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Not to pick on Sebastian Bubeck in particular, but if auto-complete-on-steroid can “blow his mind,” imagine the effects on the average user.
Developers and data practitioners use LLMs every day to generate code, synthetic data, and documentation. They too can be misled by inflated capabilities. It’s when humans over-trust their tools that mistakes happen.
TL;DR: This is an anti-hype take where you’ll understand how LLMs work, why they’re dumb, and why they’re very useful anyway — especially with a human in the loop.
If an LLM was a folder, it would have two files: the first is code you can execute and the second is a CSV (a large table) filled with numbers.
- The code defines the structure of the neural network of your model and the instructions necessary to run it. It’s like telling your computer how to organize itself to perform a certain type of calculations.
- The CSV file is a large list of numbers, called weights. These weights determine how the neurons inside your artificial neural network ( neuro-net) behave.
Think of a neuro-net as a chef trying to perfect a recipe. Each ingredient (input) can drastically change the flavor of the dish (output).
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