I've noticed Chatgpt 5.6 Sol High, on the chat interface, inventing words that are a mixture of Portuguese and English. Like "hardcodar" a mix of "hardcode" and the most common verb ending in Portuguese "-ar". Some don't have a single google hit
Courts have ruled they can, as long as they pay for the content. Yes it's amoral at best, but lawful. Using a trademark in your brand name is not. You can't create "OpenExcel" or "OpenOpenAi"
"Thank you for participating in the Stealth Union Alpha testing period. This model was Unbiased's Pareto. Use it now: https://openrouter.ai/unbiased/pareto"
I can’t tell if you are being serious or sarcastic. Assuming you are being real, and as someone who has to listen to boring youtube videos to help falling asleep, i have a few questions: how long is the story? What happens after the story ends? Like, does it turn off the voice feature or does it stay on and randomly speaks during the night?
I have it as well. This is how i explain it to regular folks: most people have an whole neural network dedicated to processing the human face, memorizing its feutures and matching them to the people in front of them. But when they look at trees they cant really distinguish a pine tree from another pine tree, unless the differences are extreme: one is brunt down, or has a weird shaped trunk, etc. If they try to recall a specific pine tree all they can recall is a messy green spiky blob. Why? Because their brain has no neural network dedicated to identifying trees. For us, the human face is as distinguishable as a tree
to be fair you need to create a dozen images to get a decent one. Many times i need to get to almost what i want using paint.net and hope chatgpt removes the aliasing/cleans up the pixel boundaries without messing anything up - i'm not complaining though, still faster and cleaner than photoshop -- when it works
I asked gemini 3.8 high to review the site I'm working on for points of high cpu/ram consumption - it failed spectacularly and also halucinated the server i/o limits
I'm seeing it more like: take the concept of "good" put it in a scale of -10 (pure evil) to +10 (pure good). These concepts and the inbetweens have been ingrained into the model, the model can multiply its weights in any combination to express any level and any in between, even -2.16541 etc. So, suppose you give it a short story and ask the model to reason about it and how a character displayed good vs evil behaviour towards the story: Internally the model is making calculation that are very nuanced and extremely precise. This calculations are not the reasoning trace. The reasoning trace itself does not influence the calculations. You may read: "John starts bad and slowly becomes good" when inside the calculations are John goes from -5.245 to -4.24 to -5.221 again, and then 2.1. What matters for nuance is the inner calculations across the many matrix layers. What you see is like an independent program that looks at "-5.245 to -4.24 to -5.221 again, and then 2.1." consults the tokenizer and outputs: "John starts bad and slowly becomes good" or even "John first bad, then good". When in reality, inside, the model as been processing something more akin to "John starts the story as a despicable person, with a redemption arc that builds slowly, he can't yet be considered a good person, certainly not the kind of good person you'd leave your dog with, but he's certainly not as bad as before" the whole time. Now, what if when you continue the conversation, what does the model receive as context? It's original nuanced sentiment, or the brute reasoning trace? That I don't know. It might be that when the reasoning trace is converted from tokens back into numbers it loses all nuance, or it might be that the trace (the words you see) are not the only thing that is being saved and is not the only thing being fed back as context
It's my understanding that the llm is not literally thinking those words, they are just the conversion of the matrix multiplication results (numbers) into the tokens. So the matrix is "multiplying" concepts and directions to come up with the final answer - which produces a somewhat readable reasoning trace. As far as the llm is concerned the reasoning trace could be random (to us) symbols. In fact, the reasoning traces are not necessarily optimized for readability as much as they are an emergent property of the way a reasoning model is trained