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This is a valid critique. Transparency for the source attribution of info boxes is lower than clicking to be redirected to the full web page with context. But

This is a valid critique. Transparency for the source attribution of info boxes is lower than clicking to be redirected to the full web page with context. But chatbots take that a lot further...and generative AI specifically cannot really do that in a way that is easy for consumers to make use of - at least it's not possible at the moment.

KRAKRISMOTT 4y ago
It's important that consumers learn this the hard way. When they start losing money, then the flaws of ML become obvious. Otherwise if we gatekeep the technology to "professionals", you end up with companies like McKinsey recommending it to governments and civil service and allowing black box ML to insidiously creep into the societal infrastructure. People without a STEM background won't understand the implications when you say that a biased dataset makes consumer lending metrics like credit scores unfair. But they will understand when their "AI" assistant purchase the wrong airline ticket or generate garbage for a school assignment
- jmathai 4y ago
  Agree. I'm not recommending gatekeeping to professionals. Ideally the product waits until it can be productively used by consumers before being launched. But the cat's out of the bag and I don't see anything slowing it down.
  - KRAKRISMOTT 4y ago
    The failure modes of ML won't go away easily. It is important that consumers are exposed to weaker versions of sophisticated LLMs, instead of being bamboozled with mostly bug-free shiny products that are the result of billion dollars worth of unpublished research. If you think current big tech customer service is bad, just imagine how much worse it can get. The constant failures of Bing and Google search with LLMs is a good thing for society, just like how the early versions of Siri struggled with getting basic speech recognition right. If lay people start treating these as omnipotent black boxes, then it would be very bad for the world.
- tablespoon 4y ago
  > It's important that consumers learn this the hard way. When they start losing money, then the flaws of ML become obvious. But they may not be harmed in ways that don't involve monetary loss, or involve monetary loss that they can quantify and attribute. > Otherwise if we gatekeep the technology to "professionals", you end up with companies like McKinsey recommending it to governments and civil service and allowing black box ML to insidiously creep into the societal infrastructure. Honestly, if it's gate-kept, it should be limited to academics and researchers. It's orders of magnitude worse for some institutional process to be built on the "judgements" of an unreliable "AI" than for some consumer to be mislead about something.
  - KRAKRISMOTT 4y ago
    > But they may not be harmed in ways that don't involve monetary loss, or involve monetary loss that they can quantify and attribute. It is better that your average Joe wastes 500 dollars over a wrong airline ticket, than to waste 500 thousand in a lawsuit because a government bureaucracy placed too much faith in consultants and efficiency buzzwords. > Honestly, if it's gate-kept, it should be limited to academics and researchers. It's orders of magnitude worse for some institutional process to be built on the "judgements" of an unreliable "AI" than for some consumer to be mislead about something. Academics and researchers are not infallible. The current loss of trust in subject matter experts is precisely because they have been held up for too long as paragons of knowledge and judgement. People's lives and livelihoods are impacted when experts make a mistake or offer bad advice. Most of the top academics that are summoned by think tanks and consult for governments are all well tenured/insulated from the worst consequences of their decisions.
    - tablespoon 4y ago
      > Academics and researchers are not infallible. The current loss of trust in subject matter experts is precisely because they have been held up for too long as paragons of knowledge and judgement. Yeah, I guess I could have put it better that it should be banned from production use by anyone, and academics and researchers should only be able to study and improve it.
- eastbayjake 4y ago
  Odd that you chose to pick on McKinsey, which has actually published quite a bit about AI/ML risk management and specifically about bias in training data: https://www.mckinsey.com/featured-insights/artificial-intelligence/tackling-bias-in-artificial-intelligence-and-in-humans https://www.mckinsey.com/featured-insights/artificial-intell...

KRAKRISMOTT 4y ago

It's important that consumers learn this the hard way. When they start losing money, then the flaws of ML become obvious. Otherwise if we gatekeep the technology to "professionals", you end up with companies like McKinsey recommending it to governments and civil service and allowing black box ML to insidiously creep into the societal infrastructure. People without a STEM background won't understand the implications when you say that a biased dataset makes consumer lending metrics like credit scores unfair. But they will understand when their "AI" assistant purchase the wrong airline ticket or generate garbage for a school assignment

jmathai 4y ago
Agree. I'm not recommending gatekeeping to professionals. Ideally the product waits until it can be productively used by consumers before being launched. But the cat's out of the bag and I don't see anything slowing it down.
- KRAKRISMOTT 4y ago
  The failure modes of ML won't go away easily. It is important that consumers are exposed to weaker versions of sophisticated LLMs, instead of being bamboozled with mostly bug-free shiny products that are the result of billion dollars worth of unpublished research. If you think current big tech customer service is bad, just imagine how much worse it can get. The constant failures of Bing and Google search with LLMs is a good thing for society, just like how the early versions of Siri struggled with getting basic speech recognition right. If lay people start treating these as omnipotent black boxes, then it would be very bad for the world.
tablespoon 4y ago
> It's important that consumers learn this the hard way. When they start losing money, then the flaws of ML become obvious. But they may not be harmed in ways that don't involve monetary loss, or involve monetary loss that they can quantify and attribute. > Otherwise if we gatekeep the technology to "professionals", you end up with companies like McKinsey recommending it to governments and civil service and allowing black box ML to insidiously creep into the societal infrastructure. Honestly, if it's gate-kept, it should be limited to academics and researchers. It's orders of magnitude worse for some institutional process to be built on the "judgements" of an unreliable "AI" than for some consumer to be mislead about something.
- KRAKRISMOTT 4y ago
  > But they may not be harmed in ways that don't involve monetary loss, or involve monetary loss that they can quantify and attribute. It is better that your average Joe wastes 500 dollars over a wrong airline ticket, than to waste 500 thousand in a lawsuit because a government bureaucracy placed too much faith in consultants and efficiency buzzwords. > Honestly, if it's gate-kept, it should be limited to academics and researchers. It's orders of magnitude worse for some institutional process to be built on the "judgements" of an unreliable "AI" than for some consumer to be mislead about something. Academics and researchers are not infallible. The current loss of trust in subject matter experts is precisely because they have been held up for too long as paragons of knowledge and judgement. People's lives and livelihoods are impacted when experts make a mistake or offer bad advice. Most of the top academics that are summoned by think tanks and consult for governments are all well tenured/insulated from the worst consequences of their decisions.
  - tablespoon 4y ago
    > Academics and researchers are not infallible. The current loss of trust in subject matter experts is precisely because they have been held up for too long as paragons of knowledge and judgement. Yeah, I guess I could have put it better that it should be banned from production use by anyone, and academics and researchers should only be able to study and improve it.
eastbayjake 4y ago
Odd that you chose to pick on McKinsey, which has actually published quite a bit about AI/ML risk management and specifically about bias in training data: https://www.mckinsey.com/featured-insights/artificial-intelligence/tackling-bias-in-artificial-intelligence-and-in-humans https://www.mckinsey.com/featured-insights/artificial-intell...