• 3 Posts
  • 32 Comments
Joined 1 year ago
cake
Cake day: June 12th, 2023

help-circle
  • Google is genuinely bad now. I switched to Ecosia which is just Bing with a simpler front end and they use their profits to plant trees. I don’t think Ecosia is particularly special though. Duck Duck Go, Bing whatever, they’re all better than Google.

    Whenever I set up a new computer then search for something, I’m always surprised at first seeing the awful layout and quality of the search results before I realize that I haven’t changed the default search from Google. It’s awful now. Seriously, how are people using it?

    My new favorite way to search is perplexity.ai. It’s an AI search tool that summarizes the loads of crap out there so you don’t need to read through the junk that people write. It provides sources, unlike using ChatGPT, which is incredibly valuable. All AIs make shit up, so having links to double check it is a must. Unlike Bing Chat, or whatever Microsoft calls it this week, you can ask follow up questions to home in on what you want.








  • Whenever I have issues with YouTube refusing to do things it used to do, I stop using it for a while and eventually they put it back. If you’re not willing to do that, I find that the NewPipe app is better than the native YouTube app. But be warned that occasionally Google makes changes that break New Pipe and you need to wait a couple days for the devs to catch up to the change.


  • I thought it was going to be something that used vision to monitor the roadways and dynamically make decisions about the lights, which would be a very different way of controlling traffic. It’s a predictive model of traffic that they used to adjust light timings, which is the same way that traffic has always been controlled, informed by a model. It’s nice that it helps, but they probably had no shortage of decent ways to model traffic that would have led to this result. My guess is no one looked into it and Google found an easy place to showcase an AI model.





  • I used Firefox when it first came out. Google and Mozzila got into a hot race to make the best browser and they both did well. Somehow I ended up using Chrome a lot more even though I thought that by the time the race ended they were pretty even. Both were very fast and had great plugin libraries. Chrome looked nicer out of the box, but Firefox is highly customizable. Since the end of that race, Chrome has gotten worse and Firefox is about the same. I’ve switched back fully to Firefox, and the only thing I miss is the “Piss off publisher frames” plugin, that I haven’t found a replacement for. It’s a nice browser.


  • I just bought a new laptop for a family member. It wasn’t very expensive, but hardware now is generally amazing. It has Windows 11. My 12 year old laptop running Windows 7 is faster for most tasks, despite far inferior hardware. Plus search actually works in 7, it’s better organized, it doesn’t come with a ton of junk you need to disable or remove (good god the default start menu on 10 is a mess), and it doesn’t look like they designed the UI over the weekend. I kept waiting for the typical MS move of fixing the dumb crap they added, but with 11 it’s clear that they’re doubling down.





  • Kethal@lemmy.worldtoScience Memes@mander.xyzanswer = sum(n) / len(n)
    link
    fedilink
    English
    arrow-up
    1
    ·
    edit-2
    1 month ago

    Seeing your comment I wondered how someone publishing in Nature could have possibly left out the use of statistics for prediction. That would be a wild oversight that only someone with little knowledge of the topic would make, and surely not something that the editors of Nature would miss. Upon clicking the link I see that they mentioned it in the very first sentence and apparently ignore it if someone happens to call the prediction model a machine learning model. Using statistical models for prediction has been used since the start of the field, and renaming things that have been used for decades as “machine learning” doesn’t suddenly make them not statistics.

    Artificial neural networks are statistical models, with numerous statistical approaches associated with their use, development and interpretation.