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This isn't surprising. Most 'AI' comes down to correlating a small number of variables (one or two) with a prediction target. The real benefit of any form of ma

This isn't surprising. Most 'AI' comes down to correlating a small number of variables (one or two) with a prediction target. The real benefit of any form of machine learning is detecting functional relationships between variables (ie, "when this AND NOT that OR this"). It's just that these relationships don't provide a real benefit in 99/100 real world use cases. In the case of a loan, if the credit score is high they'll probably qualify. If it's low, they won't. All other variables are very minor in comparison to this one, which already encapsulates almost all of the relevant information. This is by far the most common situation for practical machine learning.

sdoering 5y ago
When dealing with stakeholders in big corporations I learned that they absolutely needed AI, machine learning and data science as capabilities in the projects that they started. Even if data was only very tangentially related to the core product/project. If so, for example one needed to datascience the shit out of project metadata to create a project efficiency self optimization loop. Or what ever crap one could come up with. Else they would not be able to secure funding for the idea from upper management because they had learned that 'data is the new oil'. The second VS part always needed/needs to be how this project enables the product to be (or become) a platform where the company can the run value added services on top (value adding for the company, not the customer). Imagine a car being a platform and you can book additional horse power for the upcoming trip. Or change the background image for the instrument panel. Or pay extra to natively have access to your Spotify playlists. And then datascience the user data to recommend even more stuff resulting in $$$ (by imagination from management and fuelled by slide decks created by some junior strategists that have never even owned a car but presented by the client account lead in a lush retreat).
- IgorPartola 5y ago
  I am unclear as to whether what you are saying is that they need data science to justify their projects because when they say that an AI told them that they need $X extra for the project it carries more weight than when they say they did the analysis by hand or if the data science actually provided value in this case.
  - sdoering 5y ago
    No. They need AI in their projects to receive management buy in (and funding) because management swallowed the hook and now wants everything AI & data driven (even if it makes no sense) to be able to brag to other managers from the competition and other industries that they now use AI and are a data driven company.
    - IgorPartola 5y ago
      So you can feed them data from an RNG and say it’s from an AI and they’ll follow it?
- Gollapalli 5y ago
  Are we headed for another AI winter? Because this sounds like a recipe for another AI winter.
roenxi 5y ago
Yes. But there is also an interesting failure mode - people don't bother applying if they don't think they'll get accepted. So presumably even just a "YES" will be a fairly good model of what actually happens without necessarily capturing the real model that the bank is using. A 95% accuracy rate isn't necessarily very good because of this effect (it might be, it just isn't certain).
- sdoering 5y ago
  My SO used to work in a bank in the business loan securities back office. Believe me. Lots of people and businesses apply even if they themselves see no realistic chance of receiving a loan. And in private non business customers there are even a significant amount that have less than nothing to their name, living of welfare and still don't see, why they should not receive a loan from the bank for the newest iPhone. Or a new car. Or TV because "football's coming home". Excuse my snarkiness, but the stories she told me from her apprenticeship in the bank, when she was customer facing were not painting a good picture of humans (Btw. well off or not). But that is probably the case in any consumer facing job.
  - jacquesm 5y ago
    Excuse my snarkiness, but having actually worked for a bank and seeing the inside of the loan department from an IT perspective, the bulk of that translates into: banks will allow the people that are already wealthy to leverage to make them even more wealthy while working hard to keep those already down firmly in their place. Not to mention to take advantage of them by charging them exorbitant fees for the little bit of credit that is extended to them.
    - sdoering 5y ago
      I am with you. Shitty entitled customers calling female tellers whores that need to be f**ed to loosen up a bit (happened more than once) do not absolve banks from their abysmal policies. Or the system that not only enables but sometimes regulates/necessitates these policies. I am not siding with banks in general here. I was just exemplifying one aspect to show how it is not always black and white.amd how there can be unreasonable customers. [Edit typos]
kqr 5y ago
Yup. This has been shown over and over by Meehl and that gang. > The real benefit of any form of machine learning is detecting functional relationships between variables (ie, "when this AND NOT that OR this"). It's just that these relationships don't provide a real benefit in 99/100 real world use cases. And the real problem with humans, ironically enough, is that due to narrative fallacy, confirmation bias, etc., humans vastly overvalue the contributions of these special circumstances. If a dumb 2--3 variable rule predicts your friend will go to the movies in the weekend, you are likely to go, "Yeah, maybe. But she complained of that headache this morning, maybe there's something deeper there that also makes them not want to go to the movies." In most cases, you'll be wrong to override the dumb rule. So why do people do this? Well, the original movie-going prediction might only have been a 55 % shot. So 45 % of the time, your friend won't go to the movies, headache or not. But if they don't, you'll think you made the right call considering that headache. If they do go, you'll end up thinking, "Right, of course. They had the headache but their friend really wanted to go so of course they would endure it." In other words, you'll think of a way to frame your incorrect call as the right one. (Hindsight bias.)

sdoering 5y ago

When dealing with stakeholders in big corporations I learned that they absolutely needed AI, machine learning and data science as capabilities in the projects that they started. Even if data was only very tangentially related to the core product/project. If so, for example one needed to datascience the shit out of project metadata to create a project efficiency self optimization loop. Or what ever crap one could come up with. Else they would not be able to secure funding for the idea from upper management because they had learned that 'data is the new oil'. The second VS part always needed/needs to be how this project enables the product to be (or become) a platform where the company can the run value added services on top (value adding for the company, not the customer). Imagine a car being a platform and you can book additional horse power for the upcoming trip. Or change the background image for the instrument panel. Or pay extra to natively have access to your Spotify playlists. And then datascience the user data to recommend even more stuff resulting in $$$ (by imagination from management and fuelled by slide decks created by some junior strategists that have never even owned a car but presented by the client account lead in a lush retreat).

IgorPartola 5y ago
I am unclear as to whether what you are saying is that they need data science to justify their projects because when they say that an AI told them that they need $X extra for the project it carries more weight than when they say they did the analysis by hand or if the data science actually provided value in this case.
- sdoering 5y ago
  No. They need AI in their projects to receive management buy in (and funding) because management swallowed the hook and now wants everything AI & data driven (even if it makes no sense) to be able to brag to other managers from the competition and other industries that they now use AI and are a data driven company.
  - IgorPartola 5y ago
    So you can feed them data from an RNG and say it’s from an AI and they’ll follow it?
Gollapalli 5y ago
Are we headed for another AI winter? Because this sounds like a recipe for another AI winter.

roenxi 5y ago

Yes. But there is also an interesting failure mode - people don't bother applying if they don't think they'll get accepted. So presumably even just a "YES" will be a fairly good model of what actually happens without necessarily capturing the real model that the bank is using. A 95% accuracy rate isn't necessarily very good because of this effect (it might be, it just isn't certain).

sdoering 5y ago
My SO used to work in a bank in the business loan securities back office. Believe me. Lots of people and businesses apply even if they themselves see no realistic chance of receiving a loan. And in private non business customers there are even a significant amount that have less than nothing to their name, living of welfare and still don't see, why they should not receive a loan from the bank for the newest iPhone. Or a new car. Or TV because "football's coming home". Excuse my snarkiness, but the stories she told me from her apprenticeship in the bank, when she was customer facing were not painting a good picture of humans (Btw. well off or not). But that is probably the case in any consumer facing job.
- jacquesm 5y ago
  Excuse my snarkiness, but having actually worked for a bank and seeing the inside of the loan department from an IT perspective, the bulk of that translates into: banks will allow the people that are already wealthy to leverage to make them even more wealthy while working hard to keep those already down firmly in their place. Not to mention to take advantage of them by charging them exorbitant fees for the little bit of credit that is extended to them.
  - sdoering 5y ago
    I am with you. Shitty entitled customers calling female tellers whores that need to be f**ed to loosen up a bit (happened more than once) do not absolve banks from their abysmal policies. Or the system that not only enables but sometimes regulates/necessitates these policies. I am not siding with banks in general here. I was just exemplifying one aspect to show how it is not always black and white.amd how there can be unreasonable customers. [Edit typos]

kqr 5y ago

Yup. This has been shown over and over by Meehl and that gang. > The real benefit of any form of machine learning is detecting functional relationships between variables (ie, "when this AND NOT that OR this"). It's just that these relationships don't provide a real benefit in 99/100 real world use cases. And the real problem with humans, ironically enough, is that due to narrative fallacy, confirmation bias, etc., humans vastly overvalue the contributions of these special circumstances. If a dumb 2--3 variable rule predicts your friend will go to the movies in the weekend, you are likely to go, "Yeah, maybe. But she complained of that headache this morning, maybe there's something deeper there that also makes them not want to go to the movies." In most cases, you'll be wrong to override the dumb rule. So why do people do this? Well, the original movie-going prediction might only have been a 55 % shot. So 45 % of the time, your friend won't go to the movies, headache or not. But if they don't, you'll think you made the right call considering that headache. If they do go, you'll end up thinking, "Right, of course. They had the headache but their friend really wanted to go so of course they would endure it." In other words, you'll think of a way to frame your incorrect call as the right one. (Hindsight bias.)