People who are experiencing unrest and idleness are easy to recruit -- and if you hang out with people ages of 18-30, you'll see there's no shortage of them.
1,335 karma · joined December 4, 2019
People who are experiencing unrest and idleness are easy to recruit -- and if you hang out with people ages of 18-30, you'll see there's no shortage of them.
> But yes, thanks: I was once offered this challenge when faced with a Ren’Py problem, so I grit my teeth and posed my question to some LLM. It confidently listed several related formatting tags that would solve my problem. One teeny tiny issue: those tags did not and had never existed. Just about anything might be plausible! It can just generate Whatever! I cannot stress enough that this is worse than useless to me.
The probabilistic machine generated a probabilistic answer. Unable to figure out a use for the probabilistic machine in two tries, I threw it into the garbage.
Unfortunately, humans are also probabilistic machines. Despite speaking English for nearly a lifetime, errors are constantly produced by my finger-based output streams. So I'm okay talking to the machine that might be wrong in addition to the human that might be wrong.
> It feels like the same attitude that happened with Bitcoin, the same smug nose-wrinkling contempt. Bitcoin is the future. It’ll replace the dollar by 2020. You’re gonna be left behind. Enjoy being poor.
I mean, you were left behind. I was left behind. I am not enjoying being poor. Most of us were left behind. If we invested in Bitcoin like it was the future in 2011 we'd all be surfing around on yachts right now given the current valuation.
Every other intellectual job will presumably be gone by then too. Maybe AI will be the second great equalizer, after death.
The only thing that actually worked was knowing the target language and sitting down with multiple LLMs, going through the translation one sentence at a time with a translation memory tool wired in.
The LLMs are good, but they make lot of strange mistakes a human never would. Weird grammatical adherence to English structures, false friend mistakes that no one bilingual would make, and so on. Bizarrely many of these would not be caught between LLMs -- sometimes I would get _increasingly_ unnatural outputs instead of more natural outputs.
This is not just for English to Asian languages, even English to German or French... I shipped something to a German editor and he rewrote 50% of the lines.
LLMs are good editors and suggestors for alternatives, but I've found that if you can't actually read your target language to some degree, you're lost in the woods.
https://www.nbcnews.com/news/us-news/group-50-people-shoplif...
Yes, in the chat where a reporter was accidentally present, many of the messages were set to be disappearing. I don't know why anyone would do that if not to avoid recordkeeping laws.
> The images of the text chain show that the messages were set to disappear in one week.
https://apnews.com/article/war-plans-hegseth-signal-chat-inv...
Further, Project 2025 suggests bypassing federal record keeping legislation by simply holding in-person meetings without record.
https://www.youtube.com/watch?v=xxe55mU4DA8
Oddly, the Project 2025 training videos that presumably the members of the executive cabinet have seen say _not_ to delete messages or set messages to auto-deleting _because_ that would be in violation of federal record keeping legislation.
> Within minutes after DOGE accessed the NLRB's systems, someone with an IP address in Russia started trying to log in, according to Berulis' disclosure. The attempts were "near real-time," according to the disclosure. Those attempts were blocked, but they were especially alarming. Whoever was attempting to log in was using one of the newly created DOGE accounts — and the person had the correct username and password, according to Berulis.
If you want to deconstruct the status quo, make sure that your outcomes will be actually be better for you. Even Stalin ended up as a victim of his regime at the end of days, his physician ending up arrested and being interrogated while his health declined.
It's embarrassing that to get to the content that technofascists don't want you to see, you need to visit a secret URL.
Seems I can't delete this now either so...
This hasn't been true since last year, when automated targeting systems began entering the battlefield. They still are not as widely deployed as personnel controlled drones, but over time it appears to be shifting towards battlefield autonomy. Nuclear weapons are a cudgel, automated human-free weapons are a scalpel.
https://www.atlanticcouncil.org/blogs/ukrainealert/missiles-...
The Russia-Ukraine war has convinced me that artificial intelligence driven drone warfare has rendered nuclear warfare obsolete. The drones act as a force multiplier which can mass target the enemy with little collateral damage.
The Federal American government looks like it's playing a dangerous game -- or at least makes the assumption that individual states within it will be unable to mount a concerted response. With inflation and unemployment rising, and hundreds of thousands of federal workers out of jobs, it's possible state governments may become radicalized.
https://en.wikipedia.org/wiki/Timeline_of_events_leading_to_...
Ironic, but GPT4o works better for me at longer contexts <128k than Gemini 2.0 flash. And out to 1m is just hopeless, even though you can do it.
A discussion worth having, if we could.
For ML driven code development, I find it works best when I used it to make pure functions where I know exactly what I expect to go in and out of the function, and the LLM can simultaneously write the tests for it to ensure that it works. LLMs do not plan like humans, even when finetuned they seem to have difficulty being integrative with knowledge beyond pattern matching.
That being said, 90% of coding is pattern matching to something someone made already. And as long as I'm writing pure functions and providing suitably adequate context for what the model needs to produce, LLMs seem to work wonders. My rule of thumb is to spend 10-20 minutes specifying exactly what I need in the prompt, and then tuning that if I fail to get the expected result.
> Another early pharmacologic approach to alcoholism treatment used psychostimulants to create a feeling of well-being and obviate the euphoric effects of alcohol. Bloomberg (1939) administered amphetamine to 21 alcoholic patients, who became more alert and energetic and reported no desire to drink (see also Bowman and Jellinek, 1941). Reifenstein and Davidoff (1940) found that amphetamine was of benefit in cases of acute alcohol intoxication and recommended it to treat depression in institutionalized alcoholics (see also Bowman and Jellinek, 1941).
Of course, I'm interested, but I will let medical science do the damage first before I elect to dive in.
[1] https://ajph.aphapublications.org/doi/pdf/10.2105/AJPH.2007....
https://github.com/microsoft/unilm/blob/master/Diff-Transfor...
2. The method of tokenization/adapter is novel and uses many fewer tokens than all comparable CLIP/SigLIP-adapter models, making it _much_ faster. Attention is O(n^2) on memory/compute per sequence length.
https://en.wikipedia.org/wiki/Peanut_Corporation_of_America
The UK had mad cow disease, Italy continues to deal with diluted and falsified olive oil, the EU has had repeated horse meat scandals. Most recently Japan had a health food supplement scandal in which many people died.