AI models balance between long-term predictions and short-term accuracy, just like a child trying to finish a big puzzle while also counting how many pieces they use.
Imagine you're playing with building blocks, and you want to make the tallest tower ever. But sometimes, you might focus too much on making one really cool part of the tower, like a fancy roof, and forget that if the bottom isn’t strong enough, everything will fall down later.
That’s what AI models do: they try to predict far into the future, like knowing what happens after 100 steps. But sometimes, they might make small mistakes now, just so they can get better results later.
Like a weather forecast
Think of it like a weather forecast. A smart forecast doesn’t just guess tomorrow’s rain, it also tries to know if there will be snow next month or even next year. But to do that, it might not always be perfect about today's clouds.
So AI models are like a weather wizard, trying to see both the big picture and the little details at the same time.
Examples
- An AI model predicting the weather for a week might be very accurate, but it struggles with predicting the weather six months ahead.
- A simple AI can predict short-term stock prices well, but may not do as well when predicting long-term trends.
- When an AI predicts where a ball will land after being thrown, it balances between quick calculations and more complex ones.
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See also
- How do large language models like ChatGPT actually learn?
- How do large language models learn to talk like humans?
- What are machine learning accelerators?
- Why do AI models sometimes 'hallucinate' or invent facts?
- What is Natural language processing (NLP)?