbKdKcGJ4tM It is absolutely ridiculous how useless targeted ads actually are... and seeing a real person try to sell you this stuff really underscores the insanity.

Re: ML is not that good at predicting consumers' choices

#135

Post by kmnc »

jltsiren wrote: post #45
tomcam wrote: post #29

I can personally vouch that Amazon, Twitter, and YouTube all do horrible horrible jobs predicting my taste. And they have got worse over the years, not better

My favorite experience with Amazon: I had just preordered novel 9 of The Expanse, and I got an email recommending something else from the same authors: novel 8 of the Expanse. A more sensible recommendation engine might have assumed that someone who preorders part n+1 of a series may already have part n. Not to mention that Amazon should have known that I already had novel 8 on my Kindle. I guess generating personali…

Funny thing is, your very comment is an indirect praise of the very thing they were advertising to you, and here it is being read by thousands of people. Are we so sure absurdly terrible ads don't actually beat out actually good well tailored ones? Looking through the history of radio ads, television ads, it seems like the best ads are always the stupidest. "Head on, apply directly to the forehead!" isn't so far off from "You bought a washing machine? Buy another!". The reality is, advertising optimizes to target stupid people because stupid people spend money. It is easier to trick a moron then sell a smart man something they actually want.

Re: ML is not that good at predicting consumers' choices

#136

Post by mjevans »

Melatonic wrote: post #128
Slackwise wrote: post #10

Earlier quoted context omitted.

"Why am I sweating right now? Oh, the Nest set the temperature too high again!" And then after a few instances, I just turn off all the automation and set up a schedule like normal. Same with the "away from home" which seems to randomly think I'm away and I have no idea why. Oh, and the app doesn't show me filter reminders, only the actual device, which I never touch all the way downstairs. There's not even any statu…

The problem I think with all of these ML functions is that there is never enough of an in between full manual and full auto magic. The Nest could simply ask you a few times a day how you feel about how its doing (too hot ? too cold? too expensive?) or you could opt in somewhere and to give feedback. And then it could keep doing its behind the scenes mumbo jumbo.

How I feel is at least psychosomatically related to how the system is operating, but also how things are outside of the controlled environment.

Generally this is an algorithm that might work for one person:

* Is it equal or higher than the upper temperature outside? Run COOLing function (if it exists) until the LOWER inside temperature bound is reached.

* ELSE Is it equal or lower than the lower temperature outside? Run HEATing function (if it exists) until the UPPER inside temperature bound is reached.

In both cases DO NOT continue to run the circulation fans, at least above a whisper quite slow circulation, past the thermal shift operation. Luke-warm airflow makes the system perceptibly anemic and the results dis-satisfactory.

Re: ML is not that good at predicting consumers' choices

#137

Post by rocket_surgeron »

As far as I can tell every single penny of the hundreds of millions (billions?) of dollars that have been spent on ML recommendation engines has been wasted and the team of highly-compensated developers who spent years burning through cash building Hadoop clusters, buying GPUs, building dashboards and churning out thousands of tables and charts for execs to ponder over just to build something that recommends I rebuy something I already bought could have been replaced with a 24-year old communications major who also understands the products being sold and the people buying them.

Re: ML is not that good at predicting consumers' choices

#138

Post by danielscrubs »

zach_garwood wrote: post #124
wrycoder wrote: post #98

Earlier quoted context omitted.

I think YouTube has given up on figuring me out. They mostly offer stuff I’ve already watched or stuff on my watch list.

Same. For a long time they wanted me to watch angry white guys complaining about pop culture. But all I want to watch are PBS science shows and stuff about ancient history! Eventually, they gave up, and now half the videos they recommend are ones I already watched years ago.

collaborative filtering models on big platforms are really the worst

Re: ML is not that good at predicting consumers' choices

#139

Post by Barrin92 »

teruakohatu wrote: post #121
Barrin92 wrote: post #111

Earlier quoted context omitted.

>Of course when an AI does that, we then say its just doing statistics, not reasoning. no, AI simply doesn't do that. Even Demis Hassabis of Deepmind fame in a recent interview pointed this out. Machine learning is great on averaging out a large amount of data, which is often useful, but it doesn't generate true novelty in any human sense. AI can play Go, it can't invent Go. In the same way today's recommender system…

> but it doesn't generate true novelty in any human sense. Few humans do either, even great artists create from, or react to, the art they experienced in their life. At the end of the day this is a rather metaphysical question. A Go model is trained to play Go, not create new games. > on a genuine understanding of say, my personality That is exactly what they are trying to do. The purpose of many recommendation syste…

>to predict certain users like to listen to happy upbeat tracks on Fridays, and sad songs sung in Romance languages on Mondays.

This is still entirely superficial. There's no recommender system that could even tell what a 'sad track' is other than by human labelling, or distinguish say, a satirically sad track from the genuine thing, or accurately tell what any individual even perceives as happy or sad which is highly subjective.

It's not about metaphysics but about a very practical fact that these statistical systems fail at. Making genuinely novel human recommendations requires and understanding of human nature in general, individual nature of the person in question, and understanding of context and meaning. In short a system like this needs to be able to reason about what it is that it's recommending and who it's talking to.

There is no system that could invent a game like Go because it has nothing to do with data in any direct sense. It'd require being able to reason about what makes games compelling and aesthetically pleasing to human beings and that's not a question of mapping one button click to another.

Re: ML is not that good at predicting consumers' choices

#140

Post by majormajor »

HWR_14 wrote: post #88
aaronax wrote: post #34

ML is there to maximize business income--nothing else. If ML was benefiting me, it would know that 90% of the time I fire up Hulu I plan to watch the next episode of what I was watching last time. And it would make that a one click action. Instead I have to scroll past promotional garbage...every single time. Assholes.

I don't know why you assume the goal is "help aaronax watch what he wants quickly" vs "make sure when aaronax switches to his next series/movie it's on Hulu"

Yeah, in these cases people try to balance for both "engagement" and "retention" (probably called something like "discovery" - the more they can show you about what other options they have, and the more shows you start watching, the less you quit after the first show you watch).

The problem is that they don't have a way of collecting "annoying" metrics as easily as they do those two. So it's a big blind spot in terms of "should we tweak more in favor of one or the other."

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