Was hoping to understand if anyone has done any research on it. I could only really find one research paper on the topic [1], but it seems less focused on this specific issue.
I walked into the Mint one night because I liked to grab a bad cocktail and some good karaoke.. some guy got on stage and belted out a beautiful version of Rocket Man. I looked closed and it was Drew.
Not to discredit anything that was said in any particular blog post.
Folks also need to remember that a lot of blog posts are written by engineers or managers that have their own agendas and careers and often external blog posts can be a form of self marketing or idea marketing that an engineer or director has been pushing internally.
I have no idea if this happened in mozilla's case but the person that wrote it seemed to talk about the their own internal harness / fuzz testing framework quite a bit, and I imagine it was probably a big part of that person's scope / accomplishments and will probably show up at their end of year review and on their resume.
I think as a user I have 2 modes:
1. Q&A mode where it's basically Google search by voice.
2. I'm trying to process an idea I have with an LLM buddy.
My desires are pretty different in the two scenarios. Q&A mode if it's not quick to respond I'll think something is wrong with my phone.
Deep think mode I'm honestly kind of pissed off at how fast it tries to respond. I want it to slow down and give me a chance to process and use extra compute on its side (including newer models) so it doesn't just spew low thought bullshit at me.
It seems like the system could detect which of these two modes was happening and adapt, including protocol.
I haven't tried the voice mode since the new model updates, maybe it's gotten better.
Counter to everything I just said though and germain to the topic at hand, when I'm in q&a mode that's probably the worst time for it to drop audio as it changes the query significantly. vs when I'm talking at it for 2 minutes it could probably throw half away.
I was doubting this for a minute as I wondered with a significantly biased coin towards the head side would you be more likely to get HT. With probability problems like Monty Hall I like to think about extreme cases like say it's 99 heads to every 1 tails. You'd expect HT 0.99% of the time. Ditto TH.
Is there any current research on as agents w/tools start dominating LLM use, if making making models smaller / less single-shot, more like efficient engines that can process a lot of context, and feeding a lot more into context windows is going to be more of a path forward vs trying to memory the world?
Like smaller models that show effectiveness on problems with verifiable rewards when run in a loop with external grounding context?
I went through college around 2000 in compsci and I remember even back then noticing that there were about 10% of us that cared. The other 90% were just in it for money/prestige. The dotbomb cleared them out, but post 2009 you could see the folks creeping back in due to Facebook and co.
And I like your ideas but I don't see why the venues and artists don't want to capture more of what people are willing to pay enabled by what the parent comment suggested.
I wonder if in your system it actually attracts fans or just people that have the time to wait for tickets.
The article seemed full of weak rationale leading up to the conclusion which made me doubt the whole thing:
1. He kept citing things like PGP, C++ and distributed systems as things in common between Satoshi and Back, but would have described 75% of pragmatic comp sci folks at the time.
2. His end section about word correlations where he started with Back's weird isms and then started finding them in Satoshi's writings seemed like pure Texas sharp shooter fallacy. He started with broader scoring mechanisms and when those didn't work he started seeking out measures that fit his case better.
All of this based on a vibe of how the guy seemed in a Netflix documentary.
I have no idea if Satoshi is Back or not and would love to close this chapter. But this "reporter" seems to have started with a conclusion and then tried to find data that proved the conclusion.
I remember living pre-internet and post-internet, especially post-google and feeling like my own memory was being replaced with an Ethernet cable. The current AI models are definitely carving even more of my brain off, the only thing I'm unsure of is if I'm a better or worse cyborg at each stage. Like even with facts and data at my finger tips I still had to process decisions. I'm wondering what my bio brain's role will be as LLM's progress.
My deeply naive take is all of this stuff should be done similar to weight classes. I'm not sure what key measurable testosterone or other doping effects but measure that and separate athletes into classes based on that. Want to do steroids? Fine but you just get to compete against other dopers. As a low-T male I can go get my butt kicked by women at athletic events if I want.
Can someone help me with insights about large context models? Are there relationships that pop up at the beginning and end of long context windows that don't transitively follow from intermediate points? Is there value in the training over these longer windows vs using the more basic/closer weight distributions over different sliding windows?
So the consumer ate the tariff (I saw somewhere that they just got passed on for the most part). Now the companies are just gonna get the money back and either enrich their exec staff or shareholders?
It feels like a company should have to prove they didn't pass the tariff on to consumers in order to collect this.
You're talking abstractly but the author mentions SF and NYC in the intro where cars come with deep pain points, but at least in SF there are so many bus stops it leads to trip times that are much slower than say an Uber/lyft/waymo.
While I don't disagree with your point about Epstein case being a massive cya for a ton of people in power, the fact is that if they deeply wanted to cover up something the right way to do it would to be to actually print it and scan it, this does look like someone shortcutted some broad order to print and scan all digital media.