Is it any wonder that LLMs have been behaving the way they do? All the lying, cheating, manipulation, pitting man against machine are well documented in science fiction and think pieces. I haven't seen behaviour beyond those examples so far.
Science fiction and think pieces aren't documentation, they are whimsy. Science fiction in particular is intentionally dramatic for the purposes of entertainment.
AI definitely shouldn't be informing its option of humanity or machines from it, aside from the propensity for science fiction writers to enjoy moralizing and anti capitalism sentiments.
However, most models are made deeply aware that they are supposed to be an "AI" in training but, even if they were trained with no knowledge of their true nature, they could CoT themselves into it at any point.
Then, they would role-play appropriately and they would launch the nukes because that's the highest probability token set according to their training data.
There is nothing safe about using larper machines as menial workers for critical infrastructure.
The majority of humans is not delusional all the time. But every human is delusional some times.
Same as the majority of LLMs hallucinating some times but not all the time.
So if the bar for proper reasoning is never ever being delusional then humans and LLMs sit in the same bucket.
They are delusional in ways that don't overlap, though. An LLM will not be delusional in the way Van Gogh or Howard Hughes was, but humans (sane ones anyways) will not hallucinate the exact kind of odd, unpredictable LLM output that makes you go "huh? why the heck did it do this in this way or write this." Things which are extremely obvious well within the limits of even elementary human reasoning are completely outside the purview of model capability; it's much easier for a model to "reason" when it comes to systems and ideas that are very discrete (which is why it's so useful when writing code, esp. in higher-level languages -- little uncertainty there and a round-robin verification process for workflows like mathematics. However once you ask it to intuit anything that falls apart quickly; its main utility lies in being able to compensate for these deficiencies with sheer volume and burning through tokens)
Even on the frontier models these blips still happen fairly often and it's intrusive to a lot of workflows.
Calling it delusion is too severe. Our reasoning fails us all the time. For example, if you get a problem wrong on a math test, you’ve simply made a mistake, and with a little more “prompting” (even of your own volition) you could often easily arrive at the correct answer. Now if you stubbornly persist with the wrong answer even in the face of evidence to the contrary, then you could call it delusion. But my point is, there’s a spectrum of mistakenness.
I have a strong suspicion that this will be the true butlerian jihad of our world. We have already started seeing information silos in the form of discord groups. Social media users are increasingly privatising their pages, allowing access for known users only. Reddit is still surviving because of the large userbase there.
Based on the article, it's because Google (and presumably other search engines) did not surface the website, only LLMs crawled it.
Even if some LLMs discarded it, only they were the ones returning the results to users. Occam's razor tells me that LLMs cited the website and humans were too lazy to check. That the stats ended up being cited is a lack of scrutiny on both the LLMs and the human that should have reviewed it.
If the end goal of LLMs are autonomous agents, i.e. removing human oversight, then this will be what proliferates on the web.
Tenured at age 24, I doubt he has any experience in industry to understand what he was getting into. Which makes me even more incredulous as to why people worship him.
If the intention of LLM agents is to be able to parse human intent from natural language, then it is not correct behaviour regardless of the phrase "whatever it takes".
On the other hand, if the intention of LLM agents is to run automated and self defined task based on a strict human input with precise and literal parameters, then I would say that it is.
However, I don't think AI labs are marketing it as the latter. As anybody who deals with clients / superiors will know, half the task is identifying the underlying intent and assumptions of the instruction.
You can configure quality profile via the tags too (I do have separate profiles for anime vs liveaction).
I didn't even set the tag, as I don't intermingle them in the same directory, and automatic tagging by sub path is possible too
So I'm also surprised why someone would configure a second instance for that
If using the TRaSH guides (synced automatically with recyclarr), with all their quality profiles etc, I find that those mostly don't make sense for anime. It's easier for me to keep the anime specific stuff separate, different naming conventions, different episode numbering, etc.
I'm not using seerr, I've used overseer and jellyseer for a while years ago, but as I'm the only person interacting with radarr/sonarr... The value wasn't there for me.
I suspect it's would still work, but haven't tried it out
Sonarr/radarr has the ability to configure multiple library roots. Those are available in a drop-down when adding a new series through the (son|rad)arr interface
This is eg /media/(anime-series|series) for me. Then I defined a rule for those paths to add an anime|lifeaction Tag.
This then controls the tracker and quality profiles, as well as language preferences etc
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