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9. Build a custom GPT. If you have a paid ChatGPT plan, you can build custom GPTs that carry out specific actions. For example, if you regularly need to turn a topic into social media captions ...
De Matas also suggests a "user-centered design of the writing process" which prioritizes users over the technology. This "user-centered design" involves two core components. The first is an emphasis on reflective writing which would encourage students to view writing as a process rather than product-based like how ChatGPT can be. The second is ...
ChatGPT is a generative artificial intelligence chatbot [2] [3] developed by OpenAI and launched in 2022. It is currently based on the GPT-4o large language model (LLM). ChatGPT can generate human-like conversational responses and enables users to refine and steer a conversation towards a desired length, format, style, level of detail, and language. [4]
These people specialize in developing effective prompts for the AI program — the quality of responses you will get from ChatGPT are only as good as the questions — or prompts — you provide it.
For example, a prompt may include a few examples for a model to learn from, such as asking the model to complete "maison → house, chat → cat, chien →" (the expected response being dog), [23] an approach called few-shot learning. [24] In-context learning is an emergent ability [25] of large language models.
You may have heard about Jackson Greathouse Fall, who asked OpenAI's ChatGPT to give him instructions to turn $100 into "as much money as possible." He followed the chatbot's instructions and ...
Generative Pre-trained Transformer 4 (GPT-4) is a multimodal large language model trained and created by OpenAI and the fourth in its series of GPT foundation models. [1] It was launched on March 14, 2023, [1] and made publicly available via the paid chatbot product ChatGPT Plus, via OpenAI's API, and via the free chatbot Microsoft Copilot. [2]
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] 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.