Turns out land is still cheap and sunlight still generally free.

tits out for harambe

“It’s crucial…”

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:

Imagine buying a policitian’s cryptocurrency

Mulvad.

They don’t know. and the documentary will be bigfoot level speculation.

I’m voting for the human candidate for president, I don’t think the AI one is up to the task.

Don’t blame me, I voted for Kodos_bot

Clearly not a subscriber.

He wants the US at war before the sea change. once elected or close enough to it Harris can change her tune.

Instructions unclear. Bank account emptied by looking at link.

Yeah some of my team members use hf and it really does represent a convenience (basically a GitHub for models), but I’m sure to be clear we can’t rely on them alone. I don’t trust any company to exist or not be bought out and enshittified in 3 years.

Ikr? It really seems like the dismissiveness is coming from people either not experienced with it, or just politically angry at its existence.

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.

Yeah I skimmed a bit. I’m on like 4 hours of in flight sleep after like 24 hours of air ports and flying. If you really want me to address the points of the paper, I can, but I can also tell it doesn’t diminish my primary point: dismiss 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.

Again, dismiss at your own peril.

Because “Integrate two badly documented APIs” is precisely the kind of tasks that even the current batch of LLMs actually crush.

And I’m not worried about being replaced by the current crop. I’m worried about future frameworks on technology like greyskull running 30, or 300, or 3000 uniquely trained LLMs and other transformers at once.

Machine learning scientist.

I think you live in a nonsense world. I literally use it everyday and yes, sometimes it’s shit and it’s bad at anything that even requires a modicum of creativity. But 90% of shit doesn’t require a modicum of creativity. And my point isn’t about where we’re at, it’s about how far the same tech progressed on another domain adjacent task in three years.

Lemmy has a “dismiss AI” fetish and does so at its own peril.

It’s like the least popular opinion I have here on Lemmy, but I assure you, this is the begining.

Yes, we’ll see a dotcom style bust. But it’s not like the world today wasn’t literally invented in that time. Do you remember where image generation was 3 years ago? It was a complete joke compared to a year ago, and today, fuck no one here would know.

When code generation goes through that same cycle, you can put out an idea in plain language, and get back code that just “does” it.

I have no idea what that means for the future of my humanity.