And yet I haven't seen one ad that has a 'not interested' feature similar to YouTube. I mean, if I could stop seeing washing machines or whatever, I'd probably click it.
do you clear your cookies and html5 storage? That should wipe any personalization that's happening, but the ads will become very generic then. You can also block ads (Ublock/Umatrix)
Oh yeah I can do that, I'm just wondering why they're not too interested in direct user feedback from my end. Surely it would be useful to adjust their algos.
Since we are sharing anecdotes, I can report it's been 20 years of buying stuff on the internet and the combined billions of ad tracking research dollars spent by Amazon and Google have not yet come up with a better algorithm than to bombard me with ads for the exact same thing I just bought .
I have been looking for a shelf that’s as close to 50” wide and 10” deep as possible. No search on any site that allows you to search for such a thing as far as I can tell.
You can do this at wayfair.com. It's kind of hidden though, when you get to the shelves, click "Sort & Filter" and then scroll down until you see the dimension sliders.
It would be interesting to try to train an algorithm to detect snake oil in companies claiming AI, but I don't know how it would work with no negatives.
I don't have time to read the entire paper but I would like to share an anecdote. I worked at a company with a well staffed/funded machine learning team. They were in charge of recommendation systems - think along the lines of youtube up next videos. My team wanted better recommendations (really, less editorial intensive) so the ML team spent weeks crafting 12 or more variants of their recommendation system for our c…
Since we are sharing anecdotes, I can report it's been 20 years of buying stuff on the internet and the combined billions of ad tracking research dollars spent by Amazon and Google have not yet come up with a better algorithm than to bombard me with ads for the exact same thing I just bought .
I think amazon works pretty well.
They show me similar things to the thing I just put in my cart. Sometimes better choices that I made.
They also show related things that other people have bought, that many times I end up purchasing.
I don't have time to read the entire paper but I would like to share an anecdote. I worked at a company with a well staffed/funded machine learning team. They were in charge of recommendation systems - think along the lines of youtube up next videos. My team wanted better recommendations (really, less editorial intensive) so the ML team spent weeks crafting 12 or more variants of their recommendation system for our c…
The San Jose Mercury News had a weather-forecasting contest. It was won one year by a 12-year-old, who's algorithm was "The weather tomorrow will be the same as the weather today". A kind of AI I guess.
The author says "AI is already at or beyond human accuracy in all the tasks on this slide and is continuing to get better rapidly" and one of his examples is "Medical diagnosis from scans". That is an example of precisely the sort of snake oil hype he's berating in the social prediction category. In an extremely narrow sense of pattern recognition of some "image features", i.e. 5% of what a radiologist actually does,…
FYI, I too looked at his categorisation of what was/was advancing/was not snake oil and didn't exactly agree with all his decisions either. Medical imaging diagnosis was one of them. Speech recognition/transcription was another. I don't know if it's my accent or my speech patterns(though foreigners regularly compliment my wife and myself on our pronunciation), but the tech hasn't gotten noticeably better for me since…
I feel like it’s gotten loads better. You can watch as the voice recognition on your phone changes the words it recognizes to match the context in the sentence. Sometimes it gets a word wrong and fixes it after a half second. Google translate does magical things recognizing common phrasing constructions and bad accents, stuff that Dragon could never do. I built a lipsync pipeline for video games based on Dragon a decade ago, and it most definitely was not as good as what I have on my phone today.
Brilliant. I think YouTube has arrived at the same algorithm - it picks the videos I watched yesterday to recommend today.
Well to be fair that's how all employers also hire. If you did a good job at the last company you'll probably do a good job here. If you did a good job yesterday, you'll probably do a good job today. For the most part they are usually correct.
I remember reading Steve Jobs used a "different" technique to figure out if someone was good.
He would go around to people and say "I heard Joe sucks". If the people strongly defended Joe, he was probably pretty good. If nobody stuck up for him, Joe might indeed suck.
I don't have time to read the entire paper but I would like to share an anecdote. I worked at a company with a well staffed/funded machine learning team. They were in charge of recommendation systems - think along the lines of youtube up next videos. My team wanted better recommendations (really, less editorial intensive) so the ML team spent weeks crafting 12 or more variants of their recommendation system for our c…
Isn't sparse recommendation for videos kind of solved in netflix prize, where the winner uses SVD to extract signature characteristic and recommend videos base on that?
I feel like if the Netflix Prize results had truly solved the problem, then they’d still be using them. It seems like the video recommendations aren’t as good as they were ten years ago during the prize competition, and they’re no longer based on what I might like but rather what Netflix wants me to watch.
Since we are sharing anecdotes, I can report it's been 20 years of buying stuff on the internet and the combined billions of ad tracking research dollars spent by Amazon and Google have not yet come up with a better algorithm than to bombard me with ads for the exact same thing I just bought .
I think amazon works pretty well. They show me similar things to the thing I just put in my cart. Sometimes better choices that I made. They also show related things that other people have bought, that many times I end up purchasing.
Dirty secret:
"The customers who bought also bought" algorithm doesn't require ML/AI.
You can accomplish that with relational algebra on a precomputed data warehouse job and only for products with strong correlation. The intelligence of the customers is enough agency to instil a semblance of intelligence in the data.
I think amazon works pretty well. They show me similar things to the thing I just put in my cart. Sometimes better choices that I made. They also show related things that other people have bought, that many times I end up purchasing.
Dirty secret: "The customers who bought also bought" algorithm doesn't require ML/AI. You can accomplish that with relational algebra on a precomputed data warehouse job and only for products with strong correlation. The intelligence of the customers is enough agency to instil a semblance of intelligence in the data.
Yes, this is the straightforward ‘collaborative filtering’ algorithm. I suppose the line between ‘algorithm’ and AI/ML is not well defined though. At what point does a technique become ‘AI’? I don’t know a good answer.