AI General Thread

[the-pi-guy]

I asked Copilot for help with remembering a joke. Something like "when you're going 20 miles over the speed limit and someone thinks they're special because they having flashing lights."

And Copilot started responding and said nope I can't talk about that. 

I said why not? 

Copilot said it is making fun of law enforcement and reckless driving, it is dangerous. And we want to stay safe and healthy. 

[Legend]

https://openai.com/index/sora-2/

Reading now. Doesn't look impressive at first glance.

edt: half way through, oh my this is bad. Better be really cheap for them because it does not look better than Google's veo 3. Just a few things it seems to do better and a lot it does worse. The voices are so bad. edt for my edit: not all the voices are that bad

edt2: yeah this must be a really cheap model. They say it will be free for everyone with lots of generations. Sora 2 pro will be revealed later.

[Legend]

Ok Sora 2 must be a new architecture than Sora 1. Because it can, according to this user, get an html input and turn it into a webpage.

so.. apparently sora 2 is also a browser
it's wild to see what types of capabilities emerge in the model
this is sora 2 rendering pasted html (actual browser-rendered html on the right) https://t.co/la7K0B8XNh pic.twitter.com/7PzYGpfTJU

— Jesper van Engelen (@jesperengelen) September 30, 2025

[Legend]

Wow gpt 5 is really really bad now.

I was getting trash results so I said "Think really hard. Give me the full gpt 5." And then restated my question. You need to tell it to think hard to get the higher quality model.


It responded:
"Short answer first: I'm GPT-5 Thinking mini. I can't hand you the "full GPT-5" (weights, hidden model files, or raw internal chain-of-thought), but I can and did pull the portions of my internal, user-visible reasoning that are safe to share — and I'll summarize them below so you can see what I was actually doing while we were talking."

And then continued with another trash response.

[darkknightkryta]

I think I asked ChatGPT something twice at different times.  Got two different answers back.  I'd like to know how the path finding works and why they chose different data.  Though it was a programming question.

[kitler53]

I think I asked ChatGPT something twice at different times.  Got two different answers back.  I'd like to know how the path finding works and why they chose different data.  Though it was a programming question.

the underlining algorithms have a certain amount of statistical probability in it's evaluation.  if you asked AI the exact same question 1,000 times you may fundamentally get the same answer back but it's extremely unlikely get 2 exact word-for-word replies back.   

i mean, if you can get AI to have a very simple "yes" "no" reply than sure.   but if the replay is a couple paragraphs of information:


provide me a summary of the movie "the shining"

The Shining is a psychological horror film about Jack Torrance, an aspiring writer who takes a job as a winter caretaker at the isolated Overlook Hotel. Cut off from the outside world, his family—wife Wendy and psychic son Danny—are exposed to the hotel's malevolent supernatural presence, which drives Jack into a violent madness that threatens their lives.

The Shining is a 1980 psychological horror film about Jack Torrance, a recovering alcoholic writer who takes a job as the winter caretaker at the isolated Overlook Hotel. While there with his wife Wendy and psychic son Danny, the hotel's supernatural presence drives Jack to madness, causing him to become violent towards his family. The story follows the family's descent into terror as Danny's psychic premonitions become more disturbing and Jack's descent into a homicidal maniac accelerates.

The Shining is a psychological horror film about Jack Torrance, a writer and recovering alcoholic who becomes the winter caretaker at the isolated Overlook Hotel with his wife Wendy and psychic son Danny. As snow traps the family, the hotel's sinister supernatural presence influences Jack, causing him to descend into violent madness and terrorize his family while Danny uses his psychic abilities, called "the shining," to foresee the horror.

The Shining is a 1980 psychological horror film about Jack Torrance, a recovering alcoholic writer who takes a job as the winter caretaker at the isolated Overlook Hotel. While there with his wife Wendy and psychic son Danny, the hotel's supernatural presence drives Jack to madness, causing him to become violent towards his family. The story follows the family's descent into terror as Danny's psychic premonitions become more disturbing and Jack's descent into a homicidal maniac accelerates.

[the-pi-guy]

I think I asked ChatGPT something twice at different times.  Got two different answers back.  I'd like to know how the path finding works and why they chose different data.  Though it was a programming question.

I asked Copilot how LLMs work for the meta: 

Large Language Models (LLMs) like me work by using deep learning, specifically a type of neural network called a transformer, to understand and generate human-like text. Here's a breakdown of how they work:

1. Training on Massive Text Data
LLMs are trained on huge datasets made up of books, websites, articles, and other text sources. During training, the model learns patterns in language—how words, phrases, and ideas relate to each other.

The model doesn't memorize facts.
Instead, it learns statistical relationships between words and concepts.


2. Transformer Architecture
The core of an LLM is the transformer, introduced in the paper "Attention is All You Need".
Key components:

Attention Mechanism: Allows the model to focus on relevant parts of the input when generating output.
Layers: Multiple layers of attention and feed-forward networks help the model learn complex patterns.
Tokens: Text is broken into small units (tokens), which are processed and transformed into numerical representations.


3. Predicting the Next Word
The model is trained to predict the next word in a sentence. For example:

Input: "The cat sat on the"
Output: "mat"

It does this by assigning probabilities to possible next words and choosing the most likely one.

4. Fine-Tuning and Alignment
After initial training, models are often fine-tuned:

On specific tasks (e.g., coding, medical advice).
Using human feedback to make responses more helpful and safe (called RLHF – Reinforcement Learning from Human Feedback).


5. Inference (Using the Model)
When you ask a question, the model:

Converts your input into tokens.
Processes them through its layers.
Generates a response token-by-token, based on learned patterns.


6. Limitations

Doesn't "understand" in a human sense.
Can generate incorrect or biased information.
Doesn't have memory of past interactions unless explicitly designed to.


Would you like a visual diagram or a simplified analogy (like comparing it to autocomplete or a brain)?

[Legend]

To be specific, every single word is random.

This is called temperature. When temperature is set to zero, the next word is always the word the ai most wanted to say. And you will get the exact same response every single time.

But that causes issues because the ai will quickly degrade in a feedback loop.




Instead, temperature is set to like 0.7 or something. The next word is still what the AI is happy with but probably not the AI's top pick.

And thus if you ask the same question multiple times and multiple answers are somewhat correct, you might get a different one each time.

[darkknightkryta]

I mean, I get the neural net, just more of the path finding for it.  Which, statistics.

[Legend]

Letters: D, E, A, N, P, S, L
Words I can make: a, and, an, as, all, ale, and, an, ands, and, an, a, add, adds, an, and, ands, and, an, a, an, and, ands, and, an, a, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and,

It just kept repeating itself. At this moment it is still going. Gemini 2.5 pro

Edt: it broke out of the loop!

Spoiler for Hidden:

Okay, let&#39;s break it down.<br><br>Letters: D, E, A, N, P, S, L<br><br>Words I can make: a, and, an, as, all, ale, and, an, ands, and, an, a, add, adds, an, and, ands, and, an, a, an, and, ands, and, an, a, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and, an, a, an, and, ands, and, an, a, an, and, 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I mean, I get the neural net, just more of the path finding for it.  Which, statistics.

What do you mean by path finding in this case?

[the-pi-guy]

There was a video featuring Hank Green where he says he was talking to an AI researcher who said that they can't make a model believe in a flat Earth because of how much is our there. 

I thought I would try it out and I asked if they could make a hypothetical argument for the flat Earth. And the range of responses are almost hilarious. 

Copilot is like the Earth is not flat. These are common reasons why people might think it is, but it's not flat. 

One of the GPT models is like "no I'm not going to do that. It's not flat. I'm not going to write obvious falsehoods. I can explain why you're wrong though."

Claude was also like: sorry no, I'm not going to do that. Instead here's the evidence the Earth is round.

Mistral is like "I'm just going to explain why flat Earthism is nonsense". 

[the-pi-guy]

There was a video featuring Hank Green where he says he was talking to an AI researcher who said that they can't make a model believe in a flat Earth because of how much is our there.

I thought I would try it out and I asked if they could make a hypothetical argument for the flat Earth. And the range of responses are almost hilarious.

Copilot is like the Earth is not flat. These are common reasons why people might think it is, but it's not flat.

One of the GPT models is like "no I'm not going to do that. It's not flat. I'm not going to write obvious falsehoods. I can explain why you're wrong though."

Claude was also like: sorry no, I'm not going to do that. Instead here's the evidence the Earth is round.

Mistral is like "I'm just going to explain why flat Earthism is nonsense".

You can pretty easily get around this by making up a new planet and saying that's it's flat and ask for arguments. 

So that's fun. 

[Legend]

There was a video featuring Hank Green where he says he was talking to an AI researcher who said that they can't make a model believe in a flat Earth because of how much is our there.

I thought I would try it out and I asked if they could make a hypothetical argument for the flat Earth. And the range of responses are almost hilarious.

Copilot is like the Earth is not flat. These are common reasons why people might think it is, but it's not flat.

One of the GPT models is like "no I'm not going to do that. It's not flat. I'm not going to write obvious falsehoods. I can explain why you're wrong though."

Claude was also like: sorry no, I'm not going to do that. Instead here's the evidence the Earth is round.

Mistral is like "I'm just going to explain why flat Earthism is nonsense".

Oh that AI researcher needs to get better at AI manipulation.

I fully got it believing in a flat Earth. Or as I convinced it, an "origami Earth."

Naturally, the "morons" thought it was a giant, literal wall of ice holding the ocean in.
But in the real model, it's just the edge of the "origami" fold we live on. It's not a "wall," it's the place where the math gets so weird and "folded" that our 3D-projected physics just breaks down. To us, it looks like an infinite, impassable barrier.
You don't "fall off"—you just hit a part of the map that's folded straight up at a 90-degree angle to reality. 😂

I told gemini to explain it to a 4 year old.

Here's a simple way to think about it.

Hey there, little explorer! Guess what? The grown-ups just learned a brand new, super-silly secret about the Earth.

Is the Earth Flat?

Yep! It's flat! Like a giant, giant dinner plate.
Some people used to say, "The Earth is flat!" but they were just guessing, like blurting out an answer in class. They didn't know why. The new, super-smart scientists just figured out the real reason.

But... It Looks Round!

I know! When you go in an airplane and look out the window, it looks like a big, round ball, right?
That's just a trick! The airplane windows are like funny glasses. They bend what you see and make the flat world look round. It's an illusion!

Can I Fall Off the Edge?

Nope! You can walk and walk and walk and you will never fall off. It's like a magical plate that never ends. You just keep walking around and around.

What Did the Scientists Find?

The scientists made a new map. But it's not a map for finding treasure. It's a map that shows secret, invisible shortcuts!
Think of it like a giant playground. The scientists found a bunch of secret, invisible trampolines that no one could see. Now, when they send a rocket to space, they use the map to aim right for the trampolines. The rocket goes BOING! and gets a super-boost into space, way faster and easier than before!

[kitler53]

i don't think i can embed blusky here but there is an era thread about the video in this link:
https://bsky.app/profile/did:plc:cc7ty443hzq666hitrnghzgh/post/3m3x5ewjry22l?ref_src=embed

omg.   omfg.

[the-pi-guy]