51–60 of 185 posts
The way they went from GPT-3 to ChatGPT is really quite genius. My understanding is that it's something like this: 1. Start with GPT-3, which predicts the next word in some text and is trained on all the text on the internet 2. Take thousands of prompts, generate several responses for each of them, and have human reviewers rank the responses for each prompt from best to worst 3. The GPT model needs a massive amount o…
Rather strangely, it would seem - I just had this response: "However, I am a language model and do not have the ability to edit or revise my responses once they have been generated" Except I've had no problem getting it to do just that previously... I'm curious about its training data too, as I've managed to find a few things it knows nothing about (despite them having wikipedia pages and multiple dedicated websites…
For a given prompt, the odds that it is considered sensitive is probabilistic
Since it's an input outside if the prompting part, they can probably tune that specific aspect independently in response to controversial usages
Earlier quoted context omitted.
I don't mean to downplay how incredible the tech is, but I'm not sure I'd call this approach genius as it's the industry standard.
Then why were they the first ones to exploit it so effectively? I don’t think it was standard for GPT models.
The way they went from GPT-3 to ChatGPT is really quite genius. My understanding is that it's something like this: 1. Start with GPT-3, which predicts the next word in some text and is trained on all the text on the internet 2. Take thousands of prompts, generate several responses for each of them, and have human reviewers rank the responses for each prompt from best to worst 3. The GPT model needs a massive amount o…
Ok, but how does it take my code and fixes the bug? It's my own code, no one has seen it besides me and the model wasn't trained on it.
ChatGPT is really very simple. Imagine you could analyze a million books and identify all the words within them -- not the meanings of the words, just the actual letters they contain. Now, when someone asks you about the history of France (or why the sky is blue), you could simply pluck out of your library the most common strings of word that seem to follow the words that were in your question! It's like a kid in the…
ChatGPT can do this if you just ask. E.g., explain as if I'm 5 and it will use simple words and phrases. Explain like I'm an expert and it will use technical jargon and expect a deeper shared knowledge with which it can draw from.
You shouldn't write so authoritatively when clearly you haven't explored its capabilities.
The way they went from GPT-3 to ChatGPT is really quite genius. My understanding is that it's something like this: 1. Start with GPT-3, which predicts the next word in some text and is trained on all the text on the internet 2. Take thousands of prompts, generate several responses for each of them, and have human reviewers rank the responses for each prompt from best to worst 3. The GPT model needs a massive amount o…
ChatGPT seems to be/result in some amount of caching of responses - there is very little variation when to asking the same question multiple times. CharacterAI produces a lot more variety in comparison, making it more helpful for brainstorming. That said ChatGPT is likely closer to the truth, even if not perfect, for searches. The innovation happening lately is incredible.
How does the non-english languages part work? I thought maybe they use Google translator, but remembered that Russians have trained it to not to understand "russophobic" sentences. -- Mitä tarkoittaa ryssänvastainen, explain in English. -- Ryssänvastainen means "anti-Russian" or "anti-Russian sentiment." It refers to an attitude or behavior that is hostile or opposed to Russia or Russian interests.
The model says it has been trained in a variety of languages, but its current version is english. Its translations are bad, probably because it uses a translation model on top of its output. Sometimes it is obvious it is translating english articles from the mistakes it makes
The way they went from GPT-3 to ChatGPT is really quite genius. My understanding is that it's something like this: 1. Start with GPT-3, which predicts the next word in some text and is trained on all the text on the internet 2. Take thousands of prompts, generate several responses for each of them, and have human reviewers rank the responses for each prompt from best to worst 3. The GPT model needs a massive amount o…
> Take thousands of prompts, generate several responses for each of them, and have human reviewers rank the responses for each prompt from best to worst Recently I saw an image where Indian women sat in front of computers and the caption said they were classifying "AI" responses. I guess that's true and this kind of work is the new outsourced cheap labour in the AI age.
I really doubt the mechanical turk method is applicable or even useful for the current state of AI-generated text.
Although I hope these high quality answers don't all just come from ChatGPT one day.
I asked ChatGPT and here is its answer: Sure, I'd be happy to help. An artificial neural network (ANN) is a type of machine learning algorithm that is designed to mimic the way the human brain processes information. At its core, an ANN is composed of a large number of interconnected processing nodes, called neurons, which are organized into multiple layers. These layers are interconnected in a way that allows them to…