They detect bots but let a ton of them run free because any character having membership = revenue and an extremely significant chunk of active characters are bots. They nuked them all in 2011 I think and the game was nearly empty.
SirPugger's youtube channel has loads of videos monitoring various bot farms.
It is early in the news but I also read about the US working on directly having the nation transition. Gives me bad vibes as someone who lived through the invasion of Iraq. TBH, I am not very knowledgeable but I assume there's less sectarianism and lack of infrastructure so it is a different situation. Although with all things Trump, it's his execution and competency following through after the immediate ready decision.
I like the idea of using vintage LLMs to study explicit and implicit bias. e.g. text before mid-19th century believing in racial superiority, gender discrimination, imperial authority or slavery. Comparing that to text since then. I'm sure there are more ideas when you use temporal constraints on training data.
They also search online and return links, though? And, you can steer them when they do that to seek out more "authoritative" sources (e.g. news reports, publications by reputable organizations).
If you pay for it, ChatGPT can spend upwards of 5 minutes going out and finding you sources if you ask it to.
Those sources can than be separately verified, which is up to the user - of course.
Right, but now you are not talking about an LLM generating from it's training data - you are talking about an agent that is doing web search, and hopefully not messing it up when it summarizes it.
Yes, because most of the things that people talk about (ChatGPT, Google SERP AI summaries, etc.) currently use tools in their answers. We're a couple years past the "it just generates output from sampling given a prompt and training" era.
It depends - some queries will invoke tools such as search, some won't. A research agent will be using search, but then summarizing and reasoning about the responses to synthesize a response, so then you are back to LLM generation.
The net result is that some responses are going to be more reliable (or at least coherently derived from a single search source) than others, but at least to the casual user, maybe to most users, it's never quite clear what the "AI" is doing, and it's right enough, often enough, that they tend to trust it, even though that trust is only justified some of the time.
A side project that takes legal documents and uses TTS models to create a narrated read out of the whole document.
Part of the reason I'm building my own solution is that legal documents are often distributed in PDFs which can have all kinds of formatting issues when converted to plain text. There's also specific jargon and formatting that may or may not need to be included, or spoken, or even spoken differently, that I am finding no commercial TTS platform like ElevenLabs really accounts for well. It's all about the pre-processing and chunking.
Also, the commercial models are expensive when you're routinely throwing dozens of pages of text at it.
Unfortunately not, I've just had this one opened in my browser for ages as a reminder (after seeing it on HN IIRC) and recognised it again in the OP instantly :)
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