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Spreadsheets are one of the most commonly used software programs in the business world. Though they are most commonly used for simple tasks like budgeting and bookkeeping, spreadsheets can actually be quite powerful tools for data analysis and machine learning.
Machine learning is a form of artificial intelligence that allows computers to learn from data without being explicitly programmed. Machine learning is a subset of artificial intelligence, and it is what allows computers to do things like recognize faces or recommend products.
Using machine learning to accomplish hard tasks in spreadsheets can be powerful for a number of reasons. First, spreadsheets are already a familiar tool for many business users, so there is no need to learn a new piece of software. Second, machine learning can automate tedious and time-consuming tasks, freeing up users for more creative work. Finally, machine learning models can be easily shared and used by other users, making collaboration and knowledge-sharing easy.
PromptLoop is a machine learning platform that makes it easy to select inputs and outputs and generate a model. PromptLoop takes care of the heavy lifting, so users can focus on their data.
Recent research breakthroughs have unlocked the power of what are referred to as Large Language Models. These help power Promptloop to gather context quickly and learn exactly what you are trying to accomplish. Large language models are a type of machine learning algorithm that can be used to accomplish a variety of tasks, from understanding natural language to generating new text. These models are trained on large amounts of data, and they can be very effective at understanding and responding to real-world situations.
Few shot learning is a type of machine learning that can be used to learn from a small amount of data. This is helpful when there is not a lot of data available, or when the data is not labeled. Zero shot learning is a type of machine learning that can be used to learn from no data at all. This is helpful when there is no data available, or when the data is not labeled.