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How to recognize AI snake oil [pdf]

cs.princeton.edu

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Re: How to recognize AI snake oil [pdf]

#161

Post by keyle »

mezentius wrote: post #100
reggieband wrote: post #58

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 best performing variant happened to be random. Some years ago I heard an anecdote from a developer who had worked on a video game about American football. The gist of it was that they had tested various sophisticated systems for an AI opponent to choose a possible offensive/defensive play, but the one that the players often considered the most "intelligent" was the one that simply made random decisions. In cert…

It's quite common in games for AI to pick a random decision. Simply put, a good AI is a character/NPC that appears to have a mind of its own, and its own life. Nothing beats random at explaining someone's behaviour based on a personal history you don't know.

If AI responded/acted based on a predefined set of patterns that could be recognizable, the player would automatically feel it (pattern matching) and makes the NPC far less interesting.

Re: How to recognize AI snake oil [pdf]

#162

Post by JimmyRuska »

_gwlb wrote: post #118
reggieband wrote: post #58

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…

I worked at a larger services marketplace, helping data scientists get their models into production as A/B experiments. We had an interesting and related challenge in our search ranking algorithms: we wanted to rank order results by the predicted lifetime value of establishing a relationship between searcher and each potential service provider. In our case, a 1% increase in LTV from one of these experiments would be.…

If you have historical data to validate against, you can set a leader board on models run against older data, and always leave part of the data out and unavailable for test

https://gluebenchmark.com/leaderboard/

This encourages a simple first version and incremental complexity, rather than starting very complex 6 months in, and never having an easy baseline to compare to. A simple baseline can spawn off several creative methods of improvement to research.

The other case is that the models should be run against simple cases that are easy to understand and easy to confirm. This way there's always a human QA component available to make sure results are sensible.

Re: How to recognize AI snake oil [pdf]

#163

Post by atomicity »

JimmyRuska wrote: post #160
AareyBaba wrote: post #146

Earlier quoted context omitted.

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 hope you are joking since the industry collectively knows how much employers/interviewers value algorithms-based coding interview, which doesn't correlate strongly with performance. Even if you are talking about senior positions where they don't matter, then you should know that people hire someone they know+like who did decently well, rather than the truly best on the market.

Re: How to recognize AI snake oil [pdf]

#164

Post by ssivark »

jfarmer wrote: post #116
reggieband wrote: post #58

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…

A relevant Twitter thread: https://twitter.com/NeuroStats/status/1192679554306887681 At the risk of projecting, this has the hallmark of bad experimental design. The best experiments are designed to determine which of many theories better account for what we observe. (When I write "you" or "your" below, I don't mean YOU specifically, but anyone designing the kind of experiment you describe.) One model of gravity says…

This is a very good comment. Is this line of reasoning fleshed out and written up somewhere so I could point people to it? (Also, I would like to think more deeply about its implications)

> What models/theories of customer behavior were your experiments designed to distinguish between? My guess is "none" because someone thinking about the problem scientifically would start with a single experiment whose results are maximally dispositive and go from there.

This is how science is (at least, ought to be) done. This way, the goal is to always be improving your understanding of objective reality.

> They wouldn't spend a bunch of time up-front designing 12 distinct experiments. [...] So it wasn't really an experiment in the scientific sense, but rather a kind of random optimization exercise: do 12 somewhat-less-than-random things and see which, if any, improve the metrics we care about.

The problem is that a lot of AI salesmen tend to hype the "model-free" nature of "predictive" AI towards optimizing outcomes/goals, and people who don't know better get carried away with the bandwagon. Overly business-oriented people are susceptible to the ostrich mentality of not wanting to understand problems with bad tools -- they are too focused on the possibility of optimizing money-making. I find the movie "The big short" to be a fantastic illustration of this psychology.

It's probably going to lead to a very bad hangover, but for the moment the party's still going on and nobody likes the punch bowl being yanked away.

Re: How to recognize AI snake oil [pdf]

#165

Post by chimi »

mark-r wrote: post #147
chimi wrote: post #125

Earlier quoted context omitted.

It's hard to verbalize this, most of it is "intuition" but I think it boils down to "supervised learning is BS." Humans are smarter than computers. How can a human teach a computer how to do something when the human itself can't teach another human that something? We haven't solved that problem. The snake is eating its tail. You can't teach a human how to do something when the methodology to do that is the student tr…

> You can't teach a human how to do something when the methodology to do that is the student trying something and the teacher saying "Yes" or "No". Strangely, I recently had to complete a cognitive test that was essentially that process. I was given a series of pages, each of which had a number of shapes and a multiple choice answer. I was told whether I chose the correct answer, then the page was flipped to the next…

You're touching on the "difficulty" in verbalizing it. I see what you mean, because you did learn that the heuristic was changing with just a yes or no. I said you can't teach that way, but you clearly learned that way, so I wasn't exactly correct, but I'm not practically wrong either still I don't think.

I wonder, how would an AI perform on the same test.

What is the mathematical minimum number of questions on such a test, subsequent to the heuristic change, that could guarantee that new heuristic has been learned?

I'm curious about the test. Did it have a name? What were they testing you for?

Re: How to recognize AI snake oil [pdf]

#166

Post by [deleted] »

JimmyRuska wrote: post #160
AareyBaba wrote: post #146

Earlier quoted context omitted.

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.

[deleted]

Re: How to recognize AI snake oil [pdf]

#167

Post by virgilp »

atomicity wrote: post #163
JimmyRuska wrote: post #160

Earlier quoted context omitted.

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 hope you are joking since the industry collectively knows how much employers/interviewers value algorithms-based coding interview, which doesn't correlate strongly with performance. Even if you are talking about senior positions where they don't matter, then you should know that people hire someone they know+like who did decently well, rather than the truly best on the market.

well, that's the thing though - coding interview is pretty much a yes/no thing. What rank you get is typically based on "what rank do you have now?" and "how much do we respect your current employer?"

Re: How to recognize AI snake oil [pdf]

#168

Post by dekhn »

MadWombat wrote: post #136
Hitton wrote: post #109

Earlier quoted context omitted.

That doesn't work. Everything is called AI these days and in mountains of bullshit there are also some actually useful results, these few are not snake oil.

Somewhere out there, a biotech R&D company has developed an effective penis enlargement treatment. Unfortunately they have been having some trouble reaching potential customers.

Yes, I know folks who worked for Pfizer (which made viagra). They said they had all sorts of spam problems.

Re: How to recognize AI snake oil [pdf]

#169

Post by noodlenotes »

reggieband wrote: post #58

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…

Recommender systems are notoriously hard because it's difficult to do better than just recommending the most popular content. You can recommend more personalized content at the expense of KPIs like click-through rate.

Re: How to recognize AI snake oil [pdf]

#170

Post by dbcurtis »

mark-r wrote: post #138
JoeAltmaier wrote: post #105

Earlier quoted context omitted.

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.

I have a friend who lived in San Jose who just wrote the forecast on his whiteboard and left it there, because it never changed. It was funny, because he came from Minnesota where the weather is never the same two days in a row.

Ha! I came here to say something similar. Here in San Jose, the weather tomorrow will be the same as today, most of the time. My joke when I lived in Minnesota was: "If you don't like the weather, just wait 10 minutes. It will be different."

Gotta say though, I don't miss my slow blower even just a wee little bit.

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