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Understanding machine learning models
Improving data analysis and decision-making
How Google uses logic in machine learning
The next most common way people used LLMs to learn better was as a language tutor. This seems like a task LLMs are well-equipped for. Whatever their other flaws, they can produce grammatically correct text.
Summaries are another area where LLMs seem to excel.
Good summaries, especially those fine-tuned to your particular needs, might be a good way of navigating the large information loads we often face in knowledge work.
LLMs also can help you “ask questions” of longer texts. For instance, when reading a scientific paper you could quickly query the sample size or ask for the methodology or results.
Concepts are unexplained, context is lacking and jargon abounds.
This means that most people must rely on translators, such as general-market nonfiction or science writers, who present what experts think in a more readable format.
Many readers found benefits in using LLMs to figure out jargon and terms used within a particular community in a way that dictionary definitions often fail to elucidate.
The author doesn't seem to be sure to use LLM's here for making study plan if you are already an expert or have enough experience.
He suggests that ChatGTP might be useful when you are going to learn a completely new subject and when you no idea of how to break the curriculum.
The place LLMs seem to work really well is at the fringes of a programmer’s expertise.
Many programmers said that they found AI helpful in getting starting hints in unfamiliar languages or tools.
Their base of programming experience allowed them to make sense of and implement the output, but their unfamiliarity with the underlying language meant the AI saved them a lot of time.
Flashcards are a powerful learning tool. They’re also a pain in the butt to make.
Nonetheless, making flashcards is tedious, so getting a first draft that later review might speed up the process considerably.
The risks seem relatively limited if you confirm the cards’ correctness before putting them in your deck.
Searching via keywords is fraught because sometimes you can’t remember the exact term you used, even if the meaning is roughly the same.
Personalized LLMs that live on your hard drive and access your existing data may be a valuable application.
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