In my experience it’s surprisingly easy. I found the break in routine snaps me out of complacency, making me more alert.
In my experience it’s surprisingly easy. I found the break in routine snaps me out of complacency, making me more alert.
Really? I’ve been peeing on people just in case they had a jellyfish sting for years!
Not many. I prefer smaller trackers though. If you see a lot of popular torrents on larger trackers, you’ll have a bunch of concurrent active seeds.
If you permaseed you don’t need to know individual tracker seeing requirements.
Give an alternative a go, see if you have better luck. There’s adguard home, blocky, and Technitium DNS for you to consider.
Alternatively, the window trick should work.
I think that happened 8 years ago or so
Let me expand a little bit.
Ultimately the models come down to predicting the next token in a sequence. Tokens for a language model can be words, characters, or more frequently, character combinations. For example, the word “Lemmy” would be “lem” + “my”.
So let’s give our model the prompt “my favorite website is”
It will then predict the most likely token and add it into the input to build together a cohesive answer. This is where the T in GPT comes in, it will output a vector of probabilities.
“My favorite website is”
"My favorite website is "
“My favorite website is lem”
“My favorite website is lemmy”
“My favorite website is lemmy.”
“My favorite website is lemmy.org”
Woah what happened there? That’s not (currently) a real website. Finding out exactly why the last token was org, which resulted in hallucinating a fictitious website is basically impossible. The model might not have been trained long enough, the model might have been trained too long, there might be insufficient data in the particular token space, there might be polluted training data, etc. These models are massive and so determine why it’s incorrect in this case is tough.
But fundamentally, it made up the first half too, we just like the output. Tomorrow some one might register lemmy.org, and now it’s not a hallucination anymore.
Very difficult, it’s one of those “it’s a feature not a bug” things.
By design, our current LLMs hallucinate everything. The secret sauce these big companies add is getting them to hallucinate correct information.
When the models get it right, it’s intelligence, when they get it wrong, it’s a hallucination.
In order to fix the problem, someone needs to discover an entirely new architecture, which is entirely conceivable, but the timing is unpredictable, as it requires a fundamentally different approach.
And rage!
Those are rookie numbers, gotta pump them up!
How many charges is he currently facing? I feel like every few days there’s another indictment.
Just pour milk into the box and eat it all?
I will cut off your Johnson!
How is it implemented?
Might be time to switch banks…
You’re right, I misread the post. What sites have done that? I’ve been fortunate to never encounter any.
Yes, asking these questions is a fantastic thing.
Speaking of questions - I imagine there is a day to use the built in dev tools in the browser to verify that the particular site does this, but I don’t know how. Do you happen to know how I might?
I remember signing up for a site a few years ago and they emailed me my confirmation, with my password, in plaintext. I was absolutely shocked
I always figured they checked the plaintext locally before hashing and sending it to their server, but I don’t really know.
Soon? It’s already offensive, you bigot!