seven MQ-9 drones shot down by the group over the past several weeks. The Houthis have brought down more than a dozen of the surveillance drones (emphasis mine)

Wow, something tells me the military had some editorial input on this article. In all kinds of materials, including General Moseley’s own statements, the MQ-9 Reaper is a hunter-killer drone. (https://en.wikipedia.org/wiki/General_Atomics_MQ-9_Reaper)

The relationship correlation data makes a lot of sense if only from a bandwidth perspective.

Correct. I can definitively say “I don’t know how this happened.” But I do know it creeps me out and spurs me to speed up my privacy efforts.

@Marty_Man_X@lemmy.world and @TORFdot0@lemmy.world both make great points, both of which can certainly explain the sudden change in suggestions.

Anecdote: (a little background) I don’t typically deal with narcissistic people; I’m not troubled by narcissists in my life. My tech life is pretty well locked down, but it could always be better (working on it). And my YouTube suggestions are tightly, carefully curated to topics pertinent to my professional and personal projects.

I had an utter piece of shit contractor working for me on a project; he was a grifting, conniving, manipulative shitbag. When I outright fired his ass, he first got all self-righteous then tried to play the victim, but I wasn’t playing any of his games. My phone was sitting on the workbench next to me.

The next day, I opened YouTube because an engineer I know told me he dropped a new video on software we recently discussed. There among my suggestions were a bunch of videos on how to deal with narcissists. So somehow, in only talking with the contractor (he doesn’t use email, text, or other electronic communications), YouTube decided I was curious about dealing with narcissism. I’m morbidly curious how YouTube made that decision, and whether it was audio or “we know you’re associating with this guy who we identify as a problematic narcissist and here are some resources.”

Now, I’m just some douchecanoe on the internet and you should probably dismiss me based on that alone. But GODDAMN, the data points sure do pile up quickly on how deeply we’re being surveilled.

Per your first comment, I played around with the Vowel Filter in Grid. That certainly does seem to be a factor! Thank you!

Thanks. I was aiming for the learning process. I want to be able to hear or imagine a sound and then start linking up the oscillators and filters to get that specific sound. My end goal is to be all like… oh, take a square wave modulated by a sawtooth with a 4-pole notch here tumble-dried with an LFO insert, then hit the unison with muffler bearings and blinker fluid. Bam, There’s the sound I wanted. But with less nonsense.

OpenDroneMap. It’s a suite that provides photogrammetry, stitching, volumetric analysis, geographic correlation, and 3D model conversion from aerial and non-aerial photos. And that’s only the features that I use myself. It defaults to CPU-only rendering, so you don’t need a big bad GPU to GSD.

Even ignoring the lack of subscription cost, ODM performs at least as well as other applications I tried such as Pix4D. Professionally, I use it for year-over-year kelp bed monitoring, photosynthetic mass analysis, and home construction analysis, specifically volumetric infill needs. Personally, I use it to generate 3D models of my boat interior, which I convert to STL files for arranging infrastructure in limited spaces.

One would develop Popeye forearms gaming on that thing. Get in your arm, neck, and shoulder day while gaming!

I had a Toshiba Satellite around the time this was out. It weighed 12 pounds. That millstone went everywhere with me. Now my laptop weighs about six pounds minus the brick, and I might carry it from my desk to the settee. I look back at what our devices used to be and always think “Damn, I’ve gotten soft!”

You raise good points. Thank you for your replies. All of this still requires planet-cooking levels of power for garbage and to hurt workers.

And an additional response, because I didn’t fully answer your question. LLMs don’t reason. They traverse a data structure based on weightings relative to the occurrence frequency in their training content. Loosely speaking, it’s a graph (https://en.wikipedia.org/wiki/Graph_(abstract_data_type)). It appears like reasoning because the LLM is iterating over material that has been previously reasoned out. An LLM can’t reason through a problem that it hasn’t previously seen unlike, say, a squirrel.

By the same logic, raytracing is ancient tech that should be abandoned.

Nice straw man argument you have there.

I’ll restate, since my point didn’t seem to come across. All of the “AI” garbage that is getting jammed into everything is merely scaled up from what has been before. Scaling up is not advancement. A possible analogy would be automobiles in the late 60s and 90s: Just put in more cubic inches and bigger chassis! More power from more displacement does not mean more advanced. Continuing that analogy, 2.0L engines cranking out 400ft-lb and 500HP while delivering 28MPG average is advanced engineering. Right now, the software and hardware running LLMs are just MOAR cubic inches. We haven’t come up with more advanced data structures.

These types of solutions can have a place and can produce something adjacent to the desired results. We make great use of expert systems constantly within narrow domains. Camera autofocus systems leap to mind. When “fuzzy logic” autofocus was introduced, it was a boon to photography. Another example of narrow-ish domain ML software is medical decision support software, which I developed in a previous job in the early 2000s. There was nothing advanced about most of it; the data structures used were developed in the 50s by a medical doctor from Columbia University (Larry Weed: https://en.wikipedia.org/wiki/Lawrence_Weed). The advanced part was the computer language he also developed for quantifying medical knowledge. Any computer with enough storage, RAM, and the hardware ability to quickly traverse the data structures can be made to appear advanced when fed with enough collated data, i.e. turning data into information.

Since I never had the chance to try it out myself, how was your neural network and LLMs reasoning back in the day? Imo that’s the most impressive part, not that it can write.

It was slick for the time. It obviously wasn’t an LLM per se, but both were a form of LM. The OCR and auto-suggest for DOS were pretty shit-hot for x386. The two together inspried one of my huge projects in engineering school: a whole-book scanner* that removed page curl and gutter shadow, and then generated a text-under-image PDF. By training the software on a large body of varied physical books and retentively combing over the OCR output and retraining, the results approached what one would see in the modern suite that now comes with your scanner. I only achieved my results because I had unfettered use of a quad Xeon beast in the college library where I worked. That software drove the early digitization processes for this (which I also built): http://digitallib.oit.edu/digital/collection/kwl/search

*in contrast to most book scanning at the time, which required the book to be cut apart and the pages fed into an automatically fed scanner; lots of books couldn’t be damaged like that.

Edit: a word

No, no they’re not. These are just repackaged and scaled-up neural nets. Anyone remember those? The concept and good chunks of the math are over 200 years old. Hell, there was two-layer neural net software in the early 90s that ran on my x386. Specifically, Neural Network PC Tools by Russell Eberhart. The DIY implementation of OCR in that book is a great example of roll-your-own neural net. What we have today, much like most modern technology, is just lots MORE of the same. Back in the DOS days, there was even an ML application that would offer contextual suggestions for mistyped command line entries.

Typical of Silicon Valley, they are trying to rent out old garbage and use it to replace workers and creatives.

And Cascadia too, please.

If you look at from a different perspective, it all makes more sense. Right now, you’re trying to apply the incorrect logic and an ethical consistency to anti-trans efforts. The anti-trans efforts are a test to move the Overton Window rightward. Trans and NB people are such a tiny minority. By targeting and othering that demographic, Conservatives are testing how much the rest of the citizenry will tolerate the next steps in fascism: targeting other minorities, miscegenation, segregation, concentration camps… whatever it takes to make a white xian US.

This right here. I fell down the “wild boar problem” rabbit hole a couple years ago. I was curious about what controls have been tried and what could be done to bring things back into balance. The statistic I read said that 75000 boars must be killed per year in Texas just to keep their numbers stable there. Holy hell. That’s a lot of dangerous game hunting.

keeping a product listed that they know is not safe.

Amazon wouldn’t do THAT, would they?

Oh right, they would. https://youtu.be/B90_SNNbcoU And not only would they continue to sell the item, but suppress reviews pointing out the issues.

Anecdotally, six years ago I purchased Ancor marine wiring crimps and 314 stainless steel bolts through Amazon. The crimps were counterfeit garbage and the stainless steel rusted and galled in about two weeks of saltwater exposure. Amazon’s response was basically “contact the manufacturer for warranty.” A quick glance at Amazon listings and it’s clear things have gone further downhill since.

So I regard Amazon doubling down on supply chain fuckery as a net win. I will never shop there again after that hardware BS. And more people will come to the same conclusion that Amazon is quickly becoming the Dollar General of online sales. Add on their shitty treatment of sellers, and good manufacturers go elsewhere, further accelerating the decline.

Thank you for your service, culinary logistics veteran.

Which is why they’re not people.

But the C-suite and board are almost like humans. And that’s even better for… things.

The medical field would be categorically fuct. Just the loss of sterile packaging would have serious consequences. Minimally invasive surgeries, joint replacements, bandages that don’t adhere to wounds, stents…

Then let’s consider cordage. Mountain climbing, arborists, rescue teams, sailboats (the most efficient way to cross oceans), ships, construction… the loss of just Dyneema/UHMWPE, which is a relatively new entrant to the cordage field would have seriously negative impacts.

There is a lot of energy bound up in those long molecules, and there are no unexploited niches in balanced ecosystems. There are already bacteria that can consume certain polymers under narrow conditions. Humanity is gonna be so screwed for a long time if bacteria can slip those narrow parameters.

Thanks. And now all I can hear is “BRUSH brush ~brush brush~ FLOSS floss ~floss floss~.”