AI language learning models are computer programs that can generate natural language texts based on some input, such as a prompt, a question, or a keyword. Some examples of AI language learning models are ChatGPT, BERT, and GPT-3. These models are trained on large amounts of text data from various sources, such as books, articles, websites, and social media posts. They use deep neural networks to learn the patterns and rules of natural language, and then use them to produce new texts that are coherent, relevant, and sometimes creative.
Text-based Generative AI tools can use artificial intelligence to summarize data and provide answers quickly and efficiently; it has and can be used for creating content for websites or social media, generating reports or articles, and even writing creative works such as stories or poems. These tools can be useful when you are brainstorming topic ideas or trying to come up with keywords to use to search on a specific topic. Remember that these tools are NOT sources of knowledge -- they are fluency-based text-language generators ("large language models"), which means that they literally guess what word comes next. To understand when these tools are most (and least) useful, please scroll to the bottom of the page.
What are AI Tools like ChatGPT or Google Gemini best for? |
What are AI tools like Perplexity AI or Elicit best for? |
Explaining basic concepts or giving background information. |
Helping users navigate and understand academic research papers. |
Generating ideas and exploring related concepts, terms, and keywords about a topic. |
Offering follow-up questions to guide users deeper into a topic. |
Improving grammar, finding spelling errors, or offering better wording. |
Summarizing information from various sources.
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Exploring topics or issues for which you do not need cited sources. |
Providing direct answers to questions using cited sources. |