“The future ain’t what it used to be.”

-Yogi Berra

  • 1 Post
  • 164 Comments
Joined 1 year ago
cake
Cake day: July 29th, 2023

help-circle



  • It would just be instant validation of the power women hold.

    This is the most gendered/ sexist view I have: women, across all age groups, are more responsible then men.

    It might be because culture, it might be because whatever. But generally speaking, women of all ages hold their shit together (make payments on time, go to the doctor, finish the paper work, register to vote, etc…) at a higher rate then men. Young men in the age group of 14-24 are particularly terrible in this regard.

    If women have a woman as an option, and the premise that women can take this seat of power, they’ll break hard in that direction. Might be a conservative piece of shit like Thatcher, but it will be a very long time for men to catch up once this dam breaks. I expect both sides to double down on this strategy if it works.





  • TropicalDingdong@lemmy.worldtoTechnology@lemmy.worldWhat are your AI use cases?
    link
    fedilink
    English
    arrow-up
    19
    arrow-down
    1
    ·
    edit-2
    23 days ago

    I’ve done several AI/ ML projects at nation/ state/ landscape scale. I work mostly on issues that can be solved or at least, goals that can be worked towards using computer vision questions, but I also do all kinds of other ml stuff.

    So one example is a project I did for this group: https://www.swfwmd.state.fl.us/resources/data-maps

    Southwest Florida water management district (aka “Swiftmud”). They had been doing manual updates to a land-cover/ land use map, and wanted something more consistent, automated, and faster. Several thousands of square miles under their management, and they needed annual updates regarding how land was being used/ what cover type or condition it was in. I developed a hybrid approach using random forest, super-pixels, and UNET’s to look for regions of likely change, and then to try and identify the “to” and “from” classes of change. I’m pretty sure my data products and methods are still in use largely as I developed them. I built those out right on the back of UNET’s becoming the backbone of modern image analysis (think early 2016), which is why we still had some RF in there (dating myself).

    Another project I did was for State of California. I developed both the computer vision and statistical approaches for estimating outdoor water use for almost all residential properties in the state. These numbers I think are still in-use today (in-fact I know they are), and haven’t been updated since I developed them. That project was at a 1sq foot pixel resolution and was just about wall-to-wall mapping for the entire state, effectively putting down an estimate for every single scrap of turf grass in the state, and if California was going to allocate water budget for you or not. So if you got a nasty-gram from the water company about irrigation, my bad.

    These days I work on a small team focused on identifying features relevant for wildfire risk. I’m trying to see if I can put together a short video of what I’m working on right now as i post this.

    Example, fresh of the presses for some random house in California:











  • I mean I’ve been doing this for 20 years and have led teams from 2-3 in size to 40. I’ve been the lead on systems that have had to undergo legal review at a state level, where the output literally determines policy for almost every home in a state. So you can be as dismissive or enthusiastic as you like. I could truly actually give a shit about ley opinion cus I’m out here doing this, building it, and I see it every day.

    For any one with ears to listen, dismiss this current round at your at your own peril.



  • Dismiss at your own peril is my mantra on this. I work primarily in machine vision and the things that people were writing on as impossible or “unique to humans” in the 90s and 2000s ended up falling rapidly, and that generation of opinion pieces are now safely stored in the round bin.

    The same was true of agents for games like go and chess and dota. And now the same has been demonstrated to be coming true for languages.

    And maybe that paper built in the right caveats about “human intelligence”. But that isn’t to say human intelligence can’t be surpassed by something distinctly inhuman.

    The real issue is that previously there wasn’t a use case with enough viability to warrant the explosion of interest we’ve seen like with transformers.

    But transformers are like, legit wild. It’s bigger than UNETs. It’s way bigger than ltsm.

    So dismiss at your own peril.