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331·1 month agoThey are trying to cause riots
This is my exact read on the matter. We can’t be the only ones to see it this way. What surprises me is that more analysts and pundits aren’t talking about the economic violence from this angle. And when the masses finally reach their threshold, the Conservatives will do their “Whaaaat? So much for the tolerant Left.”
What were the handful of instances wherein violence wasn’t required to defeat fascists?
No paywall link: https://archive.ph/bn5e5
How do you think the gaskets are made?
What you propose is simple (as in simplistic), but far from easy. Content moderation at scale is extremely difficult, if not impossible. See “Masnick’s Impossibility Theorem.”
Also, deplatforming bigots is difficult and ineffective:
Holy. Fuck. I grew up on a farm. I’ve seen some shit; that just comes with the territory when livestock, heavy equipment, and farm implements are involved. Fly strike alone will have you napalming flying insects.
That video of the myiasis infection in the dude’s nose… <shiver>. Clive Barker wishes he could make horror like that.
Edit: I’m an idiot. Myiasis == fly strike. The species of fly is the variable.
1·8 months agoThe 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.
@[email protected] and @[email protected] 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.
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.
our highly trained Canadian Geese
This explains so damned much of their behavior. I for one look forward to these operatives helping us out with our myriad domestic issues.
I’m not a hunter. But I do understand a lot about environmental conservation and the need for balance. We have eliminated enough of the animals that predate on deer such that some other means, ie hunters, are required to control deer populations. The other option is mass kills, which strike me as wasteful on so many levels.
When I lived in Vermont, there was a conservation movement to attract younger people to deer hunting because natural controls just aren’t there anymore. Where I live now, a distemper outbreak decimated the coyotes, and the deer are out of control. The coyotes are finally bouncing back, but it’s going to take a while. In my small city, the deer are so rampant, it’s common to see dozens on a short bike ride through town. Their food supply is depleted enough such that most deer here appear unhealthy and undernourished. The exploded deer population have follow-on effects: increased expense for deer control measures, collisions (one almost slammed into me on my bike two days ago; not the first time), destruction of plantings to control erosion, and spreading ticks.
I would like to see prospering wild animal populations, rather than this mess we made.
the DEA visited them last year and performed “accountability audits” that uncovered violations of the federal Controlled Substances Act, namely through inadequate record keeping, according to records obtained by The Baltimore Banner.
At the scale of prisons, these pharmacies are called institutional pharmacies. The size, operation, automation, and throughput of institutional pharmacies is mind-blowing. For example, the biggest Costco pharmacies might process 300 scrips a day; institutional pharmacies generally handle 15000 to 30000 per day, with some being even larger.
The “inadequate record keeping” part is just idiocy. There exists automation and auditing software for this. I know because I wrote the last-mile portion of a suite that manages end-to-end compliance automation for institutional pharmacies. A single failed audit generally costs more than most of the auditing and compliance suites licensing fees. And even in small pharmacies, there’s usually more than one failed C-2 audit when it happens. And let’s be clear; these audits are always for C-2 drugs (opioids and stimulants).
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.
Serious question: what are y’all’s methods to prevent/deal with spicy pillows?