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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 ...
Prompt engineering is the process of structuring an instruction that can be interpreted and understood by a generative artificial intelligence (AI) model. [1] [2]A prompt is natural language text describing the task that an AI should perform. [3]
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]
On the SAT reading and writing section, GPT-4 scored a 710 out of 800, 40 points higher than GPT-3.5. On the SAT math section, GPT-4 scored 700, marking a 110 point increase from GPT-3.5.
Generative AI systems trained on words or word tokens include GPT-3, GPT-4, GPT-4o, LaMDA, LLaMA, BLOOM, Gemini and others (see List of large language models). They are capable of natural language processing , machine translation , and natural language generation and can be used as foundation models for other tasks. [ 62 ]
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 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.
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