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2. ChatGPT - Wikipedia

   en.wikipedia.org/wiki/ChatGPT

   ChatGPT's new API uses the same GPT-3.5-turbo AI model as the chatbot. This allows developers to add either an unmodified or modified version of ChatGPT to their applications. The ChatGPT API costs $0.001 per 1,000 input tokens plus $0.002 per 1,000 output tokens (about 750 words), making it ~10% the price of the original GPT-3.5 models.

3. An interview with AI: What ChatGPT says about itself - AOL

   www.aol.com/finance/interview-ai-chatgpt-says...

   ChatGPT’s default responses can sound robotic, like they’re written by a machine – which, well, they are. However, with the right cues you can condition ChatGPT to provide answers that are ...

4. ChatGPT can now respond with spoken words. But what is it ...

   www.aol.com/news/chatgpt-used-exactly-does-heres...

   Currently, you can use a basic version of ChatGPT for free at chat.openai.com, or update to ChatGPT Plus for $20 a month for access to ChatGPT-4, the latest model with the fastest response speed.

5. Former OpenAI board member reveals why Sam Altman was ... - AOL

   www.aol.com/finance/former-openai-board-member...

   Even the very launch of ChatGPT, which sparked the generative AI frenzy when it debuted in November 2022, was withheld from the board, according to Toner. "We learned about ChatGPT on Twitter ...

6. OpenAI - Wikipedia

   en.wikipedia.org/wiki/OpenAI

   OpenAI is an American artificial intelligence (AI) research organization founded in December 2015, researching artificial intelligence with the goal of developing "safe and beneficial" artificial general intelligence, which it defines as "highly autonomous systems that outperform humans at most economically valuable work."

7. Generative pre-trained transformer - Wikipedia

   en.wikipedia.org/wiki/Generative_pre-trained...

   History Initial developments. Generative pretraining (GP) was a long-established concept in machine learning applications. It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.