LLMs are making me dumber
a year ago
- #AI
- #Learning
- #Productivity
- LLMs are being used to shortcut learning processes, such as coding and math homework, leading to less depth in understanding.
- There is a trade-off between output speed and depth of learning, with urgency driven by rapidly improving AI models.
- Historical analogies like calculators and GPS suggest some skills can be offloaded, but intelligence is harder to confine.
- Arguments against LLMs include becoming just a wrapper for models and potential stagnation if models don't improve as expected.
- Arguments for LLMs include significant short-term output gains and the need to act quickly in a fast-evolving field.
- Using LLMs as tutors could balance learning and output, but repetitive tasks are crucial for ingraining skills.
- Finding balance involves automating small tasks while preserving critical thinking and long-term project skills.