• 2 Posts
  • 58 Comments
Joined 1 year ago
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Cake day: August 6th, 2023

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  • I know you are asking for something different, but since there are already a few good answers, allow me to instead to reject the premise and give you a different.

    It’s not impossible to implement an AI solution within the context your provided. The problem is that it’s going to be expensive. However, you can offer to deliver something smaller, focus on the smallest but valuable contribution you can make. While cleaning up the data is still going to be a hell of task, if the scope is small enough it can be achievable. Then, you can communicate the difficulty to scale due to data issues which can help management undestand the importance of prioritizing data quality.

    If you have a bunch of sales data, maybe you can focus on deriving purchase patterns and build a simple recommendations engine. If you want to focus on marketing, you could try lead classification. Ideas depend on the domain of the company you work for.


  • IMO it’s not about what metric is used, but how it is used. The current approach, completely avoiding any karma like mechanism, solves the farming issue, but IMO does not cater to the needs of every user.

    For example, I have ADHD and if accumulating karma gives me much needed motivation and feel good chemicals, I am going to take them.

    At the same time, holding a user to a higher regard because of their karma is stupid, it’s better to build real connections with usernames you recognise through continuous communication.

    Personally, karma was an easily digestable piece of information about how my outreach into the social media is performing. Accumulating karma helps me feel connected with the community, feel accepted.


  • While the consumption for AI train can be large, there are arguments to be made for its net effect in the long run.

    The article’s last section gives a few examples that are interesting to me from an environmental perspective. Using smaller problem-specific models can have a large effect in reducing AI emissions, since their relation to model size is not linear. AI assistance can indeed increase worker productivity, which does not necessarily decrease emissions but we have to keep in mind that our bodies are pretty inefficient meat bags. Last but not least, AI literacy can lead to better legislation and regulation.


















  • I am definitely guilt for that, but I find this approach really productive. We use small bug fixes as an opportunity to improve the code quality. Bigger PRs often introduce new features and take a lot of time, you know the other person is tired and needs to move on, so we focus on the bigger picture, requesting changes only if there is a bug or an important structural issue.