That's not how it works now. You are the product and the social media company makes its money by selling your eyes to advertisers, using your posts (including non public ones) to target the ads. Your "payment" is the free use of the website.Grelbr42 said:
Greg Bernhardt said:
PF will have a search chatbot trained on our posts at some point, yes. I don't know about monetizing though. Also, Reddit and Stack and many times larger than PF, so they have much more data to work with.jedishrfu said:
You should inform yourself quickly!Greg Bernhardt said:
6/3/21 said:
Pretty aggressive considering LLMs are likely going to put SE out of business.fresh_42 said:
Maybe SE but certainly not MO (and possibly other Overflows I don't know of). The discussions there are really on an academic level.Greg Bernhardt said:
You just wait a couple of years. I'm interested to see what Wolfram Alpha's plugin can do with ChatGPT.fresh_42 said:
I would have expected that ChatGPT would analyze at least Wikipedia in general and WA on my specific questionGreg Bernhardt said:
It can't browse the internet yet (there is an alpha model) but it is trained on a lot of wiki pages. I don't disagree there are issues, but the tech is moving quickly. We'll get there before too long.fresh_42 said:
LLMs, or Large Language Models, work by using deep learning techniques to process and understand large amounts of text data. They are trained on vast datasets to learn patterns and relationships within the data, allowing them to generate human-like text responses.
LLMs are commonly used in natural language processing tasks such as text generation, language translation, sentiment analysis, and chatbots. They can also be used for tasks like summarization, question-answering, and content recommendation.
LLMs differ from traditional machine learning models in that they have a much larger number of parameters and are trained on significantly larger datasets. This allows them to capture more complex patterns in the data and generate more nuanced responses.
Some limitations of LLMs include their tendency to generate biased or inaccurate responses based on the data they were trained on. They also require significant computational resources and data to train effectively, making them inaccessible to many researchers and organizations.
LLMs can be fine-tuned for specific tasks by providing them with additional training data that is relevant to the task at hand. By adjusting the model's parameters and hyperparameters, researchers can optimize the model for specific performance metrics and improve its accuracy on targeted tasks.