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> But I also don't think the engineers working in this space are as out to lunch as the author seems to imply. Are you at all close to this space? It sounds yo

by 3gg 5y ago

> But I also don't think the engineers working in this space are as out to lunch as the author seems to imply. Are you at all close to this space? It sounds you may be underestimating corporate politics and the lack of rigour and ethical thought with which these systems are applied. The example Cory puts on policing -- and the many other examples you can find in Evgeny Morozov's book or "The End of Trust" -- are solid proof of this.

vletal 5y ago
My first thought was that I'm not the target audience of this article. I'm a ML practitioner. This seems more like an overstated opinionated wake up call to mgmt and sales people. Is not it?
- version_five 5y ago
  Agreed. What I called a straw man in the OP could also be characterized as a simplification to get his point across to lay-audiences. (Personally I dont agree with the simplification, per my other post). It's meant for popular audiences (as someone else points out, this is from a sci-fi magazine)
- 3gg 5y ago
  If you are an ML practitioner and you think you're not part of the target audience, then you're probably part of the target audience.
foobiekr 5y ago
There are three entirely different groups at work here. The deepmind team etc type of group who actually know what they’re doing and the boundaries of what they are working with the “AI-washing” startups, corporate groups who know they are faking it and that what they’re doing is extremely limited the corporate project team types who are just doing random tool play and honestly don’t understand what they are doing or that they are absolutely clueless with no self-awareness at all I’ve worked with all three and they really are just totally different things that are all being lumped together. They also are listed in terms of increasing proportion. For every self-aware AI-washer team I’ve seen 50 “we are doing AI” Corp team types spinning out one trivial demo after another to execs who know zero.
- 3gg 5y ago
  Where does Google Vision Cloud sit in your categorization? https://algorithmwatch.org/en/google-vision-racism/ https://algorithmwatch.org/en/google-vision-racism/
  - foobiekr 5y ago
    First group. You’re observing that they aren’t doing a perfect job, which is true, but my grouping isn’t related to perfection of results.
    - 3gg 5y ago
      > The deepmind team etc type of group who actually know what they’re doing and the boundaries of what they are working with. You claim that they "know what they are doing and the boundaries of what they are working with" -- and yet they recklessly make public a racist vision product?
      - foobiekr 5y ago
        Your argument is that knowing what you are doing means error free output.
        3gg 5y ago
        It's more like applying the technology with caution and accountability when you already know beforehand that the output is not error-free.
        username90 5y ago
        They never promised that the output would be error free, having output with errors is still useful for many applications. And the issues you are talking about got fixed as soon as it was discovered and since then Google has made sure to always diversify their datasets by race. Nowadays that is common knowledge that you need to do it, but back then it wasn't obvious that a model wouldn't generalize across human races and it is much thanks to that mistake that everyone now knows it is an issue.
bhntr3 5y ago
> Are you at all close to this space? I am. > The example Cory puts on policing My most upvoted comment on this website was discussing this exact scenario. https://news.ycombinator.com/item?id=23655487 https://news.ycombinator.com/item?id=23655487 Could you perhaps clarify the generalization you're making about me and people like me so I can understand it?
- 3gg 5y ago
  Excellent. One problem in my mind that I don't see discussed enough -- and also not in your other post -- is that there is a large divide between those who use the technology (the cops in this case) and those who supply it, and there is no accountability in any of the two groups when something goes wrong. Like you write in your other post, "the system works (according to an objective function which maximizes arrests.)", and that is as far as the engineer goes. On the other hand, the cop picks up the technology and blindly applies it. To make any improvement to the system would require both groups to work together, but as far as I know, that is not happening. A recent example can be found in the adventures of Clearview AI. So from that perspective, I do think that the engineers (and the cops, and everybody else) are out to lunch, each doing their own work in a bubble and not paying enough attention to (or caring about) the side effects of the applications of this technology. Also, the lack of thought and accountability that I mention above I think is fairly general from my experience, even outside of policing. That is why I don't generally agree with the lunch statement. Guys are having a hell of a party as far as I can tell -- at the expense of horror stories suffered by the victims of these systems.
  - salawat 5y ago
    I second this. I spend a great deal of time digging through where we've positioned big data models to steer population scale behavior, and very infrequently do the implementers of the system ever stop to analyze the changes they are seeding or think beyond the first or second degree consequences once things take off. That is all part of engineering to me, so by definition, I think many in the field are in fact, out to lunch.
    - 5y ago

by 3gg 5y ago

vletal 5y ago

My first thought was that I'm not the target audience of this article. I'm a ML practitioner. This seems more like an overstated opinionated wake up call to mgmt and sales people. Is not it?

version_five 5y ago
Agreed. What I called a straw man in the OP could also be characterized as a simplification to get his point across to lay-audiences. (Personally I dont agree with the simplification, per my other post). It's meant for popular audiences (as someone else points out, this is from a sci-fi magazine)
3gg 5y ago
If you are an ML practitioner and you think you're not part of the target audience, then you're probably part of the target audience.

foobiekr 5y ago

There are three entirely different groups at work here. The deepmind team etc type of group who actually know what they’re doing and the boundaries of what they are working with the “AI-washing” startups, corporate groups who know they are faking it and that what they’re doing is extremely limited the corporate project team types who are just doing random tool play and honestly don’t understand what they are doing or that they are absolutely clueless with no self-awareness at all I’ve worked with all three and they really are just totally different things that are all being lumped together. They also are listed in terms of increasing proportion. For every self-aware AI-washer team I’ve seen 50 “we are doing AI” Corp team types spinning out one trivial demo after another to execs who know zero.

3gg 5y ago
Where does Google Vision Cloud sit in your categorization? https://algorithmwatch.org/en/google-vision-racism/ https://algorithmwatch.org/en/google-vision-racism/
- foobiekr 5y ago
  First group. You’re observing that they aren’t doing a perfect job, which is true, but my grouping isn’t related to perfection of results.
  - 3gg 5y ago
    > The deepmind team etc type of group who actually know what they’re doing and the boundaries of what they are working with. You claim that they "know what they are doing and the boundaries of what they are working with" -- and yet they recklessly make public a racist vision product?
    - foobiekr 5y ago
      Your argument is that knowing what you are doing means error free output.
      - 3gg 5y ago
        It's more like applying the technology with caution and accountability when you already know beforehand that the output is not error-free.
        username90 5y ago
        They never promised that the output would be error free, having output with errors is still useful for many applications. And the issues you are talking about got fixed as soon as it was discovered and since then Google has made sure to always diversify their datasets by race. Nowadays that is common knowledge that you need to do it, but back then it wasn't obvious that a model wouldn't generalize across human races and it is much thanks to that mistake that everyone now knows it is an issue.

bhntr3 5y ago

> Are you at all close to this space? I am. > The example Cory puts on policing My most upvoted comment on this website was discussing this exact scenario. https://news.ycombinator.com/item?id=23655487 https://news.ycombinator.com/item?id=23655487 Could you perhaps clarify the generalization you're making about me and people like me so I can understand it?

3gg 5y ago
Excellent. One problem in my mind that I don't see discussed enough -- and also not in your other post -- is that there is a large divide between those who use the technology (the cops in this case) and those who supply it, and there is no accountability in any of the two groups when something goes wrong. Like you write in your other post, "the system works (according to an objective function which maximizes arrests.)", and that is as far as the engineer goes. On the other hand, the cop picks up the technology and blindly applies it. To make any improvement to the system would require both groups to work together, but as far as I know, that is not happening. A recent example can be found in the adventures of Clearview AI. So from that perspective, I do think that the engineers (and the cops, and everybody else) are out to lunch, each doing their own work in a bubble and not paying enough attention to (or caring about) the side effects of the applications of this technology. Also, the lack of thought and accountability that I mention above I think is fairly general from my experience, even outside of policing. That is why I don't generally agree with the lunch statement. Guys are having a hell of a party as far as I can tell -- at the expense of horror stories suffered by the victims of these systems.
- salawat 5y ago
  I second this. I spend a great deal of time digging through where we've positioned big data models to steer population scale behavior, and very infrequently do the implementers of the system ever stop to analyze the changes they are seeding or think beyond the first or second degree consequences once things take off. That is all part of engineering to me, so by definition, I think many in the field are in fact, out to lunch.
  - 5y ago