VGhlcmUgaXMgbm8gZ2VudWluZSBpbnRlbGxpZ2VuY2UgLCB0aGVyZSBpcyBhcnRpZmljaWFsIHN0dXBpZGl0eS4NClRoZXJlIGlzIG5vIHNlcmVuaXR5LCB0aGVyZSBpcyBhbnhpZXR5Lg0KVGhlcmUgaXMgbm8gcGVhY2UsIHRoZXJlIGlzIHR1cm1vaWwuDQpUaGVyZSBpcyBubyBzdHJ1Y3R1cmUsIHRoZXJlIGlzIHBvcnJpZGdlLg0KVGhlcmUgaXMgbm8gb3JkZXIsIHRoZXJlIGlzIGNoYW9zLg==

  • 1 Post
  • 33 Comments
Joined 1 year ago
cake
Cake day: May 14th, 2024

help-circle
  • The Last Airbender.

    If you just forget about the avatar series for a while, and treat this as a bit of harmless fun, it’s not that bad. Well it’s not good enough that I would watch it again, nor is it bad enough to warrant all the abysmal reviews. If you expect this movie to fit in with the series, all of the hate and anger is entirely justified though.

    It all depends on how you watch this movie, and I would argue that there is a way to enjoy it. It’s not all bad.









  • Switched from Fedora to Debian. Here are my reasons:

    1. That computer doesn’t need the latest versions. Debian is new enough for me.
    2. The update GUI has been broken for years. I fixed it once, but then it broke again after a year. I’ve been installing updates from the terminal, because I can’t trust the GUI. I realized I appreciate reliability, and that’s exactly what Debian is all about.
    3. Can’t be bothered to do much admin work like that.


  • That’s a problem when you want to automate the curation and annotation process. So far, you could have just dumped all of your data into the model, but that might not be an option in the future, as more and more of the training data was generated by other LLMs.

    When that approach stops working, AI companies need to figure out a way to get high quality data, and that’s when it becomes useful to have data that was verified to be written by actual people. This way, an AI doesn’t even need to be able to curate the data, as humans have done that to some extent. You could just prioritize the small amount of verified data while still using the vast amounts of unverified data for training.


  • Math problems are a unique challenge for LLMs, often resulting in bizarre mistakes. While an LLM can look up formulas and constants, it usually struggles with applying them correctly. Sort of, like counting the hours in a week, it says it calculates 7*24, which looks good, but somehow the answer is still 10 🤯. Like, WTF? How did that happen? In reality, that specific problem might not be that hard, but the same phenomenon can still be seen in more complicated problems. I could give some other examples too, but this post is long enough as it is.

    For reliable results in math-related queries, I find it best to ask the LLM for formulas and values, then perform the calculations myself. The LLM can typically look up information reasonably accurately but will mess up the application. Just use the right tool for the right job, and you’ll be ok.