Researchers Alex Hanna and Emily M. Bender call on businesses not to succumb to this artificial “intelligence” hype.

cross-posted from: https://lemmy.ml/post/2811405

"We view this moment of hype around generative AI as dangerous. There is a pack mentality in rushing to invest in these tools, while overlooking the fact that they threaten workers and impact consumers by creating lesser quality products and allowing more erroneous outputs. For example, earlier this year America’s National Eating Disorders Association fired helpline workers and attempted to replace them with a chatbot. The bot was then shut down after its responses actively encouraged disordered eating behaviors. "

  • rockSlayer@lemmy.world
    1·
    1 year ago

    The black box isn’t being done because it’s a new idea, it’s actually the other way around. The newer idea is actually the method for easier analysis. There’s a few reasons that they aren’t doing that though.

    1. It’s a newer idea, not everything has been studied so methods will be experimental.
    2. It’s in the company’s interest to make the AI harder to analyze, because they don’t want open the door on a better algorithm from a different company/government/group.
    3. It’s cheaper up front to build a black box and then do statistical analysis the hard and expensive way. Companies would much rather spend money doing things the wrong way instead of saving money long term doing things the right way.
    • FaceDeer@kbin.social
      1·
      1 year ago

      If doing it the “wrong way” is cheap and works well, then perhaps it’s not the “wrong way.”

      There are many companies (and researchers and hobbyists now) who are doing this stuff other than OpenAI, at this point. They just broke the ice and showed what was possible.

    • Kogasa@programming.dev
      11·
      1 year ago

      There is no agent on the planet who is intentionally choosing to make their models harder to analyze. This is a ridiculous idea that you could only believe if you didn’t understand where the complexity comes from in the first place. Creating ML models that can be efficiently and effectively trained and interpreted is an extremely hard and unsolved problem, and whomever could solve it would be rolling in cash.